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From "Chris Riccomini (JIRA)" <j...@apache.org>
Subject [jira] [Created] (AIRFLOW-807) Scheduler is very slow when a .py file has many DAGs in it
Date Wed, 25 Jan 2017 19:28:26 GMT
Chris Riccomini created AIRFLOW-807:
---------------------------------------

             Summary: Scheduler is very slow when a .py file has many DAGs in it
                 Key: AIRFLOW-807
                 URL: https://issues.apache.org/jira/browse/AIRFLOW-807
             Project: Apache Airflow
          Issue Type: Bug
          Components: scheduler
    Affects Versions: 1.8.0b2
            Reporter: Chris Riccomini
            Assignee: Chris Riccomini
             Fix For: Airflow 1.8


While running Airflow 1.8.0b2 in production, we noticed a significant performance issue with
one of our DAGs.

The .py file (called db.py) generates a bunch of DAGs. This file was taking > 900 seconds
for the scheduler to process, which was introducing significant delays in our data pipeline.

We enabled slow_query log for MySQL, and saw that this query was taking more than 10 seconds
per DAG in the .py file:

{code:sql}
SELECT task_instance.task_id AS task_id, max(task_instance.execution_date) AS max_ti 
FROM task_instance 
WHERE task_instance.dag_id = 'dag1' AND task_instance.state = 'success' AND task_instance.task_id
IN ('t1', 't2') GROUP BY task_instance.task_id
{code}

This query is run inside jobs.py's manage_slas method. When running an explain, we can see
that MySQL is using the wrong index for it:

{noformat}
+----+-------------+---------------+------+----------------------------------------------------+----------+---------+-------+-------+--------------------------+
| id | select_type | table         | type | possible_keys                                
     | key      | key_len | ref   | rows  | Extra                    |
+----+-------------+---------------+------+----------------------------------------------------+----------+---------+-------+-------+--------------------------+
|  1 | SIMPLE      | task_instance | ref  | PRIMARY,ti_dag_state,ti_pool,ti_state_lkp,ti_state
| ti_state | 63      | const | 81898 | Using where; Using index |
+----+-------------+---------------+------+----------------------------------------------------+----------+---------+-------+-------+--------------------------+
{noformat}

It's using ti_state, but should be using ti_primary. We tried running ANALYZE/OPTIMIZE on
the {{task_instance}} table, but it didn't improve the query plan or performance time.

Next, we added a hint to the SqlAlchemy query object, which improved the performance by about
10x, dropping the db.py parsing down to 90 seconds.

I then got another 2x boost by simply aborting the manage_slas method at the start if the
DAG has no tasks SLAs in it (none of our DAGs do). This dropped the db.py parse time to 45-50
seconds.

This JIRA is to add a short circuit in manage_slas, and a hint for MySQL in the query.





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