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LAG and LEAD in SQL are window functions used to compare the current row with the previous row data. 1. LAG(column, offset, default) is used to obtain the data of the offset line before the current line. The default value is 1. If there is no previous line, the default is returned; 2. LEAD(column, offset, default) is used to obtain the subsequent line. They are often used in time series analysis, such as calculating sales changes, user behavior intervals, etc. For example, obtain the previous day's sales through LAG(sales, 1, 0) and calculate the difference and growth rate; obtain the next visit time through LEAD(visit_date) and calculate the number of days between them in combination with DATEDIFF. When using it, you need to pay attention to clear sorting, reasonable setting of default values, avoid repeated calls, and consider performance optimization.

When processing time series data, it is often necessary to compare the values ??of the current row with the previous row or the next row, such as viewing sales trends, user behavior intervals, etc. At this time, SQL's LAG and LEAD functions are very practical.

They are window functions that allow you to access the data of front and back lines without self-connection. It is simple to write and efficiently.

What are LAG and LEAD?

LAG and LEAD are two functions in SQL that are used to access the previous or next row of data:

• LAG(column, offset, default) : Take the column value of the offset line before the current line. The default is 1 line. If not, the default will be returned.
• LEAD(column, offset, default) : Similar, but the following line is taken

To give a simple example, suppose you have a sales record table like this:

If you want to know how much sales have changed every day compared to the previous day, you can use LAG(sales, 1, 0) to get the sales of the previous day.

How to use LAG/LEAD to analyze time series?

In actual use, there are several common scenarios that can use these two functions:

Comparison of changes in adjacent time points (such as growth volume, growth rate)

This is one of the most common uses. For example, analyzing the daily sales growth:

 SELECT 
    date,
    Sales,
    LAG(sales, 1, 0) OVER (ORDER BY date) AS prev_sales,
    sales - LAG(sales, 1, 0) OVER (ORDER BY date) AS diff,
    ROUND((sales - LAG(sales, 1, 0) OVER (ORDER BY date)) / LAG(sales, 1, 0.0) OVER (ORDER BY date) * 100, 2) AS growth_rate
FROM sales_data;

This will result in the sales difference and growth rate every day. Note that LAG is used twice here, which can actually be optimized into subqueries or CTE to avoid repeated calculations.

View the interval between events (such as user active interval)

If you have a user access log table:

If you want to see how many days there are between each visit, you can use LEAD(visit_date) to get the next visit date, and then use DATEDIFF to calculate the interval:

 SELECT
    user_id,
    Visit_date,
    LEAD(visit_date) OVER (PARTITION BY user_id ORDER BY visit_date) AS next_visit,
    DATEDIFF(LEAD(visit_date) OVER (PARTITION BY user_id ORDER BY visit_date), visit_date) AS days_between
FROM user_visits;

This method is particularly suitable for analyzing indicators such as user churn and activity frequency.

Handle the front and back comparisons within the group (such as by user and product classification)

If the data is organized in multiple dimensions, such as the access record of each user, you need to add PARTITION BY to OVER() to ensure that it is only compared before and after within the same user.

For example, find out the last purchase amount for each user:

 LAG(amount, 1, 0) OVER (PARTITION BY user_id ORDER BY purchase_date)

This way, data from different users will not be mixed together.

Several details to pay attention to when using

• The sorting must be clear : LAG/LEAD is based on ordered data, so there must be ORDER BY in OVER() , otherwise the result will be uncontrollable.
• The default value should be set reasonably : for example, there is no "non-the-day" on the first day, it can be set to 0 or NULL, and it will be decided based on business needs.
• Performance issues : Although it is more efficient than self-connection, if the data volume is large and there are many partitions, you should also pay attention to the execution plan and add an index if necessary.
• Avoid calling the same LAG/LEAD multiple times : As mentioned above, repeated calls will affect readability and performance. It is recommended to use CTE or subqueries to extract them first.

Basically that's it. Although LAG and LEAD seem simple, they are very useful in time series analysis, especially when doing year-on-year and month-on-month analysis, mastering the usage can make the query clearer and more efficient.

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