Performance Comparison - Identity() x NewId() x NewSequentialId

原文链接：http://www.codeproject.com/Articles/32597/Performance-Comparison-Identity-x-NewId-x-NewSeque
AgileWare, 16 Jan 2009

目录

Introduction

The NEWSEQUENTIALID system function is an addition to SQL Server 2005. It seeks to bring together, what used to be, conflicting requirements in SQL Server 2000; namely identity-level insert performance, and globally unique values.

Background

GUIDs (Globally Unique Identifiers) are pseudo-random 16-byte (128-bit) numbers whose generation algorithm provides a sufficiently high degree of probability that the same GUID will never be generated twice on any computer at any time. Although a GUID is a binary array, it is often represented in its hexadecimal form using the following format, for example: dbbc2827-edf8-4a2d-92ad-c1e0059304d7. This makes them suitable for the ‘distributed uniqueness requirement’ of our Orders table.

The problem would be pretty much solved there unless we have some non-trivial performance requirements, especially around insert volumes and speed. As GUIDs are effectively pseudo-random (the degree of randomness / predictability was changed by Microsoft in response to privacy concerns), they do not possess the same orderly insert benefits as identity columns when used as the clustering key. The insert IO pattern changes from sequential to random-access when using GUIDs instead of an IDENTITY column. Not only do GUIDs require far more IO to locate the insert location in a clustered index (the index has to be traversed from the root for every insert), fragmentation at the leaf level leads to less dense pages, which in turn requires defragmentation / reindexing.

Using the Code

NEWSEQUENTIALID()

Enter NEWSEQUENTIALID(), stage left. This is a new system function included in SQL Server 2005 that combines the best of both worlds. NEWSEQUENTIALID() will generate a GUID that will be greater in value than the previously generated one. It is easy to see how this function works by looking at the output the following TSQL script generates:

create table TestTable ( id uniqueidentifier default newsequentialid() not null primary key clustered, sequence int); 
go 

-- Insert 100 rows. 
declare @count int; 
set @count = 0; 
while @count < 100 begin insert TestTable (sequence) values (@count); 

   set @count = @count + 1; 
end; 
go 

select * from TestTable order by id; 
go

Output:

id sequence 
------------------------------------ ----------- 
FA780E3B-03C2-DB11-BD9F-0011D82F3F23 0 
FB780E3B-03C2-DB11-BD9F-0011D82F3F23 1 
FC780E3B-03C2-DB11-BD9F-0011D82F3F23 2 
FD780E3B-03C2-DB11-BD9F-0011D82F3F23 3 
FE780E3B-03C2-DB11-BD9F-0011D82F3F23 4 
...
5C790E3B-03C2-DB11-BD9F-0011D82F3F23 98 
5D790E3B-03C2-DB11-BD9F-0011D82F3F23 99

You’ll notice that there is a clear bit-shifting pattern that SQL Server is employing to keep the GUIDs increasing in relative value. These are the restrictions in NEWSEQUENTIALID’s use: NEWSEQUENTIALID cannot be used in arbitrary TSQL statements. It can only be specified as the DEFAULT value for an unique identifier column. More than one column in a table can use NEWSEQUENTIALID. It cannot be combined with other scalar functions, e.g., REVERSE(NEWSEQUENTIALID()) is not allowed. The network card’s MAC address is easily identifiable in the generated GUID. My laptop’s MAC address is 00-01-4A-28-64-8B. This is one of the NEWSEQUENTIALID GUIDs that was generated: 4EAC7708-30C3-DB11-B902-00014A28648B. Hence the privacy warnings in BOL: Correlating the position of the MAC address in the GUID with the bits that get incremented / shifted for each successive GUID sheds light on why the GUIDs remain globally unique. The MAC address portion of the GUID does not change.

Performance Comparison

The following test demonstrates the benefits of NEWSEQUENTIALID. The insert performance of the various clustering key variations was measured and compared. For each test, the same TestTable was recreated with a different Id column definition:

IDENTITY() 
NEWID() 
NEWSEQUENTIALID()

The following TSQL contains the DDL used to construct the tables for the three clustered keys:

-- IDENTITY 
create table TestTable ( id int identity(1,1) not null primary key clustered, sequence int not null, data char(250) not null default ''); 
go 

-- NEWID 
create table TestTable ( id uniqueidentifier default newid() not null primary key clustered, sequence int not null, data char(250) not null default ''); 
go 

-- NEWSEQUENTIALID 
create table TestTable ( id uniqueidentifier default newsequentialid() not null primary key clustered, sequence int not null, data char(250) not null default ''); 
go

For each test, 50,000 rows were inserted into the TestTable table using the following TSQL script:

-- Insert 50,000 rows. 
declare @count int; 
set @count = 0; 
while @count < 50000 begin insert TestTable (sequence) values (@count); 

   set @count = @count + 1; 
end; 
go

The following metrics were gathered after each run:

-- Get the number of read / writes for this session... 
select reads, writes from sys.dm_exec_sessions where session_id = @@spid; 

-- Get the page fragmentation and density at the leaf level. 
select index_type_desc, index_depth, page_count, avg_page_space_used_in_percent, avg_fragmentation_in_percent, record_count from sys.dm_db_index_physical_stats(db_id(), object_id('TestTable'), null, null, 'detailed') where index_level = 0; 
go

The results are quite compelling:

-	Reads	Writes	Leaf Pages	Avg Page Used	Avg Fragmentation	Record Count
IDENTITY()	0	1,683	1,667	98.9%	0.7%	50,000
NEWID()	0	5,386	2,486	69.3%	99.2%	50,000
NEWSEQUENTIALID()	0	1,746	1,725	99.9%	1.0%	50,000

Conclusion

The most striking is the number of writes required by the NEWID system function. This, coupled with the average page density of 69%, is evidence of the page splitting caused by the random distribution of inserts at the leaf level. As soon as a page fills up, it needs to be split into two pages of 50% each for the insert to complete. Not only has page splitting resulted in poor page density, it has fragmented the data pages quite badly (there is a 99% probability that the next data page is not next to the current one). In our tests, the most likely place for a free page required for the page split is at the end of the table irrespective of where the row is being inserted. Therefore, to read the rows in order, the scan needs to keep jumping back and forth between widely distributed split pages, hence the appalling fragmentation. The minor difference in page count between IDENTITY and NEWSEQUENTIALID metrics is due to the difference in size between IDENTITY’s INT (4 bytes) and NEWSEQUENTIALID’s UNQUEIDENTIFIER (16 bytes). However, note that any non-clustered index on the UNIQUEIDENTIFIER table will be four times larger than the same index on the IDENTITY table. Therefore, IDENTITY is still preferred over NEWSEQUENTIALID when choosing between the two. The NEWSEQUENTIALID system function has clearly lived up to its claim of providing GUID-like uniqueness coupled with identity-level insert performance. The number of writes, fragmentation, and page density are all inline with identity-level metrics. These benefits make NEWSEQUENTIALID a compelling feature.

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)