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SQL Performance Optimization: Count Queries
When working with large datasets, queries that count rows can become computationally expensive and slow down performance. This article explores the performance implications of count queries and provides solutions to optimize them in SQL Server.
Performance Differences in Count Queries
Consider the following SQL query on a table with over 20 million rows:
if (select count(*) from BookChapters) = 0
This query executes swiftly, as SQL Server optimizes it into:
if exists(select * from BookChapters)
Essentially, it checks for the presence of any rows rather than counting them. However, if the query is modified to:
if (select count(*) from BookChapters) = 1
or
if (select count(*) from BookChapters) > 1
execution time dramatically increases to over 10 minutes.
Understanding the Performance Gap
The performance difference stems from the fact that for count queries with conditions (e.g., = 1, > 1), SQL Server employs a different approach. It uses the narrowest non-clustered index to count rows. Since the table in the example lacks any non-clustered index, SQL Server must resort to a full table scan, leading to slow performance.
Optimization Techniques
To optimize count queries, consider the following techniques:
SELECT OBJECT_NAME(i.id) [Table_Name], i.rowcnt [Row_Count] FROM sys.sysindexes i WITH (NOLOCK) WHERE i.indid in (0,1) ORDER BY i.rowcnt desc
select sum (spart.rows) from sys.partitions spart where spart.object_id = object_id(’YourTable’) and spart.index_id < 2
select max(ROWS) from sysindexes where id = object_id(’YourTable’)
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