Search clusters that stay fast and relevant
OpenSearch clusters designed, tuned and migrated: shard strategy, relevance tuning, vector search, snapshots and moves off Elasticsearch.
Overview
Search clusters often start small and grow without a plan. Too many shards, mappings nobody remembers and no tested snapshots turn a useful service into a fragile one. Relevance drifts too, and users stop trusting results.
We design or rework OpenSearch clusters for your data volume and query pattern. That covers index and shard layout, mappings, relevance tuning, vector and hybrid search, snapshot and restore, and migration from Elasticsearch with no lost documents.
Signs you need this
- Cluster status goes yellow or red during traffic peaks or node restarts
- Heap pressure and shard counts keep rising and nobody knows the right layout
- Users complain that obvious results are missing or buried
- You have never restored a snapshot to check that it works
What you get
- Cluster design
- Node roles, instance types, storage tiers and shard sizing based on your data growth and query load.
- Index strategy
- Mappings, analysers including Arabic, index templates, rollover and lifecycle policies.
- Relevance tuning
- A judged query set, scoring changes, synonyms and boosts, with before and after results recorded.
- Vector and hybrid search
- k-NN indexes, embedding pipelines and hybrid ranking that combines keyword and semantic scores.
- Snapshots and restore
- Automated snapshots to object storage and a documented, tested restore procedure.
- Migration plan and cutover
- For Elasticsearch moves: compatibility checks, reindexing, dual writes where needed and a rollback path.
How the work runs
- 1
Health check
1 to 2 weeks
We review cluster settings, shard layout, slow logs, mappings and snapshot history.
- 2
Design
1 to 2 weeks
We agree the target layout, index strategy and, if needed, the migration approach.
- 3
Build and migrate
2 to 6 weeks
We apply changes or build the new cluster, reindex data and run both side by side until counts match.
- 4
Tune and hand over
1 to 3 weeks
We tune relevance against the judged query set, test a restore and document operations.
Timings depend on the size of your estate. The assessment fixes them before any build starts.
What you can check afterwards
- Cluster stays green through a rolling node restart
- Shard sizes and counts sit within the documented target ranges
- A snapshot has been restored into a separate cluster and verified
- The judged query set shows the relevant result in the top results for agreed key queries
Tools we work with
- OpenSearch
- Amazon OpenSearch Service
- Elasticsearch
- OpenSearch Dashboards
- Logstash
- Data Prepper
- k-NN plugin
- Terraform
- Prometheus
- Amazon S3
From our work
North American clients, delivered by the OpsBreak team
OpenSearch implementations for search and analytics
OpenSearch running in production for client workloads
Questions buyers ask
- Will migrating from Elasticsearch cause downtime?
- Usually not. We reindex into the new cluster, keep both in sync, and switch reads over once document counts and query results match.
- Should we use managed OpenSearch or run our own?
- Managed is less work for most teams. Self-managed makes sense when you need plugins, versions or regions the managed service does not offer.
- Can OpenSearch do semantic search well enough to replace a separate vector database?
- For many workloads, yes. Keeping keyword and vector search in one cluster also makes hybrid ranking and access filtering simpler.
- What access do you need?
- Admin access to the cluster or its cloud account, read access to the applications that query it, and sample queries from real users.
Talk to us about OpenSearch and search engineering.
Tell us what you run and where. We reply within one business day with questions or a quote.