Apache Spark Query Optimization on Databricks: Catalyst, AQE, and Photon Engine

A deep dive into how Spark transforms your SQL into a physical execution plan — and how Databricks layers Adaptive Query Execution and the Photon vectorized engine on top to squeeze out maximum performance. Table of Contents Why Query Optimization Matters The Catalyst Optimizer Pipeline Stage 1: Parsing — From SQL to Unresolved Logical Plan Stage 2: Analysis — Binding to the Catalog Stage 3: Logical Optimization — Rule-Based Rewrites Stage 4: Physical Planning — Strategies and Cost Models Adaptive Query Execution (AQE) The Photon Engine Reading Explain Plans Tuning Reference Table References Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization — is what separates a job that runs in 3 minutes from one that runs in 3 hours on identical hardware. Databricks compounds Spark's native Catalyst optimizer with two additional layers: Adaptive Query Execution (AQE) — re-optimizes the query at runtime using actual statistics collected mid-job Photon — a C++ vectorized execution engine that replaces the JVM-based Spark executor for eligible operators Understanding all three lets you write queries that cooperate with the engine rather than fight it. The Catalyst Optimizer Pipeline Catalyst is Spark's rule-based and cost-based query optimizer. Every query — whether written in SQL, DataFrame API, or Dataset API — passes through the same four-stage pipeline before a single byte of data is read. Stage 1: Parsing — From SQL to Unresolved Logical Plan # ── Catalyst Stage 1: Parsing ───────────────────────────────────────────────── # Spark uses ANTLR4 to parse SQL into an Abstract Syntax Tree (AST). # At this point column names are NOT validated — the plan is "unresolved". from pyspark.sql import SparkSession spark = SparkSession . builder . appName ( " catalyst-demo " ). getOrCreate () # Both of these produce identical internal representations df_api = ( spark .