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From "Aljoscha Krettek (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-4576) Low Watermark Service in JobManager for Streaming Sources
Date Wed, 02 Nov 2016 14:31:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-4576?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15629137#comment-15629137

Aljoscha Krettek commented on FLINK-4576:

I think the watermark needs to be collected across the parallel instances of an operator.
For example, if you have a source, you would collect the current watermark from all instances
of that source, combine it and then send out the minimum watermark to all those source instances.

If there are several operators in the topology that would need the watermark notification
functionality then this process has to be done for each operator separately, i.e. for all
parallel instances of one operator.

> Low Watermark Service in JobManager for Streaming Sources
> ---------------------------------------------------------
>                 Key: FLINK-4576
>                 URL: https://issues.apache.org/jira/browse/FLINK-4576
>             Project: Flink
>          Issue Type: New Feature
>          Components: JobManager, Streaming, TaskManager
>            Reporter: Tzu-Li (Gordon) Tai
>            Assignee: Tzu-Li (Gordon) Tai
>            Priority: Blocker
>             Fix For: 1.2.0
> As per discussion in FLINK-4341 by [~aljoscha] and [~StephanEwen], we need a low watermark
service in the JobManager to support transparent resharding / partition discovery for our
Kafka and Kinesis consumers (and any future streaming connectors in general for which the
external system may elastically scale up and down independently of the parallelism of sources
in Flink). The main idea is to let source subtasks that don't emit their own watermarks (because
they currently don't have data partitions to consume) emit the low watermark across all subtasks,
instead of simply emitting a Long.MAX_VALUE watermark and forbidding them to be assigned partitions
in the future.
> The proposed implementation, from a high-level: a {{LowWatermarkCoordinator}} will be
added to execution graphs, periodically triggering only the source vertices with a {{RetrieveLowWatermark}}
message. The tasks reply to the JobManager through the actor gateway (or a new interface after
FLINK-4456 gets merged) with a {{ReplyLowWatermark}} message. When the coordinator collects
all low watermarks for a particular source vertex and determines the aggregated low watermark
for this round (accounting only values that are larger than the aggregated low watermark of
the last round), it sends a {{NotifyNewLowWatermark}} message to the source vertex's tasks.
> The messages will only be relevant to tasks that implement an internal {{LowWatermarkCooperatingTask}}
interface. For now, only {{SourceStreamTask}} should implement {{LowWatermarkCooperatingTask}}.
> Source functions should implement a public {{LowWatermarkListener}} interface if they
wish to get notified of the aggregated low watermarks across subtasks. Connectors like the
Kinesis consumer can choose to emit this watermark if the subtask currently does not have
any shards, so that downstream operators may still properly advance time windows (implementation
for this is tracked as a separate issue).
> Overall, the service will include -
> New messages between JobManager <-> TaskManager:
> {{RetrieveLowWatermark(jobId, jobVertexId, taskId, timestamp)}}
> {{ReplyLowWatermark(jobId, jobVertexId, taskId, currentLowWatermark)}}
> {{NotifyNewLowWatermark(jobId, jobVertexId, taskId, newLowWatermark, timestamp)}}
> New internal task interface {{LowWatermarkCooperatingTask}} in flink-runtime
> New public interface {{LowWatermarkListener}} in flink-streaming-java
> Might also need to extend {{SourceFunction.SourceContext}} to support retrieving the
current low watermark of sources.
> Any feedback for this is appreciated!

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