Addressing Out of Memory Errors in OpenSearch

Problem:

The production OpenSearch cluster encountered frequent shutdowns due to Out of Memory (OOM) errors across all four nodes. The exact cause of the OOM issue needed further investigation, requiring specific data and logs to diagnose the root cause.

Process:

1. Resource Assessment:
   • Evaluate RAM, CPU, and disk usage on each node to identify potential resource exhaustion.
   • Determine the number of nodes in the cluster and analyze data distribution.
   • Review Java Heap size, garbage collection parameters, total shards, and indexes to understand memory usage.
2. Data Collection and Analysis:
   • Gather OpenSearch and system logs, including heap dumps during crashes.
   • Utilize Metricbeat agent for efficient metric collection and monitoring.
   • Analyze historical data to identify patterns and potential triggers for OOM errors.
3. Configuration Review and Optimization:
   • Verify consistency in OpenSearch configuration across all nodes.
   • Implement monitoring tools like Grafana for better cluster insights.
   • Address common OOM causes such as small heap size and low vm.max_map_count.

Solution:

After thorough exploration of potential solutions, we’ve identified two viable approaches to tackle the issue at hand:

1. Shard Reduction:

   To alleviate the strain on resources, we recommend decreasing the number of primary shards to a range of 10-15. However, it’s important to note that reducing the primary shard count of an existing index isn’t feasible. Therefore, the index must be recreated with the desired number of shards. For this purpose, we suggest utilizing the reindex API, which offers a straightforward method for recreating the index while adjusting the shard count. You can find detailed instructions on how to perform this operation via the following link: Reindex API Guide. By creating a new index with the recommended shard count, you can effectively manage the resource utilization within your OpenSearch cluster.

2. Query Optimization:

   Another crucial aspect to address is optimizing the queries executed on OpenSearch. By analyzing the fields and structure of the queries, we can identify opportunities to enhance efficiency and reduce resource consumption. An excellent resource for identifying and optimizing expensive queries is available on Stack Overflow at the following link: Query Optimization Guide. Additionally, implementing timeouts on queries can help prevent prolonged execution times and resource overconsumption.

In the event that these solutions do not yield the desired results, we recommend gathering sample data, mapping details, and sample queries from clients. By setting up a test environment and replicating the client’s queries, our experts can gain deeper insights into the root cause of the issue and devise further troubleshooting strategies.

Together, these approaches offer a comprehensive strategy to address the issue and optimize the performance of the clients OpenSearch cluster.

Conclusion:

While the proposed solutions aim to mitigate OOM errors, further investigation is required to ensure long-term stability. Collaboration with the client to gather sample data and queries will facilitate testing in a controlled environment to validate the effectiveness of the proposed changes. By addressing resource constraints and optimizing queries, the OpenSearch cluster can achieve better performance and reliability. Debugging tools and logging adjustments will aid in obtaining more detailed information for future troubleshooting efforts.