"I'm more wondering why the query optimizer would ever use the plan it currently does."
To put it another way, the question is why the following plan looks cheapest to the optimizer, compared with the alternatives (of which there are many).
The inner side of the join is essentially running a query of the following form for each correlated value of BrowserID
:
DECLARE @BrowserID smallint;
SELECT
tfsph.BrowserID
FROM dbo.tblFEStatsPaperHits AS tfsph
WHERE
tfsph.BrowserID = @BrowserID
OPTION (MAXDOP 1);
Note that the estimated number of rows is 185,220 (not 289,013) since the equality comparison implicitly excludes NULL
(unless ANSI_NULLS
is OFF
). The estimated cost of the above plan is 206.8 units.
Now let's add a TOP (1)
clause:
DECLARE @BrowserID smallint;
SELECT TOP (1)
tfsph.BrowserID
FROM dbo.tblFEStatsPaperHits AS tfsph
WHERE
tfsph.BrowserID = @BrowserID
OPTION (MAXDOP 1);
The estimated cost is now 0.00452 units. The addition of the Top physical operator sets a row goal of 1 row at the Top operator. The question then becomes how to derive a 'row goal' for the Clustered Index Scan; that is, how many rows should the scan expect to process before one row matches the BrowserID
predicate?
The statistical information available shows 166 distinct BrowserID
values (1/[All Density] = 1/0.006024096 = 166). Costing assumes that the distinct values are distributed uniformly over the physical rows, so the row goal on the Clustered Index Scan is set to 166.302 (accounting for the change in table cardinality since the sampled statistics were gathered).
The estimated cost of scanning the expected 166 rows is not very large (even executed 339 times, once for each change of BrowserID
) - the Clustered Index Scan shows an estimated cost of 1.3219 units, showing the scaling effect of the row goal. The unscaled operator costs for I/O and CPU are shown as 153.931, and 52.8698 respectively:
In practice, it is very unlikely that the first 166 rows scanned from the index (in whatever order they happen to be returned) will contain one each of the possible BrowserID
values. Nevertheless, the DELETE
plan is costed at 1.40921 units total, and is selected by the optimizer for that reason. Bart Duncan shows another example of this type in a recent post titled Row Goals Gone Rogue.
It is also interesting to note that the Top operator in the execution plan is not associated with the Anti Semi Join (in particular the 'short-circuiting' Martin mentions). We can start to see where the Top comes from by first disabling an exploration rule called GbAggToConstScanOrTop:
DBCC RULEOFF ('GbAggToConstScanOrTop');
GO
DELETE FROM tblFEStatsBrowsers
WHERE BrowserID NOT IN
(
SELECT DISTINCT BrowserID
FROM tblFEStatsPaperHits WITH (NOLOCK)
WHERE BrowserID IS NOT NULL
) OPTION (MAXDOP 1, LOOP JOIN, RECOMPILE);
GO
DBCC RULEON ('GbAggToConstScanOrTop');
That plan has an estimated cost of 364.912, and shows that the Top replaced a Group By Aggregate (grouping by the correlated column BrowserID
). The aggregate is not due to the redundant DISTINCT
in the query text: it is an optimization that can be introduced by two exploration rules, LASJNtoLASJNonDist and LASJOnLclDist. Disabling those two as well produces this plan:
DBCC RULEOFF ('LASJNtoLASJNonDist');
DBCC RULEOFF ('LASJOnLclDist');
DBCC RULEOFF ('GbAggToConstScanOrTop');
GO
DELETE FROM tblFEStatsBrowsers
WHERE BrowserID NOT IN
(
SELECT DISTINCT BrowserID
FROM tblFEStatsPaperHits WITH (NOLOCK)
WHERE BrowserID IS NOT NULL
) OPTION (MAXDOP 1, LOOP JOIN, RECOMPILE);
GO
DBCC RULEON ('LASJNtoLASJNonDist');
DBCC RULEON ('LASJOnLclDist');
DBCC RULEON ('GbAggToConstScanOrTop');
That plan has an estimated cost of 40729.3 units.
Without the transformation from Group By to Top, the optimizer 'naturally' chooses a hash join plan with BrowserID
aggregation before the anti semi join:
DBCC RULEOFF ('GbAggToConstScanOrTop');
GO
DELETE FROM tblFEStatsBrowsers
WHERE BrowserID NOT IN
(
SELECT DISTINCT BrowserID
FROM tblFEStatsPaperHits WITH (NOLOCK)
WHERE BrowserID IS NOT NULL
) OPTION (MAXDOP 1, RECOMPILE);
GO
DBCC RULEON ('GbAggToConstScanOrTop');
And without the MAXDOP 1 restriction, a parallel plan:
Another way to 'fix' the original query would be to create the missing index on BrowserID
that the execution plan reports. Nested loops work best with when the inner side is indexed. Estimating cardinality for semi joins is challenging at the best of times. Not having proper indexing (the large table doesn't even have a unique key!) will not help at all.
I wrote more about this in Row Goals, Part 4: The Anti Join Anti Pattern.
Best Answer
Your comma between two table means that you make a cross join and then select only the fitting rows.
do a proper JOIN with ON Clause, like
Also have an index on id2 in the table image