Joining a large and a small Dataset
A technique to improve the performance is analyzing the DataFrame size to get the best join strategy.
If the smaller DataFrame is small enough to fit into the memory of each worker, we can turn ShuffleHashJoin or SortMergeJoin into a BroadcastHashJoin. In broadcast join, the smaller DataFrame will be broadcasted to all worker nodes. Using the BROADCAST hint guides Spark to broadcast the smaller DataFrame when joining them with the bigger one:
largeDf.join(smallDf.hint("broadcast"), Seq("id"))
This way, the larger DataFrame does not need to be shuffled at all.
Recently Spark has increased the maximum size for the broadcast table from 2GB to 8GB. Thus, it is not possible to broadcast tables which are greater than 8GB.
Last modified 11mo ago
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