Databricks/ Spark Optimizers🔥
😇Do you know what are the different optimizers in Apache Spark and their use?
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🎁Tungsten and Catalyst are two major components of the Apache Spark SQL engine that work together to optimize the performance of Spark queries. They serve different purposes within the Spark SQL execution engine:
✔✔Catalyst Optimizer:
👀Purpose: Catalyst is Spark’s extensible query optimization framework. It is responsible for logical and physical query optimization.
👁Logical Optimization: Catalyst optimizes the logical plan of a Spark SQL query by applying various transformations like predicate pushdown, constant folding, and more. It aims to improve the query plan at a higher level without considering the physical execution details.
👁Physical Optimization: Catalyst generates an optimized physical execution plan based on the logical plan. It considers details like data distribution, storage format, and join strategies to come up with an efficient physical plan.
Extensibility: Catalyst is extensible, meaning developers can add custom optimization rules to enhance Spark’s optimization capabilities.
✔✔Tungsten Execution Engine:
👀Purpose: Tungsten is Spark’s execution engine designed to improve the physical execution of Spark jobs. It focuses on runtime code generation and memory management.
👁Code Generation: Tungsten translates the optimized physical plan generated by Catalyst into executable code. It generates bytecode dynamically at runtime, which can significantly improve the performance of certain operations by avoiding interpretation overhead.
🍉Memory Management: Tungsten introduces an efficient memory layout called “BinaryRegion” and provides fine-grained memory management. This helps reduce garbage collection overhead by managing memory more efficiently during query execution.
Broadcast Hash Join: Tungsten includes optimizations like Broadcast Hash Join, which can be more efficient than traditional join algorithms in certain scenarios.
🐱‍💻🐱‍💻In summary, Catalyst is responsible for optimizing the logical and physical plans of Spark SQL queries, while Tungsten focuses on improving the physical execution by utilizing runtime code generation and efficient memory management. Both work together to enhance the overall performance of Spark SQL queries. The Catalyst optimizer precedes the
Tungsten execution engine in the Spark SQL execution pipeline.