This is part 4 of an 8 part series of articles about speeding up access to a SQL Server database. This series is
based on chapter 8 “Speeding up Database Access” of my book ASP.NET Site
Performance Secrets
available at amazon.com and other book sites.
In part 1 we saw how to identify the most expensive queries. In this part 4, we'll look at fixing those
expensive queries.
- Part 1 Pinpointing missing indexes and expensive queries
- Part 2 Pinpointing other bottlenecks
- Part 3 Fixing missing indexes
- Part 4 Fixing expensive queries
- Part 5 Fixing locking issues
- Part 6 Fixing execution plan reuse
- Part 7 Fixing fragmentation
- Part 8 Fixing memory, disk and CPU issues
It makes sense to try and optimize those queries that are most expensive – because they are used heavily, or
because each single execution is just plain expensive. You already saw how to identify and create missing
indexes in part 3. Here are more ways to optimize your queries and stored procedures.
Cache aggregation queries
Aggregation statements such as COUNT and AVG are expensive, because they need to access lots of
records. If you need aggregated data for a web page, consider caching the aggregation results in a table
instead of regenerating them for each web page request. Provided you read the aggregates more often than
you update the underlying columns, this will reduce your response time and CPU usage. For example, this
code stores a COUNT aggregate in a table Aggregates:
DECLARE @n int
SELECT @n = COUNT(*) FROM dbo.Book
UPDATE Aggregates SET BookCount = @n
You could update the aggregations whenever the underlying data changes, using a trigger or as part of the
stored procedure that makes the update. Or recalculate the aggregations periodically with a SQL Server Job.
See how to create such a job at:
Keep records short
Reducing the amount of space taken per table record speeds up access. Records are stored in 8KB pages on
disk. The more records fit on a page, the fewer pages SQL Server needs to read to retrieve a given set of
records.
Here are ways to keep your records short:
Use short data types. If your values fit in a 1 byte TinyInt, don’t use a 4 byte Int. If
you store simple ASCII characters, use varchar(n) which uses 1 byte per character, instead of
nvarchar(n) which uses 2. If you store strings of fixed length, use char(n) or nchar(n) instead of
varchar(n) or nvarchar(n), saving the 2 bytes length field.
Consider storing large rarely used columns off row. Large object fields such as
nvarchar(max), varchar(max), varbinary(max) and xml fields are normally stored in row if smaller
than 8000 bytes, and replaced by a 16 bit pointer to an off row area if larger than 8000 bytes. Storing
off row means that accessing the field takes at least 2 reads instead of 1, but also makes for a much
shorter record – which may be desirable if the field is rarely accessed. To force large object fields in
a table to be always off row, use:
EXEC sp_tableoption 'mytable', 'large value types out of
row', '1'
Consider vertical partitioning. If some columns in a table are much more frequently
accessed than others, put the rarely accessed columns in a separate table. Access to the frequently
used columns will be faster, at the expense of having to JOIN to the second table when it does get
used.
Avoid repeating columns. For example, don’t do this:
| AuthorId | Author
|
Country
| Book Title 1
| Book Title
2 |
|---|
| 1
| Charles
Dickens
|
United
Kingdom
| Oliver Twist
| The Pickwick
Papers
|
| 2
| Herman
Melville
|
United
States
| Moby-Dick
|
| 3
| Leo
Tolstoy
|
Russia
| Anna
Karenina
| War and
Peace |
This solution not only creates long records, it also makes it hard to update book titles, and makes
it impossible to have more than two titles per author. Instead, store the book titles in a separate Book
table, and include an AuthorId column that refers back to the Book’s author.
Avoid duplicate values. For example, don’t do this:
| BookId |
Book Title
| Author
| Country |
|---|
| 1
| Oliver
Twist
| Charles
Dickens
| United Kingdom
|
| 2
| The
Pickwick Papers
| Charles
Dickens
| United Kingdom
|
| 3
| Moby-
Dick
| Herman
Melville
| United States
|
| 4
| Anna
Karenina
| Leo
Tolstoy
| Russia
|
| 5
| War
and Peace
| Leo
Tolstoy
| Russia |
Here the author’s name and country are duplicated for each of their books. In addition to
resulting in long records, updating author details now requires multiple record updates and an
increased risk of inconsistencies. Store authors and books in separate tables, and have the Book
records refer back to their Author records.
Considering Denormalization
Denormalization is essentially the reverse of the last two points in the previous section, “Avoid repeating
columns” and “Avoid duplicate values”.
The issue is that while these recommendations improve update speed, consistency and record sizes,
they do lead to data being spread across tables, meaning more JOINs.
For example, say you have 100 addresses, spread over 50 cities, with the cities stored in a separate
table. This will shorten the address records and make updating a city name easier, but also means having to
do a JOIN each time you retrieve an address. If a city name is unlikely to change and you always retrieve the
city along with the rest of the address, than you may be better off including the city name in the address
record itself. This solution implies having repeated content (the city name), but on the other hand you’ll have
one less JOIN.
Be careful with triggers
Triggers can be very convenient, and great for data integrity. On the other hand, they tend to be hidden from
view of developers, so they may not realize that an additional INSERT, UPDATE or DELETE carries the
overhead of a trigger.
Keep your triggers short. They run inside the transaction that caused them to fire, so locks held by
that transaction continue to be held while the trigger runs. Remember that even if you do not explicitly create
a transaction using BEGIN TRAN, each individual INSERT, UPDATE or DELETE creates its own transaction
for the duration of the operation.
When deciding what indexes to use, don’t forget to look at your triggers as well as your stored
procedures and function.
Use table variables for small temporary result sets
Consider replacing temporary tables in your stored procedures with table variables.
For example, instead of writing this:
CREATE TABLE #temp (Id INT, Name nvarchar(100))
INSERT INTO #temp
...
You would write this:
DECLARE @temp TABLE(Id INT, Name nvarchar(100))
INSERT INTO @temp
...
Table variables have these advantages over temporary tables:
SQL Server is more likely to store them in memory rather than tempdb. That means
less traffic and locking in tempdb.
No transaction log overhead.
Fewer stored procedure recompilations.
However, there are disadvantages as well:
You can’t add indexes or constraints to a table variable after it has been created. If
you need an index, it needs to be created as part of the DECLARE statement:
DECLARE @temp TABLE(Id INT primary key, Name
nvarchar(100))
They are less efficient than temporary tables when they have more than about 100 rows,
because no statistics are created for a table variable. This makes it harder for the query optimizer to
come up with an optimal execution plan.
Use Full Text Search instead of LIKE
You may be using LIKE to search for substrings in text columns, like so:
SELECT Title, Author FROM dbo.Book WHERE Title LIKE
'%Quixote'
However, unless the wildcard starts with constant text, SQL Server will not be able to use any index on the
column, and so will do a full table scan instead. Not good.
To improve this situation, consider using SQL Server’s Full Text Search feature. This automatically
creates an index for all words in the text column, leading to much faster searches. To see how to use Full
Text Search, visit:
Replacing cursors with set based code
If you use cursors, consider replacing them with set based code. Performance improvements of a 1000 times
are not uncommon. Set based code uses internal algorithms that are much better optimized than you could
ever hope to achieve with a cursor.
For more information about converting cursors to set based code, visit:
Minimise traffic from SQL Server to Web Server
Do not use SELECT *. This will return all columns. Instead, only list the specific columns you actually need.
If the web site needs only part of a long text value, only send that part, not the entire value. For
example:
SELECT LEFT(longtext, 100) AS excerpt FROM Articles WHERE
...
Object Naming
Do not start stored procedure names with sp_. SQL Server assumes stored procedure names starting with sp_
belong to system stored procedures, and always looks in the master database first to find them – even when
you prefix the name with your database name.
Prefix object names with the schema owner. This saves SQL Server time identifying objects, and
improves execution plan reusability. For example, use:
SELECT Title, Author FROM dbo.Book
Instead of:
SELECT Title, Author FROM Book
Use SET NOCOUNT ON
Always include the command SET NOCOUNT ON at the start of stored procedures and triggers. This
prevents SQL Server from sending the number of rows affected after execution of every SQL statement.
Use FILESTREAM for values over 1MB
Store BLOBs over 1MB in size in a FILESTREAM column. This stores the objects directly on the NTFS file
system instead of in the database data file. To see how to make this work, visit:
Avoid functions on columns in WHERE clauses
Using a function on a column in a WHERE clause, prevents SQL Server from using an index on that column.
Take this query:
SELECT Title, Author FROM dbo.Book WHERE LEFT(Title,
1)='D'
SQL Server doesn’t know what values the LEFT function returns, so has no choice but to scan the entire
table, executing LEFT for each column value.
However, it does know how to interpret LIKE. If you rewrite the query to:
SELECT Title, Author FROM dbo.Book WHERE Title LIKE
'D%'
SQL Server can now use an index on Title, because the LIKE string starts with constant text.
Use UNION ALL instead of UNION
The UNION clause combines the results of two SELECT statements, removing duplicates from the final
result. This is expensive – it uses a work table and executes a DISTINCT select to provide this functionality.
If you don’t mind duplicates, or if you know there will be no duplicates, use UNION ALL instead.
This simply concatenates the SELECT results together.
If the optimizer determines there will be no duplicates, it chooses UNION ALL even if you write
UNION. For example, the select statements in the following query will never return overlapping records, and
so the optimizer will replace the UNION clause with UNION ALL:
SELECT BookId, Title, Author FROM dbo.Book WHERE Author
LIKE 'J%'
UNION
SELECT BookId, Title, Author FROM dbo.Book WHERE Author LIKE 'M%'
Use EXISTS instead of COUNT to find existence of records
If you need to establish whether there are records in a result set, don’t use COUNT:
DECLARE @n int
SELECT @n = COUNT(*) FROM dbo.Book
IF @n > 0
print 'Records found'
This reads the entire table to find the number of records. Instead, use EXISTS:
IF EXISTS(SELECT * FROM dbo.Book)
print 'Records found'
This allows SQL Server to stop reading the moment it finds a record.
Combine SELECT and UPDATE
Sometimes, you need to SELECT and UPDATE the same record. For example, you may need to update a
‘LastAccessed’ column whenever you retrieve a record. One can do this is with a SELECT and an UPDATE:
UPDATE dbo.Book
SET LastAccess = GETDATE()
WHERE BookId=@BookId
SELECT Title, Author
FROM dbo.Book
WHERE BookId=@BookId
However, you can combine the SELECT into the UPDATE, like this:
DECLARE @title nvarchar(50)
DECLARE @author nvarchar(50)
UPDATE dbo.Book
SET LastAccess = GETDATE(),
@title = Title,
@author = Author
WHERE BookId=@BookId
SELECT @title, @author
That saves you some elapsed time, and it reduces the time locks are held on the record.
Conclusion
In this part, we saw how to speed up expensive queries, such as through the proper use of normalization and
denormalization, the use of full-text search and replacing cursors with set-based code.
In the next part, we'll tackle locking issues.