SQL Server中使用扩展事件捕捉所需的信息后，可以选择存放的位置。比如说内存或文件中，但无论存在哪里，其本质都是一个大XML。因此在SQL Server中读取该XML就是解析扩展事件结果的方式。
微软官方或者一些SQL Server论坛提供了使用SQL XML解析扩展事件的脚本，如代码清单1所示。
1: WITH events_cte
2: AS ( SELECT DATEADD(mi,
3: DATEDIFF(mi, GETUTCDATE(), CURRENT_TIMESTAMP),
4: xevents.event_data.value('(event/@timestamp)[1]',
5: 'datetime2')) AS [event time] ,
6: xevents.event_data.value('(event/@name)[1]',
7: 'nvarchar(128)') AS [Event Name],
8: xevents.event_data.value('(event/action[@name="client_app_name"]/value)[1]',
9: 'nvarchar(128)') AS [client app name] ,
10: xevents.event_data.value('(event/action[@name="client_hostname"]/value)[1]',
11: 'nvarchar(max)') AS [client host name] ,
12: xevents.event_data.value('(event/action[@name="sql_text"]/value)[1]',
13: 'nvarchar(max)') AS [sql_text] ,
14:
15: xevents.event_data.value('(event/action[@name="database_name"]/value)[1]',
16: 'nvarchar(max)') AS [database name] ,
17: xevents.event_data.value('(event/action[@name="username"]/value)[1]',
18: 'nvarchar(max)') AS [username] ,
19: xevents.event_data.value('(event/action[@name="duration"]/value)[1]',
20: 'bigint') AS [duration (ms)] ,
21: xevents.event_data.value('(event/action[@name="cpu_time"]/value)[1]',
22: 'bigint') AS [cpu time (ms)] ,
23: xevents.event_data.value('(event/data[@name="object_name"]/value)[1]',
24: 'nvarchar(max)') AS [OBJECT_NAME]
25: FROM sys.fn_xe_file_target_read_file('D:XeventResutlDDLAudit*.xel',
26: NULL, NULL, NULL)
27: CROSS APPLY ( SELECT CAST(event_data AS XML) AS event_data
28: ) AS xevents
29: )
30: SELECT *
31: FROM events_cte
32: ORDER BY [event time] DESC;
代码清单1.读取扩展事件文件的脚本
但代码清单1的脚本使用的是XQuery，XQuery在使用Xml的节点属性作为删选条件时，数据上千以后就会变得非常慢。因此我对上述脚本进行了改写，将XML读取出来后，变为节点的集合以关系数据格式存放，再用子查询进行筛选，这种方式读取数据基本上是秒出，如代码清单2所示。
1: WITH tt
2: AS ( SELECT MIN(event_name) AS event_name ,
3: DATEADD(hh,DATEDIFF(hh, GETUTCDATE(), CURRENT_TIMESTAMP),
4: CONVERT(DATETIME, MIN(CASE WHEN d_name = 'collect_system_time'
5: AND d_package IS NOT NULL THEN d_value
6: END))) AS [event_timestamp] ,
7: CONVERT
8: (VARCHAR(MAX), MIN(CASE WHEN d_name = 'client_hostname'
9: AND d_package IS NOT NULL THEN d_value
10: END)) AS [Client_hostname] ,
11: CONVERT
12: (VARCHAR(MAX), MIN(CASE WHEN --event_name = 'sql_batch_completed'
13: d_name = 'client_app_name'
14: THEN d_value
15: END)) AS [Client_app_name] ,
16: CONVERT
17: (VARCHAR(MAX), MIN(CASE WHEN d_name = 'database_name'
18: AND d_package IS NOT NULL THEN d_value
19: END)) AS [database_name] ,
20: CONVERT
21: (VARCHAR(MAX), MIN(CASE WHEN d_name = 'object_name'
22: THEN d_value
23: END)) AS [object_name] ,
24: CONVERT
25: (BIGINT, MIN(CASE WHEN event_name = 'sql_batch_completed'
26: AND d_name = 'duration'
27: AND d_package IS NULL THEN d_value
28: END)) AS [sql_statement_completed.duration] ,
29:
30: CONVERT
31: (VARCHAR(MAX), MIN(CASE WHEN d_name = 'sql_text'
32: THEN d_value
33: END)) AS [sql_statement_completed.sql_text] ,
34: CONVERT
35: (VARCHAR(MAX), MIN(CASE WHEN d_name = 'username'
36: AND d_package IS NOT NULL THEN d_value
37: END)) AS [username]
38: FROM ( SELECT * ,
39: CONVERT(VARCHAR(400), NULL) AS attach_activity_id
40: FROM ( SELECT event.value('(@name)[1]',
41: 'VARCHAR(400)') AS event_name ,
42: DENSE_RANK() OVER ( ORDER BY event ) AS unique_event_id ,
43: n.value('(@name)[1]',
44: 'VARCHAR(400)') AS d_name ,
45: n.value('(@package)[1]',
46: 'VARCHAR(400)') AS d_package ,
47: n.value('((value)[1]/text())[1]',
48: 'VARCHAR(MAX)') AS d_value ,
49: n.value('((text)[1]/text())[1]',
50: 'VARCHAR(MAX)') AS d_text
51: FROM ( SELECT ( SELECT
52: CONVERT(XML, target_data)
53: FROM
54: sys.dm_xe_session_targets st
55: JOIN sys.dm_xe_sessions s ON s.address = st.event_session_address
56: WHERE
57: s.name = 'DDL'
58: AND st.target_name = 'ring_buffer'
59: ) AS [x]
60: FOR
61: XML PATH('') ,
62: TYPE
63: ) AS the_xml ( x )
64: CROSS APPLY x.nodes('//event') e ( event )
65: CROSS APPLY event.nodes('*')
66: AS q ( n )
67: ) AS data_data
68: ) AS activity_data
69: GROUP BY unique_event_id
70: )
71: SELECT *
72: FROM tt
73:
代码清单2.对扩展事件结果的优化读取方式
参考资料：http://blog.wharton.com.au/2011/06/13/part-5-openxml-and-xquery-optimisation-tips/