Some systems can get overwhelmed when too many processes hit them at the same time. Airflow pools can be used to
limit the execution parallelism on arbitrary sets of tasks. The list of pools is managed in the UI
Menu -> Admin -> Pools) by giving the pools a name and assigning it a number of worker slots.
Tasks can then be associated with one of the existing pools by using the
pool parameter when creating tasks:
aggregate_db_message_job = BashOperator( task_id="aggregate_db_message_job", execution_timeout=timedelta(hours=3), pool="ep_data_pipeline_db_msg_agg", bash_command=aggregate_db_message_job_cmd, dag=dag, ) aggregate_db_message_job.set_upstream(wait_for_empty_queue)
Tasks will be scheduled as usual while the slots fill up. The number of slots occupied by a task can be configured by
pool_slots (see section below). Once capacity is reached, runnable tasks get queued and their state will show as such in the UI.
As slots free up, queued tasks start running based on the Priority Weights of the task and its
Note that if tasks are not given a pool, they are assigned to a default pool
default_pool, which is
initialized with 128 slots and can be modified through the UI or CLI (but cannot be removed).
Using multiple pool slots¶
Airflow tasks will each occupy a single pool slot by default, but they can be configured to occupy more with the
pool_slots argument if required.
This is particularly useful when several tasks that belong to the same pool don’t carry the same “computational weight”.
For instance, consider a pool with 2 slots,
Pool(pool='maintenance', slots=2), and the following tasks:
BashOperator( task_id="heavy_task", bash_command="bash backup_data.sh", pool_slots=2, pool="maintenance", ) BashOperator( task_id="light_task1", bash_command="bash check_files.sh", pool_slots=1, pool="maintenance", ) BashOperator( task_id="light_task2", bash_command="bash remove_files.sh", pool_slots=1, pool="maintenance", )
Since the heavy task is configured to use 2 pool slots, it depletes the pool when running. Therefore, any of the light tasks must queue and wait for the heavy task to complete before they are executed. Here, in terms of resource usage, the heavy task is equivalent to two light tasks running concurrently.
This implementation can prevent overwhelming system resources, which (in this example) could occur when a heavy and a light task are running concurrently. On the other hand, both light tasks can run concurrently since they only occupy one pool slot each, while the heavy task would have to wait for two pool slots to become available before getting executed.
Pools and SubDAGs do not interact as you might first expect. SubDAGs will not honor any pool you set on them at the top level; pools must be set on the tasks inside the SubDAG directly.