The Shuffle Storm
A finance pipeline computes daily account-level metrics from raw transactions and then rolls them up to multiple business hierarchies.
Scenario context
A new version replaced reduceByKey-like logic with groupByKey and several downstream transformations. Runtime doubled and shuffle traffic exploded.
Business requirement
Fix the PySpark code so the pipeline is correct, scalable, and safe to rerun.
Schema
DataFrames depend on the scenario. Assume large production-scale inputs, skewed keys, retries, and partitioned lake storage.Sample input
Use the code comments and logs to infer the input shape. Focus on the production failure mode, not local toy execution.Broken logic / code
# Broken: groupByKey shuffles all values before reducing.
metrics = raw_rdd.map(lambda r: (r.account_id, r.amount))
daily_totals = metrics.groupByKey().mapValues(lambda values: sum(values))
daily_totals.saveAsTextFile(output_path)Logs / error
[Spark] The Shuffle Storm
Stage progress: most tasks finished, one or more tasks are long-running.
Metrics to inspect: shuffle read, spill, skew ratio, executor lost count, file count.
A new version replaced reduceByKey-like logic with groupByKey and several downstream
transformations. Runtime doubled and shuffle traffic exploded.Expected output / expected logic
Corrected PySpark code or approach should reduce the failure mode, preserve correctness, and include validation/monitoring.Your attempt
Write the corrected PySpark approach
Think before revealing the answer. A partial but honest attempt is better practice than reading the model solution first.
Saved locally
Interview-style explanation
Now explain your solution as if you are in an interview: symptom, root cause, fix, edge cases, trade-offs, monitoring, and prevention.