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The Results

Now, I'm sure you are curious about the results. I promise to show them, but first I'd like to say a few things about test results in general and the environment that I used.

Disclaimer

It's extremely important to be skeptical when reading test results. To come to the right conclusion, you have to translate the tests to your environment and to your situation, and then re-execute the tests.

Environment

The equipment that I used for the tests is not real production equipment; I used only a development machine (1.7GHz, 512MB RAM). The test machine was a Windows XP machine with SP2 for SQL Server 2000.

In a new production environment, you will typically have RAID 1 (mirroring) for the log and RAID 10 (mirroring and striping) with at least four disks for the data. You will also have more CPU cache than 256KB, probably more than one CPU, and also at least 1GB of memory.

With that out of the way, let's take a look at the test results.

Test A: INSERT of Orders

In Table 1, you can find the results of the first test, in which I INSERT orders and details for those orders. The time values are recalculated so that the first result has a value of 1 (a baseline value) and all other time values shows a percentage compared to the first test result value.

As you can see in Table 1, the overhead for the GUID solution is actually huge. What is especially worrisome is the sign of bad scalability that I saw when I compared results from executions when I had different numbers of orders.

Table 1 Result of Test A: INSERT of 500 Orders

Test A

Time (with 1,000,000 Orders)

INTEGER

1

GUID

10


This big difference occurred with "only" 1,000,000 orders (and 10,000,000 detail rows). The difference was very small with a small amount of orders, so this is probably a really bad problem with 100,000,000 orders!

Something is wrong—very wrong!

Test A: INSERT of Orders, Revised

I did quick tests before comparing GUIDs and IDENTITY+INT without seeing the huge overhead that I saw when I scaled up the test a bit. (I also did the tests on NT4, and the problem is probably smaller there, as you soon will see.) This certainly worried me a lot! I tried different tricks, different indexing strategies, different fill factors, a clustered IDENTITY+INT, and so on—everything without luck. My guess was that the reason for the problem was the lack of order for the GUIDs generated at Windows 2000 (and beyond). In Listing 3, you can see a series of GUIDs generated with a couple of calls to NEWID() on SQL Server 7 on NT4. Compare that series with the one in Listing 4, which is generated with a couple of calls to NEWID() on SQL Server 2000 on Windows 2000.

Listing 3: Some GUIDs Generated with SQL Server 7 on NT4

B3BFC6B1-05A2-11D6-9FBA-00C04FF317DF
B3BFC6B2-05A2-11D6-9FBA-00C04FF317DF
B3BFC6B3-05A2-11D6-9FBA-00C04FF317DF
B3BFC6B4-05A2-11D6-9FBA-00C04FF317DF
B3BFC6B5-05A2-11D6-9FBA-00C04FF317DF

Listing 4: Some GUIDs Generated with SQL Server 2000 on Windows 2000

C87FC84A-EE47-47EE-842C-29E969AC5131
2A734AE4-E0EF-4D77-9F84-51A8365AC5A0
70E2E8DE-500E-4630-B3CB-166131D35C21
15ED815C-921C-4011-8667-7158982951EA
56B3BF2D-BDB0-4AFE-A26B-C8F59C4B5103

As you saw in Listing 3, only the eighth half byte is changed between calls. On the other hand, in Listing 4, only the 13th half byte is constant between calls. In Windows 2000, the MAC address isn't used any longer for when GUIDs are generated. Instead, the GUID is only a 16-byte random number. Well, that isn't totally true. The 13th half byte is constant, so only 15.5 bytes are random. Adam Nathan at Microsoft explained to me that the 13th half byte is the value that will point out the source of the GUID, and 4 means Microsoft. Thanks, Adam!

The guess that I had about the problem with the INSERT overhead for GUIDs was that the lack of order in the Windows 2000–generated GUIDs was giving SQL Server a hard time administering indexes under massive INSERT periods. Therefore, I tried to create an order for the GUIDs instead. I tried to CAST the current DATETIME to a BINARY(8) and put that first in the GUID. Unfortunately, that had no effect. When I investigated it further, I found that when I had a BINARY(16) value and CASTed it to a UNIQUEIDENTIFIER, some bytes were scrambled. What to do? You guessed it. I tried to compensate for the scrambling to see if that had any positive effect on throughput, but no effect occurred. Then I found out that it wasn't the first (high) byte that was important for the new ordering, but the last (low) bytes. I also learned that I didn't have to use BINARY(8) for the current DATETIME. BINARY(6) is enough for the next 77 years, so I decided to occupy only the last (low) 6 bytes with the current DATETIME. In Listing 5, you can see the result of PRINT CAST(GETDATE() AS BINARY(8)). The first (high) bytes are 0.

Listing 5: GETDATE() as BINARY(8)

0x0000919001862D20

The algorithm that I used to generate my new GUIDs is shown in Listing 6; GUIDs of that type (generated in SQL Server 2000 on Windows 2000) are shown in Listing 7. I call this type of GUIDs COMBs (for COMBined, abbreviated).

Listing 6: An Algorithm to Generate Ordered GUIDs (COMBs)

DECLARE @aGuid UNIQUEIDENTIFIER

SET @aGuid = CAST(CAST(NEWID() AS BINARY(10)) 
+ CAST(GETDATE() AS BINARY(6)) AS UNIQUEIDENTIFIER)

Listing 7: A Set of GUIDs (COMBs) Generated with the New Algorithm

E25AFE33-DB2D-4502-9BF0-919001862D20
83E689D3-8549-4094-B223-919001862D20
CC22A56D-0CD5-43C5-990E-919001862D20
D5149998-1718-468C-B1AD-919001862D20
CBD0182D-4A0E-40AC-9A4C-919001862D20

As you saw in Listing 7, those GUIDs (COMBs) have the same last 6 bytes. Those represent the DATETIME of when those five GUIDs were generated. A DATETIME has the "precision" of 1/300th of a second, and it's no problem for SQL Server to generate five GUIDs in 3ms with this algorithm, even though it is slower than using only NEWID(). As you can see, ordering the GUIDs isn't perfect because I will have several GUIDs being generated with the same DATETIME—but that doesn't really matter here.

Finally, I found something that helped with my INSERT overhead. When I used this new solution, I got the throughput table shown in Table 2.

Table 2 Result of Test A: INSERT of 500 Orders, Revised

Test A

Time (with 1,000,000 Orders)

INTEGER

1

GUID (COMB)

1.05


This was great news, and the overhead of the GUIDs was actually less than expected. Perhaps you wonder whether my algorithm to generate GUIDs in itself isn't very slow. Often when you do conversions, you see CPU cycles fly away. On the other hand, compared to writing to a database, those conversions are pretty cheap.

You also might think that this algorithm must be risky because there is no guarantee of unique GUIDs. That's correct, but the built-in algorithm makes no guarantee, either. It's just a random number. (Not even the NT4 algorithm was guaranteed, as I said before.) Okay, a 15.5-byte random number has low predictability of being duplicated. (Most of us think that the chance of winning millions in the lottery is almost zero. Still, the chance is a number that is less than 4 bytes!)

With my algorithm, the random number's size decreases from 15.5 bytes to 9.5 bytes because the last 6 bytes are occupied with the current DATETIME. Of course, the risk of generating duplicated random numbers with my algorithm is larger than with NEWID(), but it's not a problem if the numbers don't occur within the same 1/300 of a second. In my opinion, the risk of duplicates is small enough that it's acceptable. And when you have UNIQUE CONSTRAINTs, it's okay even if you happen to see two duplicated GUIDs being generated once a decennium or so.

NOTE

To judge the likelihood of generating two duplicate COMBs, I ran a quick test. I used 6 static bytes for the low bytes and then created a million COMBs without duplicates. It won't be possible for you to INSERT close to that many rows in 1/300 of a second.

I also discussed the risk with a friend who is a mathematician. He pointed out that we don't know the algorithm being used for the random number. Is it "perfect"? He also pointed out that it's very dangerous to make assumptions and conclusions about likelihood and random numbers. Even so, he said that there seemed to be a very low risk of creating duplicate COMBs.

To add some more fuel to the fire, I'd like to point out that the differences in real-life apps are probably bigger than the one I showed in Table 1 when ordinary GUIDs are used. This is because I skipped test results when CHECKPOINTing was going on—CHECKPOINTing was much more meaningful to the GUID test. When I used my custom (COMB) algorithm, CHECKPOINTing was much less frequent and was used for short intervals. That is obvious when I compare the time for INSERTing 500,000 orders when there are 500,000 orders present at the start. You can see the throughput shown in Table 3.

Table 3 Result of Test A: INSERT of 500,000 Orders

Test A

Time (with 500,000 Orders)

INTEGER

1

GUID

30

GUID (COMB)

1.1


When did you last get a wage increase of 30 times your salary? Think about it, and you'll get a feeling for what a huge difference this is!

Finally, the memory consumption of the tests was very different. The IDENTITY+INT and my new GUID (COMB) algorithm grabbed perhaps 1MB of RAM for each 500 orders INSERTed. The ordinary GUID algorithm grabbed all memory given to SQL Server (350MB in my tests) when 500 or 1,000 orders were INSERTed!

NOTE

One property of ordinary GUIDs isn't maintained with COMBs: The order of creation is not known with GUIDs. With COMBs, it is known. For some applications, this can be a problem that keeps COMBs from being used.

Is it common to see this type of raw INSERTs only? No, not that common. Most applications also need some reading of data. Now on to the second test.

Test B: Fetch of Single Order

In the second test, I fetch the customer ID from some of the orders, one by one. To get a correct set of IDs to work with, I first ran a script that loops through the table to grab 100 random IDs and PRINTs a complete stored procedure call to the stored procedure a_Order_Fetch, which is shown in GUID version in Listing 8.

Listing 8: GUID Version of a_Order_Fetch

CREATE PROCEDURE a_OrderGUID_Fetch
(@orderId UNIQUEIDENTIFIER
, @customerId UNIQUEIDENTIFIER OUTPUT)
AS
 DECLARE @anError INT

 SET @aCustomerId = 
 (SELECT om.customer_id
 FROM orderGUIDmaster om
 WHERE om.id = @orderId)

 SET @anError = @@ERROR

 RETURN @anError

Then I copied the 1,000 PRINTed rows and pasted them to another script that EXECUTEs the calls. To skew the test results as little as possible here, I first executed a DBCC DROPCLEANBUFFERS in the beginning of the script that calls the stored procedure.

Let's take a look at the results for the second test. As usual, the first value is recalculated to 1, to be used as a baseline.

Table 4 Result of Test B: SELECT of Orders

Test B

Time for Fetching 100 Orders

INTEGER

1 (baseline)

GUID (COMB)

1.1


This is fine, isn't it? But what about the dreaded 16-byte JOIN?

Test C: A Small JOIN

The most common argument for not using GUIDs for primary keys is that it's so expensive for JOINs. Perhaps you have heard this: "Use a 16-byte column to join? Are you crazy?"

I reused Test B but changed the stored procedures slightly so that the orderdetail table was also used in a JOIN, as shown in Listing 9.

Listing 9: COMB Version of a_Order_FetchWithDetail

CREATE PROCEDURE a_OrderCOMB_Fetch
(@orderId UNIQUEIDENTIFIER, @noOfRows INT OUTPUT)
AS
 DECLARE @anError INT

 SET @noOfRows = 
 (SELECT COUNT(*)
 FROM orderCOMBmaster om
 INNER JOIN orderCOMBdetail od
 ON om.id = od.ordermaster_id
 WHERE om.id = @orderId)

 SET @anError = @@ERROR

 RETURN @anError

The results of the third test case are shown in Table 5.

Table 5 Result of Test C: SELECT of orders and JOIN with Details

Test C

Time for Fetching 100 Orders

INTEGER

1 (baseline)

GUID (COMB)

1.1


Worth mentioning is that the foreign keys are clustered both in the INTEGER case and in the COMB case. As a matter of fact, it was faster not using clustered foreign keys for this specific test, but I thought that it was most natural (and probably more effective in other tests) to use clustered foreign keys.

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