Apache Cassandra: Resolving High Memory Usage issue

Problem:

The client reported high memory usage on a production Apache Cassandra node, accompanied by frequent errors related to the ThreadPoolExecutor shutting down. This led to instability in the Cassandra service, including errors like java.util.concurrent.RejectedExecutionException, and resulted in a failure to execute repairs.

Process:

Step 1: Initial Identification

The error logs provided by the client contained multiple instances of RejectedExecutionException due to the ThreadPoolExecutor shutting down, indicating a failure in managing repair tasks:

ERROR [Repair#6941:244] 2024-12-19 23:24:00,818 CassandraDaemon.java:244 - Exception in thread Thread[Repair#6941:244,5,RMI Runtime] java.util.concurrent.RejectedExecutionException: ThreadPoolExecutor has shut down

Additionally, high memory usage was observed, with the heap size configured to 16GB (-Xms16G -Xmx16G).

Step 2: Analysis by the Expert

The expert began by reviewing the client’s configuration and the error logs. Key questions were posed regarding:

• The number of nodes in the cluster and their individual resource allocations.
• Whether there was an overload of repair tasks or any inefficient Cassandra operations running at the time.
• Whether there were recent changes to the cluster’s workload, such as an increase in data volume or a backlog of repair tasks.

Step 3: Client Environment Overview

The client provided details from the nodetool status command, showing that the nodes in the cluster were up, but high disk usage and memory consumption were reported on certain nodes. The cluster consists of multiple nodes with varying data loads, with each node running a similar configuration:

UN 100.64.113.243  629.95 GiB 52      20.7%       9620770a-f526-4821-a31e-00d21a828d0a rack2

UN 100.64.113.73  947.4 GiB 52      28.5%       5e168a49-a93b-40c1-b439-cf62717a72d0 rack2

Despite having sufficient disk space on most nodes, the memory issues persisted, likely due to Cassandra’s memory management under high load.

Step 4: Root Cause Analysis and Solution Proposal

The expert’s analysis identified the following key factors contributing to the issue:

• ThreadPoolExecutor Shutdown: The ThreadPoolExecutor has shut down error typically occurs when Cassandra is unable to process repair tasks due to thread pool exhaustion. This can be a result of high repair demand or insufficient system resources.
• High Memory Usage: The heap memory configuration was set to 16GB, which may not be sufficient under the current load. This, combined with potentially excessive garbage collection (GC) operations and inefficient repair operations, led to the thread pool being overwhelmed.
• Repair Task Backlog: Cassandra’s repair tasks were likely not completing in time, creating a backlog that caused further strain on the system.

Solution:

To resolve the issue, the expert recommended the following:

• Increase Memory Allocation: Increase the heap size for Cassandra, adjusting the -Xms and -Xmx values to 32GB (or higher, depending on available resources) to handle larger data volumes and improve garbage collection efficiency.
• Optimize Repair Configuration: Configure the repair strategy to run in smaller, more manageable chunks, using the -Drepair.concurrent.repair setting to control parallel repair task execution. This helps prevent thread pool exhaustion.
• Thread Pool Adjustment: Increase the size of the thread pool by configuring the threadpool settings in the cassandra.yaml file to allocate more threads for repair tasks, thereby avoiding RejectedExecutionException.
• Regular Monitoring and Scaling: Set up monitoring tools to track memory and thread pool usage in real-time, and implement scaling solutions such as adding more nodes or adjusting the replication factor to balance the load more effectively.

Conclusion:

The Cassandra service experienced high memory usage and ThreadPoolExecutor shutdown due to excessive repair tasks, inefficient resource allocation, and insufficient heap memory. By increasing memory allocation, optimizing repair settings, and adjusting thread pool configurations, the client can improve the stability of the Cassandra cluster and prevent similar issues in the future. The expert also recommended ongoing monitoring and potentially scaling the cluster to accommodate future growth and repair needs.