Switching from PostgreSQL to ClickHouse for Improved Performance and Scalability
Momentic, the company behind an AI-driven software testing platform, recently rearchitected its caching system to handle over 2 million queries per day across 20 billion total entries, while maintaining an average response latency of around 250 ms. This improvement was made possible by transitioning from PostgreSQL to the column-oriented database ClickHouse. By Sergio De Simone
The increasing scale and complexity of AI/ML workloads necessitate more performant and scalable database solutions that traditional relational databases struggle to provide.
This move highlights a growing trend of companies optimizing their data infrastructure to meet the demands of large-scale AI applications, pushing adoption of specialized databases like ClickHouse.
The shift demonstrates a growing willingness to adopt specialized, high-performance database solutions over general-purpose ones for demanding data infrastructure needs, particularly in AI.
- · ClickHouse
- · Column-oriented databases
- · AI/ML platforms requiring high-throughput data processing
- · Momentic
- · PostgreSQL in high-scale analytical contexts
- · Traditional relational databases without specific analytical optimizations
Increased adoption and development of column-oriented databases for specific high-performance data workloads.
Pressure on general-purpose databases to improve analytical and high-throughput capabilities or risk being disintermediated by specialized solutions.
Further fragmentation of the database market as companies select niche solutions tailored to extreme performance requirements, potentially increasing infrastructure complexity.
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Read at InfoQ