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OpenSearch metrics challenge: can you spot the performance flaw?
- An analysis of metrics can identify hidden performance issues to improve system efficiency.
- This article is about a real-world case involving an error for a high-performance OpenSearch cluster.
- The OpenSearch cluster was configured with 12 nodes to meet growing performance requirements, but performance couldn't improve anymore.
- The article focuses on three high-level metrics - Indexing Data Rate, HTTP requests by response code and Search rate - to identify the flaw.
- It was found that the root cause of the anomaly was due to how queries were executed by a component leveraging a shared library that was lacking the index pattern parameter.
- The impact of the error was significant and led to an unintended consumption of resources.
- Correcting the code to always specify the index pattern in queries led to a significant improvement in performance and resource utilization.
- Horizontal scaling-in of the cluster reduced cloud costs, highlighting the importance of monitoring performance metrics.
- This experience highlights the need to pay attention to detail in analysis, monitor metrics and avoid simple coding errors.
- OpenSearch's good performances can hide issues that might go unnoticed and have severe consequences.
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