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https://issues.apache.org/jira/browse/SPARK-23206?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16340474#comment-16340474
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Edwina Lu commented on SPARK-23206:
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[~jerryshao], yes SPARK-9103 is similar, and this proposal (SPARK-23206) seems
complementary. We are also interested in netty and total memory metrics from
SPARK-9103 and SPARK-21157. It would be great to share the Heartbeat and
history logging infrastructure mentioned in both tickets. We'd also like to
have the metrics exposed via web UI and REST API.
Adding JVM used memory metrics would give an idea about Spark's JVM process,
and adding executor level information about execution and storage memory would
give insight into the unified memory region. Peak execution memory is available
at the task level, so users could calculate a peak execution memory for the
executor by multiplying with the number of concurrent tasks. Storage memory is
available in the storage tab while it is running, but it isn't visible once the
application finishes, so can be hard to examine, and would be difficult to
combine with execution memory for an overall view into the unified memory
region, at a per executor and stage level.
> Additional Memory Tuning Metrics
> --------------------------------
>
> Key: SPARK-23206
> URL: https://issues.apache.org/jira/browse/SPARK-23206
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.2.1
> Reporter: Edwina Lu
> Priority: Major
> Attachments: MemoryTuningMetricsDesignDoc.pdf
>
>
> At LinkedIn, we have multiple clusters, running thousands of Spark
> applications, and these numbers are growing rapidly. We need to ensure that
> these Spark applications are well tuned – cluster resources, including
> memory, should be used efficiently so that the cluster can support running
> more applications concurrently, and applications should run quickly and
> reliably.
> Currently there is limited visibility into how much memory executors are
> using, and users are guessing numbers for executor and driver memory sizing.
> These estimates are often much larger than needed, leading to memory wastage.
> Examining the metrics for one cluster for a month, the average percentage of
> used executor memory (max JVM used memory across executors /
> spark.executor.memory) is 35%, leading to an average of 591GB unused memory
> per application (number of executors * (spark.executor.memory - max JVM used
> memory)). Spark has multiple memory regions (user memory, execution memory,
> storage memory, and overhead memory), and to understand how memory is being
> used and fine-tune allocation between regions, it would be useful to have
> information about how much memory is being used for the different regions.
> To improve visibility into memory usage for the driver and executors and
> different memory regions, the following additional memory metrics can be be
> tracked for each executor and driver:
> * JVM used memory: the JVM heap size for the executor/driver.
> * Execution memory: memory used for computation in shuffles, joins, sorts
> and aggregations.
> * Storage memory: memory used caching and propagating internal data across
> the cluster.
> * Unified memory: sum of execution and storage memory.
> The peak values for each memory metric can be tracked for each executor, and
> also per stage. This information can be shown in the Spark UI and the REST
> APIs. Information for peak JVM used memory can help with determining
> appropriate values for spark.executor.memory and spark.driver.memory, and
> information about the unified memory region can help with determining
> appropriate values for spark.memory.fraction and
> spark.memory.storageFraction. Stage memory information can help identify
> which stages are most memory intensive, and users can look into the relevant
> code to determine if it can be optimized.
> The memory metrics can be gathered by adding the current JVM used memory,
> execution memory and storage memory to the heartbeat. SparkListeners are
> modified to collect the new metrics for the executors, stages and Spark
> history log. Only interesting values (peak values per stage per executor) are
> recorded in the Spark history log, to minimize the amount of additional
> logging.
> We have attached our design documentation with this ticket and would like to
> receive feedback from the community for this proposal.
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