ashb commented on a change in pull request #6515: [AIRFLOW-XXX] GSoD: How to make DAGs production ready URL: https://github.com/apache/airflow/pull/6515#discussion_r350232483
########## File path: docs/best-practices.rst ########## @@ -0,0 +1,296 @@ + .. Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + .. http://www.apache.org/licenses/LICENSE-2.0 + + .. Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. + +Best Practices +============== + +Running Airflow in production is seamless. It comes bundled with all the plugins and configs +necessary to run most of the DAGs. However, you can come across certain pitfalls, which can cause occasional errors. +Let's take a look at what you need to do at various stages to avoid these pitfalls, starting from writing the DAG +to the actual deployment in the production environment. + + +Writing a DAG +^^^^^^^^^^^^^^ +Creating a new DAG in Airflow is quite simple. However, there are many things that you need to take care of +to ensure the DAG run or failure does not produce unexpected results. + +Creating a task +--------------- + +You should treat tasks in Airflow equivalent to transactions in a database. It implies that you should never produce +incomplete results from your tasks. An example is not to produce incomplete data in ``HDFS`` or ``S3`` at the end of a task. + +Airflow can retry a task if it fails. Thus, the tasks should produce the same outcome on every re-run. +Some of the ways you can avoid producing a different result - + +* Do not use INSERT during a task re-run, an INSERT statement might lead to duplicate rows in your database. + Replace it with UPSERT. +* Read and write in a specific partition. Never read the latest available data in a task. + Someone may update the input data between re-runs, which results in different outputs. + A better way is to read the input data from a specific partition. You can use ``execution_date`` as a partition. + You should follow this partitioning method while writing data in S3/HDFS, as well. +* The python datetime ``now()`` function gives the current datetime object. + This function should never be used inside a task, especially to do the critical computation, as it leads to different outcomes on each run. + It's fine to use it, for example, to generate a temporary log. + +.. tip:: + + You should define repetitive parameters such as ``connection_id`` or S3 paths in ``default_args`` rather than declaring them for each task. + The ``default_args`` help to avoid mistakes such as typographical errors. + + +Deleting a task +---------------- + +Never delete a task from a DAG. In case of deletion, the historical information of the task disappears from the Airflow UI. +It is advised to create a new DAG in case the tasks need to be deleted. + + +Communication +-------------- + +Airflow executes tasks of a DAG on different servers in case you are using :doc:`Kubernetes executor <../executor/kubernetes>` or :doc:`Celery executor <../executor/celery>`. +Therefore, you should not store any file or config in the local filesystem as the next task is likely to run on a different server without access to it — for example, a task that downloads the data file that the next task processes. +In the case of :class:`Local executor <airflow.executors.local_executor.LocalExecutor>`, +storing a file on disk can make retries harder e.g., your task requires a config file that is deleted by another task in DAG. + +If possible, use ``XCom`` to communicate small messages between tasks and a good way of passing larger data between tasks is to use a remote storage such as S3/HDFS. +For example, if we have a task that stores processed data in S3 that task can push the S3 path for the output data in ``Xcom``, +and the downstream tasks can pull the path from XCom and use it to read the data. + +The tasks should also not store any authentication parameters such as passwords or token inside them. +Where at all possible, use :ref:`Connections <concepts-connections>` to store data securely in Airflow backend and retrieve them using a unique connection id. + + +Variables +--------- + +You should avoid usage of Variables outside an operator's ``execute()`` method or Jinja templates if possible, +as Variables create a connection to metadata DB of Airflow to fetch the value, which can slow down parsing and place extra load on the DB. + +Airflow parses all the DAGs in the background at a specific period. +The default period is set using ``processor_poll_interval`` config, which is by default 1 second. During parsing, Airflow creates a new connection to the metadata DB for each DAG. +It can result in a lot of open connections. + +The best way of using variables is via a Jinja template which will delay reading the value until the task execution. The template synaxt to do this is: + +.. code:: + + {{ var.value.<variable_name> }} + +or if you need to deserialize a json object from the variable : + +.. code:: + + {{ var.json.<variable_name> }} + + +.. note:: + + In general, you should not write any code outside the tasks. The code outside the tasks runs every time Airflow parses the DAG, which happens every second by default. + + +Testing a DAG +^^^^^^^^^^^^^ + +Airflow users should treat DAGs as production level code. The DAGs should have various tests to ensure that it produces expected results. +You can write a wide variety of tests for a DAG. Let's take a look at some of them. + +DAG Loader Test +--------------- + +This test should ensure that your DAG does not contain a piece of code that raises error while loading. +No additional code needs to be written by the user to run this test. + +.. code:: + + python your-dag-file.py + +Running the above command without any error ensures your DAG does not contain any uninstalled dependency, syntax errors, etc. + +You can look into :ref:`Testing a DAG <testing>` for details on how to test individual operators. + +Unit tests +----------- + +Unit tests ensure that there is no incorrect code in your DAG. You can write a unit test for your tasks as well as your DAG. + +**Unit test for loading a DAG:** + +.. code:: Review comment: (FYI this one isn't required it seems as python is configured as the default lanauge. I think) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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