DISTRIBUTED COMPUTING WITH SPARK SQL

Optimizing Query Performance Through Data Partitioning

• By partitioning the "priority" column, I effectively minimized data shuffling since the column's size was substantial, consequently improving query performance by reducing processing time.

• Leveraging data versioning, I had the ability to perform time-travel queries and access historical data snapshots or previous data versions.

Optimizing Performance with Caching and Spark UI Tuning

• By employing caching in conjunction with the Tungsten optimizer, effectively reduced the memory footprint of our data while substantially enhancing query performance through decreased computation times.

• leveraged the Spark UI to adjust the shuffle partition size from its default setting of 200 tasks to the more suitable and optimized value of 8 tasks for our specific operation. This modification led to improved query performance by significantly reducing the computation time.