Chesnay Schepler created FLINK-10241:
Summary: Reduce performance/stability impact of latency metrics
Key: FLINK-10241
URL: https://issues.apache.org/jira/browse/FLINK-10241
Project: Flink
Chesnay Schepler created FLINK-10242:
Summary: Disable latency metrics by default
Key: FLINK-10242
URL: https://issues.apache.org/jira/browse/FLINK-10242
Project: Flink
Issue Type: Sub-ta
Chesnay Schepler created FLINK-10243:
Summary: Add option to reduce latency metrics granularity
Key: FLINK-10243
URL: https://issues.apache.org/jira/browse/FLINK-10243
Project: Flink
Issu
Chesnay Schepler created FLINK-10244:
Summary: Add option to measure latency as absolute value
Key: FLINK-10244
URL: https://issues.apache.org/jira/browse/FLINK-10244
Project: Flink
Issue
Shimin Yang created FLINK-10245:
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Summary: Add DataStream HBase Sink
Key: FLINK-10245
URL: https://issues.apache.org/jira/browse/FLINK-10245
Project: Flink
Issue Type: Sub-task
Compon
Till Rohrmann created FLINK-10246:
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Summary: Harden and separate MetricQueryService
Key: FLINK-10246
URL: https://issues.apache.org/jira/browse/FLINK-10246
Project: Flink
Issue Type: Improvem
Till Rohrmann created FLINK-10247:
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Summary: Run MetricQueryService in separate thread pool
Key: FLINK-10247
URL: https://issues.apache.org/jira/browse/FLINK-10247
Project: Flink
Issue Type:
Shimin Yang created FLINK-10248:
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Summary: Add HBase Table Sink
Key: FLINK-10248
URL: https://issues.apache.org/jira/browse/FLINK-10248
Project: Flink
Issue Type: Sub-task
Components:
Andrey Zagrebin created FLINK-10249:
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Summary: Document hadoop/presto s3 file system configuration
forwarding
Key: FLINK-10249
URL: https://issues.apache.org/jira/browse/FLINK-10249
Project: Flink
PengYang created FLINK-10250:
Summary: Flink doesn't support functions that extend
RichAggregateFunction in a group window
Key: FLINK-10250
URL: https://issues.apache.org/jira/browse/FLINK-10250
Project:
Till Rohrmann created FLINK-10251:
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Summary: Handle oversized response messages in AkkaRpcActor
Key: FLINK-10251
URL: https://issues.apache.org/jira/browse/FLINK-10251
Project: Flink
Issue Ty
Till Rohrmann created FLINK-10252:
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Summary: Handle oversized metric messges
Key: FLINK-10252
URL: https://issues.apache.org/jira/browse/FLINK-10252
Project: Flink
Issue Type: Sub-task
Till Rohrmann created FLINK-10253:
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Summary: Run MetricQueryService with lower priority
Key: FLINK-10253
URL: https://issues.apache.org/jira/browse/FLINK-10253
Project: Flink
Issue Type: Sub-
Thanks for starting this design discussion Zhijiang!
I really like the idea to introduce a ShuffleService abstraction which
allows to have different implementations depending on the actual use case.
Especially for batch jobs I can clearly see the benefits of persisting the
results somewhere else.
aitozi created FLINK-10254:
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Summary: Fix check in stateBackend
Key: FLINK-10254
URL: https://issues.apache.org/jira/browse/FLINK-10254
Project: Flink
Issue Type: Improvement
Components: S
Till Rohrmann created FLINK-10255:
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Summary: Standby Dispatcher locks submitted JobGraphs
Key: FLINK-10255
URL: https://issues.apache.org/jira/browse/FLINK-10255
Project: Flink
Issue Type: Bu
Hi Fabian
Thanks for your update. The opinions on upsert streams are highly
enlightening. I think now I am agree with you that we can choose option 2
to solve the problem: Throw away empty deletes when source generate
retractions, otherwise pass empty deletes down.
As for the UpsertSink, I think
陈梓立 created FLINK-10256:
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Summary: Rework JobManagerFailsITCase and JobManagerTest into
JobMasterITCase and JobMasterHAITCase
Key: FLINK-10256
URL: https://issues.apache.org/jira/browse/FLINK-10256
Project: Fli
Hi Hequn,
That's great! Yes, let's go with option 2 (from the source's point of view)
and later extend Join and Aggregation to discard empty deletes.
I agree that the filtering at the sink should be optional and configurable
via the query configuration.
Again, thanks for starting this discussion.
Dear community,
this is the weekly community update thread #35. Please post any news and
updates you want to share with the community to this thread.
# Flink 1.7 community roadmap
The Flink community started discussing which features will be included in
the next upcoming major release. Join the
Glad to receive your positive feedbacks Till!
Actually our motivation is to support batch job well as you mentioned.
For output level, flink already has the Subpartition abstraction(writer), and
currently there are PipelinedSubpartition(memory output) and
SpillableSubpartition(one-sp-one-file
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