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https://issues.apache.org/jira/browse/SPARK-7263?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin closed SPARK-7263.
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Resolution: Later
> Add new shuffle manager which stores shuffle blocks in Parquet
> --------------------------------------------------------------
>
> Key: SPARK-7263
> URL: https://issues.apache.org/jira/browse/SPARK-7263
> Project: Spark
> Issue Type: New Feature
> Components: Shuffle
> Reporter: Matt Massie
>
> I have a working prototype of this feature that can be viewed at
> https://github.com/apache/spark/compare/master...massie:parquet-shuffle?expand=1
> Setting the "spark.shuffle.manager" to "parquet" enables this shuffle manager.
> The dictionary support that Parquet provides appreciably reduces the amount of
> memory that objects use; however, once Parquet data is shuffled, all the
> dictionary information is lost and the column-oriented data is written to
> shuffle
> blocks in a record-oriented fashion. This shuffle manager addresses this issue
> by reading and writing all shuffle blocks in the Parquet format.
> If shuffle objects are Avro records, then the Avro $SCHEMA is converted to
> Parquet
> schema and used directly, otherwise, the Parquet schema is generated via
> reflection.
> Currently, the only non-Avro keys supported is primitive types. The reflection
> code can be improved (or replaced) to support complex records.
> The ParquetShufflePair class allows the shuffle key and value to be stored in
> Parquet blocks as a single record with a single schema.
> This commit adds the following new Spark configuration options:
> "spark.shuffle.parquet.compression" - sets the Parquet compression codec
> "spark.shuffle.parquet.blocksize" - sets the Parquet block size
> "spark.shuffle.parquet.pagesize" - set the Parquet page size
> "spark.shuffle.parquet.enabledictionary" - turns dictionary encoding on/off
> Parquet does not (and has no plans to) support a streaming API. Metadata
> sections
> are scattered through a Parquet file making a streaming API difficult. As
> such,
> the ShuffleBlockFetcherIterator has been modified to fetch the entire contents
> of map outputs into temporary blocks before loading the data into the reducer.
> Interesting future asides:
> o There is no need to define a data serializer (although Spark requires it)
> o Parquet support predicate pushdown and projection which could be used at
> between shuffle stages to improve performance in the future
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