What is the Vectorized Reader in Spark and how does it improve Parquet performance?

The Vectorized Parquet Reader (introduced in Spark 2.0) reads a batch of rows at once directly into an in-memory columnar format (ColumnarBatch) rather than converting each row individually to a JVM object. This avoids object creation overhead and allows the JVM's JIT compiler to apply SIMD-style optimisations.

Key benefits:

• Reduces per-row CPU overhead significantly.
• Enables whole-stage code generation to operate on batches.
• Speeds up filters and projections applied directly to column vectors.

Enable or verify via Spark config:

spark.conf.set("spark.sql.parquet.enableVectorizedReader", "true")  # default true

For complex nested schemas (e.g., deeply nested structs/maps), the vectorized reader may fall back to row-by-row mode automatically.

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