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Bloom Pruning

Reading Parallel Execution Plans With Bloom Pruning And Composite Partitioning

You’ve probably heard sayings like “sometimes things aren’t always what they seem” and “people lie”. Well, sometimes execution plans lie. It’s not really by intent, but it is sometimes difficult (or impossible) to represent everything in a query execution tree in nice tabular format like dbms_xplan gives. One of the optimizations that was introduced back in 10gR2 was the use of bloom filters. Bloom filters can be used in two ways: 1) for filtering or 2) for partition pruning (bloom pruning) starting with 11g. Frequently the data models used in data warehousing are dimensional models (star or snowflake) and most Oracle warehouses use simple range (or interval) partitioning on the fact table date key column as that is the filter that yields the largest I/O reduction from partition pruning (most queries in a time series star schema include a time window, right!). As a result, it is imperative that the join between the date dimension and the fact table results in partition pruning. Let’s consider a basic two table join between a date dimension and a fact table. For these examples I’m using STORE_SALES and DATE_DIM which are TPC-DS tables (I frequently use TPC-DS for experiments as it uses a [...]