PySparkIntermediateBroken PySpark FixFree

The Broadcast Betrayal

An enrichment job joins a large fact table with a reference dataset that is usually small.

Scenario context

Someone forced a broadcast hint. It worked in test and smaller markets, but production now fails intermittently with broadcast timeout or executor memory pressure.

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

from pyspark.sql.functions import broadcast

events = spark.read.parquet(events_path)
customers = spark.read.parquet(customers_path)

# Broken: forced broadcast can crash executors if customers is no longer small.
enriched = events.join(broadcast(customers), on='customer_id', how='left')
enriched.write.mode('overwrite').parquet(output_path)

Logs / error

[Spark] The Broadcast Betrayal
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.
Someone forced a broadcast hint. It worked in test and smaller markets, but production now fails
intermittently with broadcast timeout or executor memory pressure.

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.