Checkpoint Chaos
A real-time fraud detection stream reads Kafka events, enriches them, and writes to a Delta sink with checkpointing.
Scenario context
After a deployment and infrastructure interruption, the stream restarts but either reprocesses events, produces duplicates, or struggles with state-store recovery.
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 import functions as F
source_df = spark.read.parquet(source_path)
# Broken: this code is functionally plausible but unsafe for production scale/reruns.
result_df = (source_df
.join(reference_df, on='id', how='left')
.groupBy('id')
.agg(F.count('*').alias('record_count')))
result_df.write.mode('append').parquet(output_path)Logs / error
[Spark] Checkpoint Chaos
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.
After a deployment and infrastructure interruption, the stream restarts but either reprocesses
events, produces duplicates, or struggles with state-store recovery.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.