ASP.NET Site Performance Secrets
Simple and proven techniques to quickly speed up your ASP.NET website
Speed up your ASP.NET website by identifying performance bottlenecks that hold back your site's performance and fixing them
Tips and tricks for writing faster code and pinpointing those areas in the code that matter most, thus saving time and energy
Drastically reduce page load times
Configure and improve compression – the single most important way to improve your site's performance
Written in a simple problem-solving manner – with a practical hands-on approach and just the right amount of theory you need to make sense of it all
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(For more resources on ASP.Net, see here.)
The reader can benefit from the previous article on Pinpointing bottlenecks for better Database Access in ASP.Net.
Now that you have pinpointed the bottlenecks to prioritize, skip to the appropriate subsection to find out how to fix those bottlenecks.
Missing indexes
Just as using an index in a book to find a particular bit of information is often much faster than reading all pages, SQL Server indexes can make finding a particular row in a table dramatically faster by cutting down the number of read operations.
This section first discusses the two types of indexes supported by SQL Server: clustered and non-clustered. It also goes into included columns, a feature of nonclustered indexes. After that, we'll look at when to use each type of index.
Clustered index
Take the following table (missingindexes.sql in the downloaded code bundle):
CREATE TABLE [dbo].[Book]( [BookId] [int] IDENTITY(1,1) NOT NULL, [Title] [nvarchar](50) NULL, [Author] [nvarchar](50) NULL, [Price] [decimal](4, 2) NULL)
Because this table has no clustered index, it is called a heap table. Its records are unordered, and to get all books with a given title, you have to read all the records. It has a very simple structure:
Let's see how long it takes to locate a record in this table. That way, we can compare against the performance of a table with an index.
To do that in a meaningful way, first insert a million records into the table (code to do this is in missingindexes.sql in the downloaded code bundle).
Tell SQL Server to show I/O and timing details of each query we run:
SET STATISTICS IO ONSET STATISTICS TIME ON
Also, before each query, flush the SQL Server memory cache:
CHECKPOINTDBCC DROPCLEANBUFFERS
Now, run the query below with a million records in the Book table:
SELECT Title, Author, Price FROM dbo.Book WHERE BookId = 5000
The results on my machine are: reads: 9564, CPU time: 109 ms, elapsed time: 808 ms.
SQL Server stores all data in 8-KB pages. This shows that it read 9564 pages, that is, the entire table.
Now, add a clustered index:
ALTER TABLE BookADD CONSTRAINT [PK_Book] PRIMARY KEY CLUSTERED ([BookId] ASC)
This puts the index on column BookId, making WHERE and JOIN statements on BookId faster. It sorts the table by BookId and adds a structure called a B-tree to speed up access:
BookId is now used the same way as a page number in a book. Because the pages in a book are sorted by page number, finding a page by page number is very fast.
Now, run the same query again to see the difference:
SELECT Title, Author, Price FROM dbo.Book WHERE BookId = 5000
The results are: reads: 2, CPU time: 0 ms, elapsed time: 32 ms.
The number of reads of 8-KB pages has gone from 9564 to 2, CPU time from 109ms to less than 1 ms, and elapsed time from 808 ms to 32 ms. That's a dramatic improvement.
Non-clustered index
Now let's select by Title instead of BookId:
SELECT Title, Author FROM dbo.Book WHERE Title = 'Don Quixote'
These results are pretty similar to what we got with the heap table, which is no wonder, seeing that there is no index on Title.
The solution obviously is to put an index on Title. However, because a clustered index involves sorting the table records on the index field, there can be only one clustered index. We've already sorted on BookId, and the table can't be sorted on Title at the same time.
The solution is to create a non-clustered index. This is essentially a duplicate of the table records, this time sorted by Title. To save space, SQL Server leaves out the other columns, such as Author and Price. You can have up to 249 non-clustered indexes on a table.
Because we still want to access those other columns in queries though, we need a way to get from the non-clustered index records to the actual table records. The solution is to add the BookId to the non-clustered records. Because BookId has the clustered index, once we have found a BookId via the non-clustered index, we can use the clustered index to get to the actual table record. This second step is called a key lookup.
Why go through the clustered index? Why not put the physical address of the table record in the non-clustered index record? The answer is that when you update a table record, it may get bigger, causing SQL Server to move subsequent records to make space. If non-clustered indexes contained physical addresses, they would all have to be updated when this happens. It's a tradeoff between slightly slower reads and much slower updates. If there is no clustered index or if it is not unique, then non-clustered index records do have the physical address.
To see what a non-clustered index will do for us, first create it as follows:
CREATE NONCLUSTERED INDEX [IX_Title] ON [dbo].[Book]([Title] ASC)
Now, run the same query again:
SELECT Title, Author FROM dbo.Book WHERE Title = 'Don Quixote'
The results are: reads: 4, CPU time: 0 ms, elapsed time: 46 ms.
The number of reads has gone from 9146 to 4, CPU time from 156 ms to less than 1 ms, and elapsed time from 1653 ms to 46 ms. This means that having a non-clustered index is not quite as good as having a clustered index, but still dramatically better than having no index at all.
Included columns
You can squeeze a bit more performance out of a non-clustered index by cutting out the key lookup—the second step where SQL Server uses the clustered index to find the actual record.
Have another look at the test query—it simply returns Title and Author. Title is already present in the non-clustered index record. If you were to add Author to the non-clustered index record as well, there would be no longer any need for SQL Server to access the table record, enabling it to skip the key lookup. It would look similar to the following:
This can be done by including Author in the non-clustered index:
CREATE NONCLUSTERED INDEX [IX_Title] ON [dbo].[Book]([Title] ASC)INCLUDE(Author)WITH drop_existing
Now, run the query again:
SELECT Title, Author FROM dbo.Book WHERE Title = 'Don Quixote'
The results are: reads: 2, CPU time: 0 ms, elapsed time: 26 ms.
The number of reads has gone from 4 to 2, and elapsed time from 46 ms to 26 ms; that's almost 50 percent improvement. In absolute terms, the gain isn't all that great, but for a query that is executed very frequently, this may be worthwhile. Don't overdo this—the bigger you make the non-clustered index records, the fewer fit on an 8KB page, forcing SQL Server to read more pages.
Selecting columns to give an index
Because indexes do create overhead, you want to carefully select the columns to give indexes. Before starting the selection process, keep the following in mind:
Putting a Primary Key on a column by default gives it a clustered index (unless you override the default). So, you may already have many columns in your database with an index. As you'll see later in the When to use a clustered index section, putting the clustered index on the ID column of a record is almost always a good idea.
Putting an index on a table column affects all queries that use that table. Don't focus on just one query.
Before introducing an index on your live database, test the index in development to make sure it really does improve performance.
Let's look at when and when not to use an index, and when to use a clustered index.
When to use an index
You can follow this decision process when selecting columns to give an index:
Start by looking at the most expensive queries.
Look at putting an index on at least one column involved in every JOIN.
Consider columns used in ORDER BY and GROUP BY clauses. If there is an index on such a column, than SQL Server doesn't have to sort the column again because the index already keeps the column values in sorted order.
Consider columns used in WHERE clauses, especially if the WHERE will select a small number of records. However, keep in mind the following:
A WHERE clause that applies a function to the column value can't use an index on that column, because the output of the function is not in the index. Take for example the following:
SELECT Title, Author FROM dbo.Book WHERE LEFT(Title, 3) = 'Don'
Putting an index on the Title column won't make this query any faster.
Likewise, SQL Server can't use an index if you use LIKE in a WHERE clause with a wild card at the start of the search string, as in the following:
SELECT Title, Author FROM dbo.Book WHERE Title LIKE '%Quixote'
However, if the search string starts with constant text instead of a wild card, an index can be used:
SELECT Title, Author FROM dbo.Book WHERE Title LIKE 'Don%'
Consider columns that have a UNIQUE constraint. Having an index on the column makes it easier for SQL Server to check whether a new value would not be unique.
The MIN and MAX functions benefit from working on a column with an index. Because the values are sorted, there is no need to go through the entire table to find the minimum or maximum.
Think twice before putting an index on a column that takes a lot of space. If you use a non-clustered index, the column values will be duplicated in the index. If you use a clustered index, the column values will be used in all nonclustered indexes. The increased sizes of the index records means fewer fit in each 8-KB page, forcing SQL Server to read more pages. The same applies to including columns in non-clustered indexes.
When not to use an index
Having too many indexes can actually hurt performance. Here are the main reasons not to use an index on a column:
The column gets updated often
The column has low specificity, meaning it has lots of duplicate values
Let's look at each reason in turn.
Column updated often
When you update a column without an index, SQL Server needs to write one 8KB page to disk, provided there are no page splits.
However, if the column has a non-clustered index, or if it is included in a nonclustered index, SQL Server needs to update the index as well, so it has to write at least one additional page to disk. It also has to update the B-tree structure used in the index, potentially leading to more page writes.
If you update a column with a clustered index, the non-clustered index records that use the old value need to be updated too, because the clustered index key is used in the non-clustered indexes to navigate to the actual table records. Secondly, remember that the table records themselves are sorted based on the clustered index. If the update causes the sort order of a record to change, that may mean more writes. Finally, the clustered index needs to keep its B-tree up-to-date.
This doesn't mean you cannot have indexes on columns that get updated; just be aware that indexes slow down updates. Test the effect of any indexes you add.
If an index is critical but rarely used, for example only for overnight report generation, consider dropping the index and recreating it when it is needed.
Low specificity
Even if there is an index on a column, the query optimizer won't always use it. Remember, each time SQL Server accesses a record via an index, it has to go through the index structure. In the case of a non-clustered index, it may have to do a key lookup as well. If you're selecting all books with price $20, and lots of books happen to have that price, than it might be quicker to simply read all book records rather than going through an index over and over again. In that case, it is said that the $20 price has low specificity.
You can use a simple query to determine the average selectivity of the values in a column. For example, to find the average selectivity of the Price column in the Book table, use (missingindexes.sql in downloaded code bundle):
SELECT COUNT(DISTINCT Price) AS 'Unique prices', COUNT(*) AS 'Number of rows', CAST((100 * COUNT(DISTINCT Price) / CAST(COUNT(*) AS REAL)) AS nvarchar(10)) + '%' AS 'Selectivity'FROM Book
If every book has a unique price, selectivity will be 100 percent. However, if half the books cost $20 and the other half $30, then average selectivity will be only 50 percent. If the selectivity is 85 percent or less, an index is likely to incur more overhead than it would save.
Some prices may occur a lot more often than other prices. To see the specificity of each individual price, you would run (missingindexes.sql in downloaded code bundle):
DECLARE @c realSELECT @c = CAST(COUNT(*) AS real) FROM BookSELECT Price, COUNT(BookId) AS 'Number of rows', CAST((1 - (100 * COUNT(BookId) / @c)) AS nvarchar(20)) + '%' AS 'Selectivity'FROM BookGROUP BY PriceORDER BY COUNT(BookId)
The query optimizer is unlikely to use a non-clustered index for a price whose specificity is below 85 percent. It figures out the specificity of each price by keeping statistics on the values in the table.
When to use a clustered index
You saw that there are two types of indexes, clustered and non-clustered, and that you can have only one clustered index. How do you determine the lucky column that will have the clustered index?
To work this out, let's first look at the characteristics of a clustered index against a non-clustered index:
Characteristic
Clustered index compared to a non-clustered index
Reading
Faster: Because there is no need for key lookups. No difference if all the required columns are included in the non-clustered index.
Updating
Slower: Not only the table record, but also all non-clustered index records potentially need to be updated.
Inserting/Deleting
Faster: With a non-clustered index, inserting a new record in the table means inserting a new record in the non-clustered index as well. With a clustered index, the table is effectively part of the index, so there is no need for the second insert. The same goes for deleting a record. On the other hand, when the record is inserted at any place in the table but the very end, the insert may cause a page split where half the content of the 8-KB page is moved to another page. Having a page split in a non-clustered index is less likely, because its records are smaller (they normally don't have all columns that a table record has), so more records fit on a page. When the record is inserted at the end of the table, there won't be a page split.
Column Size
Needs to be kept short and fast - Every non-clustered index contains a clustered index value, to do the key lookup. Every access via a non-clustered index has to use that value, so you want it to be fast for the server to process. That makes a column of type int a lot better to put a clustered index on than a column of type nvarchar(50).
If only one column requires an index, this comparison shows that you'll probably want to give it the clustered index rather than a non-clustered index.
If multiple columns need indexes, you'll probably want to put the clustered index on the primary key column:
Reading: The primary key tends to be involved in a lot of JOIN clauses, making read performance important.
Updating: The primary key should never or rarely get updated, because that would mean changing referring foreign keys as well.
Inserting/Deleting: Most often you'll make the primary key an IDENTITY column, so each new record is assigned a unique, ever increasing number. This means that if you put the clustered index on the primary key, new records are always added at the end of the table. When a record is added at the end of a table with a clustered index and there is no space in the current page, the new record goes into a new page but the rest of the data in the current page stays in the page. In other words, there is no expensive page split.
Size: Most often, the primary key is of type int, which is short and fast.
Indeed, when you set the primary key on a column in the SSMS table designer, SSMS gives that column the clustered index by default, unless another column already has the clustered index.
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