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https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Zhuoluo Yang updated FLINK-6309:
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Description:
Currently, in {{PlanFinalizer}}, we travel all the job vertices to calculate
the consumer weights of the memory and then assign the weights for each job
vertex. In case of a large job graph, e.g. with multiple joins, group reduces,
the value of consumer weights will be very high and the available memory for
each job vertex will be very low.
I think it makes more sense to calculate the consumer weights of the memory at
the job vertex level (after chaining), in order to maximize the usage ratio of
the memory.
was:
Currently, in {{PlanFinalizer}}, we travel all the job vertices to calculate
the consumer weights of the memory, and then assign the weights for each job
vertex. In case of a large job graph, e.g. with multiple joins, group reduces,
the value of consumer weights will be very high and the available memory for
each job vertex will be very low.
I think it makes more sense to calculate the consumer weights of the memory at
the job vertex level (after chaining), in order to maximize the usage ratio of
the memory.
> Memory consumer weights should be calculated in job vertex level
> ----------------------------------------------------------------
>
> Key: FLINK-6309
> URL: https://issues.apache.org/jira/browse/FLINK-6309
> Project: Flink
> Issue Type: Improvement
> Components: Optimizer
> Reporter: Kurt Young
> Assignee: Xu Pingyong
>
> Currently, in {{PlanFinalizer}}, we travel all the job vertices to calculate
> the consumer weights of the memory and then assign the weights for each job
> vertex. In case of a large job graph, e.g. with multiple joins, group
> reduces, the value of consumer weights will be very high and the available
> memory for each job vertex will be very low.
> I think it makes more sense to calculate the consumer weights of the memory
> at the job vertex level (after chaining), in order to maximize the usage
> ratio of the memory.
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