The digital media world is in the process of dramatic change. For years, the Internet has been about web sites and browser-based experiences, and the systems that drove those sites generally matched those experiences. But now, the portable world is upon us and it is formidable. With the growing need and ability to be portable comes tremendous opportunity for content providers. But it also requires substantial changes to their thinking and their systems. It requires distribution platforms, API’s and other ways to get the content to where it needs to be. But having an API is not enough. In order for content providers to take full advantage of these new platforms, they will need to, first and foremost, embrace one simple philosophy: COPE (Create Once, Publish Everywhere).
The diagram above represents NPR’s content management pipeline and how it embraces these COPE principles. The basic principle is to have content producers and ingestion scripts funnel content into a single system (or series of closely tied systems). Once there, the distribution of all content can be handled identically, regardless of content type or its destinations
COPE Through COPE, our systems have enabled incredible growth despite having a small staff and limited resources. Although the CMS is home-grown, COPE itself is agnostic as to the build or buy/integrate decision. Any system that adheres to these principles, whether it is a COTS product, home-grown, or anything in between, will see the benefits of content modularity and portability.
In this series of posts, I will be discussing these philosophies, as well as how NPR applied them and how we were able to do so much with so little (including our NPR API).
COPE is really a combination of several other closely related sub-philosophies, including:
Build content management systems (CMS), not web publishing tools (WPT)
Separate content from display
Ensure content modularity
Ensure content portability
These philosophies have a direct impact on API and distribution strategies as well. Creating an API on top of a COPE-less system will distribute the content, but there is still no guarantee that the content can actually live on any platform. COPE is dependent on these other philosophies to ensure that the content is truly portable.
Build CMS, not WPT COPE is the key difference between content management systems and web publishing tools, although these terms are often used interchangeably in our industry. The goal of any CMS should be to gather enough information to present the content on any platform, in any presentation, at any time. WPT’s capture content with the primary purpose of publishing web pages. As a result, they tend to manage the content in ways focused on delivering it to the web. Plug-ins are often available for distribution to other platforms, but applying tools on top of the native functions to manipulate the content for alternate destinations makes the system inherently unscalable. That is, for each new platform, WPT’s will need a new plug-in to tailor the presentation markup to that platform. CMS’s, on the other hand, store the content cleanly, enabling the presentation layers to worry about how to display the content not on how to transform the markup embedded within it.
True CMS’s are really just content capturing tools that are completely agnostic as to how or where the content will be viewed, whether it is a web page, mobile app, TV or radio display, etc. Additionally, platforms that don’t yet exist are able to be served by a true CMS in ways that WPT’s may not be able to (even with plug-ins). By applying COPE, NPR was able to quickly jump on advancements throughout the years like RSS, Podcasts, API’s and mobile platforms with relative ease. As an example, the public API took only about two developer months to create, and most of that time was spent on user and rights management.
This presentation shows the same NPR story displayed in a wide range of platforms. The content, through the principles of COPE, is pushed out to all of these destinations through the NPR API. Each destination, meanwhile, uses the appropriate content for that presentation layer.
Separate Content from Display Separating content from display is one of the key concepts supporting COPE. In the most basic form, this means that the presentation layer needs to be a series of templates that know how to pull in the content from the repository. This enables the presentation layer to care about how the content will look while the content can be display-agnostic, allowing it to appear on a web site, a mobile device, etc.
But to truly separate content from display, the content repository needs to also avoid storing “dirty” content. Dirty content is content that contains any presentation layer information embedded in it, including HTML, XML, character encodings, microformats, and any other markup or rich formatting information. This separation is achieved by the two other principles, content modularity and content portability
At a high level, many systems and organizations are applying the basics of COPE. They are able to distribute content to different platforms, separate content from display, etc. But to take some of these systems to the next level, enabling them to scale and adapt to our changing landscape, they will need to focus more on content modularity and portability. In my next post, I will go into more detail about NPR’s approach to content modularity and why our approach is more than just data normalization.
As I discussed in my recent blog post on ProgrammableWeb.com, Netflix has found substantial limitations in the traditional one-size-fits-all (OSFA) REST API approach. As a result, we have moved to a new, fully customizable API. The basis for our decision is that Netflix’s streaming service is available on more than 800 different device types, almost all of which receive their content from our private APIs. In our experience, we have realized that supporting these myriad device types with an OSFA API, while successful, is not optimal for the API team, the UI teams or Netflix streaming customers. And given that the key audiences for the API are a small group of known developers to which the API team is very close (i.e., mostly internal Netflix UI development teams), we have evolved our API into a platform for API development. Supporting this platform are a few key philosophies, each of which is instrumental in the design of our new system. These philosophies are as follows:
Embrace the Differences of the Devices
Separate Content Gathering from Content Formatting/Delivery
Redefine the Border Between “Client” and “Server”
Distribute Innovation
I will go into more detail below about each of these, including our implementation and what the benefits (and potential detriments) are of this approach. However, each philosophy reflects our top-level goal: to provide whatever is best for the Netflix customer. If we can improve the interaction between the API and our UIs, we have a better chance of making more of our customers happier.
Now, the philosophies…
Embrace the Differences of the Devices
The key driver for this redesigned API is the fact that there are a range of differences across the 800+ device types that we support. Most APIs (including the REST API that Netflix has been using since 2008) treat these devices the same, in a generic way, to make the server-side implementations more efficient. And there is good reason for this approach. Providing an OSFA API allows the API team to maintain a solid contract with a wide range of API consumers because the API team is setting the rules for everyone to follow.
While effective, the problem with the OSFA approach is that its emphasis is to make it convenient for the API provider, not the API consumer. Accordingly, OSFA is ignoring the differences of these devices; the differences that allow us to more optimally take advantage of the rich features offered on each. To give you an idea of these differences, devices may differ on:
Memory capacity or processing power, potentially modifying how much content it can manage at a given time
Requirements for distinct markup formats and broader device proliferation increases the likelihood of this
Document models, some devices may perform better with flatter models, others with more hierarchical
Screen real estate which may impact the content elements that are needed
Document delivery, some performing better with bits streamed across HTTP rather than delivered as a complete document
User interactions, which could influence the metadata fields, delivery method, interaction model, etc.
Our new model is designed to cut against the OSFA paradigm and embrace the differences across devices while supporting those differences equally. To achieve this, our API development platform allows each UI team to create customized endpoints. So the request/response model can be optimized for each team’s UIs to account for unique or divergent device requirements. To support the variability in our request/response model, we need a different kind of architecture, which takes us to the next philosophy…
Separate Content Gathering from Content Formatting/Delivery
In many OSFA implementations, the API is the engine that retrieves the content from the source(s), prepares that payload, and then ultimately delivers it. Historically, this implementation is also how the Netflix REST API has operated, which is loosely represented by the following image:
The above diagram shows a rainbow of colors roughly representing some of the different requests needed for the PS3, as an example, to start the Netflix experience. Other UIs will have a similar set of interactions against the OSFA REST API given that they are all required by the API to adhere to roughly the same set of rules. Inside the REST API is the engine that performs the gathering, preparation and delivery of the content (indifferent to which UI made the request).
Our new API has departed from the OSFA API model towards one that enables fine-grained customizations without compromising overall system manageability. To achieve this model, our new architecture clearly separates the operations of content gathering from content formatting and delivery. The following diagram represents this modified architecture:
In this new model, the UIs make a single request to a custom endpoint that is designed to specifically handle that request. Behind the endpoint is a handler that parses the request and calls the Java API, which gathers the content by calling back to a range of dependent services. We will discuss in later posts how we do this, particularly in how we parse the requests, trigger calls to dependencies, handle concurrency, support fallbacks, as well as other techniques we use to ensure optimized and accurate gathering of the content. For now, though, I will just say that the content gathering from the Java API is generic and independent of destination, just like the OSFA approach.
After the content has been gathered, however, it is handed off to the formatting and delivery engines which sit on top of the Java API on the server. The diagram represents this layer by showing an array of different devices resting on top of the Java API, each of which corresponds to the custom endpoints for a given UI and/or set of devices. The custom endpoints, as mentioned earlier, support optimized request/response handling for that device, which takes us to the next philosophy…
Redefine the Border Between “Client” and “Server”
The traditional definition of “client code” is all code that lives on a given device or UI. “Server code” is typically defined as the code that resides on the server. The divide between the two is the network border. This is often the case for REST APIs and that border is where the contract between the API provider and API consumer is engaged, as was the case for Netflix’s REST API, as shown below:
In our new approach, we are pushing this border back to the server, and with it goes a substantial portion of the UI-specific content processing. All of the code on the device is still considered client code, but some client code now resides on the server. In essence, the client code on the device makes a network call back to a dedicated client adapter that resides on the server behind the custom endpoint. Once back on the server, the adapter (currently written in Groovy) explodes that request out to a series of server-side calls that get the corresponding content (in some cases, roughly the same rainbow of requests that would be handled across HTTP in our old REST API). At that point, the Java APIs perform their content gathering functions and deliver the requested content back to the adapter. Once the adapter has some or all of its content, the adapter processes it for delivery, which includes pruning out unwanted fields, error handling and retries, formatting the response, and delivering the document header and body. All of this processing is custom to the specific UI. This new definition of client/server is represented in the following diagram:
There are two major aspects to this change. First, it allows for more efficient interactions between the device and the server since most calls that otherwise would be going across the network can be handled on the server. Of course, network calls are the most expensive part of the transaction, so reducing the number of network requests improves performance, in some cases by several seconds. The second key component leads us to the final (and perhaps most important) philosophy to this approach, which is the distribution of the work for building out the optimized adapters.
Distribute Innovation
One expected critique with this approach is that as we add more devices and build more UIs for A/B and multivariate tests, there will undoubtedly be myriad adapters needed to support all of these distinct request profiles. How can we innovate rapidly and support such a diverse (and growing) set of interactions? It is critical for us to support the custom adapters, but it is equally important for us to maintain a high rate of innovation across these UIs and devices.
As described above, pushing some of the client code back to the servers and providing custom endpoints gives us the opportunity to distribute the API development to the UI teams. We are able to do this because the consumers of this private API are the Netflix UI and device teams. Given that the UI teams can create and modify their own adapter code (potentially without any intervention or involvement from the API team), they can be much more nimble in their development. In other words, as long as the content is available in the Java API, the UI teams can change the code that lives on the device to support the user experience and at the same time change the adapter code to deliver the payload needed for that experience. They are no longer bound by server teams dictating the rules and/or being a bottleneck for their development. API innovation is now in the hands of the UI teams! Moreover, because these adapters are isolated from each other, this approach also diminishes the risk of harming other device implementations with tactical changes in their device-specific APIs.
Of course, one drawback to this is that UI teams are often more skilled in technologies like HTML5, CSS3, JavaScript, etc. In this system, they now need to learn server-side technologies and techniques. So far, however, this has been a relatively small issue, especially since our engineering culture is to hire very strong, senior-level engineers who are adaptable, curious and passionate about learning and implementing these kinds of solutions. Another concern is that because the UI teams are implementing server-side adapters, they have the potential to bring down the servers through infinite loops or other processes that are resource intensive. To offset this, we are working on scrubbing engines that will hopefully minimize the likelihood of such mistakes. That said, in the OSFA world, code on the device can just as easily DDOS the server, it is just potentially a bigger problem if it runs on the server.
Example of how this new system works:
A device, such as the PS3, makes a single request across the network to load the home screen (This code is written and supported by the PS3 UI team.
A Groovy adapter receives and parses the PS3 request (PS3 UI team)
The adapter explodes that one request into many requests that call the Java API to (PS3 UI team)
Each Java API calls back to a dependent service, concurrently when appropriate, to gather the content needed for that sub-request (API team)
In the Java API, if a dependent service unavailable or returns a 4xx or 5xx, the Java API returns a fallback and/or an error code to the adapter (API team)
Successful Java API transactions then return the content back to the adapter when each thread has completed (API team)
The adapter can handle the responses from each thread progressively or all together, depending on how the UI team wants to handle it (PS3 UI team)
The adapter then manipulates the content, retrieves the wanted (and prunes out the unwanted) elements, handle errors, etc. (PS3 UI team)
The adapter formats the response in preparation for delivery back across the network to the PS3, which includes everything needed for the PS3 home screen in the single payload (PS3 UI team)
The adapter finally handles the delivery of the payload across the network (PS3 UI team)
The device will then parse this optimized response and populate the UI (PS3 UI team)
We are still in the early stages of this new system. Some of our devices have fully migrated over to it, others are split between it and the REST API, and others are just getting their feet wet. In upcoming posts, we will share more about the deeper technical aspects of the system, including the way we handle concurrency, how we manage the adapters, the interaction between the adapters and the Java API, our Groovy implementation, error handling, etc. We will also continue to share the evolution of this system as we learn more about it.
Below are the slides from my presentation to the engineering team at PayPal. This presentation discusses the history and future of the Netflix API. It also goes into API design principles as well as concepts behind system scalability and resiliency.
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