26.07.2024 Problem:
The client experienced frequent warnings in their Cassandra nodes, indicated by the log entry:
INFO [CompactionExecutor:37] 2023-02-23 12:00:12,268 NoSpamLogger.java:91 – Maximum memory usage reached (536870912), cannot allocate chunk of 1048576
The client requested an investigation into the potential impact of increasing the file_cache_size_in_mb in the cassandra.yaml file, whether a restart (bounce) would be necessary, and the appropriate memory setting to eliminate these warnings.
Process:
Upon receiving the query, the expert analyzed the situation and provided initial feedback. It was clarified that these messages were not errors but debug logs indicating that the cache size was insufficient to handle the frequent queries. This situation did not disrupt data retrieval, which would continue from SSTables instead of caches, but it could impact performance due to the need for more frequent disk reads.
The expert advised against manually setting the file_cache_size_in_mb as it is dynamically managed based on the JVM heap size. Instead, the recommendation was to increase the overall memory available to Cassandra nodes. To proceed further, the expert requested detailed specifications of the nodes.
Solution:
Based on the specifications, it was concluded that the Cassandra nodes were currently handling the workload but were close to their memory limits. To prevent future performance degradation, the following steps were recommended:
Increase Node Memory: Allocate more memory to each Cassandra node. This would automatically increase the cache size, as it is dynamically adjusted based on the JVM heap size.
Monitoring: Implement real-time monitoring of memory usage using tools like Prometheus and Grafana. This would provide insights into memory usage patterns and help in fine-tuning the system.
Adjust Chunk Sizes: Evaluate and adjust the data chunk sizes if necessary. This can be an additional step to optimize memory usage and performance.
Conclusion:
By increasing the memory allocation for Cassandra nodes and implementing a robust monitoring system, the memory usage warnings were effectively addressed. These measures helped optimize the performance of the Cassandra cluster, ensuring it could handle frequent queries without hitting memory usage limits. This proactive approach not only resolved the immediate issue but also provided a framework for maintaining optimal performance and stability in the future.