Search that finds what your users mean
Elasticsearch is a distributed engine built for full-text search and analytics over large volumes of data. It is the right choice when users need fast, relevant search with filters, typo tolerance and ranking.
Elasticsearch excels at relevance scoring, faceted navigation, autocomplete and log or event analytics. It is not a primary database: data should be synced from a system of record, and keeping that sync reliable is part of the work. Clusters also need memory and operational attention. For modest catalogues, the built-in search in PostgreSQL or a hosted search service may be simpler and cheaper, and we will say so before recommending a cluster.
What we build with Elasticsearch
- Product and site search
- Faceted filters and autocomplete
- Relevance and synonym tuning
- Index sync from primary databases
- Log and event analytics
Elasticsearch questions
Do we need Elasticsearch for site search?
Not always. Small and medium catalogues can often use database full-text search. Elasticsearch earns its place when relevance, scale or faceting needs outgrow that.
How is data kept in sync with our main database?
Through event-driven updates or queued reindex jobs, with periodic full rebuilds as a safety net. We monitor drift so search results do not go stale.
How do you improve search relevance in Elasticsearch?
We tune analysers, field boosts, synonyms and fuzzy matching, then test changes against a set of real search terms with known good results. Relevance is measured, not judged by eye.
What is a mapping in Elasticsearch?
A mapping defines how each field is stored and analysed, such as full text, exact keyword, number or date. We define mappings explicitly rather than relying on automatic detection, which can guess wrongly.
How do you change an index mapping without downtime?
Most mapping changes need a new index, so we build it alongside the old one, reindex the data and then switch an alias to point at it. Searches keep working throughout.
How many shards should an Elasticsearch index have?
It depends on data size and query load, but too many small shards is a common mistake that wastes memory. We size shards based on expected growth and review them as data increases.
How do you back up Elasticsearch?
We use snapshots to a repository such as cloud object storage, scheduled with lifecycle policies. Because search data usually comes from a primary database, a full reindex is also a valid recovery path.
How is an Elasticsearch cluster secured?
We enable authentication, role-based access and TLS, and never expose the cluster directly to the internet. Applications query it through a backend that controls what each user can search.
What is the difference between Elasticsearch and OpenSearch?
OpenSearch is a fork of Elasticsearch maintained under an open-source licence, and the two share many concepts but have diverged. We check which one your hosting or cloud provider offers before building.
Can Elasticsearch handle log and event analytics?
Yes, it is widely used for logs and metrics with index lifecycle policies that move older data to cheaper storage and delete it on schedule. We size retention to what you actually need to search.
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