Welcome!

Agile Computing Authors: Liz McMillan, ManageEngine IT Matters, Craig Lowell, AppNeta Blog, Jonathan Fries

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Containers Expo Blog, @BigDataExpo, SDN Journal

@CloudExpo: Article

Can We Finally Find the Database Holy Grail? | Part 3

With the advent of Durable Distributed Cache architectures organizations can build global systems with transactional semantics

In my first post in this three part series I talked about the need for distributed transactional databases that scale-out horizontally across commodity machines, as compared to traditional transactional databases that employ a "scale-up" design.  Simply adding more machines is a quicker, cheaper and more flexible way of increasing database capacity than forklift upgrades to giant steam-belching servers. It also brings the promise of continuous availability and of geo-distributed operation.

The second post in this series provided an overview of the three historical approaches to designing distributed transactional database systems, namely: 1. Shared Disk Designs (e.g., ORACLE RAC); 2. Shared Nothing Designs (e.g. the Facebook MySQL implementation); and 3) Synchronous Commit Designs (e.g. GOOGLE F1).  All of them have some advantages over traditional client-server database systems, but they each have serious limitations in relation to cost, complexity, dependencies on specialized infrastructure, and workload-specific performance trade-offs. I noted that we are very excited about a recent innovation in distributed database design, introduced by NuoDB's technical founder Jim Starkey.  We call the concept Durable Distributed Cache (DDC), and I want to spend a little time in this third and final post talking about what it is, with a high-level overview of how it works.

Memory-Centric vs. Storage-Centric
The first insight Jim had was that all general-purpose relational databases to-date have been architected around a storage-centric assumption, and that this is a fundamental problem when it comes to scaling out.  In effect, database systems have been fancy file systems that arrange for concurrent read/write access to disk-based files such that users do not trample on each other.  The Durable Distributed Cache architecture inverts that idea, imagining the database as a set of in-memory container objects that can overflow to disk if necessary, and can be retained in backing stores for durability purposes.  Memory-Centric vs. Storage-Centric may sound like splitting hairs, but it turns out that it is really significant.  The reasons are best described by example.

Suppose you have a single, logical DDC database running on 50 servers (which is absolutely feasible to do with an ACID transactional DDC-based database).  And suppose that at some moment server 23 needs object #17.  In this case, server 23 might determine that object #17 is instantiated in memory on seven other servers.  It simply requests the object from the most responsive server.  Note that as the object was in memory, the operation involved no disk IO - it was a remote memory fetch, which is orders of magnitude faster than going to disk.

You might ask about the case in which object #17 does not exist in memory elsewhere.  In the Durable Distributed Cache architecture this is handled by certain servers "faking" that they have all the objects in memory.  In practice, of course, they are maintaining backing stores on disk, SSD or whatever they choose (in the NuoDB implementation they can use arbitrary Key/Value stores such as Amazon S3 or Hadoop HDFS).  As it relates to supplying objects, these "backing store servers" behave exactly like the other servers except they can't guarantee the same response times.

So all servers in the DDC architecture can request objects and supply objects.  They are peers in that sense (and in all other senses).  Some servers have a subset of the objects at any given time, and can therefore only supply a subset of the database to other servers.  Other servers have all the objects and can supply any of them, but will be slower to supply objects that are not resident in memory.

Let's call the servers with a subset of the objects Transaction Engines (TEs), and the other servers Storage Managers (SMs).  TEs are pure in memory servers that do not need to use disks.  They are autonomous and can unilaterally load and eject objects from memory according to their needs.  Unlike TEs, SMs can't just drop objects on the floor when they are finished with them; instead they must ensure they are safely placed in durable storage.

For readers familiar with caching architectures, you might have already recognized that these TEs are in effect a distributed DRAM cache, and the SMs are specialized TEs that ensure durability.  Hence the name Durable Distributed Cache.

Resilience to Failure
It turns out that any object state that is present on a TE is either already committed to the disk (i.e. safe on one or more SMs) or part of an uncommitted transaction that will simply fail at application level if the object goes away. This means that the database has the interesting property of being resilient to the loss of TEs.  You can shut a TE down or just unplug it and the system does not lose data.  It will lose throughput capacity of course, and any partial transactions on the TE will be reported to the application as failed transactions.  But transactional applications are designed to handle transaction failure. If you reissue the transaction at the application level it will be assigned to a different TE and will proceed to completion.  So the DDC architecture is resilient to the loss of TEs.

What about SMs?  Recall that you can have as many SMs as you like.  They are effectively just TEs that secretly stash away the objects in some durable store.  And, unless you configure it not to, each SM might as well store all the objects. Disks are cheap, which means that you have as many redundant copies of the whole database as you want.  In consequence, the DDC architecture is not only resilient to the loss of TEs, but also to the loss of SMs.

In fact, as long as you have at least one TE and one SM running, you still have a running database.  Resilience to failure is one of the longstanding but unfulfilled promises of distributed transactional databases.  The DDC architecture addresses this directly.

Elastic Scale-out and Scale-in
What happens if I add a server to a DDC database?  Think of the TE layer as a cache.  If the new TE is given work to do, it will start asking for objects and doing the assigned work.  It will also start serving objects to other TEs that need them.  In fact, the new TE is a true peer of the other TEs.  Furthermore, if you were to shut down all of the other TEs, the database would still be running, and the new TE would be the only server doing transactional work.

SMs, being specialized TEs, can also be added and shut down dynamically.  If you add an "empty" (or stale) SM to a running database, it will cycle through the list of objects and load them into its durable store, fetching them from the most responsive place as is usual.  Once it has all the objects, it will raise its hand and take part as a peer to the other SMs.  And, just as with the new TE described above, you can delete all other SMs once you have added the new SM.  The system will keep running without missing a beat or losing any data.

So the bottom line is that the DDC architecture delivers capacity on demand.  You can elastically scale-out the number of TEs and SMs and scale them back in again according to workload requirements.  Capacity on demand is a second promise of distributed databases that is delivered by the DDC architecture.

Geo-Distribution
The astute reader will no doubt be wondering about the hardest part of this distributed database problem -- namely that we are talking about distributed transactional databases.  Transactions, specifically ACID transactions, are an enormously simplifying abstraction that allows application programmers to build their applications with very clean, high-level and well-defined data guarantees.  If I store my data in an ACID transactional database, I know it will isolate my program from other programs, maintain data consistency, avoid partial failure of state changes and guarantee that stored data will still be there at a later date, irrespective of external factors.  Application programs are vastly simpler when they can trust an ACID compliant database to look after their data, whatever the weather.

The DDC architecture adopts a model of append-only updates.  Traditionally, an update to a record in a database overwrites that record, and a deletion of a record removes the record.  That may sound obvious, but there is another way, invented by Jim Starkey about 25 years ago.  The idea is to create and maintain versions of everything.  In this model, you never do a destructive update or destructive delete.  You only ever create new versions of records, and in the case of a delete, the new version is a record version that notes the record is no longer extant.  This model is called MVCC (multi-version concurrency control), and it has a number of well-known benefits, even in scale-up databases.  MVCC has even greater benefits in distributed database architectures, including DDC.

We don't have the space here to cover MVCC in detail, but it is worth noting that one of the things it does is to allow a DBMS to manage read/write concurrency without the use of traditional locks.  For example, readers don't block writers and writers do not block readers.  It also has some exotic features, including that if you wanted to you could theoretically maintain a full history of the entire database.  But as it relates to DDC and the Distributed Transactional Database challenge, there is something very neat about MVCC.  DDC leverages a distributed variety of MVCC in concert with DDC's distributed object semantics that allows almost all the inter-server communications to be asynchronous.

The implications of DDC being asynchronous are very far-reaching.  In general, it allows much higher utilization of system resources (cores, networks, disks, etc.) than synchronous models can.  But specifically, it allows the system to be fairly insensitive to network latencies, and to the location of the servers relative to each other.  Or to put it a different way, it means you can start up your next TE or SM in a remote datacenter and connect it to the running database.  Or you can start up half of the database servers in your datacenter and the other half on a public cloud.

Modern applications are distributed.  Users of a particular web site are usually spread across the globe.  Mobile applications are geo-distributed by nature.  Internet of Things (IoT) applications are connecting gazillions of consumer devices that could be anywhere at any time.  None of these applications are well served by a single big database server in a single location, or even a cluster of smaller database servers in a single location.  What they need is a single database running on a group of database servers in multiple datacenters (or cloud regions).  That can give them higher performance, datacenter failover and the potential to manage issues of data privacy and sovereignty.

The third historical promise of Distributed Transactional Database systems is Geo-Distribution.  Along with the other major promises (Resilience to Failure and Elastic Scalability), Geo-Distribution has heretofore been an unattainable dream.  The DDC architecture, with its memory-centric distributed object model and its asynchronous inter-server protocols, finally delivers on this capability.

In Summary
This short series of posts has sought to provide a quick overview of distributed database designs, with some high level commentary on the advantages and disadvantages of the various approaches.  There has been great historical success with Shared Disk, Shared Nothing and Synchronous Commit models.  We see the advanced technology companies delivering some of the most scalable systems in the world using these distributed database technologies.  But to date, distributed databases have never really delivered anything close to their full promise.  They have also been inaccessible to people and organizations that lack the development and financial resources of GOOGLE or Facebook.

With the advent of DDC architectures, it is now possible for any organization to build global systems with transactional semantics, on-demand capacity and the ability to run for 10 years without missing a beat.  The big promises of Distributed Transactional Databases are Elastic Scalability and Geo-Distribution.  We're very excited that due to Jim Starkey's Durable Distributed Cache, those capabilities are finally being delivered to the industry.

More Stories By Barry Morris

Barry Morris is CEO & Co-Founder of NuoDB, Inc. An accomplished software CEO with over 25 years of industry experience in the USA and Europe, running private and public companies ranging in scale from early startup phase to 1,000+ employees, he loves to build companies around industry-changing paradigm-shifts in technology. Morris was previously CEO of StreamBase and Iona Technologies.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@ThingsExpo Stories
Connected devices and the industrial internet are growing exponentially every year with Cisco expecting 50 billion devices to be in operation by 2020. In this period of growth, location-based insights are becoming invaluable to many businesses as they adopt new connected technologies. Knowing when and where these devices connect from is critical for a number of scenarios in supply chain management, disaster management, emergency response, M2M, location marketing and more. In his session at @Th...
"Dice has been around for the last 20 years. We have been helping tech professionals find new jobs and career opportunities," explained Manish Dixit, VP of Product and Engineering at Dice, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
What happens when the different parts of a vehicle become smarter than the vehicle itself? As we move toward the era of smart everything, hundreds of entities in a vehicle that communicate with each other, the vehicle and external systems create a need for identity orchestration so that all entities work as a conglomerate. Much like an orchestra without a conductor, without the ability to secure, control, and connect the link between a vehicle’s head unit, devices, and systems and to manage the ...
"We're a cybersecurity firm that specializes in engineering security solutions both at the software and hardware level. Security cannot be an after-the-fact afterthought, which is what it's become," stated Richard Blech, Chief Executive Officer at Secure Channels, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
In addition to all the benefits, IoT is also bringing new kind of customer experience challenges - cars that unlock themselves, thermostats turning houses into saunas and baby video monitors broadcasting over the internet. This list can only increase because while IoT services should be intuitive and simple to use, the delivery ecosystem is a myriad of potential problems as IoT explodes complexity. So finding a performance issue is like finding the proverbial needle in the haystack.
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life sett...
The WebRTC Summit New York, to be held June 6-8, 2017, at the Javits Center in New York City, NY, announces that its Call for Papers is now open. Topics include all aspects of improving IT delivery by eliminating waste through automated business models leveraging cloud technologies. WebRTC Summit is co-located with 20th International Cloud Expo and @ThingsExpo. WebRTC is the future of browser-to-browser communications, and continues to make inroads into the traditional, difficult, plug-in web ...
20th Cloud Expo, taking place June 6-8, 2017, at the Javits Center in New York City, NY, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy.
Internet-of-Things discussions can end up either going down the consumer gadget rabbit hole or focused on the sort of data logging that industrial manufacturers have been doing forever. However, in fact, companies today are already using IoT data both to optimize their operational technology and to improve the experience of customer interactions in novel ways. In his session at @ThingsExpo, Gordon Haff, Red Hat Technology Evangelist, will share examples from a wide range of industries – includin...
WebRTC is the future of browser-to-browser communications, and continues to make inroads into the traditional, difficult, plug-in web communications world. The 6th WebRTC Summit continues our tradition of delivering the latest and greatest presentations within the world of WebRTC. Topics include voice calling, video chat, P2P file sharing, and use cases that have already leveraged the power and convenience of WebRTC.
"We build IoT infrastructure products - when you have to integrate different devices, different systems and cloud you have to build an application to do that but we eliminate the need to build an application. Our products can integrate any device, any system, any cloud regardless of protocol," explained Peter Jung, Chief Product Officer at Pulzze Systems, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at 20th Cloud Expo, Ed Featherston, director/senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
"Once customers get a year into their IoT deployments, they start to realize that they may have been shortsighted in the ways they built out their deployment and the key thing I see a lot of people looking at is - how can I take equipment data, pull it back in an IoT solution and show it in a dashboard," stated Dave McCarthy, Director of Products at Bsquare Corporation, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
IoT is rapidly changing the way enterprises are using data to improve business decision-making. In order to derive business value, organizations must unlock insights from the data gathered and then act on these. In their session at @ThingsExpo, Eric Hoffman, Vice President at EastBanc Technologies, and Peter Shashkin, Head of Development Department at EastBanc Technologies, discussed how one organization leveraged IoT, cloud technology and data analysis to improve customer experiences and effici...
Fact is, enterprises have significant legacy voice infrastructure that’s costly to replace with pure IP solutions. How can we bring this analog infrastructure into our shiny new cloud applications? There are proven methods to bind both legacy voice applications and traditional PSTN audio into cloud-based applications and services at a carrier scale. Some of the most successful implementations leverage WebRTC, WebSockets, SIP and other open source technologies. In his session at @ThingsExpo, Da...
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
As data explodes in quantity, importance and from new sources, the need for managing and protecting data residing across physical, virtual, and cloud environments grow with it. Managing data includes protecting it, indexing and classifying it for true, long-term management, compliance and E-Discovery. Commvault can ensure this with a single pane of glass solution – whether in a private cloud, a Service Provider delivered public cloud or a hybrid cloud environment – across the heterogeneous enter...
The cloud promises new levels of agility and cost-savings for Big Data, data warehousing and analytics. But it’s challenging to understand all the options – from IaaS and PaaS to newer services like HaaS (Hadoop as a Service) and BDaaS (Big Data as a Service). In her session at @BigDataExpo at @ThingsExpo, Hannah Smalltree, a director at Cazena, provided an educational overview of emerging “as-a-service” options for Big Data in the cloud. This is critical background for IT and data professionals...
Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before. They have a question on their mind like “How is my application doing” but no id...
@GonzalezCarmen has been ranked the Number One Influencer and @ThingsExpo has been named the Number One Brand in the “M2M 2016: Top 100 Influencers and Brands” by Onalytica. Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR “Machine to Machine.” They then identified the top 100 most influential brands and individuals leading the discussion on Twitter.