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You review the 'as_trace.log' file and see an error like this:
2018-09-15 13:41:46,676 | 10.85.128.45:9080 | ibm | admin | | 165de0893a5-eb27836496a4554f | ERROR | spark.SparkJobRunner | OO-E(10)-P-4-T-6 => Fail to run spark job org.apache.spark.SparkException: Job aborted due to stage failure:
Task 6
in stage 1000.0 failed 4 times, most recent failure: Lost
task 6
.3 in stage 1000.0 (TID 102082, spssas123.mydomain): ExecutorLostFailure (executor 491 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 27.5 GB of 27.5 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.
A rule of thumb to use here is
spark.executor.memory + spark.yarn.executor.memoryOverhead < yarn.nodemanager.resource.memory-mb.
You can decrease spark.yarn.executor.memoryOverhead and spark.executor.memory and/or increase yarn.nodemanager.resource.memory-mb to something more than 27G. What you can increase yarn.nodemanager.resource.memory-mb to will depend on your available memory on a node.
For Ambari:
Adjust spark.executor.memory and spark.yarn.executor.memoryOverhead in the SPSS Analytic Server service configuration under "Configs -> Custom analytics.cfg"
Adjust yarn.nodemanager.resource.memory-mb in the YARN service configuration under the "Settings" tab and "Memory Node" slider.
For Cloudera Manager:
Adjust spark.executor.memory and spark.yarn.executor.memoryOverhead in the SPSS Analytic Server service configuration under "Analytic Server Advanced Configuration Snippet (Safety Valve) for analyticserver-conf/config.properties"
Adjust yarn.nodemanager.resource.memory-mb in the YARN service configuration under "Container Memory yarn.nodemanager.resource.memory-mb".
