ElasticSearch to ClickHouse Analytical Big Data Migration

ElasticSearch to ClickHouse Analytical Big Data Migration

A scalable, high-performance backend module built with Node.js and TypeScript, combining ClickHouse and Elasticsearch to handle large-scale data ingestion, analytics, and search. The solution includes automated scripts and infrastructure to migrate data from Elasticsearch to ClickHouse for long-term analytics. Designed for real-time performance, horizontal scaling, and seamless integration with BigData APIs.

Overview

This solution is a robust and scalable backend architecture built for high-throughput data ingestion, analytics, and full-text search. To support long-term analytical needs and reduce reliance on Elasticsearch, it includes automated migration scripts and infrastructure to efficiently transfer and normalize data into ClickHouse. With built-in support for auto-scaling, read replicas, and CDN delivery, the platform aims to achieve 99.9 percent uptime, a 50 percent reduction in API response times, and a 40 percent decrease in infrastructure costs. This enhances developer efficiency and delivers a high-quality user experience.

This solution is a robust and scalable backend architecture built for high-throughput data ingestion, analytics, and full-text search. To support long-term analytical needs and reduce reliance on Elasticsearch, it includes automated migration scripts and infrastructure to efficiently transfer and normalize data into ClickHouse.

With built-in support for auto-scaling, read replicas, and CDN delivery, the platform aims to achieve 99.9 percent uptime, a 50 percent reduction in API response times, and a 40 percent decrease in infrastructure costs. This enhances developer efficiency and delivers a high-quality user experience.

Problem

Modern industries like social media platforms generate massive volumes of user-generated content and metadata every hour. Businesses that rely on this data for marketing, analytics, or influencer intelligence face significant challenges:

  • Ingesting and processing hundreds of thousands of profiles and updates in near real-time.
  • Running complex filters and full-text searches with high accuracy and speed.
  • Aggregating and analyzing time-series data across millions of data points.
  • Managing data across multiple storage systems (e.g., Elasticsearch, ClickHouse) without consistency issues or performance bottlenecks.
  • Scaling infrastructure efficiently without over-provisioning or driving up costs.
  • Ensuring reliability and fault tolerance while maintaining fast developer iteration cycles.

Without a unified, scalable architecture, teams struggle with rising infrastructure costs, slow response times, data inconsistencies, and poor user experience.

Solution

The solution uses a modular open source Node.js and TypeScript backend module to manage seamless migration from Elasticsearch to ClickHouse, enabling long-term and cost-effective analytical storage while maintaining high performance. It separates real-time search, handled by Elasticsearch, from analytical workloads, handled by ClickHouse, allowing each system to operate at its best. Automated migration scripts ensure large datasets are normalized and transferred with data integrity and schema consistency maintained.

This way your backend can be built for high-throughput data ingestion, using batching, compression, and the efficient columnar storage format of ClickHouse which in a nutshell supports horizontal scaling with distributed nodes and read replicas. That's what allows and ensures low-latency queries even during heavy traffic. Designed for integration with Big Data systems, you can start working with any APIs ready for large-scale use and support full-text search, aggregation, and filtering while significantly lowering infrastructure costs and improving long-term analytics.

Features

Automated Data Migration
Includes robust scripts that automate the transfer and normalization of data from Elasticsearch to ClickHouse. These scripts ensure schema alignment, preserve historical data integrity, and reduce manual intervention during migration processes.
Grafana Dashboards
Fully integrated with Grafana for real-time observability. Visual dashboards track ingestion status, error rates, query performance, and infrastructure health, helping teams monitor data flow and system stability at a glance.
Winston Logging Integration
Winston provides detailed, timestamped logs for all backend operations. These logs are structured for easy parsing and support integration with external logging services like Loggly or ELK, enabling fast debugging and operational transparency.

Benefits

Improved Performance for Analytical Queries
ClickHouse handles complex analytical workloads with sub-second query times, even on billions of rows, enabling faster data exploration and business intelligence.
Lower Infrastructure Costs
Offloading long-term data from Elasticsearch to ClickHouse reduces storage and compute expenses. ClickHouse's columnar format and compression dramatically cut resource usage.
Real-Time & Historical Insights
Combines the power of real-time search from ElasticSearch with deep historical analysis from ClickHouse, giving users access to both current and long-term insights.
Future-Proof Approach
The module is designed with extensibility in mind, allowing for future plug-ins, additional data sources, and evolving business logic without re-architecting core components.

Questions & Answers

Why migrate from Elasticsearch to ClickHouse for analytics?

Elasticsearch is optimized for search and filtering but is not cost-effective or efficient for large-scale analytical queries over time. ClickHouse provides faster analytical performance with lower storage costs thanks to its columnar data format and compression capabilities.

Does this solution eliminate Elasticsearch completely?

No, Elasticsearch remains in use for real-time search features. The migration is focused on offloading long-term analytical workloads to ClickHouse, allowing both systems to operate efficiently within their strengths.

How is data consistency maintained during migration?

Automated migration scripts handle data normalization and transfer, ensuring schema consistency and data integrity between Elasticsearch and ClickHouse.

Is this solution suitable for real-time analytics?

While ClickHouse can handle near real-time analytics, ElasticSearch remains the primary engine for immediate user-facing search. The combination provides a balanced, hybrid approach.

Is This the Right Solution for You?

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5
July 8, 2025
Exactly what we needed to scale our analytics without breaking the project.
Author:
Marcus Li