How to Optimize SQL Query with Multiple JOINs [Complete Guide]

Modern data management rests on relational databases that provide a structured way to make data easy to understand, navigate, and query. The data querying part is handled through SQL, a language especially designed to interact with relational databases.

A JOIN statement—essentially a clause of the SELECT statement—is an integral part of SQL querying. Its importance lies in its function of combining data from two or more tables based on specific shared fields.

Despite its profound significance, using multiple JOIN statements in an SQL query creates complications that may cripple readability and cause slow responses within the system.

In this article, we’ll discuss optimization strategies that can help mitigate issues. But first, let’s understand the common challenges in optimizing SQL queries with multiple JOINs.

Three types of JOINs are primarily used in writing SQL queries. The most common among them is INNER JOIN, which returns only rows where there is a match in the JOIN condition for both tables. Imagine an intersection in a Venn diagram.

The outer JOINs (LEFT, RIGHT, FULL) include all rows from one table (specified left/right) and match rows from the other table. They’re also useful in showing all data from one table without any correspondence in the other table.

Finally, there’s the dreaded CROSS JOIN, which creates Cartesian products. It results in every possible combination of rows from joined tables, disregarding any matching criteria. Use it with caution.

Whether due to technological limitations or operator error, optimization challenges frequently occur with SQL queries with multiple JOIN statements. Here are some of them discussed:

Incomplete JOIN Conditions

This type of error occurs when the JOIN conditions between tables aren’t correctly or fully specified. If run successfully, incomplete data conditions can lead to unexpected results in query execution.

For example, imagine you have two tables:

• Customers table with columns for customer_id (primary key), name, and city
• Orders table with columns for order_id (primary key), customer_id (foreign key referencing Customers table), and order_amount

You write a query to find the names and the cities of the customers who placed orders:

SELECT c.customer_id, [c.name](http://c.name/), c.city, o.order_id, o.order_amount
FROM Customers c
JOIN Orders o 
WHERE o.order_amount > 100;  -- A filter is applied after the join

The JOIN clause is missing an essential ON clause that specifies the condition to match the rows between the tables. This error may result in empty result sets or an irrelevant dataset, depending on the database implementation.

As the likelihood of incomplete conditions occurring increases with the number of JOIN clauses, preventive strategies, in this case, may involve code review and revisions.

Data Duplication and Inaccurate Data

When working with multiple JOIN statements, data duplication issues might occur as a result of redundant or overlapping information in the tables. In most cases, this results in inflated datasets or incorrect data aggregation, which compromises the reliability of query results.

For instance, imagine tables Products (product_id, name, price) and Orders (order_id, product_id, quantity). We want to find the total quantity sold for each product. A foundational JOIN might lead to duplicates if a product appears in multiple orders. Mitigation strategies often involve the careful declaration of JOIN statements and employing aggregation functions like SUM or COUNT.

Incorrect Instance:

SELECT p.product_id, [p.name](http://p.name/), SUM(o.quantity) AS total_quantity
FROM Products p
JOIN Orders o ON p.product_id = o.product_id;

Revised instance:

SELECT p.product_id, [p.name](http://p.name/), SUM(o.quantity) AS total_quantity
FROM Products p
JOIN Orders o ON p.product_id = o.product_id
GROUP BY p.product_id;  -- Group before aggregation to avoid duplicates

Cartesian Products

This issue occurs when a JOIN statement fails to specify any conditions between two tables in an SQL query. The Cartesian products, as the name suggests, combine every row from the first table with every row from the second table, generating a massive dataset containing all possible combinations of rows from the joined table.

When working with a large number of rows, Cartesian products often consume excessive system resources and can return unintended results.

Here is a quick example of such an issue:

Imagine two tables:

• Customers table with 100 rows (customer_id, name)
• Orders table with 50 rows (order_id, customer_id, order_amount)

Without a JOIN condition, a CROSS JOIN or a missing ON clause would create a Cartesian product:

SELECT *  -- Selecting all columns from both tables
FROM Customers c
CROSS JOIN Orders o;  -- No JOIN condition specified (or using CROSS JOIN)

The query would generate 100*50 = 5000 rows of the combined dataset, even if there is no relationship between all the rows. For instance, it would combine every existing customer with each existing product, even those they haven’t bought.

Performance Issues

Multiple JOINs in SQL queries can introduce performance issues when working with large datasets or complex query structures. Inefficient query executions, lack of appropriate indexing, and incorrect JOIN algorithms may cause performance issues.

Performance issues are often resolved by properly indexing, rewriting queries, limiting result sets, and analyzing query execution plans.

Missing Indexes

Similar to an index in a book, indexing in a database allows queries to quickly locate specific rows. Without appropriate indexes, the database may resort to inefficient table scans or suboptimal JOIN strategies, increasing query execution time and resource utilization.

Several strategies, including composite indexes or covering indexes, can be employed to implement indexing. However, these strategies also add overhead during data insertion or updates. Try to find the right balance by only creating indexes on frequently used columns.

Imagine you have an e-commerce database with two tables:

• Products table: Stores information about products (product_id, name, price, category)
• Orders table: Stores order details (order_id, customer_id, product_id, quantity)

You frequently run a query to find all orders for a specific product category (e.g., “Electronics”).

Without an index, the database engine needs to scan every row in the Orders table to find the “Electronics” category. Here is how it may look in an SQL query:

SELECT *
FROM Orders o
WHERE o.category = "Electronics";

By creating an index on the category column in the Orders table, you can more quickly locate rows where the category matches “Electronics.”

Adding in Index:

CREATE INDEX idx_orders_category ON Orders(category);

Reduced Readability

SQL queries with multiple JOINs can be difficult to comprehend and pose readability challenges. Poorly structured or overly convoluted queries can hinder troubleshooting efforts and increase the likelihood of unintended outcomes.

Optimization strategies may involve consistent formatting, using table aliases, modularization (breaking down complex queries), and adding relevant comments.

Depending on the database structure and implementations, several strategies can be executed to optimize queries with multiple JOINs. We’ve discussed a few of them briefly in the previous section. Let’s explore more such strategies here:

Analyzing Queries and Rewriting

Both complex JOIN statements and multiple JOIN statements may reduce readability and performance. Consider analyzing queries frequently and rewriting them to optimize for the best performance and comprehensibility.

Using Subqueries

Breaking down detailed statements into subqueries might optimize SQL queries with multiple JOINs. While these subqueries may lengthen the code, they can break down complex logic and filter data before joining.
Original query:

SELECT [c.name](http://c.name/), o.order_id, p.product_name
FROM Customers c
JOIN Orders o ON c.customer_id = o.customer_id
JOIN Products p ON o.product_id = p.product_id
WHERE o.order_date > '2024-01-01' AND c.city = 'New York';

Rewritten with subqueries:

SELECT [c.name](http://c.name/), o.order_id, p.product_name
FROM Customers c
WHERE c.city = 'New York'
AND o.order_id IN (
SELECT order_id
FROM Orders
WHERE order_date > '2024-01-01'
);

Ensuring Proper Indexes

As mentioned, create B-tree, hash, or bitmap indexes on frequently used columns in JOIN conditions, WHERE clauses, and ORDER BY clauses. Ensuring that proper indexes are created on the columns involved in JOIN conditions can significantly improve query performance.

Using INNER JOIN Over Outer JOINs

If you only need data where there’s a match in both tables, use INNER JOIN instead of outer JOINs (LEFT, RIGHT, FULL) to reduce writing complex queries and operator errors. Outer JOINs can be slower and return more data than necessary.

For example, using LEFT JOIN:

SELECT [c.name](http://c.name/), o.order_id  -- o.order_id might be NULL for unmatched customers
FROM Customers c
LEFT JOIN Orders o ON c.customer_id = o.customer_id;

Using INNER JOIN:

SELECT [c.name](http://c.name/), o.order_id
FROM Customers c
INNER JOIN Orders o ON c.customer_id = o.customer_id;

Avoiding SELECT *

Specify the exact columns you need in the SELECT clause instead of using SELECT, which selects all the columns. This reduces the amount of data retrieved during SQL querying with multiple JOINs and dramatically improves processing speed for larger datasets with many columns.

For example, here is a query that selects all the columns:

SELECT *
FROM Customers c
INNER JOIN Orders o ON c.customer_id = o.customer_id;

The modified version with selecting only the necessary columns (say, c.name and o.order_id) could be:

SELECT [c.name](http://c.name/), o.order_id
FROM Customers c
INNER JOIN Orders o ON c.customer_id = o.customer_id;

Using EXISTS or IN

In some cases, particularly when dealing with large datasets and multiple complex JOIN conditions, using EXISTS or IN can prove to be more efficient than creating subqueries. These instances may include:

• Correlated subqueries. In case the subquery (where the EXISTS or IN is located) is correlated with the outer query, these clauses can be more efficient than JOINs.
• Sparse data. When dealing with tables that don’t have corresponding rows with the other table, these clauses can outperform Joins.
• Semi-JOINs. If you just need to check for the existence of related rows and don’t need to retrieve data, EXISTS and IN can be pretty useful.
• Database engine dependency. Some database engines may run EXISTS or IN clauses more efficiently than SQL queries with multiple Joins.

Database Denormalization

Denormalization is a database optimization technique that intentionally and carefully introduces redundancy, such as duplicating data or combining tables, into a database schema. On the surface, database denormalization appears counterproductive, but in some cases, it can optimize queries with multiple JOIN operations. However, it also introduces data integrity, data storage, and maintenance challenges.

Using Table Aliases

Table aliases are used in multiple JOIN queries to improve readability and manageability. As the name suggests, table aliases are shorter, understandable names for tables. They are particularly useful when dealing with JOINs involving multiple tables. An example of a table alias may include:

SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id

Parallel Processing

Some database systems support parallel processing, such as parallel query execution or parallelizing JOIN operations across multiple CPU cores. These can distribute the workload of complex JOINs across multiple processors and significantly improve performance for large datasets.

Here are some more tips to speed up your SQL queries with multiple JOINS:

Filtering Early

Apply WHERE clause filters on tables before joining them. This would significantly reduce the data size involved in JOINs.

Optimize JOIN order

Choose the JOIN order that minimizes the number of rows involved in subsequent JOINs.

Partitioning

Divide tables into smaller, manageable partitions based on frequently used JOIN columns. This allows the database to focus on relevant data during JOINs.

Query Caching

Cache frequently used queries with predictable results to avoid redundant execution. This can be particularly beneficial for read-heavy workloads.

We’ve discussed the most common optimization techniques for SQL queries with multiple JOINs. Depending on the database structure and engine, numerous other strategies can be adopted to minimize and optimize queries. If you’re here for an upcoming interview, feel free to explore our data science SQL interview questions and data analyst SQL interview questions.

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