Spark OutOfMemoryError is one of the most common failures in data engineering. It usually appears during shuffles, caching, or when the driver collects too much data.
Common Spark OOM symptoms
java.lang.OutOfMemoryError: Java heap spaceSparkOutOfMemoryError: Unable to acquire N bytes of memoryGC overhead limit exceeded- Stage failed with shuffle read/write in the Spark UI
Quick fixes (try in order)
1. Increase executor memory
spark.executor.memory=8g spark.executor.memoryOverhead=1g
2. Reduce shuffle pressure
spark.sql.shuffle.partitions=100 spark.sql.adaptive.enabled=true
3. Avoid collect() on large datasets
Use take(), writes, or aggregates instead of pulling full DataFrames to the driver.
4. Uncache unused data
Call .unpersist() on cached RDDs/DataFrames you no longer need.
Use the Spark UI
1. Open the Stages tab for the failed job 2. Check Shuffle Read/Write size 3. Look for skewed tasks (one task much larger than others) 4. Review Storage tab for unexpected caching
Analyze your error log with AI
Paste your stack trace into the VyomaStack Log Analyzer — choose Apache Spark or Auto-detect mode.
You get structured output:
- Root cause
- What happened
- How to fix
- Relevant spark-submit flags
Instant analysis works even when AI capacity is limited.
Prevention checklist
- Right-size executors: 4–8 GB, 2–5 cores is a solid default
- Filter early; avoid wide transformations before filters
- Monitor shuffle bytes in production jobs
- Use AQE and salting for skewed keys
Related tools
- Spark Memory Calculator — size executors before you deploy
- Spark Error Explainer — Spark-specific deep dive