I remembered the Gluster demo at Jaring over a video call, because I was the lead consultant pitching the scale-out NAS solution. It did not go well, and there were “bugs” which made the Head of IT flinched in her seat. Despite Jaring being Malaysia’s technology trailblazer, the impression of Gluster was forgettable. I stayed on the GlusterFS architecture a little while and then it dropped off my radar.
Gluster Scale Out NAS
But after the conversation last week, I am elated to revive my interest in Gluster, knowing that something big and impressive in coming into the fore very soon. Studying the architecture (again!), there are 2 parts of Gluster which excite me. One is the Brick and the other is the lack of a Metadata service.
I am guilty. I have not been tendering this blog for quite a while now, but it feels good to be back. What have I been doing? Since leaving NetApp 2 months or so ago, I have been active in the scenes again. This time I am more aligned towards data analytics and its burgeoning impact on the storage networking segment.
I was intrigued by an article posted by a friend of mine in Facebook. The article (circa 2013) was titled “Never, ever do this to Hadoop”. It described the author’s gripe with the SAN bigots. I have encountered storage professionals who throw in the SAN solution every time, because that was all they know. NAS, to them, was like that old relative smelled of camphor oil and they avoid NAS like a plague. Similar DAS was frowned upon but how things have changed. The pendulum has swung back to DAS and new market segments such as VSANs and Hyper Converged platforms have been dominating the scene in the past 2 years. I highlighted this in my blog, “Praying to the Hypervisor God” almost 2 years ago.
I agree with the author, Andrew C. Oliver. The “locality” of resources is central to Hadoop’s performance.
Consider these 2 models:
In the model on your left (Moving Data to Compute), the delivery process from Storage to Compute is HEAVY. That is because data has dependencies; data has gravity. However, if you consider the model on your right (Moving Compute to Data), delivering data processing to the storage layer is much lighter. Compute or data processing is transient, and the data in the compute layer is volatile. Once compute’s power is turned off, everything starts again from a clean slate, hence the volatile stage.
December 22 2015: I kept this blog in draft for 6 months. Now I am releasing it as NetApp acquires Solidfire.
The above is an old Chinese adage which means “True Gold fears no Fire“. That is how I would describe my revisited view and assessment of SolidFire, a high performance All-Flash array vendor which is starting to make its presence felt in South Asia.
I first blogged about SolidFire 3 years ago, and I have been following the company closely as more and more All-Flash array players entered the market over the 3 years. Many rode on the hype and momentum of flash storage, and as a result, muddied and convoluted the storage infrastructure market understanding. It seems to me spin marketing ruled the day and users could not make a difference between vendor A and vendor B, and C and D, and so on….
I have been often asked, which is the best All-Flash array today. I have always hesitated to say which is the best because there aren’t much to say, except for 2-3 well entrenched vendors. Pure Storage and EMC XtremIO come to mind but the one that had stayed under the enterprise storage radar was SolidFire, until now.