Implicit vs. Explicit Conversion

SQL Server

Everyone knows Implicit Conversion is bad

It can ruin SARGability, defeat index usage, and burn up your CPU like it needs some Valtrex. But what about explicit conversion? Is there any overhead? Turns out, SQL is just as happy with explicit conversion as it is with passing in the correct datatype in the first place.

Here’s a short demo:

One table, one million rows, two columns! Just like real life! Let’s throw some queries at it. The first one will use the wrong datatype, the second one will cast the wrong datatype as the right datatype, and the third one is our control query. It uses the right datatype.

The results shouldn’t surprise most of you. From statistics time and I/O, the first query is El Stinko. The second two were within 1ms of each other, and the reads were always the same over every execution. Very little CPU, far fewer reads.

So there you go

Explicit conversion of parameter datatypes doesn’t carry any horrible overhead. Is it easier to just pass in the correct the datatype? Yeah, probably, but you might be in a position where you can’t control the parameter datatype that’s incoming, but you can CAST or CONVERT it where it touches data.

Thanks for reading!

Brent says: the key here is that we’re taking an incoming NVARCHAR variable, and casting it in our query to be VARCHAR to match the table definition. This only works if you can guarantee that the app isn’t going to pass in unicode – but in most situations, that’s true, because the same app is also responsible for inserting/updating data in this same table, so it’s already used to working with VARCHAR data. Also, just to be clear, Erik’s talking about casting the variable – NOT every row in the table. That part still blows.

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10 Comments. Leave new

  • Thanks for your post,

    the main thing is not about implicit or explicit, the problem here is where the conversion happens, because if your query converts [c1].[TextColumn] it doesn’t matter implicit or explicit, your SARGability is down the toilet anyway.

    Implicit conversion is all about the data types precedence as opposed as explicit where we can convert to whatever we want as far as the actual data fits in the target data type.

  • John Hennesey
    May 12, 2016 11:21 am

    +1 for making me do a double-take on the Valtrex reference. “Did he really just say…” 🙂 Always witty yet informative – thanks for all the great work you guys do!

  • Mad Sql Dba
    May 12, 2016 12:06 pm

    Just thought I’d add a link to the Data Type Precedence reference:

  • Interesting, Thanks for the post.

    You examples are using Variables in the where clause, Interestingly this also translates to using strings in the where clause as well.


    SELECT *
    FROM [dbo].[Conversion] AS [c1]
    WHERE [c1].[TextColumn] = ‘A’

    Table ‘Conversion’. Scan count 1, logical reads 63, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

    SQL Server Execution Times:
    CPU time = 15 ms, elapsed time = 7 ms.


    SELECT *
    FROM [dbo].[Conversion] AS [c1]
    WHERE [c1].[TextColumn] = N’A’

    Table ‘Conversion’. Scan count 1, logical reads 739, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.

    SQL Server Execution Times:
    CPU time = 47 ms, elapsed time = 116 ms.

    I’m going to have to go back and revisit some of my slow running queries.

  • Like others have already hinted, the difference in performance has nothing to do with implicit or explicit, but more with what is actually converted. SQL Server will always do a data safe convert for an implicit converion, so in the ‘slow’ query, sql server performs a million converions (one for each row), while the ‘fast’ queries only convert the parameter, once. If you swap the datatypes in your test, so the column is of type NVARCHAR, you will see that all queries are just as fast, because now in all cases the parameter is converted and not the values in the table.

  • the article is great, but what is the actual proposed solution for solving this.

    • Make your datatypes match.

      • Thanks, my question wasn’t looking for conceptual answer, rather its a more case speficic, namely;
        an api call using jdbc driver passes nvarchar4000 variable/for varchar(25) column for a table with 20mln rows. needless to say what that does to performance.
        there isn’t a way to cast convert the variable on driver level, that is where the query is generated, for each transaction.
        that was my original question, I myself have the conceptual answer to match datatypes.

        • Erik Darling
          June 2, 2016 4:15 pm

          If you can’t change the API call to use VARCHAR, you could change the column datatype to NVARCHAR, or make a computed column based on it, casting it as NVARCHAR, and then change the query to point to that column. But if you can’t change the API call, I’m assuming you can’t change the query.

          So, process of elimination…

          • Hi Erik,
            thanks for your reply.
            changing datatype to a production table is not an option.

            implicit conversion seems to be very common issue, and usually solved on the API side, by updating the latest jdbc driver. but that driver update didn’t solve it, and the problem persists with high cxpackets.
            thanks for the answer though

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