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Subject [GitHub] ubermen opened a new pull request #3885: [AIRFLOW-3001] Add index 'ti_dag_date' to taskinstance
Date Fri, 05 Oct 2018 07:12:16 GMT
ubermen opened a new pull request #3885: [AIRFLOW-3001] Add index 'ti_dag_date' to taskinstance
URL: https://github.com/apache/incubator-airflow/pull/3885
 
 
   Sorry for recreate PR. (I had ruined master of my fork. It will be never occured again.)
   
   [ Description ]
   There was no index composed of dag_id and execution_date. So, when scheduler find all tis
of dagrun like this "select * from task_instance where dag_id = 'some_id' and execution_date
= '2018-09-01 ...'", this query will be using ti_dag_state index (I was testing it in mysql
workbench. I was expecting 'ti_state_lkp' but, it was not that case). Perhaps there's no problem
when range of execution_date is small (under 1000 dagrun), but I had experienced slow allocation
of tis when the dag had 1000+ accumulative dagrun. So, now I was using airflow with adding
new index ti_dag_date (dag_id, execution_date) on task_instance table. I have attached result
of my test :)
   
   [ Test ] I have tested using 1.10 version
   1. just running scheduler with past start_date and high concurrency. (3 years ago, 10 minute
interval)
   2. scheduler may be executing  backfill and "select tis" query (like below sequence)
   models.py > DAG.run
   jobs.py > BaseJob.run
   jobs.py > BackfillJob._execute
   jobs.py > BackfillJob._execute_for_run_dates
   jobs.py > BackfillJob._task_instances_for_dag_run
   models.py > DagRun.get_task_instances
   tis = session.query(TI).filter(
       TI.dag_id == self.dag_id,
       TI.execution_date == self.execution_date,
   )
   3. wait until enough dagruns will be accumlate.
   I can find that many slow query logs get to occur from mysql log file. (query like below
sample)
   "select * from task_instance where dag_id = 'some_id' and execution_date = '2018-09-01
...'"
   
   
   [ASIS] current
   ![image](https://user-images.githubusercontent.com/6738941/45285016-fb9ecc00-b51c-11e8-945c-c28d81aece02.png)
   
   [TOBE] after adding new index
   ![image](https://user-images.githubusercontent.com/6738941/45285019-fe012600-b51c-11e8-91fa-a66c2293ca5d.png)
   
   ### Jira
   
   - [ ] My PR addresses the following [Airflow Jira](https://issues.apache.org/jira/browse/AIRFLOW/)
issues and references them in the PR title. For example, "\[AIRFLOW-XXX\] My Airflow PR"
     - https://issues.apache.org/jira/browse/AIRFLOW-XXX
     - In case you are fixing a typo in the documentation you can prepend your commit with
\[AIRFLOW-XXX\], code changes always need a Jira issue.
   
   ### Description
   
   - [ ] Here are some details about my PR, including screenshots of any UI changes:
   
   ### Tests
   
   - [ ] My PR adds the following unit tests __OR__ does not need testing for this extremely
good reason:
   
   ### Commits
   
   - [ ] My commits all reference Jira issues in their subject lines, and I have squashed
multiple commits if they address the same issue. In addition, my commits follow the guidelines
from "[How to write a good git commit message](http://chris.beams.io/posts/git-commit/)":
     1. Subject is separated from body by a blank line
     1. Subject is limited to 50 characters (not including Jira issue reference)
     1. Subject does not end with a period
     1. Subject uses the imperative mood ("add", not "adding")
     1. Body wraps at 72 characters
     1. Body explains "what" and "why", not "how"
   
   ### Documentation
   
   - [ ] In case of new functionality, my PR adds documentation that describes how to use
it.
     - When adding new operators/hooks/sensors, the autoclass documentation generation needs
to be added.
   
   ### Code Quality
   
   - [ ] Passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
   

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