Welcome!

Microservices Expo Authors: Elizabeth White, Charles Araujo, Liz McMillan, Pat Romanski, Flint Brenton

Related Topics: Containers Expo Blog, Java IoT, Microservices Expo, Linux Containers, SDN Journal

Containers Expo Blog: Blog Post

In-Memory Computing: In Plain English

Explaining in-memory computing and defining what in-memory computing is really about

After five days (and eleven meetings) with new customers in Europe, Russia, and the Middle East, I think time is right for another refinement of in-memory computing's definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple, understandable definition of what in-memory computing is all about, what problems it solves, and what uses are a good fit for the technology.

In-Memory Computing: What Is It?
In-memory computing means using a type of middleware software that allows one to store data in RAM, across a cluster of computers, and process it in parallel. Consider operational datasets typically stored in a centralized database which you can now store in "connected" RAM across multiple computers. RAM, roughly, is 5,000 times faster than traditional spinning disk. Add to the mix native support for parallel processing, and things get very fast. Really, really, fast.

RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing.

RAM storage and parallel distributed processing are two fundamental pillars of in-memory computing. While in-memory data storage is expected of in-memory technology, the parallelization and distribution of data processing, which is an integral part of in-memory computing, calls for an explanation.

Parallel distributed processing capabilities of in-memory computing are... a technical necessity. Consider this: a single modern computer can hardly have enough RAM to hold a significant dataset. In fact, a typical x86 server today (mid-2014) would have somewhere between 32GB to 256GB of RAM. Although this could be a significant amount of memory for a single computer, that's not enough to store many of today's operational datasets that easily measure in terabytes.

To overcome this problem in-memory computing software is designed from the ground up to store data in a distributed fashion, where the entire dataset is divided into individual computers' memory, each storing only a portion of the overall dataset. Once data is partitioned - parallel distributed processing becomes a technical necessity simply because data is stored this way.

And while it makes the development of in-memory computing software challenging (literally fewer than 10 companies in the world have mastered this type of software development) - end users of in-memory computing seeking dramatic performance and scalability increas benefit greatly from this technology.

In-Memory Computing: What Is It Good For?
Let's get this out of the way first: if one wants a 2-3x performance or scalability improvements - flash storage (SSD, Flash on PCI-E, Memory Channel Storage, etc.) can do the job. It is relatively cheap and can provide that kind of modest performance boost.

To see, however, what a difference in-memory computing can make, consider this real-live example...

Last year GridGain won an open tender for one of the largest banks in the world. The tender was for a risk analytics system to provide real-time analysis of risk for the bank's trading desk (common use case for in-memory computing in the financial industry). In this tender GridGain software demonstrated one billion (!) business transactions per second on 10 commodity servers with the total of 1TB of RAM. The total cost of these 10 commodity servers? Less than $25K.

Now, read the previous paragraph again: one billion financial transactions per second on $25K worth of hardware. That is the in-memory computing difference - not just 2-3x times faster; more than 100x faster than theoretically possible even with the most expensive flash-based storage available on today's market (forget about spinning disks). And 1TB of flash-based storage alone would cost 10x of entire hardware setup mentioned.

Importantly, that performance translates directly into the clear business value:

  • you can use less hardware to support the required performance and throughput SLAs, get better data center consolidation, and significantly reduce capital costs, as well as operational and infrastructure overhead, and
  • you can also significantly extend the lifetime of your existing hardware and software by getting increased performance and improve its ROI by using what you already have longer and making it go faster.

And that's what makes in-memory computing such a hot topic these days: the demand to process ever growing datasets in real-time can now be fulfilled with the extraordinary performance and scale of in-memory computing, with economics so compelling that the business case becomes clear and obvious.

In-Memory Computing: What Are the Best Use Cases?
I can only speak for GridGain here but our user base is big enough to be statistically significant. GridGain has production customers in a wide variety of industries:

  • Investment banking
  • Insurance claim processing & modeling
  • Real-time ad platforms
  • Real-time sentiment analysis
  • Merchant platform for online games
  • Hyper-local advertising
  • Geospatial/GIS processing
  • Medical imaging processing
  • Natural language processing & cognitive computing
  • Real-time machine learning
  • Complex event processing of streaming sensor data

And we're also seeing our solutions deployed for more mundane use cases, like speeding the response time of a student registration system from 45 seconds to under a half-second.

By looking at this list it becomes pretty obvious that the best use cases are defined not by specific industry but by the underlying technical need, i.e. the need to get the ultimate best and uncompromised performance and scalability for a given task.

In many of these real-life deployments in-memory computing was an enabling technology, the technology that made these particular systems possible to consider and ultimately possible to implement.

The bottom line is that in-memory computing is beginning to unleash a wave of innovation that's not built on Big Data per se, but on Big Ideas, ideas that are suddenly attainable. It's blowing up the costly economics of traditional computing that frankly can't keep up with either the growth of information or the scale of demand.

As the Internet expands from connecting people to connecting things, devices like refrigerators, thermostats, light bulbs, jet engines and even heart rate monitors are producing streams of information that will not just inform us, but also protect us, make us healthier and help us live richer lives. We'll begin to enjoy conveniences and experiences that only existed in science fiction novels. The technology to support this transformation exists today - and it's called in-memory computing.

More Stories By Nikita Ivanov

Nikita Ivanov is founder and CEO of GridGain Systems, started in 2007 and funded by RTP Ventures and Almaz Capital. Nikita has led GridGain to develop advanced and distributed in-memory data processing technologies – the top Java in-memory computing platform starting every 10 seconds around the world today.

Nikita has over 20 years of experience in software application development, building HPC and middleware platforms, contributing to the efforts of other startups and notable companies including Adaptec, Visa and BEA Systems. Nikita was one of the pioneers in using Java technology for server side middleware development while working for one of Europe’s largest system integrators in 1996.

He is an active member of Java middleware community, contributor to the Java specification, and holds a Master’s degree in Electro Mechanics from Baltic State Technical University, Saint Petersburg, Russia.

@MicroservicesExpo Stories
"This all sounds great. But it's just not realistic." This is what a group of five senior IT executives told me during a workshop I held not long ago. We were working through an exercise on the organizational characteristics necessary to successfully execute a digital transformation, and the group was doing their ‘readout.' The executives loved everything we discussed and agreed that if such an environment existed, it would make transformation much easier. They just didn't believe it was reali...
Your homes and cars can be automated and self-serviced. Why can't your storage? From simply asking questions to analyze and troubleshoot your infrastructure, to provisioning storage with snapshots, recovery and replication, your wildest sci-fi dream has come true. In his session at @DevOpsSummit at 20th Cloud Expo, Dan Florea, Director of Product Management at Tintri, provided a ChatOps demo where you can talk to your storage and manage it from anywhere, through Slack and similar services with...
Containers are rapidly finding their way into enterprise data centers, but change is difficult. How do enterprises transform their architecture with technologies like containers without losing the reliable components of their current solutions? In his session at @DevOpsSummit at 21st Cloud Expo, Tony Campbell, Director, Educational Services at CoreOS, will explore the challenges organizations are facing today as they move to containers and go over how Kubernetes applications can deploy with lega...
The “Digital Era” is forcing us to engage with new methods to build, operate and maintain applications. This transformation also implies an evolution to more and more intelligent applications to better engage with the customers, while creating significant market differentiators. In both cases, the cloud has become a key enabler to embrace this digital revolution. So, moving to the cloud is no longer the question; the new questions are HOW and WHEN. To make this equation even more complex, most ...
Learn how to solve the problem of keeping files in sync between multiple Docker containers. In his session at 16th Cloud Expo, Aaron Brongersma, Senior Infrastructure Engineer at Modulus, discussed using rsync, GlusterFS, EBS and Bit Torrent Sync. He broke down the tools that are needed to help create a seamless user experience. In the end, can we have an environment where we can easily move Docker containers, servers, and volumes without impacting our applications? He shared his results so yo...
Don’t go chasing waterfall … development, that is. According to a recent post by Madison Moore on Medium featuring insights from several software delivery industry leaders, waterfall is – while still popular – not the best way to win in the marketplace. With methodologies like Agile, DevOps and Continuous Delivery becoming ever more prominent over the past 15 years or so, waterfall is old news. Or, is it? Moore cites a recent study by Gartner: “According to Gartner’s IT Key Metrics Data report, ...
Enterprise architects are increasingly adopting multi-cloud strategies as they seek to utilize existing data center assets, leverage the advantages of cloud computing and avoid cloud vendor lock-in. This requires a globally aware traffic management strategy that can monitor infrastructure health across data centers and end-user experience globally, while responding to control changes and system specification at the speed of today’s DevOps teams. In his session at 20th Cloud Expo, Josh Gray, Chie...
Kubernetes is a new and revolutionary open-sourced system for managing containers across multiple hosts in a cluster. Ansible is a simple IT automation tool for just about any requirement for reproducible environments. In his session at @DevOpsSummit at 18th Cloud Expo, Patrick Galbraith, a principal engineer at HPE, discussed how to build a fully functional Kubernetes cluster on a number of virtual machines or bare-metal hosts. Also included will be a brief demonstration of running a Galera MyS...
Many organizations are now looking to DevOps maturity models to gauge their DevOps adoption and compare their maturity to their peers. However, as enterprise organizations rush to adopt DevOps, moving past experimentation to embrace it at scale, they are in danger of falling into the trap that they have fallen into time and time again. Unfortunately, we've seen this movie before, and we know how it ends: badly.
Agile has finally jumped the technology shark, expanding outside the software world. Enterprises are now increasingly adopting Agile practices across their organizations in order to successfully navigate the disruptive waters that threaten to drown them. In our quest for establishing change as a core competency in our organizations, this business-centric notion of Agile is an essential component of Agile Digital Transformation. In the years since the publication of the Agile Manifesto, the conn...
"I focus on what we are calling CAST Highlight, which is our SaaS application portfolio analysis tool. It is an extremely lightweight tool that can integrate with pretty much any build process right now," explained Andrew Siegmund, Application Migration Specialist for CAST, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
In IT, we sometimes coin terms for things before we know exactly what they are and how they’ll be used. The resulting terms may capture a common set of aspirations and goals – as “cloud” did broadly for on-demand, self-service, and flexible computing. But such a term can also lump together diverse and even competing practices, technologies, and priorities to the point where important distinctions are glossed over and lost.
"I will be talking about ChatOps and ChatOps as a way to solve some problems in the DevOps space," explained Himanshu Chhetri, CTO of Addteq, in this SYS-CON.tv interview at @DevOpsSummit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.
For organizations that have amassed large sums of software complexity, taking a microservices approach is the first step toward DevOps and continuous improvement / development. Integrating system-level analysis with microservices makes it easier to change and add functionality to applications at any time without the increase of risk. Before you start big transformation projects or a cloud migration, make sure these changes won’t take down your entire organization.
The Jevons Paradox suggests that when technological advances increase efficiency of a resource, it results in an overall increase in consumption. Writing on the increased use of coal as a result of technological improvements, 19th-century economist William Stanley Jevons found that these improvements led to the development of new ways to utilize coal. In his session at 19th Cloud Expo, Mark Thiele, Chief Strategy Officer for Apcera, compared the Jevons Paradox to modern-day enterprise IT, examin...
The taxi industry never saw Uber coming. Startups are a threat to incumbents like never before, and a major enabler for startups is that they are instantly “cloud ready.” If innovation moves at the pace of IT, then your company is in trouble. Why? Because your data center will not keep up with frenetic pace AWS, Microsoft and Google are rolling out new capabilities. In his session at 20th Cloud Expo, Don Browning, VP of Cloud Architecture at Turner, posited that disruption is inevitable for comp...
When you focus on a journey from up-close, you look at your own technical and cultural history and how you changed it for the benefit of the customer. This was our starting point: too many integration issues, 13 SWP days and very long cycles. It was evident that in this fast-paced industry we could no longer afford this reality. We needed something that would take us beyond reducing the development lifecycles, CI and Agile methodologies. We made a fundamental difference, even changed our culture...
In his session at 20th Cloud Expo, Mike Johnston, an infrastructure engineer at Supergiant.io, discussed how to use Kubernetes to set up a SaaS infrastructure for your business. Mike Johnston is an infrastructure engineer at Supergiant.io with over 12 years of experience designing, deploying, and maintaining server and workstation infrastructure at all scales. He has experience with brick and mortar data centers as well as cloud providers like Digital Ocean, Amazon Web Services, and Rackspace. H...
High-velocity engineering teams are applying not only continuous delivery processes, but also lessons in experimentation from established leaders like Amazon, Netflix, and Facebook. These companies have made experimentation a foundation for their release processes, allowing them to try out major feature releases and redesigns within smaller groups before making them broadly available. In his session at 21st Cloud Expo, Brian Lucas, Senior Staff Engineer at Optimizely, discussed how by using ne...
Without a clear strategy for cost control and an architecture designed with cloud services in mind, costs and operational performance can quickly get out of control. To avoid multiple architectural redesigns requires extensive thought and planning. Boundary (now part of BMC) launched a new public-facing multi-tenant high resolution monitoring service on Amazon AWS two years ago, facing challenges and learning best practices in the early days of the new service.