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From "Jeremiah Lowin (JIRA)" <>
Subject [jira] [Commented] (AIRFLOW-14) DagRun Refactor (Scheduler 2.0)
Date Fri, 06 May 2016 21:58:12 GMT


Jeremiah Lowin commented on AIRFLOW-14:

Good question. Scheduling and evaluating pool availability actually happen separately. "Scheduling"
really means "here's a list of tasks that should be run" but the Scheduler doesn't actually
kick them off unless pool slots are available. The good news is that the slots are read from
the database, so it's robust across processes. The bad news is that a race condition already
exists that could compromise it for multiple simultaneous jobs -- see
This proposal (Airflow-14) doesn't address that problem.

> DagRun Refactor (Scheduler 2.0)
> -------------------------------
>                 Key: AIRFLOW-14
>                 URL:
>             Project: Apache Airflow
>          Issue Type: Improvement
>          Components: scheduler
>            Reporter: Jeremiah Lowin
>            Assignee: Jeremiah Lowin
>              Labels: backfill, dagrun, scheduler
> For full proposal, please see the Wiki:
> Borrowing from that page: 
> *Description of New Workflow*
> DagRuns represent the state of a DAG at a certain point in time (perhaps they should
be called DagInstances?). To run a DAG – or to manage the execution of a DAG – a DagRun
must first be created. This can be done manually (simply by creating a DagRun object) or automatically,
using methods like dag.schedule_dag(). Therefore, both scheduling new runs OR introducing
ad-hoc runs can be done by any process at any time, simply by creating the appropriate object.
> Just creating a DagRun is not enough to actually run the DAG (just as creating a TaskInstance
is not the same as actually running a task). We need a Job for that. The DagRunJob is fairly
simple in structure. It maintains a set of DagRuns that it is tasked with executing, and loops
over that set until all the DagRuns either succeed or fail. New DagRuns can be passed to the
job explicitly via DagRunJob.submit_dagruns() or by defining its DagRunJob.collect_dagruns()
method, which is called during each loop. When the DagRunJob is executing a specific DagRun,
it locks it. Other DagRunJobs will not try to execute locked DagRuns. This way, many DagRunJobs
can run simultaneously in either a local or distributed setting, and can even be pointed at
the same DagRuns, without worrying about collisions or interference.
> The basic DagRunJob loop works like this:
> - refresh dags
> - collect new dagruns
> - process dagruns (including updating dagrun states for success/failure)
> - call executor/own heartbeat
> By tweaking the DagRunJob, we can easily recreate the behavior of the current SchedulerJob
and BackfillJob. The Scheduler simply runs forever and picks up ALL active DagRuns in collect_dagruns();
Backfill generates DagRuns corresponding to the requested start/end dates and submits them
to itself prior to initiating its loop.

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