This talk, the third in a Spark series, introduces the Spark SQL parsing layer, explains its architecture and integration with ANTLR4, details core implementation classes, and presents a real‑world optimization case that reduces code complexity and improves maintainability.
This guide explains common business scenarios that require custom SQL parsing, walks through setting up Antlr4 in IntelliJ IDEA, configuring Maven dependencies, generating parser code, and provides Java examples for extracting table names from Spark SQL statements, including handling of prediction modes and execution results.
This article explores the inner workings of SQL engines, detailing the end‑to‑end process from query submission through lexical analysis, parsing, abstract syntax tree generation with ANTLR4, and subsequent logical and physical plan optimization, while also demonstrating practical Java examples for SQL parsing and rewriting.
