Day 28 of 45 · Pro lesson

Spark Interview Day

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

Teaches: Spark execution mental model, Spark UI triage framework, shuffle mechanics & skew, broadcast trade-offs, repartition vs coalesce, executor failure triage

Build: Spark Interview Lab: 5 TB skew autopsy with salting, shuffle diagnostic analyzer, and small files compaction optimizer.

•Defend the 7 mandatory Spark interview areas (slow jobs, shuffle, skew, broadcast, repartition, small files, executor death).
•Solve the 5 TB production skew incident (1.7B rows on a single customer) and 4.2M small files outage.
•Run 6 Python modules simulating skew salting, shuffle diagnostics, and broadcast analyzers.
•Pass the 8 capstone interview criteria and master the 8 blank-page verification questions.

Never just say 'add more executors' when a job is slow. Triage the Spark UI: find the slow stage, inspect task skew, and check shuffle volume.

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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