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Catalyst is Spark's query optimization framework. In the Analysis phase, Catalyst examines the query, DataFrame, unresolved logical plan, and Catalog to formulate a logical plan. This plan is then refined into an optimized logical plan during the logical optimization phase, which involves rule-based enhancements such as folding, pushdown, and pruning in Spark SQL. Subsequently, during the physical planning phase, Catalyst devises multiple physical plans from the optimized logical plan. Each physical plan specifies the methods of computation on datasets, outlining precise instructions for executing these computations.

Cost is determined by evaluating the time and memory utilized by a query. The Catalyst optimizer chooses a query path that minimizes consumption of both resources. Given that queries may follow various paths, these evaluations can grow significantly complex, especially when large datasets are involved. The most efficient physical plan is chosen based on a cost model, highlighting the cost-based optimization step. The final phase is code generation, where Catalyst implements the chosen physical plan by generating Java bytecode, which executes across the cluster nodes. It is responsible for optimizing the logical and physical plan of SQL queries. Catalyst uses a rule-based optimization engine, where optimization rules are applied in multiple phases:

• Analysis: Converts unresolved attributes and relations into a logical plan that is fully resolved.
• Logical Optimization: Applies rule-based optimizations on the logical plan, such as predicate pushdown, constant folding, and boolean expression simplification.
• Physical Planning: Converts the optimized logical plan into a physical plan that can be executed on the cluster. Different strategies are evaluated to find the most efficient way to execute a query.
• Cost Model: Optionally, Catalyst can use a cost model to choose between multiple physical plans based on their estimated cost of execution. In Spark, cost-based optimization is part of the Catalyst optimizer, which does include cost-based optimization strategies for logical planning of data queries, particularly when dealing with Spark SQL.

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