ClickHouse JOIN: Deep Dive into Nested Loop and Merge Scan Joins


Choosing the right join strategy is crucial for optimizing query performance in ClickHouse. ClickHouse supports two main types of joins: Nested-Loop Join and Merge-Scan Join. Let’s delve into each join type, explain how they work, and provide practice data set examples.

Nested-Loop Join

Nested-Loop Join is suitable when one of the tables is relatively small and fits into memory. It works by iterating through one table (the outer table) and probing the other table (the inner table) for matching rows using a nested loop.

Example Use Case:

Consider two tables: orders and customers. The orders table contains order information, including order_id and customer_id, while the customers table contains customer details, including customer_id and customer_name.

Sample Data



SELECT orders.order_id, orders.customer_id, customers.customer_name
FROM orders
INNER JOIN customers ON orders.customer_id = customers.customer_id;

Merge-Scan Join

Merge-Scan Join is suitable for large tables where both tables can be sorted on the join key. It involves merging two sorted streams of data and identifying matching rows.

Example Use Case:

Let’s continue with the same tables, orders and customers, and perform a Merge-Scan Join.

Sample Data

Same as above.


SELECT orders.order_id, orders.customer_id, customers.customer_name
FROM orders
ANY LEFT JOIN customers ON orders.customer_id = customers.customer_id;

Join Selection Considerations

  1. Data Size: Nested-Loop Join is efficient for small tables, while Merge-Scan Join is better suited for larger tables due to its sorted nature.
  2. Memory: Nested-Loop Join requires memory proportional to the size of the outer table, while Merge-Scan Join requires extra memory for sorting.
  3. Sorting: Merge-Scan Join requires both tables to be sorted on the join key, which might require additional processing.
  4. Performance: Nested-Loop Join can be slower for large tables due to the repeated probing of the inner table. Merge-Scan Join is usually faster for large tables if they are sorted.
  5. Index Usage: Merge-Scan Join works best with sorted tables and indexes, while Nested-Loop Join doesn’t depend on indexes.
  6. Complexity: Nested-Loop Join is simple to implement but may not scale well for large datasets. Merge-Scan Join can handle larger datasets efficiently.


Choosing the right join strategy in ClickHouse depends on factors like data size, memory availability, and the nature of the tables. Nested-Loop Join is suitable for small tables, while Merge-Scan Join shines with larger sorted tables. Understanding the characteristics of your data and the types of joins available helps in making informed decisions to optimize query performance.

To know more about JOINs in Clickhouse, do read the following articles:

About Shiv Iyer 227 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.