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Table Variables: an empirical approach.

Published 27 October 2011 9:06 pm

It isn’t entirely a pleasant experience to publish an article only to have it described on Twitter as ‘Horrible’, and to have it criticized on the MVP forum. When this happened to me in the aftermath of publishing my article on Temporary tables recently, I was taken aback, because these critics were experts whose views I respect. What was my crime? It was, I think, to suggest that, despite the obvious quirks, it was best to use Table Variables as a first choice, and to use local Temporary Tables if you hit problems due to these quirks, or if you were doing complex joins using a large number of rows.

What are these quirks? Well, table variables have advantages if they are used sensibly, but this requires some awareness by the developer about the potential hazards and how to avoid them. You can be hit by a badly-performing join involving a table variable. Table Variables are a compromise, and this compromise doesn’t always work out well. Explicit indexes aren’t allowed on Table Variables, so one cannot use covering indexes or non-unique indexes. The query optimizer has to make assumptions about the data rather than using column distribution statistics when a table variable is involved in a join, because there aren’t any column-based distribution statistics on a table variable. It assumes a reasonably even distribution of data, and is likely to have little idea of the number of rows in the table variables that are involved in queries. However complex the heuristics that are used might be in determining the best way of executing a SQL query, and they most certainly are, the Query Optimizer is likely to fail occasionally with table variables, under certain circumstances, and produce a Query Execution Plan that is frightful. The experienced developer or DBA will be on the lookout for this sort of problem.

In this blog, I’ll be expanding on some of the tests I used when writing my article to illustrate the quirks, and include a subsequent example supplied by Kevin Boles.

A simplified example.

We’ll start out by illustrating a simple example that shows some of these characteristics. We’ll create two tables filled with random numbers and then see how many matches we get between the two tables. We’ll forget indexes altogether for this example, and use heaps. We’ll try the same Join with two table variables, two table variables with OPTION (RECOMPILE) in the JOIN clause, and with two temporary tables.

It is all a bit jerky because of the granularity of the timing that isn’t actually happening at the millisecond level (I used DATETIME). However, you’ll see that the table variable is outperforming the local temporary table up to 10,000 rows. Actually, even without a use of the OPTION (RECOMPILE) hint, it is doing well. What happens when your table size increases?

The table variable is, from around 30,000 rows, locked into a very bad execution plan unless you use OPTION (RECOMPILE) to provide the Query Analyser with a decent estimation of the size of the table. However, if it has the OPTION (RECOMPILE), then it is smokin’. Well, up to 120,000 rows, at least. It is performing better than a Temporary table, and in a good linear fashion.

What about mixed table joins, where you are joining a temporary table to a table variable? You’d probably expect that the query analyzer would throw up its hands and produce a bad execution plan as if it were a table variable. After all, it knows nothing about the statistics in one of the tables so how could it do any better? Well, it behaves as if it were doing a recompile. And an explicit recompile adds no value at all. (we just go up to 45000 rows since we know the bigger picture now)


Now, if you were new to this, you might be tempted to start drawing conclusions. Beware! We’re dealing with a very complex beast: the Query Optimizer. It can come up with surprises

What if we change the query very slightly to insert the results into a Table Variable? We change nothing else and just measure the execution time of the statement as before.

Suddenly, the table variable isn’t looking so much better, even taking into account the time involved in doing the table insert.

OK, if you haven’t used OPTION (RECOMPILE) then you’re toast. Otherwise, there isn’t much in it between the Table variable and the temporary table. The table variable is faster up to 8000 rows and then not much in it up to 100,000 rows. Past the 8000 row mark, we’ve lost the advantage of the table variable’s speed. Any general rule you may be formulating has just gone for a walk. What we can conclude from this experiment is that if you join two table variables, and can’t use constraints, you’re going to need that Option (RECOMPILE) hint.

Count Dracula and the Horror Join.

These tables of integers provide a rather unreal example, so let’s try a rather different example, and get stuck into some implicit indexing, by using constraints.

What unusual words are contained in the book ‘Dracula’ by Bram Stoker?

Here we get a table of all the common words in the English language (60,387 of them) and put them in a table. We put them in a Table Variable with the word as a primary key, a Table Variable Heap and a Table Variable with a primary key. We then take all the distinct words used in the book ‘Dracula’ (7,558 of them). We then create a table variable and insert into it all those uncommon words that are in ‘Dracula’. i.e. all the words in Dracula that aren’t matched in the list of common words. To do this we use a left outer join, where the right-hand value is null.

The results show a huge variation, between the sublime and the gorblimey.

  • If both tables contain a Primary Key on the columns we join on, and both are Table Variables, it took 33 Ms.
  • If one table contains a Primary Key, and the other is a heap, and both are Table Variables, it took 46 Ms.
  • If both Table Variables use a unique constraint, then the query takes 36 Ms.
  • If neither table contains a Primary Key and both are Table Variables, it took 116383 Ms. Yes, nearly two minutes!!
  • If both tables contain a Primary Key, one is a Table Variables and the other is a temporary table, it took 113 Ms.
  • If one table contains a Primary Key, and both are Temporary Tables, it took 56 Ms.
  • If both tables are temporary tables and both have primary keys, it took 46 Ms.

Here we see table variables which are joined on their primary key again enjoying a  slight performance advantage over temporary tables. Where both tables are table variables and both are heaps, the query suddenly takes nearly two minutes! So what if you have two heaps and you use option Recompile? If you take the rogue query and add the hint, then suddenly, the query drops its time down to 76 Ms. If you add unique indexes, then you’ve done even better, down to half that time. Here are the text execution plans.

So where have we got to? Without drilling down into the minutiae of the execution plans we can begin to create a hypothesis. If you are using table variables, and your tables are relatively small, they are faster than temporary tables, but as the number of rows increases you need to do one of two things: either you need to have a primary key on the column you are using to join on, or else you need to use option (RECOMPILE) If you try to execute a query that is a join, and both tables are table variable heaps, you are asking for trouble, well- slow queries, unless you give the table hint once the number of rows has risen past a point (30,000 in our first example, but this varies considerably according to context).

Kevin’s Skew

In describing the table-size, I used the term ‘relatively small’. Kevin Boles produced an interesting case where a single-row table variable produces a very poor execution plan when joined to a very, very skewed table. In the original, pasted into my article as a comment, a column consisted of 100000 rows in which the key column was one number (1) . To this was added eight rows with sequential numbers up to 9. When this was joined to a single-tow Table Variable with a key of 2 it produced a bad plan.

This problem is unlikely to occur in real usage, and the Query Optimiser team probably never set up a test for it. Actually, the skew can be slightly less extreme than Kevin made it. The following test showed that once the table had 54 sequential rows in the table, then it adopted exactly the same execution plan as for the temporary table and then all was well.

Undeniably, real data does occasionally cause problems to the performance of joins in Table Variables due to the extreme skew of the distribution. We’ve all experienced Perfectly Poisonous Table Variables in real live data. As in Kevin’s example, indexes merely make matters worse, and the OPTION (RECOMPILE) trick does nothing to help. In this case, there is no option but to use a temporary table. However, one has to note that once the slight de-skew had taken place, then the plans were identical across a huge range.


Where you need to hold intermediate results as part of a process, Table Variables offer a good alternative to temporary tables when used wisely. They can perform faster than a temporary table when the number of rows is not great. For some processing with huge tables, they can perform well when only a clustered index is required, and when the nature of the processing makes an index seek very effective. Table Variables are scoped to the batch or procedure and are unlikely to hang about in the TempDB when they are no longer required. They require no explicit cleanup. Where the number of rows in the table is moderate, you can even use them in joins as ‘Heaps’, unindexed. Beware, however, since, as the number of rows increase, joins on Table Variable heaps can easily become saddled by very poor execution plans, and this must be cured either by adding constraints (UNIQUE or PRIMARY KEY) or by adding the OPTION (RECOMPILE) hint if this is impossible. Occasionally, the way that the data is distributed prevents the efficient use of Table Variables, and this will require using a temporary table instead.

Tables Variables require some awareness by the developer about the potential hazards and how to avoid them. If you are not prepared to do any performance monitoring of your code or fine-tuning, and just want to pummel out stuff that ‘just runs’ without considering namby-pamby stuff such as indexes, then stick to Temporary tables. If you are likely to slosh about large numbers of rows in temporary tables without considering the niceties of processing just what is required and no more, then temporary tables provide a safer and less fragile means-to-an-end for you.

12 Responses to “Table Variables: an empirical approach.”

  1. a.kukso says:

    Hi Phil,

    I really liked this article.
    You considered execution time as the key measure of optimality.
    But cpu usage is very important too. Your experiments have been performed in single user mode.
    I have observed high cpu usage for table variable with RECOMPILE option(espcially for queries running simultaneously). Recompilations significantly reduce parallelism !
    If I want to get robust and reliable execution plan I use temporary table + hint ” KEEP PLAN” (in onder to increase recompilation threshold).

  2. Phil Factor says:

    Yes, good point. I chose to start with simple execution times just to keep things as simple as possible. I’ll re-run them with CPU as well as time. To get consistent timings I ran as the sole user on the server. It would be interesting to also see if there is a specific problem with Table Variables under high concurrency.
    Hmm. Nothing I can see unusual in the @@CPU_BUSY  or in the ‘last_worker_time’
    Here are the @@CPU_BUSY stats from  a ‘Dracula’ run. (along with elapsed time)
    both PK both TVs took 20 Ms., CPU usage 1
    one PK both TVs took 33 Ms., CPU usage 2
    Both Unique both TVs took 36 Ms., CPU usage 1
    no PK both TVs took 113513 Ms., CPU usage 3547
    no PK both TVs-recompile took 76 Ms., CPU usage 3
    both PK one TV one TT took 116 Ms., CPU usage 3
    one PK one Heap both TT took 56 Ms., CPU usage 1
    both PK both TT took 46 Ms., CPU usage 1

  3. TheSQLGuru says:

    Phil, I probably missed it, but I don’t see any of your sample code nor a link to such code. Can you provide that please?

    I would also like information on what query plans were being generated, especially on the 2 table unindexed join count(*) scenarios. One would think that all of those tests would do straight-forward scan-hash plans and it would be quite interesting to me if query differences existed if the same scan-hash plan was used. What is more likely I think is that table vars may have produced nested loop plans which can be quite fast time wise but lead to massive logical IOs (and perhaps more CPU?). Curious to dig into your queries.

    Also, a few questions about machine config:
    1) how many CPUs and RAM allocated?
    2) physical or virtual machine?
    3) what queries were parallizing and which were single threaded?
    4) Cost threshold for parallelism setting?


  4. Phil Factor says:

    Good idea. I’ll add a few comments and post the sources and plans. As I write this, the Dracula script is in a slight state of flux as I decided over the weekend to try for all combinations of table with both PKs and as heaps. I’ll post the results by updating the blog. As you’d probably guess, temporary and permanent tables behave very similarly in joins with TVs

  5. pelsql says:

    In your article you didn’t mentioned the possibility to add the clustered clause to the primary key. For large join volumes, the optimizer is much more favorable to use this type of index.

    The query optimizer weight clustered index choice as being less costly over using ordinary index and when I watch query plan i observe a strong skew in the optimizer in using clustered index. Since there is no statistics, seeking clustered index wins over other join methods that use no index probably more often seeking ordinary indexes.

    In context where you have an idea that the nature of the data that goes into a table makes its mostly favorable to use index seeks over other join methods, clustered index is usually best.

    But there is a price to pay. Probably there is more work to load data into a table variable that have a primary key clustered constraint, so someone have to figure out if the job to be done at seeking the table is significant over its loading.

    One may argues that ordinary indexes over a table variable don’t make a big difference in access times, since indexes leaves have a physical pointer, and the seek price to go to the record is probably weak because most of them are probably into the cache. That is exact, but I’m not sure that the query optimizer takes that into account.

    It would be great to rerun your tests with large number of rows and with the clustered clause in the primary key constraint.

  6. TheSQLGuru says:

    I have removed useless (often harmful from a performance perspective) clustered PKs from table vars and temp tables at clients more times than I can remember. There is definitely a price to pay to ensure order, although it is less for an identity. But then you need to evaluate usage of said temp object to see if the PK (or any other index) is actually useful AND if it is if it makes the query more efficient in total than if you just had a simple heap temp object.

  7. Phil Factor says:

    Yes.I’d say that there is no general rule here, though for any particular routine I’m developing I usually like to do timing and CPU tests with, and without, PKs. I’ll certainly agree with Kevin that you can sometimes get remarkable performance gains by removing a PK, (and in the case of Temp tables a whole lot of unused indexes), but I see this less often with PKs on Table variables than with Temp tables. One standard thing I do with a badly performing routine I have to deal with is to go ‘index-cold-turkey’ (removing them all) and pop them in one at a time in a test harness. I’m surprised how often the routine goes better before I’ve added in any new indexes.

    p.s. Apologies to Kevin for not having the facts he asked for yet. Pressure of work!

  8. TheSQLGuru says:

    No worries Phil – I am completely swamped myself!

  9. Kate09Da says:

    these was good!keepit up!

  10. paulmcmillan says:

    I think the main issue with Tabel Variables isn’t the fact they are good for some sorts of data and bad for other.. It is just that the results themselves can be non-predicable.

    If we have a system that runs fine with Table Variables 364 days year (365 in 2012) because we only have 1% price change then great.. But if it fails on 31 December because VAT has gone up or down affecting everything we have a problem.. and that is my experience (simplied example) they work ok with predicatable data but not so well with un-predicable data and the later example is that which is going to burn you

  11. paulmcmillan says:

    PS. I meant ‘price change affecting 1% of products’

  12. pelsql says:

    I agree table variables are useful tools when you can predict volume and your predictions have a stability in time.

    For example having a clustered index is no good because it add to loading time, but if you know that loading time is going to be a short part of the treatment, and you are going to use intensively this index it will be good.

    Your assumptions need to be good at all times, because there is no data distribution statistics to help the optimizer do things differently when volume change.

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