URL: http://explainextended.com/2009/07/16/inner-join-vs-cross-apply/
URL: http://stackoverflow.com/questions/1139160/when-should-i-use-cross-apply-over-inner-join
EXPLAIN EXTENDED
How to create fast database queries
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INNER JOIN vs. CROSS APPLY
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From Stack Overflow:
Can anyone give me a good example of when
CROSS APPLYmakes a difference in those cases whereINNER JOINwill work as well?
This is of course SQL Server.
A quick reminder on the terms.
INNER JOIN is the most used construct in SQL: it joins two tables together, selecting only those row combinations for which a JOIN condition is true.
This query:
1.SELECT *
2.FROM table1
3.JOIN table2
4.ON table2.b = table1.a
reads:
For each row from
table1, select all rows fromtable2where the value of fieldbis equal to that of fielda
Note that this condition can be rewritten as this:
1.SELECT *
2.FROM table1, table2
3.WHERE table2.b = table1.a
, in which case it reads as following:
Make a set of all possible combinations of rows from
table1andtable2and of this set select only those rows where the value of fieldbis equal to that of fielda
These conditions are worded differently, but they yield the same result and database systems are aware of that. Usually both these queries are optimized to use the same execution plan.
The former syntax is called ANSI syntax, and it is generally considered more readable and is recommended to use.
However, it didn’t get into Oracle until recently, that’s why there are many hardcore Oracle developers that are just used to the latter syntax.
Actually, it’s a matter of taste.
To use JOINs (with whatever syntax), both sets you are joining must be self-sufficient, i. e. the sets should not depend on each other. You can query both sets without ever knowing the contents on another set.
But for some tasks the sets are not self-sufficient. For instance, let’s consider the following query:
We table
table1andtable2.table1has acolumncalledrowcount.For each row from
table1we need to select firstrowcountrows fromtable2, ordered bytable2.id
We cannot formulate a join condition here. The join condition, should it exists, would involve the row number, which is not present in table2, and there is no way to calculate a row number only from the values of columns of any given row in table2.
That’s where the CROSS APPLY can be used.
CROSS APPLY is a Microsoft’s extension to SQL, which was originally intended to be used with table-valued functions (TVF‘s).
The query above would look like this:
01.SELECT *
02.FROM table1
03.CROSS APPLY
04.(
05.SELECT TOP (table1.rowcount) *
06.FROM table2
07.ORDER BY
08.id
09.) t2
For each from
table1, select firsttable1.rowcountrows fromtable2ordered byid
The sets here are not self-sufficient: the query uses values from table1 to define the second set, not to JOINwith it.
The exact contents of t2 are not known until the corresponding row from table1 is selected.
I previously said that there is no way to join these two sets, which is true as long as we consider the sets as is. However, we can change the second set a little so that we get an addicional calculated field we can later join on.
The first option to do that is just count all preceding rows in a subquery:
01.SELECT *
02.FROM table1 t1
03.JOIN (
04.SELECT t2o.*,
05.(
06.SELECT COUNT(*)
07.FROM table2 t2i
08.WHERE t2i.id <= t2o.id
09.) AS rn
10.FROM table2 t2o
11.) t2
12.ON t1.rowcount = t2.rn
The second option is to use a window function, also available in SQL Server since version 2005:
1.SELECT *
2.FROM table1 t1
3.JOIN (
4.SELECT t2o.*, ROW_NUMBER() OVER (ORDER BY id) AS rn
5.FROM table2 t2o
6.) t2
7.ON t1.rowcount = t2.rn
This functions returns the ordinal number a row would have be the ORDER BY condition used in the function applied to the whole query.
This is essentially the same result as the subquery used in the previous query.
Now, let's create the sample tables and check all these solutions for efficiency:
01.SET NOCOUNT ON
02.GO
03.DROP TABLE [20090716_cross].table1
04.DROP TABLE [20090716_cross].table2
05.DROP SCHEMA [20090716_cross]
06.GO
07.CREATE SCHEMA [20090716_cross]
08.CREATE TABLE table1
09.(
10.id INT NOT NULL PRIMARY KEY,
11.row_count INT NOT NULL
12.)
13.CREATE TABLE table2
14.(
15.id INT NOT NULL PRIMARY KEY,
16.value VARCHAR(20) NOT NULL
17.)
18.GO
19.BEGIN TRANSACTION
20.DECLARE @cnt INT
21.SET @cnt = 1
22.WHILE @cnt <= 100000
23.BEGIN
24.INSERT
25.INTO [20090716_cross].table2 (id, value)
26.VALUES (@cnt, 'Value ' + CAST(@cnt AS VARCHAR))
27.SET @cnt = @cnt + 1
28.END
29.INSERT
30.INTO [20090716_cross].table1 (id, row_count)
31.SELECT TOP 5
32.id, id % 2 + 1
33.FROM [20090716_cross].table2
34.ORDER BY
35.id
36.COMMIT
37.GO
table2 contains 100,000 rows with sequential ids.
table1 contains the following:
id
row_count
1
2
2
1
3
2
4
1
5
2
Now let's run the first query (with COUNT):
01.SELECT *
02.FROM [20090716_cross].table1 t1
03.JOIN (
04.SELECT t2o.*,
05.(
06.SELECT COUNT(*)
07.FROM [20090716_cross].table2 t2i
08.WHERE t2i.id <= t2o.id
09.) AS rn
10.FROM [20090716_cross].table2 t2o
11.) t2
12.ON t2.rn <= t1.row_count
13.ORDER BY
14.t1.id, t2.id
id
row_count
id
value
rn
1
2
1
Value 1
1
1
2
2
Value 2
2
2
1
1
Value 1
1
3
2
1
Value 1
1
3
2
2
Value 2
2
4
1
1
Value 1
1
5
2
1
Value 1
1
5
2
2
Value 2
2
8 rows fetched in 0.0000s (498.4063s)
Table 'table1'. Scan count 2, logical reads 200002, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 100000, logical reads 8389920, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'table2'. Scan count 4, logical reads 1077, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. SQL Server Execution Times: CPU time = 947655 ms, elapsed time = 498385 ms.
This query, as was expected, is very unoptimal. It runs for more than 500 seconds.
Here's the query plan:
SELECT
Sort
Compute Scalar
Parallelism (Gather Streams)
Inner Join (Nested Loops)
Inner Join (Nested Loops)
Clustered Index Scan ([20090716_cross].[table2])
Compute Scalar
Stream Aggregate
Eager Spool
Clustered Index Scan ([20090716_cross].[table2])
Clustered Index Scan ([20090716_cross].[table1])
For each row selected from table2, it counts all previous rows again an again, never recording the intermediate result. The complexity of such an algorithm is O(n^2), that's why it takes so long.
Let's run he second query, which uses ROW_NUMBER():
01.SELECT *
02.FROM [20090716_cross].table1 t1
03.JOIN (
04.SELECT t2o.*, ROW_NUMBER() OVER (ORDER BY id) AS rn
05.FROM [20090716_cross].table2 t2o
06.) t2
07.ON t2.rn <= t1.row_count
08.ORDER BY
09.t1.id, t2.id
id
row_count
id
value
rn
1
2
1
Value 1
1
1
2
2
Value 2
2
2
1
1
Value 1
1
3
2
1
Value 1
1
3
2
2
Value 2
2
4
1
1
Value 1
1
5
2
1
Value 1
1
5
2
2
Value 2
2
8 rows fetched in 0.0006s (0.5781s)
Table 'Worktable'. Scan count 1, logical reads 214093, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'table2'. Scan count 1, logical reads 522, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'table1'. Scan count 1, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. SQL Server Execution Times: CPU time = 578 ms, elapsed time = 579 ms.
This is much faster, only 0.5 ms.
Let's look into the query plan:
SELECT
Inner Join (Nested Loops)
Clustered Index Scan ([20090716_cross].[table1])
Lazy Spool
Sequence Project (Compute Scalar)
Compute Scalar
Segment
Clustered Index Scan ([20090716_cross].[table2])
This is much better, since this query plan keeps the intermediate results while calculating the ROW_NUMBER.
However, it still calculates ROW_NUMBERs for all 100,000 of rows in table2, then puts them into a temporary index over rn created by Lazy Spool, and uses this index in a nested loop to range the rns for each row fromtable1.
Calculating and indexing all ROW_NUMBERs is quite expensive, that's why we see 214,093 logical reads in the query statistics.
Finally, let's try a CROSS APPLY:
01.SELECT *
02.FROM [20090716_cross].table1 t1
03.CROSS APPLY
04.(
05.SELECT TOP (t1.row_count) *
06.FROM [20090716_cross].table2
07.ORDER BY
08.id
09.) t2
10.ORDER BY
11.t1.id, t2.id
id
row_count
id
value
1
2
1
Value 1
1
2
2
Value 2
2
1
1
Value 1
3
2
1
Value 1
3
2
2
Value 2
4
1
1
Value 1
5
2
1
Value 1
5
2
2
Value 2
8 rows fetched in 0.0004s (0.0008s)
Table 'table2'. Scan count 5, logical reads 10, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. Table 'table1'. Scan count 1, logical reads 2, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0. SQL Server Execution Times: CPU time = 0 ms, elapsed time = 1 ms.
This query is instant, as it should be.
The plan is quite simple:
SELECT
Inner Join (Nested Loops)
Clustered Index Scan ([20090716_cross].[table1])
Top
Clustered Index Scan ([20090716_cross].[table2])
For each row from table1, it just takes first row_count rows from table2. So simple and so fast.
Summary:
While most queries which employ CROSS APPLY can be rewritten using an INNER JOIN, CROSS APPLY can yield better execution plan and better performance, since it can limit the set being joined yet before the join occurs.
