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https://issues.apache.org/jira/browse/SPARK-8597?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14606554#comment-14606554
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Vlad Ionescu commented on SPARK-8597:
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Actually I've used an ExternalAppendOnlyMap<String, InternalRow> to group all
rows to their partition path. After that, I just iterate through the List of
InternalRows for each key (key = partition path) and write them using the same
OutputWriter. In this way I'll have only one OutputWriter opened at one time.
What do you think about the performance overhead caused by using this external
map? Will the execution time decrease a lot?
Thank you!
> DataFrame partitionBy memory pressure scales extremely poorly
> -------------------------------------------------------------
>
> Key: SPARK-8597
> URL: https://issues.apache.org/jira/browse/SPARK-8597
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.4.0
> Reporter: Matt Cheah
> Priority: Blocker
> Attachments: table.csv
>
>
> I'm running into a strange memory scaling issue when using the partitionBy
> feature of DataFrameWriter.
> I've generated a table (a CSV file) with 3 columns (A, B and C) and 32*32
> different entries, with size on disk of about 20kb. There are 32 distinct
> values for column A and 32 distinct values for column B and all these are
> combined together (column C will contain a random number for each row - it
> doesn't matter) producing a 32*32 elements data set. I've imported this into
> Spark and I ran a partitionBy("A", "B") in order to test its performance.
> This should create a nested directory structure with 32 folders, each of them
> containing another 32 folders. It uses about 10Gb of RAM and it's running
> slow. If I increase the number of entries in the table from 32*32 to 128*128,
> I get Java Heap Space Out Of Memory no matter what value I use for Heap Space
> variable.
> Scala code:
> {code}
> var df = sqlContext.read.format("com.databricks.spark.csv").option("header",
> "true").load("table.csv")
> df.write.partitionBy("A", "B").mode("overwrite").parquet("table.parquet”)
> {code}
> How I ran the Spark shell:
> {code}
> bin/spark-shell --driver-memory 16g --master local[8] --packages
> com.databricks:spark-csv_2.10:1.0.3
> {code}
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