Multiple Identity Inserts

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This week I want to share something that surprised me about using SQL Server's SET IDENTITY_INSERT statement.

I started with two tables with identity columns defined:

CREATE TABLE dbo.[User]
(
    Id int identity,
    UserName varchar(40)
);
CREATE TABLE dbo.StupidQuestions
(
    Id bigint identity,
    UserId int,
    Question varchar(400)
);


INSERT INTO dbo.[User] (UserName) VALUES ('Jim');
INSERT INTO dbo.[User] (UserName) VALUES ('Jane');
INSERT INTO dbo.[User] (UserName) VALUES ('Jin');
INSERT INTO dbo.[User] (UserName) VALUES ('Joyce');

INSERT INTO dbo.StupidQuestions (UserId,Question) VALUES (1,'Is smooth peanut butter better than chunky?');
INSERT INTO dbo.StupidQuestions (UserId,Question) VALUES (1,'Do I really need to backup my production databases?');
INSERT INTO dbo.StupidQuestions (UserId,Question) VALUES (2,'How to grant developers SA access?');
INSERT INTO dbo.StupidQuestions (UserId,Question) VALUES (3,'I''m getting an error about not being able to add any more indexes to my table - how do I increase the limit?');
INSERT INTO dbo.StupidQuestions (UserId,Question) VALUES (4,'How can I include more than 32 columns in my index key?');
GO

I wanted to copy the data from these two tables into two other tables:

CREATE TABLE dbo.User_DEV
(
    Id int identity,
    UserName varchar(40)
);

CREATE TABLE dbo.StupidQuestions_DEV
(
    Id bigint identity,
    UserId int,
    Question varchar(400)
);

This would allow me to safely test some changes on these _DEV table copies without breaking my original tables.

The next step was to write a couple of INSERT INTO SELECT statements:

INSERT INTO dbo.User_DEV
SELECT Id,UserName FROM dbo.[User]

INSERT INTO dbo.StupidQuestions_DEV
SELECT Id,UserId,Question FROM dbo.StupidQuestions

And of course as soon as I executed them SQL Server threw an error stating that I can't INSERT data into tables containing identity columns without first enabling identity inserts:

An explicit value for the identity column in table 'dbo.User_DEV' can only be specified when a column list is used and IDENTITY_INSERT is ON.

Ok, simple enough to fix: we just need to do what the error message says and SET IDENTITY_INSERT ON for both tables:

SET IDENTITY_INSERT dbo.User_DEV ON;  
SET IDENTITY_INSERT dbo.StupidQuestions_DEV ON;  

And... it still didn't work:

IDENTITY_INSERT is already ON for table 'IdentityTest.dbo.User_DEV'. Cannot perform SET operation for table 'dbo.StupidQuestions_DEV'.

One at a time

Although I've probably moved data around like this hundreds (thousands?) of times before, I've never encountered this particular error.

Apparently SQL Server only allows one table to have the IDENTITY_INSERT property enabled at a time within each session. The solution therefore is straightforward: enable identity inserts and copy each table's data one at a time:

SET IDENTITY_INSERT dbo.User_DEV ON; 
INSERT INTO dbo.User_DEV (Id,UserName)
SELECT Id,UserName FROM dbo.[User];
SET IDENTITY_INSERT dbo.User_DEV OFF; 

SET IDENTITY_INSERT dbo.StupidQuestions_DEV ON;
INSERT INTO dbo.StupidQuestions_DEV (Id,UserId,Question)
SELECT Id,UserId,Question FROM dbo.StupidQuestions
SET IDENTITY_INSERT dbo.StupidQuestions_DEV OFF;

20/20

In hindsight, I think I've never encountered this error before because I normally use the the Export Data Wizard in SSMS or a dedicated SSIS package to move data around. Either of those options are typically easier than writing T-SQL to move data across servers or for repeatability for when I need to regularly refresh tables with test data.

However, when using either of those options I've never paid attention to the implementation details, causing me to assume I knew how SQL Server handles identity inserts.

Trailing Spaces in SQL Server

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A long time ago I built an application that captured user input. One feature of the application was to compare the user's input against a database of values.

The app performed this text comparison as part of a SQL Server stored procedure, allowing me to easily update the business logic in the future if necessary.

One day, I received an email from a user saying that the value they were typing in was matching with a database value that they knew shouldn't match. That is the day I discovered SQL Server's counter intuitive equality comparison when dealing with trailing space characters.

Padded white space

You are probably aware that the CHAR data type pads the value with spaces until the defined length is reached:

DECLARE @Value CHAR(10) = 'a'
SELECT
    @Value AS OriginalValue,
    LEN(@Value) AS StringLength,
    DATALENGTH(@Value) AS DataLength,
    CAST(@Value AS BINARY) AS StringToHex;

image

The LEN() function shows the number of characters in our string, while the DATALENGTH() function shows us the number of bytes used by that string.

In this case, DATALENGTH is equal to 10. This result is due to the padded spaces occurring after the character "a" in order to fill the defined CHAR length of 10. We can confirm this by converting the value to hexadecimal. We see the value 61 ("a" in hex) followed by nine "20" values (spaces).

If we change our variable's data type to VARCHAR, we'll see the value is no longer padded with spaces:

DECLARE @Value VARCHAR(10) = 'a'
SELECT
    @Value AS OriginalValue,
    LEN(@Value) AS StringLength,
    DATALENGTH(@Value) AS DataLength,
    CAST(@Value AS BINARY) AS StringToHex;

image-1

Given that one of these data types pads values with space characters while the other doesn't, what happens if we compare the two?

DECLARE 
    @CharValue CHAR(10) = '',
    @VarcharValue VARCHAR(10) = ''
SELECT
    IIF(@CharValue=@VarcharValue,1,0) AS ValuesAreEqual,
    DATALENGTH(@CharValue) AS CharBytes,
    DATALENGTH(@VarcharValue) AS VarcharBytes

image-2

In this case SQL Server considers both values equal, even though we can confirm that the DATALENGTHs are different.

This behavior doesn't only occur with mixed data type comparisons however. If we compare two values of the same data type, with one value containing several space characters, we experience something...unexpected:

DECLARE 
    @NoSpaceValue VARCHAR(10) = '',
    @MultiSpaceValue VARCHAR(10) = '    '
SELECT
    IIF(@NoSpaceValue=@MultiSpaceValue,1,0) AS ValuesAreEqual,
    DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
    DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-3

Even though our two variables have different values (a blank compared to four space characters), SQL Server considers these values equal.

If we add a character with some trailing whitespace we'll see the same behavior:

DECLARE 
    @NoSpaceValue VARCHAR(10) = 'a',
    @MultiSpaceValue VARCHAR(10) = 'a     '
SELECT
    IIF(@NoSpaceValue=@MultiSpaceValue,1,0) AS ValuesAreEqual,
    DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
    DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-4

Both values are clearly different, but SQL Server considers them to be equal to each other. Switching our equal sign to a LIKE operator changes things slightly:

DECLARE 
   @NoSpaceValue VARCHAR(10) = 'a',
   @MultiSpaceValue VARCHAR(10) = 'a     '
SELECT
   IIF(@NoSpaceValue LIKE @MultiSpaceValue,1,0) AS ValuesAreEqual,
   DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
   DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-5

Even though I would think that a LIKE without any wildcard characters would behave just like an equal sign, SQL Server doesn't perform these comparisons the same way.

If we switch back to our equal sign comparison and prefix our character value with spaces we'll also notice a different result:

DECLARE 
    @NoSpaceValue VARCHAR(10) = 'a',
    @MultiSpaceValue VARCHAR(10) = '    a'
SELECT
    IIF(@NoSpaceValue=@MultiSpaceValue,1,0) AS ValuesAreEqual,
    DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
    DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-6

SQL Server considers two values equal regardless of spaces occurring at the end of a string. Spaces preceding a string however, no longer considered a match.

What is going on?

ANSI

While counter intuitive, SQL Server's functionality is justified. SQL Server follows the ANSI specification for comparing strings, adding white space to strings so that they are the same length before comparing them. This explains the phenomena we are seeing.

It does not do this with the LIKE operator however, which explains the difference in behavior.

Comparisons when extra spaces matter

Let's say we want to do a comparison where the difference in trailing spaces matters.

One option is to use the LIKE operator as we saw a few examples back. This is not the typical use of the LIKE operator however, so be sure to comment and explain what your query is attempting to do by using it. The last thing you want is some future maintainer of your code to switch it back to an equal sign because they don't see any wild card characters.

Another option that I've seen is to perform a DATALENGTH comparison in addition to the value comparison:

DECLARE 
    @NoSpaceValue VARCHAR(10) = 'a',
    @MultiSpaceValue VARCHAR(10) = 'a    '
SELECT
    IIF(@NoSpaceValue = @MultiSpaceValue AND DATALENGTH(@NoSpaceValue) = DATALENGTH(@MultiSpaceValue),1,0) AS ValuesAreEqual,
    DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
    DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-7

This solution isn't right for every scenario however. For starters, you have no way of knowing if SQL Server will execute your value comparison or DATALENGTH predicate first. This could wreck havoc on index usage and cause poor performance.

A more serious problem can occur if you are comparing fields with different data types. For example, when comparing a VARCHAR to NVARCHAR data type, it's pretty easy to create a scenario where your comparison query using DATALENGTH will trigger a false positive:

DECLARE 
    @NoSpaceValue VARCHAR(10) = 'a ',
    @MultiSpaceValue NVARCHAR(10) = 'a'
SELECT
    IIF(@NoSpaceValue = @MultiSpaceValue AND DATALENGTH(@NoSpaceValue) = DATALENGTH(@MultiSpaceValue),1,0) AS ValuesAreEqual,
    DATALENGTH(@NoSpaceValue) AS NoSpaceBytes,
    DATALENGTH(@MultiSpaceValue) AS MultiSpaceBytes

image-8

Here the NVARCHAR stores 2 bytes for every character, causing the DATALENGTHs of a single character NVARCHAR to be equal to a character + a space VARCHAR value.

The best thing to do in these scenarios is understand your data and pick a solution that will work for your particular situation.

And maybe trim your data before insertion (if it makes sense to do so)!

Joker's Wild

This past weekend I had a blast presenting Joker's Wild with Erin Stellato (blog|twitter), Andy Mallon (blog|twitter), and Drew Furgiuele (blog|twitter).

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Table of contents:

  • What is Joker's Wild? Watch this to witness Andy's amazing PowerPoint animation skills (0:00)
  • Bert demos SQL injection (2:25)
  • Erin recollects desserts (9:55)
  • Andy shares an automation tip (18:55)
  • Andy explains an ANSI standard (23:10)
  • Drew describes containers (27:02)

While a video doesn't quite give you the same experience as being in the room with dozens of other data professionals laughing and shouting along, hopefully it gives you an idea.

Here's a behind-the-scenes peek at how it all came together.

A Different Kind Of Presentation

I've wanted to do a "fun" SQL Server presentation for a while; something that would be lighthearted while still delivering (some) educational value.

I ran some ideas past Erin after SQL Saturday Cleveland earlier this year. We came up with several concepts ideas we could incorporate into the presentation (thanks to Paul Popovich and Luis Gonzalez for also helping us generate a lot of these ideas) and at that point I think Erin came up with the name "Joker's Wild."

Blind Commitment

Fast forward a few months: occasionally I'd talk about the presentation idea with people but still wasn't any closer to actually making it real.

Then a few days before the SQL Saturday Columbus submission deadline, Erin reached out to ask if we were going to submit. We recruited Andy and Drew to help present and submitted an abstract:

Come one, come all to the greatest (and only) SQL Server variety show at SQL Saturday Columbus.

This session features a smattering of lightning talks covering a range of DBA- and developer-focused SQL Server topics, interspersed with interactive games to keep the speakers and audience on their toes.

Plan for plenty of sarcasm, laughs, and eye rolls in this thoughtfully structured yet highly improvised session.

We can't guarantee what you'll learn, but we do promise a great time!

*Slot machine will not generate real money for "winners"

Structure

If that abstract reads a little vague, it's because at that point we didn't know exactly what we wanted to do yet. Once our session was selected though it was time to come up with a concrete plan (big thank you to David Maxwell and Peter Shore for giving us the opportunity to try something like this).

After some discussion, Erin, Andy, Drew, and I came up with the following structure:

  1. The audience will choose the lightning talk topic
  2. We will spin the "Wheel of Misfortune" to determine the presentation style, including:
    • Slides I didn't write
    • Random slide timing
    • Who has the clicker?
  3. We will play some SQL Server themed Jeopardy and Pictionary with the audience

After our first meeting Andy created the world's most versatile PowerPoint presentation that would run the show. Seriously, if you haven't watched the video above yet, go watch it - that introduction is all PowerPoint goodness created by him.

The Session and Final Thoughts

I'm incredibly happy with how it all went. The session was planned but a lot of it was still left up to a highly improvised performance. I had a lot of fun preparing and presenting, and I think the session was well received by the audience. Jeopardy and Pictionary were a lot of fun too, even though I ran out of video recording space so I couldn't include them in the video.

I hope we have another opportunity to present this session again in the future.

Thank you again David and Peter for letting us do this session as part of SQL Saturday Columbus.

Thank you to our audience for taking a risk on attending a session you didn't know much about. Also for your great participation.

And thank you Erin, Andy, and Drew for helping do something fun and different.

CHOOSE() in SQL Server

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While I know I don't utilize most of the features available in SQL Server, I like to think I'm at least aware that those features exist.

This week I found a blind-spot in my assumption however. Even though it shipped in SQL Server 2012, the SQL Server CHOOSE function is a feature that I think I'm seeing for the first time this past week.

CHOOSE is CASE

CHOOSE returns the n-th item from a comma-delimited list.

Whenever learning a new feature in SQL Server I try to think of a good demo I could build to test out the functionality. In this case the immediate example that came to mind was building something that would provide a lookup of values:

SELECT 
    [key],
    [value],
    [type],
    CHOOSE(type+1,'null','string','int','boolean','array','object') AS JsonType
FROM
    OPENJSON(N'{
        "Property1":null,
        "Property2":"a",
        "Property3":3,
        "Property4":false,
        "Property5":[1,2,"3"],
        "Property6":{
            "SubProperty1":"a"
        }
    }');

In this case, the OPENJSON function returns a "type" field that indicates the datatype of that particular JSON property's value. The issue is that the "type" column is numeric and I can never remember what type of data each number represents.

The above query solves this by using CHOOSE to create a lookup of values. Since OPENJSON returns results starting with 0, we need to use type+1 in order to get the 1-based CHOOSE function to work correctly:

2019-06-03-21-08-58

If we look at the CHOOSE function's scalar operator properties in the execution plan, we'll see that this function is just a fancy alias for a more verbose CASE statement:

[Expr1000] = Scalar Operator(
    CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(1) 
    THEN 'null' 
    ELSE 
        CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(2) 
        THEN 'string' 
        ELSE 
            CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(3) 
            THEN 'int' 
            ELSE 
                CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(4) 
                THEN 'boolean' 
                ELSE 
                    CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(5) 
                    THEN 'array' 
                    ELSE 
                        CASE WHEN (CONVERT_IMPLICIT(int,OPENJSON_DEFAULT.[type],0)+(1))=(6) 
                        THEN 'object' 
                        ELSE NULL END 
                    END 
                END 
            END 
        END 
    END
)

The Set-Based Way

I think one of the reasons I've never used CHOOSE is because I would hate typing up all of those lookup values and trapping them in a SELECT statement, never to be used again.

Previously, I would have stored the lookup values in table and joined them with the OPENJSON results to accomplish the same end result:

DROP TABLE IF EXISTS #JsonType;
CREATE TABLE #JsonType
(
    Id tinyint,
    JsonType varchar(20),
    CONSTRAINT PK_JsonTypeId PRIMARY KEY CLUSTERED (Id)
);

INSERT INTO #JsonType VALUES (0,'null');
INSERT INTO #JsonType VALUES (1,'string');
INSERT INTO #JsonType VALUES (2,'int');
INSERT INTO #JsonType VALUES (3,'boolean');
INSERT INTO #JsonType VALUES (4,'array');
INSERT INTO #JsonType VALUES (5,'object');

SELECT 
    j.[key],
    j.[value],
    j.[type],
    t.JsonType
FROM
    OPENJSON(N'{
        "Property1":null,
        "Property2":"a",
        "Property3":3,
        "Property4":false,
        "Property5":[1,2,"3"],
        "Property6":{
                        "SubProperty1":"a"
                    }
    }') j
    INNER JOIN #JsonType t
        ON j.[type] = t.Id

While more initial setup is involved with this solution, it's more flexible long-term. With a centralized set of values, there's no need to update the CHOOSE function in all of your queries when you can update the values in a single lookup table.

And while I didn't bother performance testing it, by virtue of being a scalar function, CHOOSE will probably perform worse in many real-world scenarios when compared to the table-based lookup approach (eg. large datasets, parallel plans, etc...).

CHOOSE What Works For You

I'm not surprised that it took me this long to learn about the CHOOSE function: while a simplified way to write certain CASE statements, I can't think of many (any?) scenarios where I would prefer to use it over a CASE or a lookup-table solution.

12 Ways To Rewrite SQL Queries for Better Performance

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Over the past several week's I've been exploring ways to rewrite queries to improve execution performance.

I learned a lot of these techniques over time from trial an error, attending presentations, reading blog posts, speaking to other dbas and developers, etc... but never knew of a good resource that summarized these techniques in one place.

This post will be a quick round-up of everything I've covered so far, as well as 8 additional techniques that I use occasionally but don't necessarily require a full detailed post to explain them.

Why Rewrite Queries?

I often find myself working in environments where modifying indexes or changing server settings is out of the question when performance tuning. I usually run into these scenarios when dealing with:

  • Vendor databases
  • "Fragile" systems
  • Not enough disk space
  • Limited tooling/ad hoc analysis
  • Features limited by security software

While solving the root cause of a performance problem is always preferable, sometimes the only way I'm able to fix problems in these environments is by rewriting the queries.

I decided to write this summary post because it is a resource I would have loved to have when starting out. Sometimes it can be easy to get "writer's block" when trying to think of ways to rewrite a SQL query, so hopefully this list of techniques can provide ideas and get your creative juices flowing.

So, without further ado, here is a list of 12 techniques in no particular order that you can use to rewrite your queries to change their performance.

12 Ways to Refactor a Query to Change Performance

1. Window functions vs GROUP BY

Sometimes window functions rely a little too much on tempdb and blocking operators to accomplish what you ask of them. While using them is always my first choice because of their simple syntax, if they perform poorly you can usually rewrite them as an old-fashioned GROUP BY to achieve better performance.

2. Correlated subqueries vs derived tables

Many people like using correlated subqueries because the logic is often easy to understand, however switching to derived table queries often produces better performance due to their set-based nature.

3. IN vs UNION ALL

When filtering rows of data on multiple values in tables with skewed distributions and non-covering indexes, writing your logic into multiple statements joined with UNION ALLs can sometimes generate more efficient execution plans than just using IN or ORs.

4. Temporary Staging Tables

Sometimes the query optimizer struggles to generate an efficient execution plan for complex queries. Breaking a complex query into multiple steps that utilize temporary staging tables can provide SQL Server with more information about your data. They also cause you to write simpler queries which can cause the optimizer to generate more efficient execution plans as well as allow it to reuse result sets more easily.

5. Forcing Table Join Orders

Sometimes outdated statistics and other insufficient information can cause the SQL Server query optimizer to join tables in a less than ideal sequence. Adam Machanic has a fantastic presentation on forcing table join order with blocking operators without having to resort to join hints.

6. DISTINCT with few unique values

Using the DISTINCT operator is not always the fastest way to return the unique values in a dataset. In particular, Paul White uses recursive CTEs to return distinct values on large datasets with relatively few unique values. This is a great example of solving a problem using a very creative solution.

7. Eliminate UDFs

UDFs often cause poor query performance due to forcing serial plans and causing inaccurate estimates. One way to possibly improve the performance of queries that call UDFs is to try and inline the UDF logic directly into the main query. With SQL Server 2019 this will be something that happens automatically in a lot of cases, but as Brent Ozar points out you might occasionally have to manually inline a UDF's functionality to get the best performance.

8. Create UDFs

Sometimes a poorly configured server will parallelize queries too frequently and cause poorer performance than their serially equivalent plan. In those cases, putting the troublesome query logic into a scalar or multi-statement table-valued function might improve performance since they will force that part of the plan to run serially. Definitely not a best practice, but it is one way to force serial plans when you can't change the cost threshold for parallelism.

9. Data Compression

Not only does data compression save space, but on certain workloads it can actually improve performance. Since compressed data can be stored in fewer pages, read disk speeds are improved, but maybe more importantly the compressed data allows more to be stored in SQL Server's buffer pool, increasing the potential for SQL Server to reuse data already in memory.

10. Indexed Views

When you can't add new indexes to existing tables, you might be able to get away with creating a view on those tables and indexing the view instead. This works great for vendor databases where you can't touch any of the existing objects.

11. Switch cardinality estimators

The newer cardinality estimator introduced in SQL Server 2014 improves the performance of many queries. However, in some specific cases it can make queries perform more slowly. In those cases, a simple query hint is all you need to force SQL Server to change back to the legacy cardinality estimator.

12. Copy the data

If you can't get better performance by rewriting a query, you can always copy the data you need to a new table in a location where you CAN create indexes and do whatever other helpful transformations you need to do ahead of time.

...And more

By no means is this list exhaustive. There are so many ways to rewrite queries, and not all of them will work all the time.

The key is to think about what the query optimizer knows about your data and why it's choosing the plan it is. Once you understand what it's doing, you can start getting creative with various query rewrites that address that issue.