let's say that you want to select all rows from one table that have a corresponding row in another one (the data in the other table is not important, only the presence of a corresponding row is important). From what I know about DB2, this kinda query is better performing when written as a correlated query with a EXISTS clause rather than a INNER JOIN. Is that the same for SQL Server? Or doesn't it make any difference whatsoever?
I just ran a test query and the two statements ended up with the exact same execution plan. Of course, for just about any performance question I would recommend running the test on your own environment; With SQL server Management Studio this is easy (or SQL Query Analyzer if your running 2000). Just type both statements into a query window, select Query|Include Actual Query Plan. Then run the query. Go to the results tab and you can easily see what the plans are and which one had a higher cost.
Odd: it's normally more natural for me to write these as a correlated query first, at which point I have to then go back and re-factor to use a join because in my experience the sql server optimizer is more likely to get that right.
But don't take me too seriously. For all I have 26K rep here and one of only 2 current sql topic-specific badges, I'm actually pretty junior in terms of sql knowledge (It's all about the volume! ;) ); certainly I'm no DBA. In practice, you will of course need to profile each method to gauge it's actual performance. I would expect the optimizer to recognize what you're asking for and handle either query in the optimal way, but you never know until you check.
As everyone notes, it all boils down to the optimizer. I'd suggest writing it in whatever way feels more natural to you, then making sure the optimizer can figure out the most effective query plan (gather statistics, create an index, whatever). The SQL Server optimizer is pretty good overall, so long as you give it the information it needs to work with.
Use the join. It might not make much of a difference in performance if you have small tables, but if the "outer" table is very large then it will need to do the EXISTS sub-query for each row. If your tables are indexed on the common columns then it should be far quicker to do the INNER JOIN. BTW, if you want to find all rows that are NOT in the second table, use a LEFT JOIN and test for NULL in the second table--it is much faster than using EXISTS when you have very large tables and indexes.
Probably the best performance is with a join to a derived table. Exists would probably be next (and might be faster). The worst performance would be with a subquery inside the select as it would tend to run row by row instead of as a set.
However, all things being equal and database performance being very dependent on the database design. I would try out all possible methods and see which are faster in your circumstances.
Related
This is a question about SQL Server 2008 R2
I'm not a DBA, by far. I'm a java developer, who has to write SQL from time to time. (mostly embedded in code). I want to know if I did something wrong here, and if so, what I can do to avoid it to happen again.
Q1:
SELECT something FROM (SELECT * FROM T1 WHERE condition1) JOIN ...
Q1 features 14 joins
Q2 is the same as Q1, with one exception. (SELECT * FROM T1 WHERE condition1) is executed before, and stored in a temp table.
This is not a correlated sub-query.
Q2:
SELECT * INTO #tempTable FROM T1 WHERE condition1
SELECT something FROM #tempTable JOIN ...
again, 14 joins.
The thing that puzzles me now is that Q1 took > 2min, (tried it a few times, to avoid caching to play a role) while Q2 (both queries combined) took 2sec!!! What gives?
Why it's not recommended to use subqueries?
Database Optimizer (regardless of what database you are using) can not always properly optimize such query (with subqueries). In this case, the problem to the optimizer is to choose the right way to join result sets. There are several algorithms for joining two result sets. The choice of the algorithm depends on the number of records which are contained in one and in the other result set. In case if you join two physical tables (subquery is not a physical table), the database can easily determine the amount of data in two result sets by the available statistics. If one of result sets is a subquery then to understand how many records it returns is very difficult. In this case the database can choose wrong query plan of join, so that will lead to a dramatic reduction in the performance of the query.
Rewriting the query with using temporary tables is intended to simplify the database optimizer. In the rewritten query all result sets participating in joins will be physical tables and the database will easily determine the length of each result set. This will allow the database to choose the guaranteed fastest of all possible query plans. Moreover, the database will make the right choice no matter what are the conditions. The rewritten query with temporary tables would work well on any database, this is especially important in the development of portable solutions. In addition, the rewritten query is easier to read, easier to understand and to debug.
It is understood that rewriting the query with temporary tables can lead to some slowdown due to additional expenses: creation of temporary tables. If the database will not be mistaken with the choice of the query plan, it will perform the old query faster than a new one. However, this slowdown will always be negligible. Typically the creation of a temporary table takes a few milliseconds. That is, the delay can not have a significant impact on system performance, and usually can be ignored.
Important! Do not forget to create indexes for temporary tables. The index fields should include all fields that are used in join conditions.
There are lot of things to tackle here, indexes, execution plans, etc. Testing and comparing results is the way to go.
You could take a look to the usual suspects, indexes. Take a look into the execution plan and compare them. Make sure the WHERE clause is using the correct ones. Ensure you are using the indexes on your JOINs.
These answers sure will help you a lot.
Performance: Subquery or Joining
Is there a speed difference between CTE , SubQuery and Temp tables?
One of my jobs it to maintain our database, usually we have troubles with lack of performance while getting reports and working whit that base.
When I start looking at queries which our ERP sending to database I see a lot of totally needlessly subselect queries inside main queries.
As I am not member of developers which is creator of program we using, they do not like much when I criticize they code and job. Let say they do not taking my review as serious statements.
So I asking you few questions about subselect in SQL
Does subselect is taking a lot of more time then left outer joins?
Does exists any blog, article or anything where I subselect is recommended not to use ?
How I can prove that if we avoid subselesct in query that query is going to be faster ?
Our database server is MSSQL2005
"Show, Don't Tell" - Examine and compare the query plans of the queries identified using SQL Profiler. Particularly look out for table scans and bookmark lookups (you want to see index seeks as often as possible). The 'goodness of fit' of query plans depends on up-to-date statistics, what indexes are defined, the holistic query workload.
Execution Plan Basics
Understanding More Complex Query Plans
Using SQL Server Profiler (2005 Version)
Run the queries in SQL Server Management Studio (SSMS) and turn on Query->Include Actual Execution Plan (CTRL+M)
Think yourself lucky they're only subselects (which in some cases the optimiser will produce equivalent 'join plans') and not correlated sub-queries!
Identify a query that is performing a high number of logical reads, re-write it using your preferred technique and then show how few logicals reads it does by comparison.
Here's a tip. To get the total number of logical reads performed, wrap a query in question with:
SET STATISTICS IO ON
GO
-- Run your query here
SET STATISTICS IO OFF
GO
Run your query, and switch to the messages tab in the results pane.
If you are interested in learning more, there is no better book than SQL Server 2008 Query Performance Tuning Distilled, which covers the essential techniques for monitoring, interpreting and fixing performance issues.
One thing you can do is to load SQL Profiler and show them the cost (in terms of CPU cycles, reads and writes) of the sub-queries. It's tough to argue with cold, hard statistics.
I would also check the query plan for these queries to make sure appropriate indexes are being used, and table/index scans are being held to a minimum.
In general, I wouldn't say sub-queries are bad, if used correctly and the appropriate indexes are in place.
I'm not very familiar with MSSQL, as we are using postrgesql in most of our applications. However there should exist something like "EXPLAIN" which shows you the execution plan for the query. There you should be able to see the various steps that a query will produce in order to retrieve the needed data.
If you see there a lot of table scans or loop join without any index usage it is definitely a hint for a slow query execution. With such a tool you should be able to compare the two queries (one with the join, the other without)
It is difficult to state which is the better ways, because it really highly depends on the indexes the optimizer can take in the various cases and depending on the DBMS the optimizer may be able to implicitly rewrite a subquery-query into a join-query and execute it.
If you really want to show which is better you have to execute both and measure the time, cpu-usage and so on.
UPDATE:
Probably it is this one for MSSQL -->QueryPlan
From my own experience both methods can be valid, as for example an EXISTS subselect can avoid a lot of treatment with an early break.
Buts most of the time queries with a lot of subselect are done by devs which do not really understand SQL and use their classic-procedural-programmer way of thinking on queries. Then they don't even think about joins, and makes some awfull queries. So I prefer joins, and I always check subqueries. To be completly honnest I track slow queries, and my first try on slow queries containing subselects is trying to do joins. Works a lot of time.
But there's no rules which can establish that subselect are bad or slower than joins, it's just that bad sql programmer often do subselects :-)
Does subselect is taking a lot of more time then left outer joins?
This depends on the subselect and left outer joins.
Generally, this construct:
SELECT *
FROM mytable
WHERE mycol NOT IN
(
SELECT othercol
FROM othertable
)
is more efficient than this:
SELECT m.*
FROM mytable m
LEFT JOIN
othertable o
ON o.othercol = m.mycol
WHERE o.othercol IS NULL
See here:
NOT IN vs. NOT EXISTS vs. LEFT JOIN / IS NULL: SQL Server
Does exists any blog, article or anything where subselect is recommended not to use ?
I would steer clear of the blogs which blindly recommend to avoid subselects.
They are implemented for a reason and, believe it or not, the developers have put some effort into optimizing them.
How I can prove that if we avoid subselesct in query that query is going to be faster ?
Write a query without the subselects which runs faster.
If you post your query here we possibly will be able to improve it. However, a version with the subselects may turn out to be faster.
Try rewriting some of the queries to elminate the sub-select and compare runtimes.
Share and enjoy.
say i have a table
Id int
Region int
Name nvarchar
select * from table1 where region = 1 and name = 'test'
select * from table1 where name = 'test' and region = 1
will there be a difference in performance?
assume no indexes
is it the same with LINQ?
Because your qualifiers are, in essence, actually the same (it doesn't matter what order the where clauses are put in), then no, there's no difference between those.
As for LINQ, you will need to know what query LINQ to SQL actually emits (you can use a SQL Profiler to find out). Sometimes the query will be the simplest query you can think of, sometimes it will be a convoluted variety of such without you realizing it, because of things like dependencies on FKs or other such constraints. LINQ also wouldn't use an * for select.
The only real way to know is to find out the SQL Server Query Execution plan of both queries. To read more on the topic, go here:
SQL Server Query Execution Plan Analysis
Should it? No. SQL is a relational algebra and the DBMS should optimize irrespective of order within the statement.
Does it? Possibly. Some DBMS' may store data in a certain order (e.g., maintain a key of some sort) despite what they've been told. But, and here's the crux: you cannot rely on it.
You may need to switch DBMS' at some point in the future. Even a later version of the same DBMS may change its behavior. The only thing you should be relying on is what's in the SQL standard.
Regarding the query given: with no indexes or primary key on the two fields in question, you should assume that you'll need a full table scan for both cases. Hence they should run at the same speed.
I don't recommend the *, because the engine should look for the table scheme before executing the query. Instead use the table fields you want to avoid unnecessary overhead.
And yes, the engine optimizes your queries, but help him :) with that.
Best Regards!
For simple queries, likely there is little or no difference, but yes indeed the way you write a query can have a huge impact on performance.
In SQL Server (performance issues are very database specific), a correlated subquery will usually have poor performance compared to doing the same thing in a join to a derived table.
Other things in a query that can affect performance include using SARGable1 where clauses instead of non-SARGable ones, selecting only the fields you need and never using select * (especially not when doing a join as at least one field is repeated), using a set-bases query instead of a cursor, avoiding using a wildcard as the first character in a a like clause and on and on. There are very large books that devote chapters to more efficient ways to write queries.
1 "SARGable", for those that don't know, are stage 1 predicates in DB2 parlance (and possibly other DBMS'). Stage 1 predicates are more efficient since they're parts of indexes and DB2 uses those first.
What techniques can be applied effectively to improve the performance of SQL queries? Are there any general rules that apply?
Use primary keys
Avoid select *
Be as specific as you can when building your conditional statements
De-normalisation can often be more efficient
Table variables and temporary tables (where available) will often be better than using a large source table
Partitioned views
Employ indices and constraints
Learn what's really going on under the hood - you should be able to understand the following concepts in detail:
Indexes (not just what they are but actually how they work).
Clustered indexes vs heap allocated tables.
Text and binary lookups and when they can be in-lined.
Fill factor.
How records are ghosted for update/delete.
When page splits happen and why.
Statistics, and how they effect various query speeds.
The query planner, and how it works for your specific database (for instance on some systems "select *" is slow, on modern MS-Sql DBs the planner can handle it).
The biggest thing you can do is to look for table scans in sql server query analyzer (make sure you turn on "show execution plan"). Otherwise there are a myriad of articles at MSDN and elsewhere that will give good advice.
As an aside, when I started learning to optimize queries I ran sql server query profiler against a trace, looked at the generated SQL, and tried to figure out why that was an improvement. Query profiler is far from optimal, but it's a decent start.
There are a couple of things you can look at to optimize your query performance.
Ensure that you just have the minimum of data. Make sure you select only the columns you need. Reduce field sizes to a minimum.
Consider de-normalising your database to reduce joins
Avoid loops (i.e. fetch cursors), stick to set operations.
Implement the query as a stored procedure as this is pre-compiled and will execute faster.
Make sure that you have the correct indexes set up. If your database is used mostly for searching then consider more indexes.
Use the execution plan to see how the processing is done. What you want to avoid is a table scan as this is costly.
Make sure that the Auto Statistics is set to on. SQL needs this to help decide the optimal execution. See Mike Gunderloy's great post for more info. Basics of Statistics in SQL Server 2005
Make sure your indexes are not fragmented. Reducing SQL Server Index Fragmentation
Make sure your tables are not fragmented. How to Detect Table Fragmentation in SQL Server 2000 and 2005
Use a with statment to handle query filtering.
Limit each subquery to the minimum number of rows possible.
then join the subqueries.
WITH
master AS
(
SELECT SSN, FIRST_NAME, LAST_NAME
FROM MASTER_SSN
WHERE STATE = 'PA' AND
GENDER = 'M'
),
taxReturns AS
(
SELECT SSN, RETURN_ID, GROSS_PAY
FROM MASTER_RETURNS
WHERE YEAR < 2003 AND
YEAR > 2000
)
SELECT *
FROM master,
taxReturns
WHERE master.ssn = taxReturns.ssn
A subqueries within a with statement may end up as being the same as inline views,
or automatically generated temp tables. I find in the work I do, retail data, that about 70-80% of the time, there is a performance benefit.
100% of the time, there is a maintenance benefit.
I think using SQL query analyzer would be a good start.
In Oracle you can look at the explain plan to compare variations on your query
Make sure that you have the right indexes on the table. if you frequently use a column as a way to order or limit your dataset an index can make a big difference. I saw in a recent article that select distinct can really slow down a query, especially if you have no index.
The obvious optimization for SELECT queries is ensuring you have indexes on columns used for joins or in WHERE clauses.
Since adding indexes can slow down data writes you do need to monitor performance to ensure you don't kill the DB's write performance, but that's where using a good query analysis tool can help you balanace things accordingly.
Indexes
Statistics
on microsoft stack, Database Engine Tuning Advisor
Some other points (Mine are based on SQL server, since each db backend has it's own implementations they may or may not hold true for all databases):
Avoid correlated subqueries in the select part of a statement, they are essentially cursors.
Design your tables to use the correct datatypes to avoid having to apply functions on them to get the data out. It is far harder to do date math when you store your data as varchar for instance.
If you find that you are frequently doing joins that have functions in them, then you need to think about redesigning your tables.
If your WHERE or JOIN conditions include OR statements (which are slower) you may get better speed using a UNION statement.
UNION ALL is faster than UNION if (And only if) the two statments are mutually exclusive and return the same results either way.
NOT EXISTS is usually faster than NOT IN or using a left join with a WHERE clause of ID = null
In an UPDATE query add a WHERE condition to make sure you are not updating values that are already equal. The difference between updating 10,000,000 records and 4 can be quite significant!
Consider pre-calculating some values if you will be querying them frequently or for large reports. A sum of the values in an order only needs to be done when the order is made or adjusted, rather than when you are summarizing the results of 10,000,000 million orders in a report. Pre-calculations should be done in triggers so that they are always up-to-date is the underlying data changes. And it doesn't have to be just numbers either, we havea calculated field that concatenates names that we use in reports.
Be wary of scalar UDFs, they can be slower than putting the code in line.
Temp table tend to be faster for large data set and table variables faster for small ones. In addition you can index temp tables.
Formatting is usually faster in the user interface than in SQL.
Do not return more data than you actually need.
This one seems obvious but you would not believe how often I end up fixing this. Do not join to tables that you are not using to filter the records or actually calling one of the fields in the select part of the statement. Unnecessary joins can be very expensive.
It is an very bad idea to create views that call other views that call other views. You may find you are joining to the same table 6 times when you only need to once and creating 100,000,00 records in an underlying view in order to get the 6 that are in your final result.
In designing a database, think about reporting not just the user interface to enter data. Data is useless if it is not used, so think about how it will be used after it is in the database and how that data will be maintained or audited. That will often change the design. (This is one reason why it is a poor idea to let an ORM design your tables, it is only thinking about one use case for the data.) The most complex queries affecting the most data are in reporting, so designing changes to help reporting can speed up queries (and simplify them) considerably.
Database-specific implementations of features can be faster than using standard SQL (That's one of the ways they sell their product), so get to know your database features and find out which are faster.
And because it can't be said too often, use indexes correctly, not too many or too few. And make your WHERE clauses sargable (Able to use indexes).
Consider the following 2 queries:
select tblA.a,tblA.b,tblA.c,tblA.d
from tblA
where tblA.a not in (select tblB.a from tblB)
select tblA.a,tblA.b,tblA.c,tblA.d
from tblA left outer join tblB
on tblA.a = tblB.a where tblB.a is null
Which will perform better? My assumption is that in general the join will be better except in cases where the subselect returns a very small result set.
RDBMSs "rewrite" queries to optimize them, so it depends on system you're using, and I would guess they end up giving the same performance on most "good" databases.
I suggest picking the one that is clearer and easier to maintain, for my money, that's the first one. It's much easier to debug the subquery as it can be run independently to check for sanity.
non-correlated sub queries are fine. you should go with what describes the data you're wanting. as has been noted, this likely gets rewritten into the same plan, but isn't guaranteed to! what's more, if table A and B are not 1:1 you will get duplicate tuples from the join query (as the IN clause performs an implicit DISTINCT sort), so it's always best to code what you want and actually think about the outcome.
Well, it depends on the datasets. From my experience, if You have small dataset then go for a NOT IN if it's large go for a LEFT JOIN. The NOT IN clause seems to be very slow on large datasets.
One other thing I might add is that the explain plans might be misleading. I've seen several queries where explain was sky high and the query run under 1s. On the other hand I've seen queries with excellent explain plan and they could run for hours.
So all in all do test on your data and see for yourself.
I second Tom's answer that you should pick the one that is easier to understand and maintain.
The query plan of any query in any database cannot be predicted because you haven't given us indexes or data distributions. The only way to predict which is faster is to run them against your database.
As a rule of thumb I tend to use sub-selects when I do not need to include any columns from tblB in my select clause. I would definitely go for a sub-select when I want to use the 'in' predicate (and usually for the 'not in' that you included in the question), for the simple reason that these are easier to understand when you or someone else has come back and change them.
The first query will be faster in SQL Server which I think is slighty counter intuitive - Sub queries seem like they should be slower. In some cases (as data volumes increase) an exists may be faster than an in.
It should be noted that these queries will produce different results if TblB.a is not unique.
From my observations, MSSQL server produces same query plan for these queries.
I created a simple query similar to the ones in the question on MSSQL2005 and the explain plans were different. The first query appears to be faster. I am not a SQL expert but the estimated explain plan had 37% for query 1 and 63% for the query 2. It appears that the biggest cost for query 2 is the join. Both queries had two table scans.