Feature/cdap 16709 pipeline performance cp #12234
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Add a new set of classes for AutoJoiner, which can be used instead of the current Joiner interface. This new API leaves all of the implementation details up to the application, which will allow the app to perform the join in better ways. For example, in the Spark program, it will allow using broadcast joins. Plugin developers are responsible for returning a JoinDefinition based on information about incoming stages. This change includes the JoinDefinition as well as all the classes required to create a definition. It also includes validation logic to make sure the plugin cannot create a definition that tries to join on a field that doesn't exist, or tries to join on fields that have mismatched types, or any other type of error.
Implemented auto join for batch spark pipelines. Added a join method to SparkCollection that takes in the list of other SparkCollections that it should be joined to. RDDCollection converts RDDs into Datasets and uses the Dataset join method to implement the join. This allows Spark to broadcast small datasets automatically, and to use sort merge join instead of shuffle hash join, which has better memory characteristics. As part of this, added a separate RDDCollection implementation for Spark1 and Spark2, since the Spark API for joins is not compatible.
Implemented AutoJoiner for mapreduce by creating a bridge that implements the old Joiner API using the JoinDefinition from the new AutoJoiner API.
Changed the 'dropNullKeys' property to 'nullSafe' since it more accurately describes what is happening. Null keys are not dropped in outer joins, they just do not count as being equal to a null key on the other side. Implemented by using Spark's null safe equality when configured to do so and normal equality otherwise.
Honor the broadcast flag set in the JoinDefinition when joining multiple DataFrames. Added a small tweak to the join logic to join all non-broadcasted datasets first in order to ensure that both sides of the join are not broadcast, and to reduce the amount of data that is being shuffled in non-broadcast intermediate joins.
Implemented the nullSafe flag for mapreduce auto join. This was done by filtering out records on the map side if they come from an optional stage and have a null key or a field in the key that is null.
Implemented auto-join for spark streaming by using the same JoinerBridge that is used for MapReduce. This means auto-joins in streaming pipelines will have the same characteristics as normal joins, meaning they will be executed as shuffle hash joins. This is probably ok, as only data within the micro batch is being joined, which means it shouldn't be too likely to go OOM assuming there is enough executor memory.
yaojiefeng
approved these changes
Jun 1, 2020
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