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PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
While both Postgres and ClickHouse serve different purposes, the key distinction lies in how they handle replication and sharding. Postgres is primarily designed for transactional workloads (OLTP), where data consistency and durability are prioritized. On the other hand, ClickHouse is tailored for analytical workloads (OLAP), and optimized for high-speed querying and large-scale data analysis.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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ClickHouse is highly compatible with a wide range of data tools, including ETL/ELT processes and BI tools like Apache Superset. It supports virtually all common data formats, making integration seamless across diverse ecosystems.
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Apache JMeter
Apache JMeter open-source load testing tool for analyzing and measuring the performance of a variety of services
To further evaluate the performance of your ClickHouse installation, consider using load testing tools like Apache JMeter or k6 to simulate increased query loads. Measure how query response times change as you add more nodes to the cluster.
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ClickHouse is rapidly gaining traction for its unmatched speed and efficiency in processing big data. Cloudflare, for example, uses ClickHouse to process millions of rows per second and reduce memory usage by over four times, making it a key player in large-scale analytics. With its advanced features and real-time query performance, ClickHouse is becoming a go-to choice for companies handling massive datasets. In this article, we'll explore why ClickHouse is increasingly favored for analytics, its key features, and how to deploy it on Kubernetes. We'll also cover some best practices for scaling ClickHouse to handle growing workloads and maximize performance.
