Memory Management in Cassandra: A Complete Guide

Introduction

Memory management in Apache Cassandra plays a crucial role in ensuring optimal performance, stability, and scalability. Cassandra uses a combination of Java heap memory and off-heap memory to handle large volumes of data efficiently while minimizing garbage collection (GC) pauses.

In this guide, we’ll explore how Cassandra manages memory, key configuration parameters, and best practices for tuning it in production environments.

1. Understanding Cassandra’s Memory Architecture

Cassandra uses memory for different internal operations such as caching, compaction, and data buffering. It mainly divides memory usage into two categories:

1.1 On-Heap Memory

Managed by the JVM (Java Virtual Machine).
Stores metadata, Bloom filters, and small objects.
Too much heap memory can lead to long garbage collection pauses.

1.2 Off-Heap Memory

Allocated outside the JVM heap.
Used for memtables, compression metadata, and caches.
Reduces GC pressure and improves performance.

2. Key Memory Components in Cassandra

2.1 Memtables

Memtables are in-memory data structures that store recently written data before it is flushed to disk as SSTables.

Configured using memtable_heap_space_in_mb and memtable_offheap_space_in_mb.
When full, memtables are flushed to disk.

2.2 Row Cache and Key Cache

Row Cache: Stores entire rows for faster reads.
Key Cache: Caches partition key locations within SSTables.
Configurable in cassandra.yaml with parameters like key_cache_size_in_mb and row_cache_size_in_mb.

2.3 Bloom Filters

Help quickly determine if a partition exists in an SSTable.
Use off-heap memory for efficiency.

3. JVM Heap and Garbage Collection (GC)

Cassandra’s performance is highly influenced by JVM tuning. Incorrect heap sizing can cause GC delays or OutOfMemory errors.

3.1 Recommended Heap Size

For production, set heap size between 8GB and 16GB.
Example configuration in cassandra-env.sh:

MAX_HEAP_SIZE="8G"
HEAP_NEWSIZE="800M"

3.2 GC Tuning

Use G1GC (Garbage First Garbage Collector) for modern Cassandra versions.
Avoid frequent full GCs by keeping the heap small enough for fast collections.

4. Off-Heap Caching and Native Memory

4.1 Off-Heap Cache

Cassandra uses off-heap buffers to store data efficiently without impacting GC.

4.2 Native Transport and Buffer Pools

The native transport protocol (CQL) also uses direct memory buffers for network communication. Proper configuration ensures smooth request handling under high load.

5. Monitoring Memory Usage

You can monitor Cassandra memory metrics using tools such as:

nodetool info → Provides memory usage statistics.
JMX metrics → Offers JVM and off-heap usage data.
Prometheus + Grafana → Recommended for production-level monitoring.

6. Best Practices for Memory Management

✅ Keep heap size within 8–16GB.
✅ Use off-heap caching for Bloom filters and compression metadata.
✅ Avoid enabling the row cache unless needed.
✅ Monitor GC logs regularly.
✅ Enable G1GC for better pause-time control.

7. Common Issues and Solutions

Problem	Cause	Solution
High GC pause	Large heap size	Reduce heap to ≤16GB
OutOfMemoryError	Misconfigured memtable or cache	Tune `memtable_heap_space_in_mb`
Slow reads	Inefficient cache usage	Enable key cache, disable row cache

Conclusion

Effective memory management in Cassandra ensures high performance and system stability. By balancing on-heap and off-heap usage, optimizing GC, and tuning caches wisely, you can achieve consistent throughput even under heavy workloads.