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Search and log-analytics clusters can fail from poor shard design, fast-growing retention, expensive aggregations and low disk headroom, not only memory pressure. OpenSearch recommends dedicated cluster-manager nodes for production.
OpenSearch documentation says three dedicated cluster-manager nodes is the right approach for almost all production use cases. Data nodes carry storage/search/indexing workloads and should be balanced across zones.
Cluster-manager nodes manage cluster state, data nodes store/search shards, ingest nodes can handle pipelines and coordinating nodes fan out queries.
Elastic gives a general guideline of 10–50 GB shards for many use cases and below 200 million documents per shard; the real optimum should be benchmarked.
Replicas, merges, segment overhead and watermark headroom increase storage beyond primary data.
Adapt endpoint and authentication to your cluster.
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curl -s http://localhost:9200/_cluster/health?prettycurl -s http://localhost:9200/_cat/nodes?vcurl -s http://localhost:9200/_cat/shards?v | head -n 40curl -s http://localhost:9200/_cat/indices?v&s=store.size:desc | head -n 30Adding nodes alone does not fix oversharding or poor index lifecycle policies.
They share ancestry but are separate projects. APIs overlap significantly, but version and plugin compatibility must be checked.
Elastic gives 10–50 GB as a general guideline for many workloads, but benchmark search, recovery and ingest.
It can run, but it has no replica redundancy or node-failure tolerance; critical production should consider multi-node.
Share daily ingest, retention, index count, query type and shard layout; we can size nodes, RAM and NVMe.