Identifying and Recommending Missing Indexes for Better SQL Server Performance
When optimizing SQL Server performance, identifying missing indexes can be an effective way to reduce query costs and improve response times. However, analyzing index recommendations can be resource-intensive. It is often best to schedule this analysis during off-peak hours and store the results for further in-depth review.
SQL Query to Detect Missing Indexes
The following SQL query calculates a metric to prioritize potential missing indexes based on their impact and usage. It also generates the recommended CREATE INDEX statements for implementation:
SELECT
migs.avg_total_user_cost * (migs.avg_user_impact / 100.0) * (migs.user_seeks + migs.user_scans) AS improvement_measure,
'CREATE INDEX [IX_' + CONVERT(VARCHAR, mig.index_group_handle) + '_' + CONVERT(VARCHAR, mid.index_handle) + '_' + LEFT(PARSENAME(mid.STATEMENT, 1), 32) + ']' +
' ON ' + mid.STATEMENT +
' (' + ISNULL(mid.equality_columns, '') +
CASE
WHEN mid.equality_columns IS NOT NULL AND mid.inequality_columns IS NOT NULL THEN ','
ELSE ''
END + ISNULL(mid.inequality_columns, '') + ') ' +
ISNULL(' INCLUDE (' + mid.included_columns + ')', '') AS create_index_statement,
migs.*,
mid.database_id,
mid.[object_id]
FROM
[sys].dm_db_missing_index_groups AS mig
INNER JOIN
[sys].dm_db_missing_index_group_stats AS migs
ON migs.group_handle = mig.index_group_handle
INNER JOIN
[sys].dm_db_missing_index_details AS mid
ON mig.index_handle = mid.index_handle
WHERE
migs.avg_total_user_cost * (migs.avg_user_impact / 100.0) *
(migs.user_seeks + migs.user_scans) > 10
ORDER BY
migs.avg_total_user_cost * migs.avg_user_impact *
(migs.user_seeks + migs.user_scans) DESC;
Why Missing Indexes Matter
Missing indexes can significantly degrade database performance by forcing SQL Server to perform expensive scans instead of efficient seeks. By regularly reviewing and applying index recommendations, you can ensure your database runs smoothly and efficiently, especially under heavy load.
