[jira] [Closed] (FLINK-2119) Add ExecutionGraph support for leg-wise scheduling

["Stephan Ewen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-2119?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Stephan Ewen closed FLINK-2119.
-------------------------------
    Resolution: Won't Fix

> Add ExecutionGraph support for leg-wise scheduling
> --------------------------------------------------
>
>                 Key: FLINK-2119
>                 URL: https://issues.apache.org/jira/browse/FLINK-2119
>             Project: Flink
>          Issue Type: Improvement
>          Components: Scheduler
>    Affects Versions: 0.10.0
>            Reporter: Ufuk Celebi
>
> Scheduling currently happens by lazily unrolling the ExecutionGraph from the sources.
> 1. All sources are scheduled for execution.
> 2. Their results trigger scheduling and deployment of the receiving tasks (either on the first available buffer or when all are produced [pipelined vs. blocking exchange]).
> For certain batch jobs this can be problematic as many tasks will be running at the same time and consume task manager resources like executionslots and memory. For these jobs, it is desirable to schedule the ExecutionGraph in with different strategies.
> With respect to the ExecutionGraph, the current limitation is that data availability for a result always triggers scheduling of the consuming tasks. This needs to be more general to allow different scheduling strategies.
> Consider the following example:
> {code}
>           [ union ]
>          /         \
>         /           \
>   [ source 1 ]  [ source 2 ]
> {code}
> Currently, both sources are scheduled concurrently and the "faster" one triggers scheduling of the union. It is desirable to first allow source 1 to completly produce its result, then trigger scheduling of source 2, and only then schedule the union.
> The required changes in the ExecutionGraph are conceptually straight-forward: instead of going through the list of result consumers and scheduling them, we need to be able to run a more general action. For normal operation, this will still schedule the consumer task, but we can also configure it to kick of the next source etc.



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