[jira] [Updated] (FLINK-15981) Control the direct memory in FileChannelBoundedData.FileBufferReader
[ https://issues.apache.org/jira/browse/FLINK-15981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhijiang updated FLINK-15981: - Fix Version/s: 1.12.0 > Control the direct memory in FileChannelBoundedData.FileBufferReader > > > Key: FLINK-15981 > URL: https://issues.apache.org/jira/browse/FLINK-15981 > Project: Flink > Issue Type: Improvement > Components: Runtime / Network >Affects Versions: 1.10.0, 1.10.1, 1.11.0, 1.10.2, 1.11.1 >Reporter: Jingsong Lee >Assignee: Zhijiang >Priority: Critical > Labels: pull-request-available > Fix For: 1.12.0 > > > Now, the default blocking BoundedData is FileChannelBoundedData. In its > reader, will create new direct buffer 64KB. > When parallelism greater than 100, users need configure > "taskmanager.memory.task.off-heap.size" to avoid direct memory OOM. It is > hard to configure, and it cost a lot of memory. Consider 1000 parallelism, > maybe we need 1GB+ for a task manager. > This is not conducive to the scenario of less slots and large parallelism. > Batch jobs could run little by little, but memory shortage would consume a > lot. > If we provided N-Input operators, maybe things will be worse. This means the > number of subpartitions that can be requested at the same time will be more. > We have no idea how much memory. > Here are my rough thoughts: > * Obtain memory from network buffers. > * provide "The maximum number of subpartitions that can be requested at the > same time". -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (FLINK-15981) Control the direct memory in FileChannelBoundedData.FileBufferReader
[ https://issues.apache.org/jira/browse/FLINK-15981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] ASF GitHub Bot updated FLINK-15981: --- Labels: pull-request-available (was: ) > Control the direct memory in FileChannelBoundedData.FileBufferReader > > > Key: FLINK-15981 > URL: https://issues.apache.org/jira/browse/FLINK-15981 > Project: Flink > Issue Type: Improvement > Components: Runtime / Network >Affects Versions: 1.10.0, 1.10.1, 1.11.0, 1.10.2, 1.11.1 >Reporter: Jingsong Lee >Priority: Critical > Labels: pull-request-available > > Now, the default blocking BoundedData is FileChannelBoundedData. In its > reader, will create new direct buffer 64KB. > When parallelism greater than 100, users need configure > "taskmanager.memory.task.off-heap.size" to avoid direct memory OOM. It is > hard to configure, and it cost a lot of memory. Consider 1000 parallelism, > maybe we need 1GB+ for a task manager. > This is not conducive to the scenario of less slots and large parallelism. > Batch jobs could run little by little, but memory shortage would consume a > lot. > If we provided N-Input operators, maybe things will be worse. This means the > number of subpartitions that can be requested at the same time will be more. > We have no idea how much memory. > Here are my rough thoughts: > * Obtain memory from network buffers. > * provide "The maximum number of subpartitions that can be requested at the > same time". -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (FLINK-15981) Control the direct memory in FileChannelBoundedData.FileBufferReader
[ https://issues.apache.org/jira/browse/FLINK-15981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhijiang updated FLINK-15981: - Affects Version/s: 1.10.1 1.11.0 1.10.2 1.11.1 > Control the direct memory in FileChannelBoundedData.FileBufferReader > > > Key: FLINK-15981 > URL: https://issues.apache.org/jira/browse/FLINK-15981 > Project: Flink > Issue Type: Improvement > Components: Runtime / Network >Affects Versions: 1.10.0, 1.10.1, 1.11.0, 1.10.2, 1.11.1 >Reporter: Jingsong Lee >Priority: Critical > > Now, the default blocking BoundedData is FileChannelBoundedData. In its > reader, will create new direct buffer 64KB. > When parallelism greater than 100, users need configure > "taskmanager.memory.task.off-heap.size" to avoid direct memory OOM. It is > hard to configure, and it cost a lot of memory. Consider 1000 parallelism, > maybe we need 1GB+ for a task manager. > This is not conducive to the scenario of less slots and large parallelism. > Batch jobs could run little by little, but memory shortage would consume a > lot. > If we provided N-Input operators, maybe things will be worse. This means the > number of subpartitions that can be requested at the same time will be more. > We have no idea how much memory. > Here are my rough thoughts: > * Obtain memory from network buffers. > * provide "The maximum number of subpartitions that can be requested at the > same time". -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (FLINK-15981) Control the direct memory in FileChannelBoundedData.FileBufferReader
[ https://issues.apache.org/jira/browse/FLINK-15981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Zhijiang updated FLINK-15981: - Fix Version/s: (was: 1.10.1) (was: 1.11.0) > Control the direct memory in FileChannelBoundedData.FileBufferReader > > > Key: FLINK-15981 > URL: https://issues.apache.org/jira/browse/FLINK-15981 > Project: Flink > Issue Type: Improvement > Components: Runtime / Network >Affects Versions: 1.10.0 >Reporter: Jingsong Lee >Priority: Critical > > Now, the default blocking BoundedData is FileChannelBoundedData. In its > reader, will create new direct buffer 64KB. > When parallelism greater than 100, users need configure > "taskmanager.memory.task.off-heap.size" to avoid direct memory OOM. It is > hard to configure, and it cost a lot of memory. Consider 1000 parallelism, > maybe we need 1GB+ for a task manager. > This is not conducive to the scenario of less slots and large parallelism. > Batch jobs could run little by little, but memory shortage would consume a > lot. > If we provided N-Input operators, maybe things will be worse. This means the > number of subpartitions that can be requested at the same time will be more. > We have no idea how much memory. > Here are my rough thoughts: > * Obtain memory from network buffers. > * provide "The maximum number of subpartitions that can be requested at the > same time". -- This message was sent by Atlassian Jira (v8.3.4#803005)
[jira] [Updated] (FLINK-15981) Control the direct memory in FileChannelBoundedData.FileBufferReader
[ https://issues.apache.org/jira/browse/FLINK-15981?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Xintong Song updated FLINK-15981: - Fix Version/s: 1.10.1 > Control the direct memory in FileChannelBoundedData.FileBufferReader > > > Key: FLINK-15981 > URL: https://issues.apache.org/jira/browse/FLINK-15981 > Project: Flink > Issue Type: Improvement > Components: Runtime / Network >Affects Versions: 1.10.0 >Reporter: Jingsong Lee >Priority: Critical > Fix For: 1.10.1, 1.11.0 > > > Now, the default blocking BoundedData is FileChannelBoundedData. In its > reader, will create new direct buffer 64KB. > When parallelism greater than 100, users need configure > "taskmanager.memory.task.off-heap.size" to avoid direct memory OOM. It is > hard to configure, and it cost a lot of memory. Consider 1000 parallelism, > maybe we need 1GB+ for a task manager. > This is not conducive to the scenario of less slots and large parallelism. > Batch jobs could run little by little, but memory shortage would consume a > lot. > If we provided N-Input operators, maybe things will be worse. This means the > number of subpartitions that can be requested at the same time will be more. > We have no idea how much memory. > Here are my rough thoughts: > * Obtain memory from network buffers. > * provide "The maximum number of subpartitions that can be requested at the > same time". -- This message was sent by Atlassian Jira (v8.3.4#803005)
