How Can I Simulate the LAG Function in MySQL to Calculate Differences Between Consecutive Rows?

Simulate the LAG function in MySQL

There is no direct LAG function in MySQL, but we can cleverly use user-defined variables to simulate its function and calculate the difference between consecutive rows.

Suppose we have the following table:

<code>| time                | company | quote |
+---------------------+---------+-------+
| 0000-00-00 00:00:00 | GOOGLE  |    40 |
| 2012-07-02 21:28:05 | GOOGLE  |    60 |
| 2012-07-02 21:28:51 | SAP     |    60 |
| 2012-07-02 21:29:05 | SAP     |    20 |</code>

We can simulate the LAG function using the following query:

<code class="language-sql">SET @quot=-1;
SELECT time, company, @quot AS lag_quote, @quot:=quote AS curr_quote
FROM stocks
ORDER BY company, time;</code>

This query:

• Initialize user-defined variable @quot to -1.
• Calculate the difference between the current row quote and the previous row's quote (although the difference is not calculated directly here, but how to get the previous row of data is shown).
• Store the current row's quote value into @quot for use in the next iteration.

Format results

To get the desired format:

<code>GOOGLE | 20
SAP    | 40</code>

We can use nested subqueries:

<code class="language-sql">SET @quot=0,@latest=0,company='';
SELECT B.* 
FROM (
    SELECT A.time, A.change, IF(@comp=A.company, 0, 1) AS LATEST, @comp:=A.company AS company
    FROM (
        SELECT time, company, quote - @quot AS change, @quot:=quote AS curr_quote
        FROM stocks
        ORDER BY company, time
    ) A
    ORDER BY company, time DESC
) B
WHERE B.LATEST = 1;</code>

This query:

• Calculate the difference of quote and store it in the change column.
• Use the IF statement and the variable @comp to identify the first row (LATEST=1) in each company grouping.
• Filter the results to include only the first row of each company group.

Conclusion

The above method effectively simulates the LAG function in MySQL, providing a practical and efficient way for the analysis of time series data. It should be noted that the two methods are implemented in slightly different ways. The first one more directly shows how to access the previous row of data, while the second one is more focused on calculating the difference and formatting the output results. Which method you choose depends on your specific needs.

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