adaptive-query-execution

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sushmakl95 / spark-performance-optimization-playbook

Battle-tested Apache Spark tuning patterns with reproducible benchmarks. 10 techniques (partition pruning, broadcast joins, AQE, skew handling, Z-ORDER, and more) — each paired with measured before/after speedups runnable on a laptop.

Updated Apr 23, 2026
Python

nagaraju-12 / pyspark-optimization-topics

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This project demonstrates key PySpark performance optimization techniques using a synthetic banking transactions dataset (~5,000 records). Built using Databricks and Delta Lake.

pyspark data-engineering parquet partitioning databricks etl-pipeline bucketing delta-lake broadcast-join spark-optimization spark-performance adaptive-query-execution

Updated Mar 3, 2026
Python

sivananda1995 / spark-skew-doctor

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Diagnoses Spark data skew from the job's own event log - no cluster, no JVM - and tells key skew apart from a straggler by comparing task time against bytes read. Then measures the fixes against each other on real Spark 4: across 16 cells, salting won 0. Broadcast 6.5x, AQE 1.5x, salting 0.81x.

python benchmarking big-data apache-spark distributed-computing pyspark data-engineering performance-tuning profiling shuffle query-optimization observability salting spark4 broadcast-join adaptive-query-execution data-skew spark-event-log

Updated Aug 19, 2026
Python

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