Case Study: Migrating Slow Elasticsearch Setup to Redis Search for Faster Query Performance

Introduction

Modern applications—e-commerce platforms, SaaS tools, content delivery networks, and real-time analytics systems—rely on fast and accurate search to deliver seamless user experiences.

However, many businesses struggle with slow Elasticsearch setups due to high indexing latency, resource-intensive queries, and complex cluster management. As search workloads scale, Elasticsearch can become a bottleneck, leading to delays, increased costs, and reduced customer satisfaction.

To address these challenges, many organizations are migrating to Redis Search (powered by RediSearch)—a high-speed, in-memory, full-text search engine that delivers results 10x faster than Elasticsearch while maintaining efficiency and scalability.

This case study explores how Redis Search transformed a data-heavy analytics company, significantly reducing query times and infrastructure costs while improving search efficiency.

Business Problem

Before migration, the client used Elasticsearch to power real-time search on their customer insights platform, which indexed millions of data points daily. However, they faced several major issues:

High Query Latency & Slow Performance

Elasticsearch queries took 3-5 seconds, impacting real-time analytics and dashboard load times.
Performance degraded under high concurrent search loads, affecting user experience.

Expensive & Complex Cluster Management

Elasticsearch required costly multi-node clusters to maintain performance.
Frequent reindexing and cluster rebalancing led to downtime and high DevOps overhead.

High CPU & Memory Consumption

Running Elasticsearch at scale demanded significant computing resources, increasing cloud costs.
Cache misses forced repeated disk I/O, slowing down queries further.

Limited Real-Time Capabilities

Elasticsearch had high indexing latency, making real-time data retrieval difficult.
Search suggestions and auto-completion were slow, affecting usability.

Lack of Efficient Data Storage for Small & Medium-Sized Indexes

For datasets under 50 million records, Elasticsearch was overkill, consuming unnecessary resources.

Given these issues, the company needed a high-performance, low-latency alternative.

Solution: Implementing Kafka Connector for Redis

Why Redis Search?

Redis Search (RediSearch) is an in-memory, high-performance search engine that provides:

Sub-millisecond search queries (even under heavy loads).
Full-text search with advanced filtering and ranking.
Efficient indexing without the need for periodic rebalancing.
Lightweight memory footprint, reducing infrastructure costs.
Seamless integration with existing Redis-based architectures.

Key Benefits

10x Faster Query Performance

Query latency dropped from 3-5 seconds to under 100ms, improving dashboard load times.

40% Lower Infrastructure Costs

Migrating to Redis Search eliminated unnecessary Elasticsearch clusters, reducing cloud spend.

Real-Time Indexing & Retrieval

Data updates were indexed instantly, enabling live search & real-time analytics.

60% Less Memory & CPU Usage

Redis efficiently stored indexes in-memory, reducing the need for expensive disk-based operations.

Simplified Cluster Management

No need for frequent reindexing or shard reallocation, reducing DevOps overhead.

Advanced Search Capabilities

Implemented fuzzy search, auto-complete, and facet filtering with minimal latency.

Real World Impact

Conclusion

Migrating from Elasticsearch to Redis Search was a game-changer for this company. By eliminating search bottlenecks and reducing costs, they achieved:

Ultra-fast, real-time search performance
Lower operational costs and simplified infrastructure
Scalability without the need for complex cluster management

For businesses facing slow search performance with Elasticsearch, Redis Search offers an efficient, cost-effective, and future-proof alternative.