What is Catalyst Optimizer?
How the Catalyst optimizer uses rule-based and cost-based techniques on tree-structured query plans to make Spark SQL queries faster, more efficient and easier to extend
Summary
- See how Catalyst sits at the core of Spark SQL as an extensible optimizer built on Scala features like pattern matching and quasiquotes to support new rules and data types
- Understand how Catalyst represents queries as trees and applies rules across analysis, logical optimization, physical planning and code generation phases to create efficient execution plans
- Discover how Catalyst supports both rule-based and cost-based optimization and exposes extension points for external data sources and user-defined types
At the core of Spark SQL is the Catalyst optimizer, which leverages advanced programming language features (e.g. Scala’s pattern matching and quasi quotes) in a novel way to build an extensible query optimizer. Catalyst is based on functional programming constructs in Scala and designed with these key two purposes:
- Easily add new optimization techniques and features to Spark SQL
- Enable external developers to extend the optimizer (e.g. adding data source specific rules, support for new data types, etc.)
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Catalyst contains a general library for representing trees and applying rules to manipulate them. On top of this framework, it has libraries specific to relational query processing (e.g., expressions, logical query plans), and several sets of rules that handle different phases of query execution: analysis, logical optimization, physical planning, and code generation to compile parts of queries to Java bytecode. For the latter, it uses another Scala feature, quasiquotes, that makes it easy to generate code at runtime from composable expressions. Catalyst also offers several public extension points, including external data sources and user-defined types. As well, Catalyst supports both rule-based and cost-based optimization.
