HUS VM is not virtual storage appliance

I was very confused with an recent HDS announcement, and it has been at the back of my mind for several weeks now.

On the last week of September 2012, HDS announced their Hitachi Unified Storage VM, aimed at small/medium enterprises (SMEs). Nothing wrong with that, except the VM part. I am not sure if it was the Computerworld author’s mistake, but he specifically mentioned VM as “virtual machine”. Check out the link here and the screenshot below:

It got me a bit riled up thinking this was some kind of virtual storage ala VMware Virtual Storage Appliance or NetApp ONTAP-V or even the early innovation of HP Lefthand Virtual SAN Appliance. Apparently not!

I did some short investigation and found Nigel Poulton’s blog which gave a fantastic dissection about the HUS VM. The VM is not virtual machine, but Virtual Midrange!

The HUS VM architecture is deep in ASICs, given HDS long history in ASICs design and manufacturing. SiliconFS, is the NAS front end, while the iSCSI and FC part are being serviced from the same HDS microcode of the higher end HDS VSP. Here’s a look at the hardware architectural diagram from Nigel’s blog:

There are plenty of bells and whistles in the HUS VM, armed with plenty of 8Gbps FC ports, SAS 6Gbps backend, SSDs, and software such as Dynamic Provisioning (thin provisioning) and Dynamic Tiering.

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Swiss army of data management

Back in 2000, before I joined NetApp, I bought one of my first storage technology books. It was “The Holy Grail of Data Storage Management” by Jon William Toigo. The book served me very well, because it opened up my eyes about the storage networking and data management world.

I mean, I have been doing storage for 7 years before the year 2000, but I was an implementation and support engineer. I installed my first storage arrays in 1993, the trusty but sometimes odd, SPARCstorage Array 1000. These “antiques” were running 0.25Gbps Fibre Channel, and that nationwide bank project gave me my first taste and insights of SAN. Point-to-point, but nonetheless SAN.

Then at Sun from 1997-2000, I was implementing the old Storage Disk Packs with FastWide SCSI, moving on to the A5000 Photons (remember these guys?) and was trained on the A7000, Sun’s acquisition of Encore way back in the late nineties. Then there was “Purple”, the T300s which I believe came from the acquisition of MaxStrat.

The implementation and support experience was good but my world opened up when I joined NetApp in mid-2000. And from the Jon Toigo’s book, I learned one of the most important lessons that I have carried with me till this day – “Data Storage Management is 3x more expensive that the data storage equipment itself“. Given the complexity of the data today compared to the early 2000s, I would say that it is likely to be 4-5x more expensive.

And yet, I am still perplexed that many customers and prospects still cannot see the importance and the gravity of data storage management, and more precisely, data management itself.

A couple of months ago, I had to opportunity to work on an RFP for project in Singapore. The customer had thousands of tapes storing digital media files in addition to tens of TBs running on IBM N-series storage (translated to a NetApp FAS3xxx). They wanted to revamp their architecture, and invited several vendors in Singapore to propose. I was working for a friend, who is an EMC reseller. But when I saw that tapes figured heavily in their environment, and the other resellers were proposing EMC Isilon and NetApp C-Mode, I thought that these resellers were just trying to stuff a square peg into a round hole. They had not addressed the customer’s issues and problems at all, and was just merely proposing storage for the sake of storage capacity. Sure, EMC Isilon is great for the media and entertainment business, but EMC Isilon is not the data management solution for this customer’s situation. Neither was NetApp with the C-Mode solution.

What the customer needed to solve was a data management solution, one that involved

  • Single namespace for video editors and programmers, regardless of online disk storage or archived tape storage
  • Transparent and automated storage tiering and addressing the value of the data to the storage media
  • A backup tier which kept a minimum 2 recent copies for file restoration in case of disasters
  • An archived tier which they could share with their counterparts in other regions
  • A transparent replication tier which would allow them to implement a simplified disaster recovery mechanism with their counterparts in Japan and China

And these were the key issues that needed to be addressed, not the scale-out, usual snapshot mechanism. These features are good for a primary, production storage infrastructure, but this customer’s business operations had about 70-80% data and files which were offline in tapes. I took the liberty to advise my friend to look into Quantum StorNext, because the solution could solve the business problem NOT solving it from an IT point of view. Continue reading

“I want to put in my own hard disk”

I want to put in my own hard disk“.

If a customer ever utter that sentence, it will trigger a storage vendor meltdown. Panic buttons, alarm bells, and everything else that will lead a salesman to go berserk. That’s a big NO, NO!

For decades, storage vendors have relied on proprietary hardware to keep customers in line, and have customers continue to sign hefty maintenance contracts until the next tech refresh. The maintenance contract, with support, software upgrades and hardware spares replacement, defines the storage networking industry that we are in. Even as some vendors have commoditized their hardware on the x86 platforms, and on standard enterprise hard disk drives (HDDs), NICs and HBAs, that openness and convenience of commodity hardware savings are usually not passed on the customers.

It is easy to explain to customers that keeping their enterprise data in reliable and high performance storage hardware with performance optimization and special firmware is paramount, and any unwarranted and unvalidated hardware would put the customer’s data at high risk.

There is a choice now. The ripple of enterprise-grade, open storage kernel and file system has just started its first ring, and we hope that this small ripple will reverberate across the storage industry in the next few years.

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Feed us with Filr

I have checked about the progress of Novell Filr, which has generated a lot of buzz on the web. I blogged about it here and here and I was hoping to get a job to review the product. But I didn’t get the job yet, because the product will only be available in Q4 of 2012.

That’s a long time to come to market, considering that from the time it was announced in Novell BrainShare in November 2011. And the competitors are gearing up as well. There is Dropbox for the enterprise called Dropbox for Teams, and VMware is doing something along the same lines called Project Octopus. I am pretty sure there are plenty of other vendors who are already offering something equivalent to what the Novell Filr can do.

I browsed around the web for more info about the Novell Filr, hoping that it won’t be like a blackhole after Novell’s announcement. Fortunately, I found more details which I thought was interesting but it took a while after 5 or 6 Google pages.

The set of slides I found belonged to Anthony Priestman of Novell. The slides started with the Novell Filr ease of installation.

  • Local Administrator/Password
  • Server Name
  • IP Address
  • Finalize the configuration with a browser

In a nutshell Novell Filr is a virtualization service. It virtualizes the backend NTFS shares, CIFS shares, identity management through Active Directory or Novell eDirectory, as well as access control and security to present a “merged-view” of files and folders from different disparate sources.

Going deeper, the Novell Filr becomes the orchestrator and broker, linking up the backend to the front end with ease and simplicity. Even though it sounds easy, the entire architecture and its implementation is complicated because there are so many components and services involved.

Therefore, to make file services and authentication services matters easy and simple should be considered genius, and we shall see how Novell Filr pans out when it is released. I have no doubt in my mind that it will be easy and simple.

Here’s a deeper look at the architecture:

The Filr integrates with both Novell eDirectory and Windows Active Directory for authentication and file access control. I

One of the new concepts is called File Spaces. This is great, because this is going to do away with drive letters that we are so used to in mapped drives concept in Windows. There is a running joke that the number of mapped drives in Windows will run out after the letter Z:.

File Spaces allows a simple folder to represent any Windows shares, NAS CIFS shares or Novell NSS volumes. This is based on UNC (Universal Naming Convention), so it is straightforward. File Spaces also allows users to right-click to share their files easily, probably similar to how you share Google Docs files when you want to invite team members to collaborate on files. And it will update you on notifications after files have been updated or modified. This ease-of-sharing, of course, is governed with higher, company-wide policies about file sharing, both internally and externally (across firewalls as well)

Both the most powerful feature of File Spaces is the ability to have a single, “merged-view” of all files and folders with all types of devices, from tablets, to smartphones to laptops. The slide below explains a bit of File Spaces “Merged Views”:

The view of files and folders will look like the following below:

The Novell Filr concept and technology is going to define the new file sharing, home and user directories landscape in IT. The archaic concept of mapped drives will slowly fade away, and the Filr will bring forth new frontiers of tight and secure enterprise user and resource management, but with the ease of use and simplicity of sharing concepts of social media. 

Some older implementations from Novell will eventually be replaced by Filr. iFolder, Netstorage, and QuickFinder will go the way of the dodos, while the next generation Filr will become the flagship of the new Novell.

This sounds dandy. Unfortunately, I personally am worried about how Novell’s new owner, Attachmate will be good to Novell. Right now, the future of Novell seems like business as usual but that’s not good. Novell has been losing mindshare and they had better make their stand with a strong product like Novell Filr.

Like I said earlier, the product is shipping in late of 2012. It was announced in late 2011. That’s a whole 12 months in which Novell could do much better by feeding the minds of followers of Novell Filr. Let them and people like me, learn more about the technology. Let us help spread the idea and word and keep the Novell Filr interests up and the fire burning until the launch date.

Protogon File System

I was out shopping yesterday and I was tempted to have lunch at Bar-B-Q Plaza, a popular Thai, Japanese-style hot plate barbeque restaurant in this neck of the woods. The mascot of this restaurant is Bar-B-Gon, a dragon-like character and it is obviously a word play of barbeque and dragon.

As I was reading the news this morning about the upcoming Windows Server 8 launch, I found out that ever popular, often ridiculed NTFS (NT File System) of Windows will be going away. It will be replaced by Protogon, a codename for the new file system that Microsoft is about to release. Protogon? A word play of prototype and dragon?

The new file system, with backward compatibility with NTFS, will be called ReFS or Resilient File System. And the design objectives of what Microsoft calls “next generation” file system are clear and adept to the present day requirements. I notably mentioned present day requirements for a reason, because when I went through the key features of ReFS, the concepts and the ideas are not exactly “next generation“. Many of these features are already present with most storage vendors we know of, but perhaps for the people in the Windows world, these features might sound “next generation” to them.

ReFS, to me, is about time. NTFS has been around for a long, long time. It was first known in the wild in the 1993, and gain prominence and wide acceptance in Windows 2000 as the “enterprise-ready” file system. Indeed it was, because that was the time Microsoft Windows started its dominance into the data centers when the Unix vendors were still bickering about their version of open standards. Active Directory (AD) and NTFS were the 2 key technologies that slowly, but surely, removed Unix’s strengths in the data centers.

But over the years, as the storage networking technologies like SAN and NAS were developing and maturing, I see the NTFS being little developed to meet the strengths of these storage networking technologies and relevant protocols in the data world. When I did  a little bit of system administration on Windows (2000, 2003 notably), I could feel that NTFS was developed with direct-attached storage (DAS) or internal disks in mind. Definitely not full taking advantage of the strengths of Fibre Channel or iSCSI SAN. It was only in Windows Server 2008, that I felt Microsoft finally had enough pussyfooting with SAN and NAS, and introduced a more decent disk storage management that incorporates features that works well natively with SAN. Now, Microsoft can no longer sit quietly without acknowledging the need to build enterprise-ready technologies related to storage networking and data management. And the core in the new Microsoft Windows Server 8 engine for that is the ReFS.

One of the key technology objectives in the design of ReFS is backward compatibility. Windows has a huge market to address and they cannot just shove NTFS away. The way they did was to maintain the upper level API and file semantics and having a new core file system engine as shown in the diagram below:

ReFS is positioned with resiliency in mind. Here are a few resilient features:

  • Ability to isolate fault and perform data salvation on parts of the file system without taking the entire file system or volume offline. The goal of REFS here is to be ONLINE and serving data all the time!
  • Checksumming data and metadata for integrity. It verifies all data, and in some cases, auto-correcting corrupted data
  • Optional integrity streams that ensures protection for all forms of file-level data corruption. When enabled, whenever a file is changed, the modified copy is written to a different area of the disk than that of the original file. This way, even if the write operation is interrupted and the modified file is lost, the original file is still intact. (Doesn’t this sounds like COW with snapshots?) When combined with Storage Spaces (we will talk about this later), which can store a copy of all files in a storage array on more than one physical disk, ReFS gives Windows a way to automatically find and open an uncorrupted version of a file In the event that a file on one of the physical disks becomes corrupted. Microsoft does not recommend integrity streams for applications or systems with a specific type of storage layout or applications which want better control in the disk storage, for example databases.
  • Data scrubbing for latent disk errors. There is an tool, integrity.exe which runs and manages the data scrubbing and integrity policies. The file attribute, FILE_ATTRIBUTE_NO_SCRUB_DATA, will allow certain applications to skip this options and have these applications control integrity policies beyond what ReFS has to offer.
  • Shared storage pools across machines for additional fault tolerance and load balancing (ala Oracle RAC perhaps?)
  • Protection against bit rot. Silent data corruption, which I have blogged about many, many moons ago.

End-to-end resilient architecture is the goal in mind.

From a file structure standpoint, here’s how ReFS looks like:

ReFS is Copy-on-Write (COW). As you know, I am a big fan of any file systems but COW is one that I am most familiar with. NetApp’s Data ONTAP, Oracle Solaris, ZFS and the upcoming Linux BTRFS are all implementations of COW. Similar to BTRFS, ReFS uses a B+ tree implementation and as described in Wikipedia,

ReFS uses B+ trees for all on-disk structures including metadata and file data. The file size, total volume size, number of files in a directory and number of directories in a volume are limited by 64-bit numbers, which translates to maximum file size of 16 Exbibytes, maximum volume size of 1 Yobibyte (with 64 KB clusters), which allows large scalability with no practical limits on file and directory size (hardware restrictions still apply). Metadata and file data are organized into tables similar to relational database. Free space is counted by a hierarchal allocator which includes three separate tables for large, medium, and small chunks. File names and file paths are each limited to a 32 KB Unicode text string.

In ReFS, Microsoft introduces Storage Spaces. And the concept is very, very similar to what ZFS is, with the seamless implementation of a volume manager, RAID management, and highly resilient file system. And ZFS is 10 years old. So much for ReFS being “next generation“.  But here is a series of screenshots of how Storage Spaces looks like:

And similar to this “flexible volume management” ala ONTAP FlexVol and ZFS file systems, you can add disk drives on the fly, and grow your volumes online and real time.

ReFS inherits many of the NTFS features as it inches towards the Windows Server 8 launch date. Some of the features mentioned were the BitLocker encryption, Access Control List (ACL) for security (naturally), Symbolic Links, Volume Snapshots, File IDs and Opportunistic Locking (Oplocks).

ReFS is intended to scale to as what Microsoft says, “to extreme limits“. Here is a table describing those limits:

ReFS new technology will certainly bring Windows to the stringent availability and performance requirements of modern day file systems, but the storage networking world is also evolving into the cloud computing space. Object-based file systems are also getting involved as market trends dictate new requirements and file systems, in order to survive, must continue to evolve.

Microsoft’s file system, NTFS took a long time to come to this present version, ReFS, but can Microsoft continue to innovate to change the rules of the data storage game? We shall see …

VAAI to go!

First of all, let me apologize. I am guilty of not updating my blogs as regularly as I did in the past. Things got a bit crazy after Christmas and I had to juggle several things that demand more of my attention but I am confident things will sort itself out soon enough.

Today’s topic is about VMware’s VAAI (vSphere vStorage API for Array Integration). This feature was announced more than 3 years ago but was only introduced in vSphere 4.1 July 2010 and now with newer enhancements in the latest release of vSphere 5.0.

What is this VAAI and what does this mean from a storage perspective?

When VMware came into prominence in version 3.0/3.5 time, the whole world revolved around the ESX hypervisor. It tried to do everything on its own, in its own proprietary nature. Given its nascent existence then, ESX had to do what it had to do and control everything with its hypervisor universe. Yes, it was a good move then and it did what it was supposed to do. This was back when server virtualization was in its infancy, and resources requirements were less demanding.

Hence when VMware wants to initialize VMs, or create VMDK files on the datastore, or creating clones or snapshots, or even executing VMotion and Storage VMotion, it tends to execute it at the hypervisor level. For example, when creating virtual disks with VMFS, most of the commands to initialization of the disks were done at the VMFS level. Zeroing the virtual disks would mean sending zeroing commands to the actual physical disks on the shared storage. And this would go on back and forth, taxing the CPU cycles and memory on the hypervisor layer, and sending wasteful and unnecessary zeroes over the network to the storage array. This was very inefficient, wasteful and degrades the performance tremendously, especially at the hypervisor layer (compute and memory).

There are also other operations such as virtual disks locking that locks up the entire LUN that housed several datastores. Again, not good.

But VMware took off like a rocket, and quickly established itself as a Tier 1, enterprise server virtualization solution addressing the highest demands of the enterprise. It is also defining the future of Cloud Computing, building exorbitant requirements as it pushes forward. And VMware began to realize that if the hypervisor is to scale, it needs to leave the I/O operations to the “experts”, and the “experts” here being the respective storage array itself.

So, in version 4.1, VAAI (vStorage API for Array Integration) was introduced as an API suite, following 3 other earlier APIs – vStorage API for Site Recovery Manager (SRM), vStorage API for Data Protection and vStorage API for Multipathing.

In a nutshell, as I have mentioned before, VAAI offloads I/O and storage related operations to the VAAI-capable storage array (leave it to the experts) as shown in the diagram below:


Of course, the storage vendors themselves has to rework their array OS layer to integrate with the VAAI API. You can say that the VAAI are “hooks” that enhances the storage connectivity and communications with vSphere’s hypervisor. But then again, if you look at it from the other angle, vSphere need the storage vendors more in order for its universe to scale. Good thing VMware has a big, big market share. Imagine if there are no takers for the VAAI APIs. That would be a strange predicament instead.

What is the big deal that we get from VAAI? The significant and noticeable benefit is increase performance. By offloading the I/O functionality and operations to the storage array itself, the hypervisor and the compute and memory resource are not bogged down, resulting in higher performance and better response time to serve its VMs and other VM operations.

I am going off to another meeting and I shall write of VAAI in more details later. Until the next entry, adios and have a great year ahead.

Is there IOPS for Cloud Storage? – Nasuni style

I was in Singapore last week attending the Cloud Infrastructure Services course.

In the class, one of the foundation components of Cloud Computing is of course, storage. As the students and the instructor talked about Storage, one very interesting argument surfaced. It revolved around the storage, if it was offered on the cloud. A lot of people assumed that Cloud Storage would be for their databases, and their virtual machines, which of course, is true when the communication between the applications, virtual machines and databases are in the local area network of the Cloud Service Provider (CSP).

However, if the storage is offered through the cloud to applications that are sitting on-premise in the customer’s server room, then we have to think twice of how we perceive Cloud Storage. In this aspect, the Cloud Storage offered by the CSP is a Infrastructure-as-a-Service (IaaS), where the key service is Storage. We have to differentiate that this Storage functions as a data container, and usually not for I/O performance reasons.

Though this concept probably will be easily understood by storage professionals like us, this can cause a bit confusion for someone new to the concept of Cloud Computing and Cloud Storage. This confusion, unfortunately, is caused by many of us who are vendors or solution providers, or even publications and magazines. We are responsible to disseminate correct information to customers, but due to our lack of knowledge and experience in this extremely new market of Cloud Storage, we have created the FUDs (Fear, Uncertainty and Doubt) and hype.

Therefore, it is the duty of this blogger to clear the vapourware, and hopefully pass on the right information to accelerate  the adoption of Cloud Storage in the near future. At this moment, given the various factors such as network costs, high network latency and lack of key network technologies similar to LAN in Cloud Computing, Cloud Storage is, most of the time, for data storage containership and archiving only. And there are no IOPS or any performance related statistics related to Cloud Storage. If any engineer or vendor tells you that they have the fastest Cloud Storage in the industry, do me a favour. Give him/her a knock on the head for me!

Of course, as technologies evolve, this could change in the near future. For now, Cloud Storage is a container, NOT a high performance storage in the cloud. It is usually not meant for transactional data. There are many vendors in the Cloud Storage space from real CSPs to storage companies offering re-packaged storage boxes that are “cloud-ready”. A good example of a CSP offering Cloud Storage is Amazon S3 (Simple Storage Service). And storage vendors such as EMC and HDS are repackaging and rebranding their storage technologies as object storage, ready for the cloud. EMC Atmos is really a repackaged and rebranded Centera, with some slight modifications, while HDS , using their Archiving solution, has HCP (aka HCAP). There’s nothing wrong with what EMC and HDS have done, but before the overhyping of the world of Cloud Computing, these platforms were meant for immutable data archiving reasons. Just thought you should know.

One particular company that captured my imagination and addresses the storage performance portion is Nasuni. Of course, they are quite inventive with the Cloud Storage Gateway approach. Nasuni comes up with a Cloud Storage Gateway filer appliance, which can be either a physical 1U server or as a VMware or Hyper-V virtual appliance sitting on-premise at the customer’s site.

The key to this is “on-premise”, which allows access to data much faster because they are locally-cached in the Nasuni filer appliance itself. This Nasuni filer piece addresses the Cloud Storage “performance” piece but Nasuni do not claim any performance statistics with such implementation. The clever bit is that this addresses data or files that are transactional in nature, i.e. NFS or CIFS, to serve data or files “locally”. (I wonder if Nasuni filer has iSCSI as well. Hmmmm….)

In the Nasuni architecture, they “break up” their “Cloud Storage” into 2 pieces. Piece #1 sits on-premise, at the customer site, and acts as a bridge to the Piece #2, that is sitting in a Cloud Storage. From a simplified view, have a look at the diagram below:



Piece #1 is the component that handles some of the transactional traffic related to files. In a more technical diagram below, you can see that the Nasuni filer addresses the file sharing portion, using the local disks on the filer appliance as a local caching mechanism.


Furthermore, older file pieces are whiffed away to the any Cloud Storage using the Cloud Connector interface, hence giving the customer a sense that their storage capacity needs can be limitless if they want to (for a fee, of course). At the same time, the Nasuni filer support thin provisioning and snapshots. How cool is that!

The Cloud Storage piece (Piece #2) is used for the data container and archiving reasons. This component can be sitting and hosted at Amazon S3, Microsoft Azure, Rackspace Cloud Files, Nirvanix Storage Delivery Network and Iron Mountain Archive Services Platform.

The data communication and transfer between the Nasuni filer is secure, encrypted, deduplication and compressed, giving it the efficiency and security that most customers would be concerned about. The diagram below explains the dat communication and data transfer bit.


In this manner, the Nasuni filer can replace traditional NAS platforms and can potentially provide a much lower total cost of ownership (TCO) in the long run. Nasuni does not pretend to be a NAS replacement. To me, this concept is very inventive and could potentially change the way we perceive file sharing and file server, obscuring and blurring concept of NAS.

Again, I would like to reiterate that Nasuni does not attempt to say their solution is a NAS or a performance-based Cloud Storage but what they have cleverly packaged seems to be appealing to customers. Their customer base has grown 78% in Q2 of 2011. It’s just too bad they are not here in Malaysia or this part of the world (yet).

IOPS in Cloud Storage? Not yet.


A little yellow elephant

By now, I believe most of you in the storage networking world would have heard of Hadoop. Hadoop was created by Doug Cutting, while he and his team was working on an open source web search engine called Nutch. The easily recognized little yellow elephant, Hadoop, was Doug Cutting’s son toy, which he made as Hadoop’s mascot. Pretty cool!

And today, Hadoop has become THE platform for Big Data applications. Why?

As I have mentioned before, everything that we do or don’t do, generates data, either as a direct product or in-direct product. I am blogging right now and I am creating data. I was in Singapore the whole of this week and everywhere I go in the MRT stations, I am being watched by the video cameras they have at the station. A new friend in class said that Singapore is the second most “watched” city after London, where there are video cameras mounted everywhere, either discreetly or indiscreetly. And that’s just video data. And there’s plenty of other human activities that generate tons and tons of data.

IDC Digital Universe Report for 2011 said that we have generated 1.8ZB (zettabyte) of data this year alone. I mentioned in my previous blog that this is a gold mine and companies are scrambling to tap on massive amount of data.  Extracting valuable information to anticipate the next trend or predict that next evolution in human preference is akin to the Gold Rush in the wild, wild west in the late 19th century. Folks, Big Data is going to be this generation’s “Digital Gold Rush”.

Sieving, filtering and processing gazillions of data (more unstructured than structured) will not work in defined, well-formatted relational databases. The data model of relational databases will simply break down. And of course, there are different schools of thoughts of different data models, but the Hadoop model seems to be gaining momentum and mind share of data scientists. That is because of Hadoop’s capability to deal with massive unstructured data, processing it and producing results in a small amount of time.

One way to process the pool of massive data is parallel programming. In parallel programming, multi-threading is commonly deployed to achieve the performance and effects of programming. But implementing multi-threading in parallel programming is difficult. Developers often has to deal with LWP (lightweight processes), semaphores, shared memory, mutex (mutually exclusive) locking and so on. Hence this style of programming works with different states on shared data, often resulting in different results in different states, even when using the same programming expression.

Hadoop belongs to another school of programming known as functional programming, where the different states on shared data concept is removed. With that in mind, the dependency on different states is also removed, resulting in a much easier and simpler parallel programming implementation. Hadoop borrows ideas from the MapReduce software framework made well known by Google and the Google File System.

Before, we get to know Hadoop, we must know MapReduce. MapReduce is a framework which allows very large data sets to be processed with a very large set of computer nodes in a cluster. Typically the computational processing is executed in a distributed fashion, spread across many computer nodes and final results are consolidated from the sub-results of these distributed processing nodes.

According to Wikipedia, the 2 key functions of Map Reduce are map() and reduce(). That’s pretty obvious. The extract below was taken from the Wikipedia definition, and explains both functions very well.

“Map” step: The master node takes the input, partitions it up into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes the smaller problem, and passes the answer back to its master node.

“Reduce” step: The master node then collects the answers to all the sub-problems and combines them in some way to form the output – the answer to the problem it was originally trying to solve.

The diagram below probably can simplify the concept of MapReduce to the readers.


Hadoop is one of the open-source implementations of MapReduce. It is one of the projects of Apache Foundation, and the project has sparked a brand-new niche of data search, data management and data science. The diagram below will allow our readers to juxtapose MapReduce and Hadoop, and comparing them in the simplest fashion.

Hadoop primary development platform is Java. Hadoop’s architecture consists mainly of 2 components – Hadoop Common and a Hadoop-compatible file system, as shown in the diagram below.

Hadoop MapReduce layer above is the file/object access interface to the Hadoop-compatible file system below. HDFS is Hadoop Distributed File System is just one of a few Hadoop-compatible file systems. Other file systems include:

  • Amazon S3 File System as part of the Amazon EC2 Infrastructure-as-a-Service (IaaS) cloud platform
  • CloudStore – a similar Hadoop-like implementation using C++ and also inspired by Google File System
  • FTP file systems
  • HTTP and HTTPS read-only file systems
  • Any file systems accessible with the file:// URL nomenclature

But the main engine of Hadoop is in the MapReduce layer. The 2 core components in this layer is JobTracker and TaskTracker. Both has their own individual roles to play and collectively, they are key cogs in the Hadoop distributed data processing model.

Below are extract I picked up from Wikipedia.

JobTracker submits MapReduce jobs to client applications. The JobTracker pushes work out to available TaskTracker nodes in the cluster, striving to keep the work as close to the data as possible. With a rack-aware filesystem, the JobTracker knows which node contains the data, and which other machines are nearby. If the work cannot be hosted on the actual node where the data resides, priority is given to nodes in the same rack. This reduces network traffic on the main backbone network. If a TaskTracker fails or times out, that part of the job is rescheduled. The TaskTracker on each node spawns off a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the running job crashes the JVM. A heartbeat is sent from the TaskTracker to the JobTracker every few minutes to check its status. The Job Tracker and TaskTracker status and information is exposed by Jetty and can be viewed from a web browser. Jetty is a Java-based HTTP server, among other things

JobTracker records what it is up to in the filesystem. When a JobTracker starts up, it looks for any such data, so that it can restart work from where it left off.


By default Hadoop uses first-in, first-out (FIFO), and optional 5 scheduling priorities to schedule jobs from a work queue. In version 0.19 the job scheduler was refactored out of the JobTracker, and added the ability to use an alternate scheduler (such as the Fair scheduler or the Capacity scheduler).

Fair scheduler

The fair scheduler was developed by Facebook. The goal of the fair scheduler is to provide fast response times for small jobs and QoS (Quality of Service) for production jobs. The fair scheduler has three basic concepts.

  1. Jobs are grouped into Pools.
  2. Each pool is assigned a guaranteed minimum share.
  3. Excess capacity is split between jobs.

By default jobs that are uncategorized go into a default pool. Pools have to specify the minimum number of map slots, reduce slots, and a limit on the number of running jobs.

Capacity scheduler

The capacity scheduler was developed by Yahoo. The capacity scheduler supports several features which are similar to the fair scheduler.

  • Jobs are submitted into queues.
  • Queues are allocated a fraction of the total resource capacity.
  • Free resources are allocated to queues beyond their total capacity.
  • Within a queue a job with a high level of priority will have access to the queue’s resources.

I took most the extract below from Wikipedia, and I don’t claim to be a knowledgeable person on Hadoop. All the credits go to Wikipedia editors to put Hadoop in layman terms.

Hadoop has certainly won the hearts of the new digital gold rush, Big Data and is slowly becoming a force to be reckoned with among data scientists. Hadoop implementations are powering new frontiers in processing and mining the ever growing data capacity, giving solution providers a simple programming methodology and data model to gain more insights into the vast seas of data and information.

Hadoop has many fans, and slowly becoming the data platform for large companies such as Yahoo!, Facebook, IBM, Amazon, Apple, eBay and many more. Facebook even claims to have the largest Hadoop clusters in the world, growing to 30PB in July of 2011.

This little yellow elephant is going places and one to watch out for.

btrfs – a better butter from a better COW?

There’s better a lot of chatter about the default file system in the recently released Fedora 16. Fedora 16 was released on November 8, 2011. For months, there were rumours that the default file system for Fedora 16 will be btrfs (btree file system, or better known as butter-FS). But after the release, the default file system of Fedora 16 is still ext4, much to the dismay of many. btrfs holds a lot of potential because in the space of less than 4 years, it has moved up the hierarchy to be one of the top file systems in the Linux ecosystem.

File systems in Linux has not seen had a knight in shining armour for the long time. The file systems kings of the Linux world were ext2/3 for the RedHat and Debian flavoured distros and reiserfs for the SuSE  flavoured distros. ext4 is now default in most Linux distros but the concept of  ext2/3/4 has not changed much since its inception decades ago.

At the same time, reiserfs  had a lot of promise as well, but its development and progress have lost it lustre after its principal developer, Han Reiser was convicted of the murder of his wife a few years ago. If you are KPC (Malaysian Chinese colloquialism, meaning busybody), you can read the news here.

btrfs is going to be the new generation of file systems for Linux and even Ted T’so, the CTO of Linux Foundation and principal developer admitted that he believed btrfs is the better direction because “it offers improvements in scalability, reliability, and ease of management”.

For those who has studied computer science, B-Tree is a data structure that is used in databases and file systems. B-Tree is an excellent data structure to store billions and billions of objects/data and is able to provide fast data retrieval in logarithmic time. And the B-Tree implementation is already present in some of the file systems such as JFS, XFS and ReiserFS. However, these file systems are not shadow-paging filesystems (popularly known as copy-on-write file systems).

You see, B-Tree, in its native form of implementation, is very incompatible with COW file systems. In fact, the implementation was thought of impossible, until someone by the name of Ohad Rodeh came along. He presented a paper in Usenix FAST ’07 which described the challenges of combining the B-Tree concept with shadow paging file systems. The solution, as he described it, was to introduce insert/remove key into the tree structure, and removing the dependency of intra-leaf linking.

Chris Mason, one of the developers of reiserfs, took Ohad’s idea and created a shadow-paging file system based on the B-Tree idea.

Traditional file systems tend to follow the idea of the Berkeley Fast File Systems. Different cylinder groups have its own inode, bitmap and disk blocks. The used space in one cylinder group cannot be shared to another cylinder group, resulting in wastage. At the same time, performance can be an issue as the disk read/head frequently have to move to the inodes to find out where the next used or free blocks will be. In a way, it looks like the diagram below.

Chris Mason’s idea of btrfs made the file system looked like this:

Today, a quick check in btrfs wiki page, shows that the main Btrfs features available at the moment include:

  • Extent based file storage
  • 2^64 byte == 16 EiB maximum file size
  • Space-efficient packing of small files
  • Space-efficient indexed directories
  • Dynamic inode allocation
  • Writable snapshots, read-only snapshots
  • Subvolumes (separate internal filesystem roots)
  • Checksums on data and metadata
  • Compression (gzip and LZO)
  • Integrated multiple device support
  • RAID-0, RAID-1 and RAID-10 implementations
  • Efficient incremental backup
  • Background scrub process for finding and fixing errors on files with redundant copies
  • Online filesystem defragmentation

And Chris Mason and his team have still plenty more to offer for btrfs. It will likely be the default file system in Fedora 17 and at the rate it is going, could be the file system of choice for RedHat, Debian and SuSE distros very soon.

There’s been a lot of comparisons between btrfs and ZFS, since both are part of Oracle now. ZFS is obviously a much more mature file system, with more enterprise features and more robust, (Incidentally, ZFS just celebrated its 10th birthday on Halloween 2011 – see Matt Ahren’s blog) and the btrfs is the rising star in the Linux world. But at this moment, the 2 file systems are set apart in their market positioning and deployment.

ZFS is based on the CDDL (Common Development and Distribution License) while btrfs is based on GNU GPL, open source licensing. There are controversies surrounding CDDL licensing scheme, while is incompatible with GNU GPL scheme.

Oracle can count itself very lucky to have 2 of the most promising and prominent COW file systems around. It will be interesting to see what Oracle will do next. As a proponent of innovation, community and sharing, I sincerely hope that both file systems will continue to thrive in Oracle’s brutal, sales-driven organization. We certainly don’t want to see controversies about dual ownership BS of Oracle and mySQL that could end in 2015.