Join tables in ClickHouse without equal expressions

ClickHouse

join tables

SQL

database

unequal joins

Join tables in ClickHouse without equal expressions

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1In the realm of data analytics and database management, ClickHouse stands out as an open-source columnar database management system capable of real-time data processing. Known for its exceptional speed in handling analytical queries, ClickHouse provides various capabilities to interact with data effectively. Among these capabilities, join operations are crucial, allowing users to merge data from multiple tables based on related keys. Typically, joins rely on equal expressions, but there are scenarios where join operations without equal expressions become necessary. This article delves into the technical details of such joins in ClickHouse, providing examples and explanations.
2
3### Understanding Non-Equal Joins in ClickHouse
4
5In relational databases, joins are generally performed using equality conditions, such as matching an ID from one table with an ID from another table. However, there are cases where a relationship between tables relies on non-equal conditions. While ClickHouse predominantly supports equal joins using the `JOIN ... ON ...` syntax, there can be workarounds or alternative methods to achieve non-equal joins.
6
7#### Custom Join Conditions
8
9ClickHouse does not natively support non-equal joins directly due to its columnar storage architecture optimized for rapid data retrieval via hash joins on equality. However, users can emulate non-equal joins using subqueries, array joins, or cross joins combined with filtering conditions. Let's explore some of these techniques:
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11#### Cross Joins with Filtering
12
13One approach to simulate non-equal joins is to perform a cross join and apply filter conditions that replicate the desired non-equal logic. A cross join combines every row of one table with every row of another table, forming a Cartesian product. This set can then be filtered down.
14
15**Example:**
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17Consider two tables: `orders` and `discounts`. We aim to find discounts that are active during the order date range but do not match directly on equality.
18
19```sql
20SELECT
21    o.order_id,
22    o.order_date,
23    d.discount_rate
24FROM
25    orders AS o
26CROSS JOIN
27    discounts AS d
28WHERE
29    o.order_date >= d.start_date
30    AND o.order_date <= d.end_date;

In this example, a cross join generates combinations of orders and discounts that are subsequently filtered based on the order date falling within the discount's active date range.

Array Joins

Another technique involves using array data structures in combination with ARRAY JOIN. This is particularly useful for attributes that can naturally expand into arrays, allowing the join to operate across elements of the array.

Example:

Suppose each order can have multiple applicable promo codes, and you want to check if any promo code falls within a period specified in another table.

sql

1SELECT
2    o.order_id,
3    o.order_date,
4    d.discount_rate
5FROM
6    orders AS o
7ARRAY JOIN o.promo_codes AS pc
8CROSS JOIN
9    discounts AS d
10WHERE
11    pc = d.promo_code
12    AND o.order_date BETWEEN d.start_date AND d.end_date;

Here, ARRAY JOIN unfolds each order record by its promo_codes array, enabling a natural join with the discounts table on a non-equal condition.

Performance Considerations

Non-equal joins can be resource-intensive due to the combinatory nature of operations like cross join. Users should be cautious about performance implications, especially on large data sets. It is often beneficial to preprocess data to fit scenarios where equal joins are possible or employ ClickHouse’s materialized views to optimize non-equal join scenarios.

Summary

Understanding and effectively utilizing non-equal joins in ClickHouse requires a good grasp of the underlying data architecture and creative query strategies. Though ClickHouse does not cater to non-equal joins directly, techniques leveraging cross joins, filtering, and array expansions can be employed.

Technique	Description	Use Case Example
Cross Joins	Combines every row from two tables, apply filter conditions	Orders overlapping with discount active dates
Array Joins	Expands array elements, allows joining with separate tables	Orders with multiple promo codes
Subqueries	Use subqueries to filter results post-join	Dynamic range checks within joined data
Performance Tips	Preprocessing and Materialized views can improve performance	Optimizing frequent complex join conditions

This article aimed to shed light on the concept and techniques to handle non-equal joins in ClickHouse, providing you with the foundational knowledge and practical tools to tackle complex query requirements.