[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: auto-deprioritized-major auto-deprioritized-minor auto-unassigned
(was: auto-deprioritized-major auto-unassigned stale-minor)
Priority: Not a Priority (was: Minor)
This issue was labeled "stale-minor" 7 days ago and has not received any
updates so it is being deprioritized. If this ticket is actually Minor, please
raise the priority and ask a committer to assign you the issue or revive the
public discussion.
> 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: API / DataSet
>Reporter: Kurt Young
>Priority: Not a Priority
> Labels: auto-deprioritized-major, auto-deprioritized-minor,
> auto-unassigned
>
> 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 the 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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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: auto-deprioritized-major auto-unassigned stale-minor (was:
auto-deprioritized-major auto-unassigned)
I am the [Flink Jira Bot|https://github.com/apache/flink-jira-bot/] and I help
the community manage its development. I see this issues has been marked as
Minor but is unassigned and neither itself nor its Sub-Tasks have been updated
for 180 days. I have gone ahead and marked it "stale-minor". If this ticket is
still Minor, please either assign yourself or give an update. Afterwards,
please remove the label or in 7 days the issue will be deprioritized.
> 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: API / DataSet
>Reporter: Kurt Young
>Priority: Minor
> Labels: auto-deprioritized-major, auto-unassigned, stale-minor
>
> 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 the 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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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: auto-deprioritized-major auto-unassigned (was: auto-unassigned
stale-major)
Priority: Minor (was: Major)
This issue was labeled "stale-major" 7 days ago and has not received any
updates so it is being deprioritized. If this ticket is actually Major, please
raise the priority and ask a committer to assign you the issue or revive the
public discussion.
> 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: API / DataSet
>Reporter: Kurt Young
>Priority: Minor
> Labels: auto-deprioritized-major, auto-unassigned
>
> 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 the 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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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: auto-unassigned stale-major (was: auto-unassigned)
I am the [Flink Jira Bot|https://github.com/apache/flink-jira-bot/] and I help
the community manage its development. I see this issues has been marked as
Major but is unassigned and neither itself nor its Sub-Tasks have been updated
for 30 days. I have gone ahead and added a "stale-major" to the issue". If this
ticket is a Major, please either assign yourself or give an update. Afterwards,
please remove the label or in 7 days the issue will be deprioritized.
> 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: API / DataSet
>Reporter: Kurt Young
>Priority: Major
> Labels: auto-unassigned, stale-major
>
> 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 the 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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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: auto-unassigned (was: stale-assigned)
> 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: API / DataSet
>Reporter: Kurt Young
>Assignee: Xu Pingyong
>Priority: Major
> Labels: auto-unassigned
>
> 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 the 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.
--
This message was sent by Atlassian Jira
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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Flink Jira Bot updated FLINK-6309:
--
Labels: stale-assigned (was: )
> 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: API / DataSet
>Reporter: Kurt Young
>Assignee: Xu Pingyong
>Priority: Major
> Labels: stale-assigned
>
> 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 the 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.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhuoluo Yang updated FLINK-6309:
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 the 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 the 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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[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhuoluo Yang updated FLINK-6309:
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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhuoluo Yang updated FLINK-6309:
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 the whole job vertexes to calculate
the memory consumer weights, and then assign the weights for each job vertex.
In a case of a large job graph, e.g. with multiple joins, group reduces, the
consumer weights will be high and the usable memory for each job vertex will be
very low.
I think it makes more sense to calculate the memory consumer weights in job
vertex level (after chaining) to maximize the memory utility.
> 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.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
[jira] [Updated] (FLINK-6309) Memory consumer weights should be calculated in job vertex level
[
https://issues.apache.org/jira/browse/FLINK-6309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Zhuoluo Yang updated FLINK-6309:
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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