Implicit Conversions In SQL Server – A Silent Perf Killer

I recently had the opportunity to work with a developer at the day job. He was using a loop (for what he was doing it actually makes sense 🙂 ) and he had a query that ran great when he stripped the query out of the process but when running in the loop it took 120ms and read 5079 pages of data. This running repeatedly in a loop had a huge cost to the total process time of this batch process… He was confused, it was the same query, what was happening??

I asked if the queries were really the same and he basically said yes, the only difference was he was supplying the variable value instead of using the variable…

AHA! The smell of implicit conversion started to invade the air. He showed me the procedure and it was declaring the variable as NVARCHAR. The column in the table was a VARCHAR. Change of his SQL script from the procedure to declare the variable as NVARCHAR just like the loop alone showed the IO_Statistics that matched the earlier profiler results: more reads.

Why?

Implicit Conversion

When you have are trying to compare data across data types, depending on precedence constraints SQL Server may have to perform an implicit conversion. So? For most of these implicit conversions (if not all) you have the potential to turn your query into a non-index friendly one. A properly formed where clause that has the ability to use an index is sometimes referred to as a SARG or Search Argument. Various no-no’s can prevent a query from being a SARG. Famous things like using a function to the left of the where clause and often times forgotten things like mismatching your data types. SQL’s query optimizer has to convert the data and the index essentially becomes of no use because the data is not indexed for every possible data type it could be converted to.

Let’swatch this happen… First create a table:

And populate that table with some random data

Then we can create some indexes (or indices, your choice, I say indexes…)

Now the fun begins. We have a table with some random character data stored in a VARCHAR column. The queries below should show us the risk of not matching our data types between columns and variables and even between columns that should be related in joins.

First Up – Let’s query that using an NVARCHAR variable… IO and Time Statistics will be enabled to show us the difference

When I run that query, I see it did 301 reads and the elapsed time was 103ms. Is that horrible? You may not think so but hopefully you took my advice when I went on about Empirical Evidence and you enjoy testing your code and thinking through options and possibilities.

Remember, this is a small table (100k rows and each row is quite narrow). So you have to ask yourself the question in terms of what it is doing… It did 300 reads and took 101 ms to bring back one row on my machine (with the random nature you may get 0 or more rows but with such a small sample for the random data you will probably get none, you’ll see close to the same reads and most likely duration, however). So is that acceptable to me? Not really. What if the table grows? I added an index on that column. I would expect to see an index seek. The non-clustered index includes the clustered key from Col1 and this is a two column table so the non-clustered index on Col2 handles the query..

But when I look at the query plan, I see that it isn’t using an index seek but an index scan. If I hover over that scan, I see something that looks a bit odd:

What's this CONVERT_IMPLICIT?

What's this CONVERT_IMPLICIT?

The predicate, what I am searching for, is CONVERT_IMPLICIT(nvarchar(10), Col2,0)=@variable.. I never typed CONVERT anywhere, hence the “IMPLICIT”. The column is stored as a VARCHAR, my variable is an NVARCHAR. Yes those are both dealing with string data of variable character lengths but they are not the same. A look at SQL’s data type precedence helps show you when and where/why implicit conversions happen. Because of the differences between these two data types they can’t be compared one against the other unless they are converted. As most developers seem to know by now, a function on the left side of the equals sign in a where clause can’t use an index. Well we didn’t put one here but the conversion did for us…

So how does the alternative look? Obviously the alternative is to use the right data type. Let’s give it a shot:

Voila! 3 reads now, an index seek in the query plan and according to the time less than 1ms (0 ms elapsed). So simply paying attention to the data type means a 100x improvement in time and reads each.

The moral should be clear: Pay attention to your data types…

While I have your attention regarding data types, remember that NVARCHAR takes twice the amount of space as VARCHAR. If you need the features of NVARCHAR, if you need to support UNICODE data (or have a strong chance of doing so… Being an “International” company is not a strong chance, how do you store your data? How is your data used? Ask those types of questions). Choosing NVARCHAR and never using it can cost you in terms of table size, index size, backup size. This all means additional storage space required (Money and Physical space) and it also means that one call to the disks brings back less information (Performance consideration).

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