Elasticsearch That Doesn't Break Your Budget
Index discipline, retention strategy, operational runbooks. We tame Elasticsearch—high performance, reasonable costs, no surprises.
What we deliver with Elasticsearch
Index templates and mappings
Explicit mappings instead of dynamic ones, standard field types, and templates applied consistently so searches behave the same everywhere.
ILM and tiering
Hot, warm, cold, and delete phases set from retention policy and query patterns, so old data costs less without becoming unreachable.
Query and cluster performance
Shard sizing, refresh intervals, and query rewrites based on measured slow logs rather than guesswork.
Security and access
Role-based access per index pattern, TLS, and audit logging, with API keys scoped to the service that needs them.
Operations runbook
Documented procedures for red and yellow cluster states, snapshot restore, and capacity growth, tested before they are needed.
How we work with Elasticsearch
Almost every struggling cluster we see has the same two causes: dynamic mappings left to guess field types, and a daily index per day with no lifecycle policy. Both are cheap to fix early and expensive to fix after a year of data has accumulated.
We set templates and ILM first, then use slow logs to find the queries that matter. Snapshot and restore is proven with an actual restore into a scratch cluster, because a backup nobody has restored is a hope, not a backup.
Elasticsearch in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Mapping explosion
Dynamic mapping turns unbounded JSON keys into thousands of fields, and the cluster refuses writes. Explicit templates with strict mappings stop it.
One shard per daily index, forever
Thousands of tiny shards exhaust heap and slow every search. Shard sizing plus ILM reduces cluster size and cost.
Fielddata on analysed text fields
Sorting or aggregating on text triggers heap spikes and circuit breakers. Keyword sub-fields and doc values fix the query, not the symptom.
When to use Elasticsearch
Log and event platforms where search speed and retention both matter.
Product search that has outgrown database LIKE queries.
SIEM and investigation workloads needing fast filtering over months of data.