What would you do if your Spark job fails at 50% completion?

First, check the Spark UI and logs to identify the exact stage or task that failed. Look for exception messages, out‑of‑memory errors, or network timeouts. If the failure is due to a transient issue, enable Spark's built‑in retry mechanism by setting spark.task.maxFailures and spark.stage.maxConsecutiveAttempts. For long‑running jobs, enable checkpointing (rdd.checkpoint()) so that only the last few stages need to be recomputed. If the failure is data‑related (e.g., corrupt partition), isolate the problematic partition using rdd.mapPartitionsWithIndex and reprocess it separately. Adjust executor memory or shuffle partitions (spark.sql.shuffle.partitions) to reduce memory pressure. Finally, consider using a fault‑tolerant storage format like Parquet and enable compression to reduce I/O. After making changes, re‑run the job and monitor the metrics to ensure the issue is resolved.