01
Shard allocation & JVM heap tuning
Fixing unassigned shards and tuning heap and garbage-collection settings to prevent cluster freezes.
Technologies — Databases & storage
Elasticsearch & OpenSearch cluster optimization
We tune Elasticsearch and OpenSearch clusters for fast, relevant search, stable JVM heaps and lower storage costs.
Core capabilities
Why we build with Elasticsearch & OpenSearch
02
Index lifecycle management (ILM)
Automated hot-warm-cold tiering that moves ageing logs and metrics to cheaper storage.
03
Search relevance tuning
Custom tokenizers, edge n-grams, fuzzy matching and BM25 score tuning for natural search.
Use cases
Where Elasticsearch & OpenSearch fits
Enterprise full-text search
Searching large collections of unstructured PDFs, tickets and customer documents with typo tolerance.
Centralized log aggregation
High-volume application logs and audit events with real-time Kibana or OpenSearch Dashboards views.
How we staff it
Elasticsearch & OpenSearch engineers you interview first
Seniority and experience are agreed in the proposal, and you interview every engineer before they start.
Working-hours overlap is agreed for each engagement and written into the statement of work — the shared window, who shifts hours, and how handoffs work outside it.
Technical FAQs
Frequently asked engineering questions
Common culprits are excessive small shards, high-cardinality aggregations, or poorly tuned fielddata. We restructure indexes and optimize mappings.
Usually, yes. OpenSearch forked from Elasticsearch 7.10, so indices from 7.10 and earlier can move by snapshot and restore; newer versions need reindexing or a Logstash pipeline, and client libraries may need changes.
Ecosystem
Related technologies
Planning an Elasticsearch & OpenSearch project?
Tell us about your architecture, backlog and team. We'll reply within one business day with an honest read on whether we can help.