How to Use JOINs in ClickHouse

How do I perform JOINs in ClickHouse?

JOINs combine rows from multiple tables in ClickHouse, supporting INNER, LEFT, ANY, ALL, and ASOF strategies for fast analytical queries.

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Description

Why use JOINs in ClickHouse?

ClickHouse JOINs enrich fact data with dimensions at query time, avoiding duplication and enabling real-time analytics. They are perfect for adding customer details to order summaries or attaching product metadata to sales events.

What JOIN types are available?

ClickHouse supports INNER, LEFT, RIGHT, FULL, and CROSS JOINs. Modifiers ANY and ALL control duplicate handling, while ASOF aligns rows on nearest timestamp. GLOBAL executes the JOIN on remote shards first.

How to write a basic INNER JOIN?

Write SELECT columns FROM table1 INNER JOIN table2 ON condition. INNER is implicit, but always add ON or USING to define matching keys clearly.

Example: match customers with orders

The query below lists each order with the customer’s name and email for easy reporting.

How do ANY and ALL modifiers work?

ANY returns the first matching row from the right table, eliminating duplicates fast. ALL returns every match, producing a Cartesian subset; use it only when duplicates are essential.

When should I use ASOF JOIN?

ASOF JOIN pairs rows on the closest preceding timestamp, ideal for merging clickstream events with slowly changing product prices or exchange rates.

Can I filter during the JOIN?

Yes. Apply WHERE or PREWHERE to either table before JOIN to cut data early. You can also add conditions inside ON to limit matches.

Best practices for performant JOINs?

Match on low-cardinality keys, set join_use_nulls = 1 for NULL safety, prefer ANY for large datasets, and pre-aggregate right-hand tables with SELECT DISTINCT.

Common mistakes and fixes

Using ALL when ANY suffices balloons result size; switch to ANY. Forgetting ON causes CROSS JOIN behaviour; always specify keys.

Why How to Use JOINs in ClickHouse is important

How to Use JOINs in ClickHouse Example Usage


-- Show each order with customer info and product count
SELECT
    o.id AS order_id,
    c.name AS customer_name,
    c.email,
    o.order_date,
    o.total_amount,
    COUNT(oi.product_id) AS items
FROM Orders o
ANY LEFT JOIN Customers c ON o.customer_id = c.id
LEFT JOIN OrderItems oi ON o.id = oi.order_id
GROUP BY order_id, customer_name, email, order_date, total_amount
ORDER BY order_date DESC;

How to Use JOINs in ClickHouse Syntax


SELECT [columns]
FROM left_table
[INNER | LEFT | RIGHT | FULL | CROSS] [ANY | ALL | ASOF] JOIN right_table
    [ON left_table.key = right_table.key OR left_table.key < right_table.key]
    [USING (key1, key2)]
    [GLOBAL]             -- execute sub-query on remote shards first
    [ARRAY JOIN] array_col
WHERE conditions
GROUP BY columns
ORDER BY columns
LIMIT n;

Common Mistakes

Using ALL JOINs on high-cardinality keys causes explosive result sets. Fix by switching to ANY or pre-aggregating the right table with DISTINCT.
Omitting the ON or USING clause turns the statement into a CROSS JOIN, producing unexpected Cartesian products. Always define join keys explicitly.

Frequently Asked Questions (FAQs)

Can I JOIN more than two tables in ClickHouse?

Yes. Chain multiple JOIN clauses: table1 JOIN table2 ON … JOIN table3 ON … . Keep each ON condition specific to avoid Cartesian explosions.

What is the difference between ANY and ALL JOINs?

ANY keeps only the first matching row from the right table, improving speed and memory. ALL returns every match, preserving duplicates but costing more resources.

How do I speed up large JOINs?

Create JOIN-optimized tables with ORDER BY join keys, use GLOBAL for distributed clusters, pre-filter with WHERE, and keep the right table small using DISTINCT.