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Related to Spark - Joining 2 PairRDD elements
When doing a regular join in pig, the last table in the join is not brought into memory but streamed through instead, so if A has small cardinality per key and B large cardinality, it is significantly better to do join A, B than join A by B, from performance perspective (avoiding spill and OOM)
Is there a similar concept in spark? I didn't see any such recommendation, and wonder how is it possible? The implementation looks to me pretty much the same as in pig: https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
Or am I missing something?
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Summary. Table join order matters for reducing the number of rows that the rest of the query needs to process. By default SQL Server gives you no control over the join order - it uses statistics and the query optimizer to pick what it thinks is a good join order.
The default join operation in Spark includes only values for keys present in both RDDs, and in the case of multiple values per key, provides all permutations of the key/value pair. The best scenario for a standard join is when both RDDs contain the same set of distinct keys.
In order to explain join with multiple tables, we will use Inner join, this is the default join in Spark and it's mostly used, this joins two DataFrames/Datasets on key columns, and where keys don't match the rows get dropped from both datasets.
It does not make a difference, in spark the RDD will only be brought into memory if it is cached. So in spark to achieve the same effect you can cache the smaller RDD. Another thing you can do in spark which I'm not sure that pig does, is if all RDD's being joined have the same partitioner no shuffle needs to be done.
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