Spark的Lost executor错误问题

最新推荐文章于 2025-05-14 08:57:44 发布

訾零

最新推荐文章于 2025-05-14 08:57:44 发布

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分类专栏： Spark 文章标签： Spark lost executor

本文链接：https://blog.youkuaiyun.com/lingeio/article/details/92586193

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本文主要探讨Spark on Yarn运行时出现lost executor的问题。一是executor - memory或executor - cores设置不合理，超过Yarn可调度资源上限，如CPU核心数超上限，修改executor - cores可解决；二是物理内存不够，可加大内存来解决问题。

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1.

19/06/17 09:50:52 WARN cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Requesting driver to remove executor 2 for reason Container marked as failed: container_1560518528256_0014_01_000003 on host: hadoop-master. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Killed by external signal

19/06/17 09:50:52 ERROR cluster.YarnScheduler: Lost executor 2 on hadoop-master: Container marked as failed: container_1560518528256_0014_01_000003 on host: hadoop-master. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Killed by external signal

19/06/17 09:50:52 WARN scheduler.TaskSetManager: Lost task 22.0 in stage 0.0 (TID 17, hadoop-master, executor 2): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container marked as failed: container_1560518528256_0014_01_000003 on host: hadoop-master. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Killed by external signal

19/06/17 09:50:52 WARN scheduler.TaskSetManager: Lost task 21.0 in stage 0.0 (TID 16, hadoop-master, executor 2): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container marked as failed: container_1560518528256_0014_01_000003 on host: hadoop-master. Exit status: 143. Diagnostics: Container killed on request. Exit code is 143
Container exited with a non-zero exit code 143
Killed by external signal

Spark on Yarn运行时，Yarn调度的executor资源不够，所以kill掉了executor，出现lost executor的情况。

大多是 executor-memory 或者 executor-cores 设置不合理，超过了Yarn可以调度资源的最高上限（内存或者CPU核心）。

如，3台服务器，32核心，64GB内存

yarn.scheduler.maximum-allocation-mb = 68G

设置：

num-executors = 30（一个节点上运行10个executor）

executor-memory = 6G （每个executor分配6G内存）

executor-cores = 5 （每个executor分配5个核心）

每个节点上executor占用的内存：10*6.5 (有512堆内内存) = 65G 没超出上限

每个节点上executor占用的核心：10*5 = 50 超过上限，错误

修改 executor-cores = 3 问题解决

内存设置问题同上

2.

19/10/25 10:25:14 ERROR cluster.YarnScheduler: Lost executor 9 on cdh-master: Container killed by YARN for exceeding memory limits. 9.5 GB of 9 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead or disabling yarn.nodemanager.vmem-check-enabled because of YARN-4714.
19/10/25 10:25:14 WARN scheduler.TaskSetManager: Lost task 0.1 in stage 7.0 (TID 690, cdh-master, executor 9): ExecutorLostFailure (executor 9 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 9.5 GB of 9 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead or disabling yarn.nodemanager.vmem-check-enabled because of YARN-4714.

问题很明显，物理内存不够。

sudo -uhdfs spark-submit \
--class com.sm.analysis.AnalysisRetained \
--master yarn \
--deploy-mode client \
--driver-memory 3G \
--driver-cores 3 \
--num-executors 3 \
--executor-memory 8g \
--executor-cores 5 \
--jars /usr/java/jdk1.8.0_211/lib/mysql-connector-java-5.1.47.jar \
--conf spark.default.parallelism=30 \
/data4/liujinhe/tmp/original-analysis-1.0-SNAPSHOT.jar \

本来 executor 分配 8G，加上堆外内存（9 x 1024m x 0.07‬ = 645m，增量512m，所以取整1024m）1G，9G内存不够，所以加大内存为10G。