Deep Dive into ClickHouse SORT Operation and Index Scan

Introduction

In ClickHouse, a SORT operation is used to sort data in a specified order, either ascending or descending. Sorting data can improve query performance when the data is accessed in a sorted order, such as when using an ORDER BY clause in a query. However, sorting large volumes of data can be resource-intensive and time-consuming.

ClickHouse uses an ordered property of Index Scans to optimize queries that access data in a sorted order. An index is a data structure that stores a sorted list of values along with pointers to the corresponding rows in a table. When an index scan is performed on a table, the index is used to access the data in a sorted order. This can improve query performance by reducing the amount of data that needs to be scanned.

Example 1: Sorting Data by Date

Suppose you have a table containing sales data for a company, with the following columns:

  • date (Date type)
  • product (String type)
  • sales (Float type)

To sort the data by date in ascending order, you can use the following query:

SELECT *
FROM sales
ORDER BY date ASC

This query will perform a SORT operation on the data to order it by date in ascending order.

To optimize this query using the ordered property of Index Scans, you can create an index on the date column:

CREATE INDEX date_index ON sales (date) TYPE Sorted

This will create an index on the date column with the Sorted type, which indicates that the data is already sorted in the same order as the index. Now, when you execute the query, ClickHouse can use the index to access the data in a sorted order more efficiently:

SELECT *
FROM sales
WHERE date >= '2022-01-01' AND date < '2023-01-01'
ORDER BY date ASC

This query will use the ordered property of Index Scans to access the data in a sorted order using the date_index, and will return the data for the year 2022 sorted by date in ascending order.

Example 2: Sorting Data by Multiple Columns

Suppose you have a table containing employee data for a company, with the following columns:

  • department (String type)
  • salary (Float type)
  • start_date (Date type)
  • name (String type)

To sort the data by department in ascending order, and then by salary in descending order within each department, you can use the following query:

SELECT *
FROM employees
ORDER BY department ASC, salary DESC

This query will perform a SORT operation on the data to order it by department in ascending order, and then by salary in descending order within each department.

To optimize this query using the ordered property of Index Scans, you can create an index on both the department and salary columns:

CREATE INDEX dept_salary_index ON employees (department, salary) TYPE Sorted

This will create an index on both the department and salary columns with the Sorted type, which indicates that the data is already sorted in the same order as the index. Now, when you execute the query, ClickHouse can use the index to access the data in a sorted order more efficiently:

SELECT *
FROM employees
WHERE start_date >= '2020-01-01' AND start_date < '2023-01-01'
ORDER BY department ASC, salary DESC

This query will use the ordered property of Index Scans to access the data in a sorted order using the dept_salary_index, and will return the employee data sorted by department in ascending order, and then by salary in descending order within each department for the time period between 2020 and 2022.

Conclusion

In summary, SORT operations in ClickHouse are used to sort data in a specified order, and can improve query performance when the data is accessed in a sorted order. The ordered property of Index Scans is used to optimize queries that access data in a sorted order, by using an index to access the data more efficiently. Creating an index with the Sorted type can significantly improve query performance, especially for large datasets.

To read more about Indexes in ClickHouse, do consider reading the following articles

About Shiv Iyer 225 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.