[jira] [Created] (SPARK-20925) Out of Memory Issues With org.apache.spark.sql.DataFrameWriter#partitionBy

["Jeffrey Quinn (JIRA)" <ji...@apache.org>]

Jeffrey Quinn created SPARK-20925:
-------------------------------------

             Summary: 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.



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