Temporary Tables vs. Table Variables and Their Effect on SQL Server Performance

 

Temporary Tables vs. Table Variables and Their Effect on SQL Server Performance

September 13th, 2007

Original http://www.sqlsolutions.com/articles/articles/Temporary_Tables_vs._Table_Variables_and_Their_Effect_on_SQL_Server_Performance.htm 

 

There are 3 major theoretical differences between temporary tables: create table #T (...) and table-variables declare @T table (...) The first one is that transaction logs are not recorded for the table-variables. Hence, they are out of scope of the transaction mechanism, as is clearly visible from this example: create table #T (s varchar(128)) declare @T table (s varchar(128)) insert into #T select 'old value #' insert into @T select 'old value @' begin transaction   update #T set s='new value #'   update @T set s='new value @' rollback transaction select * from #T select * from @T s              --------------- old value # s                  --------------- new value @ After declaring our temporary table #T and our table-variable @T, we assign each one with the same 'old value' string. Then, we begin a transaction that updates their contents. At this point, both will now contain the same 'new value' string. But when we rollback the transaction, as you can see, the table-variable @T retained its value instead of reverting back to the 'old value' string. This happened because, even though the table-variable was updated within the transaction, it is not a part of the transaction itself. The second major difference is that any procedure with a temporary table cannot be pre-compiled, while an execution plan of procedures with table-variables can be statically compiled in advance. Pre-compiling a script gives a major advantage to its speed of execution. This advantage can be dramatic for long procedures, where recompilation can be too pricy. Finally, table-variables exist only in the same scope as variables. Contrary to the temporary tables, they are not visible in inner stored procedures and in exec(string) statements. Also, they cannot be used in insert/exec statement. But let's compare both in terms of performance. At first, we prepare a test table with 1 million records: create table NUM (n int primary key, s varchar(128)) GO set nocount on declare @n int set @n=1000000 while @n>0 begin   insert into NUM     select @n,'Value: '+convert(varchar,@n)   set @n=@n-1   end GO Now we prepare our test procedure T1: create procedure T1   @total int as   create table #T (n int, s varchar(128))   insert into #T select n,s from NUM     where n%100>0 and n<=@total   declare @res varchar(128)   select @res=max(s) from NUM     where n<=@total and       not exists(select * from #T       where #T.n=NUM.n) GO Called with a parameter, which we will vary from 10, 100, 1000, 10'000, 100'000 up to 1'000'000, it copies the given number of records into a temporary table (with some exceptions, it skips records where n is divisible by 100), and then finds a max(s) of such missing records. Of course, the more records we give, the longer the execution is. To measure the execution time precisely, I use the code: declare @t1 datetime, @n int set @t1=getdate() set @n=100-- (**) while @n>0 begin   exec T1 1000 -- (*)   set @n=@n-1 end select datediff(ms,@t1,getdate()) GO (*) is a parameter to our procedure, it is varied from 10 to 1'000'000 (**) if an execution time is too short, I repeat the same loop 10 or 100 times. I run the code several times to get a result of a 'warm' execution. The results can be found in Table 1 (see below). Now let's try to improve our stored procedure by adding a primary key to the temporary table: create procedure T2   @total int as   create table #T (n int primary key, s varchar(128))   insert into #T select n,s from NUM     where n%100>0 and n<=@total   declare @res varchar(128)   select @res=max(s) from NUM     where n<=@total and       not exists(select * from #T       where #T.n=NUM.n) GO Then, lets create a third one. With a clustered index, it works much better. But let's create the index AFTER we insert data into the temporary table - usually, it is better: create procedure T3   @total int as   create table #T (n int, s varchar(128))   insert into #T select n,s from NUM     where n%100>0 and n<=@total   create clustered index Tind on #T (n)   declare @res varchar(128)   select @res=max(s) from NUM     where n<=@total and       not exists(select * from #T       where #T.n=NUM.n) GO Surprise! It not only takes longer for the big amounts of data; merely adding 10 records take an additional 13 milliseconds. The problem is that 'create index' statements force SQL server to recompile stored procedures, and slows down the execution significantly. Now let's try the same using table-variables: create procedure V1   @total int as   declare @V table (n int, s varchar(128))   insert into @V select n,s from NUM     where n%100>0 and n<=@total   declare @res varchar(128)   select @res=max(s) from NUM     where n<=@total and       not exists(select * from @V V       where V.n=NUM.n) GO To our surprise, this version is not significantly faster than the version with the temporary table. This is a result of a special optimization SQL server has for the create table #T statements in the very beginning of a stored procedure. For the whole range of values, V1 works better or the same as T1. Now let's try the same with a primary key: create procedure V2   @total int as   declare @V table (n int primary key, s varchar(128))   insert into @V select n,s from NUM     where n%100>0 and n<=@total   declare @res varchar(128)   select @res=max(s) from NUM     where n<=@total and       not exists(select * from @V V       where V.n=NUM.n) GO The result is much better, but T2 outruns this version.       In some cases, SQL 2005 was much faster then SQL 2000 (marked with green). But in many cases, especially with huge amounts of data, procedures that used table variables took much longer (highlighted with red). In 4 cases, I even gave up waiting. Conclusion: 1. There is no universal rule of when and where to use temporary tables or table variables. Try them both and experiment. 2. In your tests, verify both sides of the spectrum - small amount/number of records and the huge data sets. 3. Be careful with migrating to SQL 2005 when you use complicated logic in your stored procedures. The same code can run 10-100 times slower on SQL server 2005!