Solving Kafka java.lang.OutOfMemoryError: GC overhead limit exceeded

Introduction

Apache Kafka is a popular distributed event streaming platform that is widely used for building real-time data pipelines and streaming applications. However, developers often encounter the dreaded java.lang.OutOfMemoryError: GC overhead limit exceeded error while working with Kafka. This Java error is thrown when the Garbage Collector (GC) has spent too much time collecting a small amount of heap and is unable to free a substantial amount of memory. In a Kafka environment, this issue can lead to consumer or producer failures, broker outages, and can degrade the overall system performance.

In this article, we will discuss the reasons behind this error and explore practical solutions to fix it.

Reasons for the Error

• Heap Space Misconfiguration: Kafka’s heap space may be insufficient for the volume of data it is handling, causing excessive GC with little memory reclaimed each time.
• Inefficient Code: Poorly written Kafka consumers, producers, or custom partitioners can lead to memory leaks.
• Resource Intensive Processing: Functions like serialization, deserialization, and high throughput data operations can consume substantial heap if not managed carefully.

Solutions to the Error

Solution #1 – Increase Heap Size

Increasing Kafka’s heap size can give the Java VM more memory to manage and reduce the frequency of garbage collection, hence possibly avoiding the out of memory error.

1. Find the Kafka server startup script which is typically kafka-server-start.sh or the service configuration file.
2. Locate the KAFKA_HEAP_OPTS variable and increase the -Xmx value which sets the maximum heap size.
3. Restart the Kafka broker for the changes to take effect.

Notes: Ensure that the server has enough physical memory to support the increased heap size to avoid swapping. Swapping can lead to significant performance degradation.

Solution #2 – Optimize Kafka Configurations

Fine-tuning Kafka producer and consumer configurations such as batch.size, linger.ms, and max.poll.records, can alleviate memory pressure.

1. Analyze the data throughput and adjust the batch.size parameter to optimize the batch of records to send per request.
2. Adjust linger.ms to control the maximum time to buffer data in the producer.
3. Decrease max.poll.records in the consumer configuration to reduce the number of records fetched per poll.

Example:

KafkaProducer<String, String> producer = new KafkaProducer<>(producerProperties);
producerProperties.put("batch.size", 16384);
producerProperties.put("linger.ms", 5);

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(consumerProperties);
consumerProperties.put("max.poll.records", 100);

Notes: Be aware that changing these configurations can affect the latency and throughput. Testing is recommended to find the best configuration that balances performance with resource utilization.

Solution #3 – Profile and Debug to Identify Memory Leaks

Using profiling tools to find and fix memory leaks in your custom producers, consumers, or other parts of the Kafka application can solve the memory issue at its core.

1. Choose a profiling tool such as VisualVM, YourKit or JProfiler.
2. Connect the profiler to your running Kafka application.
3. Identify unusual memory consumption patterns and trace them back to specific lines of code.
4. Refactor and fix the identified memory leaks in the application’s codebase.
5. Test the changes to confirm the memory leak has been resolved.

Notes: Profiling a running application can incur overhead, so it should be done in a test environment or during off-peak hours for production systems. Once memory leaks are identified and fixed, continuous monitoring is recommended.

Conclusion

The java.lang.OutOfMemoryError: GC overhead limit exceeded error in Kafka can be a significant blocker to system stability and performance, but with careful tuning of heap settings and consumer/producer configurations, as well as profiling for memory leaks, recovery is achievable. Always remember that changes to the system should be thoroughly tested before rolling out to production environments.