Predicate Pushdown

Concepts covered: paPredicatePushdown, paBloomFilter, paQueryPlanning

Predicate pushdown is the technique of moving filter conditions as close to the storage layer as possible so the engine reads only the bytes that could match. Partition pruning is the coarsest form of pushdown. File-level statistics, row-group min/max, and bloom filters are finer forms. A well-designed Parquet file plus a smart query engine produces queries that scan a tiny fraction of the table while returning the same answer. The wins compound with partitioning and columnar layout. The Three Levels of Pushdown Each level skips bytes the previous level could not. A query that filters on event_date prunes by partition, then by row group statistics within each partition's file, then by column page statistics. By the time the engine reads actual data, it has often pruned 99% of the table wit

About This Interactive Section

This section is part of the Storage Layers and Table Formats: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

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