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如何识别 SQL Server 中需要添加索引的查询

  • 2025-07-09
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  • 本文字数:2741 字

    阅读完需:约 9 分钟

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?


  • 性能瓶颈:全表扫描(Table Scan)可能导致简单查询耗时数秒

  • 资源浪费:未使用索引的查询消耗额外 CPU 和 I/O 资源

  • 隐性成本:缺失索引可能使关键业务操作延迟数倍

据统计,合理添加索引可使查询性能提升 10-100 倍(来源:Microsoft SQL Server 性能调优白皮书)


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;
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结果解读:

  • improvement_measure:综合改进指标(值越大优先级越高)

  • improvement_percent:预估查询性能提升百分比

  • seek_operations:该索引可能被使用的次数


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;
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关键指标:

  • avg_logical_reads > 1000 表示严重 I/O 问题

  • 执行计划中出现 Index Scan 警告


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;
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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';
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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;
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决策依据:

  • user_updates > 10 * (user_seeks + user_scans) → 考虑删除索引

  • user_lookups 过高 → 需要优化 INCLUDED 列


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;
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五、避坑指南

  1. 索引覆盖陷阱:包含过多 INCLUDED 列会显著增大索引体积

  2. 参数嗅探问题:使用OPTION(RECOMPILE)解决参数敏感查询

  3. 锁升级风险:单索引超过 8KB 可能引发锁升级

  4. 统计信息滞后:开启AUTO_UPDATE_STATISTICS_ASYNC


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:

  • 改进潜力(improvement_measure) > 100,000 的索引

  • 逻辑读取(avg_logical_reads) > 5000 的查询

  • 扫描次数(total_scans) > 10,000 的热点列


文章转载自:LuoCore

原文链接:https://www.cnblogs.com/LuoCore/p/18972388

体验地址:http://www.jnpfsoft.com/?from=001YH

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