🚀🚀 I spent 6 hours learning how Apache Spark plans the execution for us

Here are the key insights I found

Apache Spark SQL (2015): Spark introduced Spark SQL, a new module that combines the power of relational processing with Spark’s procedural API. It allows developers to write declarative queries using a DataFrame API while benefiting from Spark's optimized storage and execution.

🔩 Catalyst Optimizer: Spark SQL's Catalyst optimizer enhances query performance through four key phases:

◉ Analysis: Builds an unresolved logical plan and resolves attributes using predefined rules.

◉ Logical Optimization: Applies rule-based optimizations like predicate pushdown and projection pruning.

◉ Physical Planning: Generates multiple physical plans and selects the best one using a cost model

◉ Code Generation: Transforms queries into Java bytecode for efficient execution, especially on CPU-bound datasets.

🌊 Adaptive Query Execution (2020): With Apache Spark 3, Adaptive Query Execution (AQE) was introduced. AQE dynamically adjusts query plans based on runtime statistics, ensuring optimal performance even when initial statistics are outdated or unavailable.

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