Category Archives: Performance Benchmark
Happy Lunar New Year! The Chinese around world has just ushered in the Year of the Water Dragon yesterday. To all my friends and family, and readers of my blog, I wish you a prosperous and auspicious Chinese New Year!
Over the holidays, I have been keeping up with the progress of Solid State Drives (SSDs). I am sure many of us are mesmerized by SSDs and the storage vendors are touting the best of SSDs have to offer. But let me tell you one thing – you are probably getting the least of what the best SSDs have to offer. You might be puzzled why I say things like this.
Let me share with a common sales pitch. Most (if not all) storage vendors will tout performance (usually IOPS) as the greatest benefits of SSDs. The performance numbers have to be compared to something, and that something is your regular spinning Hard Disk Drives (HDDs). The slowest SSDs in terms of IOPS is about 10-15x faster than the HDDs. A single SSD can at least churn 5,000 IOPS when compared to the fastest 15,000 RPM HDDs, which churns out about 200 IOPS (depending on HDD vendors). Therefore, the slowest SSDs can be 20-25x faster than the fastest HDDs, when measured in IOPS.
But the intent of this blogger is to share with you more about SSDs. There’s more to know because SSDs are not built the same. There are write-bias SSDs, read-bias SSDs; there are SLC (single level cell) and MLC (multi level cell) SSDs and so on. How do you differentiate them if Vendor A touts their SSDs and Vendor B touts their SSDs as well? You are not comparing SSDs and HDDs anymore. How do you know what questions to ask when they show you their performance statistics?
SNIA has recently released a set of methodology called “Solid State Storage (SSS) Performance Testing Specifications (PTS)” that helps customers evaluate and compare the SSD performance from a vendor-neutral perspective. There is also a whitepaper related to SSS PTS. This is something very important because we have to continue to educate the community about what is right and what is wrong.
In a recent webcast, the presenters from the SNIA SSS TWG (Technical Working Group) mentioned a few questions that I think we as vendors and customers should think about when working with an SSD sales pitch. I thought I share them with you.
- Was the performance testing done at the SSD device level or at the file system level?
- Was the SSD pre-conditioned before the testing? If so, how?
- Was the performance results taken at a steady state?
- How much data was written during the testing?
- Where was the data written to?
- What data pattern was tested?
- What was the test platform used to test the SSDs?
- What hardware or software package(s) used for the testing?
- Was the HBA bandwidth, queue depth and other parameters sufficient to test the SSDs?
- What type of NAND Flash was used?
- What is the target workload?
- What was the percentage weight of the mix of Reads and Writes?
- Are there warranty life design issue?
I thought that these questions were very relevant in understanding SSDs’ performance. And I also got to know that SSDs behave differently throughout the life stages of the device. From a performance point of view, there are 3 distinct performance life stages
- Fresh out of the box (FOB)
- Steady State
As you can see from the graph below, a SSD, fresh out of the box (FOB) displayed considerable performance numbers. Over a period of time (the graph shown minutes), it transitioned into a mezzanine stage of lower IOPS and finally, it normalized to the state called the Steady State. The Steady State is the desirable test range that will give the most accurate type of IOPS numbers. Therefore, it is important that your storage vendor’s performance numbers should be taken during this life stage.
Another consideration when understanding the SSDs’ performance numbers are what type of tests used? The test could be done at the file system level or at the device level. As shown in the diagram below, the test numbers could be taken from many different elements through the stack of the data path.
Performance for cached data would given impressive numbers but it is not accurate. File system performance will not be useful because the data travels through different layers, masking the true performance capability of the SSDs. Therefore, SNIA’s performance is based on a synthetic device level test to achieve consistency and a more accurate IOPS numbers.
There are many other factors used to determine the most relevant performance numbers. The SNIA PTS test has 4 main test suite that addresses different aspects of the SSD’s performance. They are:
- Write Saturation test
- Latency test
- IOPS test
- Throughput test
Good morning, afternoon, evening, Ladies & Gentlemen, wherever you are.
Today, we are going to learn how to bake, errr … I mean, make a storage performance model. Before we begin, allow me to set the stage.
Don’t you just hate it when you are asked to do storage performance sizing and you don’t have a freaking idea how to get started? A typical techie would probably say, “Aiya, just use the capacity lah!”, and usually, they will proceed to size the storage according to capacity. In fact, sizing by capacity is the worst way to do storage performance modeling.
Bear in mind that storage is not a black box, although some people wished it was. It is not black magic when it comes to performance sizing because things can be applied in a very scientific and logical manner.
SNIA (Storage Networking Industry Association) has made a storage performance modeling methodology (that’s quite a mouthful), and basically simplified it into these few key ingredients. This recipe is for storage performance modeling in general and I am advising you guys out there to engage your storage vendors professional services. They will know their storage solutions best.
And I am going to say to you – Don’t be cheap and not engage professional services – to get to the experts out there. I was having a chat with an consultant just now at McDonald’s. I have known this friend of mine for about 6-7 years now and his name is Sugen Sumoo, the Director of DBORA Consulting. They specialize in Oracle and database performance tuning and performance forecasting and it is something that a typical DBA can’t do, because DBORA Consulting is the Professional Service that brings expertise and value to Oracle customers. Likewise, you have to engage your respective storage professional services as well.
In a cook book or a cooking show, you are presented with the ingredients used and in this recipe for storage performance modeling, the ingredients (in no particular order) are:
- Application block size
- Read and Write ratio
- Application access patterns
- Working set size
- IOPS or throughput
- Demand intensity
Application Block Size
First of all, the storage is there to serve applications. We always have to look from the applications’ point of view, not storage’s point of view. Different applications have different block size. Databases typically range from 8K-64K and backup applications usually deal with larger block sizes. Video applications can have 256K block sizes or higher. It all depends.
The best way is to find out from the DBA, email administrator or application developers. The unfortunate thing is most so-called technical people or administrators in Malaysia doesn’t have a clue about the applications they manage. So, my advice to you storage professionals, do your research on the application and take the default value. These clueless fellas are likely to take the default.
Read and Write ratio
Applications behave differently at different times of the day, and at different times of the month (no, it’s not PMS). At the end of the financial year or calendar, there are some tasks that these applications do as well. But in a typical day, there are different weightage or percentage of read operations versus write operations.
Most OLTP (online transaction processing)-based applications tend to be read heavy and write light, but we need to find out the ratio. Typically, it can be a 2:1 ratio or 60%:40%, but it is best to speak to the application administrators about the ratio. DSS (Decision Support Systems) and data warehousing applications could have much higher reads than writes while a seismic-analysis applications can have multiple writes during the analysis periods. It all depends.
To counter the “clueless” administrators, ask lots of questions. Find out the workflow of several key tasks and ask what that particular tasks do at different checkpoints of the application’s processing. If you are lazy (please don’t be lazy, because it degrades your value as a storage professional), use a rule of thumb.
Application access patterns
Applications behave differently in general. They can be sequential, like backup or video streaming. They can be random like emails, databases at certain times of the day, and so on. All these behavioral patterns affect how we design and size the disks in the storage.
Some RAID levels tend to work well with sequential access and others, with random access. It is not difficult to find out about the applications’ pattern and if you read more about the different RAID-levels in storage, you can easily identify the type of RAID levels suitable for each type of behavioral patterns.
Working set size
This variable is a bit more difficult to determine. This means that a chunk of the application has to be loaded into a working area, usually memory and cache memory, to be used and abused by the application users.
Unless someone is well versed with the applications, one would not be able to determine how much of the applications would be placed in memory and in cache memory. Typically, this can only be determined after the application has been running for some time.
The flexibility of having SSDs, especially the DRAM-type of SSDs, are very useful to ensure that there is sufficient “working space” for these applications.
IOPS or Throughput
According to SNIA model, for I/O less than 64K, IOPS should be used as a yardstick to do storage performance modeling. Anything larger, use throughput, in which MB/sec is the measurement unit.
The application guy would be able to tell you what kind of IOPS their application is expecting or what kind of throughput they want. Again, ask a lot of questions, because this will help you determine the type of disks and the kind of performance you give to the application guys.
If the application guy is clueless again, ask someone more senior or ask the vendor. If the vendor engineers cannot give you an answer, then they should not be working for the vendor.
This part is usually overlooked when it comes to performance sizing. Demand intensity refers to how intense is the I/O requests. It could come from 1 channel or 1 part of the applications, or it could come from several parts of the applications in parallel. It is as if the storage is being ‘bombarded’ by applications and this is the part that is hard to determine as well.
In some applications, the degree of intensity or parallelism can be tuned and to find out, ask the application administrator or developer. If not, ask the vendor. Also do a lot of research on the application’s architecture.
And one last thing. What I have learned is to add buffers to the storage performance model. Typically I would add about 10-20% extra but you never know. As storage professionals, I would strongly encourage to engage professional services, because it is worthwhile, especially in the early stages of the sizing. It is usually a more expensive affair to size it after the applications have been installed and running.
“Failure to plan is planning to fail”. The recipe isn’t that difficult. Go figure it out.
Thanks for my buddy, Chew Boon of HDS who put me on alert about the new leader of the SPECSfs benchmark results. NetApp “world record” has been broken 13 days later by Avere Systems.
Avere has posted the result of 1,564,404 NFS ops/sec with an ORT (overall response time) of 0.99 msec. This benchmark was done by 44 nodes, using 6.808 TB of memory, with 800 HDDs.
Earlier this month, NetApp touted fantastic results and quickly came out with a TR comparing their solution with EMC Isilon. Here’s a table of the comparison
But those numbers are quickly made irrelevant by Avere FXT, and Avere claims to have the world record title with the “smallest footprint ever”. Here’s a comparison in Avere’s blog, with some photos to boot.
If you have not heard of Avere, they are basically the core team of Spinnaker. NetApp acquired Spinnaker in 2003 to create the clustered file systems from the Spinnaker technology. The development and integration of Spinnaker into NetApp’s Data ONTAP took years and was buggy, and this gave the legroom for competitors like Isilon to take market share in the clustered NAS/scale-out NAS landscape.
Meanwhile, NetApp finally came did come good with the Spinnaker technology and with ONTAP 8.0.1 and 8.1, the codes of both platforms merged into one.
The Spinnaker team went on develop a new technology called the “A-3 Architecture” (shown below) and positioned itself as a NAS Accelerator.
The company has 2 series of funding and now has a high performance systems to compete with the big boys. The name, Avere Systems, is still pretty much unknown in this part of the world and this “world record” will help position them stronger.
But as I have said before, benchmarking are just ways to have bigger bragging rights. It is a game of leapfrogging, and pretty soon this Avere record will be broken. It is nice while it lasts.
A few days after I wrote about the performance benchmark bag of tricks, EMC was the first to fire the first salvo at NetApp’s SPECSfs2008 world records on NFS IOPS.
EMC is obviously using all its ammo to deflate NetApp chest thumping act, with Storagezilla‘s blog. Mark Twomey, who is the alter ego of Storagezilla posted several observations about NetApp apparent use of disk short stroking to artificially boost its performance numbers. This puts NetApp against the wall, with Alex MacDonald (who incidentally is SNIA NFSv4 co-chairman) of the office of the CTO responding hard to Storagezilla’s observation.
The news of this appeared in The Register. Read all about it.
With no letting up, the article also mentioned EMC Isilon’s CTO, Rob Pegler, adding more fuel to the fire.
I spoke about short stroking as some of the tricks used to gain better numbers in benchmark. And I also mentioned that these numbers have little use to the real work and I would like to add that these numbers are just there for marketing reasons. So, for you readers out there, benchmark is really not big of a deal.
Have a great weekend!
First of all, congratulations to NetApp for beating EMC Isilon in the latest SPECSfs2008 benchmark for NFS IOPS. The news is everywhere and here’s one here.
EMC Isilon was blowing its horns several months ago when it hit 1,112,705 IOPS recorded from a 140-node S200 cluster with 3,360 disk drives and a overall response time of 2.54 msecs. Last week, NetApp became top dog, pounding its chest with 1,512,784 IOPS on a 24 x FAS6240 nodes with an overall response time of 1.53msecs. There were 1,728 450GB, 15,000rpm disk drives and the FAS6240s were fitted with Flash Cache.
And with each benchmark that you and I have seen before and after, we will see every storage vendors trying to best the other and if they did, their horns will be blaring, the fireworks are out and they will pounding their chests like Tarzan, saying “Who’s your daddy?” The euphoria usually doesn’t last long as performance records are broken all the time.
However, the performance benchmark results are not to be taken in verbatim because they are not true representations of real life, production environment. 2 years ago, the magazine, the defunct Byte and Switch (which now is part of Network Computing), did a 9-year study on File Systems and Storage Benchmarking. In a very interesting manner, it revealed that a lot of times, benchmarks results are merely reduced to single graphs which has little information about the details of how the benchmark was conducted, how long the benchmark took and so on.
The paper, published by Avishay Traeger and Erez Zadok from Stony Brook University and Nikolai Joukov and Charles P. Wright from the IBM T.J. Watson Research Center entitled, “A Nine Year Study of File System and Storage Benchmarking” studied 415 file systems from 106 published results and the article quoted:
Based on this examination the paper makes some very interesting observations and conclusions that are, in many ways, very critical of the way “research” papers have been written about storage and file systems.
Therefore, the paper highlighted the way the benchmark was done and the way the benchmark results were reported and judging by the strong title (It was titled “Lies, Damn Lies and File Systems Benchmarks”) of the online article that reviewed the study, benchmarks are not the pictures that says a thousand words.
Be it TPC-C, SPC1 or SPECSfs benchmarks, I have gone through some interesting experiences myself, and there are certain tricks of the trade, just like in a magic show. Some of the very common ones I come across are
- Short stroking – a method to format a drive so that only the outer sectors of the disk platter are used to store data. This practice is done in I/O-intensive environments to increase performance.
- Shortened test – performance tests that run for several minutes to achieve the numbers rather than prolonged periods (which mimics real life)
- Reporting aggregated numbers – Note the number of nodes or controllers used to achieve the numbers. It is not ONE controller than can achieve the numbers, but an aggregated performance results factored by the number of controllers