Partition pruning in Spark is a performance optimization that limits the number of files and partitions that Spark reads when querying. After partitioning the data, queries that match certain partition filter criteria improve performance by allowing Spark to only read a subset of the directories and files. When partition filters are present, the catalyst optimizer pushes down the partition filters. The scan reads only the directories that match the partition filters, thus reducing disk I/O.
However, in reality data engineers don’t just execute a single query, or single filter in their queries, and the common case is that they actually have dimensional tables, small tables that they need to join with a larger fact table. So in this case, we can no longer apply static partition pruning because the filter is on one side of the join, and the table that is more appealing and more attractive to prune is on the other side of the join. So, we have a problem now.
And that’s where dynamic partition pruning comes into play.