Technologies

Technology

Elasticsearch

Search and analytics at a scale PostgreSQL LIKE queries cannot touch.

Elasticsearch for full-text search, log analytics, and real-time data exploration when your query patterns have outgrown what a relational database can serve efficiently.

Elasticsearch
<50ms
Search at 100M records
Horizontal
Scaling model
Full-text + analytics
Combined

Our Take

Why we use Elasticsearch

When users search 10 million product records with typo tolerance, when you need to aggregate billions of log events in real time, or when you need faceted filtering with live count updates — that is when Elasticsearch justifies its operational overhead. For search-heavy applications, it delivers query performance that PostgreSQL cannot match at scale.

What We Build With It

Product search engines
Log analytics and observability platforms
Content discovery platforms
Security event correlation (SIEM)
Business intelligence dashboards

Honest Assessment

When Elasticsearch is the right choice. And when it is not.

Strengths

Sub-second full-text search at massive scale
Powerful aggregations for analytics
Flexible schema with JSON documents
Horizontal scaling via sharding
Kibanaa for out-of-box visualization

Limitations

Operational complexity vs hosted Algolia
Eventual consistency requires architecture consideration
Memory intensive
Schema changes require reindexing
Overkill for small datasets

Ready to build with Elasticsearch?

Get a free architecture consultation. We will tell you if Elasticsearch is the right fit for your project.

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