devcrafter/clickhouse — explained in plain English
Analysis updated 2026-07-17 · repo last pushed 2024-11-25
Build real-time dashboards showing metrics without slowing down your application.
Store and analyze every user click, page view, and feature interaction at scale.
Process server logs quickly across millions of events.
Run fast reports over billions of rows instead of waiting minutes or hours.
| devcrafter/clickhouse | allentdan/shape_based_matching | benagastov/bindweb-nim-wasm-compiler | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | C++ | C++ | C++ |
| Last pushed | 2024-11-25 | 2019-03-01 | — |
| Maintenance | Stale | Dormant | — |
| Setup difficulty | moderate | moderate | easy |
| Complexity | 4/5 | 3/5 | 5/5 |
| Audience | data | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Simple single-command install, but designing for column-based analytics takes some learning.
ClickHouse is a database designed specifically for analyzing huge amounts of data quickly. Instead of waiting minutes or hours for reports to generate, ClickHouse can give you answers in seconds, even when you're working with billions of rows of data. It's free, open-source, and used by companies that need to spot trends, track metrics, and understand patterns in real-time. The key to its speed is how it stores data. Traditional databases arrange information by row, think of a spreadsheet where each row is a complete record. ClickHouse does the opposite: it groups data by column. So all your dates are stored together, all your prices together, all your user IDs together. When you want to analyze just a few columns (like "show me average revenue by month"), the database only needs to read those specific columns instead of fetching entire rows. This is faster and uses less memory. You'd use ClickHouse if you're running a data analytics platform, building dashboards, processing logs from servers, or tracking user behavior across millions of events. For example, a SaaS company might store every user click, page view, and feature interaction in ClickHouse, then build dashboards that show real-time statistics without slowing down their application. The README doesn't go into detail about pricing or specific performance benchmarks, but it emphasizes that setup is simple, you can install it with a single command. The project is mature and actively maintained by a distributed team. They publish monthly releases, host community calls to discuss new features, and run meetups around the world. The README points to extensive documentation, video tutorials, and community channels if you want to learn more or get help.
An open-source database built to analyze billions of rows of data in seconds instead of minutes or hours.
Mainly C++. The stack also includes C++.
Stale — no commits in 1-2 years (last push 2024-11-25).
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly data.
This repo across BitVibe Labs
double-check against the repo, no cap.