Welcome!

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Cloud Security, @DXWorldExpo, SDN Journal

@CloudExpo: Article

Bringing Intelligence to REST

The API Economy has nearly run its course.

Whether you're a Cloud Computing aficionado, an enterprise integration specialist, or an IT executive, it's hard to have a conversation today without mention of REST. Representational State Transfer, or REST to the cognoscenti, is an architectural style that treats distributed computing problems as though they were Web problems. On the Web you have browsers chatting via HTTP to Web servers, and those servers can work whatever magic they need to in order to serve up the full wealth we've all come to expect from the World Wide Web. Take those basic Web patterns, extend them to general distributed computing problems (including Cloud and legacy integration), and voila! You have REST.

However, while REST has achieved substantial success in simplifying software interfaces and thus facilitating many forms of integration, it is still inherently inflexible. What works well for humans using browsers often doesn't apply to arbitrary software clients. And most fundamentally, REST does not address the most difficult distributed computing challenge of all: how to deal with dynamic business context.

Freeing Ourselves from REST's Four Constraints
The majority of RESTafarians, as the aforementioned cognoscenti have so eloquently dubbed themselves, treat REST as an Application Programming Interface (API) style. After all, REST does call for a uniform interface, a requirement that in one fell swoop addresses many of the knottier problems of Web Services and other, more tightly coupled API styles that came before. Compared to the complexities of Web Service operations or object-oriented remote method calls, REST's uniform interface is the essence of simplicity. Furthermore, there's no question that REST's uniform interface requirement is at the heart of what analysts like to refer to as the API Economy.

But there is more to REST than a uniform interface. In fact, REST isn't an API style at all. It's an architectural style. As an architectural style, REST consists of a set of constraints on software architecture. In other words, feel free to follow what architectural rules you like, but if you want to follow REST you must comply with the following RESTful constraints:

  1. Separation of resources from representations
  2. Manipulation of resources by representations
  3. Self-descriptive messages
  4. Hypermedia as the engine of application state, or HATEOAS

Let's take a quick tour of these constraints to put them in plain language. In so doing, we'll also why REST fails to adequately address the problem of dynamic business context.

The first constraint is essentially the encapsulation requirement. Resources are abstractions of capabilities on a server, while the representations are what the resources provide to the client. For example, a resource might be a php script running on a Web server, and the representation might be an HTML file it returns when a browser makes a GET request of it. But the browser never, ever gets the php itself; it only sees the HTML. The php is forever hidden from view.

The second constraint calls for the uniform interface. The only way that clients are able to interact with resources is by following hyperlinks in representations - in other words, making GET, POST, PUT, or DELETE requests to the URI of the resource, assuming we're using HTTP as our transport protocol, which we usually are.

The third, self-descriptive message constraint is actually quite straightforward: all the data as well as all the metadata the resource needs to process a request must be contained in that request, and correspondingly, the resource must send all necessary metadata in the representation response to the client that the client will need to understand the representation. In other words, REST requires that there be no out-of-band metadata: information pertinent to the interaction that doesn't actually appear in the interaction. Furthermore, the interaction must be stateless: the resource isn't expected to keep track of any information pertinent to any particular client.

The problem with this third constraint, of course, is that out-of-band metadata is very handy in many situations. Take security-related metadata, for example. REST calls for all such metadata to be in every request, which led to the development of the OAuth (Open Authorization) standard. Yes, OAuth is quite powerful and Web friendly. Yes, OAuth is making inroads into the enterprise. But do you really want to restrict the security protocols for all of your interactions to OAuth and nothing but OAuth? Probably not.

If you have a more complex interaction than a simple request, then the ban on out-of-band metadata becomes increasingly impractical. For example, let's say you're trying to support a complex business process by building a composite application. You're trying to follow REST so you're composing resources. But then you find you need to somehow deal with a range of policies, business rules, or other out-of-band metadata that impact the behavior of your composite application for certain users but not others. REST alone simply doesn't deal well with such complexities.

And then there's the fourth constraint: the dreaded HATEOAS. REST separates state information into two types: resource state and application state. Resource state is shared or persisted state information on the server, while application state is specific to the individual client, who negotiates the application (think abstracted Web site) by following hyperlinks. The HATEOAS constraint hammers home the fact that the point of REST is building distributed hypermedia systems, where the client is responsible for running hypermedia-based applications. In other words, the hypermedia contain the business context for the interactions between client and server.

Hypermedia drive the Web, of course, but once you start breaking down REST's notions of client and server, however, then the power of hypermedia starts to wane. After all, enterprises often want to build or leverage business applications that offer more than a simple Web site, especially when there's a shared business context across nodes, where those nodes are more than just clients and servers. Hypermedia - and REST - simply weren't built for such complex, dynamic situations.

The Devil in the Details
There's a very good reason why REST eschews out-of-band metadata and shared business context beyond the scope of hypermedia: both of these requirements are inherently dynamic, and furthermore, depend upon multiple actors - actors who may change over time. By constraining the architecture to avoid such complexities, REST provides a useful set of simplifications that have provided unquestionable value throughout the API economy.

The challenges in the section above, however, go well beyond REST. The problems we're discussing have plagued software interfaces in general, from the earliest screen-scraping programs to object-oriented APIs to Web Services to today's RESTful APIs. The entire notion of a software interface is an agreement between the people building the software provider and consumer endpoints that the interface behaves a particular way. Loose coupling, after all, relies upon an interface contract that fixes the behavior of the API so that the parties involved can make various decisions about their software under the covers without breaking the interaction. But woe to those who dare to change the contract, or who want to consider metadata the contract knows nothing about!

Out-of-band metadata and business context outside hypermedia applications are by definition exterior to the contract, and thus aren't amenable to any distributed computing architectural style that relies too heavily on static APIs. Therein lies the essential challenge of the API. To those analysts trumpeting the API Economy I say: the API Economy has nearly run its course. We've solved as many problems as we're going to solve with contracted software interfaces. But the business stakeholders still aren't happy. After all, it's their context - the business context - that APIs (whether RESTful or not) are so woefully unable to deal with. It's time for another approach.

The EnterpriseWeb Take
I'm not saying that we don't need APIs, of course, or that REST doesn't serve a useful purpose. I am declaring, however, that something critically important is missing from this picture. APIs are far too static to address issues of dynamic business context. We tried to address these issues with the SOA intermediary (typically an ESB), where the intermediary executed policy-based routing and transformation rules to abstract a set of inflexible Service interfaces, thus providing the illusion of flexibility, much as a flip deck provides the illusion of motion. But even the most successful implementers of SOA were still unable to deal with most out-of-band metadata - and dynamic business context? That nut no one has been able to crack.

What we really need is an entirely different kind of intermediary. A smarter intermediary that knows how to deal with all types of metadata and furthermore, can resolve the more difficult challenge of business context - in real time, where the business lives. In the next issue of Loosely-Coupled I'll discuss how such a smarter intermediary might actually work. And naturally, if you want to see one in action, drop us a line.

Image credit: lin440315

More Stories By Jason Bloomberg

Jason Bloomberg is a leading IT industry analyst, Forbes contributor, keynote speaker, and globally recognized expert on multiple disruptive trends in enterprise technology and digital transformation. He is ranked #5 on Onalytica’s list of top Digital Transformation influencers for 2018 and #15 on Jax’s list of top DevOps influencers for 2017, the only person to appear on both lists.

As founder and president of Agile Digital Transformation analyst firm Intellyx, he advises, writes, and speaks on a diverse set of topics, including digital transformation, artificial intelligence, cloud computing, devops, big data/analytics, cybersecurity, blockchain/bitcoin/cryptocurrency, no-code/low-code platforms and tools, organizational transformation, internet of things, enterprise architecture, SD-WAN/SDX, mainframes, hybrid IT, and legacy transformation, among other topics.

Mr. Bloomberg’s articles in Forbes are often viewed by more than 100,000 readers. During his career, he has published over 1,200 articles (over 200 for Forbes alone), spoken at over 400 conferences and webinars, and he has been quoted in the press and blogosphere over 2,000 times.

Mr. Bloomberg is the author or coauthor of four books: The Agile Architecture Revolution (Wiley, 2013), Service Orient or Be Doomed! How Service Orientation Will Change Your Business (Wiley, 2006), XML and Web Services Unleashed (SAMS Publishing, 2002), and Web Page Scripting Techniques (Hayden Books, 1996). His next book, Agile Digital Transformation, is due within the next year.

At SOA-focused industry analyst firm ZapThink from 2001 to 2013, Mr. Bloomberg created and delivered the Licensed ZapThink Architect (LZA) Service-Oriented Architecture (SOA) course and associated credential, certifying over 1,700 professionals worldwide. He is one of the original Managing Partners of ZapThink LLC, which was acquired by Dovel Technologies in 2011.

Prior to ZapThink, Mr. Bloomberg built a diverse background in eBusiness technology management and industry analysis, including serving as a senior analyst in IDC’s eBusiness Advisory group, as well as holding eBusiness management positions at USWeb/CKS (later marchFIRST) and WaveBend Solutions (now Hitachi Consulting), and several software and web development positions.

Latest Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will d...
While DevOps most critically and famously fosters collaboration, communication, and integration through cultural change, culture is more of an output than an input. In order to actively drive cultural evolution, organizations must make substantial organizational and process changes, and adopt new technologies, to encourage a DevOps culture. Moderated by Andi Mann, panelists discussed how to balance these three pillars of DevOps, where to focus attention (and resources), where organizations might...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Le...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
CI/CD is conceptually straightforward, yet often technically intricate to implement since it requires time and opportunities to develop intimate understanding on not only DevOps processes and operations, but likely product integrations with multiple platforms. This session intends to bridge the gap by offering an intense learning experience while witnessing the processes and operations to build from zero to a simple, yet functional CI/CD pipeline integrated with Jenkins, Github, Docker and Azure...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Dhiraj Sehgal works in Delphix's product and solution organization. His focus has been DevOps, DataOps, private cloud and datacenters customers, technologies and products. He has wealth of experience in cloud focused and virtualized technologies ranging from compute, networking to storage. He has spoken at Cloud Expo for last 3 years now in New York and Santa Clara.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure ...
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.