OpenSearch Performance and Monitoring
summary
Covers concrete steps to cut search load: choose field types to avoid dual indexing and unnecessary full‑text analysis, use filters to drop unneeded events before ingest, and tune mappings to reduce query scope. Uses the OpenSearch Python library to poll cluster health and APIs for indices, snapshot status, and replica counts, and surfaces problems in Grafana dashboards that are already driving alerts. Emphasizes mapping: keyword vs text decisions and measured reduction in query volume.
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