Page Free Space

The SQL Server documentation has this to say about page splits:

When a new row is added to a full index page, the Database Engine moves approximately half the rows to a new page to make room for the new row. This reorganization is known as a page split. A page split makes room for new records, but can take time to perform and is a resource intensive operation. Also, it can cause fragmentation that causes increased I/O operations.

Given that, how can a SELECT statement be responsible for page splits?

Well, I suppose we could SELECT from a function that adds rows to a table variable as part of its internal implementation, but that would clearly be cheating, and no fun at all from a blogging point of view.

The following table summarizes the results from my last two articles, Enforcing Uniqueness for Performance and Avoiding Uniqueness for Performance. It shows the CPU time used when performing 5 million clustered index seeks into a unique or non-unique index:

In test 1, making the clustered index unique improved performance by around 40%.

In test 2, making the same change reduced performance by around 70% (on 64-bit systems – more on that later).

In my last post, Enforcing Uniqueness for Performance, I showed how using a unique index could speed up equality seeks by around 40%.

A little while back, I posted a short series on seeks and scans:

• When is a Seek not a Seek?
• So…is it a Seek or a Scan?
• Seeking Without Indexes

One of the things I highlighted in the middle post was the difference between a singleton seek and a range scan:

• A singleton equality seek always retrieves exactly one row, and is guaranteed to do so because a unique index exists to enforce it.

• A range scan seeks down the B-tree to a starting (or ending) point, and scans forward (or backward) from that point using the next or previous page pointers.

Today’s short post shows how much faster a singleton seek is, compared with a range scan, even when both return exactly the same number of records.

Introduction

You probably already know that it’s important to be aware of data types when writing queries, and that implicit conversions between types can lead to poor query performance.

Some people have gone so far as to write scripts to search the plan cache for CONVERT_IMPLICIT elements, and others routinely inspect plans for that type of thing when tuning.

Now, that’s all good, as far as it goes. It may surprise you to learn that not all implicit conversions are visible in query plans, and there are other important factors to consider too.