Optimising plans with XML readers

Executing the query from here to pull the deadlock events out of the default extended events session

SELECT CAST (
    REPLACE (
        REPLACE (
            XEventData.XEvent.value ('(data/value)[1]', 'varchar(max)'),
            '', ''),
        '', '')
    AS XML) AS DeadlockGraph
FROM (SELECT CAST (target_data AS XML) AS TargetData
    FROM sys.dm_xe_session_targets st
    JOIN sys.dm_xe_sessions s ON s.address = st.event_session_address
    WHERE [name] = 'system_health') AS Data
CROSS APPLY TargetData.nodes ('//RingBufferTarget/event') AS XEventData (XEvent)
    WHERE XEventData.XEvent.value('@name', 'varchar(4000)') = 'xml_deadlock_report';

takes about 20 minutes to complete on my machine. The stats reported are

Table 'Worktable'. Scan count 0, logical reads 68121, physical reads 0, read-ahead reads 0, 
         lob logical reads 25674576, lob physical reads 0, lob read-ahead reads 4332386.

 SQL Server Execution Times:
   CPU time = 1241269 ms,  elapsed time = 1244082 ms.

Slow Plan XML

If I remove the WHERE clause it completes in less than a second returning 3,782 rows.

Similarly if I add OPTION (MAXDOP 1) to the original query that speeds things up too with the stats now showing massively fewer lob reads.

Table 'Worktable'. Scan count 0, logical reads 15, physical reads 0, read-ahead reads 0,
                lob logical reads 6767, lob physical reads 0, lob read-ahead reads 6076.

 SQL Server Execution Times:
   CPU time = 639 ms,  elapsed time = 693 ms.

Faster Plan XML

So my question is


Can anyone explain what's going on? Why is the original plan so
catastrophically worse and is there any reliable way of avoiding the
problem?

Addition:

I've also found that changing the query to INNER HASH JOIN improves things to some extent (but it still takes > 3 mins) as the DMV results are so small I doubt that the Join type itself is responsible though and presume something else must have changed. Stats for that

```

The reason for the performance difference lies in how scalar expressions are handled in the execution engine. In this case, the expression of interest is:

[Expr1000] = CONVERT(xml,DM_XE_SESSION_TARGETS.[target_data],0)

This expression label is defined by a Compute Scalar operator (node 11 in the serial plan, node 13 in the parallel plan). Compute Scalar operators are different from other operators (SQL Server 2005 onward) in that the expression(s) they define are not necessarily evaluated at the position they appear in the visible execution plan; evaluation can be deferred until the result of the computation is required by a later operator.

In the present query, the target_data string is typically large, making the conversion from string to XML expensive. In slow plans, the string to XML conversion is performed every time a later operator that requires the result of Expr1000 is rebound.

Rebinding occurs on the inner side of a nested loops join when a correlated parameter (outer reference) changes. Expr1000 is an outer reference for most of the nested loops joins in this execution plan. The expression is referenced multiple times by several XML Readers, both Stream Aggregates, and by a start-up Filter. Depending on the size of the XML, the number of times the string is converted to XML can easily number in the millions.

The call stacks below show examples of the target_data string being converted to XML (ConvertStringToXMLForES - where ES is the Expression Service):

Start-up Filter

XML Reader (TVF Stream internally)

Stream Aggregate

Converting the string to XML each time any of these operators rebind explains the performance difference observed with the nested loops plans. This is regardless of whether parallelism is used or not. It just so happens that the optimizer chooses a hash join when the MAXDOP 1 hint is specified. If MAXDOP 1, LOOP JOIN is specified, performance is poor just as with the default parallel plan (where the optimizer chooses nested loops).

How much performance increases with a hash join depends on whether Expr1000 appears on the build or probe side of the operator. The following query locates the expression on the probe side:

SELECT CAST (
    REPLACE (
        REPLACE (
            XEventData.XEvent.value ('(data/value)[1]', 'varchar(max)'),
            '', ''),
        '', '')
    AS XML) AS DeadlockGraph
FROM (SELECT CAST (target_data AS XML) AS TargetData
    FROM sys.dm_xe_sessions s
    INNER HASH JOIN sys.dm_xe_session_targets st ON s.address = st.event_session_address
    WHERE [name] = 'system_health') AS Data
CROSS APPLY TargetData.nodes ('//RingBufferTarget/event') AS XEventData (XEvent)
WHERE XEventData.XEvent.value('@name', 'varchar(4000)') = 'xml_deadlock_report';

I have reversed the written order of the joins from the version shown in the question, because join hints (INNER HASH JOIN above) also force the order for the whole query, just as if FORCE ORDER had been specified. The reversal is necessary to ensure Expr1000 appears on the probe side. The interesting part of the execution plan is:

With the expression defined on the probe side, the value is cached:

Evaluation of Expr1000 is still deferred until the first operator needs the value (the start-up filter in the stack trace above) but the computed value is cached (CValHashCachedSwitch) and reused for later calls by the XML Readers and Stream Aggregates. The stack trace below shows an example of the cached value being reused by an XML Reader.

When the join order is forced such that the definition of Expr1000 occurs on the build side of the hash join, the situation is different:

SELECT CAST (
    REPLACE (
        REPLACE (
            XEventData.XEvent.value ('(data/value)[1]', 'varchar(max)'),
            '', ''),
        '', '')
    AS XML) AS DeadlockGraph
FROM (SELECT CAST (target_data AS XML) AS TargetData
    FROM sys.dm_xe_session_targets st 
    INNER HASH JOIN sys.dm_xe_sessions s ON s.address = st.event_session_address
    WHERE [name] = 'system_health') AS Data
CROSS APPLY TargetData.nodes ('//RingBufferTarget/event') AS XEventData (XEvent)
WHERE XEventData.XEvent.value('@name', 'varchar(4000)') = 'xml_deadlock_report'

A hash join reads its build input completely to construct a hash table before it starts probing for matches. As a result, we have to store all the values, not just the one per thread being worked on from the probe side of the plan. The hash join therefore uses a tempdb work table to store the XML data, and every access to the result of Expr1000 by later operators requires an expensive trip to tempdb:

The following shows more details of the slow access path:

If a merge join is forced the input rows are sorted (a blocking operation, just like the build input to a hash join) resulting in a similar arrangement where slow access via a tempdb sort-optimized worktable is required because of the size of the data

StackExchange Database Administrators Q#30862, answer score: 41