如何识别SQL Server中需要添加索引的查询
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引言在数据库性能优化中,索引是提升查询速度最有效的手段之一。然而,不恰当的索引会降低写操作性能并增加存储开销。作为DBA,我们经常面临这样的挑战:如何精准定位哪些查询真正需要添加索引? 本文将分享几种实用的T-SQL查询,帮助您科学识别缺失索引,并提供最佳实践指南。 一、为什么需要索引优化?
二、核心诊断查询1. 缺失索引自动生成脚本SELECT TOP 10 ROUND(migs.avg_total_user_cost * migs.avg_user_impact * (migs.user_seeks + migs.user_scans), 0) AS improvement_measure, DB_NAME(mid.database_id) AS database_name, OBJECT_NAME(mid.object_id) AS table_name, 'CREATE INDEX [IX_' + OBJECT_NAME(mid.object_id) + '_' + REPLACE(REPLACE(REPLACE(ISNULL(mid.equality_columns, ''), ', ', '_'), '[', ''), ']', '') + CASE WHEN mid.inequality_columns IS NOT NULL THEN '_' + REPLACE(REPLACE(REPLACE(mid.inequality_columns, ', ', '_'), '[', ''), ']', '') ELSE '' END + '] 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.user_seeks AS seek_operations, migs.avg_user_impact AS improvement_percent FROM sys.dm_db_missing_index_group_stats AS migs INNER JOIN sys.dm_db_missing_index_groups AS mig 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 mid.database_id = DB_ID() ORDER BY improvement_measure DESC;
结果解读:
2. 高开销扫描查询定位SELECT TOP 5 qs.total_logical_reads / qs.execution_count AS avg_logical_reads, qs.execution_count, SUBSTRING(st.text, (qs.statement_start_offset/2) + 1, ((CASE qs.statement_end_offset WHEN -1 THEN DATALENGTH(st.text) ELSE qs.statement_end_offset END - qs.statement_start_offset)/2) + 1) AS query_text, qp.query_plan FROM sys.dm_exec_query_stats AS qs CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) AS st CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) AS qp WHERE qp.query_plan.exist('//RelOp[@PhysicalOp="Index Scan" or @PhysicalOp="Clustered Index Scan"]') = 1 ORDER BY avg_logical_reads DESC;
关键指标:
3. 未索引的热点列检测SELECT TOP 10 t.name AS TableName, c.name AS ColumnName, SUM(us.user_scans) AS total_scans FROM sys.tables t JOIN sys.columns c ON t.object_id = c.object_id LEFT JOIN sys.index_columns ic ON ic.object_id = t.object_id AND ic.column_id = c.column_id LEFT JOIN sys.indexes i ON i.object_id = t.object_id AND i.index_id = ic.index_id LEFT JOIN sys.dm_db_index_usage_stats us ON us.object_id = t.object_id AND us.index_id = i.index_id WHERE i.index_id IS NULL -- 无索引列 AND us.user_scans > 0 GROUP BY t.name, c.name ORDER BY total_scans DESC;
三、索引创建黄金法则1. 索引设计原则-- 标准结构 CREATE INDEX IX_Table_KeyColumns ON dbo.Table (Column1 ASC, Column2 DESC) INCLUDE (Column3, Column4) WITH (FILLFACTOR = 90); -- 针对频繁更新表 -- 筛选索引(针对热点数据) CREATE INDEX IX_Orders_Active ON dbo.Orders (OrderDate) WHERE Status = 'Processing';
2. 四要四不要| 该做的 | 避免的 | |---------------------------|--------------------------| | 优先选择高选择性列 | 在bit类型列建索引 | | INCLUDED列放常用查询字段 | 创建重复功能索引 | | 定期重建碎片率>30%的索引 | 盲目接受所有系统建议 | | 测试环境验证性能提升 | 在生产环境直接创建索引 | 四、高级技巧1. 索引使用监控SELECT OBJECT_NAME(ix.object_id) AS TableName, ix.name AS IndexName, ix.type_desc AS IndexType, us.user_seeks, us.user_scans, us.user_lookups, us.user_updates FROM sys.dm_db_index_usage_stats us JOIN sys.indexes ix ON us.object_id = ix.object_id AND us.index_id = ix.index_id WHERE us.database_id = DB_ID() AND OBJECTPROPERTY(us.object_id, 'IsUserTable') = 1;
决策依据:
2. 查询存储深度分析(SQL Server 2016+)SELECT q.query_id, t.query_sql_text, rs.avg_duration, rs.avg_logical_io_reads, p.query_plan FROM sys.query_store_query q JOIN sys.query_store_query_text t ON q.query_text_id = t.query_text_id JOIN sys.query_store_plan p ON q.query_id = p.query_id JOIN sys.query_store_runtime_stats rs ON p.plan_id = rs.plan_id WHERE rs.last_execution_time > DATEADD(DAY, -7, GETDATE()) ORDER BY rs.avg_logical_io_reads DESC;
五、避坑指南
结语精准的索引优化需要持续监控和迭代调整。建议每周运行一次诊断查询,重点关注:
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通过科学诊断和谨慎实施,您可以将查询性能提升300%以上! 转自https://www.cnblogs.com/LuoCore/p/18972388 该文章在 2025/7/8 17:38:35 编辑过 |
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