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Computing the Running total in SQL Server is vital for cumulative sum calculations in reports, financial assessments, and monitoring trends. Summing values row by row according to a defined order, like transaction IDs, aids in preserving an accurate cumulative total. In this blog, we will delve into the various techniques for determining the running total in SQL Server, complete with examples for each method.

Table of Contents:

• Why is it important to calculate the SQL Server Running Total?
• Techniques to Compute the Running Total in SQL Server
  • Technique 1: Utilizing SUM() with OVER(ORDER BY) Clause in SQL Server
  • Technique 2: Utilizing Correlated Subquery in SQL Server
  • Technique 3: Utilizing CTE with SUM() in SQL Server
  • Technique 4: Utilizing Variables in the SELECT Statement in SQL Server
• Different Methods for Calculating the Running Total
  • Technique 1: Utilizing the Windows Function in MySQL
  • Technique 2: Utilizing Recursive CTE in SQLite
• Comparison of Performance Across Methods
• Practical Examples
• Recommended Practices
• Summary

Why is it important to calculate the SQL Server Running Total?

Calculating the running total in SQL Server facilitates the identification of patterns and trends within the dataset, aiding in decision-making by allowing the monitoring of accumulated totals over time. These principles are predominantly employed in business intelligence and performance evaluation to juxtapose calculated figures against established targets. Implementing SQL-based solutions guarantees effective, scalable, and real-time calculations within the database.

Techniques to Compute the Running Total in SQL Server

Prior to diving into the various techniques, let’s set up a Transactional Log table and populate it with values for demonstration purposes in the upcoming methods.

CREATE TABLE Tran_Log (
    Tran_ID INT PRIMARY KEY IDENTITY(1,1),
    EventSeq INT,
    Tran_Value DECIMAL(10,2)
);

INSERT INTO Tran_Log (EventSeq, Tran_Value) VALUES
(1, 120.00),
(2, 180.50),
(3, 210.75),
(4, 260.00),
(5, 300.25);
SELECT * FROM Tran_Log;

This illustrates the table’s appearance once created and populated with values.

Technique 1: Utilizing SUM() with OVER(ORDER BY) Clause in SQL Server

One of the most efficient methods for determining a running total involves the use of Window functions, which eliminate the need for subqueries or JOINS.

Syntax:

SELECT 
    col1, 
    col2, 
    SUM(aggregate) OVER (ORDER BY colname) 
FROM table;

Example:

SELECT 
    Tran_ID,
    EventSeq,
    Tran_Value,
    SUM(Tran_Value) OVER (ORDER BY EventSeq) AS AccumulatedValue
FROM Tran_Log;

Output:

Explanation: In this case, the cumulative total is calculated sequentially from row to row using the SUM(Tran_Value) OVER (ORDER BY EventSeq) function.

Technique 2: Utilizing Correlated Subquery in SQL Server

A correlated subquery computes the running total based on all preceding values within the dataset prior to the current record.

Syntax:

SELECT 
    main_table.column1, 
    main_table.column2, 
    (SELECT SUM(sub_table.aggregate_column) 
     FROM table_name sub_table 
     WHERE sub_table.order_column <= main_table.order_column) 
FROM table_name main_table
ORDER BY main_table.order_column;

Example:

SELECT 
    TL_A.Tran_ID,
    TL_A.EventSeq,
    TL_A.Tran_Value,
    (
        SELECT SUM(TL_B.Tran_Value)
        FROM Tran_Log TL_B
        WHERE TL_B.EventSeq <= TL_A.EventSeq
    ) AS AggregatedTotal
FROM Tran_Log TL_A
ORDER BY TL_A.EventSeq;

Output:

Explanation: In this instance, the subquery executes for each row in TL_A, summing all transactional values where TL_B.EventSeq is less than or equal to TL_A.EventSeq.

Technique 3: Utilizing CTE with SUM() in SQL Server

A Common Table Expression (CTE) serves as a temporary result set formatted logically. Instead of relying on a subquery for each row, we can derive the total by using a recursive CTE combined with the SUM() function.

Syntax:

WITH CTE AS (
    SELECT 
        colA, 
        colB, 
        SUM(column) OVER (ORDER BY colm_name) AS Total
    FROM table_name
)
SELECT * FROM CTE;

Example:

WITH EventTotals AS (
    SELECT 
        Tran_ID,
        EventSeq,
        Tran_Value,
        SUM(Tran_Value) OVER (ORDER BY EventSeq) AS CTERunningTotal
    FROM
``````html
Tran_Log
)
SELECT * FROM EventTotals;

Result:

Clarification: In this context, the CTE retains interim results, while the SUM(Tran_Value) OVER (ORDER BY EventSeq) function guarantees that each row sums up all preceding rows for display.

Method 4: Leveraging Variables in the SELECT Statement within SQL Server

Utilizing Variables in the SELECT Statement allows for a variable assignment throughout the execution process that consistently updates as each row is sequentially handled.

Formula:

DECLARE @RunningTotal DECIMAL(10,2) = 0;
SELECT 
    column1, 
    column2, 
    SET @RunningTotal = @RunningTotal + aggregate_column 
FROM table_name
ORDER BY order_column;

Illustration:

DECLARE @CumulativeSum DECIMAL(10,2) = 0;
SELECT 
    Tran_ID,
    EventSeq,
    Tran_Value,
    RunningTotal = CAST(0 AS DECIMAL(10,2)) 
INTO #TempRunningTotal
FROM Tran_Log
ORDER BY EventSeq;
UPDATE #TempRunningTotal
SET @CumulativeSum = RunningTotal = @CumulativeSum + Tran_Value;
SELECT * FROM #TempRunningTotal;
DROP TABLE #TempRunningTotal; 

Result:

Clarification: In this scenario, the @CumulativeSum variable is defined to hold the running total, which is updated for each row by adding SalesAmount through the UPDATE Statement.

Alternate Methods for Determining the Running Total 

There are several techniques to calculate the running total, including the utilization of Window functions in MySQL or employing a Recursive CTE in SQLite. 

Method 1: Utilizing Window Functions in MySQL

This approach employs Window functions to derive cumulative sums without the necessity for subqueries or JOINS.

Formula:

SELECT 
    col1, 
    col2, 
    SUM(aggregate) OVER (ORDER BY colname) 
FROM table;

Illustration:

CREATE TABLE Tran_Log (
    Tran_ID INT PRIMARY KEY AUTO_INCREMENT,
    EventSeq INT,
    Tran_Value DECIMAL(10,2)
);

INSERT INTO Tran_Log (EventSeq, Tran_Value) VALUES
(1, 130.00),
(2, 150.50),
(3, 280.75),
(4, 278.00),
(5, 301.25);

SELECT 
    EventSeq, 
    Tran_Value, 
    SUM(Tran_Value) OVER (ORDER BY EventSeq) AS Running_Total
FROM Tran_Log;

Result:

Clarification: Here, the cumulative total is computed from row to row with the usage of the SUM(Tran_Value) OVER (ORDER BY EventSeq) function.

Method 2: Applying Recursive CTE in SQLite

A recursive common table expression (CTE), which allows multiple references to the same CTE, proves advantageous for calculations that incorporate running totals.

Formula:

WITH RECURSIVE CTE AS (
    SELECT KeyID, Value 
    FROM YrTab
    WHERE KeyID = (SELECT MIN(KeyID) FROM YourTable)
    UNION ALL
    SELECT yt.KeyID, yt.Value, pr.Cumulative + yt.Value
    FROM YrTab yt
    JOIN pr ON yt.KeyID = pr.KeyID + 1
)
SELECT * FROM tab_name;

Illustration:

CREATE TABLE Tran_Log (
    Tran_ID INTEGER PRIMARY KEY AUTOINCREMENT,
    EventSeq INTEGER,
    Tran_Value DECIMAL(10,2)
);

INSERT INTO Tran_Log (EventSeq, Tran_Value) VALUES
(1, 120.00),
(2, 180.50),
(3, 210.75),
(4, 260.00),
(5, 300.25);

WITH RECURSIVE Log_Tracker AS (
    SELECT Tran_ID, EventSeq, Tran_Value, Tran_Value AS RunningTotal
    FROM Tran_Log
    WHERE Tran_ID = (SELECT MIN(Tran_ID) FROM Tran_Log)
    UNION ALL
    SELECT t.Tran_ID, t.EventSeq, t.Tran_Value, lt.RunningTotal + t.Tran_Value
    FROM Tran_Log t
    JOIN Log_Tracker lt ON t.Tran_ID = lt.Tran_ID + 1
)
SELECT * FROM Log_Tracker;

Result:

Clarification: Start with the earliest transaction using MIN(Tran_ID) to calculate the running total, then recursively add each subsequent Tran_Value to the last total through the join on Tran_ID +1.

Performance Assessment of Each Technique

Technique	Application Scenario	Advantages	Disadvantages
SUM() with OVER (ORDER BY)	Best for sequential datasets in substantial datasets	Advanced for accumulating totals	Incompatible with earlier versions of SQL
Correlated Subquery	Applicable while dealing with smaller datasets	Straightforward to implement	Suboptimal performance due to row-by-row processing
CTE with SUM()	Beneficial where further transformations are required	Simple to understand and execute	CTEs could compromise performance if materialized repeatedly in extensive queries
Variables in the SELECT statement	Effectively functions when the sequence is established	Processes smaller datasets effectively	Does not accommodate parallel execution

Practical Instances

1. Expense Monitoring System

An organization aims to monitor the total expenditures over a period.

Illustration:

CREATE TABLE Bud_Spend (
    DeptID INT,
    SpendDt DATE,
    AmountSpent DECIMAL(10,2)
);

INSERT INTO Bud_Spend VALUES
(1, '2024-01-10', 1000),
(1, '2024-03-15', 1500),
(1, '2024-05-20', 1300),
(2, '2024-02-05', 2000),
(2, '2024-04-18', 1800),
(2, '2024-06-25', 1600);

SELECT 
    DeptID,
    SpendDt,
    AmountSpent,
    SUM(AmountSpent) OVER (PARTITION BY DeptID ORDER BY SpendDt) AS TotalSpend
FROM Bud_Spend;

Result:
“`

Clarification: In this instance, the cumulative amount expended can be computed based on the SpendDt by utilizing the SUM(AmountSpent) OVER (PARTITION BY DeptID ORDER BY SpendDt) function.

2. Score Card Monitoring

An educational institution intends to monitor the performance scores of a participant in a cricket match.

Illustration:

CREATE TABLE Play_Sco (
    Ply_ID INT,
    Ply_Dt DATE,
    Score INT
);

INSERT INTO Play_Sco VALUES
(10, '2024-01-05', 50),
(10, '2024-01-10', 70),
(10, '2024-01-15', 80),
(11, '2024-02-01', 60),
(11, '2024-02-08', 90),
(11, '2024-02-15', 75);

SELECT 
    Ply_ID,
    Ply_Dt,
    Score,
    SUM(Score) OVER (PARTITION BY Ply_ID ORDER BY Ply_Dt) AS RunningScore
FROM Play_Sco;

Result:

Clarification:In this example, the total score of a participant is determined using the SUM(Score) OVER (PARTITION BY Ply_ID ORDER BY Ply_Dt) function.

Optimal Practices

• Utilize window functions: Window functions enhance execution speed as they are more efficient compared to correlated subqueries and cursors.
• Steer Clear of Cursors with Extensive Data Sets: Cursors process each row individually, leading to reduced performance. It is advisable to avoid cursors with large datasets; instead, consider set-based methods, including CTE or window functions.
• Implement Proper Indexing: Adding an index to the columns can significantly improve performance, particularly with large datasets.

Final Thoughts

The most proficient approach to compute the total is by employing the Windows function; however, based on the specific scenario and performance needs, alternative strategies such as CTE, Correlated subqueries, and variables can also be adopted. Throughout this article, you have acquired insights into various techniques to compute the running total in SQL Server.

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Calculate Running Total in SQL Server – Frequently Asked Questions

The article Calculate Running Total in SQL Server first appeared on Intellipaat Blog.