IN vs. JOIN with large rowsets

Question

I'm wanting to select rows in a table where the primary key is in another table. I'm not sure if I should use a JOIN or the IN operator in SQL Server 2005. Is there any significant performance difference between these two SQL queries with a large dataset (i.e. millions of rows)?

SELECT * FROM a WHERE a.c IN (SELECT d FROM b)  SELECT a.* FROM a JOIN b ON a.c = b.d 

Answer 1

Update:

This article in my blog summarizes both my answer and my comments to another answers, and shows actual execution plans:

• IN vs. JOIN vs. EXISTS

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SELECT  * FROM    a WHERE   a.c IN (SELECT d FROM b)  SELECT  a.* FROM    a JOIN    b ON      a.c = b.d 

These queries are not equivalent. They can yield different results if your table b is not key preserved (i. e. the values of b.d are not unique).

The equivalent of the first query is the following:

SELECT  a.* FROM    a JOIN    (         SELECT  DISTINCT d         FROM    b         ) bo ON      a.c = bo.d 

If b.d is UNIQUE and marked as such (with a UNIQUE INDEX or UNIQUE CONSTRAINT), then these queries are identical and most probably will use identical plans, since SQL Server is smart enough to take this into account.

SQL Server can employ one of the following methods to run this query:

• If there is an index on a.c, d is UNIQUE and b is relatively small compared to a, then the condition is propagated into the subquery and the plain INNER JOIN is used (with b leading)

• If there is an index on b.d and d is not UNIQUE, then the condition is also propagated and LEFT SEMI JOIN is used. It can also be used for the condition above.

• If there is an index on both b.d and a.c and they are large, then MERGE SEMI JOIN is used

• If there is no index on any table, then a hash table is built on b and HASH SEMI JOIN is used.

Neither of these methods reevaluates the whole subquery each time.

See this entry in my blog for more detail on how this works:

• Counting missing rows: SQL Server

There are links for all RDBMS's of the big four.