ClickHouse JOIN: Mastering Hash Joins


In ClickHouse, Hash Joins are a powerful method for combining data from multiple tables efficiently. They fall under the category of “merge joins,” where ClickHouse takes advantage of sorted data structures to speed up the joining process. Hash Joins are particularly beneficial when dealing with large datasets and complex join conditions.

How Hash Joins Work?

  1. Hashing: The first step in a Hash Join is to hash the join keys from both tables. A hash function is applied to the join keys, creating a set of hash values. These hash values determine how rows from the two tables will be matched.
  2. Partitioning: ClickHouse then partitions both tables based on their hash values. Rows with the same hash value are grouped together in the same partitions. This step essentially creates smaller, more manageable datasets.
  3. Joining: Finally, ClickHouse compares the partitions from the two tables. Rows with matching hash values are joined together to produce the final result set.

Use Cases of Hash Joins

Hash Joins in ClickHouse are extremely useful in scenarios where you need to combine data from two or more tables efficiently. Some common use cases include:

  1. Data Warehousing: Hash Joins are often used in data warehousing environments where large volumes of data from various sources need to be combined for analysis.
  2. Joining Fact and Dimension Tables: In data modeling, fact tables (containing transactional data) are joined with dimension tables (containing descriptive information) using Hash Joins.
  3. Aggregating Data: When you need to aggregate data based on certain criteria, Hash Joins can be used to combine data from multiple sources before aggregation.

Real-life Implementation and Benefits

Let’s consider a real-life example:

Scenario: A retail company wants to analyze its sales data by joining information from a “Sales” table and a “Products” table.


FROM Sales
INNER JOIN Products ON Sales.ProductID = Products.ProductID


  1. Efficiency: Hash Joins are highly efficient, even with large datasets. They partition data based on hash values, reducing the amount of data that needs to be compared during the join operation.
  2. Scalability: ClickHouse can parallelize Hash Joins, allowing them to scale with the number of CPU cores available, making them suitable for big data scenarios.
  3. Flexibility: Hash Joins can handle various join conditions, including complex ones involving multiple columns.
  4. Optimized Memory Usage: ClickHouse’s memory management ensures that Hash Joins perform well without consuming excessive memory.


In summary, Hash Joins in ClickHouse are a powerful tool for efficiently combining data from multiple tables. They are suitable for a wide range of use cases and can significantly enhance query performance, making them a valuable feature in ClickHouse’s arsenal for data analysis and processing.

To know more about Clickhouse JOINs, do visit 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.