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https://issues.apache.org/jira/browse/SPARK-20925?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16031412#comment-16031412
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Jeffrey Quinn commented on SPARK-20925:
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Yes you are correct, the iterator seems to clean itself up once consumed, so
the comment "It is the caller's responsibility to call `cleanupResources()`
after consuming this iterator." is a bit inaccurate.
This is perhaps a red herring then, but will try and update when we can try
Spark 2.1.1 on EMR and confirm if it solves this issue. Amazon should hopefully
upgrade by the end of the summer given their previous schedule of releases..
> Out of Memory Issues With org.apache.spark.sql.DataFrameWriter#partitionBy
> --------------------------------------------------------------------------
>
> Key: SPARK-20925
> URL: https://issues.apache.org/jira/browse/SPARK-20925
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Jeffrey Quinn
>
> Observed under the following conditions:
> Spark Version: Spark 2.1.0
> Hadoop Version: Amazon 2.7.3 (emr-5.5.0)
> spark.submit.deployMode = client
> spark.master = yarn
> spark.driver.memory = 10g
> spark.shuffle.service.enabled = true
> spark.dynamicAllocation.enabled = true
> The job we are running is very simple: Our workflow reads data from a JSON
> format stored on S3, and write out partitioned parquet files to HDFS.
> As a one-liner, the whole workflow looks like this:
> ```
> sparkSession.sqlContext
> .read
> .schema(inputSchema)
> .json(expandedInputPath)
> .select(columnMap:_*)
> .write.partitionBy("partition_by_column")
> .parquet(outputPath)
> ```
> Unfortunately, for larger inputs, this job consistently fails with containers
> running out of memory. We observed containers of up to 20GB OOMing, which is
> surprising because the input data itself is only 15 GB compressed and maybe
> 100GB uncompressed.
> The error message we get indicates yarn is killing the containers. The
> executors are running out of memory and not the driver.
> ```Caused by: org.apache.spark.SparkException: Job aborted due to stage
> failure: Task 184 in stage 74.0 failed 4 times, most recent failure: Lost
> task 184.3 in stage 74.0 (TID 19110, ip-10-242-15-251.ec2.internal, executor
> 14): ExecutorLostFailure (executor 14 exited caused by one of the running
> tasks) Reason: Container killed by YARN for exceeding memory limits. 21.5 GB
> of 20.9 GB physical memory used. Consider boosting
> spark.yarn.executor.memoryOverhead.```
> We tried a full parameter sweep, including using dynamic allocation and
> setting executor memory as high as 20GB. The result was the same each time,
> with the job failing due to lost executors due to YARN killing containers.
> We were able to bisect that `partitionBy` is the problem by progressively
> removing/commenting out parts of our workflow. Finally when we get to the
> above state, if we remove `partitionBy` the job succeeds with no OOM.
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