Fixing High Memory Usage in MappingCassandraConverter: A Practical Guide for Java Developers

As your Java application scales, performance bottlenecks like memory leaks or excessive memory usage can sneak in—silently degrading your system over time. One such culprit developers may encounter when working with Apache Cassandra in Spring Data is MappingCassandraConverter.

In this post, we’ll explore a real-world scenario where memory was slowly building up in a Spring Boot application. Using VisualVM, we traced the issue to MappingCassandraConverter, which was responsible for retaining over 55% of the used memory. Digging deeper, we found the CassandraTypeResolver and a massive number of ConcurrentHashMapNode instances at the root of the problem.

🧠 What Is MappingCassandraConverter?

The MappingCassandraConverter is part of Spring Data for Apache Cassandra. It handles the conversion between your Java domain objects and Cassandra’s native data types.

While generally efficient, misconfiguration or incorrect usage patterns can cause this converter to retain excessive memory—especially through internal caching mechanisms used to optimize type resolution.

🚩 The Symptom: Memory Slowly Builds Up

Using VisualVM, we observed that:

• Heap memory usage increased slowly but steadily.
• Manual garbage collection had little effect.
• A retained size analysis showed MappingCassandraConverter was holding more than half of the heap memory.
• The culprit: CassandraTypeResolver → ConcurrentHashMap → Many ConcurrentHashMapNode entries.

🔍 Step-by-Step Diagnosis with VisualVM

Step 1: Profile the Application
Attach VisualVM to your running application and enable memory sampling.

Step 2: Capture a Heap Dump
Once memory usage grows suspiciously large, capture a heap dump and analyze retained sizes.

Step 3: Filter by Retained Memory
Sort objects by retained memory. You’ll likely find MappingCassandraConverter near the top.

Step 4: Drill into CassandraTypeResolver
Navigate inside the MappingCassandraConverter object to inspect its fields. You’ll discover a ConcurrentHashMap in CassandraTypeResolver holding thousands of nodes.

⚠️ Root Cause: Unbounded Cache Growth

The issue usually stems from unbounded growth of an internal ConcurrentHashMap used for caching resolved Cassandra types.

Possible causes:

• Repeated addition of unique keys with no eviction policy.
• Inefficient mapping or type resolution logic.
• Excessive use of dynamic schemas or reflection.

🛠️ Solutions and Mitigation Strategies

1. Review Your Entity Mappings

• Simplify and reduce the number of distinct types.
• Avoid using dynamic or frequently changing domain models unnecessarily.

2. Enable Explicit Caching Strategy

If you’re using custom resolvers, make sure to implement size limits or eviction strategies on internal caches.

3. Singleton vs Prototype Scope

Ensure MappingCassandraConverter and CassandraTypeResolver are not being instantiated multiple times unnecessarily (check for improper bean scopes in Spring).

4. Upgrade Dependencies

Spring Data and DataStax drivers frequently release performance improvements. Update to the latest versions.

5. Add Monitoring and Alerts

• Use JMX or Prometheus metrics to monitor heap usage and memory allocation over time.
• Set alerts for abnormal trends.

6. Analyze Application Access Patterns

• Do you run thousands of small, diverse queries?
• Are there excessive schema inspections at runtime?
  Optimize those use cases.

🧪 Bonus Tip: Use a Tool Like Eclipse MAT

For deeper inspection:

• Load your heap dump in Eclipse Memory Analyzer Tool (MAT).
• Run the Leak Suspects Report.
• Identify the largest dominator trees.
• Trace references to ConcurrentHashMapNode.

This can help pinpoint what code is holding onto memory the longest.

Conclusion

When working with Spring Data and Apache Cassandra, MappingCassandraConverter can quietly become a memory hog if not monitored carefully. Understanding how it retains data through CassandraTypeResolver and its internal ConcurrentHashMap is key to resolving memory issues.

By applying the tips in this article—profiling with VisualVM, reviewing your mappings, optimizing cache usage, and upgrading libraries—you can keep your memory usage in check and ensure your Cassandra-powered application performs smoothly at scale.

✅ TL;DR

• MappingCassandraConverter can retain a large portion of heap due to internal caching in CassandraTypeResolver.
• Use VisualVM to trace retained memory and heap usage.
• The memory issue is often caused by an unbounded ConcurrentHashMap.
• Optimize mappings, control cache size, and monitor usage over time.