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https://issues.apache.org/jira/browse/SPARK-20564?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hua Liu updated SPARK-20564:
----------------------------
Description:
When we used more than 2000 executors in a spark application, we noticed a
large number of executors cannot connect to driver and as a result they were
marked as failed. In some cases, the failed executor number reached twice of
the requested executor count and thus applications retried and may eventually
fail.
This is because that YarnAllocator requests all missing containers every
spark.yarn.scheduler.heartbeat.interval-ms (default 3 seconds). For example,
YarnAllocator can ask for and get over 2000 containers in one request, and then
launch them almost simultaneously. These thousands of executors try to retrieve
spark props and register with driver. However, driver handles executor
registration, stop, removal and spark props retrieval in one thread, and it can
not handle such a large number of RPCs within a short period of time. As a
result, some executors cannot retrieve spark props and/or register. These
failed executors are then marked as failed, cause executor removal and
aggravate the overloading of driver, which causes more executor failures.
This patch adds an extra configuration
spark.yarn.launchContainer.count.simultaneously, which caps the maximal
containers driver can ask for and launch in every
spark.yarn.scheduler.heartbeat.interval-ms. As a result, the number of
executors grows steadily. The number of executor failures is reduced and
applications can reach the desired number of executors faster.
was:
When we used more than 2000 executors in a spark application, we noticed a
large number of executors cannot connect to driver and as a result they were
marked as failed. In some cases, the failed executor number reached twice of
the requested executor count and thus applications retried and may eventually
fail.
This is because that YarnAllocator requests all missing containers every
spark.yarn.scheduler.heartbeat.interval-ms (default 3 seconds). For example,
YarnAllocator can ask for and get over 2000 containers in one request, and then
launch them. These thousands of executors try to retrieve spark props and
register with driver. However, driver handles executor registration, stop,
removal and spark props retrieval in one thread, and it can not handle such a
large number of RPCs within a short period of time. As a result, some executors
cannot retrieve spark props and/or register. These failed executors are then
marked as failed, cause executor removal and aggravate the overloading of
driver, which causes more executor failures.
This patch adds an extra configuration
spark.yarn.launchContainer.count.simultaneously, which caps the maximal
containers driver can ask for and launch in every
spark.yarn.scheduler.heartbeat.interval-ms. As a result, the number of
executors grows steadily. The number of executor failures is reduced and
applications can reach the desired number of executors faster.
> a lot of executor failures when the executor number is more than 2000
> ---------------------------------------------------------------------
>
> Key: SPARK-20564
> URL: https://issues.apache.org/jira/browse/SPARK-20564
> Project: Spark
> Issue Type: Improvement
> Components: Deploy
> Affects Versions: 1.6.2, 2.1.0
> Reporter: Hua Liu
>
> When we used more than 2000 executors in a spark application, we noticed a
> large number of executors cannot connect to driver and as a result they were
> marked as failed. In some cases, the failed executor number reached twice of
> the requested executor count and thus applications retried and may eventually
> fail.
> This is because that YarnAllocator requests all missing containers every
> spark.yarn.scheduler.heartbeat.interval-ms (default 3 seconds). For example,
> YarnAllocator can ask for and get over 2000 containers in one request, and
> then launch them almost simultaneously. These thousands of executors try to
> retrieve spark props and register with driver. However, driver handles
> executor registration, stop, removal and spark props retrieval in one thread,
> and it can not handle such a large number of RPCs within a short period of
> time. As a result, some executors cannot retrieve spark props and/or
> register. These failed executors are then marked as failed, cause executor
> removal and aggravate the overloading of driver, which causes more executor
> failures.
> This patch adds an extra configuration
> spark.yarn.launchContainer.count.simultaneously, which caps the maximal
> containers driver can ask for and launch in every
> spark.yarn.scheduler.heartbeat.interval-ms. As a result, the number of
> executors grows steadily. The number of executor failures is reduced and
> applications can reach the desired number of executors faster.
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