[
https://issues.apache.org/jira/browse/FLINK-4576?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15631405#comment-15631405
]
Tzu-Li (Gordon) Tai commented on FLINK-4576:
--------------------------------------------
Ah, right. Got lost on this part mid-discussion.
However, I have the feeling that letting all operators interact with this
service (as pointed out previously, basically all stream operators need to work
with the service) there's a bit of overkill in trying to solve what initially
brought out the discussion of the low watermark service in the first place:
some source subtasks like FlinkKafkaConsumer may initially be idle with no
partitions to read from, in which we would want to emit the global low
watermark across subtasks for them, instead of the max value watermark. So, in
the beginning we were trying to aim for a source-only solution.
But honestly I currently don't have better ideas on how to achieve that,
because we allow users to assign watermarks at sources and in middle of
topologies.
> 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!
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
