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Disk Queue Length vs. Disk Latency Times: Which is Best for Measuring Database Performance

Introduction

 

There are several common metrics that people use to look for performance bottlenecks. They are usually:

 

-Disk queue length

-Disk latency/transfer times

-MB/sec

-IO/sec

 

For the rest of this note, I’ll focus on the top two.

 

 

Disk Queue Length

 

The most common performance monitoring metric people quote is usually disk queue length. While this is important, now days it’s almost impossible to use as an accurate metric because the SAN used in an enterprise database application abstracts the DBA from a lot of the physical characteristics needed to make a reasonable judgment about what is good or bad.

 

The usual “rule of thumb” for “bad performance” is a queue length greater than 2 for a disk drive. But when you have a SAN with 100 drives in the cabinet, you have no idea how many are being used for your “drive F:” for your data file.

 

For example, the DBA watches perfmon and sees the disk queue length is 10 on drive F:. While this is a big number, it’s relative. If the F: is one physical disk, yes, you have a problem. If the F: is a logical disk composed of 8 physical disks, then it’s not an issue. Why? Because 8 disks x 2 queue length = 16… and the DBA sees 10… so 10 is less than 16 (our threshold for pain)… hence there is not an issue.

 

The main problem with queue length is this: very few people can ever provide me with a comprehensive disk map of their SAN showing all the logical volumes. For example, how many disks are participating in an EMC Sym hyper volume.

 

The following is a very small example of what a mapping would look like. Each of the “73” are 73G disk drives. You can see multiple drives participating in the LUN (Note, this is RAID 0+1 so you see 2 73G drives making up a 59.39G formatted capacity):

 

 

RG0, R1, 60.39 GB

RG1, R1, 60.39 GB

RG2, R1, 60.39 GB

RG3, R1, 66.61 GB

 

73

73

73

73

73

73

73

73

 

00_00

00_01

00_02

00_03

00_04

00_05

00_06

00_07

 

Private System Partition

 

LUN 30 (1 GB), Unallocated (65.61 GB)

 

LUN 0 (1 GB), Unallocated (59.39 GB)

LUN 10 (1 GB), Unallocated (59.39 GB)

LUN 20 (1 GB), Unallocated (59.39 GB)

 
 

 

Without a map like this, disk queue length is meaningless to me. I have nothing to calculate what the queue length should be less than.

 

With regards to SQL Server, it will not let the transaction log volume have a queue length of more than 3. Thus, the DBA may never see a “problem”. The reason for this is SQL Server is trying not to swarm the disk drives and will throttle back the work to the log. Since the database follows the Write Ahead Log (WAL) protocol, if it can’t write to the log fast enough, it will not be able to write to the data volumes either… and it will slow down the whole system’s throughput.

 

Per the previous posting on throughput testing, if you don’t have enough spindles to support the volume of data being pushed through the system, the whole box will slow down. This would be especially important in an enterprise app such as SAP or PeopleSoft that run large batch jobs and are writing to the transaction log constantly.

 

Disk Transfer Time

 

As you can read from above, disk queue length can be deceptive. You need a lot of information to make a good judgment.

 

Disk transfer time is what the disk sub system is reporting to the operating system. It abstracts the DBA from having to know the absolute physical characteristics of the underlying disk.

 

The key point is that it keeps the DBA from rat holing on a lot of very specific information that most people don’t have… namely, a comprehensive disk mapping of their SAN. It also distills a lot of other complicated measurements down to a few simple ones that mean the most.

 

For example, if I have a disk queue length of 20 on my F: drive, but my response time is less than 5ms… then everything is working great. What this probably means is that the F: drive has 20 (or more) spindles behind it or that there is massive caching taking place.

 

In the context of disk latency, the following is an example of modern performance characteristics of a SCSI disk drive:

 

Maxtor Atlas 15K II

Ultra320 SCSI

Average 3ms, Max 8ms

Max Sustained 98 MB/sec

Burst 320 MB/sec

8M Cache

15K RPM

 

Source Maxtor: http://www.maxtor.com/_files/maxtor/en_us/documentation/data_sheets/atlas_15kii_data_sheet.pdf

 

To monitor the times:

  1. Start Windows perfmon.
  2. Use counter “Logical Disk”
  3. Select “Avg Disk/Sec” and choose Read, Write or Transfer depending on what you want to monitor. I typically choose Transfer.

Note, when you read the perfmon data and see a number like “.010” this means 10 milliseconds.

Additionally, when monitoring a SAN, they often have their own perfmon counter you need to use. For example, EMC and Veritas have their own.

 

There is plenty of room for intelligent people to argue on the following data, but I use the following table to determine the meaning of the data:

 

10 ms

 

Good

10 ms

20 ms

Reasonable

20 ms

50 ms

Busy

50 ms +

 

Bad

 

 

I have a good friend that will credibly argue that anything more than 1 ms response times is a poorly configured SAN. He has customers that get in the 250 to 500 microsecond responds times.

Note, many times the performance problems are tied to firmware revisions, HBA configuration, and/or BIOS issues.

 

 

Summary

 

When you start focusing on response time, which is what really matters most, the queue length starts to become irrelevant. If you can get some kind of super drive that can handle everything, terrific! (Think: Solid State RAM Drives.)

 

What the latency metric does is cut through a lot of the red tape that architects usually have. This metric gives the DBA the ability to just tell the SAN architect when they lay out the LUNs is, “Slice the disk in a way where I get 5 ms disk access times. The rest is up to you.”

 

If the database can get sustained 5ms disk access times with a heavy stress test (ex. SQLIOSTRESS) then it can probably handle the load of the user base with the app running… which is what you ultimately want for success.

 

Thanks to Frank McBath

 

 
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