Gentle Introduction to Elasticsearch
By Sandip Parida
Elasticsearch is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.
How Elasticsearch Works
Unlike traditional databases that search through records sequentially, Elasticsearch uses inverted indexes. Instead of mapping documents to their contents (like a book’s table of contents), it maps contents to their documents (like a book’s index at the back).
This data-to-index approach enables incredibly fast search operations regardless of dataset size.
Communication
Elasticsearch communicates through HTTP REST interfaces, making it accessible from any programming language or tool that can make HTTP requests.
Horizontal Scalability
Elasticsearch supports horizontal scaling through shards distributed across nodes. As your data grows, you add more nodes rather than upgrading existing hardware. The system handles data distribution and replication automatically.
Benchmark: Elasticsearch vs PostgreSQL
We ran performance comparisons across different dataset sizes:
| Records | PostgreSQL | Elasticsearch |
|---|---|---|
| 200K | 0.012s | 0.006s |
| 600K | 0.058s | 0.006s |
| 1.2M | 0.138s | 0.006s |
The results are striking: Elasticsearch maintained approximately 0.006 seconds response time regardless of dataset size, while PostgreSQL degraded significantly — making Elasticsearch 20x+ faster on large datasets.
When to Use Elasticsearch
- Full-text search across large datasets
- Real-time analytics and aggregations
- Log and event data analysis
- Geospatial search
- Application search (e-commerce, content platforms)
Conclusion
Elasticsearch shines when you need fast, flexible search across large volumes of data. Its inverted index architecture and horizontal scalability make it an essential tool for modern applications.