🚨 Spark Executor Lost: `java.lang.OutOfMemoryError: Java heap space` & Memory Overhead

DevsCampus — Thu, 22 Oct 2026 12:36:40 +0000

Is your Apache Spark job failing because an executor runs out of memory?

This is a common issue in large-scale data processing, especially when handling large datasets, shuffle operations, joins, and aggregations.

🔍 Why does it happen?

Java Heap Space: The executor JVM runs out of heap memory while processing data.
Memory Overhead Exceeded: The executor exceeds the memory available for JVM overhead, native allocations, and other processes.
Data Skew: Some partitions contain significantly more data than others.
Excessive Caching: Persisting too much data can increase memory pressure.

🛠️ How can you fix it?

✅ Tune spark.executor.memory and spark.executor.memoryOverhead when memory allocation is insufficient.

✅ Increase partitioning where appropriate to reduce the amount of data processed by individual tasks.

✅ Avoid unnecessary caching and unpersist data when it is no longer needed.

✅ Use the Spark UI to investigate failed tasks, shuffle spill, garbage collection, and executor memory.

Remember: Increasing memory alone may not solve the problem. Identifying the actual bottleneck is essential for reliable Spark applications.

📚 Want to learn how to troubleshoot this error in detail?

Read the full article on DevsCampus for more insights into Spark executor failures, memory configuration, and troubleshooting techniques.

🌐 Visit: https://devscampus.com/troubleshooting/spark-oom-executor-lost-failure

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Kafka TimeoutException: Failed to update metadata in 60000 ms

DevsCampus — Sun, 18 Oct 2026 05:17:36 +0000

Kafka clients (producers and consumers) must first fetch cluster metadata — which broker leads each topic partition — before they can send or read. When a client cannot obtain that metadata within its timeout, it throws TimeoutException: Failed to update metadata. This stalls entire event pipelines and is almost always a connectivity or broker-advertisement problem, not a client bug.

📚 Want to understand the root cause and troubleshoot it step by step?
Read the complete guide on DevCampus:
🔗 https://devscampus.com/troubleshooting/kafka-timeoutexception-failed-to-update-metadata
Follow DevCampus for practical tutorials, troubleshooting guides, and resources for software engineers and data engineers.

ApacheKafka #Kafka #DataEngineering #BigData #DistributedSystems #DevOps #DevCampus

DEV Community: DevsCampus

🚨 **Spark Executor Lost: `java.lang.OutOfMemoryError: Java heap space` & Memory Overhead**

Kafka TimeoutException: Failed to update metadata in 60000 ms

ApacheKafka #Kafka #DataEngineering #BigData #DistributedSystems #DevOps #DevCampus

🚨 Spark Executor Lost: `java.lang.OutOfMemoryError: Java heap space` & Memory Overhead