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https://issues.apache.org/jira/browse/SPARK-5075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14271706#comment-14271706
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Brad Willard commented on SPARK-5075:
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I wanted to add that this is greatly exacerbated by a dataset with nested
structure. If a json object has a nested json object, and your queries include
attributes on the nested objects, this correlates with the memory explosion.
I'm tempted to say spark should not let you use nested json objects. It murders
performance everywhere.
> Memory Leak when repartitioning SchemaRDD or running queries in general
> -----------------------------------------------------------------------
>
> Key: SPARK-5075
> URL: https://issues.apache.org/jira/browse/SPARK-5075
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core
> Affects Versions: 1.2.0
> Environment: spark-ec2 launched 10 node cluster of type c3.8xlarge
> Reporter: Brad Willard
> Labels: ec2, json, memory-leak, memory_leak, parquet, pyspark,
> repartition, s3
>
> I'm trying to repartition a json dataset for better cpu optimization and save
> in parquet format for better performance. The Json dataset is about 200gb
> from pyspark.sql import SQLContext
> sql_context = SQLContext(sc)
> rdd = sql_context.jsonFile('s3c://some_path')
> rdd = rdd.repartition(256)
> rdd.saveAsParquetFile('hdfs://some_path')
> In ganglia when the dataset first loads it's about 200G in memory which is
> expected. However once it attempts the repartition, it balloons over 2.5x in
> memory which is never returned making any subsequent operations fail from
> memory errors.
> https://s3.amazonaws.com/f.cl.ly/items/3k2n2n3j35273i2v1Y3t/Screen%20Shot%202015-01-04%20at%201.20.29%20PM.png
> I'm also seeing a similar memory leak behavior when running repeated queries
> on a dataset.
> rdd = sql_context.parquetFile('hdfs://some_path')
> rdd.registerTempTable('events')
> sql_context.sql( anything )
> sql_context.sql( anything )
> sql_context.sql( anything )
> sql_context.sql( anything )
> will result in a memory usage pattern of.
> http://cl.ly/image/180y2D3d1A0X
> It seems like intermediate results are not being garbage collected or
> something. Eventually I have to kill my session to keep running queries.
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