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The fastest analytics database on the planet. Let's understand what it is, why it exists, and why companies like Uber, Cloudflare, and eBay trust it with billions of rows.
ClickHouse is an open-source columnar database designed for online analytical processing (OLAP). It was created by Yandex (Russia's Google) and can process billions of rows per second.
Imagine you have a giant library with millions of books. Each book has: title, author, year, genre, pages, and price.
Traditional database (row-based): It's like storing each book on its own shelf — title, author, year, genre, pages, price all together. If you want to find the average price of all books, you have to walk to EVERY shelf and read EVERY book's price tag. Slow!
ClickHouse (column-based): It's like having separate filing cabinets — one for all titles, one for all authors, one for all prices. Want the average price? Just open the "price" cabinet. You don't touch anything else. FAST!
Yandex (Russia's largest search engine) needed to analyze web analytics data — billions of page views per day. No existing database was fast enough, so they built ClickHouse.
Yandex open-sourced ClickHouse on GitHub. The world discovered a database that could query billions of rows in milliseconds.
The original creators left Yandex and founded ClickHouse Inc., raising $250M+ to build ClickHouse Cloud.
Used by Uber, Cloudflare, eBay, Spotify, Lyft, Deutsche Bank, and thousands more. Over 1 billion rows processed per second on a single server.
Databases come in two flavors, and understanding the difference is crucial:
OLTP (PostgreSQL, MySQL) = The cash register. It handles one transaction at a time — "Alice bought shoes for $50." Fast for individual operations. Like a cashier scanning items one by one.
OLAP (ClickHouse) = The accountant. At the end of the month, the accountant asks: "What were total sales by category across all 500 stores for the last 3 years?" That's analyzing millions of transactions at once. ClickHouse is that accountant — insanely fast at crunching massive amounts of data.
| Feature | OLTP (PostgreSQL) | OLAP (ClickHouse) |
|---|---|---|
| Purpose | Process transactions | Analyze data |
| Queries | INSERT, UPDATE, DELETE one row | SELECT with GROUP BY on millions of rows |
| Speed | Fast for single rows | Fast for billions of rows |
| Storage | Row-based | Column-based |
| Use Case | E-commerce checkout, banking | Dashboards, reports, analytics |
| Updates | Frequent updates/deletes | Mostly append-only (insert) |
| Example Query | SELECT * FROM orders WHERE id = 123 | SELECT country, SUM(revenue) FROM orders GROUP BY country |
ClickHouse isn't just "a little faster" — it's often 100-1000x faster than traditional databases for analytics. Here's why:
Instead of storing data row by row, ClickHouse stores each column separately. When you query SELECT AVG(price) FROM products, it only reads the "price" column — ignoring name, description, category, etc. Less data read = faster query.
Imagine you need to add 1 to a million numbers. A normal database does it one number at a time: "1+1=2, 2+1=3, 3+1=4..."
ClickHouse uses vectorized execution — it processes numbers in batches of thousands at once, using your CPU's SIMD instructions. It's like having 1000 workers each adding 1 to their number simultaneously. Same work, 1000x faster.
Because columns contain similar data types (all integers, all strings of similar length), ClickHouse compresses data 10x better than row databases. Less data on disk = less data to read = faster queries.
Traditional databases create an index entry for every single row. ClickHouse creates one entry per ~8,192 rows. For 8.87 million rows, that's only ~1,083 index entries — fits entirely in CPU cache!
Cloudflare processes 32 million HTTP requests per second. They use ClickHouse to analyze this traffic in real-time. Their analytics dashboard queries billions of rows and returns results in under 100 milliseconds. Try doing that with PostgreSQL!
You wouldn't use a race car to go grocery shopping (too impractical). And you wouldn't use a shopping cart to win a race (too slow).
ClickHouse is the race car — built for speed on the analytics track. PostgreSQL is the reliable family car — great for everyday transactions. Use the right tool for the right job!
Q1: ClickHouse stores data in _____ format.
Q2: ClickHouse is best suited for which type of workload?
Q3: Which company originally created ClickHouse?
Install ClickHouse locally and on Docker, connect with the CLI and GUI tools.
Understand columnar storage, compression, sparse indexes, and the MergeTree engine family.
Choose the right engine (MergeTree, ReplacingMergeTree, SummingMergeTree) and design efficient schemas.
Write blazing-fast SELECT queries, GROUP BY, window functions, and array operations.
Tune queries, use materialized views, projections, and understand the query pipeline.
Replication, sharding, monitoring, backups, and real-world architecture patterns.
Checked against the ClickHouse documentation in October 2026. The JSON type has been production-ready since version 25.3. ClickHouse Cloud rewrites a MergeTree create onto the matching SharedMergeTree engine.
ClickHouse is a column store for analytical queries. It is a poor primary database for many small single-row updates. MergeTree is the family you will use. On ClickHouse Cloud you still write MergeTree in the CREATE, and the service stores it as SharedMergeTree.
CREATE TABLE events (
day Date,
user_id UInt64,
name String
)
ENGINE = MergeTree
ORDER BY (day, user_id)What do you write in CREATE TABLE on ClickHouse Cloud?