Rethinking data processing frameworks systems in real time

“Row, row, row your boat, gently down the stream…”

Except the stream isn’t gentle at all in the data processing’s new context.

For many of us in the storage infrastructure and data management world, the well known framework is storing and retrieve data from a storage media. That media could be a disk-based storage array, a tape, or some cloud storage where the storage media is abstracted from the users and the applications. The model of post processing the data after the data has safely and persistently stored on that media is a well understood and a mature one. Users, applications and workloads (A&W) process this data in its resting phase, retrieve it, work on it, and write it back to the resting phase again.

There is another model of data processing that has been bubbling over the years and now reaching a boiling point. Still it has not reached its apex yet. This is processing the data in flight, while it is still flowing as it passes through processing engine. The nature of this kind of data is described in one 2018 conference I chanced upon a year ago.

letgo marketplace processing numbers in 2018

  • * NRT = near real time

From a storage technology infrastructure perspective, this kind of data processing piqued my curiosity immensely. And I have been studying this burgeoning new data processing model in my spare time, and where it fits, bringing the understanding back into the storage infrastructure and data management side.

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How well do you know your data and the storage platform that processes the data

Last week was consumed by many conversations on this topic. I was quite jaded, really. Unfortunately many still take a very simplistic view of all the storage technology, or should I say over-marketing of the storage technology. So much so that the end users make incredible assumptions of the benefits of a storage array or software defined storage platform or even cloud storage. And too often caveats of turning on a feature and tuning a configuration to the max are discarded or neglected. Regards for good storage and data management best practices? What’s that?

I share some of my thoughts handling conversations like these and try to set the right expectations rather than overhype a feature or a function in the data storage services.

Complex data networks and the storage services that serve it

I/O Characteristics

Applications and workloads (A&W) read and write from the data storage services platforms. These could be local DAS (direct access storage), network storage arrays in SAN and NAS, and now objects, or from cloud storage services. Regardless of structured or unstructured data, different A&Ws have different behavioural I/O patterns in accessing data from storage. Therefore storage has to be configured at best to match these patterns, so that it can perform optimally for these A&Ws. Without going into deep details, here are a few to think about:

  • Random and Sequential patterns
  • Block sizes of these A&Ws ranging from typically 4K to 1024K.
  • Causal effects of synchronous and asynchronous I/Os to and from the storage

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The future of Fibre Channel in the Cloud Era

The world has pretty much settled that hybrid cloud is the way to go for IT infrastructure services today. Straddled between the enterprise data center and the infrastructure-as-a-service in public cloud offerings, hybrid clouds define the storage ecosystems and architecture of choice.

A recent Blocks & Files article, “Broadcom server-storage connectivity sales down but recovery coming” caught my attention. One segment mentioned that the server-storage connectivity sales was down 9% leading me to think “Is this a blip or is it a signal that Fibre Channel, the venerable SAN (storage area network) protocol is on the wane?

Fibre Channel Sign

Thus, I am pondering the position of Fibre Channel SANs in the cloud era. Where does it stand now and in the near future? Continue reading

Data Sovereignty – A boon or a bane?

Data across borders – Data Sovereignty

I really did not want to write Data Sovereignty in the way I have written it now. I wanted to write it in a happy manner, but as recent circumstances appeared, the outlook began to dim. I apologize if my commentary is bleak.

Last week started very well. I was preparing for the iXsystems™ + Nextcloud webinar on Wednesday, August 25th 2021. After talking to the wonderful folks at Nextcloud (Thanks Markus, Uwe and Maxime!), the central theme of the webinar was on Data Sovereignty and Data Control. The notion of GDPR (General Data Protection Regulation) has already  permeated into EU (European Union) entities, organizations and individuals alike, and other sovereign states around the world are following suit. Prominent ones on my radar in the last 2 years were the California Consumer Privacy Act (CCPA) and Vietnam Personal Data Protection Act 2020.

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Where are your files living now?

[ This is Part One of a longer conversation ]

EMC2 (before the Dell® acquisition) in the 2000s had a tagline called “Where Information Lives™**. This was before the time of cloud storage. The tagline was an adage of enterprise data storage, proper and contemporaneous to the persistent narrative at the time – Data Consolidation. Within the data consolidation stories, thousands of files and folders moved about the networks of the organizations, from servers to clients, clients to servers. NAS (Network Attached Storage) was, and still is the work horse of many, many organizations.

[ **Side story ] There was an internal anti-EMC joke within NetApp® called “Information has a new address”.

EMC tagline “Where Information Lives”

This was a time where there were almost no concerns about Shadow IT; ransomware were less known; and most importantly, almost everyone knew where their files and folders were, more or less (except in Oil & Gas upstream – to be told in later in this blog). That was because there were concerted attempts to consolidate data, and inadvertently files and folders, in the organization.

Even when these organizations were spread across the world, there were distributed file technologies at the time that could deliver files and folders in an acceptable manner. Definitely not as good as what we have today in a cloudy world, but acceptable. I personally worked a project setting up Andrew File Systems for Intel® in Penang in the mid-90s, almost joined Tacit Networks in the mid-2000s, dabbled on Microsoft® Distributed File System with NetApp® and Windows File Servers while fixing the mountains of issues in deploying the worldwide GUSto (Global Unified Storage) Project in Shell 2006. Somewhere in my chronological listings, Acopia Networks (acquired by F5) and of course, EMC2 Rainfinity and NetApp® NuView OEM, Virtual File Manager.

The point I am trying to make here is most IT organizations had a good grip of where the files and folders were. I do not think this is very true anymore. Do you know where your files and folders are living today? 

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Storage IO straight to GPU

The parallel processing power of the GPU (Graphics Processing Unit) cannot be denied. One year ago, nVidia® overtook Intel® in market capitalization. And today, they have doubled their market cap lead over Intel®,  [as of July 2, 2021] USD$510.53 billion vs USD$229.19 billion.

Thus it is not surprising that storage architectures are changing from the CPU-centric paradigm to take advantage of the burgeoning prowess of the GPU. And 2 announcements in the storage news in recent weeks have caught my attention – Windows 11 DirectStorage API and nVidia® Magnum IO GPUDirect® Storage.

nVidia GPU

Exciting the gamers

The Windows DirectStorage API feature is only available in Windows 11. It was announced as part of the Xbox® Velocity Architecture last year to take advantage of the high I/O capability of modern day NVMe SSDs. DirectStorage-enabled applications and games have several technologies such as D3D Direct3D decompression/compression algorithm designed for the GPU, and SFS Sampler Feedback Streaming that uses the previous rendered frame results to decide which higher resolution texture frames to be loaded into memory of the GPU and rendered for the real-time gaming experience.

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Plotting the Crypto Coin Storage Farm

The recent craze of the Chia cryptocurrency got me excited. Mostly because it uses storage as the determinant for the Proof-of-Work consensus algorithm in a blockchain network. Yes, I am always about storage. 😉

I am not a Bitcoin miner nor am I a Chia coin farmer, and my knowledge and experience in both are very shallow. But I recently became interested in the 2 main activities of Chia – plotting and farming, because they both involved storage. I am writing this blog to find out more and document about my learning experience.

[ NB: This blog does not help you make money. It is just informational from a storage technology perspective. ]

Chia Cryptocurrency

Proof of Space and Time

Bitcoin is based on Proof-of-Work (PoW). In a nutshell, there is a complex mathematical puzzle to be solved. Bitcoin miners compete to solve this puzzle and the process uses high computational processing to solve it. Once solved, the miners are rewarded for their work.

Newer entrants like Filecoin and Chia coin (XCH) use an alternate method which is Proof-of-Space (PoS) to validate and verify the transactions. Instead of miners, Chia coin farmers have to prove to have a legitimate amount of disk and/or memory space to solve a mathematical puzzle, conceptually similar to the one in Bitcoin mining. In the beginning, this was great for folks who have unused disk space that can be “rented” out to store the crypto stuff (Note: I am not familiar with the terminology yet, and I did not want to use the word “crypto tokens” incorrectly). Storj was one of the early vendors that I remember in this space touting this method but I have not followed them for a while. Their business model might have changed.

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A FreeNAS Compression Tale

David vs Goliath Credit: Miguel Robledo of https://www.artstation.com/miguel_robledo

David vs Goliath

It was an underdog tale worthy of the biblical book of Samuel. When I first caught wind of how FreeNAS™ compression prowess was going against NetApp® compression and deduplication in one use case, I had to find out more. And the results in this use case was quite impressive considering that FreeNAS™ (now known as TrueNAS® CORE) is the free, open source storage operating system and NetApp® Data ONTAP, is the industry leading, enterprise, “king of the hill” storage data management software.

Certainly a David vs Goliath story.

Compression in FreeNAS

Ah, Compression! That technology that is often hidden, hardly seen and often forgotten.

Compression is a feature within FreeNAS™ that seldom gets the attention. It works, and certainly is a mature form of data footprint reduction (DFR) technology, along with data deduplication. It is switched on by default, and is the setting when creating a dataset, as shown below:

Dataset creation with Compression (lz4) turned on

The default compression algorithm is lz4 which is fast but poor in compression ratio compared to gzip and bzip2. However, lz4 uses less CPU cycles to perform its compression and decompression processing, and thus the impact on FreeNAS™ and TrueNAS® is very low.

NetApp® ONTAP, if I am not wrong, uses lzopro as default – a commercial and optimized version of the open source LZO compression library. In addition, NetApp also has their data deduplication technology as well, something OpenZFS has to improve upon in the future.

The DFR report

This brings us to the use case at one of iXsystems™ customers in Taiwan. The data to be reduced are mostly log files at the end user, and the version of FreeNAS™ is 11.2u7. There are, of course, many factors that affect the data reduction ratio, but in this case of 4 scenarios,  the end user has been running this in production for over 2 months. The results:

FreeNAS vs NetApp Data Footprint Reduction

In 2 of the 4 scenarios, FreeNAS™ performed admirably with just the default lz4 compression alone, compared to NetApp® which was running both their inline compression and deduplication.

The intention to post this report is not to show that FreeNAS™ is better in every case. It won’t be, and there are superior data footprint reduction tech out there which can outperform it. But I would expect potential and existing end users to leverage on the compression capability of FreeNAS™ which is getting better all the time.

A better compression algorithm

Followers of OpenZFS are aware of the changing of times with OpenZFS version 2.0. One exciting update is the introduction of the zstd compression algorithm into OpenZFS late last year, and is already in TrueNAS® CORE and Enterprise version 12.x.

What is zstd? zstd is a fast compression algorithm that aims to be as efficient (or better) than gzip, but with better speed closer to lz4, relatively. For a long time, the gzip compression algorithm, from levels 1-9, has been serving very good compression ratio compared to many compression algorithms, lz4 included.

However, the efficiency came at a higher processing price and thus took a longer time. At the other end, lz4 is fast and lightweight, but its reduction ratio efficiency is very poor. zstd intends to be the in-between of gzip and lz4. In the latest results published by Facebook’s github page,

zstd performance benchmark against other compression algorithms

For comparison, zstd (level -1) performed very well against zlib, the data compression library in gzip. It was made known there are 22 levels of compression in zstd but I do not know how many levels are accepted in the OpenZFS development.

At the same time, compression takes advantage of multi-core processing, and actually can speed up disk I/O response because the original dataset to be processed is smaller after the compression reduction.

While TrueNAS® still defaults lz4 compression as of now, you can probably change the default compression with a command

# zfs set compression=zstd-6 pool/dataset

Your choice

TrueNAS® and FreeNAS™ support multiple compression algorithms. lz4, gzip and now zstd. That gives the administrator a choice to assign the right compression algorithm based on processing power, storage savings, and time to get the best out of the data stored in the datasets.

As far as the David vs Goliath tale goes, this real life use case was indeed a good one to share.