Cosmos is a computing platform that combines the best aspects of microservices with asynchronous workflows and serverless functions. Its sweet spot is applications that involve resource-intensive algorithms coordinated via complex, hierarchical workflows that last anywhere from minutes to years. It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation.
This article will explain why we built Cosmos, how it works and share some of the things we have learned along the way.
The Media Cloud Engineering and Encoding Technologies teams at Netflix jointly operate a system to process incoming media files from our partners and studios to make them playable on all devices. The first generation of this system went live with the streaming launch in 2007. The second generation added scale but was extremely difficult to operate. The third generation, called Reloaded, has been online for about seven years and has proven to be stable and massively scalable.
When Reloaded was designed, we were a small team of developers operating a constrained compute cluster, and focused on one use case: the video/audio processing pipeline. As time passed the number of developers more than tripled, the breadth and depth of our use cases expanded, and our scale increased more than tenfold, the monolithic architecture significantly slowed down the delivery of new features. We could no longer expect everyone to possess the specialized knowledge that was necessary to build and deploy new features. Dealing with production issues became an expensive chore that placed a tax on all developers because infrastructure code was all mixed up with application code. The centralized data model that had served us well when we were a small team became a liability.
Our response was to create Cosmos, a platform for workflow-driven, media-centric microservices. The first-order goals were to preserve our current capabilities while offering:
Observability via built-in logging, tracing, monitoring, alerting and error classification.
Modularity An opinionated framework for structuring a service and enabling both compile-time and run-time modularity.
Productivity Local development tools including specialized test runners, code generators, and a command line interface.
Delivery A fully-managed continuous-delivery system of pipelines, continuous integration jobs, and end to end tests. When you merge your pull request, it makes it to production without manual intervention.
While we were at it, we also made improvements to scalability, reliability, security, and other system qualities.
A Cosmos service is not a microservice but there are similarities. A typical microservice is an API with stateless business logic which is autoscaled based on request load. The API provides strong contracts with its peers while segregating application data and binary dependencies from other systems.
A Cosmos service retains the strong contracts and segregated data/dependencies of a microservice, but adds multi-step workflows and computationally intensive asynchronous serverless functions. In the diagram below of a typical Cosmos service, clients send requests to a Video encoder service API layer. A set of rules orchestrate workflow steps and a set of serverless functions power domain-specific algorithms. Functions are packaged as Docker images and bring their own media-specific binary dependencies (e.g. debian packages). They are scaled based on queue size, and may run on tens of thousands of different containers. Requests may take hours or days to complete.
Separation of concerns
Cosmos has two axes of separation. On the one hand, logic is divided between API, workflow and serverless functions. On the other hand, logic is separated between application and platform. The platform API provides media-specific abstractions to application developers while hiding the details of distributed computing. For example, a video encoding service is built of components that are scale-agnostic: API, workflow, and functions. They have no special knowledge about the scale at which they run. These domain-specific, scale-agnostic components are built on top of three scale-aware Cosmos subsystems which handle the details of distributing the work:
Optimus, an API layer mapping external requests to internal business models.
Plato, a workflow layer for business rule modeling.
Stratum, a serverless layer called for running stateless and computational-intensive functions.
The subsystems all communicate with each other asynchronously via Timestone, a high-scale, low-latency priority queuing system. Each subsystem addresses a different concern of a service and can be deployed independently through a purpose-built managed Continuous Delivery process. This separation of concerns makes it easier to write, test, and operate Cosmos services.
Layering of services
Cosmos supports decomposition and layering of services. The resulting modular architecture allows teams to concentrate on their area of specialty and control their APIs and release cycles.
For example, the video service mentioned above is just one of many used to create streams that can be played on devices. These services, which also include inspection, audio, text, and packaging, are orchestrated using higher-level services. The largest and most complex of these is Tapas, which is responsible for taking sources from studios and making them playable on the Netflix service. Another high-level service is Sagan, which is used for studio operations like marketing clips or daily production editorial proxies.
When a new title arrives from a production studio, it triggers a Tapas workflow which orchestrates requests to perform inspections, encode video (multiple resolutions, qualities, and video codecs), encode audio (multiple qualities and codecs), generate subtitles (many languages), and package the resulting outputs (multiple player formats). Thus, a single request to Tapas can result in hundreds of requests to other Cosmos services and thousands of Stratum function invocations.
The trace below shows an example of how a request at a top level service can trickle down to lower level services, resulting in many different actions. In this case the request took 24 minutes to complete, with hundreds of different actions involving 8 different Cosmos services and 9 different Stratum functions.
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