The Making of VES: the Cosmos Microservice for Netflix Video Encoding | by Netflix Know-how Weblog | Apr, 2024

Liwei Guo, Vinicius Carvalho, Anush Moorthy, Aditya Mavlankar, Lishan Zhu

That is the second publish in a multi-part collection from Netflix. See right here for Half 1 which offers an summary of our efforts in rebuilding the Netflix video processing pipeline with microservices. This weblog dives into the main points of constructing our Video Encoding Service (VES), and shares our learnings.

Cosmos is the subsequent technology media computing platform at Netflix. Combining microservice structure with asynchronous workflows and serverless capabilities, Cosmos goals to modernize Netflix’s media processing pipelines with improved flexibility, effectivity, and developer productiveness. Previously few years, the video staff inside Encoding Applied sciences (ET) has been engaged on rebuilding your entire video pipeline on Cosmos.

This new pipeline consists of quite a few microservices, every devoted to a single performance. One such microservice is Video Encoding Service (VES). Encoding is a vital part of the video pipeline. At a excessive stage, it takes an ingested mezzanine and encodes it right into a video stream that’s appropriate for Netflix streaming or serves some studio/manufacturing use case. Within the case of Netflix, there are a variety of necessities for this service:

• Given the wide selection of gadgets from cellphones to browsers to Sensible TVs, a number of codec codecs, resolutions, and high quality ranges have to be supported.
• Chunked encoding is a should to fulfill the latency necessities of our enterprise wants, and use instances with completely different ranges of latency sensitivity have to be accommodated.
• The aptitude of steady launch is essential for enabling quick product innovation in each streaming and studio areas.
• There’s a large quantity of encoding jobs every single day. The service must be cost-efficient and take advantage of use of obtainable assets.

On this tech weblog, we are going to stroll via how we constructed VES to attain the above targets and can share quite a few classes we realized from constructing microservices. Please be aware that for simplicity, now we have chosen to omit sure Netflix-specific particulars that aren’t integral to the first message of this weblog publish.

A Cosmos microservice consists of three layers: an API layer (Optimus) that takes in requests, a workflow layer (Plato) that orchestrates the media processing flows, and a serverless computing layer (Stratum) that processes the media. These three layers talk asynchronously via a home-grown, priority-based messaging system known as Timestone. We selected Protobuf because the payload format for its excessive effectivity and mature cross-platform assist.

To assist service builders get a head begin, the Cosmos platform offers a strong service generator. This generator options an intuitive UI. With a couple of clicks, it creates a primary but full Cosmos service: code repositories for all 3 layers are created; all platform capabilities, together with discovery, logging, tracing, and so forth., are enabled; launch pipelines are arrange and dashboards are readily accessible. We will instantly begin including video encoding logic and deploy the service to the cloud for experimentation.

Optimus

Because the API layer, Optimus serves because the gateway into VES, which means service customers can solely work together with VES via Optimus. The outlined API interface is a robust contract between VES and the exterior world. So long as the API is secure, customers are shielded from inner adjustments in VES. This decoupling is instrumental in enabling sooner iterations of VES internals.

As a single-purpose service, the API of VES is sort of clear. We outlined an endpoint encodeVideo that takes an EncodeRequest and returns an EncodeResponse (in an async means via Timestone messages). The EncodeRequest object comprises details about the supply video in addition to the encoding recipe. All the necessities of the encoded video (codec, decision, and so forth.) in addition to the controls for latency (chunking directives) are uncovered via the info mannequin of the encoding recipe.

//protobuf definition 
message EncodeRequest {
VideoSource video_source = 1;//supply to be encoded
Recipe recipe = 2; //together with encoding format, decision, and so forth.
}
message EncodeResponse {
OutputVideo output_video = 1; //encoded video
Error error = 2; //error message (non-obligatory)
}
message Recipe {
Codec codec = 1; //together with codec format, profile, stage, and so forth.
Decision decision = 2;
ChunkingDirectives chunking_directives = 3;
...
}

Like every other Cosmos service, the platform robotically generates an RPC consumer based mostly on the VES API knowledge mannequin, which customers can use to construct the request and invoke VES. As soon as an incoming request is acquired, Optimus performs validations, and (when relevant) converts the incoming knowledge into an inner knowledge mannequin earlier than passing it to the subsequent layer, Plato.

Like every other Cosmos service, the platform robotically generates an RPC consumer based mostly on the VES API knowledge mannequin, which customers can use to construct the request and invoke VES. As soon as an incoming request is acquired, Optimus performs validations, and (when relevant) converts the incoming knowledge into an inner knowledge mannequin earlier than passing it to the subsequent layer, Plato.

The workflow layer, Plato, governs the media processing steps. The Cosmos platform helps two programming paradigms for Plato: ahead chaining rule engine and Directed Acyclic Graph (DAG). VES has a linear workflow, so we selected DAG for its simplicity.

In a DAG, the workflow is represented by nodes and edges. Nodes symbolize phases within the workflow, whereas edges signify dependencies — a stage is barely able to execute when all its dependencies have been accomplished. VES requires parallel encoding of video chunks to fulfill its latency and resilience targets. This workflow-level parallelism is facilitated by the DAG via a MapReduce mode. Nodes will be annotated to point this relationship, and a Scale back node will solely be triggered when all its related Map nodes are prepared.

For the VES workflow, we outlined 5 Nodes and their related edges, that are visualized within the following graph:

• Splitter Node: This node divides the video into chunks based mostly on the chunking directives within the recipe.
• Encoder Node: This node encodes a video chunk. It’s a Map node.
• Assembler Node: This node stitches the encoded chunks collectively. It’s a Scale back node.
• Validator Node: This node performs the validation of the encoded video.
• Notifier Node: This node notifies the API layer as soon as your entire workflow is accomplished.

On this workflow, nodes such because the Notifier carry out very light-weight operations and will be straight executed within the Plato runtime. Nevertheless, resource-intensive operations have to be delegated to the computing layer (Stratum), or one other service. Plato invokes Stratum capabilities for duties corresponding to encoding and assembling, the place the nodes (Encoder and Assembler) publish messages to the corresponding message queues. The Validator node calls one other Cosmos service, the Video Validation Service, to validate the assembled encoded video.

Stratum

The computing layer, Stratum, is the place media samples will be accessed. Builders of Cosmos companies create Stratum Features to course of the media. They will carry their very own media processing instruments, that are packaged into Docker photos of the Features. These Docker photos are then revealed to our inner Docker registry, a part of Titus. In manufacturing, Titus robotically scales cases based mostly on the depths of job queues.

VES must assist encoding supply movies into a wide range of codec codecs, together with AVC, AV1, and VP9, to call a couple of. We use completely different encoder binaries (referred to easily as “encoders”) for various codec codecs. For AVC, a format that’s now 20 years previous, the encoder is sort of secure. However, the latest addition to Netflix streaming, AV1, is constantly going via energetic enhancements and experimentations, necessitating extra frequent encoder upgrades. ​​To successfully handle this variability, we determined to create a number of Stratum Features, every devoted to a selected codec format and will be launched independently. This method ensures that upgrading one encoder won’t affect the VES service for different codec codecs, sustaining stability and efficiency throughout the board.

Throughout the Stratum Operate, the Cosmos platform offers abstractions for widespread media entry patterns. No matter file codecs, sources are uniformly offered as regionally mounted frames. Equally, for output that must be endured within the cloud, the platform presents the method as writing to an area file. All particulars, corresponding to streaming of bytes and retrying on errors, are abstracted away. With the platform caring for the complexity of the infrastructure, the important code for video encoding within the Stratum Operate might be so simple as follows.

ffmpeg -i enter/supplypercent08d.j2k -vf ... -c:v libx264 ... output/encoding.264

Encoding is a resource-intensive course of, and the assets required are carefully associated to the codec format and the encoding recipe. We carried out benchmarking to know the useful resource utilization sample, notably CPU and RAM, for various encoding recipes. Primarily based on the outcomes, we leveraged the “container shaping” function from the Cosmos platform.

We outlined quite a few completely different “container shapes”, specifying the allocations of assets like CPU and RAM.

# an instance definition of container form
group: containerShapeExample1
assets:
numCpus: 2
memoryInMB: 4000
networkInMbp: 750
diskSizeInMB: 12000

Routing guidelines are created to assign encoding jobs to completely different shapes based mostly on the mix of codec format and encoding decision. This helps the platform carry out “bin packing”, thereby maximizing useful resource utilization.

After we accomplished the event and testing of all three layers, VES was launched in manufacturing. Nevertheless, this didn’t mark the tip of our work. Fairly the opposite, we believed and nonetheless do {that a} important a part of a service’s worth is realized via iterations: supporting new enterprise wants, enhancing efficiency, and bettering resilience. An necessary piece of our imaginative and prescient was for Cosmos companies to have the flexibility to constantly launch code adjustments to manufacturing in a protected method.

Specializing in a single performance, code adjustments pertaining to a single function addition in VES are typically small and cohesive, making them straightforward to overview. Since callers can solely work together with VES via its API, inner code is actually “implementation particulars” which can be protected to alter. The express API contract limits the check floor of VES. Moreover, the Cosmos platform offers a pyramid-based testing framework to information builders in creating assessments at completely different ranges.

After testing and code overview, adjustments are merged and are prepared for launch. The discharge pipeline is totally automated: after the merge, the pipeline checks out code, compiles, builds, runs unit/integration/end-to-end assessments as prescribed, and proceeds to full deployment if no points are encountered. Usually, it takes round half-hour from code merge to function touchdown (a course of that took 2–4 weeks in our earlier technology platform!). The brief launch cycle offers sooner suggestions to builders and helps them make obligatory updates whereas the context continues to be contemporary.

When working in manufacturing, the service continuously emits metrics and logs. They’re collected by the platform to visualise dashboards and to drive monitoring/alerting methods. Metrics deviating an excessive amount of from the baseline will set off alerts and might result in automated service rollback (when the “canary” function is enabled).

VES was the very first microservice that our staff constructed. We began with primary information of microservices and realized a large number of classes alongside the way in which. These learnings deepened our understanding of microservices and have helped us enhance our design selections and choices.

Outline a Correct Service Scope

A precept of microservice structure is {that a} service needs to be constructed for a single performance. This sounds simple, however what precisely qualifies a “single performance”? “Encoding video” sounds good however wouldn’t “encode video into the AVC format” be an much more particular single-functionality?

After we began constructing the VES, we took the method of making a separate encoding service for every codec format. Whereas this has benefits corresponding to decoupled workflows, rapidly we have been overwhelmed by the event overhead. Think about {that a} consumer requested us so as to add the watermarking functionality to the encoding. We wanted to make adjustments to a number of microservices. What’s worse, adjustments in all these companies are very comparable and basically we’re including the identical code (and assessments) repeatedly. Such sort of repetitive work can simply put on out builders.

The service offered on this weblog is our second iteration of VES (sure, we already went via one iteration). On this model, we consolidated encodings for various codec codecs right into a single service. They share the identical API and workflow, whereas every codec format has its personal Stratum Features. Up to now this appears to strike an excellent stability: the widespread API and workflow reduces code repetition, whereas separate Stratum Features assure impartial evolution of every codec format.

The adjustments we made aren’t irreversible. If sometime sooner or later, the encoding of 1 explicit codec format evolves into a completely completely different workflow, now we have the choice to spin it off into its personal microservice.

Be Pragmatic about Knowledge Modeling

At first, we have been very strict about knowledge mannequin separation — we had a robust perception that sharing equates to coupling, and coupling may result in potential disasters sooner or later. To keep away from this, for every service in addition to the three layers inside a service, we outlined its personal knowledge mannequin and constructed converters to translate between completely different knowledge fashions.

We ended up creating a number of knowledge fashions for features corresponding to bit-depth and backbone throughout our system. To be truthful, this does have some deserves. For instance, our encoding pipeline helps completely different bit-depths for AVC encoding (8-bit) and AV1 encoding (10-bit). By defining each AVC.BitDepth and AV1.BitDepth, constraints on the bit-depth will be constructed into the info fashions. Nevertheless, it’s debatable whether or not the advantages of this differentiation energy outweigh the downsides, particularly a number of knowledge mannequin translations.

Ultimately, we created a library to host knowledge fashions for widespread ideas within the video area. Examples of such ideas embody body price, scan sort, coloration house, and so forth. As you’ll be able to see, they’re extraordinarily widespread and secure. This “widespread” knowledge mannequin library is shared throughout all companies owned by the video staff, avoiding pointless duplications and knowledge conversions. Inside every service, further knowledge fashions are outlined for service-specific objects.

Embrace Service API Adjustments

This may occasionally sound contradictory. We’ve got been saying that an API is a robust contract between the service and its customers, and preserving an API secure shields customers from inner adjustments. That is completely true. Nevertheless, none of us had a crystal ball once we have been designing the very first model of the service API. It’s inevitable that at a sure level, this API turns into insufficient. If we maintain the idea that “the API can not change” too dearly, builders could be pressured to seek out workarounds, that are nearly definitely sub-optimal.

There are lots of nice tech articles about gracefully evolving API. We consider we even have a novel benefit: VES is a service inner to Netflix Encoding Applied sciences (ET). Our two customers, the Streaming Workflow Orchestrator and the Studio Workflow Orchestrator, are owned by the workflow staff inside ET. Our groups share the identical contexts and work in direction of widespread targets. If we consider updating API is in the perfect curiosity of Netflix, we meet with them to hunt alignment. As soon as a consensus to replace the API is reached, groups collaborate to make sure a easy transition.

That is the second a part of our tech weblog collection Rebuilding Netflix Video Pipeline with Microservices. On this publish, we described the constructing strategy of the Video Encoding Service (VES) intimately in addition to our learnings. Our pipeline features a few different companies that we plan to share about as nicely. Keep tuned for our future blogs on this subject of microservices!