Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/7004#issuecomment-116164570
Also, to clarify: is this primarily intended to improve the performance of
programs written against the Spark Core API? For Spark SQL + DataFrames, I
think the [spark-avro](https://github.com/databricks/spark-avro) library will
convert the Avro records into Spark SQL's internal Row representation, which
should be more efficient to serialize and shuffle. I'd be curious to know
whether you could see most of these benefits for simpler workflows by using
Dataframes and leaving the serialization up to that.
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