Karthik Divi
·3 min read

ClickHouse Online Compiler and SQL Editor

onecompileronline-compilerdatabaseclick-house

ClickHouse was originally built at Yandex to power their web analytics. It is a column-oriented database designed for one thing: running analytical queries on large datasets fast. We are talking billions of rows, aggregated in seconds. It achieves this by storing data in columns rather than rows, which means operations like SUM, COUNT, and GROUP BY only read the columns they need.

Running ClickHouse locally usually means Docker or a package install, and while that is fine for ongoing work, it is overkill when you just want to test a query or learn the syntax.

Writing ClickHouse SQL in the browser

ClickHouse uses a SQL dialect that is mostly standard but has its own table engines, data types, and functions. If you are coming from PostgreSQL or MySQL, most of your SQL knowledge transfers, but there are differences worth discovering hands-on.

An online ClickHouse editor is useful when you want to:

  • Experiment with ClickHouse-specific functions like arrayJoin, quantile, or argMax
  • Test a GROUP BY with aggregation before running it against a production cluster
  • Learn how ClickHouse's MergeTree engine family works
  • Share a query with teammates who do not have ClickHouse installed

ClickHouse code example

Here is an example that creates a table, loads sample event data, and runs an aggregation -- the kind of workload ClickHouse was built for:

CREATE TABLE page_events (
  event_date Date,
  user_id UInt32,
  page String,
  duration_ms UInt32,
  country String
) ENGINE = MergeTree()
ORDER BY (event_date, user_id);

INSERT INTO page_events VALUES
  ('2025-03-01', 1001, '/home', 320, 'US'),
  ('2025-03-01', 1002, '/pricing', 1500, 'DE'),
  ('2025-03-01', 1001, '/docs', 4200, 'US'),
  ('2025-03-01', 1003, '/home', 280, 'IN'),
  ('2025-03-02', 1002, '/docs', 3100, 'DE'),
  ('2025-03-02', 1004, '/pricing', 900, 'US'),
  ('2025-03-02', 1001, '/home', 150, 'US');

SELECT
  country,
  COUNT(*) AS total_events,
  AVG(duration_ms) AS avg_duration,
  COUNT(DISTINCT user_id) AS unique_users
FROM page_events
GROUP BY country
ORDER BY total_events DESC;

A few things to notice: the MergeTree engine and the ORDER BY in the table definition are ClickHouse-specific. The ORDER BY determines how data is physically sorted on disk, which directly affects query performance. This is the kind of detail you want to play with before designing your production schema.

Why ClickHouse is worth testing online first

ClickHouse clusters in production can be complex -- sharding, replication, materialized views. But the SQL itself is something you can learn and iterate on in an isolated environment. An online editor strips away the infrastructure and lets you focus on the query.

This is especially practical when:

  • You are evaluating ClickHouse for an analytics project and want to feel out the SQL dialect
  • You are debugging a slow aggregation and want to test a rewritten version in isolation
  • You need to demonstrate a ClickHouse query pattern during a team discussion
  • You are preparing for a role that involves ClickHouse and want to practice

Run ClickHouse queries on OneCompiler

OneCompiler supports ClickHouse with its full SQL dialect, including MergeTree engines and ClickHouse-specific functions. Write a query, run it, see results.

Start here: ClickHouse Online Compiler on OneCompiler

If you work with analytics at scale or are considering ClickHouse for a new project, this is a fast way to get your hands on the SQL without provisioning anything.