Kubernetes is on fire. Last week VMware® released the State of Kubernetes 2020 report which surveyed companies with 1,000 employees and above. Results were not surprising as the adoptions of this nascent technology are booming. But persistent storage remained the nagging concern for the Kubernetes serving the infrastructure resources to applications instances running in the containers of a pod in a cluster.
The standardization of storage resources have settled with CSI (Container Storage Interface). Storage vendors have almost, kind of, sort of agreed that the API objects such as PersistentVolumes, PersistentVolumeClaims, StorageClasses, along with the parameters would be the way to request the storage resources from the Pre-provisioned Volumes via the CSI driver plug-in. There are already more than 50 vendor specific CSI drivers in Github.
Kubernetes and the CSI (Container Storage Interface) logos
The CSI plug-in method is the only way for Kubernetes to scale and keep its dynamic, loadable storage resource integration with external 3rd party vendors, all clamouring to grab a piece of this burgeoning demands both in the cloud and in the enterprise.
It was a surprise move and the first thing that came to my mind was “Who is Talon Storage?” I have seen that name appeared in Tech Target and CRN last year but never took the time to go in depth about their technology. I took a quick check of their FAST™ software technology with the video below:
[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at the event. The content of this blog is of my own opinions and views ]
A funny photo (below) came up on my Facebook feed a couple of weeks back. In an honest way, it depicted how a developer would think (or the lack of thinking) about the storage infrastructure designs and models for the applications and workloads. This also reminded me of how DBAs used to diss storage engineers. “I don’t care about storage, as long as it is RAID 10“. That was aeons ago 😉
The world of developers and the world of infrastructure people are vastly different. Since cloud computing birthed, both worlds have collided and programmable infrastructure-as-code (IAC) have become part and parcel of cloud native applications. Of course, there is no denying that there is friction.
In the world of software development and delivery, DevOps has taken a liking to containers. Containers make it easier to host and manage life-cycle of web applications inside the portable environment. It packages up application code other dependencies into building blocks to deliver consistency, efficiency, and productivity. To scale to a multi-applications, multi-cloud with th0usands and even tens of thousands of microservices in containers, the Kubernetes factor comes into play. Kubernetes handles tasks like auto-scaling, rolling deployment, computer resource, volume storage and much, much more, and it is designed to run on bare metal, in the data center, public cloud or even a hybrid cloud.
[ Disclosure: I was invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees were covered by GestaltIT, the organizer and I was not obligated to blog or promote the vendors’ technologies presented at this event. The content of this blog is of my own opinions and views ]
Cloud computing will have challenges processing data at the outer reach of its tentacles. Edge Computing, as it melds with the Internet of Things (IoT), needs a different approach to data processing and data storage. Data generated at source has to be processed at source, to respond to the event or events which have happened. Cloud Computing, even with 5G networks, has latency that is not sufficient to how an autonomous vehicle react to pedestrians on the road at speed or how a sprinkler system is activated in a fire, or even a fraud detection system to signal money laundering activities as they occur.
Furthermore, not all sensors, devices, and IoT end-points are connected to the cloud at all times. To understand this new way of data processing and data storage, have a look at this video by Jay Kreps, CEO of Confluent for Kafka® to view this new perspective.
Data is continuously and infinitely generated at source, and this data has to be compiled, controlled and consolidated with nanosecond precision. At Storage Field Day 19, an interesting open source project, Pravega, was introduced to the delegates by DellEMC. Pravega is an open source storage framework for streaming data and is part of Project Nautilus.
The data generated at source (end-points, sensors, devices) is serialized, timestamped (as event occurs), continuous and infinite. These are the properties of a time series data stream, and to make sense of the streaming data, new data formats such as Avro, Parquet, Orc pepper the landscape along with the more mature JSON and XML, each with its own strengths and weaknesses.
You can learn more about these data formats in the 2 links below:
Many time series projects started as DIY projects in many organizations. And many of them are still DIY projects in production systems as well. They depend on tribal knowledge, and these databases are tied to an unmanaged storage which is not congruent to the properties of streaming data.
At the storage end, the technologies today still rely on the SAN and NAS protocols, and in recent years, S3, with object storage. Block, file and object storage introduce layers of abstraction which may not be a good fit for streaming data.
[Disclosure: I am invited by GestaltIT as a delegate to their Storage Field Day 19 event from Jan 22-24, 2020 in the Silicon Valley USA. My expenses, travel, accommodation and conference fees will be covered by GestaltIT, the organizer and I am not obligated to blog or promote the vendors’ technologies to be presented at this event. The content of this blog is of my own opinions and views]
This is NOT an advertisement for coloured balls.
This is the license to brag for the vendors in the next 2 weeks or so, as we approach the 2020 new year. This, of course, is the latest 2019 IDC Marketscape for Object-based Storage, released last week.
My object storage mentions
I have written extensively about Object Storage since 2011. With different angles and perspectives, here are some of them:
Something triggered my thoughts a few days ago. A few of us got together talking about climate change and a friend asked how green was the datacenter in IT. With cloud computing booming, I would say that green computing isn’t really the hottest thing at present. That in turn, leads us to one of the most voracious energy beasts in the datacenter, storage. Where is green storage in the equation?
What is green?
Over the past decade, several storage related technologies were touted as more energy efficient. These include
Tape – when tapes are offline, they do not consume power and do not require cooling
Virtualization – Virtualization reduces the number of servers and desktops, and of course storage too
MAID (Massive Array of Independent Disks) – the arrays spin down the HDDs if idle for a period of time
SSD (Solid State Drives) – Compared to HDDs, SSDs consume much less power, and overall reduce the cooling needs
Data Footprint Reduction – Deduplication, compression and other technologies to reduce copies of data
SMR (Shingled Magnetic Recording) Drives – Higher areal density means less drives but limited by physics.
The largest gorilla in storage technology
HDDs still dominate the market and they are the biggest producers of heat and vibration in a storage array, along with the redundant power supplies and fans. Until and unless SSDs dominate, we have to live with the fact that storage disk drives are not green. The statistics from Statistica below forecasts that in 2021, the shipment of SSDs will surpass HDDs.
Today the areal density of HDDs have increased. With SMR (shingled magnetic recording), the areal density jumped about 25% more than the 1Tb/inch (Terabit per inch) in the CMR (conventional magnetic recording) drives. The largest SMR in the market today is 16TB from Seagate with 18TB SMR in the horizon. That capacity is going to grow significantly when EAMR (energy assisted magnetic recording) – which counts heat assisted and microwave assisted – drives enter the market next year. The areal density will grow to 1.6Tb/inch with a roadmap to 4.0Tb/inch. Continue reading →
[Disclosure: I was invited by Commvault as a Media person and Social Ambassador to their Commvault GO 2019 Conference and also a Tech Field Day eXtra delegate from Oct 13-17, 2019 in the Denver CO, USA. My expenses, travel, accommodation and conference fees were covered by Commvault, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]
This trip to the Commvault GO conference was pretty much a mission to find answers to their Hedvig acquisition just a month ago. It was an unprecedented move for Commvault and I, as an industry observer and pundit, took the news positively. I wrote in my blog about Commvault’s big bet and I liked their boldness in their approach.
But the news did not bode well back here in Malaysia. The local technology news portal, Data Storage Asean picked up the news in a rather unconvinced way. 2 long time Commvault partners I spoke to were obviously unhappy because the acquisition made little sense to them on the back of closing of the Commvault Malaysia office just weeks before this with more unsettling rumours of the Commvault team in Asia Pacific. The broken trust and the fear of what the future held for the Commvault customers in Malaysia and in the region were riding along with me on this trip.
But I have seen the beginning of the Commvault transformation from the Commvault GO conferences I have attended since 2017. This is my 3rd Commvault GO and I ended Day 1 with good vibes.
I was at the 9th Openstack Malaysia anniversary this morning, celebrating the inception of the OpenInfra brand. The OpenInfra branding, announced almost a year ago, represented a change of the maturing phase of the OpenStack project but many have been questioning its growing irrelevance. The foundational infrastructure components – Compute (Nova), Image (Glance), Object Storage (Swift) – are being shelved further into the back closet as the landscape evolved in recent years.
The writing is on the wall
Through the storage lens, I already griped about the conundrum of OpenStack storage in Malaysia in last year’s 8th anniversary. And at the thick of this conundrum is OpenStack Swift. The granddaddy of OpenStack storage has not gotten much attention from technology vendors and service providers alike. For one, storage vendors have their own object storage offering, and has little incentive to place OpenStack Swift into their technology development. Continue reading →
I have been following Intel for a few years now, a big part was for their push of the 3D Xpoint technology. Under the Optane brand, Intel has several forms of media types, addressing persistent memory to storage class and solid state storage. Intel, in recent years, has been more forefront with their larger technology portfolio and it is not just about their processors anymore. One of the bright areas I am seeing myself getting more engrossed in (and involved into) is their IoT (Internet of Things) portfolio, and it has been very exciting so far.
Intel IoT and Deep Learning Frameworks
The efforts of the Intel IoTG (Internet of Things Group) in Asia Pacific are recognized rapidly. The drive of the Industry 4.0 revolution is strong. And I saw the brightest spark of the Intel folks pushing the Industry 4.0 message on homeground Malaysia.
After the large showing by Intel at the Semicon event 2 months ago, they turned up a notch in Penang at their own Intel IoT Summit 2019, which concluded last week.
At the event, Intel brought out their solid engineering geeks. There were plenty of talks and workshops on Deep Learning, AI, Neural Networks, with chatters on Nervana, Nauta and Saffron. Despite all the technology and engineering prowess of Intel was showcasing, there was a worrying gap.
The hype of Deep Learning (DL), Machine Learning (ML) and Artificial Intelligence (AI) has reached an unprecedented frenzy. Every infrastructure vendor from servers, to networking, to storage has a word to say or play about DL/ML/AI. This prompted me to explore this hyped ecosystem from a storage perspective, notably from a storage performance requirement point-of-view.
One question on my mind
There are plenty of questions on my mind. One stood out and that is related to storage performance requirements.
Reading and learning from one storage technology vendor to another, the context of everyone’s play against their competitors seems to be “They are archaic, they are legacy. Our architecture is built from ground up, modern, NVMe-enabled“. And there are more juxtaposing, but you get the picture – “We are better, no doubt“.
Are the data patterns and behaviours of AI different? How do they affect the storage design as the data moves through the workflow, the data paths and the lifecycle of the AI ecosystem?