predicatepushdown

Predicate pushdown is a query optimization technique where filter conditions from a query are pushed down to the data source or storage layer so they can be evaluated before data is read or processed by the query engine. The goal is to reduce I/O and computational work by discarding non-matching data as early as possible.

How it works: During planning, the system analyzes predicates from WHERE clauses (and sometimes JOIN conditions) and determines which can be evaluated by the storage layer. If the data source supports filtering, the predicate is translated into a storage-level operation, such as index lookups, partition pruning, or block-level filtering in columnar formats. For formats like Parquet or ORC, this can mean skipping whole row groups or pages. In relational databases, the storage engine may apply filters via indexes or partitioning. Some predicates, especially complex expressions or non-deterministic functions, may not be pushdownable and are evaluated later in the processing pipeline.

Scope and limitations: Predicate pushdown is highly dependent on the capabilities of the data source and the available statistics. Pushdown is most effective with columnar formats, partitioned data, and well-indexed storage. Limitations arise when predicates cannot be translated into storage operations, when statistics are stale or unavailable, or when data sources impose restrictions that prevent early filtering. While pushdown generally improves performance by reducing data movement, it can incur planning or translation overhead and requires careful semantic correctness to ensure results remain unchanged.

Benefits: The primary advantages are lower I/O, reduced memory usage, and faster query execution, especially for large datasets and analytic workloads. The technique is widely used in data warehouses, data lakes, and big data processing engines.