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In the early years, many companies adopted Elasticsearch to serve as their distributed log aggregator. Elasticsearch is able to provide near-real-time indexing and full-text search functionalities on large volumes of logs.
With its popularity, a number of companies not only use Elasticsearch in the domain of search but also extend their usages to analytic queries. But, in recent years, I have observed more companies adapting ClickHouse* over Elasticsearch for their OLAP workloads.
In particular, ContentSquare reported queries in ClickHouse are 4 times faster, and 11 times cheaper; Uber moved to ClickHouse to manage service logs at massive scale and observed 10x performance increase.
So, you may wonder why ClickHouse is so powerful and whether you should I include it in your analytic toolkit?
This free eBook compares Elasticsearch and ClickHouse from an architectural perspective and provides a point-to-point functional comparison on considerations of OLAP systems, including data model, data ingestion, query, enterprise support, and ecosystem.
This eBook has six chapters:
- Introduction
- Architecture
- Indexing and caching
- Functionality
- Enterprise support
- Conclusion
About ByteHouse
ByteHouse, part of ByteDance, provides data warehousing products and solutions for both cloud and on-premise deployment offering speed, scalability, and low maintenance and cost.
ByteDance is a global technology platform dedicated to creating innovative solutions to entertainment, collaboration, education, and business operations.
*ClickHouse is a trademark of ClickHouse, Inc.
