How we accelerated search across a large dataset with Elasticsearch

Category: Database Optimization / Search Performance / Infrastructure

Technologies: Elasticsearch, Database Optimization, Search Infrastructure

A growing volume of content in the database had made search slow on a client's website. Here is how we moved search operations to Elasticsearch and restored a responsive experience for users.

The problem: search could no longer keep up with the data

As the client's project grew, the amount of content in its database increased significantly.

This began to affect the performance of the built-in search. Users had to wait longer for results because search queries took too long to process.

The existing search mechanism could no longer cope with the volume of data the project was handling.

To keep developing the service, the client needed a solution that could process search queries efficiently without continually increasing the load on the primary database.

The solution: moving search operations to Elasticsearch

After analyzing the situation, we decided to use Elasticsearch, a dedicated search engine designed to index and quickly search large volumes of data.

Instead of running search operations directly in the primary database, we moved search functionality to Elasticsearch.

This separated two distinct tasks:

• The primary database continued to store and process content.
• Elasticsearch handled search queries against indexed data.

This approach let us use a dedicated search engine rather than asking the primary database to perform resource-intensive search operations.

The result: fast search across a large content collection

After the switch to Elasticsearch, search performance improved substantially.

Users could find the information they needed faster, and search operations were no longer executed directly in the primary database using the previous method.

The solution did not require reducing the amount of content or limiting search capabilities for the sake of performance.

The main result was substantially faster search across the existing volume of data.

The takeaway: use the right tool for each task

As a project grows, an architecture that worked well with a small amount of data may no longer meet its needs.

In these situations, increasing server capacity is not always the best solution.

Sometimes, moving a resource-intensive function into a separate, dedicated component is far more effective.

For this project, Elasticsearch helped accelerate search and separate the search workload from the primary database.

Is your website slow when working with large volumes of data?

Slow search, high database load and lengthy query processing may indicate that the application's architecture needs a review.

The engineers at inSave Hosting help optimize server infrastructure, databases and application performance.

Contact us to discuss your requirements and find a suitable solution.