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https://issues.apache.org/jira/browse/SPARK-23429?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Edwina Lu updated SPARK-23429:
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Description:
Add new executor level memory metrics ( jvmUsedMemory, onHeapExecutionMemory,
offHeapExecutionMemory, onHeapStorageMemory, offHeapStorageMemory,
onHeapUnifiedMemory, and offHeapUnifiedMemory), and expose these via the
executors REST API. This information will help provide insight into how
executor and driver JVM memory is used, and for the different memory regions.
It can be used to help determine good values for spark.executor.memory,
spark.driver.memory, spark.memory.fraction, and spark.memory.storageFraction.
Add an ExecutorMetrics class, with jvmUsedMemory, onHeapExecutionMemory,
offHeapExecutionMemory, onHeapStorageMemory, and offHeapStorageMemory. This
will track the memory usage at the executor level. The new ExecutorMetrics will
be sent by executors to the driver as part of the Heartbeat. A heartbeat will
be added for the driver as well, to collect these metrics for the driver.
Modify the EventLoggingListener to log ExecutorMetricsUpdate events if there is
a new peak value for one of the memory metrics for an executor and stage. Only
the ExecutorMetrics will be logged, and not the TaskMetrics, to minimize
additional logging. Analysis on a set of sample applications showed an increase
of 0.25% in the size of the Spark history log, with this approach.
Modify the AppStatusListener to collect snapshots of peak values for each
memory metric. Each snapshot has the time, jvmUsedMemory, executionMemory and
storageMemory, and list of active stages.
Add the new memory metrics (snapshots of peak values for each memory metric) to
the executors REST API.
This is a subtask for SPARK-23206. Please refer to the design doc for that
ticket for more details.
was:
Add new executor level memory metrics ( jvmUsedMemory, executionMemory,
storageMemory, and unifiedMemory), and expose these via the executors REST API.
This information will help provide insight into how executor and driver JVM
memory is used, and for the different memory regions. It can be used to help
determine good values for spark.executor.memory, spark.driver.memory,
spark.memory.fraction, and spark.memory.storageFraction.
Add an ExecutorMetrics class, with jvmUsedMemory, executionMemory, and
storageMemory. This will track the memory usage at the executor level. The new
ExecutorMetrics will be sent by executors to the driver as part of the
Heartbeat. A heartbeat will be added for the driver as well, to collect these
metrics for the driver.
Modify the EventLoggingListener to log ExecutorMetricsUpdate events if there is
a new peak value for one of the memory metrics for an executor and stage. Only
the ExecutorMetrics will be logged, and not the TaskMetrics, to minimize
additional logging. Analysis on a set of sample applications showed an increase
of 0.25% in the size of the Spark history log, with this approach.
Modify the AppStatusListener to collect snapshots of peak values for each
memory metric. Each snapshot has the time, jvmUsedMemory, executionMemory and
storageMemory, and list of active stages.
Add the new memory metrics (snapshots of peak values for each memory metric) to
the executors REST API.
This is a subtask for SPARK-23206. Please refer to the design doc for that
ticket for more details.
> Add executor memory metrics to heartbeat and expose in executors REST API
> -------------------------------------------------------------------------
>
> Key: SPARK-23429
> URL: https://issues.apache.org/jira/browse/SPARK-23429
> Project: Spark
> Issue Type: Sub-task
> Components: Spark Core
> Affects Versions: 2.2.1
> Reporter: Edwina Lu
> Priority: Major
>
> Add new executor level memory metrics ( jvmUsedMemory, onHeapExecutionMemory,
> offHeapExecutionMemory, onHeapStorageMemory, offHeapStorageMemory,
> onHeapUnifiedMemory, and offHeapUnifiedMemory), and expose these via the
> executors REST API. This information will help provide insight into how
> executor and driver JVM memory is used, and for the different memory regions.
> It can be used to help determine good values for spark.executor.memory,
> spark.driver.memory, spark.memory.fraction, and spark.memory.storageFraction.
> Add an ExecutorMetrics class, with jvmUsedMemory, onHeapExecutionMemory,
> offHeapExecutionMemory, onHeapStorageMemory, and offHeapStorageMemory. This
> will track the memory usage at the executor level. The new ExecutorMetrics
> will be sent by executors to the driver as part of the Heartbeat. A heartbeat
> will be added for the driver as well, to collect these metrics for the driver.
> Modify the EventLoggingListener to log ExecutorMetricsUpdate events if there
> is a new peak value for one of the memory metrics for an executor and stage.
> Only the ExecutorMetrics will be logged, and not the TaskMetrics, to minimize
> additional logging. Analysis on a set of sample applications showed an
> increase of 0.25% in the size of the Spark history log, with this approach.
> Modify the AppStatusListener to collect snapshots of peak values for each
> memory metric. Each snapshot has the time, jvmUsedMemory, executionMemory and
> storageMemory, and list of active stages.
> Add the new memory metrics (snapshots of peak values for each memory metric)
> to the executors REST API.
> This is a subtask for SPARK-23206. Please refer to the design doc for that
> ticket for more details.
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