Category Archives: Reliability

Apple chomps Anobit

A few days ago, Apple paid USD$500 million to buy an Israeli startup, Anobit, a maker of flash storage technology.

Obviously, one of the reasons Apple did so is to move up a notch to differentiate itself from the competition and positions itself as a premier technology innovator. It has won the MP3 war with its iPod, but in the smartphones, tablets and notebooks space, Apple is being challenged strongly.

Today, flash storage technology is prevalent, and the demand to pack more capacity into a small real-estate of flash will eventually lead to reliability issues. The most common type of NAND flash storage is the MLC (multi-level cells) versus the more expensive type called SLC (single level cells).

But physically and the internal-build of MLC and SLC are the exactly the same, except that in SLC, one cell contains 1 bit of data. Obviously this means that 2 or more bits occupy one cell in MLC. That’s the only difference from a physical structure of NAND flash. However, if you can see from the diagram below, SLCs has advantages over MLCs.

NAND Flash uses electrical voltage to program a cell and it is always a challenge to store bits of data in a very, very small cell. If you apply too little voltage, the bit in the cell does not register and will result in something unreadable or an error. If you apply too much voltage, the adjacent cells are disturbed and resulting in errors in the flash. Voltage leak is not uncommon.

The demands of packing more and more data (i.e. more bits) into one cell geometry results in greater unreliability. Though the reliability of  the NAND Flash storage is predictable, i.e. we would roughly know when it will fail, we will eventually reach a point where the reliability of MLCs will no longer be desirable if we continue the trend of packing more and more capacity.

That’s when Anobit comes in. Anobit has designed and implemented architectural changes of the way NAND Flash storage is used. The technology in laymen terms comes in 2 stages.

  1. Error reduction – by understanding what causes flash impairment. This could be cross-coupling, read disturbs, data retention impairments, program disturbs, endurance impairments
  2. Error Correction and Signal Processing – Advanced ECC (error-correcting code), and introducing the patented (and other patents pending) Memory Signal Processing (TM) to improve the reliability and performance of the NAND Flash storage as show in the diagram below:

In a nutshell, Anobit’s new and innovative approach will result in

  • More reliable MLCs
  • Better performing MLCs
  • Cheaper NAND Flash technology

This will indeed extend the NAND Flash technology into greater innovation of flash storage technology in the near future. Whatever Apple will do with Anobit’s technology is anybody’s guess but one thing is certain. It’s going to propel Apple into newer heights.

Storage must go on a diet

Nowadays, the capacity of the hard disk drives (HDDs) are really big. 3TB is out and 4TB is in the horizon. What’s next?

For small-medium businesses in Malaysia, depending on their data requirements and applications, 3-10TB is pretty sufficient  and with room to grow as well. Therefore, a 6TB requirement can be easily satisfied with 2 x 3TB HDDs.

If I were the customer, why would I buy a storage array, with the software licenses and other stuff that will not only increase my cost of equipment acquisition and data management, it will also increase the complexity of my IT infrastructure? I could just slot HDDs into my existing server, RAID it with RAID-0 (not a good idea but to save costs, most customers would do that) and I have a 6TB volume! It’s cheaper, easier to manage with Windows or Linux, and my system administrator doesn’t have to fuss about lack of storage experience.

And RAID isn’t really keeping up with the tremendous growth of HDD’s capacity as well. In fact, RAID is at risk. RAID (especially RAID 5/6) just cannot continue provide the LUN or volume reliability and data availability because it just takes too damn long to rebuild the volume after the failure of a disk.

Back in the days where HDDs were less than 500GB, RAID-5 would still hold up but after passing the 1TB mark, RAID-6 became more prevalent. But now, that 1TB has ballooned to 3TB and RAID-6 is on shaky ground. What’s next? RAID-7? ZFS has RAID-Z3, triple parity but come on, how many vendors have that? With triple parity or stronger RAID (is there one?), the price of the storage array is going to get too costly.

Experts have been speaking about parity-declustering,  but that’s something that a few vendors have right now. Panasas, founded by one of forefathers of RAID, Garth Gibson, comes to mind. In fact, Garth Gibson and Mark Holland of Cargenie-Mellon University’s Parallel Data Lab (PDL) presented a paper about parity-declustering more than 10 years ago.

Let’s get back to our storage fatty. Yes, our storage is getting fat, obese, rotund or whatever you want to call it. And storage vendors have been pushing a concept in hope that storage administrators and customers can take advantage of it. It is called Storage Optimization or Storage Efficiency.

Here are a few ways you can consider to put your storage on a diet.

  • Compression
  • Thin Provisioning
  • Deduplication
  • Storage Tiering
  • Tapes and SSDs
To me, compression has not taken the storage world by storm. But then again, there aren’t many vendors that tout compression as a feature for storage optimization. Most of them rather prefer to push the darling of data reduction, data deduplication, as the main feature for save more space. Theoretically, data deduplication makes more sense when the data is inactive, and has high occurrence of duplicated data. That is why secondary storage such  as backup deduplication targets like Data Domain, HP StoreOnce, Quantum DXi can publish 20:1 rates and over time, that rate can get even higher.
NetApp also has been pushing their A-SIS data deduplication on primary storage. Yes, it helps with the storage savings in primary but when the need for higher data transfer rates and time to access “manipulated” data (deduped or compressed), it is likely that compression is a better choice for primary, active data.
So who has compression? NetApp ONTAP 8.0.1 has compression now and IBM with its Storewize V7000 started as a compression device. Read about IBM Storewize in my blog here. Dell has Ocarina Networks, which was recently unleashed. I am a big fan of Ocarina Networks and I wrote about the technology in my previous blog. EMC, during the Celerra days of DART has compression but I don’t hear much about it in their VNX. Compression is there, believe me, embedded all the loads of EMC marketing.
Thin Provisioning is now a must-have and standard feature of all storage vendors. What is Thin Provisioning? The diagram below shows you:
In the past, storage systems aren’t so intelligent. You ask for 10TB, you are given 10TB and that 10TB is “deducted” from the storage capacity. That leads to wastage and storage inefficiencies. Today, Thin Provisioning will give you 10TB but storage capacity is consumed as it is being used. The capacity is not pre-allocated as in the past. Thin provisioning is a great diet pill for bloated storage projects. 
Another up and coming feature is storage tiering. Storage tiering, when associated to storage optimization, should include hierarchical storage management (HSM) and tape-out as well. Storage optimization solutions should not offer only in the storage array itself. Storage tiering within the storage array is available with most vendors – IBM EasyTier, EMC FAST2, Dell Fluid Data Management and many others. But what about data being moved out of the storage array? What about reducing the capacity of the data online or near-line? Why not put them offline if there isn’t a need for it?
I term this as Active Archiving, something I learned while I was at EMC. Here’s a look at EMC’s style of Active Archiving:
Active Archiving promotes the concept of data archiving and is not unique only to EMC. Almost all storage vendors, either natively or with 3rd party vendors, can perform fairly efficient data archiving in one way or another. One of the software that I liked (and not unique!) is Quantum Stornext. Here’s a video of how Quantum Stornext helps reduce the fat of the storage.
With the single-copy sharing feature of Quantum Stornext to multiple disparate OSes, there are lesser duplicate files in storage as well.
Tapes have been getting a bad name in the past few years. It has been repositioned and repurposed as an archive medium rather than a backup medium. But tape is the greenest and most powerful storage diet pill around. And we should not be discount tapes because tapes are fighting back. Pretty soon you will be hearing about Linear Tape File System (LTFS). In a nutshell, Linear Tape File System (LTFS) allows you to use the tape almost as if it were a hard disk. You can drag and drop files from your server to the tape, see the list of saved files using a standard operating system directory (no backup software catalog needed), and use point and click to restore. How cool is that!
And Solid State Drives (SSDs) makes sense as well.
There are times that we need IOPS and using spinning drives, we have to set up many disk spindles to achieve the IOPS that we want.  For example, using the diagram below from the godfather of storage, Greg Schulz,
The set of 16 spinning HDD drives on the left can only deliver 3,520 IOPS. The problem is, we have wasted a lot of disk space, as seen in the diagram below. This design, which most customer would be accustomed to, may look cheaper but in actual fact, is NOT.
If the price of a Fibre Channel HDD is RM2,000, the total of 16 would make up RM32,000.00. That is not inclusive of additional power and cooling and rack space and also the data management costs. Assuming the SSDs costs 5 times more than the Fibre Channel HDD. SSDs are capable of delivering very high IOPS. Here I am putting a modest 5,000 IOPS per SSDs. With just 2 SSDs (as the right design suggests), the total costs is only RM20,000. It has greater performance room to grow, and also savings in data management, power and cooling.
Folks, consider SSDs as part of your storage diet plan.
All these features are available, in whole or in part, and they are part of the storage technology offerings that is out there. With all these being said, are you doing something about it? Get off your lazy bum and start managing your storage and put your storage on a diet!!!

Playing with NetApp … After Rightsizing

It has been a tough week for me and that’s why I haven’t been writing much this week. So, right now, right after dinner, I am back on keyboard again, continuing where I have left off with NetApp’s usable capacity.

A blog and a half ago, I wrote about the journey of getting NetApp’s usable capacity and stopping up to the point of the disk capacity after rightsizing. We ended with the table below.

Manufacturer Marketing Capacity NetApp Rightsized Capacity
36GB 34.0/34.5GB*
72GB 68GB
144GB 136GB
300GB 272GB
600GB 560GB
1TB 847GB
2TB 1.69TB
3TB 2.48TB

* The size of 34.5GB was for the Fibre Channel Zone Checksum mechanism employed prior to ONTAP version 6.5 of 512 bytes per sector. After ONTAP 6.5, block checksum of 520 bytes per sector was employed for greater data integrity protection and resiliency.

At this stage, the next variable to consider is RAID group sizing. NetApp’s ONTAP employs 2 types of RAID level – RAID-4 and the default RAID-DP (a unique implementation of RAID-6, employing 2 dedicated disks as double parity).

Before all the physical hard disk drives (HDDs) are pooled into a logical construct called an aggregate (which is what ONTAP’s FlexVol is about), the HDDs are grouped into a RAID group. A RAID group is also a logical construct, in which it combines all HDDs into data or parity disks. The RAID group is the building block of the Aggregate.

So why a RAID group? Well, first of all, (although likely possible), it is not prudent to group a large number of HDDs into a single group with only 2 parity drives supporting the RAID. Even though one can maximize the allowable, aggregated capacity from the HDDs, the data reconstruction or data resilvering operation following a HDD failure (disks are supposed to fail once in a while, remember?) would very much slow the RAID operations to a trickle because of the large number of HDDs the operation has to address. Therefore, it is best to spread them out into multiple RAID groups with a recommended fixed number of HDDs per RAID group.

RAID group is important because it is used to balance a few considerations

  • Performance in recovery if there is a disk reconstruction or resilvering
  • Combined RAID performance and availability through a Mean Time Between Data Loss (MTBDL) formula

Different ONTAP versions (and also different disk types) have different number of HDDs to constitute a RAID group. For ONTAP 8.0.1, the table below are its recommendation.

So, given a large pool of HDDs, the NetApp storage administrator has to figure out the best layout and the optimal number of HDDs to get to the capacity he/she wants. And there is also a best practice to set aside 2 HDDs for a RAID-DP configuration with every 30 or so HDDs. Also, it is best practice to take the default recommended RAID group size most of the time.

I would presume that this is all getting very confusing, so let me show that with an example. Let’s use the common 2TB SATA HDD and let’s assume the customer has just bought a 100 HDDs FAS6000. From the table above, the default (and recommended) RAID group size is 14. The customer wants to have maximum usable capacity as well. In a step-by-step guide,

  1. Consider the hot sparing best practice. The customer wants to ensure that there will always be enough spares, so using the rule-of-thumb of 2 HDDs per 30 HDDs, 6 disks are set aside as hot spares. That leaves 94 HDDs from the initial 100 HDDs.
  2. There is a root volume, rootvol, and it is recommended to put this into an aggregate of its own so that it gets maximum performance and availability. To standardize, the storage administrator configures 3 HDDs as 1 RAID group to create the rootvol aggregate, aggr0. Even though the total capacity used by the rootvol is just a few hundred GBs, it is not recommended to place data into rootvol. Of course, this situation cannot be avoided in most of the FAS2000 series, where a smaller HDDs count are sold and implemented. With 3 HDDs used up as rootvol, the customer now has 91 HDDs.
  3. With 91 HDDs, and using the default RAID group size of 14, for the next aggregate of aggr1, the storage administrator can configure 6 x full RAID group of 14 HDDs (6 x 14 = 84) and 1 x partial RAID group of 7. (91/14 = 6 remainder 7). And 84 + 7 = 91 HDDs.
  4. RAID-DP requires 2 disks per RAID group to be used as parity disks. Since there are a total of 7 RAID groups from the 91 HDDs, 14 HDDs are parity disks, leaving 77 HDDs as data disks.

This is where the rightsized capacity comes back into play again. 77 x 2TB HDDs is really 77 x 1.69TB = 130.13TB from an initial of 100 x 2TB = 200TB.

If you intend to create more aggregates (in our example here, we have only 2 aggregates – aggr0 and aggr1), there will be more consideration for RAID group sizing and parity disks, further reducing the usable capacity.

This is just part 2 of our “Playing with NetApp Capacity” series. We have not arrived at the final usable capacity yet and I will further share that with you over the weekend.


I have to get this off my chest. Oracle’s Solaris ZFS is better than NetApp’s ONTAP WAFL! There! I said it!

I have been studying both similar Copy-on-Write (COW) file systems at the data structure level for a while now and I strongly believe ZFS is a better implementation of the COW file systems (also known as “shadow-paging” file system) than WAFL. How are both similar and how are both different? The angle we are looking at is not performance but about resiliency and reliability.

(Note: btrfs or “Butter File System” is another up-and-coming COW file system under GPL license and is likely to be the default file system for the coming Fedora 16)

In Computer Science, COW file system are tree-like data structures as shown below. They are different than the traditional Berkeley Fast File System data structure as shown below:

As some of you may know, Berkeley Fast File System is the foundation of some modern day file systems such as Windows NTFS, Linux ext2/3/4, and Veritas VxFS.

COW file system is another school of thought and this type of file system is designed in a tree-like data structure.

In a COW file system or more rightly named shadow-paging file system, the original node of the data block is never modified. Instead, a copy of the node is created and that copy is modified, i.e. a shadow of the original node is created and modified. Since the node is linked to a parent node and that parent node is linked to a higher parent node and so on all the way to the top-most root node, each parent and higher-parent nodes are modified as it traverses through the tree ending at the root node.

The diagram below shows the shadow-paging process in action as modifications of the node copy and its respective parent node copies traverse to the top of the tree data structure. The diagram is from ZFS but the same process applies to WAFL as well.

As each data block of either the leaf node (the last node in the tree) or the parent nodes are being modified, pointers to either the original data blocks or the copied data blocks are modified accordingly relative to the original tree structure, until the last root node at the top of the shadow tree is modified. Then, the COW file system commit is considered complete. Take note that the entire process of changing pointers and modifying copies of the nodes of the data blocks is done is a single I/O.

The root at the top for ZFS is called uberblock and called fsinfo in WAFL. Because an exact shadow of the tree-like file system is created when the data blocks are modified, this also gives birth to how snapshots are created in a COW file system. It’s all about pointers, baby!

Here’s how it looks like with the original data tree and the snapshot data tree once the shadow paging modifications are complete.

However, there are a few key features from the data integrity and reliability point of view where ZFS is better than WAFL. Let me share that with you.

In a nutshell, ZFS is a layered architecture that looks like this

The Data Management Unit (DMU) layer is one implementation that ensures stronger data integrity. The DMU maintains a checksum on the data in each data block by storing the checksum in the parent’s blocks. Thus if something is messed up in the data block (possibly by Silent Data Corruption), the checksum in the parent’s block will be able to detect it and also repair the data corruption if there is sufficient data redundancy information in the data tree.

WAFL will not be able to detect such data corruptions because the checksum is applied at the disk block level and the parity derived during the RAID-DP write does not flag this such discrepancy. An old set of slides I found portrayed this comparison as shown below.

Another cool feature that addresses data resiliency is the implementation of ditto blocks. Ditto blocks stores 3 copies of the metadata and this allows the recovery of lost metadata even if 2 copies of the metadata are deleted.

Therefore, the ability of ZFS to survive data corruption, metadata deletion is stronger when compared to WAFL .This is not discredit NetApp’s WAFL. It is just that ZFS was built with stronger features to address the issues we have with storing data in modern day file systems.

There are many other features within ZFS that have improved upon NetApp’s WAFL. One such feature is the implementation of RAID-Z/Z2/Z3. RAID-Z is a superset implementation of the traditional RAID-5 but with a different twist. Instead of using fixed stripe width like RAID-4 or RAID-DP, RAID-Z/Z2 uses a dynamic variable stripe width. This addressed the parity RAID-4/5 “write hole” flaw, where incomplete or partial stripes will result in a “hole” that leads to file system fragmentation. RAID-Z/Z2 address this by filling up all blocks with variable stripe width. A parity can be calculated and assigned with any striped width, as shown below.

Other really cool stuff are Hybrid Storage Pool and the ability to create software-based caching using fast disk drives such as SSDs. This approach of creating ReadZilla (read caching) and LogZilla (write caching) eliminates the need for proprietary NVRAM as implemented in NetApp’s WAFL.

The only problem is, despite the super cool features of ZFS, most Oracle (not Sun) sales does not have much clue how to sell ZFS storage. NetApp, with its well trained and tuned, sales force is beating Oracle to pulp.

Using simple MTBF to determine reliability to Finance

The other day, a prospect was requesting quotations after quotations from a friend of mine to make so-called “apple-to-apple” comparison with another storage vendor. But it was difficult to have that sort of comparisons because one guy would propose SAS, and the other SATA and so on. I was roped in by my friend to help. So in the end I asked this prospect, which 3 of these criteria matters to him most – Performance, Capacity or Reliability.

He gave me an answer and the reliability criteria was leading his requirement. Then he asked me if I could help determine in a “quick-and-dirty manner” by using MTBF (Mean Time Between Failure) of the disks to convince his finance about the question of reliability.

Well, most HDD vendors published their MTBF as a measuring stick to determine the reliability of their manufactured disks. MTBF is by no means accurate but it is useful to define HDD reliability in a crude manner. If you have seen the components that goes into a HDD, you would be amazed that the HDD components go through a tremendously stressed environment. The Read/Write head operating at a flight height (head gap)  between the platters thinner than a human hair and the servo-controlled technology maintains the constant, never-lagging 7200/10,000/15,000 RPM days-after-days, months-after-months, years-after-years. And it yet, we seem to take the HDD for granted, rarely thinking how much technology goes into it on a nanoscale. That’s technology at its best – bringing something so complex to make it so simple for all of us.

I found that the Seagate Constellation.2 Enterprise-class 3TB 7200 RPM disk MTBF is 1.2 million hours while the Seagate Cheetah 600GB 10,000 RPM disk MTBF is 1.5 million hours. So, the Cheetah is about 30% more reliable than the Constellation.2, right?

Wrong! There are other factors involved. In order to achieve 3TB usable, a RAID 1 (average write performance, very good read performance) would require 2 units of 3TB 7200 RPM disks. On the other hand, using a 10, 000 RPM disks, with the largest shipping capacity of 600GB, you would need 10 units of such HDDs. RAID-DP (this is NetApp by the way) would give average write performance (better than RAID 1 in some cases) and very good read performance (for sequential access).

So, I broke down the above 2 examples to this prospect (to achieve 3TB usable)

  1. Seagate Constellation.2 3TB 7200 RPM HDD MTBF is 1.2 million hours x 2 units
  2. Seagate Cheetah 600GB 10,000 RPM HDD MTBF is 1.5 million hours x 10 units

By using a simple calculation of

    RF (Reliability Factor) = MTBF/#HDDs

the prospect will be able to determine which of the 2 HDD types above could be more reliable.

In case #1, RF is 600,000 hours and in case #2, the RF is 125,000 hours. Suddenly you can see that the Constellation.2 HDDs which has a lower MTBF has a higher RF compared to the Cheetah HDDs. Quick and simple, isn’t it?

Note that I did not use the SAS versus SATA technology into the mixture because they don’t matter. SAS and SATA are merely data channels that drives data in and out of the spinning HDDs. So, folks, don’t be fooled that a SAS drive is more reliable than a SATA drive. Sometimes, they are just the same old spinning HDDs. In fact, the mentioned Seagate Constellation.2 HDD (3TB, 7200 RPM) has both SAS and SATA interface.

Of course, this is just one factor in the whole Reliability universe. Other factors such as RAID-level, checksum, CRC, single or dual-controller also determines the reliability of the entire storage array.

In conclusion, we all know that the MTBF alone does not determine the reliability of the solution the prospect is about to purchase. But this is one way you can use to help the finance people to get the idea of reliability.