Implementing Self-Joins in ClickHouse: Techniques, Use Cases, and Best Practices

A self-join in ClickHouse joins a table with itself using aliases. This technique helps compare rows within the same table, find relationships between records, and analyze hierarchical data.

Let’s explore how to implement self-joins in ClickHouse:

1. Use Case Example: Find Rows That Share a Common Key

Consider a table named employees with this schema:

CREATE TABLE employees
(
    employee_id UInt32,
    manager_id UInt32,
    employee_name String,
    salary UInt32
) ENGINE = MergeTree()
ORDER BY employee_id;

Here:

• employee_id is the ID of an employee.
• manager_id is the ID of the manager for that employee.
• Our goal is to find all employees along with their managers’ details.

2. Basic Self-Join Query

Here’s how to join the employees table with itself using aliases:

SELECT
    e1.employee_id AS employee_id,
    e1.employee_name AS employee_name,
    e1.salary AS employee_salary,
    e2.employee_id AS manager_id,
    e2.employee_name AS manager_name,
    e2.salary AS manager_salary
FROM employees AS e1
LEFT JOIN employees AS e2
    ON e1.manager_id = e2.employee_id;

Explanation:

• AS e1 and AS e2: Aliases distinguish between the two instances of the employees table.
• ON e1.manager_id = e2.employee_id: The join condition matches e1 (employee) with e2 (manager).
• LEFT JOIN: Preserves employee records even when they have no manager (NULL).

3. Use Case: Finding Hierarchical Relationships

For hierarchical data (like employees reporting to managers), you can use multiple self-joins.

Here’s how to find employees, their managers, and their managers’ managers:

SELECT
    e1.employee_name AS employee_name,
    e2.employee_name AS manager_name,
    e3.employee_name AS grand_manager_name
FROM employees AS e1
LEFT JOIN employees AS e2
    ON e1.manager_id = e2.employee_id
LEFT JOIN employees AS e3
    ON e2.manager_id = e3.employee_id;

4. Self-Join for Conditional Comparison

Self-joins with additional conditions help compare rows within the same table.

Example: Find employees with matching salaries:

SELECT
    e1.employee_id AS employee_1_id,
    e1.employee_name AS employee_1_name,
    e2.employee_id AS employee_2_id,
    e2.employee_name AS employee_2_name,
    e1.salary AS shared_salary
FROM employees AS e1
JOIN employees AS e2
    ON e1.salary = e2.salary
    AND e1.employee_id != e2.employee_id;

Explanation:

• e1.salary = e2.salary: Matches rows with the same salary.
• e1.employee_id != e2.employee_id: Prevents matching employees with themselves.

5. Performance Considerations for Self-Joins

1. Table Design:
   • Use an efficient ORDER BY clause for proper sorting and fast lookups.
   • Use PRIMARY KEY and indexes where relevant for filtering.
2. Partitioning:
   • For large tables, consider partitioning by manager_id or salary to reduce scan ranges.
3. Filtering:
   • Apply WHERE conditions to pre-filter rows and minimize comparisons.
4. Limit Rows:
   • Use LIMIT or specific filters during development to improve query performance.

6. Example with Filtering

To query only employees with salaries above 50,000:

SELECT
    e1.employee_id,
    e1.employee_name,
    e2.employee_id AS manager_id,
    e2.employee_name AS manager_name
FROM employees AS e1
LEFT JOIN employees AS e2
    ON e1.manager_id = e2.employee_id
WHERE e1.salary > 50000;

Summary

Self-joins in ClickHouse combine a table with itself using aliases and JOIN conditions. They excel at:

• Comparing rows within the same table.
• Building hierarchical relationships.
• Finding records with common attributes.

When properly optimized with filtering and indexing, self-joins perform well even on large ClickHouse tables.

Further Reading

Connect Prometheus to Your ClickHouse® Cluster

ClickHouse Projections: A Complete Guide to Query Optimization

Guide for Query optimization | ClickHouse Docs

Efficient Strategies for Purging Data in ClickHouse: Real-Life Use Cases and Detailed Implementation

Enhancing ClickHouse Performance: Strategic Insights on Partitioning, Indexing, and Monitoring

Mastering Performance Tuning in ClickHouse: Tips for Inspecting Statistics Objects

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