Running total for Oracle:
SELECT somedate, somevalue,
SUM(somevalue) OVER(ORDER BY somedate
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
AS RunningTotal
FROM Table
from http://www.codeproject.com/Articles/300785/Calculating-simple-running-totals-in-SQL-Server
Introduction
One typical question is, how to calculate running totals in SQL Server. There are several ways of doing it and this article tries to explain a few of them.
Test environment
First we need a table for the data. To keep things simple, let‘s create a table with just an auto incremented id and a value field.
-------------------------------------------------------------------- -- table for test -------------------------------------------------------------------- CREATE TABLE RunTotalTestData ( id int not null identity(1,1) primary key, value int not null );
And populate it with some data:
-------------------------------------------------------------------- -- test data -------------------------------------------------------------------- INSERT INTO RunTotalTestData (value) VALUES (1); INSERT INTO RunTotalTestData (value) VALUES (2); INSERT INTO RunTotalTestData (value) VALUES (4); INSERT INTO RunTotalTestData (value) VALUES (7); INSERT INTO RunTotalTestData (value) VALUES (9); INSERT INTO RunTotalTestData (value) VALUES (12); INSERT INTO RunTotalTestData (value) VALUES (13); INSERT INTO RunTotalTestData (value) VALUES (16); INSERT INTO RunTotalTestData (value) VALUES (22); INSERT INTO RunTotalTestData (value) VALUES (42); INSERT INTO RunTotalTestData (value) VALUES (57); INSERT INTO RunTotalTestData (value) VALUES (58); INSERT INTO RunTotalTestData (value) VALUES (59); INSERT INTO RunTotalTestData (value) VALUES (60);
The scenario is to fetch a running total when the data is ordered ascending by the id field.
Correlated scalar query
One very traditional way is to use a correlated scalar query to fetch the running total so far. The query could look like:
--------------------------------------------------------------------
-- correlated scalar
--------------------------------------------------------------------
SELECT a.id, a.value, (SELECT SUM(b.value)
FROM RunTotalTestData b
WHERE b.id <= a.id)
FROM RunTotalTestData a
ORDER BY a.id;
When this is run, the results are:
id value running total -- ----- ------------- 1 1 1 2 2 3 3 4 7 4 7 14 5 9 23 6 12 35 7 13 48 8 16 64 9 22 86 10 42 128 11 57 185 12 58 243 13 59 302 14 60 362
So there it was. Along with the actual row values, we have a running total. The scalar query simply fetches the sum of the value field from the rows where the ID is equal or less than the value of the current row. Let us look at the execution plan:
What happens is that the database fetches all the rows from the table and using a nested loop, it again fetches the rows from which the sum is calculated. This can also be seen in the statistics:
Table ‘RunTotalTestData‘. Scan count 15, logical reads 30, physical reads 0...
Using join
Another variation is to use join. Now the query could look like:
--------------------------------------------------------------------
-- using join
--------------------------------------------------------------------
SELECT a.id, a.value, SUM(b.Value)
FROM RunTotalTestData a,
RunTotalTestData b
WHERE b.id <= a.id
GROUP BY a.id, a.value
ORDER BY a.id;
The results are the same but the technique is a bit different. Instead of fetching the sum for each row, the sum is created by using a GROUP BY clause. The rows are cross joined restricting the join only to equal or smaller ID values in B. The plan:
The plan looks somewhat different and what actually happens is that the table is read only twice. This can be seen more clearly with the statistics.
Table ‘RunTotalTestData‘. Scan count 2, logical reads 31...
The correlated scalar query has a calculated cost of 0.0087873 while the cost for the join version is 0.0087618. The difference isn‘t much but then again it has to be remembered that we‘re playing with extremely small amounts of data.
Using conditions
In real-life scenarios, restricting conditions are often used, so how are conditions applied to these queries. The basic rule is that the condition must be defined twice in both of these variations. Once for the rows to fetch and the second time for the rows from which the sum is calculated.
If we want to calculate the running total for odd value numbers, the correlated scalar version could look like the following:
--------------------------------------------------------------------
-- correlated scalar, subset
--------------------------------------------------------------------
SELECT a.id, a.value, (SELECT SUM(b.value)
FROM RunTotalTestData b
WHERE b.id <= a.id
AND b.value % 2 = 1)
FROM RunTotalTestData a
WHERE a.value % 2 = 1
ORDER BY a.id;
The results are:
id value runningtotal -- ----- ------------ 1 1 1 4 7 8 5 9 17 7 13 30 11 57 87 13 59 146
And with the join version, it could be like:
--------------------------------------------------------------------
-- with join, subset
--------------------------------------------------------------------
SELECT a.id, a.value, SUM(b.Value)
FROM RunTotalTestData a,
RunTotalTestData b
WHERE b.id <= a.id
AND a.value % 2 = 1
AND b.value % 2 = 1
GROUP BY a.id, a.value
ORDER BY a.id;
When actually having more conditions, it can be quite painful to maintain the conditions correctly. Especially if they are built dynamically.
Calculating running totals for partitions of data
If the running total needs to be calculated to different partitions of data, one way to do it is just to use more conditions in the joins. For example, if the running totals would be calculated for both odd and even numbers, the correlated scalar query could look like:
--------------------------------------------------------------------
-- correlated scalar, partitioning
--------------------------------------------------------------------
SELECT a.value%2, a.id, a.value, (SELECT SUM(b.value)
FROM RunTotalTestData b
WHERE b.id <= a.id
AND b.value%2 = a.value%2)
FROM RunTotalTestData a
ORDER BY a.value%2, a.id;
The results:
even id value running total ---- -- ----- ------------- 0 2 2 2 0 3 4 6 0 6 12 18 0 8 16 34 0 9 22 56 0 10 42 98 0 12 58 156 0 14 60 216 1 1 1 1 1 4 7 8 1 5 9 17 1 7 13 30 1 11 57 87 1 13 59 146
So now the partitioning condition is added to the WHERE clause of the scalar query. When using the join version, it could be similar to:
--------------------------------------------------------------------
-- with join, partitioning
--------------------------------------------------------------------
SELECT a.value%2, a.id, a.value, SUM(b.Value)
FROM RunTotalTestData a,
RunTotalTestData b
WHERE b.id <= a.id
AND b.value%2 = a.value%2
GROUP BY a.value%2, a.id, a.value
ORDER BY a.value%2, a.id;
With SQL Server 2012
SQL Server 2012 makes life much more simpler. With this version, it‘s possible to define an ORDER BY clause in the OVER clause.
So to get the running total for all rows, the query would look:
-------------------------------------------------------------------- -- Using OVER clause -------------------------------------------------------------------- SELECT a.id, a.value, SUM(a.value) OVER (ORDER BY a.id) FROM RunTotalTestData a ORDER BY a.id;
The syntax allows to define the ordering of the partition (which in this example includes all rows) and the summary is calculated in that order.
To define a condition for the data, it doesn‘t have to be repeated anymore. The running total for odd numbers would look like:
-------------------------------------------------------------------- -- Using OVER clause, subset -------------------------------------------------------------------- SELECT a.id, a.value, SUM(a.value) OVER (ORDER BY a.id) FROM RunTotalTestData a WHERE a.value % 2 = 1 ORDER BY a.id;
And finally, partitioning would be:
-------------------------------------------------------------------- -- Using OVER clause, partition -------------------------------------------------------------------- SELECT a.value%2, a.id, a.value, SUM(a.value) OVER (PARTITION BY a.value%2 ORDER BY a.id) FROM RunTotalTestData a ORDER BY a.value%2, a.id;
What about the plan? It‘s looking very different. For example, the simple running total for all rows looks like:
And the statistics:
Table ‘Worktable‘. Scan count 15, logical reads 85, physical reads 0... Table ‘RunTotalTestData‘. Scan count 1, logical reads 2, physical reads 0...
Even though the scan count looks quite high at first glance, it isn‘t targeting the actual table but a worktable. The worktable is used to store intermediate results which are then read in order to create the calculated results.
The calculated cost for this query is now 0.0033428 while previously with the join version, it was 0.0087618. Quite an improvement.
References
- SUM (SQL Server 2008 R2)
- OVER (SQL Server 2008 R2)
- SUM (SQL Server ‘Denali‘)
- OVER (SQL Server ‘Denali‘)
from http://geekswithblogs.net/Rhames/archive/2008/10/28/calculating-running-totals-in-sql-server-2005---the-optimal.aspx
I had always believed there were three different methods for calculating a running total using TSQL:
1. Use a nested sub-query
2. Use a self join
3. Use Cursors
My own personal preference was to use the cursors option. If the cursor guidelines are followed, I‘ve always found this to be the quickest, because the other two methods involve multiple scans of the table. The key for the cursor method is to ensure the data you are "cursoring" through is in the correct order, as the query optimzier does not understand cursors. This usually means cursoring through the data by clustered index, or copying the data into a temp table / table var first, in the relevant order.
A blog posted by Garth Wells back in 2001 gives these three techniques (http://www.sqlteam.com/article/calculating-running-totals)
I came across a fourth technique for the running total calculation, which is related to the cursor method. Like the cursor method, it involves a single scan of the source table, then inserting the calculated running total for each row into a temp table or table variable. However, instead of using a cursor, it makes use of the following UPDATE command syntax:
UPDATE table
SET variable = column = expression
The TSQL to calculate the running total is:
DECLARE @SalesTbl TABLE (DayCount smallint, Sales money, RunningTotal money)
DECLARE @RunningTotal money
INSERT INTO @SalesTbl
SET @RunningTotal = 0
SELECT DayCount, Sales, null
FROM Sales
ORDER BY DayCount
UPDATE @SalesTbl
SET @RunningTotal = RunningTotal = @RunningTotal + Sales
FROM @SalesTbl
SELECT * FROM @SalesTbl
I tested this query along with the other three methods on a simple set of test data (actually the same test data from Garth Wells’ blog mentioned above).
The results of my test runs are:
| Method | Time Taken |
| Nested sub-query | 9300 ms |
| Self join | 6100 ms |
| Cursor | 400 ms |
| Update to local variable | 140 ms |
I was surprised just how much faster using the “Update to a local variable” method was. I expected it to be similar to the cursor method, as both involve a single scan of the source table, and both calculate the running total once only for each row in the table. The Nested Sub-query and Self join methods are so much slower because they involve the repeated recalculation of all of the previous running totals.
Note: There is a pretty big assumption in using the “Update to local variable” method. This is that the Update statement will update the rows in the temp table in the correct order. There is no simple way to specify the order for an Update statement, so potentially this method could fail, although I have not seen this actually happen yet!
I think that if I use a table variable, then the update will probably be in the correct order, because there are no indexes for the query optimizer to use, and parallellism will not occur. However, I can‘t be sure about this!
The following script was used to create the test data:
CREATE TABLE Sales (DayCount smallint, Sales money)
CREATE CLUSTERED INDEX ndx_DayCount ON Sales(DayCount)
go
INSERT Sales VALUES (1,120)
INSERT Sales VALUES (2,60)
INSERT Sales VALUES (3,125)
INSERT Sales VALUES (4,40)
DECLARE @DayCount smallint, @Sales money
SET @DayCount = 5
SET @Sales = 10
WHILE @DayCount < 5000
BEGIN
INSERT Sales VALUES (@DayCount,@Sales)
SET @DayCount = @DayCount + 1
SET @Sales = @Sales + 15
END
The queries used in my tests for the other three methods are posted below:
1. Nested Sub-query. ALSO KNOW AS correlated scalar query
SELECT DayCount,
Sales,
Sales+COALESCE((SELECT SUM(Sales)
FROM Sales b
WHERE b.DayCount < a.DayCount),0)
AS RunningTotal
FROM Sales a
ORDER BY DayCount
2. Self join
SELECT a.DayCount,
a.Sales,
SUM(b.Sales)
FROM Sales a
INNER JOIN Sales b
ON (b.DayCount <= a.DayCount)
GROUP BY a.DayCount,a.Sales
ORDER BY a.DayCount,a.Sales
3. Cursor
DECLARE @SalesTbl TABLE (DayCount smallint, Sales money, RunningTotal money)
DECLARE @DayCount smallint,
@Sales money,
@RunningTotal money
SET @RunningTotal = 0
DECLARE rt_cursor CURSOR
FOR
SELECT DayCount, Sales
FROM Sales
ORDER BY DayCount
OPEN rt_cursor
FETCH NEXT FROM rt_cursor INTO @DayCount,@Sales
WHILE @@FETCH_STATUS = 0
BEGIN
SET @RunningTotal = @RunningTotal + @Sales
INSERT @SalesTbl VALUES (@DayCount,@Sales,@RunningTotal)
FETCH NEXT FROM rt_cursor INTO @DayCount,@Sales
END
CLOSE rt_cursor
DEALLOCATE rt_cursor
SELECT * FROM @SalesTb
参考 http://stackoverflow.com/questions/860966/calculate-a-running-total-in-sqlserver
CTE:
with CTE_RunningTotal
as
(
select T.ord, T.total, T.total as running_total
from #t as T
where T.ord = 0
union all
select T.ord, T.total, T.total + C.running_total as running_total
from CTE_RunningTotal as C
inner join #t as T on T.ord = C.ord + 1
)
select C.ord, C.total, C.running_total
from CTE_RunningTotal as C
option (maxrecursion 0)
SQL Server 2012 Sum() Over()
select id,somedate,somevalue, sum(somevalue) over(order by somedate rows unbounded preceding) as runningtotal
from TestTable
Cross Apply: very simmilar to the correlated scalar query
select t.id,t.somedate,t.somevalue,rt.runningTotal
from TestTable t cross apply (select sum(somevalue) as runningTotal from TestTable where somedate <= t.somedate ) as rt
order by t.somedate
Calculating simple running totals in SQL Server