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Stephan Ewen commented on FLINK-23402: -------------------------------------- Can we do one of the following: (1) Either change {{RuntimeExecutionMode}} as originally suggested to capture the shuffle mode as well, so the shuffle mode is only configurable for batch execution cases. (2) Or rename the {{ShuffleMode}} to {{BatchExecutionShuffleMode}}, make it clear that this is only interpreted for batch execution, and drop the {{AUTOMATIC}} setting. I would be quite fine with (2), but currently find it weird that - Either you need to set the {{ShuffleMode}} to {{AUTOMATIC}} so that you do not get into conflicts with the {{RuntimeExecutionMode}}. This voids the main benefit of having two settings, namely being able to say "pick execution mode automatic, but if it is batch, use setting X for shuffles" - Or you actually can set the {{ShuffleMode}} independent from {{RuntimeExecutionMode}} (and the setting gets ignored for STREAMING), in which case, what is the point of {{ShuffleMode.AUTOMATIC}} ? > Expose a consistent GlobalDataExchangeMode > ------------------------------------------ > > Key: FLINK-23402 > URL: https://issues.apache.org/jira/browse/FLINK-23402 > Project: Flink > Issue Type: Sub-task > Components: API / DataStream > Reporter: Timo Walther > Assignee: Timo Walther > Priority: Major > Labels: pull-request-available > > The Table API makes the {{GlobalDataExchangeMode}} configurable via > {{table.exec.shuffle-mode}}. > In Table API batch mode the StreamGraph is configured with > {{ALL_EDGES_BLOCKING}} and in DataStream API batch mode > {{FORWARD_EDGES_PIPELINED}}. > I would vote for unifying the exchange mode of both APIs so that complex SQL > pipelines behave identical in {{StreamTableEnvironment}} and > {{TableEnvironment}}. Also the feedback a got so far would make > {{ALL_EDGES_BLOCKING}} a safer option to run pipelines successfully with > limited resources. > [~lzljs3620320] > {quote} > The previous history was like this: > - The default value is pipeline, and we find that many times due to > insufficient resources, the deployment will hang. And the typical use of > batch jobs is small resources running large parallelisms, because in batch > jobs, the granularity of failover is related to the amount of data processed > by a single task. The smaller the amount of data, the faster the fault > tolerance. So most of the scenarios are run with small resources and large > parallelisms, little by little slowly running. > - Later, we switched the default value to blocking. We found that the better > blocking shuffle implementation would not slow down the running speed much. > We tested tpc-ds and it took almost the same time. > {quote} > [~dwysakowicz] > {quote} > I don't see a problem with changing the default value for DataStream batch > mode if you think ALL_EDGES_BLOCKING is the better default option. > {quote} > In any case, we should make this configurable for DataStream API users and > make the specific Table API option obsolete. -- This message was sent by Atlassian Jira (v8.3.4#803005)