How to run
docker-compose build
docker-compose up
Setup
Have 3 DAGs:
- ~240 Tasks, executes every hour, runs for about 45-50 minutes total
- ~600 Tasks, executes every 5 days, runs for days
- ~10 Tasks, executes on trigger, runs for 10-50 minutes
DAGs run on the default_pool with max_active_tasks set to 2.
My AirFlow config file has the following:
parallelism = 32
default_pool_task_slot_count = 6
executor = LocalExecutor
default_task_weight_rule = absolute
parallelism was set initially like that. default_pool_task_slot_count was set to 6, because it seemed rational that if all of my 3 dags are executing at the same time, the maximum amount of tasks that can be executed is 3*2=6.
The problem:
Almost every time any one of the DAGs is executed, no tasks from other DAGs will start until all the tasks from the first one finish. That is, if slow DAG_2 with 600 tasks starts, DAG_1 will have to wait for days to start even a single Task.
The Logs for the Scheduler look like this:
scheduler_1 |
scheduler_1 | [2022-02-09 10:02:27,116] {scheduler_job.py:288} INFO - 4 tasks up for execution:
scheduler_1 |
scheduler_1 |
scheduler_1 |
scheduler_1 |
scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:322} INFO - Figuring out tasks to run in Pool(name=default_pool) with 4 open slots and 4 task instances ready to be queued
scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:349} INFO - DAG Slow_DAG has 2/2 running and queued tasks
scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:357} INFO - Not executing
since the number of tasks running or queued from DAG Slow_DAG is >= to the DAG's max_active_tasks limit of 2 scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:349} INFO - DAG Slow_DAG has 2/2 running and queued tasks scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:357} INFO - Not executing
since the number of tasks running or queued from DAG Slow_DAG is >= to the DAG's max_active_tasks limit of 2 scheduler_1 | [2022-02-09 10:02:27,119] {scheduler_job.py:349} INFO - DAG Slow_DAG has 2/2 running and queued tasks scheduler_1 | [2022-02-09 10:02:27,120] {scheduler_job.py:357} INFO - Not executing
since the number of tasks running or queued from DAG Slow_DAG is >= to the DAG's max_active_tasks limit of 2 scheduler_1 | [2022-02-09 10:02:27,120] {scheduler_job.py:349} INFO - DAG Slow_DAG has 2/2 running and queued tasks scheduler_1 | [2022-02-09 10:02:27,120] {scheduler_job.py:357} INFO - Not executing
since the number of tasks running or queued from DAG Slow_DAG is >= to the DAG's max_active_tasks limit of 2 scheduler_1 | [2022-02-09 10:02:27,120] {scheduler_job.py:410} INFO - Setting the following tasks to queued state: scheduler_1 |
I have fixed this by setting
parallelism = 1000
default_pool_task_slot_count = 999
The reason why this occurs and why the solution works
We need to look at this method of the Scheduler: https://github.com/apache/airflow/blob/2.2.3/airflow/jobs/scheduler_job.py#L229
In it, the overall logic is the following:
- Receive
max_tis(==parallelism - active running tasksfrom the config) in the arguments. In my case that would be 30 - Calculate how many free slots in all all Pools we have. In my case that would be 4.
- Select the minimum and update the
max_tisvariable:max_tis = min(4, 30) = 4 - Query the DB for tasks that are Scheduled in an unpaused DAGs that are running normally and order them by DAG execution date.
- Limit the query by
max_tis. - Loop over each returned task and check if we can run it. Run, if possible.
Now, since the tasks are ordered by execution date, this will return us tasks for a DAG that started first, which lets say was DAG_2. It has over 600 tasks. The LIMIT operation will return 4 of them. Each off these tasks cannot be run since there are already 2 tasks running and the max_active_tasks of the DAG is 2. Thus, we just wait till one of these tasks finish and start another task from the same DAG.
1 Jan 07, 2022
3 Sep 20, 2021
407 Nov 17, 2022
1 Feb 03, 2022
4 Oct 01, 2021
3 Jul 26, 2022
61 Oct 08, 2022
18 Dec 26, 2022
1 Nov 19, 2021
2 Nov 11, 2021
3 Sep 11, 2022
2 Jan 09, 2022
0 Feb 07, 2022
4 Sep 20, 2022
6 May 16, 2022
1 Jan 15, 2022
6 May 26, 2022
6.9k Dec 27, 2022
156 Sep 28, 2022
27 Dec 08, 2022