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As SQL query performance improvement is a very debating topic between developers and the other user community. Users always wants a fast response on their data retrieval action and developers put forth their best efforts to provide the data in the minimum time span, however, there is no straightforward way to define what is the best performance. Sometime it’s debatable what is good and what bad performance of a query is but overall we have to follow best practices during development, a good developer may provide the best query response to users and avoid such discussions. We can choose multiple ways to improve SQL query performance, which falls under various categories like re-writing the SQL query, creation and use of Indexes, proper management of statistics….. and other best practices. Here are some top tips for this regards.

Avoid Correlated Subqueries

Subqueries are a very powerful and useful feature of the SQL standard. Subqueries can be categorized as either correlated or uncorrelated queries. A correlated subquery is one that is dependent on the outer query to be processed. These types of subqueries can be very inefficient and should be avoided. We can use temp table and join with outer query or other method to get the result.

Eliminate Cursors from the Query

Try to remove cursors from the query and use set-based query; set-based query is more efficient than cursor-based. A good SQL programmer must develop the mental discipline to explore set-based possibilities thoroughly before falling back on the intuitive procedural solution If there is a need to use cursor than avoid dynamic cursors as it tends to limit the choice of plans available to the query optimizer. For example, dynamic cursor limits the optimizer to using nested loop joins.

Avoid Multiple Joins in a Single Query

Try to avoid writing a SQL query using multiple joins that includes outer joins, cross apply, outer apply and other complex sub queries. It reduces the choices for Optimizer to decide the join order and join type. Sometime, Optimizer is forced to use nested loop joins, irrespective of the performance consequences for queries with excessively complex cross apply or sub queries

Avoid Use of Non-correlated Scalar Sub Query

We can re-write our query to remove non-correlated scalar sub query as a separate query instead of part of the main query and store the output in a variable, which can be referred to in the main query or later part of the batch. This will give better options to Optimizer, which may help to return accurate cardinality estimates along with a better plan.

Avoid Multi-statement Table Valued Functions (TVFs)

Multi-statement TVFs are more costly than inline TFVs. SQL Server expands inline TFVs into the main query like it expands views but evaluates multi-statement TVFs in a separate context from the main query and materializes the results of multi-statement into temporary work tables. The separate context and work table make multi-statement TVFs costly.

Creation and Use of Indexes

As many DBA/Developer creates index to magically reduce the data retrieval time but have a reverse effect on DML operations, which may degrade query performance. With this fact, Indexing is a challenging task, but could help to improve SQL query performance and give us best query response time.

Index on Highly Selective

Selectivity define the percentage of qualifying rows in the table (qualifying number of rows/total number of rows). If the ratio of the qualifying number of rows to the total number of rows is low, the index is highly selective and is most useful. A non-clustered index is most useful if the ratio is around 5% or less, which means if the index can eliminate 95% of the rows from consideration. If index is returning more than 5% of the rows in a table, it probably will not be used; either a different index will be chosen or created or the table will be scanned. Here is a query to find the index status.
SELECT statement AS [database.scheme.table],
column_id , column_name, column_usage,
migs.user_seeks, migs.user_scans,
migs.last_user_seek, migs.avg_total_user_cost,
migs.avg_user_impact
FROM sys.dm_db_missing_index_details AS mid
CROSS APPLY sys.dm_db_missing_index_columns(mid.index_handle)
INNER JOIN sys.dm_db_missing_index_groups AS mig ON mig.index_handle = mid.index_handle
INNER JOIN sys.dm_db_missing_index_group_stats AS migs ON mig.index_group_handle=migs.group_handle
ORDER BY migs.user_seeks, migs.user_scans
GO

Column order in an Index

Order or position of a column in an index also plays a vital role to improve SQL query performance. An index can help to improve the SQL query performance if the criteria of the query matches the columns that are left most in the index key. As a best practice, most selective columns should be placed leftmost in the key of a non-clustered index. The above query will let us know about Column positioning also.

Drop Unused Indexes

Dropping unused indexes can help to speed up data modifications without affecting data retrieval. Also, we need to define a strategy for batch processes that run infrequently and use certain indexes. In such cases, creating indexes in advance of batch processes and then dropping them when the batch processes are done helps to reduce the overhead on the database. Here is a query to find unused indexes.

SELECT ‘[‘+ DB_NAME() + ‘].[‘ + su.[name] + ‘].[‘ + o.[name] + ‘]’ AS [statement] ,i.[name] AS [index_name] ,
ddius.[user_seeks] + ddius.[user_scans] + ddius.[user_lookups] AS [user_reads] ,ddius.[user_updates] AS [user_writes] ,
SUM(SP.rows) AS [total_rows]
FROM sys.dm_db_index_usage_stats ddius
INNER JOIN sys.indexes i ON ddius.[object_id] = i.[object_id] AND i.[index_id] = ddius.[index_id]
INNER JOIN sys.partitions SP ON ddius.[object_id] = SP.[object_id] AND SP.[index_id] = ddius.[index_id]
INNER JOIN sys.objects o ON ddius.[object_id] = o.[object_id]
INNER JOIN sys.sysusers su ON o.[schema_id] = su.[UID]
WHERE ddius.[database_id] =DB_ID() — current database only
AND OBJECTPROPERTY(ddius.[object_id], ‘IsUserTable’)= 1  AND ddius.[index_id] > 0
GROUP BY su.[name] ,o.[name] ,i.[name] ,ddius.[user_seeks] + ddius.[user_scans] + ddius.[user_lookups] ,ddius.[user_updates] HAVING ddius.[user_seeks] + ddius.[user_scans] + ddius.[user_lookups] = 0
ORDER BY ddius.[user_updates] DESC,su.[name] ,o.[name] ,i.[name ]

Statistic Creation and Updates

We have to take care of statistic creation and regular updates for computed columns and multi-columns referred in the query, the query optimizer uses information about the distribution of values in one or more columns of a table statistics to estimate the cardinality, or number of rows, in the query result. These cardinality estimates enable the query optimizer to create a high-quality query plan.

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