Subqueries in PostgreSQL

Introduction

What is a Subquery?

Do you know how subquery works, and how it helps to enhance the performance of any database?

A subquery is an SQL query nested within another SQL query and it is used to retrieve data that will be used in the main query's execution. We will see what is main query and subquery are. Subqueries can be inserted into a SQL statement in several different places, including the SELECT, FROM, WHERE HAVING, and VALUES clauses. Subqueries are particularly useful for performing complex queries, filtering data, and performing operations.

Role of Subqueries in Relational Database Systems and PostgreSQL

Subqueries can be used to retrieve data from one or more tables and return it as a result set to the main query. It is used to calculate aggregate functions (e.g., SUM, COUNT, AVG) on a subset of data within the main query.

Subqueries are often used in the WHERE clause of a query to filter rows based on certain conditions.

Basics of Subqueries

Definition and Core Concepts

In database management systems, a query is a statement to interact with a database to retrieve, manipulate, or modify data.

For example:

We have an employee’s table that stores the following records:

Suppose we want to find the employee(s) with the most recent hire date, then it is a two-step process:

• First, we have to find the maximum hiring date using the max function.
• Second, using the result, we apply the SELECT statement to find the required employee.

Output:

Now, we have to find those employees who have a salary greater than this maximum salary.

Output:

This method is not efficient, as we have to use two queries one by one to get our desired result. Instead, we can use a subquery to make it simpler like this:

Output:

Differentiating between Main Queries and Subqueries

Main queries are the primary SQL statements for producing the final result, while subqueries are nested queries that provide intermediate data to be used by the main query. Subqueries are correlated but the main query is not.

Types of Subqueries in PostgreSQL

Scalar Subqueries

A scalar subquery is a query that returns a single value. It can be used in various parts of a query where a single value is expected, such as in the SELECT clause, WHERE clause, or HAVING clause.

Row Subqueries

A row subquery is a query that returns a single row with multiple columns. It can be used in situations where a single row is expected, such as in the FROM clause.

Column subqueries

It retrieves data from one or more columns in a subquery and then uses that result as a column or value in the main query's result set. Column subqueries is a powerful tool for performing complex data retrieval and manipulation in SQL.

Table Subqueries

A table subquery returns a result set or a table. It can be used in the FROM clause to treat the subquery as a temporary table or to join it with other tables.

WHERE Clause and Subqueries

The WHERE clause is used to filter rows from a table based on specified conditions. When you use subqueries in SQL, you can also use the WHERE clause in different ways to filter the subquery results. For example:

You can use the WHERE clause in a subquery to filter the results of the subquery itself. Let us see one by one.

Using Subqueries with Operators Like IN, NOT IN, EXISTS, and NOT EXISTS

Subqueries using operators like the IN operator:

You can use the IN operator in subqueries to filter data based on a list of values. Suppose you have a database with two tables: customers and orders. You want to retrieve a list of customers who have placed orders. For example:

Output:

Subqueries using operators like the NOT IN operator:

You can use the NOT IN operator in a subquery to select rows from one table that do not have matching values in another table. For example:

Output:

Subqueries using operators like the EXIST operator:

In PostgreSQL, you can use subqueries with the EXISTS operator to check for the existence of rows that meet certain conditions in a subquery. For example:

Output:

Subqueries using operators like the NOT EXIST operator:

This operator is used in a subquery to retrieve rows from one table that do not have corresponding records in another table based on a specified condition.

For example:

Output:

Comparative Subqueries with Operators Like >, <, >=, and <=

They are used to compare values in the result of a subquery with values from the main query. For Example:

Greater Than Operator (>):

It is used to find records in the main query where a column's value is greater than the result of the subquery. For example:

Output:

Less Than Operator (<):

It is used to find records where a column's value is less than the result of a subquery. For example:

Output:

Greater Than OR EQUAL TO Operator (>=):

To find records where a column's value is greater than or equal to the result of a subquery, you can use the >= operator. For example:-

Output:

Less Than OR EQUAL TO Operator (<=):

To find records where a column's value is less than or equal to the result of a subquery, use the <= operator. For Example:

Output:

FROM and JOIN Clauses with Subqueries

In SQL, you can use subqueries within the FROM and JOIN clauses to retrieve and manipulate data from multiple tables.

Subqueries in the FROM Clause:

It is used to create a subquery table. This derived table can then be used like any other table in your main query. For example:

Output:

In this example, the subquery calculates the average salary from the employees' table, and the main query then selects employees with a salary greater than the calculated average.

Using Subqueries as Derived Tables in the FROM Clause

It is an SQL technique for creating temporary result sets that can be used in subsequent parts of your query. For example:

You want to find employees who have been hired after the average hire date in the Engineering department.

Output:

In this query, the inner subquery calculates the average hire date for each department, and the outer query joins the avg_hire from the derived table with the main employee table using the JOIN clause, matching records based on the department.

JOIN Operations with Subqueries

It is used when you need to combine data from different tables using subqueries to define one or both sides of the join condition.

Let's say you want to find employees who belong to the same department as a specified employee:

Output:

The inner subquery retrieves the department of the specified employee and the outer query performs an INNER JOIN between the main employee table and the subquery (e2) using the common column department and finally returns the first names, last names, and departments of employees who share the same department.

SELECT Clause and Subqueries

The SELECT clause and subqueries are used together to retrieve specific data from a database. Subqueries can be used in the SELECT clause to fetch additional data or perform calculations on the result set.

Incorporating Subqueries in the SELECT Statement

It allows you to retrieve additional information or perform calculations on the data selected.

Let's say you want to retrieve a list of employees along with their department names, and for each employee, you want to display the name of the department they work in. For example:

Output:

Creating Computed Columns Using Subqueries

You can create computed columns using subqueries by performing calculations or aggregations within a subquery and then selecting the result as a column in your query.

Let's say you want to calculate the total number of employees in each department. You can use a subquery to calculate this and include it as a computed column in your result:

Output:

Advanced Concepts: Correlated Subqueries

Definition and Characteristics of Correlated Subqueries

They are subqueries that reference one or more columns from the outer query. Correlated subqueries are executed for each row of the outer query and are used for row-by-row processing or filtering.

Let's assume you have two tables: orders and customers.

Now, you want to find all orders where the order total is greater than the average order total for a specific customer. You can use a correlated subquery for this task:

Output:

In this query, the outer query selects the order_id, customer_id, and order_total from the orders table.

The correlated subquery calculates the average order total for each customer by using the condition WHERE customer_id = o.customer_id. It references the customer_id from the outer query then it compares the order_total of each order with the average order total for its corresponding customer, filtering out orders where the order total is greater.

Practical Examples and Use Cases

Correlated subqueries are powerful and versatile, and have many practical scenarios in SQL.

• Finding Nth Highest or Lowest Value:
  You can use correlated subqueries to find the nth highest or lowest value in a dataset.
• Calculating Running Totals or Running Averages:
  You can use correlated subqueries to calculate running totals or running averages.
• Checking for Existence of Related Records:
  You can use correlated subqueries to check for the existence of related records in other tables.

Performance Considerations with Subqueries

Correlated subqueries require row-by-row processing, which can be slower than set-based operations. Be careful of performance implications while dealing with large datasets. Subqueries with aggregate functions can be resource-intensive. Consider using other methods like window functions when dealing with aggregates.

When to Use Subqueries vs. JOINs?

Subqueries are useful when you want to filter rows based on conditions from another table.

It is used when you need to retrieve a single value that you want to compare with or use in the main query.

While JOINs are best for combining data from multiple tables into a single result set. They are best for combining data from multiple tables into a single result set.

Tips for Optimizing Subquery Performance

Subqueries can lead to slow query execution and resource-intensive database operations. Here are some tips for optimizing subqueries:

• Avoid Correlated Subqueries:
  Correlated subqueries can be slower than non-correlated subqueries because they are executed for each row in the outer query.
• Avoid Large Subquery Results:
  Try to limit the size of subquery results or use more efficient methods.

Utilizing Indexes and the EXPLAIN Command to Understand Subquery Execution

This explains how your database engine executes subqueries and optimizes their performance.

Ensure that the columns used in subquery conditions or JOINs are properly indexed. You can create indexes using the CREATE INDEX statement.

The EXPLAIN command provides insight into how the database engine plans to execute a query, including subqueries, and is used before running a query to analyze the execution plan.

Common Mistakes and Pitfalls

Common mistakes and pitfalls when working with subqueries in SQL can lead to query errors and suboptimal performance

Avoiding Nested Subqueries When not Necessary

Nesting subqueries can lead to inefficient queries. While subqueries are powerful, try to use them when they can achieve the same result more efficiently and with greater readability.

Recognizing and Resolving Common Errors

There are common errors that have been detected and resolved:

• Syntax Errors:
  Subqueries should have the correct syntax. Common syntax errors include missing parentheses, incorrect subquery placement, and not specifying aliases.
• Data Type Mismatch:
  Subqueries must return values of compatible data types. Ensure that the data types align between the subquery and the outer query.

Ensuring Subqueries Return the Expected Number of Rows

• Subquery Result Size:
  Be cautious about the result size of subqueries. If a subquery returns multiple rows when you expect a single result, it can lead to errors or incorrect query results.

• Use of Aggregate Functions:
  When using aggregate functions in subqueries, be aware of how these functions impact the result set.

• Handling NULL Values:
  Consider how subqueries handle NULL values. Subqueries that return NULL when not expected can affect the behavior of the main query.

Real-world Applications of Subqueries

Here are some common real-world applications of subqueries:

Data Analysis and Aggregation

• Financial Analysis:
  Subqueries are used to calculate various financial metrics, such as finding the total sales for each product category or calculating the average monthly revenue for a company.

• Sales and Inventory Management:
  Subqueries are employed to analyze sales data, identify top-selling products, calculate inventory turnover rates, and generate reports on product performance.

Complex Filtering Conditions

• Geospatial Analysis:
  Subqueries can filter and analyze geospatial data by specifying conditions like finding the closest points of interest within a certain radius of a location.

• Fraud Detection:
  Subqueries are employed to identify suspicious transactions by comparing individual transaction data with historical patterns and customer behavior.

Data Transformations and Computations

• Data Cleansing:
  Subqueries can be used to clean and transform data, such as converting data types, correcting errors, and standardizing data values.

• Calculating Derived Metrics:
  Subqueries help compute derived metrics that are not present in the original dataset, such as calculating customer churn rates based on subscription history.

Alternatives to Subqueries

There are complementary techniques that can be used to achieve similar results in different scenarios.

Common Table Expressions (CTEs) with the WITH clause

CTEs allow you to define temporary result sets within a query using the WITH clause. It can improve query readability and maintainability by breaking complex queries into smaller, more manageable parts.

LATERAL Joins

LATERAL joins enable you to reference columns from previous tables in the FROM clause, making it possible to perform correlated subquery-like operations.

Views and Temporary Tables

Views are saved SQL queries that can be referenced as if they were tables. They are useful for simplifying complex queries by encapsulating subqueries into reusable objects.