How to optimize JOIN operations in ClickHouse?

Introduction

Join operations in ClickHouse can be optimized in several ways to improve query performance. Here are some best practices to optimize join operations in ClickHouse, along with real-life data examples:

1. Use the correct data types and compression algorithm: Using the correct data types and compression algorithm can significantly improve the performance of join operations. For example, if the data being joined is numeric, use the Int32 or Float32 data type, and if the data is string, use the FixedString data type. In addition, choose the appropriate compression algorithm based on the data type and query patterns.

Example: Suppose we have two tables sales and customers, where sales contains the following data:

+----+-----------+-------+--------+
| id | date      | amount| cust_id|
+----+-----------+-------+--------+
| 1  | 2022-03-01| 100   | 1      |
| 2  | 2022-03-02| 200   | 2      |
| 3  | 2022-03-03| 150   | 1      |
+----+-----------+-------+--------+

And customers contains the following data:

+--------+-----------+
| cust_id| name      |
+--------+-----------+
| 1      | John Doe  |
| 2      | Jane Smith|
| 3      | Bob Jones |
+--------+-----------+

To join these tables efficiently, we can use the Int32 data type for the cust_id column and the LZ4 compression algorithm to reduce the disk space required to store the data.

CREATE TABLE sales (
  id UInt32,
  date Date,
  amount Int32,
  cust_id Int32
) ENGINE = MergeTree
PARTITION BY toYYYYMM(date)
ORDER BY (date, id)
SETTINGS index_granularity = 8192;

CREATE TABLE customers (
  cust_id Int32,
  name String
) ENGINE = MergeTree
PARTITION BY cust_id
ORDER BY cust_id
SETTINGS index_granularity = 8192;

2. Use the correct join algorithm: ClickHouse supports several join algorithms, including Hash Join, Merge Join, and Distributed Join. Choosing the right join algorithm can significantly improve query performance.

Example: Suppose we have two tables orders and customers, where orders contains the following data:

+----+------------+-------+--------+
| id | order_date | total | cust_id|
+----+------------+-------+--------+
| 1  | 2022-03-01 | 100   | 1      |
| 2  | 2022-03-02 | 200   | 2      |
| 3  | 2022-03-03 | 150   | 1      |
+----+------------+-------+--------+

And customers contains the following data:

+--------+-----------+
| cust_id| name      |
+--------+-----------+
| 1      | John Doe  |
| 2      | Jane Smith|
| 3      | Bob Jones |
+--------+-----------+

To join these tables efficiently, we can use the Distributed Join algorithm to parallelize the join operation across multiple nodes.

SELECT customers.name, orders.order_date, orders.total
FROM orders
DISTRIBUTED BY cust_id
JOIN customers
ON customers.cust_id = orders.cust_id

3. Use the correct partitioning and sorting key: Choosing the right partitioning and sorting key can improve join performance by minimizing data shuffling and reducing the number of rows that need to be joined.

Example: Suppose we have two tables users and orders, where users contains the following data:

+----+---------+-------------+-------+
| id | username| email       | status|
+----+---------+-------------+-------+
| 1  | john    | john@abc.com| 1     |
| 2  | jane    | jane@abc.com| 2     |
| 3  | bob     | bob@abc.com | 1     |
+----+---------+-------------+-------+

And orders contains the following data:

+----+------------+-------+--------+
| id | order_date | total | user_id|
+----+------------+-------+--------+
| 1  | 2022-03-01 | 100   | 1      |
| 2  | 2022-03-02 | 200   | 2      |
| 3  | 2022-03-03 | 150   | 1      |
+----+------------+-------+--------+

To join these tables efficiently, we can partition users and orders by user_id and sort orders by user_id and order_date.

CREATE TABLE users (
  id UInt32,
  username String,
  email String,
  status Int32
) ENGINE = MergeTree
PARTITION BY id
ORDER BY (id)
SETTINGS index_granularity = 8192;

CREATE TABLE orders (
  id UInt32,
  order_date Date,
  total Int32,
  user_id UInt32
) ENGINE = MergeTree
PARTITION BY user_id
ORDER BY (user_id, order_date)
SETTINGS index_granularity = 8192;

4. Use ClickHouse’s Materialized Views: Materialized views in ClickHouse can precompute frequently used joins and aggregations, improving query performance by reducing the amount of data that needs to be processed.

Example: Suppose we have two tables orders and customers, where orders contains the following data:

+----+------------+-------+--------+
| id | order_date | total | cust_id|
+----+------------+-------+--------+
| 1  | 2022-03-01 | 100   | 1      |
| 2  | 2022-03-02 | 200   | 2      |
| 3  | 2022-03-03 | 150   | 1      |
+----+------------+-------+--------+

And customers contains the following data:

+--------+-----------+
| cust_id| name      |
+--------+-----------+
| 1      | John Doe  |
| 2      | Jane Smith|
| 3      | Bob Jones |
+--------+-----------+

To join these tables efficiently, we can create a materialized view that precomputes the join

CREATE MATERIALIZED VIEW orders_customers_mv
ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, id)
AS
SELECT orders.id, orders.order_date, orders.total, customers.name
FROM orders
JOIN customers
ON customers.cust_id = orders.cust_id;

Then we can query the materialized view instead of joining the tables every time.

SELECT id, order_date, total, name
FROM orders_customers_mv;

By following these best practices, you can optimize join operations in ClickHouse and improve query performance for large-scale analytical workloads.