Which of those two variants is more advised in general SQL practice:
Lets consider table A with columns: 1,2,3 and table B with columns 3,4. Filtering table with select first:
select col2,col4 from
(select col1,col2 from tabA
where tabA.col3='sth') as t
join tabB using (col2);
or using plain join?:
select col2,col4 from tabA
join tabB using(col2)
where col3='sth';
We can assume where clause matches 1 row. Tables are of similar size. Does Oracle planner deal with such joins properly ot it's gonna create huge joined table and then filter it?
Test it yourself on the real tables using explain plans to learn how many rows are evaluated. I don't believe it is possible to know which would be better, or if there would be any difference. The available indexes make a difference to the optimizer's choice of approach for example.
Regarding your 2 examples I don't like "natural join" syntax ("using") so the first option below is I believe the more common approach (where clause refers directly to the "from table"):
select a.col2,b.col4
from tabA a
inner join tabB b on a.col2 = b.col2
where a.col3='sth'
;
but you could also try a join condition like this:
select b.col2,b.col4
from tabB b
inner join tabA a on b.col2 = a.col2 and a.col3='sth'
;
note this reverses the table relationship.
Your second version is an excellent way of writing the query, with the minor exceptions that col4 and col3 are not qualified:
select col2, col4
from tabA a join
tabB b
using (col2)
where a.col3 = 'sth';
Just for the record, this is not a natural join. That is a separate construct in SQL -- and rather abominable.
In my experience, Oracle has a good query optimizer. It will know how to optimize the query for the given conditions. The subquery should make no difference whatsoever to the query plan. Oracle is not going to filter before the join, unless that is the right thing to do.
The only time an inner filter will perform better than an outer filter is in (usually complex) cases where the query optimizer chooses a different query plan for one query versus the other.
Think of it in the same way as a query hint
By reorganizing the query you can influence the query plan without explicitly doing so with a hint
As far as which is more advised, whichever is easiest to read is usually best. Even if one performs better than the other because of where you put your filter, you should instead focus on having the correct indexes to ensure good performance.
Related
I was recently going through a lot of SQL code where Join sections were filled with complex subqueries, and started wondering if there is any benefit of joining subquery with limited column selection vs joining entire table and selecting only necessary columns.
To ilustrate that:
Let's say we have 2 tables: Table1, Table2 each with columns (PK, FK, a, b ,c, d, e, f).
I want to join Table1 with Table2, but retrieve only a few fields from Table2.
Which is more efficient, what are the benefits of each?
SELECT
Table1.*,
Table2.a,
Table2.b
FROM Table1
LEFT JOIN Table2 ON Table1.PK = Table2.FK
OR
SELECT
Table1.*,
Table2sub.*
FROM Table1
LEFT JOIN (SELECT FK, a, b FROM Table2) AS Table2sub ON Table1.PK = Table2sub.FK
SQL is a descriptive language, not a procedural language. That is, a SQL query describes what the result set looks like, not how the result is produced.
In fact, what the engine runs is called a directed acyclic graph (DAG) -- and that looks nothing like a query. The SQL engine first parses the query, then compiles it, then optimizes it to produce the DAG.
SQL Server has a good optimizer. It is not going to be confused by subqueries. Some SQL compilers are not quite as smart and will materialize the subquery -- which could have a big impact on performance.
If you look at the execution plans, you will see that they are the same in this case.
I have an insert-select statement that needs to only insert rows where a particular identifier of the row does not exist in either of two other tables. Which of the following would be faster?
INSERT INTO Table1 (...)
SELECT (...) FROM Table2 t2
WHERE ...
AND NOT EXISTS (SELECT 'Y' from Table3 t3 where t2.SomeFK = t3.RefToSameFK)
AND NOT EXISTS (SELECT 'Y' from Table4 t4 where t2.SomeFK = t4.RefToSameFK AND ...)
... or...
INSERT INTO Table1 (...)
SELECT (...) FROM Table2 t2
WHERE ...
AND t2.SomeFK NOT IN (SELECT RefToSameFK from Table3)
AND t2.SomeFK NOT IN (SELECT RefToSameFK from Table4 WHERE ...)
... or do they perform about the same? Additionally, is there any other way to structure this query that would be preferable? I generally dislike subqueries as they add another "dimension" to the query that increases runtime by polynomial factors.
Usually it does not matter if NOT IN is slower / faster than NOT EXISTS, because they are NOT equivalent in presence of NULL. Read:
NOT IN vs NOT EXISTS
In these cases you almost always want NOT EXISTS, because it has the usually expected behaviour.
If they are equivalent, it is likely that your database already has figured that out and will generate the same execution plan for both.
In the few cases where both options are aquivalent and your database is not able to figure that out, it is better to analyze both execution plans and choose the best options for your specific case.
You could use a LEFT OUTER JOIN and check if the value in the RIGHT table is NULL. If the value is NULL, the row doesn't exist. That is one way to avoid subqueries.
SELECT (...) FROM Table2 t2
LEFT OUTER JOIN t3 ON (t2.someFk = t3.ref)
WHERE t3.someField IS NULL
It's dependent on the size of the tables, the available indices, and the cardinality of those indices.
If you don't get the same execution plan for both queries, and if neither query plans out to perform a JOIN instead of a sub query, then I would guess that version two is faster. Version one is correlated and therefore would produce many more sub queries, version two can be satisfied with three queries total.
(Also, note that different engines may be biased in one direction or another. Some engines may correctly determine that the queries are the same (if they really are the same) and resolve to the same execution plan.)
For bigger tables, it's recomended to use NOT EXISTS/EXISTS, because the IN clause runs the subquery a lot of times depending of the architecture of the tables.
Based on cost optimizer:
There is no difference.
Given:
Table y
id int clustered index
name nvarchar(25)
Table anothertable
id int clustered Index
name nvarchar(25)
Table someFunction
does some math then returns a valid ID
Compare:
SELECT y.name
FROM y
WHERE dbo.SomeFunction(y.id) IN (SELECT anotherTable.id
FROM AnotherTable)
vs:
SELECT y.name
FROM y
JOIN AnotherTable ON dbo.SomeFunction(y.id) ON anotherTable.id
Question:
While timing these two queries out I found that at large data sets the first query using IN is much faster then the second query using an INNER JOIN. I do not understand why can someone help explain please.
Execution Plan
Generally speaking IN is different from JOIN in that a JOIN can return additional rows where a row has more than one match in the JOIN-ed table.
From your estimated execution plan though it can be seen that in this case the 2 queries are semantically the same
SELECT
A.Col1
,dbo.Foo(A.Col1)
,MAX(A.Col2)
FROM A
WHERE dbo.Foo(A.Col1) IN (SELECT Col1 FROM B)
GROUP BY
A.Col1,
dbo.Foo(A.Col1)
versus
SELECT
A.Col1
,dbo.Foo(A.Col1)
,MAX(A.Col2)
FROM A
JOIN B ON dbo.Foo(A.Col1) = B.Col1
GROUP BY
A.Col1,
dbo.Foo(A.Col1)
Even if duplicates are introduced by the JOIN then they will be removed by the GROUP BY as it only references columns from the left hand table. Additionally these duplicate rows will not alter the result as MAX(A.Col2) will not change. This would not be the case for all aggregates however. If you were to use SUM(A.Col2) (or AVG or COUNT) then the presence of the duplicates would change the result.
It seems that SQL Server doesn't have any logic to differentiate between aggregates such as MAX and those such as SUM and so quite possibly it is expanding out all the duplicates then aggregating them later and simply doing a lot more work.
The estimated number of rows being aggregated is 2893.54 for IN vs 28271800 for JOIN but these estimates won't necessarily be very reliable as the join predicate is unsargable.
Your second query is a bit funny - can you try this one instead??
SELECT y.name
FROM dbo.y
INNER JOIN dbo.AnotherTable a ON a.id = dbo.SomeFunction(y.id)
Does that make any difference?
Otherwise: look at the execution plans! And possibly post them here. Without knowing a lot more about your tables (amount and distribution of data etc.) and your system (RAM, disk etc.), it's really really hard to give a "globally" valid statement
Well, for one thing: get rid of the scalar UDF that is implied by dbo.SomeFunction(y.id). That will kill your performance real good. Even if you replace it with a one-row inline table-valued function it will be better.
As for your actual question, I have found similar results in other situations and have been similarly perplexed. The optimizer just treats them differently; I'll be interested to see what answers others provide.
My question is similar to this SQL order of operations but with a little twist, so I think it's fair to ask.
I'm using Teradata. And I have 2 tables: table1, table2.
table1 has only an id column.
table2 has the following columns: id, val
I might be wrong but I think these two statements give the same results.
Statement 1.
SELECT table1.id, table2.val
FROM table1
INNER JOIN table2
ON table1.id = table2.id
WHERE table2.val<100
Statement 2.
SELECT table1.id, table3.val
FROM table1
INNER JOIN (
SELECT *
FROM table2
WHERE val<100
) table3
ON table1.id=table3.id
My questions is, will the query optimizer be smart enough to
- execute the WHERE clause first then JOIN later in Statement 1
- know that table 3 isn't actually needed in Statement 2
I'm pretty new to SQL, so please educate me if I'm misunderstanding anything.
this would depend on many many things (table size, index, key distribution, etc), you should just check the execution plan:
you don't say which database, but here are some ways:
MySql EXPLAIN
SQL Server SET SHOWPLAN_ALL (Transact-SQL)
Oracle EXPLAIN PLAN
what is explain in teradata?
Teradata Capture and compare plans faster with Visual Explain and XML plan logging
Depending on the availability of statistics and indexes for the tables in question the query rewrite mechanism in the optimizer will may or may not opt to scan Table2 for records where val < 100 before scanning Table1.
In certain situations, based on data demographics, joins, indexing and statistics you may find that the optimizer is not eliminating records in the query plan when you feel that it should. Even if you have a derived table such as the one in your example. You can force the optimizer to process a derived table by simply placing a GROUP BY in your derived table. The optimizer is then obligated to resolve the GROUP BY aggregate before it can consider resolving the join between the two tables in your example.
SELECT table1.id, table3.val
FROM table1
INNER JOIN (
SELECT table2.id, tabl2.val
FROM table2
WHERE val<100
GROUP BY 1,2
) table3
ON table1.id=table3.id
This is not to say that your standard approach should be to run with this through out your code. This is typically one of my last resorts when I have a query plan that simply doesn't eliminate extraneous records earlier enough in the plan and results in too much data being scanned and carried around through the various SPOOL files. This is simply a technique you can put in your toolkit to when you encounter such a situation.
The query rewrite mechanism is continually being updated from one release to the next and the details about how it works can be found in the SQL Transaction Processing Manual for Teradata 13.0.
Unless I'm missing something, Why do you even need Table1??
Just query Table2
Select id, val
From table2
WHERE val<100
or are you using the rows in table1 as a filter? i.e., Does table1 only copntain a subset of the Ids in Table2??
If so, then this will work as well ...
Select id, val
From table2
Where val<100
And id In (Select id
From table1)
But to answer your question, Yes the query optimizer should be intelligent enough to figure out the best order in which to execute the steps necessary to translate your logical instructions into a physical result. It uses the strored statistics that the database maintains on each table to determine what to do (what type of join logic to use for example), as wekll as what order to perform the operations in in order to minimize Disk IOs and processing costs.
Q1. execute the WHERE clause first then JOIN later in Statement 1
The thing is, if you switch the order of inner join, i.e. table2 INNER JOIN table1, then I guess WHERE clause can be processed before JOIN operation, during the preparation phase. However, I guess even if you don't change the original query, the optimizer should be able to switch their order, if it thinks the join operation will be too expensive with fetching the whole row, so it will apply WHERE first. Just my guess.
Q2. know that table 3 isn't actually needed in Statement 2
Teradata will interpret your second query in such way that the derived table is necessary, so it will keep processing table 3 involved operation.
I'm wanting to select rows in a table where the primary key is in another table. I'm not sure if I should use a JOIN or the IN operator in SQL Server 2005. Is there any significant performance difference between these two SQL queries with a large dataset (i.e. millions of rows)?
SELECT *
FROM a
WHERE a.c IN (SELECT d FROM b)
SELECT a.*
FROM a JOIN b ON a.c = b.d
Update:
This article in my blog summarizes both my answer and my comments to another answers, and shows actual execution plans:
IN vs. JOIN vs. EXISTS
SELECT *
FROM a
WHERE a.c IN (SELECT d FROM b)
SELECT a.*
FROM a
JOIN b
ON a.c = b.d
These queries are not equivalent. They can yield different results if your table b is not key preserved (i. e. the values of b.d are not unique).
The equivalent of the first query is the following:
SELECT a.*
FROM a
JOIN (
SELECT DISTINCT d
FROM b
) bo
ON a.c = bo.d
If b.d is UNIQUE and marked as such (with a UNIQUE INDEX or UNIQUE CONSTRAINT), then these queries are identical and most probably will use identical plans, since SQL Server is smart enough to take this into account.
SQL Server can employ one of the following methods to run this query:
If there is an index on a.c, d is UNIQUE and b is relatively small compared to a, then the condition is propagated into the subquery and the plain INNER JOIN is used (with b leading)
If there is an index on b.d and d is not UNIQUE, then the condition is also propagated and LEFT SEMI JOIN is used. It can also be used for the condition above.
If there is an index on both b.d and a.c and they are large, then MERGE SEMI JOIN is used
If there is no index on any table, then a hash table is built on b and HASH SEMI JOIN is used.
Neither of these methods reevaluates the whole subquery each time.
See this entry in my blog for more detail on how this works:
Counting missing rows: SQL Server
There are links for all RDBMS's of the big four.
Neither. Use an ANSI-92 JOIN:
SELECT a.*
FROM a JOIN b a.c = b.d
However, it's best as an EXISTS
SELECT a.*
FROM a
WHERE EXISTS (SELECT * FROM b WHERE a.c = b.d)
This remove the duplicates that could be generated by the JOIN, but runs just as fast if not faster
Speaking from experience on a Table with 49,000,000 rows I would recommend LEFT OUTER JOIN.
Using IN, or EXISTS Took 5 minutes to complete where the LEFT OUTER JOIN finishes in 1 second.
SELECT a.*
FROM a LEFT OUTER JOIN b ON a.c = b.d
WHERE b.d is not null -- Given b.d is a primary Key with index
Actually in my query I do this across 9 tables.
The IN is evaluated (and the select from b re-run) for each row in a, whereas the JOIN is optimized to use indices and other neat paging tricks...
In most cases, though, the optimizer would likely be able to construct a JOIN out of a correlated subquery and end up with the same execution plan anyway.
Edit: Kindly read the comments below for further... discussion about the validity of this answer, and the actual answer to the OP's question. =)
Aside from going and actually testing it out on a big swath of test data for yourself, I would say use the JOINS. I've always had better performance using them in most cases compared to an IN subquery, and you have a lot more customization options as far as how to join, what is selected, what isn't, etc.
They are different queries with different results. With the IN query you will get 1 row from table 'a' whenever the predicate matches. With the INNER JOIN query you will get a*b rows whenever the join condition matches.
So with values in a of {1,2,3} and b of {1,2,2,3} you will get 1,2,2,3 from the JOIN and 1,2,3 from the IN.
EDIT - I think you may come across a few answers in here that will give you a misconception. Go test it yourself and you will see these are all fine query plans:
create table t1 (t1id int primary key clustered)
create table t2 (t2id int identity primary key clustered
,t1id int references t1(t1id)
)
insert t1 values (1)
insert t1 values (2)
insert t1 values (3)
insert t1 values (4)
insert t1 values (5)
insert t2 values (1)
insert t2 values (2)
insert t2 values (2)
insert t2 values (3)
insert t2 values (4)
select * from t1 where t1id in (select t1id from t2)
select * from t1 where exists (select 1 from t2 where t2.t1id = t1.t1id)
select t1.* from t1 join t2 on t1.t1id = t2.t1id
The first two plans are identical. The last plan is a nested loop, this difference is expected because as I mentioned above the join has different semantics.
From MSDN documentation on Subquery Fundamentals:
Many Transact-SQL statements that
include subqueries can be
alternatively formulated as joins.
Other questions can be posed only with
subqueries. In Transact-SQL, there is
usually no performance difference
between a statement that includes a
subquery and a semantically equivalent
version that does not. However, in
some cases where existence must be
checked, a join yields better
performance. Otherwise, the nested
query must be processed for each
result of the outer query to ensure
elimination of duplicates. In such
cases, a join approach would yield
better results.
In the example you've provided, the nested query need only be processed a single time for each of the outer query results, so there should be no performance difference. Checking the execution plans for both queries should confirm this.
Note: Though the question itself didn't specify SQL Server 2005, I answered with that assumption based on the question tags. Other database engines (even different SQL Server versions) may not optimize in the same way.
Observe the execution plan for both types and draw your conclusions. Unless the number of records returned by the subquery in the "IN" statement is very small, the IN variant is almost certainly slower.
I would use a join, betting that it'll be a heck of a lot faster than IN. This presumes that there are primary keys defined, of course, thus letting indexing speed things up tremendously.
It's generally held that a join would be more efficient than the IN subquery; however the SQL*Server optimizer normally results in no noticeable performance difference. Even so, it's probably best to code using the join condition to keep your standards consistent. Also, if your data and code ever needs to be migrated in the future, the database engine may not be so forgiving (for example using a join instead of an IN subquery makes a huge difference in MySql).
Theory will only get you so far on questions like this. At the end of the day, you'll want to test both queries and see which actually runs faster. I've had cases where the JOIN version took over a minute and the IN version took less than a second. I've also had cases where JOIN was actually faster.
Personally, I tend to start off with the IN version if I know I won't need any fields from the subquery table. If that starts running slow, I'll optimize. Fortunately, for large datasets, rewriting the query makes such a noticeable difference that you can simply time it from Query Analyzer and know you're making progress.
Good luck!
Ive always been a supporter of the IN methodology. This link contains details of a test conducted in PostgresSQL.
http://archives.postgresql.org/pgsql-performance/2005-02/msg00327.php