Coding SQL Server triggers for multi-row operations

It has been a while since I wrote some of my best practices posts. I decided to revisit these posts again to see if anything has changed, I also wanted to see if I could add some additional info.

Today I decided to revisit the post about coding triggers for multi-row operations

There are many forum posts and questions on stackoverflow where people have trigger code. However  these triggers are coded incorrectly because they don't account for multi-row operations. 

The one thing you have to remember is that a trigger fires per batch not per row, if you are lucky you will get an error...if you are not lucky you will not get an error but it might take a while before you notice that you are missing a whole bunch of data

Let's take a look at exactly what happens, first create these two tables

CREATE TABLE Test(id int identity not null primary key, 
   SomeDate datetime not null)
GO

CREATE TABLE  TestHistory(id int  not null, 
   InsertedDate datetime not null)
GO

Now create the following trigger.

CREATE  TRIGGER trTest
    ON Test
    FOR INSERT
    AS
     
    IF @@ROWCOUNT =0
    RETURN
     
    DECLARE @id int
    SET @id = (SELECT id 
    FROM inserted)
    
    INSERT TestHistory (id,InsertedDate)
    SELECT @id, getdate()
    
    GO

The trigger you just created is very simple, it basically inserts a row into the history table every time an insert happens in the test table


Run this insert statement which only inserts one row

INSERT Test(SomeDate) values(getdate())

Now run this to see what is in the history table

SELECT * FROM TestHistory

1 2017-10-14 08:49:16.227


That all works fine, what happens when we try to insert 2 rows?

INSERT Test(SomeDate)
SELECT getdate()
UNION ALL
SELECT  dateadd(dd,1,getdate() )

Here is the error.

Server: Msg 512, Level 16, State 1, Procedure trTest, Line 11
Subquery returned more than 1 value. This is not permitted when the subquery follows =, !=, <, <= , >, >= or when the subquery is used as an expression.
The statement has been terminated.


As you can see the trigger blew up with an error. Let's try something else.
What would happen if you coded the trigger in this way

ALTER TRIGGER trTest
    ON Test
    FOR INSERT
    AS
     
    IF @@ROWCOUNT =0
    RETURN
     
    DECLARE @id int
    SELECT @id = id 
    FROM inserted
    
    INSERT TestHistory (id,InsertedDate)
    SELECT @id, getdate()
    
    GO

Now insert one row

INSERT Test(SomeDate) VALUES (getdate())

We look again what is in the history table, as you can see we have id 1 and 4, this is because id 2 and 3 failed and were rolled back when we did the insert earlier

SELECT * FROM TestHistory

1 2017-10-14 08:49:16.227
4 2017-10-14 08:50:37.647


Here is where it gets interesting, run this code

INSERT Test(SomeDate)
SELECT getdate()
UNION ALL
SELECT dateadd(dd,1,getdate() )

That runs fine but when we look now we are missing one of the rows in the history table

SELECT * FROM TestHistory

1 2017-10-14 08:49:16.227
4 2017-10-14 08:50:37.647
5 2017-10-14 08:51:06.270


let's try that same insert statement again

INSERT Test(SomeDate)
SELECT getdate()
UNION ALL
SELECT dateadd(dd,1,getdate() )

Now we are again missing a row in the history table

SELECT * FROM TestHistory

1 2017-10-14 08:49:16.227
4 2017-10-14 08:50:37.647
5 2017-10-14 08:51:06.270
7 2017-10-14 08:52:09.447


The problem is with this line of code

SELECT @id = id FROM inserted

@id will only hold the value for one of the rows that was returned in the result set


Here is how you would change the trigger to work correctly

ALTER TRIGGER trTest
    ON Test
    FOR INSERT
    AS
     
    IF @@ROWCOUNT =0
    RETURN
     
        
    INSERT TestHistory (id,InsertedDate)
    SELECT id, getdate()
    FROM inserted
    
GO

Now run the single insert statement again

INSERT Test(SomeDate) VALUES (getdate())

That row was inserted, we can check the history table to see what is there now

SELECT * FROM TestHistory

1 2017-10-14 08:49:16.227
4 2017-10-14 08:50:37.647
5 2017-10-14 08:51:06.270
7 2017-10-14 08:52:09.447
9 2017-10-14 08:52:57.990


Finally, we can again test with the insert statement that will insert 2 rows

INSERT Test(SomeDate)
SELECT getdate()
UNION ALL
SELECT dateadd(dd,1,getdate() )

Let's check the history table again

SELECT  * FROM TestHistory

1 2017-10-14 08:49:16.227
4 2017-10-14 08:50:37.647
5 2017-10-14 08:51:06.270
7 2017-10-14 08:52:09.447
9 2017-10-14 08:52:57.990
11 2017-10-14 08:53:40.693
10 2017-10-14 08:53:40.693

And as you can see both rows were inserted into the history table

So what is worse in this case? The error message or the fact that the code didn't blow up but that the insert wasn't working correctly? I'll take an error message any time over the other problem.

Sargable Queries...

It has been a while since I wrote some of my best practices posts. I decided to revisit these posts again to see if anything has changed, I also wanted to see if I could add some additional info.

Today we are going to look at sargable queries. You might ask yourself, what is this weird term sargable. Sargable comes from searchable argument, sometimes also referred as Search ARGument ABLE. What that means is that the query will be able to use an index, a seek will be performed instead of a scan. In general any time you have a function wrapped around a column, an index won't be used

Some examples that are not sargable

WHERE LEFT(Name,1) = 'S'
WHERE Year(SomeDate) = 2012
WHERE OrderID * 3 = 33000

Those three should be rewritten like this in order to become sargable

WHERE Name LIKE 'S%'
WHERE SomeDate >= '20120101' AND SomeDate < '20130101'
WHERE OrderID = 33000/3

Let's create a table, insert some data so that we can look at the execution plan
Create this simple table

CREATE TABLE Test(SomeID varchar(100))

Let's insert some data that will start with a letter followed by some digits

INSERT Test
SELECT LEFT(v2.type,1) +RIGHT('0000' + CONVERT(varchar(4),v1.number),4) 
FROM master..spt_values v1
CROSS JOIN (SELECT DISTINCT LEFT(type,1) AS type 
FROM master..spt_values) v2
WHERE v1.type = 'p'

That insert should have generated 32768 rows

Now create this index on that table

CREATE CLUSTERED INDEX cx_test ON Test(SomeID)

Let's take a look at the execution plan, hit CTRL + M, this will add the execution plan once the query is done running

SELECT * FROM Test
WHERE SomeID LIKE 's%'

SELECT * FROM Test
WHERE LEFT(SomeID,1) = 's'

Here is what the plans looks like

As you can see it is 9% versus 91% between the two queries, that is a big difference
Hit CTRL + M again to disable the inclusion of the plan

Run this codeblock, it will give you the plans in a text format

SET SHOWPLAN_TEXT ON
GO

SELECT * FROM Test
WHERE SomeID LIKE 's%'

SELECT * FROM Test
WHERE LEFT(SomeID,1) = 's'
GO

SET SHOWPLAN_TEXT OFF
GO

Here are the two plans

|--Clustered Index Seek(OBJECT:([master].[dbo].[Test].[cx_test]),
SEEK:([master].[dbo].[Test].[SomeID] >= 'Rþ' AND [master].[dbo].[Test].[SomeID] < 'T'),
WHERE:([master].[dbo].[Test].[SomeID] like 's%') ORDERED FORWARD)
|--Clustered Index Scan(OBJECT:([master].[dbo].[Test].[cx_test]),
WHERE:(substring([master].[dbo].[Test].[SomeID],(1),(1))='s'))

As you can see the top one while looking more complicated is actually giving you a seek

Making a case sensitive search sargable

Now let's take a look at how we can make a case sensitive search sargable as well
In order to do a search and make it case sensitive, you have to have a case sensitive collation, if your table is not created with a case sensitive collation then you can supply it as part of the query

Here is an example to demonstrate what I mean

This is a simple table created without a collation

CREATE TABLE TempCase1 (Val CHAR(1))
INSERT TempCase1 VALUES('A')
INSERT TempCase1 VALUES('a')

Running this select statement will return both rows

SELECT * FROM TempCase1
WHERE Val = 'A' 

Val
-----
A
a

Now create the same kind of table but with a case sensitive collation

CREATE TABLE TempCase2 (Val CHAR(1) COLLATE SQL_Latin1_General_CP1_CS_AS)
INSERT TempCase2 VALUES('A')
INSERT TempCase2 VALUES('a')

Run the same query

SELECT * FROM TempCase2
WHERE Val = 'A' 

Val
-----
A

As you can see you only get the one row now that matches the case

To return both rows, you can supply the case insensitive collation in the query itself

SELECT * FROM TempCase1
WHERE Val = 'A' COLLATE SQL_Latin1_General_CP1_CI_AS

Val
-----
A
a

Now let's take a look at how we can make the case sensitive search sargable

First create this table and insert some data

CREATE TABLE TempCase (Val CHAR(1))
 
INSERT TempCase VALUES('A')
INSERT TempCase VALUES('B')
INSERT TempCase VALUES('C')
INSERT TempCase VALUES('D')
INSERT TempCase VALUES('E')
INSERT TempCase VALUES('F')
INSERT TempCase VALUES('G')
INSERT TempCase VALUES('H')

Now we will insert some lowercase characters

INSERT TempCase
SELECT LOWER(Val) FROM TempCase

Now we will create our real table which will have 65536 rows

CREATE TABLE CaseSensitiveSearch (Val VARCHAR(50))

We will do a couple of cross joins to generate the data for our queries

INSERT CaseSensitiveSearch
SELECT t1.val + t2.val + t3.val + t4.val
FROM TempCase t1
CROSS JOIN TempCase t2
CROSS JOIN TempCase t3
CROSS JOIN TempCase t4

Create an index on the table

CREATE INDEX IX_SearchVal ON CaseSensitiveSearch(Val)

Just like before, if we run this we will get back the exact value we passed in and also all the upper case and lower case variations

SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' 

Here are the results of that query
Val
-----
AbCd
ABcd
Abcd
ABCd
aBCd
abCd
aBcd
abcd
abCD
aBcD
abcD
aBCD
ABCD
AbCD
ABcD
AbcD

If you add the case sensitive collation to the query, you will get only what matches your value

SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS

Here is the result, it maches what was passed in
Val
---
ABCD

The problem with the query above is that it will cause a scan. So what can we do, how can we make it perform better? It is simple... combine the two queries

First grab all case sensitive and case insensitive values and then after that filter out the case insensitive values

Here is what that query will look like

SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS
AND Val LIKE 'ABCD'

AND Val LIKE 'ABCD' will result in a seek, but now when it also does the Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS part, it only returns the row that matches your value

If you run both queries, you can look at the plan difference (hit CTRL + M so that the plan is included)

SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS



SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS
AND Val LIKE 'ABCD'

Here is the plan

As you can see, there is a big difference between the two

Here is the plan in text as well

SET SHOWPLAN_TEXT ON
GO
 
SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS



SELECT * FROM CaseSensitiveSearch
WHERE Val = 'ABCD' COLLATE SQL_Latin1_General_CP1_CS_AS
AND Val LIKE 'ABCD'

GO
 
SET SHOWPLAN_TEXT OFF
GO

|--Table Scan(OBJECT:([tempdb].[dbo].[CaseSensitiveSearch]),
WHERE:(CONVERT_IMPLICIT(varchar(50),[tempdb].[dbo].[CaseSensitiveSearch].[Val],0)=CONVERT(varchar(8000),[@1],0)))
|--Index Seek(OBJECT:([tempdb].[dbo].[CaseSensitiveSearch].[IX_SearchVal]), SEEK:([tempdb].[dbo].[CaseSensitiveSearch].[Val] >= 'ABCD'
AND [tempdb].[dbo].[CaseSensitiveSearch].[Val] <= 'ABCD'),
WHERE:(CONVERT_IMPLICIT(varchar(50),[tempdb].[dbo].[CaseSensitiveSearch].[Val],0)='ABCD' AND [tempdb].[dbo].[CaseSensitiveSearch].[Val] like 'ABCD') ORDERED FORWARD)

I really wish Microsoft would take the time to internally rewrite these two queries when it hits the optimizer

WHERE LEFT(Name,1) = 'S'
WHERE Year(SomeDate) = 2012

It should create these and then performance would be much better

WHERE Name LIKE 'S%'
WHERE SomeDate >= '20120101' AND SomeDate < '20130101'

I think there are probably some SQL Server consultants cursing me now for even trying to suggest this :-)

Data types storage differences

It has been a while since I wrote some of my best practices posts. I decided to revisit these posts again to see if anything has changed, I also wanted to see if I could add some additional info.

Today we are going to take a look at how data types can have an impact in queries and also the size of your database.

Char vs NChar

SQL Server has two data types to store character data[1], both of them come in fixed and variable length sizes. The char and varchar data type uses one byte of store to store one character, the nchar and nvarchar data type uses two bytes of store to store one character. The nchar and nvarchar data types are used to store unicode of data

Let's think about that for a second, what we are saying is that the char and varchar data type can store twice the number of characters in the same amount of store as the nchar and nvarchar data type. Why does this matter, space is cheap right? True, space is getting cheaper but we are also storing more and more data every year.

Now think about what happens you have everything stored as unicode data

• What happens to your backup and restore process, will it be faster or slower, will the files be bigger if not compressed?
• What about when transferring the results to and from your database server, are the packets able to store the same number of characters.
• What about the amount of data on a page, what does this do to indexes and index lookups, how does it affect index maintenance?

If you don't need it, then don't use unicode data.
Some examples of what I have seen stored in nchar and nvarchar when realy you shouldn't:

Zip Code for US addresses
US addresses
Social Security Numbers (which were stored in plain text none the less)
Integer data (enforced by constraints or the app layer to make sure these were only digits)

Let's take a quick look by running some T-SQL

First create these two tables

CREATE TABLE TestChar (SomeCol char(10))
GO

CREATE TABLE TestNChar (SomeCol nchar(10))
GO

CREATE index ix_test on TestChar(SomeCol)
GO
CREATE index ix_test on TestNChar(SomeCol)
GO

Now populate each with some data

INSERT TestChar
SELECT TOP 1000000 '1234567890'
FROM sys.sysobjects c1
CROSS JOIN sys.sysobjects c2
CROSS JOIN sys.sysobjects c3
CROSS JOIN sys.sysobjects c4
GO

INSERT TestNChar
SELECT TOP 1000000 '1234567890'
FROM sys.sysobjects c1
CROSS JOIN sys.sysobjects c2
CROSS JOIN sys.sysobjects c3
CROSS JOIN sys.sysobjects c4
GO

Let's see how much space is used by each tables

EXEC sp_spaceused 'TestChar'
EXEC sp_spaceused 'TestNChar'

42768 KB
62736 KB

If you looked at the reserved column, you will see that the nchar data is using about 20 MB more than the char data

Implicit conversions

Besides the storage increase there is also a problem when querying for data that looks like varchar but is stored as unicode. Run the code below.

SET SHOWPLAN_TEXT ON
GO
DECLARE @v varchar(10) = '0123456789'

SELECT * FROM TestChar WHERE SomeCol LIKE  @v +'%'
GO

SET SHOWPLAN_TEXT OFF
GO

Here is the plan for that query

  |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1008], [Expr1009], [Expr1010]))
       |--Compute Scalar(DEFINE:([Expr1008]=LikeRangeStart([@v]+'%'), [Expr1009]=LikeRangeEnd([@v]+'%'), [Expr1010]=LikeRangeInfo([@v]+'%')))
       |    |--Constant Scan
       |--Index Seek(OBJECT:([Performance].[dbo].[TestChar].[ix_test]), SEEK:([Performance].[dbo].[TestChar].[SomeCol] > [Expr1008] AND [Performance].[dbo].[TestChar].[SomeCol] < [Expr1009]),  WHERE:([Performance].[dbo].[TestChar].[SomeCol] like [@v]+'%') ORDERED FORWARD)

If we look at the plan we can see that this looks pretty good

Usually people will sometimes change the datatype of a column but will not change any code that access this column. Let's now change the data type of the column to nchar

DROP INDEX TestChar.ix_test
GO

ALTER TABLE TestChar ALTER COLUMN SomeCol nchar(10)
GO

CREATE INDEX ix_test on TestChar(SomeCol)
GO

Run the query that gives you the plan again

SET SHOWPLAN_TEXT ON
GO
DECLARE @v varchar(10) = '0123456789'

SELECT * FROM TestChar WHERE SomeCol LIKE  @v +'%'
GO

SET SHOWPLAN_TEXT OFF
GO

Here is the plan

  |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1008], [Expr1009], [Expr1010]))
       |--Compute Scalar(DEFINE:([Expr1008]=LikeRangeStart(CONVERT_IMPLICIT(nvarchar(11),[@v]+'%',0)), [Expr1009]=LikeRangeEnd(CONVERT_IMPLICIT(nvarchar(11),[@v]+'%',0)), [Expr1010]=LikeRangeInfo(CONVERT_IMPLICIT(nvarchar(11),[@v]+'%',0))))
       |    |--Constant Scan
       |--Index Seek(OBJECT:([Performance].[dbo].[TestChar].[ix_test]), SEEK:([Performance].[dbo].[TestChar].[SomeCol] > [Expr1008] AND [Performance].[dbo].[TestChar].[SomeCol] < [Expr1009]),  WHERE:([Performance].[dbo].[TestChar].[SomeCol] like CONVERT_IMPLICIT(nvarchar(11),[@v]+'%',0)) ORDERED FORWARD)

As you can see, there is a conversion going on right now.

In order to get rid of the conversion, use the correct data types

SET SHOWPLAN_TEXT ON
GO
DECLARE @v nvarchar(10) = '0123456789'

SELECT * FROM TestChar WHERE SomeCol LIKE  @v +'%'
GO

SET SHOWPLAN_TEXT OFF
GO

  |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1008], [Expr1009], [Expr1010]))
       |--Compute Scalar(DEFINE:([Expr1008]=LikeRangeStart([@v]+N'%'), [Expr1009]=LikeRangeEnd([@v]+N'%'), [Expr1010]=LikeRangeInfo([@v]+N'%')))
       |    |--Constant Scan
       |--Index Seek(OBJECT:([Performance].[dbo].[TestChar].[ix_test]), SEEK:([Performance].[dbo].[TestChar].[SomeCol] > [Expr1008] AND [Performance].[dbo].[TestChar].[SomeCol] < [Expr1009]),  WHERE:([Performance].[dbo].[TestChar].[SomeCol] like [@v]+N'%') ORDERED FORWARD)

Implicit conversions also were an issue when ORMs first burst onto the scene. If you used NHibernate or LINQ to SQL with .NET, since strings in .NET are unicode, all text would be sent over as unicode and you would see all kinds on conversions.

Using larger datatypes when it is not needed

I see this problem mostly with the integer data types. Below is a list of the integer data types together with their storage size and range

tinyint
Storage size is 1 byte. Integer data from 0 through 255.

smallint
Storage size is 2 bytes. Integer data from -2^15 (-32,768) through 2^15 - 1 (32,767).

int
Storage size is 4 bytes. Integer data from -2^31 (-2,147,483,648) through 2^31 - 1 (2,147,483,647).

bigint
Storage size is 8 bytes. Integer data from -2^63 (-9,223,372,036,854,775,808) through 2^63-1 (9,223,372,036,854,775,807).

Now imagine facebook with a billion users decided to use bigint as CountryID in their Country table, this key is then uses as a foreign key in the user demographics table. This is wasteful,either use a smallint since we won't go through 32 thousand countries in the foreseeable feature or use the 2 or 3 character ISO code. 

The problem is even worse if you have a compound 6 column key and it is used as a foreign key in tons of other tables...that was real fun to clean up....use a surrogate 1 column key in that case...but be sure to test....normalize till it hurts then denormalize till it works....I will cover normalization in another post...just wanted to mention it

[1] I know there is text and ntext but those are deprecated

Sizing database files

It has been a while since I wrote some of my best practices posts. I decided to revisit these posts again to see if anything has changed, I also wanted to see if I could add some additional info.

This post will demonstrate that there is a difference in performance if you don't size your database file accordingly. It is a good practice to have your database sized correctly for the next 6 to 12 months, you don't want your server wasting cycles with growing files all the time.

Figure out how big your files are now, figure out how much they will grow in the next year and size your files accordingly, check back every month or so to see if your estimates were correct.

By default SQL Server will create databases with very small files when you create a database and you don't specify the sizes. If you have people creating databases on your servers, consider adding a DDL trigger to notify you when a new DB is added so that you can talk to the database creator and size the files. You also can change the defaults on the server so that you don't have the 10% growth either.

First let's see what the difference is when we have a database where the files will have to grow versus one where the files are big enough for the data that will be inserted.

Here we are creating two databases, one with much bigger files than the other one

The TestBigger database is correctly sized for the data that will be inserted

CREATE DATABASE [TestBigger]
 ON  PRIMARY 
( NAME = N'TestBigger', FILENAME = N'f:\TempTestBigger.mdf' , 
SIZE = 509600KB , FILEGROWTH = 1024KB )
 LOG ON 
( NAME = N'TestBigger_log', FILENAME = N'f:\TempTestBigger_log.ldf' , 
SIZE = 502400KB , FILEGROWTH = 10%)
GO

The TestSmaller database is very small, files will have to be expanded many times to accommodate all the data I will be inserting

CREATE DATABASE [TestSmaller]
 ON  PRIMARY 
( NAME = N'TestSmaller', FILENAME = N'f:\TempTestSmaller.mdf' , 
SIZE = 5280KB , FILEGROWTH = 1024KB )
 LOG ON 
( NAME = N'TestSmaller_log', FILENAME = N'f:\TempTestSmaller_log.ldf' , 
SIZE = 504KB , FILEGROWTH = 10%)
GO

These two stored proc calls are just to verify that the files match with what we specified, you can use sp_helpdb to check the size of a database that you created when you don't specify the file sizes

EXEC sp_helpdb 'TestBigger'

name         filename             filegroup SIZE
TestBigger f:\TempTestBigger.mdf      PRIMARY 509632 KB
TestBigger_log f:\TempTestBigger_log.ldf NULL 502400 KB

EXEC sp_helpdb 'TestSmaller'

name         filename              filegroup SIZE
TestSmaller f:\TempTestSmaller.mdf      PRIMARY 5280 KB
TestSmaller_log f:\TempTestSmaller_log.ldf NULL  512 KB

Next, we are creating two identical tables, one in each database

USE TestSmaller
GO
CREATE TABLE test (SomeName VARCHAR(100), 
SomeID VARCHAR(36), SomeOtherID VARCHAR(100), SomeDate DATETIME)

USE TestBigger
GO
CREATE TABLE test (SomeName VARCHAR(100),
 SomeID VARCHAR(36), SomeOtherID VARCHAR(100), SomeDate DATETIME)

This query is just used so that the data is cached for the two inserts later on, this way the data doesn't have to be fetched from disk for either inserts, you can discard the results after the query is done

USE master
GO


SELECT TOP 1000000 c1.name,NEWID(),NEWID(),GETDATE() 
FROM sys.sysobjects c1
CROSS JOIN sys.sysobjects c2
CROSS JOIN sys.sysobjects c3
CROSS JOIN sys.sysobjects c4

Here is the first insert into the bigger database

INSERT TestBigger.dbo.test
SELECT TOP 1000000 c1.name,NEWID(),NEWID(),GETDATE() 
FROM sys.sysobjects c1
CROSS JOIN sys.sysobjects c2
CROSS JOIN sys.sysobjects c3
CROSS JOIN sys.sysobjects c4

Here is the second insert into the smaller database

INSERT TestSmaller.dbo.test
SELECT TOP 1000000 c1.name,NEWID(),NEWID(),GETDATE() 
FROM sys.sysobjects c1
CROSS JOIN sys.sysobjects c2
CROSS JOIN sys.sysobjects c3
CROSS JOIN sys.sysobjects c4

On several machines I tested on, it takes half the time or less to insert the data in the bigger database compared to the smaller database. On some machines it is almost 5 times faster to insert into the bigger database.

How about on your machine, do you see that the insert into the bigger database takes less than half the time it takes to insert into the smaller database?

Check the sizes of the databases again

EXEC sp_helpdb 'TestBigger'

name         filename            filegroup size
TestBigger f:\TempTestBigger.mdf      PRIMARY 509632 KB
TestBigger_log f:\TempTestBigger_log.ldf NULL 502400 KB

EXEC sp_helpdb 'TestSmaller'

name         filename              filegroup size
TestSmaller f:\TempTestSmaller.mdf      PRIMARY 215296 KB
TestSmaller_log f:\TempTestSmaller_log.ldf NULL 427392 KB

As you can see, the bigger database did not expand, the smaller database expanded a lot.

Autogrow
If you do use autogrow, then make sure you don't use the default 10%, take a look at this message

Date 10/03/2017 12:57:56 PM
Log SQL Server (Current - 11/25/2012 5:00:00 AM)
Source spid62
Message
Autogrow of file 'MyDB_Log' in database 'MyDB' took 104381 milliseconds. Consider using ALTER DATABASE to set a smaller FILEGROWTH for this file.

See that, it took a long time, you don't want to grow a one terabyte file by ten percent, that would be one hundred gigabytes, that is huge. Use something smaller and don't use percent, the bigger the file gets the longer it will take to expand the file.

File placement
Separate the log files from the data files by placing them on separate hard drives. Placing the files on separate drives allows I/O activity to occur at the same time for both the data and log files. Instead of having huge files consider having smaller files in separate filegroups. Put different tables used in the same join queries in different filegroups as well. This will improve performance, because of parallel disk I/O searching for joined data.

Put heavily accessed tables and the nonclustered indexes that belong to those tables on different filegroups. This will improve performance, because of parallel I/O if the files are located on different physical disks. Just remember that you can't separate the clustered indexes from the base table, you can only do this for non clustered indexes. Of course people can get very creative, I have worked with a database once where each table was placed in its own filegroups, there were hundreds of files....what a mess

Tempdb
There are all kinds of recommendations about how many data files you should have for tempdb. Start with 4 files and add more files if you see contention. Paul Randal, has a detailed post here: A SQL Server DBA myth a day: (12/30) tempdb should always have one data file per processor core.

If you can, place tempdb on its own physical drive as well, separated from the user databases.

Consider Solid State hard drives or flash storage for tempdb

See also http://support.microsoft.com/kb/2154845 for recommendations by Microsoft Customer Service and Support

Test, test, test
Never ever blindly follow what you read on the internet, make sure that you test it out first on a QA server before promoting the changes to production!!