How can we optimize our Elasticsearch cluster running on AWS OpenSearch to reduce memory pressure and query latency under heavy load?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can we optimize our Elasticsearch cluster running on AWS OpenSearch to reduce memory pressure and query latency under heavy load?
1 real search · 1 real-search pass
site:docs.aws.amazon.com/opensearch-service/latest/developerguide performance tuning memory pressure shard sizing query latency JVM heap circuit breakersGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
site:docs.aws.amazon.com/opensearch-service/latest/developerguide performance tuning memory pressure shard sizing query latency JVM heap circuit breakers Google ↗
- Choosing the number of shards · docs.aws.amazon.com
- AOSPERF01-BP03 Check the number of shards per GiB of heap memory · docs.aws.amazon.com
- AOSPERF01-BP01 Maintain shard sizes at recommended ranges · docs.aws.amazon.com
- Troubleshooting Amazon OpenSearch Service · docs.aws.amazon.com
- Operational best practices for Amazon OpenSearch Service · docs.aws.amazon.com
- Auto-Tune for Amazon OpenSearch Service · docs.aws.amazon.com
- Recommended CloudWatch alarms for Amazon OpenSearch Service · docs.aws.amazon.com
- Monitoring OpenSearch cluster metrics with Amazon CloudWatch · docs.aws.amazon.com