Day 26 of 45 · Pro lesson

Spark Performance

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: BroadcastHashJoin (<100MB), partition pruning, Parquet predicate pushdown, when to cache vs unpersist, data skew key salting, small files coalesce

Build: Refactor a slow Spark job with 6 bottlenecks (wide scan, missing broadcast, shuffle skew, coalesce(1)) into a 95% faster pipeline.

•Complete 15 drills on broadcast hash joins, partition pruning, predicate pushdown, and key salting.
•Run 6 Python modules comparing slow vs optimized execution plans and skew remediation.
•Simulate 5 production outages: broadcast join OOM, 4-hour NULL join skew, and blind caching GC freezes.
•Defend 10 senior performance scenarios and pass the blank-page optimization challenge.

The fastest data is data Spark never reads or moves. Use broadcast joins for small dimensions and salt skewed keys.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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