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Optimize Cloud Composer via Better Airflow DAGs

Hosting, orchestrating, and managing data pipelines is a complex process for any business.  Google Cloud offers Cloud Composer – a fully managed workflow orchestration service – enabling businesses to create, schedule, monitor, and manage workflows that span across clouds and on-premises data centers. Cloud Composer is built on the popular Apache Airflow open source project and operates using the Python programming language.  Apache Airflow allows users to create directed acyclic graphs (DAGs) of tasks, which can be scheduled to run at specific intervals or triggered by external events.

This guide contains a generalized checklist of activities when authoring Apache Airflow DAGs.  These items follow best practices determined by Google Cloud and the open source community.  A collection of performant DAGs will enable Cloud Composer to work optimally and standardized authoring will help developers manage hundreds or even thousands of DAGs.  Each item will benefit your Cloud Composer environment and your development process.

Get Started

1. Standardize file names. Help other developers browse your collection of DAG files.
a. ex)

2. DAGs should be deterministic.
a. A given input will always produce the same output.

3. DAGs should be idempotent. 
a. Triggering the DAG multiple times has the same effect/outcome.

4. Tasks should be atomic and idempotent. 
a. Each task should be responsible for one operation that can be re-run independently of the others. In an atomized task, a success in part of the task means a success of the entire task.

5. Simplify DAGs as much as possible.
a. Simpler DAGs with fewer dependencies between tasks tend to have better scheduling performance because they have less overhead. A linear structure (e.g. A -> B -> C) is generally more efficient than a deeply nested tree structure with many dependencies. 

Standardize DAG Creation

6. Add an owner to your default_args.
a. Determine whether you’d prefer the email address / id of a developer, or a distribution list / team name.

7. Use with DAG() as dag: instead of dag = DAG()
a. Prevent the need to pass the dag object to every operator or task group.

8. Set a version in the DAG ID. 
a. Update the version after any code change in the DAG.
b. This prevents deleted Task logs from vanishing from the UI, no-status tasks generated for old dag runs, and general confusion of when DAGs have changed.
c. Airflow open-source has plans to implement versioning in the future. 

9. Add tags to your DAGs.
a. Help developers navigate the Airflow UI via tag filtering.
b. Group DAGs by organization, team, project, application, etc. 

10. Add a DAG description. 
a. Help other developers understand your DAG.

11. Pause your DAGs on creation. 
a. This will help avoid accidental DAG runs that add load to the Cloud Composer environment.

12. Set catchup=False to avoid automatic catch ups overloading your Cloud Composer Environment.

13. Set a dagrun_timeout to avoid dags not finishing, and holding Cloud Composer Environment resources or introducing collisions on retries.

14. Set SLAs at the DAG level to receive alerts for long-running DAGs.
a. Airflow SLAs are always defined relative to the start time of the DAG, not to individual tasks.
b. Ensure that sla_miss_timeout is less than the dagrun_timeout.
c. Example: If your DAG usually takes 5 minutes to successfully finish, set the sla_miss_timeout to 7 minutes and the dagrun_timeout to 10 minutes.  Determine these thresholds based on the priority of your DAGs.

15. Ensure all tasks have the same start_date by default by passing arg to DAG during instantiation

16. Use a static start_date with your DAGs. 
a. A dynamic start_date is misleading, and can cause failures when clearing out failed task instances and missing DAG runs.

17. Set retries as a default_arg applied at the DAG level and get more granular for specific tasks only where necessary. 
a. A good range is 1–4 retries. Too many retries will add unnecessary load to the Cloud Composer environment.

Example putting all the above together:

code_block[StructValue([(u’code’, u’import airflowrnfrom airflow import DAGrnfrom airflow.operators.bash_operator import BashOperatorrnrn# Define default_args dictionary to specify default parameters of the DAG, such as the start date, frequency, and other settingsrndefault_args = {rn ‘owner’: ‘me’,rn ‘retries’: 2, # 2-4 retries maxrn ‘retry_delay’: timedelta(minutes=5),rn ‘is_paused_upon_creation’: True,rn ‘catchup’: False,rn}rnrn# Use the `with` statement to define the DAG object and specify the unique DAG ID and default_args dictionaryrnwith DAG(rn ‘dag_id_v1_0_0′, #versioned IDrn default_args=default_args,rn description=’This is a detailed description of the DAG’, #detailed descriptionrn start_date=datetime(2022, 1, 1), # Static start datern dagrun_timeout=timedelta(minutes=10), #timeout specific to this dagrn sla_miss_timeout=timedelta(minutes=7), # sla miss less than timeoutrn tags=[‘example’, ‘versioned_dag_id’], # tags specific to this dagrn schedule_interval=None,rn) as dag:rn # Define a task using the BashOperatorrn task = BashOperator(rn task_id=’bash_task’,rn bash_command=’echo “Hello World”‘rn )’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e33a31090>)])]

18. Define what should occur for each callback function. (send an email, log a context, message slack channel, etc.).  Depending on the DAG you may be comfortable doing nothing. 
a. success
b. failure
c. sla_miss
d. retry


code_block[StructValue([(u’code’, u’from airflow import DAGrnfrom airflow.operators.python_operator import PythonOperatorrnrndefault_args = {rn ‘owner’: ‘me’,rn ‘retries’: 2, # 2-4 retries maxrn ‘retry_delay’: timedelta(minutes=5),rn ‘is_paused_upon_creation’: True,rn ‘catchup’: False,rn}rnrndef on_success_callback(context):rn # when a task in the DAG succeedsrn print(f”Task {context[‘task_instance_key_str’]} succeeded!”)rnrndef on_sla_miss_callback(context):rn # when a task in the DAG misses its SLArn print(f”Task {context[‘task_instance_key_str’]} missed its SLA!”)rnrndef on_retry_callback(context):rn # when a task in the DAG retriesrn print(f”Task {context[‘task_instance_key_str’]} retrying…”)rnrndef on_failure_callback(context):rn # when a task in the DAG failsrn print(f”Task {context[‘task_instance_key_str’]} failed!”)rnrn# Create a DAG and set the callbacksrnwith DAG(rn ‘dag_id_v1_0_0′,rn default_args=default_args,rn description=’This is a detailed description of the DAG’,rn start_date=datetime(2022, 1, 1), rn dagrun_timeout=timedelta(minutes=10),rn sla_miss_timeout=timedelta(minutes=7),rn tags=[‘example’, ‘versioned_dag_id’],rn schedule_interval=None,rn on_success_callback=on_success_callback, # what to do on successrn on_sla_miss_callback=on_sla_miss_callback, # what to do on sla missrn on_retry_callback=on_retry_callback, # what to do on retryrn on_failure_callback=on_failure_callback # what to do on failurern) as dag:rnrn def example_task(**kwargs):rn # This is an example task that will be part of the DAGrn print(f”Running example task with context: {kwargs}”)rnrn # Create a task and add it to the DAGrn task = PythonOperator(rn task_id=”example_task”,rn python_callable=example_task,rn provide_context=True,rn )’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e33a31490>)])]

19. Use Task Groups to organize Tasks.


code_block[StructValue([(u’code’, u’# Use the `with` statement to define the DAG object and specify the unique DAG ID and default_args dictionaryrnwith DAG(rn ‘example_dag’,rn default_args=default_args,rn schedule_interval=timedelta(hours=1),rn) as dag:rn # Define the first task grouprn with TaskGroup(name=’task_group_1′) as tg1:rn # Define the first task in the first task grouprn task_1_1 = BashOperator(rn task_id=’task_1_1′,rn bash_command=’echo “Task 1.1″‘,rn dag=dag,rn )’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e42683150>)])]

Reduce the Load on Your Composer Environment

20. Use Jinja Templating / Macros instead of python functions.
a. Airflow’s template fields allow you to incorporate values from environment variables and jinja templates into your DAGs. This helps make your DAGs idempotent (meaning multiple invocations do not change the result) and prevents unnecessary function execution during Scheduler heartbeats.
b. The Airflow engine passes a few variables by default that are accessible in all templates.

Contrary to best practices, the following example defines variables based on datetime Python functions:

code_block[StructValue([(u’code’, u”# Variables used by tasksrn# Bad example – Define today’s and yesterday’s date using datetime modulerntoday = = – timedelta(1)”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229ca90>)])]

If this code is in a DAG file, these functions execute on every Scheduler heartbeat, which may not be performant. Even more importantly, this doesn’t produce an idempotent DAG. You can’t rerun a previously failed DAG run for a past date because is relative to the current date, not the DAG execution date.

A better way of implementing this is by using an Airflow Variable as such:

code_block[StructValue([(u’code’, u”# Variables used by tasksrn# Good example – Define yesterday’s date with an Airflow variablernyesterday = {{ yesterday_ds_nodash }}”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c750>)])]

21. Avoid creating your own additional Airflow Variables. 
a. The metadata database stores these variables and requires database connections to retrieve them. This can affect the performance of the Cloud Composer Environment. Use Environment Variables or Google Cloud Secrets instead.

22. Avoid running all DAGs on the exact same schedules (disperse workload as much as possible). 
a. Prefer to use cron expressions for schedule intervals compared to airflow macros or time_deltas. This allows a more rigid schedule and it’s easier to spread out workloads throughout the day, making it easier on your Cloud Composer environment.
b. can help with generating specific cron expression schedules.  Check out the examples here.


code_block[StructValue([(u’code’, u’schedule_interval=”*/5 * * * *”, # every 5 minutes.rnrn schedule_interval=”0 */6 * * *”, # at minute 0 of every 6th hour.’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c090>)])]

23. Avoid XComs except for small amounts of data. 
a. These add storage and introduce more connections to the database. 
b. Use JSON dicts as values if absolutely necessary. (one connection for many values inside dict)

24. Avoid adding unnecessary objects in the dags/ Google Cloud Storage path. 
a. If you must, add an .airflowignore file to GCS paths that the Airflow Scheduler does not need to parse. (sql, plug-ins, etc.)

25. Set execution timeouts for tasks.


code_block[StructValue([(u’code’, u”# Use the `PythonOperator` to define the taskrntask = PythonOperator(rn task_id=’my_task’,rn python_callable=my_task_function,rn execution_timeout=timedelta(minutes=30), # Set the execution timeout to 30 minutesrn dag=dag,rn)”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c350>)])]

26. Use Deferrable Operators over Sensors when possible. 
a. A deferrable operator can suspend itself and free up the worker when it knows it has to wait, and hand off the job of resuming it to a Trigger. As a result, while it suspends (defers), it is not taking up a worker slot and your cluster will have fewer/lesser resources wasted on idle Operators or Sensors.


code_block[StructValue([(u’code’, u’PYSPARK_JOB = {rn “reference”: { “project_id”: “PROJECT_ID” },rn “placement”: { “cluster_name”: “PYSPARK_CLUSTER_NAME” },rn “pyspark_job”: {rn “main_python_file_uri”: “gs://dataproc-examples/pyspark/hello-world/”rn },rn}rnrnDataprocSubmitJobOperator(rn task_id=”dataproc-deferrable-example”,rn job=PYSPARK_JOB,rn deferrable=True,rn )’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229cd10>)])]

27. When using Sensors, always define mode, poke_interval, and timeout. 
a. Sensors require Airflow workers to run.
b. Sensor checking every n seconds (i.e. poke_interval < 60)? Use mode=poke. A sensor in mode=poke will continuously poll every n seconds and hold Airflow worker resources. 
c. Sensor checking every n minutes (i.e. poke_interval >= 60)? Use mode=reschedule. A sensor in mode=reschedule will free up Airflow worker resources between poke intervals.


code_block[StructValue([(u’code’, u’table_partition_sensor = BigQueryTablePartitionExistenceSensor(rn project_id=”{{ project_id }}”,rn task_id=”bq_check_table_partition”,rn dataset_id=”{{ dataset }}”,rn table_id=”comments_partitioned”,rn partition_id=”{{ ds_nodash }}”,rn mode=”reschedule”rn poke_interval=60,rn timeout=60 * 5rn )’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229cc50>)])]

28. Offload processing to external services (BigQuery, Dataproc, Cloud Functions, etc.) to minimize load on the Cloud Composer environment.
a. These services usually have their own Airflow Operators for you to utilize.

29. Do not use sub-DAGs.
a. Sub-DAGs were a feature in older versions of Airflow that allowed users to create reusable groups of tasks within DAGs. However, Airflow 2.0 deprecated sub-DAGs because they caused performance and functional issues.

30. UsePub/Subfor DAG-to-DAG dependencies.
a. Here is an example for multi-cluster / dag-to-dag dependencies. 

31. Make DAGs load faster.
a. Avoid unnecessary “Top-level” Python code. DAGs with many imports, variables, functions outside of the DAG will introduce greater parse times for the Airflow Scheduler and in turn reduce the performance and scalability of Cloud Composer / Airflow.
b. Moving imports and functions within the DAG can reduce parse time (in the order of seconds).
c. Ensure that developed DAGs do not increase DAG parse times too much.


code_block[StructValue([(u’code’, u”import airflowrnfrom airflow import DAGrnfrom airflow.operators.python_operator import PythonOperatorrnrn# Define default_args dictionaryrndefault_args = {rn ‘owner’: ‘me’,rn ‘start_date’: datetime(2022, 11, 17),rn}rnrn# Use with statement and DAG context manager to instantiate the DAGrnwith DAG(rn ‘my_dag_id’,rn default_args=default_args,rn schedule_interval=timedelta(days=1),rn) as dag:rn # Import module within DAG blockrn import my_module # DO THISrnrn # Define function within DAG blockrn def greet(): # DO THISrn greeting = my_module.generate_greeting()rn print(greeting)rnrn # Use the PythonOperator to execute the functionrn greet_task = PythonOperator(rn task_id=’greet_task’,rn python_callable=greetrn )”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c6d0>)])]

Improve Development and Testing

32. Implement “self-checks” (via Sensors or Deferrable Operators).
a. To ensure that tasks are functioning as expected, you can add checks to your DAG. For example, if a task pushes data to a BigQuery partition, you can add a check in the next task to verify that the partition generates and that the data is correct.


code_block[StructValue([(u’code’, u’# ————————————————————rn # Transform source data and transfer to partitioned tablern # ————————————————————rnrn create_or_replace_partitioned_table_job = BigQueryInsertJobOperator(rn task_id=”create_or_replace_comments_partitioned_query_job”,rn configuration={rn “query”: {rn “query”: ‘sql/create_or_replace_comments_partitioned.sql’,rn “useLegacySql”: False,rn }rn },rn location=”US”,rn )rnrn create_or_replace_partitioned_table_job_error = dummy_operator.DummyOperator(rn task_id=”create_or_replace_partitioned_table_job_error”,rn trigger_rule=”one_failed”,rn )rnrn create_or_replace_partitioned_table_job_ok = dummy_operator.DummyOperator(rn task_id=”create_or_replace_partitioned_table_job_ok”, trigger_rule=”one_success”rn )rnrn # ————————————————————rn # Determine if today’s partition exists in comments_partitionedrn # ————————————————————rnrn table_partition_sensor = BigQueryTablePartitionExistenceSensor(rn project_id=”{{ project_id }}”,rn task_id=”bq_check_table_partition”,rn dataset_id=”{{ dataset }}”,rn table_id=”comments_partitioned”,rn partition_id=”{{ ds_nodash }}”,rn mode=”reschedule”rn poke_interval=60,rn timeout=60 * 5rn )rnrn create_or_replace_partitioned_table_job >> [rn create_or_replace_partitioned_table_job_error,rn create_or_replace_partitioned_table_job_ok,rn ]rn create_or_replace_partitioned_table_job_ok >> table_partition_sensor’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c890>)])]

33. Look for opportunities to dynamically generate similar tasks/task groups/DAGs via Python code.
a. This can simplify and standardize the development process for DAGs. 


code_block[StructValue([(u’code’, u’import airflowrnfrom airflow import DAGrnfrom airflow.operators.python_operator import PythonOperatorrnrndef create_dag(dag_id, default_args, task_1_func, task_2_func):rn with DAG(dag_id, default_args=default_args) as dag:rn task_1 = PythonOperator(rn task_id=’task_1′,rn python_callable=task_1_func,rn dag=dagrn )rn task_2 = PythonOperator(rn task_id=’task_2′,rn python_callable=task_2_func,rn dag=dagrn )rn task_1 >> task_2rn return dagrnrndef task_1_func():rn print(“Executing task 1”)rnrndef task_2_func():rn print(“Executing task 2″)rnrndefault_args = {rn ‘owner’: ‘me’,rn ‘start_date’: airflow.utils.dates.days_ago(2),rn}rnrnmy_dag_id = create_dag(rn dag_id=’my_dag_id’,rn default_args=default_args,rn task_1_func=task_1_func,rn task_2_func=task_2_funcrn)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e4229c2d0>)])]

34. Implement unit-testing for your DAGs


code_block[StructValue([(u’code’, u’from airflow import modelsrnfrom airflow.utils.dag_cycle_tester import test_cyclernrnrndef assert_has_valid_dag(module):rn “””Assert that a module contains a valid DAG.”””rnrn no_dag_found = Truernrn for dag in vars(module).values():rn if isinstance(dag, models.DAG):rn no_dag_found = Falsern test_cycle(dag) # Throws if a task cycle is found.rnrn if no_dag_found:rn raise AssertionError(‘module does not contain a valid DAG’)’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e7e30435750>)])]

35. Perform local development via the Composer Local Development CLI Tool.
a. Composer Local Development CLI tool streamlines Apache Airflow DAG development for Cloud Composer 2 by running an Airflow environment locally. This local Airflow environment uses an image of a specific Cloud Composer version.

36. If possible, keep a staging Cloud Composer Environment to fully test the complete DAG run before deploying in the production.
a. Parameterize your DAG to change the variables, e.g., the output path of Google Cloud Storage operation or the database used to read the configuration. Do not hard code values inside the DAG and then change them manually according to the environment.

37. Use a Python linting tool such as Pylint or Flake8 for standardized code.

38. Use a Python formatting tool such as Black or YAPF for standardized code.

Next Steps

In summary, this blog provides a comprehensive checklist of best practices for developing Airflow DAGs for use in Google Cloud Composer. By following these best practices, developers can help ensure that Cloud Composer is working optimally and that their DAGs are well-organized and easy to manage.

For more information about Cloud Composer, check out the following related blog posts and documentation pages:

What is Cloud Composer? 

Deutsche Bank uses Cloud Composer workload automation

Using Cloud Build to keep Airflow Operators up-to-date in your Composer environment

Writing DAGs (workflows) | Cloud Composer

Cloud BlogRead More



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