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https://issues.apache.org/jira/browse/FLINK-33354?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated FLINK-33354:
-----------------------------------
Labels: pull-request-available (was: )
> Cache TaskInformation and JobInformation to avoid deserializing duplicate big
> objects
> -------------------------------------------------------------------------------------
>
> Key: FLINK-33354
> URL: https://issues.apache.org/jira/browse/FLINK-33354
> Project: Flink
> Issue Type: Sub-task
> Components: Runtime / Task
> Affects Versions: 1.18.0, 1.17.1
> Reporter: Rui Fan
> Assignee: Rui Fan
> Priority: Major
> Labels: pull-request-available
>
> The background is similar to FLINK-33315.
> A hive table with a lot of data, and the HiveSource#partitionBytes is 281MB.
> When slotPerTM = 4, one TM will run 4 HiveSources at the same time.
>
> How the TaskExecutor to submit a large task?
> # TaskExecutor#loadBigData will read all bytes from file to
> SerializedValue<TaskInformation>
> ** The SerializedValue<TaskInformation> has a byte[]
> ** It will cost the heap memory
> ** It will be great than 281 MB, because it not only stores
> HiveSource#partitionBytes, it also stores other information of
> TaskInformation.
> # Generate the TaskInformation from SerializedValue<TaskInformation>
> ** TaskExecutor#submitTask calls the
> tdd.getSerializedTaskInformation()..deserializeValue()
> ** tdd.getSerializedTaskInformation() is SerializedValue<TaskInformation>
> ** It will generate the TaskInformation
> ** TaskInformation includes the Configuration
> {color:#9876aa}taskConfiguration{color}
> ** The {color:#9876aa}taskConfiguration{color} includes
> StreamConfig#{color:#9876aa}SERIALIZEDUDF{color}
>
> {color:#172b4d}Based on the above process, TM memory will have 2 big byte
> array for each task:{color}
> * {color:#172b4d}The SerializedValue<TaskInformation>{color}
> * {color:#172b4d}The TaskInformation{color}
> When one TM runs 4 HiveSources at the same time, it will have 8 big byte
> array.
> In our production environment, this is also a situation that often leads to
> TM OOM.
> h2. Solution:
> These data is totally same due to the PermanentBlobKey is same. We can add a
> cache for it to reduce the memory and cpu cost.
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