How LEAD works in SQL? Best LEAD examples

How LEAD works in SQL? Best LEAD examples

When writing SQL queries, there are times when we need to access data from the following row within the same result set. That’s where the LEAD function comes in handy. It’s one of the window functions in SQL that helps retrieve the next row’s value without requiring a self-join. Let’s explore how LEAD works, and I’ll show you some practical examples.

What is the LEAD Function in SQL?

The LEAD function allows us to look ahead in a dataset and fetch the value from the row that follows the current one. This is extremely useful when working with ordered data, such as time-series records or sequential transactions.

The syntax for LEAD looks like this:

LEAD(column_name, offset, default_value) 
OVER (PARTITION BY partition_column ORDER BY order_column)
  • column_name: The column from which we retrieve the next row’s value.
  • offset (optional): Determines how many rows ahead we want to look. If omitted, it defaults to 1.
  • default_value (optional): A value returned when there is no next row.
  • OVER: Defines the partitioning and ordering criteria.

Basic Example of LEAD

Let’s start with a simple example. Suppose we have a table called sales:

id customer sale_date amount
1 Alice 2024-06-01 100
2 Bob 2024-06-02 200
3 Charlie 2024-06-03 150

Now, we want to check the sales amount for the next transaction. We can use LEAD like this:

SELECT 
    id, 
    customer, 
    sale_date, 
    amount, 
    LEAD(amount) OVER (ORDER BY sale_date) AS next_amount
FROM sales;

This would produce:

id customer sale_date amount next_amount
1 Alice 2024-06-01 100 200
2 Bob 2024-06-02 200 150
3 Charlie 2024-06-03 150 NULL

Notice how next_amount holds the amount for the following row, and the last row has NULL since there’s no next transaction.

Using LEAD with Partitioning

If we want to find the next sale for individual customers (rather than across the entire dataset), we can partition the results using PARTITION BY. Let’s modify our query:

SELECT 
    id, 
    customer, 
    sale_date, 
    amount, 
    LEAD(amount) OVER (PARTITION BY customer ORDER BY sale_date) AS next_amount
FROM sales;

With partitioning, SQL treats each customer’s transactions independently, rather than looking at the next row in the entire dataset.

LEAD with a Custom Offset

By default, LEAD looks one row ahead, but we can customize this by specifying an offset. For example, if we want to look two transactions ahead instead of one:

SELECT 
    id, 
    customer, 
    sale_date, 
    amount, 
    LEAD(amount, 2) OVER (ORDER BY sale_date) AS amount_2_ahead
FROM sales;

This fetches the amount from the second next row.

Handling NULL Values with LEAD

What if we don’t want NULL when there’s no next row? We can specify a default value:

SELECT 
    id, 
    customer, 
    sale_date, 
    amount, 
    LEAD(amount, 1, 0) OVER (ORDER BY sale_date) AS next_amount
FROM sales;

Here, if there’s no next row, SQL will return 0 instead of NULL.

Practical Use Cases for LEAD

Now that we understand how LEAD works, let’s discuss some real-world use cases:

  1. Comparing Current and Next Row Data – Useful in stock price analysis, session tracking, and time-series data calculations.
  2. Detecting Gaps Between Events – Helps in checking patterns like delivery delays or time differences between transactions.
  3. Calculating Retention Rates – Analyzing user activity in SaaS products by checking the next login time.

Conclusion

The LEAD function in SQL is an efficient way to grab values from the following row without using a complex self-join. Whether you’re analyzing trends, detecting gaps, or working with sequential data, this function can make your life easier. Try experimenting with different offsets, partitions, and default values, and you’ll see just how powerful LEAD can be in SQL queries.

 

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