MASTER TABLE
x------x--------------------x
| Id | Name |
x------x--------------------x
| 1 | A |
| 2 | B |
| 3 | C |
x------x--------------------x
DETAILS TABLE
x------x--------------------x-------x
| Id | PERIOD | QTY |
x------x--------------------x-------x
| 1 | 2014-01-13 | 10 |
| 1 | 2014-01-11 | 15 |
| 1 | 2014-01-12 | 20 |
| 2 | 2014-01-06 | 30 |
| 2 | 2014-01-08 | 40 |
x------x--------------------x-------x
I am getting the same results when LEFT JOIN
and OUTER APPLY
is used.
LEFT JOIN
SELECT T1.ID,T1.NAME,T2.PERIOD,T2.QTY
FROM MASTER T1
LEFT JOIN DETAILS T2 ON T1.ID=T2.ID
OUTER APPLY
SELECT T1.ID,T1.NAME,TAB.PERIOD,TAB.QTY
FROM MASTER T1
OUTER APPLY
(
SELECT ID,PERIOD,QTY
FROM DETAILS T2
WHERE T1.ID=T2.ID
)TAB
Where should I use LEFT JOIN
AND where should I use OUTER APPLY
The OUTER APPLY operator returns all the rows from the left table expression irrespective of its match with the right table expression. For those rows for which there are no corresponding matches in the right table expression, it contains NULL values in columns of the right table expression.
CROSS APPLY returns only rows from the outer table that produce a result set from the table-valued function. It other words, result of CROSS APPLY doesn't contain any row of left side table expression for which no result is obtained from right side table expression. CROSS APPLY work as a row by row INNER JOIN.
OUTER APPLY resembles LEFT JOIN, but has an ability to join table-evaluated functions with SQL Tables. OUTER APPLY's final output contains all records from the left-side table or table-evaluated function, even if they don't match with the records in the right-side table or table-valued function.
The OUTER APPLY join is a variant of the LEFT OUTER JOIN with correlation support. The usage is similar to the CROSS APPLY join, but it returns all rows from the table on the left side of the join. If the right side of the join returns no rows, the corresponding columns in the output contain NULLs.
A LEFT JOIN
should be replaced with OUTER APPLY
in the following situations.
1. If we want to join two tables based on TOP n
results
Consider if we need to select Id
and Name
from Master
and last two dates for each Id
from Details
table.
SELECT M.ID,M.NAME,D.PERIOD,D.QTY
FROM MASTER M
LEFT JOIN
(
SELECT TOP 2 ID, PERIOD,QTY
FROM DETAILS D
ORDER BY CAST(PERIOD AS DATE)DESC
)D
ON M.ID=D.ID
which forms the following result
x------x---------x--------------x-------x
| Id | Name | PERIOD | QTY |
x------x---------x--------------x-------x
| 1 | A | 2014-01-13 | 10 |
| 1 | A | 2014-01-12 | 20 |
| 2 | B | NULL | NULL |
| 3 | C | NULL | NULL |
x------x---------x--------------x-------x
This will bring wrong results ie, it will bring only latest two dates data from Details
table irrespective of Id
even though we join with Id
. So the proper solution is using OUTER APPLY
.
SELECT M.ID,M.NAME,D.PERIOD,D.QTY
FROM MASTER M
OUTER APPLY
(
SELECT TOP 2 ID, PERIOD,QTY
FROM DETAILS D
WHERE M.ID=D.ID
ORDER BY CAST(PERIOD AS DATE)DESC
)D
Here is the working : In LEFT JOIN
, TOP 2
dates will be joined to the MASTER
only after executing the query inside derived table D
. In OUTER APPLY
, it uses joining WHERE M.ID=D.ID
inside the OUTER APPLY
, so that each ID
in Master
will be joined with TOP 2
dates which will bring the following result.
x------x---------x--------------x-------x
| Id | Name | PERIOD | QTY |
x------x---------x--------------x-------x
| 1 | A | 2014-01-13 | 10 |
| 1 | A | 2014-01-12 | 20 |
| 2 | B | 2014-01-08 | 40 |
| 2 | B | 2014-01-06 | 30 |
| 3 | C | NULL | NULL |
x------x---------x--------------x-------x
2. When we need LEFT JOIN
functionality using functions
.
OUTER APPLY
can be used as a replacement with LEFT JOIN
when we need to get result from Master
table and a function
.
SELECT M.ID,M.NAME,C.PERIOD,C.QTY
FROM MASTER M
OUTER APPLY dbo.FnGetQty(M.ID) C
And the function goes here.
CREATE FUNCTION FnGetQty
(
@Id INT
)
RETURNS TABLE
AS
RETURN
(
SELECT ID,PERIOD,QTY
FROM DETAILS
WHERE ID=@Id
)
which generated the following result
x------x---------x--------------x-------x
| Id | Name | PERIOD | QTY |
x------x---------x--------------x-------x
| 1 | A | 2014-01-13 | 10 |
| 1 | A | 2014-01-11 | 15 |
| 1 | A | 2014-01-12 | 20 |
| 2 | B | 2014-01-06 | 30 |
| 2 | B | 2014-01-08 | 40 |
| 3 | C | NULL | NULL |
x------x---------x--------------x-------x
3. Retain NULL
values when unpivoting
Consider you have the below table
x------x-------------x--------------x
| Id | FROMDATE | TODATE |
x------x-------------x--------------x
| 1 | 2014-01-11 | 2014-01-13 |
| 1 | 2014-02-23 | 2014-02-27 |
| 2 | 2014-05-06 | 2014-05-30 |
| 3 | NULL | NULL |
x------x-------------x--------------x
When you use UNPIVOT
to bring FROMDATE
AND TODATE
to one column, it will eliminate NULL
values by default.
SELECT ID,DATES
FROM MYTABLE
UNPIVOT (DATES FOR COLS IN (FROMDATE,TODATE)) P
which generates the below result. Note that we have missed the record of Id
number 3
x------x-------------x
| Id | DATES |
x------x-------------x
| 1 | 2014-01-11 |
| 1 | 2014-01-13 |
| 1 | 2014-02-23 |
| 1 | 2014-02-27 |
| 2 | 2014-05-06 |
| 2 | 2014-05-30 |
x------x-------------x
In such cases an APPLY
can be used(either CROSS APPLY
or OUTER APPLY
, which is interchangeable).
SELECT DISTINCT ID,DATES
FROM MYTABLE
OUTER APPLY(VALUES (FROMDATE),(TODATE))
COLUMNNAMES(DATES)
which forms the following result and retains Id
where its value is 3
x------x-------------x
| Id | DATES |
x------x-------------x
| 1 | 2014-01-11 |
| 1 | 2014-01-13 |
| 1 | 2014-02-23 |
| 1 | 2014-02-27 |
| 2 | 2014-05-06 |
| 2 | 2014-05-30 |
| 3 | NULL |
x------x-------------x
In your example queries the results are indeed the same.
But OUTER APPLY
can do more: For each outer row you can produce an arbitrary inner result set. For example you can join the TOP 1 ORDER BY ...
row. A LEFT JOIN
can't do that.
The computation of the inner result set can reference outer columns (like your example did).
OUTER APPLY
is strictly more powerful than LEFT JOIN
. This is easy to see because each LEFT JOIN
can be rewritten to an OUTER APPLY
just like you did. It's syntax is more verbose, though.
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