A Storage Area Network (SAN) may mean two different things.
To the storage professionals, it could narrowly mean the switched fabric
between the host servers and the sophisticated disk arrays that actually store
data. To most other folks, however, the distinction between the switched fabric
and the disk arrays means very little. Rather, they view the fabric and the
disk arrays -- practically everything behind the drive and beyond the host server
comprising the SAN. This article assumes this most common view.
Two points are worth highlighting further.
First, a SAN is not a monolithic entity. If you take a
switch-based Fibre-Channel SAN as an example, the I/O path that is considered
part of the SAN starts from the host bus adapters (HBA) all the way to the
eventual disk media on the disk array. Hardware components on this I/O path
typically include switches, inter-switch links, front-side adapters, disk array
cache and processors, disk controllers, and disk drive media. In addition,
there are layers of software on this path, including various drivers, firmware,
and APIs. Every single component on this I/O path has the potential to
significantly alter the performance characteristics of a drive presented from the
The second point is that switch-based Fibre-Channel SAN is
not the only type of SAN. But it is the most commonly deployed type in the enterprise,
and is the focus of this article.
To simplify discussions in the rest of this article, a drive
from a directly-attached storage will be referred to as a DAS drive, and
a drive presented from a SAN as a SAN drive.
What is meant by "faster"?
To address the question of which is faster, you must be
clear about what you mean by "faster". In general, you can use three key metrics to quantify the
performance of a disk I/O path:
- I/Os per second (IOps)
- Megabytes per second (MBps)
IOps measures how many I/O requests can be satisfied by the
disk I/O path in a second. For a given disk I/O path, this metric is generally in
reverse proportion to the size of the I/O requests. That is, the larger the I/O
requests, the lower the IOps. This is intuitive. After all, it takes more time
to process a 256KB I/O request than it does an 8KB request.
MBps measures how much data can be pumped through the disk
I/O path. If you view the I/O path as a pipeline, MBps measures how big the
pipeline is and how much pressure it can sustain. So, the bigger the pipeline is
and the more pressure it can handle, the more megabytes of data it can push
through. For a given I/O path, MBps is in direct proportion to the size of the
I/O requests. That is, the larger the I/O requests, the higher the MBps. Larger
I/Os give you better throughput because they incur less disk seek time penalty than
Note that for I/O requests of a fixed block size, MBps is simply
IOps times the block size in megabytes.
I/O latency--also known as I/O response time--measures how
fast an I/O request can be processed by the disk I/O subsystem. For a given I/O
path, it is in reserve proportion to the size of the I/O request. As mentioned
previously, a larger I/O request takes longer to complete.
Generally speaking, you should pay more attention to IOps
and I/O latency when it comes to small-sized I/O requests, and be more
concerned with MBps when dealing with large-sized I/O requests. But whether you
report IOps or MBps, you should always keep I/O latency in the picture. Note
that as you push a disk I/O subsystem to sustain higher IOps or MBps, the average
I/O latency will continue to go up. After a certain point, the latency will
increase exponentially as you put even more I/O requests on the system. If you
obtain a good IOps or MBps number at the expense of a very high I/O latency,
that IOps or MBps number may not be very useful in practice.
What does all this have to do with the question of which is
faster, SAN or directly-attached storage? Well, these three metrics define
precisely what is meant by "faster". So when you say one is faster than the other,
you have to be explicit about what measure you are referring to.
Let us examine the question along these three performance
measures, and let's start with the I/O latency measure first.
Disk I/O latency
SANs almost always come out a loser when it competes with
DAS on I/O latency under light load. If you don't believe me, you can measure
it yourself, and you don't need any fancy tool to obtain convincing data
The latency of an I/O request depends on the current load
condition of the I/O path. If the I/O path is heavily loaded, there will be
contention among the I/O requests for use of certain components on the I/O
path, and consequently it'll take longer to process the I/O request.
To measure the latency of an I/O path under the best
possible condition, you should issue single-threaded I/O requests, i.e.
synchronous I/Os. If you are using an I/O benchmark tool such as IOMeter or sqlio.exe,
for instance, you can configure a single worker thread to issue small
sequential writes with a queue depth of one. This is meaningful since it
simulates the SQL Server database transaction logging I/O activities.
Alternatively, you can use a simple SQL Server script to
measure the latency of an I/O path. The following T-SQL script is an example
that can help you ascertain the best possible disk I/O latency (or response
time) when committing a SQL Server database transaction. The script is designed
to generate I/Os only on the database transaction log file; there is negligible
I/O on the data file because the script repeatedly writes to the same single
data page and that page is not flushed to disk until a checkpoint is issued.
The script assumes that D is a directly-attached internal
drive and E is a drive presented to the same server from a SAN.
-- create a small database on the local internal drive D
CREATE DATABASE io_test_D ON PRIMARY
(NAME=N'io_test_d_data', FILENAME=N'd:\io_test_d.mdf', SIZE=255)
(NAME=N'io_test_d_log', FILENAME=N'd:\io_test_d.ldf', SIZE=2000);
ALTER DATABASE io_test_D SET RECOVERY SIMPLE;
-- create a small database on the SAN drive E
CREATE DATABASE io_test_E ON PRIMARY
(NAME=N'io_test_e_data', FILENAME=N'e:\io_test_e.mdf', SIZE=255)
(NAME=N'io_test_e_log', FILENAME=N'e:\io_test_e.ldf', SIZE=2000);
ALTER DATABASE io_test_E SET RECOVERY SIMPLE;
-- Create two identical tables, one in each database
CREATE TABLE test (i CHAR(200) NOT NULL);
INSERT test VALUES('ABC');
CREATE TABLE test (i CHAR(200) NOT NULL);
INSERT test VALUES('ABC');
-- Run the following script on database io_test_D and io_test_E.
-- Run the script multiple times in each database, and alternate
-- the test runs to get consistent measures
SET NOCOUNT ON;
DECLARE @i int,
SET @i = 1;
SET @start = getdate();
WHILE @i < 10
SET i = cast(@i as CHAR(200));
SET @i = @i + 1;
SELECT 'Duration' = datediff(ms, @start, getdate()),
'Latency' = datediff(ms, @start, getdate())/10000.0;
What you will find is that the best I/O response time on
your D drive is in the 100-microsecond range, whereas the best I/O response
time on the E drive may be in the 300~500-microsecond range and could be
This is intuitive because a typical I/O path between the
host and the SAN media (or the SAN cache) is significantly longer than a
typical I/O path between the host and a directly attached storage media (or its
cache). In other words, an I/O request would have to travel through more
components before it can be considered hardened on a SAN drive than it would on
a directly attached storage. No matter how small a latency each of these
components may incur, they add up.
IOps and MBps
IOps is really a measure of the multi-tasking capability of
the disk subsystem, whereas MBps is a measure of how big the disk I/O pipeline
is. SANs -- especially higher end SANs -- shine on these measures, although from a
specific host server you may only have access to a fraction of what the SAN has
Higher end SANs are often deployed through a Fibre-Channel
switch based fabric. It offers point-to-point links through the fabric, and a
host is often configured to have multiple links through the fabric. Other
components on the I/O path can be similarly configured to offer I/O path
parallelism. This enables higher end SANs to scale their ability to process I/O
requests and to scale the size of the I/O pipeline.
Note that the higher end disk arrays are in fact powerful
multi-processor computers with a large amount of memory for cache. They are
ideal for handling multiple concurrent small-sized I/O requests. The faster and
the more the processors are in these disk arrays, the higher IOps we can
expect. Likewise, the larger the cache in the disk arrays, the higher IOps we
MBps, on the other hand, may leave much to be desired on a drive
presented to a specific host, especially in a large shared environment where
one has to distribute the I/O resources among as many hosts as possible while
still deliver adequate performance for each host. If you view a SAN drive as
consisting of one or more paths of many I/O components, it is then a question of
whether enough I/O path parallelism is configured for the drive.
For instance, if a server is equipped with two older 1-Gbps
host bus adapters (HBAs), its MBps throughput would be capped at about 200MB
per second no matter how powerful the rest of the SAN is. Replacing the 1-Gbps
HBAs with two newer 4-Gbps HBAs or adding more HBAs may improve the throughput,
if the HBAs are indeed the throughput bottleneck. But the SAN drive throughput could
also be limited by the maximum throughput of the inter-switch links in the SAN
switched fabric. Further down the I/O paths, the front-side adapter ports on
the disk array, the cache in the disk array, the disk controllers, and the disk
spindles can all become the bottleneck limiting the MBps throughput of the SAN
In short, there can be a host of reasons why the MBps
throughput of a particular SAN drive is limited.
However, this by no means suggests that you can't configure
a SAN drive to deliver the MBps throughput you want (up to a reasonable limit).
For instance, to get 1000MBps on a SAN drive, you may need to plug in three
4-Gbps HBAs, configure enough inter-switch links (depending on the throughput
of each link), configure enough front-side adapter port (again depending on the
port throughput), configure enough number of disk controllers, and sufficient
number of spindles. The challenge is that most of these SAN components are not
under your control, and in some SAN environments you may have to jump through the
hoops to get all the desired configurations done.
Configuring DAS, on the other hand, is a lot simpler as you
most likely have full control of all the key decision factors: the choice of
disk controllers (i.e. the maximum throughput of each controller and the number
of controllers), the choice of drives (i.e. the maximum throughput of each
drive and its size), and how many drives to hang off each controller.
For instance, if you need 1000MBps throughput, you can plug
in four 300MBps SATA RAID controllers to your host server, and hook up enough SATA
drives to each controller.
Two simple examples
So how does a SAN drive stack up against a DAS drive? Well,
I hope the discussions so far--though scratching only the surface of the topic--have
helped to convince you that there is no absolute winner, and it all depends on
how the drives are configured. In general and given sufficient resources, you
can configure a SAN drive to outperform a DAS drive in terms of throughput.
Similarly, with sufficient resources, you can configure a DAS drive to
outperform a SAN drive.
If that's not convincing enough, let me give you two simple
Example 1 - SAN beating DAS
I had a server with a SAN drive and an internal local drive
which was a mirrored set of two drives. To assess their performance, I ran an
identical series of disk I/O benchmark tests on both the SAN drive and the DAS
drive, respectively. The following table summarizes some of the results:
|8K sequential writes
|8K sequential reads
|128K sequential writes
|128K sequential reads
The SAN drive far outperformed the DAS drive on all four I/O
workloads. In fact, on the I/O throughput, I'd expect any decently configured
SAN drive to beat an internal mirrored set--that you typically find on a server--by
a large margin.
Example 2 - DAS beating SAN
It is an easy job to show that you can configure a DAS drive
to beat a SAN drive. Take the SAN drive in the previous example. The best
throughput it could achieve was around 207MB per second. Now, take a look at
the DAS drives that came with the Sun Fire X4500 server as described in one of Jim
Gray's articles. With six SATA controllers and 48 7K-rpm drives, you could
get about 2.6GB per second, more than 10 times better in throughput than the SAN drive in the previous example could offer in throughput.
I hope we have established that it doesn't help or make
sense to make unqualified blanket statements such as that SAN performs better
than DAS or that DAS performs better than SAN.
Keep in mind that performance is only one of the many
factors in deciding whether to place the database files on SAN or DAS. Since
it's likely that you can configure either to meet your performance requirements
(subject to the constraints of your particular environment), these other
factors -- such as cost, back/restore requirements, data replication requirements,
company storage policies and directions, and data center infrastructure
constraints -- are often more important than the performance factors.
Having made the case that you can configure either SAN to
outperform DAS or DAS to outperform SAN, I should point out that, for very high
I/O performance requirements, in most enterprise environments you'll probably find
that it's easier to configure a DAS drive to outperform a SAN drive because of
the highly shared and strictly managed nature of a large-scale SAN.
That may sound like a bad deal with SAN. However, it is not at
all an indictment of SAN as an enterprise storage solution, but a necessary
consequence of its main purposes. Large-scale enterprise SANs are not deployed
to meet the extreme I/O requirements of a very few applications. While they should
and do meet the performance requirements of most applications, their main
benefits lie in improved sharing of the storage resources, scalability,
manageability, and availability. For instance, your very large databases can
benefit tremendously from the SAN-based backup solutions, and the servers in a
large data center can take advantage of the SAN-level replication technologies
to effectively meet the application disaster recovery requirements en mass. DAS
can't provide this kind of scalability.