CATALYST OPTIMIZER | SPARK INTERVIEW QUESTION

In Apache Spark, Catalyst is the query optimization framework that powers the query planning and execution stages in Spark SQL. It is designed to optimize SQL queries and DataFrame operations to achieve better performance. Catalyst uses a combination of rule-based and cost-based optimizations to transform logical query plans into physical execution plans, ultimately improving the efficiency of queries.
Key Components of Catalyst Optimizer:
Logical Plan:
The initial representation of a query is parsed into a logical plan. This is a high-level description of the operations that need to be performed, but it doesn't specify how these operations will be executed.
Rule-based Optimization (RBO):
Catalyst applies a series of predefined rules to the logical plan to simplify and optimize it. Examples include constant folding, predicate pushdown, and projection pruning.
It transforms the logical plan into a more efficient version by applying these rules iteratively.
Cost-based Optimization (CBO):
With CBO, the optimizer selects the most efficient plan by analyzing statistics (such as data size, distribution, and cardinality) to estimate the cost of different physical plans.
Spark uses CBO when statistics are available, allowing it to make decisions like choosing the best join strategy or avoiding unnecessary shuffles.
Physical Plan Generation:
Once the logical plan is optimized, it is converted into a physical plan that defines how the actual execution will take place. Spark chooses the most efficient physical operators (like sort merge join, broadcast join, etc.) to execute the query.
Execution:
Finally, the physical plan is executed by Spark, which breaks down the operations into RDD transformations or actions and runs them in parallel across the Spark cluster.
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[@DigambarAatkar]

nice explanation, easy to understand .keep it up.