Netflix API - Separation of Concerns

Editor's Notes

1. Most companies focus on a small handful of device implementations, most notably Android and iOS devices.
2. At Netflix, we have more than 1,000 different device types that we support. Across those devices, there is a high degree of variability. As a result, we have seen inefficiencies and problems emerge across our implementations. Those issues also translate into issues with the API interaction.
3. For example, screen size could significantly affect what the API should deliver to the UI. TVs with bigger screens that can potentially fit more titles and more metadata per title than a mobile phone. Do we need to send all of the extra bits for fields or items that are not needed, requiring the device itself to drop items on the floor? Or can we optimize the deliver of those bits on a per-device basis?
4. Different devices have different controlling functions as well. For devices with swipe technologies, such as the iPad, do we need to pre-load a lot of extra titles in case a user swipes the row quickly to see the last of 500 titles in their queue? Or for up-down-left-right controllers, would devices be more optimized by fetching a few items at a time when they are needed? Other devices support voice or hand gestures or pointer technologies. How might those impact the user experience and therefore the metadata needed to support them?
5. The technical specs on these devices differ greatly. Some have significant memory space while others do not, impacting how much data can be handled at a given time. Processing power and hard-drive space could also play a role in how the UI performs, in turn potentially influencing the optimal way for fetching content from the API. All of these differences could result in different potential optimizations across these devices.
6. Many UI teams needing metadata means many requests to the API team. In the one-size-fits-all API world, we essentially needed to funnel these requests and then prioritize them. That means that some teams would need to wait for API work to be done. It also meant that, because they all shared the same endpoints, we were often adding variations to the endpoints resulting in a more complex system as well as a lot of spaghetti code. Make teams wait due to prioritization was exacerbated by the fact that tasks took longer because the technical debt was increasing, causing time to build and test to increase. Moreover, many of the incoming requests were asking us to do more of the same kinds of customizations. This created a spiral that would be very difficult to break out of…
7. Many other companies have seen similar issues and have introduced orchestration layers that enable more flexible interaction models.
8. Odata, HYQL, ql.io, rest.li and others are examples of orchestration layers. They address the same problems that we have seen, but we have approached the solution in a very different way.
9. We evolved our discussion towards what ultimately became a discussion between resource-based APIs and experience-based APIs.
10. The original OSFA API was very resource oriented with granular requests for specific data, delivering specific documents in specific formats.
11. The interaction model looked basically like this, with (in this example) the PS3 making many calls across the network to the OSFA API. The API ultimately called back to dependent services to get the corresponding data needed to satisfy the requests.
12. In this mode, there is a very clear divide between the Client Code and the Server Code. That divide is the network border.
13. And the responsibilities have the same distribution as well. The Client Code handles the rendering of the interface (as well as asking the server for data). The Server Code is responsible of gathering, formatting and delivering the data to the UIs.
14. And ultimately, it works. The PS3 interface looks like this and was populated by this interaction model.
15. But we believe this is not the optimal way to handle it. In fact, assembling a UI through many resource-based API calls is akin to pointillism paintings. The picture looks great when fully assembled, but it is done by assembling many points put together in the right way.
16. We have decided to pursue an experience-based approach instead. Rather than making many API requests to assemble the PS3 home screen, the PS3 will potentially make a single request to a custom, optimized endpoint.
17. In an experience-based interaction, the PS3 can potentially make asingle request across the network border to a scripting layer (currently Groovy), in this example to provide the data for the PS3 home screen. The call goes to a very specific, custom endpoint for the PS3 or for a shared UI. The Groovy script then interprets what is needed for the PS3 home screen and triggers a series of calls to the Java API running in the same JVM as the Groovy scripts. The Java API is essentially a series of methods that individually know how to gather the corresponding data from the dependent services. The Java API then returns the data to the Groovy script who then formats and delivers the very specific data back to the PS3.
18. In this model, the border between Client Code and Server Code is no longer the network border. It is now back on the server. The Groovy is essentially a client adapter written by the client teams.
19. And the distribution of work changes as well. The client teams continue to handle UI rendering, but now are also responsible for the formatting and delivery of content. The API team, in terms of the data side of things, is responsible for the data gathering and hand-off to the client adapters. Of course, the API team does many other things, including resiliency, scaling, dependency interactions, etc. This model is essentially a platform for API development.
20. If resource-based APIs assemble data like pointillism, experience-based APIs assemble data like a photograph. The experience-based approach captures and delivers it all at once.