Tag Archives: Netflix

Mythbusting the Analytics Journey

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/mythbusting-the-analytics-journey-58d692ea707e

Part of our series on who works in Analytics at Netflix — and what the role entails

by Alex Diamond

This Q&A aims to mythbust some common misconceptions about succeeding in analytics at a big tech company.

This isn’t your typical recruiting story. I wasn’t actively looking for a new job and Netflix was the only place I applied. I didn’t know anyone who worked there and just submitted my resume through the Jobs page 🤷🏼‍♀️ . I wasn’t even entirely sure what the right role fit would be and originally applied for a different position, before being redirected to the Analytics Engineer role. So if you find yourself in a similar situation, don’t be discouraged!

How did you come to Netflix?

Movies and TV have always been one of my primary sources of joy. I distinctly remember being a teenager, perching my laptop on the edge of the kitchen table to “borrow” my neighbor’s WiFi (back in the days before passwords 👵🏻), and streaming my favorite Netflix show. I felt a little bit of ✨magic✨ come through the screen each time, and that always stuck with me. So when I saw the opportunity to actually contribute in some way to making the content I loved, I jumped at it. Working in Studio Data Science & Engineering (“Studio DSE”) was basically a dream come true.

Not only did I find the subject matter interesting, but the Netflix culture seemed to align with how I do my best work. I liked the idea of Freedom and Responsibility, especially if it meant having autonomy to execute projects all the way from inception through completion. Another major point of interest for me was working with “stunning colleagues”, from whom I could continue to learn and grow.

What was your path to working with data?

My road-to-data was more of a stumbling-into-data. I went to an alternative high school for at-risk students and had major gaps in my formal education — not exactly a head start. I then enrolled at a local public college at 16. When it was time to pick a major, I was struggling in every subject except one: Math. I completed a combined math bachelors + masters program, but without any professional guidance, networking, or internships, I was entirely lost. I had the piece of paper, but what next? I held plenty of jobs as a student, but now I needed a career.

A visual representation of all the jobs I had in high school and college: From pizza, to gourmet rice krispie treats, to clothing retail, to doors and locks

After receiving a grand total of *zero* interviews from sending out my resume, the natural next step was…more school. I entered a PhD program in Computer Science and shortly thereafter discovered I really liked the coding aspects more than the theory. So I earned the honor of being a PhD dropout.

A visual representation of all the hats I’ve worn

And here’s where things started to click! I used my newfound Python and SQL skills to land an entry-level Business Intelligence Analyst position at a company called Big Ass Fans. They make — you guessed it — very large industrial ventilation fans. I was given the opportunity to branch out and learn new skills to tackle any problem in front of me, aka my “becoming useful” phase. Within a few months I’d picked up BI tools, predictive modeling, and data ingestion/ETL. After a few years of wearing many different proverbial hats, I put them all to use in the Analytics Engineer role here. And ever since, Netflix has been a place where I can do my best work, put to use the skills I’ve gathered over the years, and grow in new ways.

What does an ordinary day look like?

As part of the Studio DSE team, our work is focused on aiding the movie-making process for our Netflix Originals, leading all the way up to a title’s launch on the service. Despite the affinity for TV and movies that brought me here, I didn’t actually know very much about how they got made. But over time, and by asking lots of questions, I’ve picked up the industry lingo! (Can you guess what “DOOD” stands for?)

My main stakeholders are members of our Studio team. They’re experts on the production process and an invaluable resource for me, sharing their expertise and providing context when I don’t know what something means. True to the “people over process” philosophy, we adapt alongside our stakeholders’ needs throughout the production process. That means the work products don’t always fit what you might imagine a traditional Analytics Engineer builds — if such a thing even exists!

A typical production lifecycle

On an ordinary day, my time is generally split evenly across:

  • 🤝📢 Speaking with stakeholders to understand their primary needs
  • 🐱💻 Writing code (SQL, Python)
  • 📊📈 Building visual outputs (Tableau, memos, scrappy web apps)
  • 🤯✍️ Brainstorming and vision planning for future work

Some days have more of one than the others, but variety is the spice of life! The one constant is that my day always starts with a ridiculous amount of coffee. And that it later continues with even more coffee. ☕☕☕

My road-to-data was more of a stumbling-into-data.

What advice would you give to someone just starting their career in data?

🐾 Dip your toes in things. As you try new things, your interests will evolve and you’ll pick up skills across a broad span of subject areas. The first time I tried building the front-end for a small web app, it wasn’t very pretty. But it piqued my interest and after a few times it started to become second nature.

💪 Find your strengths and weaknesses. You don’t have to be an expert in everything. Just knowing when to reach out for guidance on something allows you to uplevel your skills in that area over time. My weakness is statistics: I can use it when needed but it’s just not a subject that comes naturally to me. I own that about myself and lean on my stats-loving peers when needed.

🌸 Look for roles that allow you to grow. As you grow in your career, you’ll provide impact to the business in ways you didn’t even expect. As a business intelligence analyst, I gained data science skills. And in my current Analytics Engineer role, I’ve picked up a lot of product management and strategic thinking experience.

This is what I look like.

☝️ One Last Thing

I started off my career with the vague notion of, “I guess I want to be a data scientist?” But what that’s meant in practice has really varied depending on the needs of each job and project. It’s ok if you don’t have it all figured out. Be excited to try new things, lean into strengths, and don’t be afraid of your weaknesses — own them.

If this post resonates with you and you’d like to explore opportunities with Netflix, check out our analytics site, search open roles, and learn about our culture. You can also find more stories like this here.

Mythbusting the Analytics Journey was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Life of a Netflix Partner Engineer — The case of extra 40 ms

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/life-of-a-netflix-partner-engineer-the-case-of-extra-40-ms-b4c2dd278513

Life of a Netflix Partner Engineer — The case of the extra 40 ms

By: John Blair, Netflix Partner Engineering

The Netflix application runs on hundreds of smart TVs, streaming sticks and pay TV set top boxes. The role of a Partner Engineer at Netflix is to help device manufacturers launch the Netflix application on their devices. In this article we talk about one particularly difficult issue that blocked the launch of a device in Europe.

The mystery begins

Towards the end of 2017, I was on a conference call to discuss an issue with the Netflix application on a new set top box. The box was a new Android TV device with 4k playback, based on Android Open Source Project (AOSP) version 5.0, aka “Lollipop”. I had been at Netflix for a few years, and had shipped multiple devices, but this was my first Android TV device.

All four players involved in the device were on the call: there was the large European pay TV company (the operator) launching the device, the contractor integrating the set-top-box firmware (the integrator), the system-on-a-chip provider (the chip vendor), and myself (Netflix).

The integrator and Netflix had already completed the rigorous Netflix certification process, but during the TV operator’s internal trial an executive at the company reported a serious issue: Netflix playback on his device was “stuttering.”, i.e. video would play for a very short time, then pause, then start again, then pause. It didn’t happen all the time, but would reliably start to happen within a few days of powering on the box. They supplied a video and it looked terrible.

The device integrator had found a way to reproduce the problem: repeatedly start Netflix, start playback, then return to the device UI. They supplied a script to automate the process. Sometimes it took as long as five minutes, but the script would always reliably reproduce the bug.

Meanwhile, a field engineer for the chip vendor had diagnosed the root cause: Netflix’s Android TV application, called Ninja, was not delivering audio data quickly enough. The stuttering was caused by buffer starvation in the device audio pipeline. Playback stopped when the decoder waited for Ninja to deliver more of the audio stream, then resumed once more data arrived. The integrator, the chip vendor and the operator all thought the issue was identified and their message to me was clear: Netflix, you have a bug in your application, and you need to fix it. I could hear the stress in the voices from the operator. Their device was late and running over budget and they expected results from me.

The investigation

I was skeptical. The same Ninja application runs on millions of Android TV devices, including smart TVs and other set top boxes. If there was a bug in Ninja, why is it only happening on this device?

I started by reproducing the issue myself using the script provided by the integrator. I contacted my counterpart at the chip vendor, asked if he’d seen anything like this before (he hadn’t). Next I started reading the Ninja source code. I wanted to find the precise code that delivers the audio data. I recognized a lot, but I started to lose the plot in the playback code and I needed help.

I walked upstairs and found the engineer who wrote the audio and video pipeline in Ninja, and he gave me a guided tour of the code. I spent some quality time with the source code myself to understand its working parts, adding my own logging to confirm my understanding. The Netflix application is complex, but at its simplest it streams data from a Netflix server, buffers several seconds worth of video and audio data on the device, then delivers video and audio frames one-at-a-time to the device’s playback hardware.

A diagram showing content downloaded to a device into a streaming buffer, then copied into the device decode buffer.
Figure 1: Device Playback Pipeline (simplified)

Let’s take a moment to talk about the audio/video pipeline in the Netflix application. Everything up until the “decoder buffer” is the same on every set top box and smart TV, but moving the A/V data into the device’s decoder buffer is a device-specific routine running in its own thread. This routine’s job is to keep the decoder buffer full by calling a Netflix provided API which provides the next frame of audio or video data. In Ninja, this job is performed by an Android Thread. There is a simple state machine and some logic to handle different play states, but under normal playback the thread copies one frame of data into the Android playback API, then tells the thread scheduler to wait 15 ms and invoke the handler again. When you create an Android thread, you can request that the thread be run repeatedly, as if in a loop, but it is the Android Thread scheduler that calls the handler, not your own application.

To play a 60fps video, the highest frame rate available in the Netflix catalog, the device must render a new frame every 16.66 ms, so checking for a new sample every 15ms is just fast enough to stay ahead of any video stream Netflix can provide. Because the integrator had identified the audio stream as the problem, I zeroed in on the specific thread handler that was delivering audio samples to the Android audio service.

I wanted to answer this question: where is the extra time? I assumed some function invoked by the handler would be the culprit, so I sprinkled log messages throughout the handler, assuming the guilty code would be apparent. What was soon apparent was that there was nothing in the handler that was misbehaving, and the handler was running in a few milliseconds even when playback was stuttering.

Aha, Insight

In the end, I focused on three numbers: the rate of data transfer, the time when the handler was invoked and the time when the handler passed control back to Android. I wrote a script to parse the log output, and made the graph below which gave me the answer.

A graph showing time spent in the thread handler and audio data throughput.
Figure 2: Visualizing Audio Throughput and Thread Handler Timing

The orange line is the rate that data moved from the streaming buffer into the Android audio system, in bytes/millisecond. You can see three distinct behaviors in this chart:

  1. The two, tall spiky parts where the data rate reaches 500 bytes/ms. This phase is buffering, before playback starts. The handler is copying data as fast as it can.
  2. The region in the middle is normal playback. Audio data is moved at about 45 bytes/ms.
  3. The stuttering region is on the right, when audio data is moving at closer to 10 bytes/ms. This is not fast enough to maintain playback.

The unavoidable conclusion: the orange line confirms what the chip vendor’s engineer reported: Ninja is not delivering audio data quickly enough.

To understand why, let’s see what story the yellow and grey lines tell.

The yellow line shows the time spent in the handler routine itself, calculated from timestamps recorded at the top and the bottom of the handler. In both normal and stutter playback regions, the time spent in the handler was the same: about 2 ms. The spikes show instances when the runtime was slower due to time spent on other tasks on the device.

The real root cause

The grey line, the time between calls invoking the handler, tells a different story. In the normal playback case you can see the handler is invoked about every 15 ms. In the stutter case, on the right, the handler is invoked approximately every 55 ms. There are an extra 40 ms between invocations, and there’s no way that can keep up with playback. But why?

I reported my discovery to the integrator and the chip vendor (look, it’s the Android Thread scheduler!), but they continued to push back on the Netflix behavior. Why don’t you just copy more data each time the handler is called? This was a fair criticism, but changing this behavior involved deeper changes than I was prepared to make, and I continued my search for the root cause. I dove into the Android source code, and learned that Android Threads are a userspace construct, and the thread scheduler uses the epoll() system call for timing. I knew epoll() performance isn’t guaranteed, so I suspected something was affecting epoll() in a systematic way.

At this point I was saved by another engineer at the chip supplier, who discovered a bug that had already been fixed in the next version of Android, named Marshmallow. The Android thread scheduler changes the behavior of threads depending whether or not an application is running in the foreground or the background. Threads in the background are assigned an extra 40 ms (40000000 ns) of wait time.

A bug deep in the plumbing of Android itself meant this extra timer value was retained when the thread moved to the foreground. Usually the audio handler thread was created while the application was in the foreground, but sometimes the thread was created a little sooner, while Ninja was still in the background. When this happened, playback would stutter.

Lessons learned

This wasn’t the last bug we fixed on this platform, but it was the hardest to track down. It was outside of the Netflix application, in a part of the system that was outside of the playback pipeline, and all of the initial data pointed to a bug in the Netflix application itself.

This story really exemplifies an aspect of my job I love: I can’t predict all of the issues that our partners will throw at me, and I know that to fix them I have to understand multiple systems, work with great colleagues, and constantly push myself to learn more. What I do has a direct impact on real people and their enjoyment of a great product. I know when people enjoy Netflix in their living room, I’m an essential part of the team that made it happen.

Life of a Netflix Partner Engineer — The case of extra 40 ms was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

How Netflix Scales its API with GraphQL Federation (Part 2)

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/how-netflix-scales-its-api-with-graphql-federation-part-2-bbe71aaec44a

In our previous post and QConPlus talk, we discussed GraphQL Federation as a solution for distributing our GraphQL schema and implementation. In this post, we shift our attention to what is needed to run a federated GraphQL platform successfully — from our journey implementing it to lessons learned.

Netflix GraphQL Federation

Our Journey so Far

Over the past year, we’ve implemented the core infrastructure pieces necessary for a federated GraphQL architecture as described in our previous post:

Studio Edge Architecture Diagram
Studio Edge Architecture

The first Domain Graph Service (DGS) on the platform was the former GraphQL monolith that we discussed in our first post (Studio API). Next, we worked with a few other application teams to make DGSs that would expose their APIs alongside the former monolith. We had our first Studio applications consuming the federated graph, without any performance degradation, by the end of the 2019. Once we knew that the architecture was feasible, we focused on readying it for broader usage. Our goal was to open up the Studio Edge platform for self-service in April 2020.

April 2020 was a turbulent time with the pandemic and overnight transition to working remotely. Nevertheless, teams started to jump into the graph in droves. Soon we had hundreds of engineers contributing directly to the API on a daily basis. And what about that Studio API monolith that used to be a bottleneck? We migrated the fields exposed by Studio API to individually owned DGSs without breaking the API for consumers. The original monolith is slated to be completely deprecated by the end of 2020.

This journey hasn’t been without its challenges. The biggest challenge was aligning on this strategy across the organization. Initially, there was a lot of skepticism and dissent; the concept was fairly new and would require high alignment across the organization to be successful. Our team spent a lot of time addressing dissenting points and making adjustments to the architecture based on feedback from developers. Through our prototype development and proactive partnership with some key critical voices, we were able to instill confidence and close crucial gaps.

Once we achieved broad alignment on the idea, we needed to ensure that adoption was seamless. This required building robust core infrastructure, ensuring a great developer experience, and solving for key cross-cutting concerns.

Core Infrastructure

Our GraphQL Gateway is based on Apollo’s reference implementation and is written in Kotlin. This gives us access to Netflix’s Java ecosystem, while also giving us the robust language features such as coroutines for efficient parallel fetches, and an expressive type system with null safety.

The schema registry is developed in-house, also in Kotlin. For storing schema changes, we use an internal library that implements the event sourcing pattern on top of the Cassandra database. Using event sourcing allows us to implement new developer experience features such as the Schema History view. The schema registry also integrates with our CI/CD systems like Spinnaker to automatically setup cloud networking for DGSs.

Developer Education & Experience

In the previous architecture, only the monolith Studio API team needed to learn GraphQL. In Studio Edge, every DGS team needs to build expertise in GraphQL. GraphQL has its own learning curve and can get especially tricky for complex cases like batching & lookahead. Also, as discussed in the previous post, understanding GraphQL Federation and implementing entity resolvers is not trivial either.

We partnered with Netflix’s Developer Experience (DevEx) team to build out documentation, training materials, and tutorials for developers. For general GraphQL questions, we lean on the open source community plus cultivate an internal GraphQL community to discuss hot topics like pagination, error handling, nullability, and naming conventions.

DGS Framework & Developer Tools

To make it easy for backend engineers to build a GraphQL DGS, the DevEx team built a “DGS Framework” on top of GraphQL Java and Spring Boot. The framework takes care of all the cross-cutting concerns of running a GraphQL service in production while also making it easier for developers to write GraphQL resolvers. In addition, DevEx built robust tooling for pushing schemas to the Schema Registry and a Self Service UI for browsing the various DGS’s schemas. Check out their conference talk and expect a future blog post from our colleagues. The DGS framework is planned to be open-sourced in early 2021.

Schema Governance

Netflix’s studio data is extremely rich and complex. Early on, we anticipated that active schema management would be crucial for schema evolution and overall health. We had a Studio Data Architect already in the org who was focused on data modeling and alignment across Studio. We engaged with them to determine graph schema best practices to best suit the needs of Studio Engineering.

Our goal was to design a GraphQL schema that was reflective of the domain itself, not the database model. UI developers should not have to build Backends For Frontends (BFF) to massage the data for their needs, rather, they should help shape the schema so that it satisfies their needs. Embracing a collaborative schema design approach was essential to achieving this goal.

Schema Design Workflow Diagram
Schema Design Workflow

The collaborative design process involves feedback and reviews across team boundaries. To streamline schema design and review, we formed a schema working group and a managed technical program for on-boarding to the federated architecture. While reviews add overhead to the product development process, we believe that prioritizing the quality of the graph model will reduce the amount of future changes and reworking needed. The level of review varies based on the entities affected; for the core federated types, more rigor is required (though tooling helps streamline that flow).

We have a deprecation workflow in place for evolving the schema. We’ve leveraged GraphQL’s deprecation feature and also track usage stats for every field in the schema. Once the stats show that a deprecated field is no longer used, we can make a backward incompatible change to remove the field from the schema.

Clients with Deprecated Field Usage
Clients with Deprecated Field Usage

We embraced a schema-first approach instead of generating our schema from existing models such as the Protobuf objects in our gRPC APIs. While Protobufs and gRPC are excellent solutions for building service APIs, we prefer decoupling our GraphQL schema from those layers to enable cleaner graph design and independent evolvability. In some scenarios, we implement generic mapping code from GraphQL resolvers to gRPC calls, but the extra boilerplate is worth the long-term flexibility of the GraphQL API.

Underlying our approach is a foundation of “context over control”, which is a key tenet of Netflix’s culture. Instead of trying to hold tight control of the entire graph, we give guidance and context to product teams so that they can apply their domain knowledge to make a flexible API for their domain. As this architecture matures, we will continue to monitor schema health and develop new tooling, processes, and best practices where needed.


In our previous architecture, observability was achieved through manual analysis and routing via the API team, which scaled poorly. For our federated architecture, we prioritized solving observability needs in a more scalable manner. We prioritized three areas:

  • Alerting — report when something goes awry
  • Discovery — easily determine what isn’t working
  • Diagnosis — debug why something isn’t working

Our guiding metrics in this space are mean time to resolution (MTTR) and service level objectives and indicators (SLO/SLI).

We teamed up with experts from Netflix’s Telemetry team. We integrated the Gateway and DGS architectural components with Zipkin, the internal distributed tracing tool Edgar, and application monitoring tool TellTale. In GraphQL, almost every response is a 200 with custom errors in the error block. We introspect these custom error codes from the response and emit them to our metrics server, Atlas. These integrations created a great foundation of rich visibility and insights for the consumers and developers of the GraphQL API.

Trace for a Federated Request Lifecycle
Edgar Trace for a Federated Request Lifecycle
Timeline View for a Federated Request lifecycle
Timeline View for a Federated Request

Distributed Log Correlation helps with debugging more complex server issues. By surfacing the application level logging details for all systems involved in processing a request, we gain deeper insights into what happened across the stack. Developers can easily see what was happening around the same time as a given request, to inspect surrounding factors that might have impacted an interaction.

Log correlation across multiple services for a request lifecycle
Logs across multiple services for a Federated Request

To solve the “who do I ask about…” routing problem, we integrated deep linking from GraphQL types and fields to their owning team’s support channels. Finding support is now as simple as clicking a link from a trace, which helps shorten MTTR and reduce the number of times the gateway team needs to get involved.

Securing the Federated Graph

Our goal is to enable robust and consistent security practices across the federated architecture. To achieve this, we partnered with the security experts at Netflix to build security into the graph. Let’s look at two essential parts of our security solution: AuthN and AuthZ.


All of our product experiences in the Studio space require an authenticated account, so we restrict the GraphQL Gateway access to only trusted authenticated callers. Additionally, Graph Introspection is restricted to Netflix internal developers.


Before Studio Edge, authorization logic was fragmented across teams. Some teams implemented authorization in their BFFs, some in microservices, and others did both for good measure. The result was often a different authorization story for a given piece of data depending on which UI a user was accessing it through. UI teams also found themselves needing to implement (and re-implement) authorization checks with each new frontend.

In Studio Edge, we delegated the authorization responsibility to DGS owners. This resulted in consistent authorization for the same user across different applications. Plus, Product Managers, Engineers and the Security team can easily get a bird’s eye view of who has access to each data type and how.

We have multiple authorization offerings within Netflix: from a simple system that grants access based on user identity to a more granular system that brings in the concept of roles and capabilities. DGS developers can choose a solution based on their needs. Then they simply annotate their resolvers with @Secured annotation and configure that to use one of the available systems. If needed, more complex authorization can be implemented in the resolver or in downstream systems.

Future of Authorization

We are currently prototyping a GraphQL-aware authorization solution. The Schema Registry automatically generates Access Control Groups (ACGs) for each field and its corresponding type when its schema is registered. Product managers & DGS Engineers decide membership and rules for these generated ACGs. Since the ACGs map to a field in GraphQL, the DGS framework then automatically applies the rules associated with the ACG during execution.

Architecting for Failure

The GraphQL Gateway is the single entry point for all requests; a failure on the gateway can cause significant disruptions. Following Netflix engineering best practices, we assume failures will happen and design ways to mitigate the impact of those failures. These are our design principles for ensuring the gateway layer is resilient:

  1. Single purpose
  2. Stateless service
  3. Demand controlled
  4. Multi-region
  5. Sharded by functionality

First, we focus the responsibilities of the gateway layer on a single purpose: parse client queries, then build and execute query plans. By reducing the scope, we limit the range of problems that can occur. We aim to perform any additional resource-intensive operations off-box with the exception of logging and metrics. Taking on additional unrelated logic in the gateway layer could increase surface area for failures in this critical tier.

Second, we run multiple stateless instances of the gateway service. Any gateway instance is able to generate and execute a query plan for any request. When we do code changes to the gateway layer, we rigorously test them before rolling out to production.

Third, we seek to balance the resources each request consumes through applying demand control. We rate-limit callers to avoid overloading the underlying databases that are the source of most of our domain elements. We also run a static query cost calculation on all incoming queries and reject expensive queries to avoid gridlock in gateway and DGS resources. Our partners understand these tradeoffs and work with us to meet these requirements, reworking expensive queries and reducing high volume callers.

Fourth, we deploy our gateway layer to multiple AWS regions around the world. This allows us to limit the blast radius for problems that inevitably arise. When problems happen, we can fail over to another region to ensure our clients are minimally impacted.

Last, we deploy multiple functional shards of our gateway layer. The code is the same in each shard and incoming requests are routed based on category. For example, GraphQL subscriptions generally result in long-lived connections while Queries & Mutations are short-lived. We use a separate fleet of instances for Subscriptions so “running out of connections” does not affect the availability of Queries and Mutations.

There is more we can do to improve resilience. We have plans to do canary deployments and analysis for gateway deployments and, eventually, schema changes. Today, our gateway dynamically updates its schema by polling the schema registry. We are in the process of decoupling these by storing the federation config in a versioned S3 bucket, making the gateway resilient to schema registry failures.

Closing Thoughts

GraphQL and Federation have been a productivity multiplier for Studio applications. Motivated by this, we’ve recently prototyped using GraphQL Federation for the Netflix consumer app search page on iOS & Android. To do this, we created three DGSs to provide the data for a minimal portion of the consumer graph. We are sending a small subset of users to this alternative stack and measuring high-level metrics. We are excited to see the results and explore further applicability in the Netflix consumer space.

Despite our positive experience, GraphQL Federation is early in its maturity lifecycle and may not be the best fit for every team or organization. Learning GraphQL and DGS development, running a federation layer, and doing a migration requires high commitment from partner teams and seamless cross-functional collaboration. If you’re considering going in this direction, we recommend checking out Apollo’s SaaS offering for Federation and the many online resources for learning GraphQL. For ecosystems like ours with a large swath of microservices that need to be aggregated together, the development velocity and improved operability has made the transition worth it.

In closing, we want to hear from you! If you have already implemented federation or tried to solve this problem with another approach, we would love to learn more. Sharing knowledge is one of the ways our industry learns and improves rapidly. Finally, if you’d like to be a part of solving complex and interesting problems like this at Netflix scale, check out our jobs page or reach out to us directly.

By Tejas Shikhare, Edited by Philip Fisher-Ogden

Additional Credits: Stephen Spalding, Jennifer Shin, Robert Reta, Antoine Boyer, Bruce Wang, David Simmer

How Netflix Scales its API with GraphQL Federation (Part 2) was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Supporting content decision makers with machine learning

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/supporting-content-decision-makers-with-machine-learning-995b7b76006f

by Melody Dye*, Chaitanya Ekanadham*, Avneesh Saluja*, Ashish Rastogi
* contributed equally

Netflix is pioneering content creation at an unprecedented scale. Our catalog of thousands of films and series caters to 195M+ members in over 190 countries who span a broad and diverse range of tastes. Content, marketing, and studio production executives make the key decisions that aspire to maximize each series’ or film’s potential to bring joy to our subscribers as it progresses from pitch to play on our service. Our job is to support them.

The commissioning of a series or film, which we refer to as a title, is a creative decision. Executives consider many factors including narrative quality, relation to the current societal context or zeitgeist, creative talent relationships, and audience composition and size, to name a few. The stakes are high (content is expensive!) as is the uncertainty of the outcome (it is difficult to predict which shows or films will become hits). To mitigate this uncertainty, executives throughout the entertainment industry have always consulted historical data to help characterize the potential audience of a title using comparable titles, if they exist. Two key questions in this endeavor are:

  • Which existing titles are comparable and in what ways?
  • What audience size can we expect and in which regions?

The increasing vastness and diversity of what our members are watching make answering these questions particularly challenging using conventional methods, which draw on a limited set of comparable titles and their respective performance metrics (e.g., box office, Nielsen ratings). This challenge is also an opportunity. In this post we explore how machine learning and statistical modeling can aid creative decision makers in tackling these questions at a global scale. The key advantage of these techniques is twofold. First, they draw on a much wider range of historical titles (spanning global as well as niche audiences). Second, they leverage each historical title more effectively by isolating the components (e.g., thematic elements) that are relevant for the title in question.

Our approach is rooted in transfer learning, whereby performance on a target task is improved by leveraging model parameters learned on a separate but related source task. We define a set of source tasks that are loosely related to the target tasks represented by the two questions above. For each source task, we learn a model on a large set of historical titles, leveraging information such as title metadata (e.g., genre, runtime, series or film) as well as tags or text summaries curated by domain experts describing thematic/plot elements. Once we learn this model, we extract model parameters constituting a numerical representation or embedding of the title. These embeddings are then used as inputs to downstream models specialized on the target tasks for a smaller set of titles directly relevant for content decisions (Figure 1). All models were developed and deployed using metaflow, Netflix’s open source framework for bringing models into production.

To assess the usefulness of these embeddings, we look at two indicators: 1) Do they improve the performance on the target task via downstream models? And just as importantly, 2) Are they useful to our creative partners, i.e. do they lend insight or facilitate apt comparisons (e.g., revealing that a pair of titles attracts similar audiences, or that a pair of countries have similar viewing behavior)? These considerations are key in informing subsequent lines of research and innovation.

Figure 1: Similar title identification and audience sizing can be supported by a common learned title embedding.

Similar titles

In entertainment, it is common to contextualize a new project in terms of existing titles. For example, a creative executive developing a title might wonder: Does this teen movie have more of the wholesome, romantic vibe ofTo All the Boys I’ve Loved Before or more of the dark comedic bent of The End of the F***ing World? Similarly, a marketing executive refining her “elevator pitch” might summarize a title with: “The existential angst of Eternal Sunshine of the Spotless Mind meets the surrealist flourishes of The One I Love.”

To make these types of comparisons even richer we “embed” titles in a high-dimensional space or “similarity map,” wherein more similar titles appear closer together with respect to a spatial distance metric such as Euclidean distance. We can then use this similarity map to identify clusters of titles that share common elements (Figure 2), as well as surface candidate similar titles for an unlaunched title.

Notably, there is no “ground truth” about what is similar: embeddings optimized on different source tasks will yield different similarity maps. For example, if we derive our embeddings from a model that classifies genre, the resulting map will minimize the distance between titles that are thematically similar (Figure 2). By contrast, embeddings derived from a model that predicts audience size will align titles with similar performance characteristics. By offering multiple views into how a given title is situated within the broader content universe, these similarity maps offer a valuable tool for ideation and exploration for our creative decision makers.

Figure 2: T-SNE visualization of embeddings learned from content categorization task.

Transfer learning for audience sizing

Another crucial input for content decision makers is an estimate of how large the potential audience will be (and ideally, how that audience breaks down geographically). For example, knowing that a title will likely drive a primary audience in Spain along with sizable audiences in Mexico, Brazil, and Argentina would aid in deciding how best to promote it and what localized assets (subtitles, dubbings) to create ahead of time.

Predicting the potential audience size of a title is a complex problem in its own right, and we leave a more detailed treatment for the future. Here, we simply highlight how embeddings can be leveraged to help tackle this problem. We can include any combination of the following as features in a supervised modeling framework that predicts audience size in a given country:

  • Embedding of a title
  • Embedding of a country we’d like to predict audience size in
  • Audience sizes of past titles with similar embeddings (or some aggregation of them)
Figure 3: How we can use transfer-learned embeddings to help with demand prediction.

As an example, if we are trying to predict the audience size of a dark comedic title in Brazil, we can leverage the aforementioned similarity maps to identify similar dark comedies with an observed audience size in Brazil. We can then include these observed audience sizes (or some weighted average based on similarity) as features. These features are interpretable (they are associated with known titles and one can reason/debate about whether those titles’ performances should factor into the prediction) and significantly improve prediction accuracy.

Learning embeddings

How do we produce these embeddings? The first step is to identify source tasks that will produce useful embeddings for downstream model consumption. Here we discuss two types of tasks: supervised and self-supervised.


A major motivation for transfer learning is to “pre-train” model parameters by first learning them on a related source task for which we have more training data. Inspecting the data we have on hand, we find that for any title on our service with sufficient viewing data, we can (1) categorize the title based on who watched it (a.k.a. “content category”) and (2) observe how many subscribers watched it in each country (“audience size”). From this title-level information, we devise the following supervised learning tasks:

  • {metadata, tags, summaries} → content category
  • {metadata, tags, summaries, country} → audience size in country

When implementing specific solutions to these tasks, two important modeling decisions we need to make are selecting a) a suitable method (“encoder”) for converting title-level features (metadata, tags, summaries) into an amenable representation for a predictive model and b) a model (“predictor”) that predicts labels (content category, audience size) given an encoded title. Since our goal is to learn somewhat general-purpose embeddings that can plug into multiple use cases, we generally prefer parameter-rich models for the encoder and simpler models for the predictor.

Our choice of encoder (Figure 4) depends on the type of input. For text-based summaries, we leverage pre-trained models like BERT to provide context-dependent word embeddings that are then run through a recurrent neural network style architecture, such as a bidirectional LSTM or GRU. For tags, we directly learn tag representations by considering each title as a tag collection, or a “bag-of-tags”. For audience size models where predictions are country-specific, we also directly learn country embeddings and concatenate the resulting embedding to the tag or summary-based representation. Essentially, conversion of each tag and country to its resulting embedding is done via a lookup table.

Likewise, the predictor depends on the task. For category prediction, we train a linear model on top of the encoder representation, apply a softmax operation, and minimize the negative log likelihood. For audience size prediction, we use a single hidden-layer feedforward neural network to minimize the mean squared error for a given title-country pair. Both the encoder and predictor models are optimized via backpropagation, and the representation produced by the optimized encoder is used in downstream models.

Figure 4: encoder architectures to handle various kinds of title-related inputs. For text summaries, we first convert each word to its context-dependent representation via BERT or a related model, followed by a biGRU to convert the sequence of embeddings to a single (final-state) representation. For tags, we compute the average tag representation (since each title is associated with multiple tags).


Knowledge graphs are abstract graph-based data structures which encode relations (edges) between entities (nodes). Each edge in the graph, i.e. head-relation-tail triple, is known as a fact, and in this way a set of facts (i.e. “knowledge”) results in a graph. However, the real power of the graph is the information contained in the relational structure.

At Netflix, we apply this concept to the knowledge contained in the content universe. Consider a simplified graph whose nodes consist of three entity types: {titles, books, metadata tags} and whose edges encode relationships between them (e.g., “Apocalypse Now is based on Heart of Darkness” ; “21 Grams has a storyline around moral dilemmas”) as illustrated in Figure 5. These facts can be represented as triples (h, r, t), e.g. (Apocalypse Now, based_on, Heart of Darkness), (21 Grams, storyline, moral dilemmas). Next, we can craft a self-supervised learning task where we randomly select edges in the graph to form a test set, and condition on the rest of the graph to predict these missing edges. This task, also known as link prediction, allows us to learn embeddings for all entities in the graph. There are a number of approaches to extract embeddings and our current approach is based on the TransE algorithm. TransE learns an embedding F that minimizes the average Euclidean distance between (F(h) + F(r)) and F(t).

Figure 5: Left: Illustration of a graph relating titles, books, and thematic elements to each other. Right: Illustration of translational embeddings in which the sum of the head and relation embeddings approximates the tail embedding.

The self-supervision is crucial since it allows us to train on titles both on and off our service, expanding the training set considerably and unlocking more gains from transfer learning. The resulting embeddings can then be used in the aforementioned similarity models and audience sizing models models.


Making great content is hard. It involves many different factors and requires considerable investment, all for an outcome that is very difficult to predict. The success of our titles is ultimately determined by our members, and we must do our best to serve their needs given the tools and data we have. We identified two ways to support content decision makers: surfacing similar titles and predicting audience size, drawing from various areas such as transfer learning, embedding representations, natural language processing, and supervised learning. Surfacing these types of insights in a scalable manner is becoming ever more crucial as both our subscriber base and catalog grow and become increasingly diverse. If you’d like to be a part of this effort, please contact us!.

Supporting content decision makers with machine learning was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Toward a Better Quality Metric for the Video Community

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/toward-a-better-quality-metric-for-the-video-community-7ed94e752a30

by Zhi Li, Kyle Swanson, Christos Bampis, Lukáš Krasula and Anne Aaron

Over the past few years, we have been striving to make VMAF a more usable tool not just for Netflix, but for the video community at large. This tech blog highlights our recent progress toward this goal.

VMAF is a video quality metric that Netflix jointly developed with a number of university collaborators and open-sourced on Github. VMAF was originally designed with Netflix’s streaming use case in mind, in particular, to capture the video quality of professionally generated movies and TV shows in the presence of encoding and scaling artifacts. Since its open-sourcing, we have started seeing VMAF being applied in a wider scope within the open-source community. To give a few examples, VMAF has been applied to live sports, video chat, gaming, 360 videos, and user generated content. VMAF has become a de facto standard for evaluating the performance of encoding systems and driving encoding optimizations.

VMAF stands for Video Multi-Method Assessment Fusion. It leans on Human Visual System modeling, or the simulation of low-level neural-circuits to gather evidence on how the human brain perceives quality. The gathered evidence is then fused into a final predicted score using machine learning, guided by subjective scores from training datasets. One aspect that differentiates VMAF from other traditional metrics such as PSNR or SSIM, is that VMAF is able to predict more consistently across spatial resolutions, across shots, and across genres (for example. animation vs. documentary). Traditional metrics, such as PSNR, are already able to do a good job evaluating the quality for the same content on a single resolution, but they often fall short when predicting quality across shots and different resolutions. VMAF fills this gap. For more background information, interested readers may refer to our first and second tech blogs on VMAF.

Recently, we migrated VMAF’s license from Apache 2.0 to BSD+Patent to allow for increased compatibility with other existing open source projects. In the rest of this blog, we highlight three other areas of recent development, as our efforts toward making VMAF a better quality metric for the community.

*The runtime ratio between the floating-point & optimized vmafossexec vs. the fixed-point & optimized vmaf executable, measured in the single-thread mode.

Speed Optimization

Improving the speed performance of VMAF has been a major theme over the past several years. Through low-level code optimization and vectorization, we sped up VMAF’s execution by more than 4x in the past. We also introduced frame-level multithreading and frame skipping, that allow VMAF to run in real time for 4K videos.

Most recently, we teamed up with Facebook and Intel to make VMAF even faster. This work took place in two steps. First, we worked with Ittiam to convert from the original floating-point based representation to fixed-point; and second, Intel implemented vectorization on the fixed-point data pipeline.

This work has allowed us to squeeze out another 2x speed gain on average while maintaining the numerical accuracy at the first decimal digit of the final score. The figure above shows the relative speed improvement under Intel Advanced Vector Extension 2 (Intel AVX2) and Intel AVX-512 intrinsics, for video at 4K, full HD and SD resolutions. Also notice that this is an ongoing effort, so stay tuned for more speed improvements.

New libvmaf API

The new BSD+Patent license allows for increased compatibility with existing open source projects. This brings us to the second area of development, which is on how VMAF can be integrated with them. For historical reasons, the libvmaf C library has been a minimal solution to integrate VMAF with FFmpeg. This year, we invested heavily on revamping the API. Today, we are annoucing the release of libvmaf v2.0.0. It comes with a new API that is much easier to use, integrate and extend.

This table above highlights the features achieved by the new API. A number of areas are worth highlighting:

  • It is extensible without breaking the API.
  • It is easy to add a new feature extractor. And this can easily support future evolution of the VMAF algorithms.
  • It becomes very flexible to allocate memory and incrementally calculate VMAF at the frame level.

The last feature makes it possible to integrate VMAF in an encoding loop, guiding encoding decisions iteratively on a frame-by-frame basis.

“No Enhancement Gain” Mode

One unique feature about VMAF that differentiates it from traditional metrics such as PSNR and SSIM is that VMAF can capture the visual gain from image enhancement operations, which aim to improve the subjective quality perceived by viewers.

The examples above demonstrate an original frame (a) and its enhanced versions by sharpening (b), and histogram equalization (c), and their corresponding VMAF scores. As one can notice, the visual improvement achieved by the enhancement operations are reflected in the VMAF scores. Most recently, a tune=vmaf mode was introduced in the libaom library as an option to perform quality-optimized AV1 encoding. This mode achieves BD-rate gain mostly by performing frame-based image sharpening prior to video compression (e). For a comparison, AV1 encoding without image sharpening is demonstrated in (d).

This is a good demonstration of how VMAF can drive perceptual optimization of video codecs. However, in codec evaluation, it is often desirable to measure the gain achievable from compression without taking into account the gain from image enhancement during pre-processing. As demonstrated by the block diagram above, since it is difficult to strictly separate an encoder from its pre-processing step (especially for proprietary encoders), it may become difficult to use VMAF to assess the pure compression gain. This dilemma is well aligned with two voices we have heard from the community: users seem to like the fact that VMAF could capture the enhancement gains, but at the same time, they have expressed concerns that such enhancement could be overused (or abused).

We think that there is value in disregarding enhancement gain that is not part of a codec. We also believe that there is value in preserving enhancement gain in many cases to reflect the fact that enhancement can improve the visual quality perceived by the end viewers. Our solution to this dilemma is to introduce a new mode called VMAF NEG (“neg” stands for “no enhancement gain”). And we propose the following:

  • Use the NEG mode for codec evaluation purposes to assess the pure effect coming from compression.
  • Use the “default” mode to assess compression and enhancement combined.

How does VMAF NEG mode work? To make the long story short: we can detect the magnitude of the VMAF gain coming from image enhancement, and subtract this effect from the measurement. The grayscale map in (f) above demonstrates the magnitude of the image sharpening performed in tune=vmaf. And we can subtract this effect from the VMAF scores. The VMAF NEG scores are also shown in (a) ~ (e) above. As we can see, the VMAF scores are largely muted by the enhancement subtraction in the NEG mode. More details about VMAF NEG mode can be found in this tech memo.

What Comes Next

We are committed to improve the accuracy and performance of VMAF in the long run. Over the past several years, through field testing and feedback from the users, we have learned extensively about the existing algorithm’s strengths and weaknesses. We believe that there is still plenty of room for improvement.

The NEG mode is our first step toward more accurately quantifying the perceptual gain without image enhancement. When operating in its regular mode, it is known that VMAF tends to overpredict perceptual quality when image enhancement operations, like oversharpening, lead to quality degradation. We plan to address this in future versions, by imposing limits on the enhancement attainable.

We have identified a number of other areas for further improvement, for example, to better predict perceived quality under challenging cases, such as banding and blockiness in the shades. Other potential areas of improvement include better model temporal masking effects in high motion sequences and also more accurately capture the effects of encoding videos generated from noisy sources. We will continue to leverage Human Visual System modeling, subjective testing and machine learning as we work toward a better quality metric for the video community.

Toward a Better Quality Metric for the Video Community was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Keeping Netflix Reliable Using Prioritized Load Shedding

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/keeping-netflix-reliable-using-prioritized-load-shedding-6cc827b02f94

How viewers are able to watch their favorite show on Netflix while the infrastructure self-recovers from a system failure

By Manuel Correa, Arthur Gonigberg, and Daniel West

Getting stuck in traffic is one of the most frustrating experiences for drivers around the world. Everyone slows to a crawl, sometimes for a minor issue or sometimes for no reason at all. As engineers at Netflix, we are constantly reevaluating how to redesign traffic management. What if we knew the urgency of each traveler and could selectively route cars through, rather than making everyone wait?

In Netflix engineering, we’re driven by ensuring Netflix is there when you need it to be. Yet, as recent as last year, our systems were susceptible to metaphorical traffic jams; we had on/off circuit breakers, but no progressive way to shed load. Motivated by improving the lives of our members, we’ve introduced priority-based progressive load shedding.

The animation below shows the behavior of the Netflix viewer experience when the backend is throttling traffic based on priority. While the lower priority requests are throttled, the playback experience remains uninterrupted and the viewer is able to enjoy their title. Let’s dig into how we accomplished this.

Failure can occur due to a myriad of reasons: misbehaving clients that trigger a retry storm, an under-scaled service in the backend, a bad deployment, a network blip, or issues with the cloud provider. All such failures can put a system under unexpected load, and at some point in the past, every single one of these examples has prevented our members’ ability to play. With these incidents in mind, we set out to make Netflix more resilient with these goals:

  1. Consistently prioritize requests across device types (Mobile, Browser, and TV)
  2. Progressively throttle requests based on priority
  3. Validate assumptions by using Chaos Testing (deliberate fault injection) for requests of specific priorities

The resulting architecture that we envisioned with priority throttling and chaos testing included is captured below.

High level playback architecture with priority throttling and chaos testing

Building a request taxonomy

We decided to focus on three dimensions in order to categorize request traffic: throughput, functionality, and criticality. Based on these characteristics, traffic was classified into the following:

  • NON_CRITICAL: This traffic does not affect playback or members’ experience. Logs and background requests are examples of this type of traffic. These requests are usually high throughput which contributes to a large percentage of load in the system.
  • DEGRADED_EXPERIENCE: This traffic affects members’ experience, but not the ability to play. The traffic in this bucket is used for features like: stop and pause markers, language selection in the player, viewing history, and others.
  • CRITICAL: This traffic affects the ability to play. Members will see an error message when they hit play if the request fails.

Using attributes of the request, the API gateway service (Zuul) categorizes the requests into NON_CRITICAL, DEGRADED_EXPERIENCE and CRITICAL buckets, and computes a priority score between 1 to 100 for each request given its individual characteristics. The computation is done as a first step so that it is available for the rest of the request lifecycle.

Most of the time, the request workflow proceeds normally without taking the request priority into account. However, as with any service, sometimes we reach a point when either one of our backends is in trouble or Zuul itself is in trouble. When that happens requests with higher priority get preferential treatment. The higher priority requests will get served, while the lower priority ones will not. The implementation is analogous to a priority queue with a dynamic priority threshold. This allows Zuul to drop requests with a priority lower than the current threshold.

Finding the best place to throttle traffic

Zuul can apply load shedding in two moments during the request lifecycle: when it routes requests to a specific back-end service (service throttling) or at the time of initial request processing, which affects all back-end services (global throttling).

Service throttling

Zuul can sense when a back-end service is in trouble by monitoring the error rates and concurrent requests to that service. Those two metrics are approximate indicators of failures and latency. When the threshold percentage for one of these two metrics is crossed, we reduce load on the service by throttling traffic.

Global throttling

Another case is when Zuul itself is in trouble. As opposed to the scenario above, global throttling will affect all back-end services behind Zuul, rather than a single back-end service. The impact of this global throttling can cause much bigger problems for members. The key metrics used to trigger global throttling are CPU utilization, concurrent requests, and connection count. When any of the thresholds for those metrics are crossed, Zuul will aggressively throttle traffic to keep itself up and healthy while the system recovers. This functionality is critical: if Zuul goes down, no traffic can get through to our backend services, resulting in a total outage.

Introducing priority-based progressive load shedding

Once we had the prioritization piece in place, we were able to combine it with our load shedding mechanism to dramatically improve streaming reliability. When we’re in a bad situation (i.e. any of the thresholds above are exceeded), we progressively drop traffic, starting with the lowest priority. A cubic function is used to manage the level of throttling. If things get really, really bad the level will hit the sharp side of the curve, throttling everything.

The graph above is an example of how the cubic function is applied. As the overload percentage increases (i.e. the range between the throttling threshold and the max capacity), the priority threshold trails it very slowly: at 35%, it’s still in the mid-90s. If the system continues to degrade, we hit priority 50 at 80% exceeded and then eventually 10 at 95%, and so on.

Given that a relatively small amount of requests impact streaming availability, throttling low priority traffic may affect certain product features but will not prevent members pressing “play” and watching their favorite show. By adding progressive priority-based load shedding, Zuul can shed enough traffic to stabilize services without members noticing.

Handling retry storms

When Zuul decides to drop traffic, it sends a signal to devices to let them know that we need them to back off. It does this by indicating how many retries they can perform and what kind of time window they can perform them in. For example:

{ “maxRetries” : <max-retries>, “retryAfterSeconds”: <seconds> }

Using this backpressure mechanism, we can stop retry storms much faster than we could in the past. We automatically adjust these two dials based on the priority of the request. Requests with higher priority will retry more aggressively than lower ones, also increasing streaming availability.

Validating which requests are right for the job

To validate our request taxonomy assumptions on whether a specific request fell into the NON_CRITICAL, DEGRADED, or CRITICAL bucket, we needed a way to test the user’s experience when that request was shed. To accomplish this, we leveraged our internal failure injection tool (FIT) and created a failure injection point in Zuul that allowed us to shed any request based on a supplied priority. This enabled us to manually simulate a load shedded experience by blocking ranges of priorities for a specific device or member, giving us an idea of which requests could be safely shed without impacting the user.

Continually ensuring those requests are still right for the job

One of the goals here is to reduce members’ pain by shedding requests that are not expected to affect the user’s streaming experience. However, Netflix changes quickly and requests that were thought to be noncritical can unexpectedly become critical. In addition, Netflix has a wide variety of client devices, client versions, and ways to interact with the system. To make sure we weren’t causing members pain when throttling NON_CRITICAL requests in any of these scenarios, we leveraged our infrastructure experimentation platform ChAP.

This platform allows us to stage an A/B experiment that will allocate a small number of production users to either a control or treatment group for 45 minutes while throttling a range of priorities for the treatment group. This lets us capture a variety of live use cases and measure the impact to their playback experience. ChAP analyzes the members’ KPIs per device to determine if there is a deviation between the control and the treatment groups.

In our first experiment, we detected a race condition in both Android and iOS devices for a low priority request that caused sporadic playback errors. Since we practice continuous experimentation, once the initial experiments were run and the bugs were fixed, we scheduled them to run on a periodic basis. This allows us to detect regressions early and keep users streaming.

Experiment regression detection before and after fix (SPS indicates streaming availability)

Reaping the benefits

In 2019, before progressive load shedding was in place, the Netflix streaming services experienced an outage that resulted in a sizable percentage of members who were not able to play for a period of time. In 2020, days after the implementation was deployed, the team started seeing the benefit of the solution. Netflix experienced a similar issue with the same potential impact as the outage seen in 2019. Unlike then, Zuul’s progressive load shedding kicked in and started shedding traffic until the service was in a healthy state without impacting members’ ability to play at all.

The graph below shows a stable streaming availability metric stream per second (SPS) while Zuul is performing progressive load shedding based on request priority during the incident. The different colors in the graph represent requests with different priority being throttled.

Members were happily watching their favorite show on Netflix while the infrastructure was self-recovering from a system failure.

We are not done yet

For future work, the team is looking into expanding the use of request priority for other use cases like better retry policies between devices and back-ends, dynamically changing load shedding thresholds, tuning the request priorities using Chaos Testing as a guiding principle, and other areas that will make Netflix even more resilient.

If you’re interested in helping Netflix stay up in the face of shifting systems and unexpected failures, reach out to us. We’re hiring!

Keeping Netflix Reliable Using Prioritized Load Shedding was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

A Day in the Life of a Content Analytics Engineer

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/a-day-in-the-life-of-a-content-analytics-engineer-eb0250b993be

Part of our series on who works in Analytics at Netflix — and what the role entails

by Rocio Ruelas

Back when we were all working in offices, my favorite days were Monday, Wednesday, and Friday. Those were the days with the best hot breakfast, and I’ve always been a sucker for free food. I started the day by arriving at the LA office right before 8am and finding a parking spot close to the entrance. I would greet the familiar faces at the reception desk and take a moment to check out which Netflix Original was currently being projected across the lobby. Take the elevator uninterrupted up to the top floor. Grab myself a plate of scrambled eggs, salsa, and bacon. Pour myself some coffee. Then sit at a small table next to the floor-to-ceiling windows with a clear view of the Hollywood sign.

My morning journey from lobby to elevators to breakfast (Photo Credit: Netflix)

During the day, the LA office buzzes with excitement and conversation. My time in the morning is like the calm before the storm — a chance to reflect before my head is full of numbers and figures. I often think about all the things that led me to becoming a Netflix employee. From my family immigrating to the United States from Mexico when I was very young to the teachers and professors that encouraged a low income student like me to dream big. It has been a journey and I’m grateful to be at a place that values the voice I bring to the table.

At the time of posting we’re working from home due to the pandemic, so my days look a bit different: The hot breakfasts are not as consistent and conversations are mainly with my dog. We still find ways to keep connected, but I for one am looking forward to when the office is fully open and I can look out to the Hollywood sign again.

Ok. But what do I actually do? (Besides eating breakfast)

What do I do at Netflix?

I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. My team focuses on innovating and maintaining the metrics Netflix uses to understand performance of our shows and films on the service. We partner closely with the business strategy team to provide as much information as we can to our content executives, so that — combined with their industry experience — they can make the best decisions for Netflix.

Being an Analytics Engineer is like being a hybrid of a librarian 📚 and a Swiss army knife 🛠️: Two good things to have on hand when you’re not quite sure what you will need. Like a librarian, I have access to an encyclopedia of knowledge about our content data and have become the resident expert in one of our most important internal metrics. And like a Swiss army knife, I possess a multitude of tools to get the job done — be it SQL, Jupyter Notebooks, Tableau, or Google Sheets.

One of my favorite things about being an Analytics Engineer is the variety. I have some days where I am brainstorming and collaborating with amazing colleagues and other days where I can put my headphones on to work out a tough problem or build a dashboard.

One of my current projects involves understanding how viewing habits have evolved over the past several years. We started out with a small working group where we brainstormed the key questions to address, what data we could use to answer said questions, and came up with a work plan for how the analysis might take shape. Then I put on my headphones and got to work, writing SQL and using Tableau to present the data in a useful way. We met frequently to discuss our findings and iterate on the analysis. The great thing about these working groups is that we each contribute different skills and ideas. We benefit from both our individual strengths and our willingness to collaborate — Our values of Selflessness and Inclusion, in action.

How did I become interested in Analytics?

I did not set out from the start to be an Analyst. I never had a 5 year plan and my path has been a winding one.

Yours truly, featuring part of my extensive Netflix apparel collection
Yours truly, featuring part of my extensive Netflix apparel collection

In college, I majored in Physics because it was “the science that explains all the other sciences”. But what I ended up liking most about it was the math. Between that and the fact that there aren’t many entry-level physics jobs, I pursued a PhD in Applied Mathematics. This turned out to be a wise choice as I avoided entering the workforce right before the 2008 recession.

I loved grad school. The lectures, the research, and most of all the lifelong friendships. But as much as I enjoyed being a student, the academic track wasn’t for me. So without much of a plan I headed back home to California after graduation.

Looking around to see what I could do with my Applied Math background, I quickly settled on Data Science. I wasn’t well versed in it but I knew it was in demand. I started my new data science career as an analyst at a small marketing company. I had an incredible boss who encouraged me to learn new skills on the job. I honed my SQL and Python skills and implemented a clustering model. I also got my first introduction to working for an actual business.

Later on I went to Hulu to grow in the core skills of a data scientist. But while the predictive modeling I was doing was interesting and challenging, I missed being close to the business. As an analyst, I got to attend more meetings with the decision makers and be part of the conversation.

So by the time the opportunity arose to interview for a position at Netflix, I had figured out that Analytics was the best area for me.

It has been a journey and I’m grateful to be at a place that values the voice I bring to the table.

Why Netflix?

Growing up I watched a lot of TV. I mean a lot of TV. But I never thought I could actually work in the TV and Film business. I feel incredibly fortunate to be working at a job I am passionate about and to be at a company that brings joy to people around the world.

Even though I’d been a loyal Netflix customer since the DVD days, I had not heard about their unique culture until I started interviewing. When I did read the culture doc (which I recently learned is also published in Spanish and 12 other languages!), it sounded pretty intimidating. Phrases like “high performance” and “dream team” made me imagine an almost gladiator-style workplace. But I quickly learned this wasn’t the case. Through a combination of my existing network, the interview process, and other online resources about the company, I found that folks are actually very friendly and helpful! Everyone just wants to do their best work and help you do your best work too. Think more The Great British Baking Show and less Hell’s Kitchen. Selflessness really is embraced as an important Netflix value.

Having been here for 3 years now, I can say that working at Netflix is really special. The company is always evolving, big decisions are made in a transparent way, and I’m encouraged to voice my thoughts. But the single most important factor is the people. My Content Analytics teammates continuously impress me not only with their quality of work, but also with their kindness and mutual trust. This foundation makes innovating more fun, lets us be open about our passions outside of work, and means we genuinely enjoy each other’s company. That balance is crucial for me and is why this truly is the place where I can do my best work.

If this post resonates with you and you’d like to explore opportunities with Netflix, check out our analytics site, search open roles, and learn about our culture. You can also find more stories like this here.

A Day in the Life of a Content Analytics Engineer was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Building Netflix’s Distributed Tracing Infrastructure

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/building-netflixs-distributed-tracing-infrastructure-bb856c319304

by Maulik Pandey

Our Team — Kevin Lew, Narayanan Arunachalam, Elizabeth Carretto, Dustin Haffner, Andrei Ushakov, Seth Katz, Greg Burrell, Ram Vaithilingam, Mike Smith and Maulik Pandey

@Netflixhelps Why doesn’t Tiger King play on my phone?” — a Netflix member via Twitter

This is an example of a question our on-call engineers need to answer to help resolve a member issue — which is difficult when troubleshooting distributed systems. Investigating a video streaming failure consists of inspecting all aspects of a member account. In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Now let’s look at how we designed the tracing infrastructure that powers Edgar.

Distributed Tracing: the missing context in troubleshooting services at scale

Prior to Edgar, our engineers had to sift through a mountain of metadata and logs pulled from various Netflix microservices in order to understand a specific streaming failure experienced by any of our members. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. The process started with manual pull of member account information that was part of the session. The next step was to put all puzzle pieces together and hope the resulting picture would help resolve the member issue. We needed to increase engineering productivity via distributed request tracing.

If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls. We could also get contextual information about the streaming session by joining relevant traces with account metadata and service logs. This insight led us to build Edgar: a distributed tracing infrastructure and user experience.

Figure 1. Troubleshooting a session in Edgar

When we started building Edgar four years ago, there were very few open-source distributed tracing systems that satisfied our needs. Our tactical approach was to use Netflix-specific libraries for collecting traces from Java-based streaming services until open source tracer libraries matured. By 2017, open source projects like Open-Tracing and Open-Zipkin were mature enough for use in polyglot runtime environments at Netflix. We chose Open-Zipkin because it had better integrations with our Spring Boot based Java runtime environment. We use Mantis for processing the stream of collected traces, and we use Cassandra for storing traces. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage. Traces collected from various microservices are ingested in a stream processing manner into the data store. The following sections describe our journey in building these components.

Trace Instrumentation: how will it impact our service?

That is the first question our engineering teams asked us when integrating the tracer library. It is an important question because tracer libraries intercept all requests flowing through mission-critical streaming services. Safe integration and deployment of tracer libraries in our polyglot runtime environments was our top priority. We earned the trust of our engineers by developing empathy for their operational burden and by focusing on providing efficient tracer library integrations in runtime environments.

Distributed tracing relies on propagating context for local interprocess calls (IPC) and client calls to remote microservices for any arbitrary request. Passing the request context captures causal relationships between microservices during runtime. We adopted Open-Zipkin’s B3 HTTP header based context propagation mechanism. We ensure that context propagation headers are correctly passed between microservices across a variety of our “paved road” Java and Node runtime environments, which include both older environments with legacy codebases and newer environments such as Spring Boot. We execute the Freedom & Responsibility principle of our culture in supporting tracer libraries for environments like Python, NodeJS, and Ruby on Rails that are not part of the “paved road” developer experience. Our loosely coupled but highly aligned engineering teams have the freedom to choose an appropriate tracer library for their runtime environment and have the responsibility to ensure correct context propagation and integration of network call interceptors.

Our runtime environment integrations inject infrastructure tags like service name, auto-scaling group (ASG), and container instance identifiers. Edgar uses this infrastructure tagging schema to query and join traces with log data for troubleshooting streaming sessions. Additionally, it became easy to provide deep links to different monitoring and deployment systems in Edgar due to consistent tagging. With runtime environment integrations in place, we had to set an appropriate trace data sampling policy for building a troubleshooting experience.

Stream Processing: to sample or not to sample trace data?

This was the most important question we considered when building our infrastructure because data sampling policy dictates the amount of traces that are recorded, transported, and stored. A lenient trace data sampling policy generates a large number of traces in each service container and can lead to degraded performance of streaming services as more CPU, memory, and network resources are consumed by the tracer library. An additional implication of a lenient sampling policy is the need for scalable stream processing and storage infrastructure fleets to handle increased data volume.

We knew that a heavily sampled trace dataset is not reliable for troubleshooting because there is no guarantee that the request you want is in the gathered samples. We needed a thoughtful approach for collecting all traces in the streaming microservices while keeping low operational complexity of running our infrastructure.

Most distributed tracing systems enforce sampling policy at the request ingestion point in a microservice call graph. We took a hybrid head-based sampling approach that allows for recording 100% of traces for a specific and configurable set of requests, while continuing to randomly sample traffic per the policy set at ingestion point. This flexibility allows tracer libraries to record 100% traces in our mission-critical streaming microservices while collecting minimal traces from auxiliary systems like offline batch data processing. Our engineering teams tuned their services for performance after factoring in increased resource utilization due to tracing. The next challenge was to stream large amounts of traces via a scalable data processing platform.

Mantis is our go-to platform for processing operational data at Netflix. We chose Mantis as our backbone to transport and process large volumes of trace data because we needed a backpressure-aware, scalable stream processing system. Our trace data collection agent transports traces to Mantis job cluster via the Mantis Publish library. We buffer spans for a time period in order to collect all spans for a trace in the first job. A second job taps the data feed from the first job, does tail sampling of data and writes traces to the storage system. This setup of chained Mantis jobs allows us to scale each data processing component independently. An additional advantage of using Mantis is the ability to perform real-time ad-hoc data exploration in Raven using the Mantis Query Language (MQL). However, having a scalable stream processing platform doesn’t help much if you can’t store data in a cost efficient manner.

Storage: don’t break the bank!

We started with Elasticsearch as our data store due to its flexible data model and querying capabilities. As we onboarded more streaming services, the trace data volume started increasing exponentially. The increased operational burden of scaling ElasticSearch clusters due to high data write rate became painful for us. The data read queries took an increasingly longer time to finish because ElasticSearch clusters were using heavy compute resources for creating indexes on ingested traces. The high data ingestion rate eventually degraded both read and write operations. We solved this by migrating to Cassandra as our data store for handling high data ingestion rates. Using simple lookup indices in Cassandra gives us the ability to maintain acceptable read latencies while doing heavy writes.

In theory, scaling up horizontally would allow us to handle higher write rates and retain larger amounts of data in Cassandra clusters. This implies that the cost of storing traces grows linearly to the amount of data being stored. We needed to ensure storage cost growth was sub-linear to the amount of data being stored. In pursuit of this goal, we outlined following storage optimization strategies:

  1. Use cheaper Elastic Block Store (EBS) volumes instead of SSD instance stores in EC2.
  2. Employ better compression technique to reduce trace data size.
  3. Store only relevant and interesting traces by using simple rules-based filters.

We were adding new Cassandra nodes whenever the EC2 SSD instance stores of existing nodes reached maximum storage capacity. The use of a cheaper EBS Elastic volume instead of an SSD instance store was an attractive option because AWS allows dynamic increase in EBS volume size without re-provisioning the EC2 node. This allowed us to increase total storage capacity without adding a new Cassandra node to the existing cluster. In 2019 our stunning colleagues in the Cloud Database Engineering (CDE) team benchmarked EBS performance for our use case and migrated existing clusters to use EBS Elastic volumes. By optimizing the Time Window Compaction Strategy (TWCS) parameters, they reduced the disk write and merge operations of Cassandra SSTable files, thereby reducing the EBS I/O rate. This optimization helped us reduce the data replication network traffic amongst the cluster nodes because SSTable files were created less often than in our previous configuration. Additionally, by enabling Zstd block compression on Cassandra data files, the size of our trace data files was reduced by half. With these optimized Cassandra clusters in place, it now costs us 71% less to operate clusters and we could store 35x more data than our previous configuration.

We observed that Edgar users explored less than 1% of collected traces. This insight leads us to believe that we can reduce write pressure and retain more data in the storage system if we drop traces that users will not care about. We currently use a simple rule based filter in our Storage Mantis job that retains interesting traces for very rarely looked service call paths in Edgar. The filter qualifies a trace as an interesting data point by inspecting all buffered spans of a trace for warnings, errors, and retry tags. This tail-based sampling approach reduced the trace data volume by 20% without impacting user experience. There is an opportunity to use machine learning based classification techniques to further reduce trace data volume.

While we have made substantial progress, we are now at another inflection point in building our trace data storage system. Onboarding new user experiences on Edgar could require us to store 10x the amount of current data volume. As a result, we are currently experimenting with a tiered storage approach for a new data gateway. This data gateway provides a querying interface that abstracts the complexity of reading and writing data from tiered data stores. Additionally, the data gateway routes ingested data to the Cassandra cluster and transfers compacted data files from Cassandra cluster to S3. We plan to retain the last few hours worth of data in Cassandra clusters and keep the rest in S3 buckets for long term retention of traces.

Table 1. Timeline of Storage Optimizations

Secondary advantages

In addition to powering Edgar, trace data is used for the following use cases:

Application Health Monitoring

Trace data is a key signal used by Telltale in monitoring macro level application health at Netflix. Telltale uses the causal information from traces to infer microservice topology and correlate traces with time series data from Atlas. This approach paints a richer observability portrait of application health.

Resiliency Engineering

Our chaos engineering team uses traces to verify that failures are correctly injected while our engineers stress test their microservices via Failure Injection Testing (FIT) platform.

Regional Evacuation

The Demand Engineering team leverages tracing to improve the correctness of prescaling during regional evacuations. Traces provide visibility into the types of devices interacting with microservices such that changes in demand for these services can be better accounted for when an AWS region is evacuated.

Estimate infrastructure cost of running an A/B test

The Data Science and Product team factors in the costs of running A/B tests on microservices by analyzing traces that have relevant A/B test names as tags.

What’s next?

The scope and complexity of our software systems continue to increase as Netflix grows. We will focus on following areas for extending Edgar:

  • Provide a great developer experience for collecting traces across all runtime environments. With an easy way to to try out distributed tracing, we hope that more engineers instrument their services with traces and provide additional context for each request by tagging relevant metadata.
  • Enhance our analytics capability for querying trace data to enable power users at Netflix in building their own dashboards and systems for narrowly focused use cases.
  • Build abstractions that correlate data from metrics, logging, and tracing systems to provide additional contextual information for troubleshooting.

As we progress in building distributed tracing infrastructure, our engineers continue to rely on Edgar for troubleshooting streaming issues like “Why doesn’t Tiger King play on my phone?”. Our distributed tracing infrastructure helps in ensuring that Netflix members continue to enjoy a must-watch show like Tiger King!

We are looking for stunning colleagues to join us on this journey of building distributed tracing infrastructure. If you are passionate about Observability then come talk to us.

Building Netflix’s Distributed Tracing Infrastructure was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introducing the first video in our new series, Verified, featuring Netflix’s Jason Chan

Post Syndicated from Stephen Schmidt original https://aws.amazon.com/blogs/security/introducing-first-video-new-series-verified-featuring-netflix-jason-chan/

The year has been a profoundly different one for us all, and like many of you, I’ve been adjusting, both professionally and personally, to this “new normal.” Here at AWS we’ve seen an increase in customers looking for secure solutions to maintain productivity in an increased work-from-home world. We’ve also seen an uptick in requests for training; it’s clear, a sense of community and learning are critically important as workforces physically distance.

For these reasons, I’m happy to announce the launch of Verified: Presented by AWS re:Inforce. I’m hosting this series, but I’ll be joined by leaders in cloud security across a variety of industries. The goal is to have an open conversation about the common issues we face in securing our systems and tools. Topics will include how the pandemic is impacting cloud security, tips for creating an effective security program from the ground up, how to create a culture of security, emerging security trends, and more. Learn more by following me on Twitter (@StephenSchmidt), and get regular updates from @AWSSecurityInfo. Verified is just one of the many ways we will continue sharing best practices with our customers during this time. You can find more by reading the AWS Security Blog, reviewing our documentation, visiting the AWS Security and Compliance webpages, watching re:Invent and re:Inforce playlists, and/or reviewing the Security Pillar of Well Architected.

Our first conversation, above, is with Jason Chan, Vice President of Information Security at Netflix. Jason spoke to us about the security program at Netflix, his approach to hiring security talent, and how Zero Trust enables a remote workforce. Jason also has solid insights to share about how he started and grew the security program at Netflix.

“In the early days, what we were really trying to figure out is how do we build a large-scale consumer video-streaming service in the public cloud, and how do you do that in a secure way? There wasn’t a ton of expertise in that, so when I was building the security team at Netflix, I thought, ‘how do we bring in folks from a variety of backgrounds, generalists … to tackle this problem?’”

He also gave his view on how a growing security team can measure ROI. “I think it’s difficult to have a pure equation around that. So what we try to spend our time doing is really making sure that we, as a team, are aligned on what is the most important—what are the most important assets to protect, what are the most critical risks that we’re trying to prevent—and then make sure that leadership is aligned with that, because, as we all know, there’s not unlimited resources, right? You can’t hire an unlimited number of folks or spend an unlimited amount of money, so you’re always trying to figure out how do you prioritize, and how do you find where is going to be the biggest impact for your value?”

Check out Jason’s full interview above, and stay tuned for further videos in this series. If you have an idea or a topic you’d like covered in this series, please drop us a comment below. Thanks!

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.


Steve Schmidt

Steve is Vice President and Chief Information Security Officer for AWS. His duties include leading product design, management, and engineering development efforts focused on bringing the competitive, economic, and security benefits of cloud computing to business and government customers. Prior to AWS, he had an extensive career at the Federal Bureau of Investigation, where he served as a senior executive and section chief. He currently holds 11 patents in the field of cloud security architecture. Follow Steve on Twitter.

How Our Paths Brought Us to Data and Netflix

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/how-our-paths-brought-us-to-data-and-netflix-4eced44a6872

Part of our series on who works in Analytics at Netflix — and what the role entails

by Julie Beckley & Chris Pham

This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

Photo from a team curling offsite — There’s us to the right!

[Chris] Julie and I joined the Streaming DSE team at Netflix a few years ago and have been close colleagues and friends since then. At work, we regularly lean on each other for help based on our respective areas of expertise — I bring my breadth of big data tools and technologies while Julie has been building statistical models for the past decade. Outside of work, we share a love of good food and coffee, exchanging tips on making espresso.

1. What was your path to working in data?

[Julie] I took a traditional path to data science. Since mathematics was my favorite subject in school, I decided to pursue it for my bachelors degree at McGill University (while indulging in French culture in the beautiful city of Montreal). Over the course of the four years it became clear that I enjoyed combining analytical skills with solving real world problems, so a PhD in Statistics was a natural next step. After completing my education, I was still not certain whether I wanted a job in academia or industry. I took a role as a Research Staff Member at IBM Research, which served as a middle ground with a joint focus on real world applications, academic research, and even allowed me to teach a graduate Machine Learning course! I then transitioned to a full industry role at Netflix.

[Chris] I initially wanted to build a career in consulting after receiving my graduate degree in Economics because I had a passion for analytical problem solving and statistical modeling. A role in data science eventually seemed like a natural transition, but it wasn’t without its hurdles: With my consulting background, I had to go through a few other roles first while learning how to code on the side. A lot of my learning and training was self-guided until 2016, when a manager at my last company took a chance on me and helped me make the rare transfer from a role in HR to Data Science.

2. Tell me about some of the exciting projects you’re a part of.

[Julie] Chris and I have the same primary stakeholders (or engineering team that we support): Encoding Technologies. They are continuously innovating compression algorithms to efficiently send high quality audio and video files to our customers over the internet. I focus on improving experimentation methodology to test how well the newest files are working: do they need less bits to stream while providing a higher video quality? Do they cause less errors? My work is typically developed in R or Python. I love the cross-functional nature of my work, as it allows me to learn from others and creatively explore new statistical methodologies to improve the Netflix service.

[Chris] When I first started working with Encoding Technologies, there was so much data waiting to be translated into actionable insights. It was fun starting from almost nothing and transforming all of that data into self-serve tools and dashboards for the team to understand their contribution to the Netflix streaming experience. These projects have involved using Spark, Python, SQL, Tableau, and Jupyter notebooks. Over the last year, I’ve spent a lot of time analyzing data to inform how we roll out new encoding innovations to the diverse ecosystem of devices that stream Netflix.

3. How do your projects impact the business at Netflix?

[Julie] Encoding experimentation (and more broadly, streaming experimentation) is critical for ensuring our customers have a good Quality of Experience when watching Netflix. In other words, the content you’re about to watch needs to load quickly with high video quality. When we test new encodes, we need effective data science methods to quickly and accurately understand whether customers are having a better experience. With these insights, the engineering teams can quickly understand what’s working well and what needs to be improved. It’s super exciting to see the impact of my work when I hear from friends and family that Netflix is streaming well for them!

[Chris] There’s a lot of things to consider when we roll out a new compression algorithm. Which devices get this treatment? What is the benefit to the streaming experience? Is the benefit uniform, or do certain cohorts of members — such as those who stream over a cellular connection — benefit more? How does a decision of this scale affect the efficiency of our globally distributed content delivery network, Open Connect? It’s one big optimization problem that requires balancing several different factors. Streaming DSE is at the center of it all, bringing together different teams at Netflix and using data to drive decisions that impact our members around the world.

4. What does it take to succeed at Netflix in a data role?

[Julie] One of the special things about working at Netflix is that a diverse set of skills and backgrounds is truly appreciated, since there are many ways to add value to the company. From my experience, being proactive in pushing forward on your ideas is key. The values in the Netflix culture document allow for a framework where everyone is a leader to work well — this is because we expect initiative, direct and candid feedback, and transparency in everything we do. This leads to a great environment where I am constantly challenged, learning, and receiving constructive feedback on how I can do better!

[Chris] I think a big part of our jobs is continuously thinking about how data can benefit our stakeholders. Julie and I will never know as much about video and audio compression algorithms as our talented Encoding Technologies team, but we should be the ones most familiar with the data: How to access, analyze, and visualize it; how to transform it into metrics that act as strong and accurate proxies for a member’s experience; and how to guide others to draw the right conclusions from data so they can act on it. Writing memos is a big part of Netflix culture, which I’ve found has been helpful for sharing ideas, soliciting feedback, and documenting project details. So writing well, especially the ability to translate technical concepts for a non-technical audience, is also very useful.

5. What piece of advice would you pass along to those just starting out their career in data?

[Julie] One piece of advice I would pass along (and wish I could give to my younger self) is not to stress and try to plan every step of your data science career. Your career is long (and unpredictable!), so as long as you work hard and stay motivated, it will move in an exciting direction.

[Chris] Everyone wants to build fancy models or tools, but fewer are willing to do the foundational things like cleaning the data and writing the documentation. I’ve found that volunteering and being proactive (no matter the task) has been an effective way of building trust with others, and it opened my career up to many more opportunities early on.

If this post resonates with you and you’d like to explore opportunities with Netflix, check out our analytics site, search open roles, and learn about our culture. You can also find more stories like this here.

How Our Paths Brought Us to Data and Netflix was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Analytics at Netflix: Who we are and what we do

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/analytics-at-netflix-who-we-are-and-what-we-do-7d9c08fe6965

Analytics at Netflix: Who We Are and What We Do

An Introduction to Analytics and Visualization Engineering at Netflix

by Molly Jackman & Meghana Reddy

Explained: Season 1 (Photo Credit: Netflix)

Across nearly every industry, there is recognition that data analytics is key to driving informed business decision-making. But there is far less agreement on what that term “data analytics” actually means — or what to call the people responsible for the work.

Even within Netflix, we have many groups that do some form of data analysis, including business strategy and consumer insights. But here we are talking about Netflix’s Data Science and Engineering group, which specializes in analytics at scale. The group has technical, engineering-oriented roles that fall under two broad category titles: “Analytics Engineers” and “Visualization Engineers.” In this post, we refer to these two titles collectively as the “analytics role.” These professionals come from a wide range of backgrounds and bring different skills to their work, while sharing a common drive to generate and scale business impact through data.

Individuals in these roles possess deep business context and are thought leaders alongside their business counterparts. This enables them to fully understand where their partners are coming from.

What’s the purpose of the analytics role at Netflix?

When you think about data at Netflix, what comes to mind? Oftentimes it is our content recommendation algorithm or the online delivery of video to your device at home. Both are integral parts of the business, but far from the whole picture. Data is used to inform a wide range of questions — ‘How can we make the product experience even better?’, ‘Which shows and films bring the most joy to our members?’, ‘Who can we partner with to expand access to our service in new markets?’. Our Analytics and Visualization Engineers are taking on these and other big questions for the company, informing decision-making across every corner of the business.

We align our analytic teams with business area verticals
We align our analytic teams with business area verticals

Since the problem space is so varied, we align our analytics professionals with the listed business area verticals rather than organizing them within a single functional horizontal. The expectation is that individuals in these roles possess deep business context and are thought leaders alongside their business counterparts. This enables them to fully understand where their partners are coming from. It also means Analytics and Visualization Engineers are a specialized resource and a rare commodity. There are many more questions and stakeholders than analytics team members, and the job is not to take on every request. Instead, these individual contributors are given freedom to choose their projects and are responsible for prioritizing the ones that will have the most business impact (and deprioritizing the rest). This requires a lot of judgment and embodies our “context not control” culture.

“OK, but what do they actually do…?”

What does the job entail?

You’ve probably caught on to some common themes: People in the analytics role are highly connected to the business, solve end-to-end problems, and are directly responsible for improving business outcomes. But what makes this group really shine are their differences. They come from lots of backgrounds, which yields different perspectives on how to approach problems. We use the catch-all titles of Analytics and Visualization Engineers so as to not get too hung up on specific credentials. Instead, people are empowered to leverage their unique skills to make Netflix better.

A couple other defining characteristics of the role are full ownership of the problem (in Netflix lingo, you are the “informed captain” of your space) and creating trustworthy outputs. These are only possible through the one-two punch of deep business context 👊 and technical excellence 👊. Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards — always with an eye towards actionable insights.

We use the catch-all titles of Analytics and Visualization Engineers so as to not get too hung up on specific credentials. Instead, people are empowered to leverage their unique skills to make Netflix better.

Because these professionals vary in their expertise, so too does their day-to-day. Below are three broadly defined personas to help illustrate some of the different backgrounds, motivations, and activities of individuals in the analytics role at Netflix. Many of our colleagues have come in with expertise that spans multiple personas. Others have grown into new areas as part of their professional development at Netflix. Ultimately, these skills are all on a continuum, some broad and some deep, and these are just a few examples of such expertise. So if you find yourself connecting with any part of these descriptions, the analytics role could be for you.

  • The Analyst is motivated by delivering metrics, findings, or dashboards that drive analytical insights and business decisions. They love to communicate their discoveries to nontechnical audiences, explain caveats, and debate analytic choices and strategic implications with peers and stakeholders. Their expertise is descriptive analytic methodology, but they have the necessary tools to be scrappy (e.g. coding, math, stats), and do what’s required to answer the highest priority business questions.
  • The Engineer enjoys making data available by piping it in from new sources in optimal ways, building robust data models, prototyping systems, and doing project-specific engineering. They’re still analysts at heart but, similar to data engineers, they have a deep understanding of data warehouse capabilities and are pros at data processing optimization and performance tuning. Being at this intersection of disciplines allows them to produce full-stack outputs, layering visualizations and analytics on their projects.
  • The Visualizer is passionate about the scalability, beauty, and functionality of dashboards and their capability for telling a visual story. They also have an eye for principled engineering, i.e. managing the data under the surface. They want to pick the perfect chart type for the narrative while also focusing on delivering key analytic insights. They may use industry tools (e.g. Tableau, Looker, Power BI) to their fullest extent, developing a deeper understanding of analytics by examining these tools under the hood. Or they may create sophisticated visuals from scratch and build the type of custom UI that enterprise tools don’t offer (e.g. JavaScript web apps).

Introducing Analytics at Netflix

Whether you’re a data professional, student, or Netflix enthusiast, we invite you to meet our stunning colleagues and hear their stories. If this series resonates with you and you’d like to explore opportunities with us, check out our analytics site, search open roles, and learn about our culture.

Welcome to Analytics at Netflix!

Related Posts:

Analytics at Netflix: Who we are and what we do was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Edgar: Solving Mysteries Faster with Observability

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/edgar-solving-mysteries-faster-with-observability-e1a76302c71f

Edgar helps Netflix teams troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata.

by Elizabeth Carretto

Everyone loves Unsolved Mysteries. There’s always someone who seems like the surefire culprit. There’s a clear motive, the perfect opportunity, and an incriminating footprint left behind. Yet, this is Unsolved Mysteries! It’s never that simple. Whether it’s a cryptic note behind the TV or a mysterious phone call from an unknown number at a critical moment, the pieces rarely fit together perfectly. As mystery lovers, we want to answer the age-old question of whodunit; we want to understand what really happened.

For engineers, instead of whodunit, the question is often “what failed and why?” When a problem occurs, we put on our detective hats and start our mystery-solving process by gathering evidence. The more complex a system, the more places to look for clues. An engineer can find herself digging through logs, poring over traces, and staring at dozens of dashboards.

All of these sources make it challenging to know where to begin and add to the time spent figuring out what went wrong. While this abundance of dashboards and information is by no means unique to Netflix, it certainly holds true within our microservices architecture. Each microservice may be easy to understand and debug individually, but what about when combined into a request that hits tens or hundreds of microservices? Searching for key evidence becomes like digging for a needle in a group of haystacks.

Example call graph in Edgar

In some cases, the question we’re answering is, “What’s happening right now??” and every second without resolution can carry a heavy cost. We want to resolve the problem as quickly as possible so our members can resume enjoying their favorite movies and shows. For teams building observability tools, the question is: how do we make understanding a system’s behavior fast and digestible? Quick to parse, and easy to pinpoint where something went wrong even if you aren’t deeply familiar with the inner workings and intricacies of that system? At Netflix, we’ve answered that question with a suite of observability tools. In an earlier blog post, we discussed Telltale, our health monitoring system. Telltale tells us when an application is unhealthy, but sometimes we need more fine-grained insight. We need to know why a specific request is failing and where. We built Edgar to ease this burden, by empowering our users to troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata.

What is Edgar?

Edgar is a self-service tool for troubleshooting distributed systems, built on a foundation of request tracing, with additional context layered on top. With request tracing and additional data from logs, events, metadata, and analysis, Edgar is able to show the flow of a request through our distributed system — what services were hit by a call, what information was passed from one service to the next, what happened inside that service, how long did it take, and what status was emitted — and highlight where an issue may have occurred. If you’re familiar with platforms like Zipkin or OpenTelemetry, this likely sounds familiar. But, there are a few substantial differences in how Edgar approaches its data and its users.

  • While Edgar is built on top of request tracing, it also uses the traces as the thread to tie additional context together. Deriving meaningful value from trace data alone can be challenging, as Cindy Sridharan articulated in this blog post. In addition to trace data, Edgar pulls in additional context from logs, events, and metadata, sifting through them to determine valuable and relevant information, so that Edgar can visually highlight where an error occurred and provide detailed context.
  • Edgar captures 100% of interesting traces, as opposed to sampling a small fixed percentage of traffic. This difference has substantial technological implications, from the classification of what’s interesting to transport to cost-effective storage (keep an eye out for later Netflix Tech Blog posts addressing these topics).
  • Edgar provides a powerful and consumable user experience to both engineers and non-engineers alike. If you embrace the cost and complexity of storing vast amounts of traces, you want to get the most value out of that cost. With Edgar, we’ve found that we can leverage that value by curating an experience for additional teams such as customer service operations, and we have embraced the challenge of building a product that makes trace data easy to access, easy to grok, and easy to gain insight by several user personas.

Tracing as a foundation

Logs, metrics, and traces are the three pillars of observability. Metrics communicate what’s happening on a macro scale, traces illustrate the ecosystem of an isolated request, and the logs provide a detail-rich snapshot into what happened within a service. These pillars have immense value and it is no surprise that the industry has invested heavily in building impressive dashboards and tooling around each. The downside is that we have so many dashboards. In one request hitting just ten services, there might be ten different analytics dashboards and ten different log stores. However, a request has its own unique trace identifier, which is a common thread tying all the pieces of this request together. The trace ID is typically generated at the first service that receives the request and then passed along from service to service as a header value. This makes the trace a great starting point to unify this data in a centralized location.

A trace is a set of segments representing each step of a single request throughout a system. Distributed tracing is the process of generating, transporting, storing, and retrieving traces in a distributed system. As a request flows between services, each distinct unit of work is documented as a span. A trace is made up of many spans, which are grouped together using a trace ID to form a single, end-to-end umbrella. A span:

  • Represents a unit of work, such as a network call from one service to another (a client/server relationship) or a purely internal action (e.g., starting and finishing a method).
  • Relates to other spans through a parent/child relationship.
  • Contains a set of key value pairs called tags, where service owners can attach helpful values such as urls, version numbers, regions, corresponding IDs, and errors. Tags can be associated with errors or warnings, which Edgar can display visually on a graph representation of the request.
  • Has a start time and an end time. Thanks to these timestamps, a user can quickly see how long the operation took.

The trace (along with its underlying spans) allows us to graphically represent the request chronologically.

Sample timeline view of a trace, based on Jaegar UI’s timeline view

Adding context to traces

With distributed tracing alone, Edgar is able to draw the path of a request as it flows through various systems. This centralized view is extremely helpful to determine which services were hit and when, but it lacks nuance. A tag might indicate there was an error but doesn’t fully answer the question of what happened. Adding logs to the picture can help a great deal. With logs, a user can see what the service itself had to say about what went wrong. If a data fetcher fails, the log can tell you what query it was running and what exact IDs or fields led to the failure. That alone might give an engineer the knowledge she needs to reproduce the issue. In Edgar, we parse the logs looking for error or warning values. We add these errors and warnings to our UI, highlighting them in our call graph and clearly associating them with a given service, to make it easy for users to view any errors we uncovered.

Example view of errors associated with a service, including an error parsed from a log

With the trace and additional context from logs illustrating the issue, one of the next questions may be how does this individual trace fit into the overall health and behavior of each service. Is this an anomaly or are we dealing with a pattern? To help answer this question, Edgar pulls in anomaly detection from a partner application, Telltale. Telltale provides Edgar with latency benchmarks that indicate if the individual trace’s latency is abnormal for this given service. A trace alone could tell you that a service took 500ms to respond, but it takes in-depth knowledge of a particular service’s typical behavior to make a determination if this response time is an outlier. Telltale’s anomaly analysis looks at historic behavior and can evaluate whether the latency experienced by this trace is anomalous. With this knowledge, Edgar can then visually warn that something happened in a service that caused its latency to fall outside of normal bounds.

Sample latency analysis

Edgar should reduce burden, not add to it

Presenting all of this data in one interface reduces the footwork of an engineer to uncover each source. However, discovery is only part of the path to resolution. With all the evidence presented and summarized by Edgar, an engineer may know what went wrong and where it went wrong. This is a huge step towards resolution, but not yet cause for celebration. The root cause may have been identified, but who owns the service in question? Many times, finding the right point of contact would require a jump into Slack or a company directory, which costs more time. In Edgar, we have integrated with our services to provide that information in-app alongside the details of a trace. For any service configured with an owner and support channel, Edgar provides a link to a service’s contact email and their Slack channel, smoothing the hand-off from one party to the next. If an engineer does need to pass an issue along to another team or person, Edgar’s request detail page contains all the context — the trace, logs, analysis — and is easily shareable, eliminating the need to write a detailed description or provide a cascade of links to communicate the issue.

Edgar’s request detail page

A key aspect of Edgar’s mission is to minimize the burden on both users and service owners. With all of its data sources, the sheer quantity of data could become overwhelming. It is essential for Edgar to maintain a prioritized interface, built to highlight errors and abnormalities to the user and assist users in taking the next step towards resolution. As our UI grows, it’s important to be discerning and judicious in how we handle new data sources, weaving them into our existing errors and warnings models to minimize disruption and to facilitate speedy understanding. We lean heavily on focus groups and user feedback to ensure a tight feedback loop so that Edgar can continue to meet our users’ needs as their services and use cases evolve.

As services evolve, they might change their log format or use new tags to indicate errors. We built an admin page to give our service owners that configurability and to decouple our product from in-depth service knowledge. Service owners can configure the essential details of their log stores, such as where their logs are located and what fields they use for trace IDs and span IDs. Knowing their trace and span IDs is what enables Edgar to correlate the traces and logs. Beyond that though, what are the idiosyncrasies of their logs? Some fields may be irrelevant or deprecated, and teams would like to hide them by default. Alternatively, some fields contain the most important information, and by promoting them in the Edgar UI, they are able to view these fields more quickly. This self-service configuration helps reduce the burden on service owners.

Initial log configuration in Edgar

Leveraging Edgar

In order for users to turn to Edgar in a situation when time is of the essence, users need to be able to trust Edgar. In particular, they need to be able to count on Edgar having data about their issue. Many approaches to distributed tracing involve setting a sample rate, such as 5%, and then only tracing that percentage of request traffic. Instead of sampling a fixed percentage, Edgar’s mission is to capture 100% of interesting requests. As a result, when an error happens, Edgar’s users can be confident they will be able to find it. That’s key to positioning Edgar as a reliable source. Edgar’s approach makes a commitment to have data about a given issue.

In addition to storing trace data for all requests, Edgar implemented a feature to collect additional details on-demand at a user’s discretion for a given criteria. With this fine-grained level of tracing turned on, Edgar captures request and response payloads as well as headers for requests matching the user’s criteria. This adds clarity to exactly what data is being passed from service to service through a request’s path. While this level of granularity is unsustainable for all request traffic, it is a robust tool in targeted use cases, especially for errors that prove challenging to reproduce.

As you can imagine, this comes with very real storage costs. While the Edgar team has done its best to manage these costs effectively and to optimize our storage, the cost is not insignificant. One way to strengthen our return on investment is by being a key tool throughout the software development lifecycle. Edgar is a crucial tool for operating and maintaining a production service, where reducing the time to recovery has direct customer impact. Engineers also rely on our tool throughout development and testing, and they use the Edgar request page to communicate issues across teams.

By providing our tool to multiple sets of users, we are able to leverage our cost more efficiently. Edgar has become not just a tool for engineers, but rather a tool for anyone who needs to troubleshoot a service at Netflix. In Edgar’s early days, as we strove to build valuable abstractions on top of trace data, the Edgar team first targeted streaming video use cases. We built a curated experience for streaming video, grouping requests into playback sessions, marked by starting and stopping playback for a given asset. We found this experience was powerful for customer service operations as well as engineering teams. Our team listened to customer service operations to understand which common issues caused an undue amount of support pain so that we could summarize these issues in our UI. This empowers customer service operations, as well as engineers, to quickly understand member issues with minimal digging. By logically grouping traces and summarizing the behavior at a higher level, trace data becomes extremely useful in answering questions like why a member didn’t receive 4k video for a certain title or why a member couldn’t watch certain content.

An example error viewing a playback session in Edgar

Extending Edgar for Studio

As the studio side of Netflix grew, we realized that our movie and show production support would benefit from a similar aggregation of user activity. Our movie and show production support might need to answer why someone from the production crew can’t log in or access their materials for a particular project. As we worked to serve this new user group, we sought to understand what issues our production support needed to answer most frequently and then tied together various data sources to answer those questions in Edgar.

The Edgar team built out an experience to meet this need, building another abstraction with trace data; this time, the focus was on troubleshooting production-related use cases and applications, rather than a streaming video session. Edgar provides our production support the ability to search for a given contractor, vendor, or member of production staff by their name or email. After finding the individual, Edgar reaches into numerous log stores for their user ID, and then pulls together their login history, role access change log, and recent traces emitted from production-related applications. Edgar scans through this data for errors and warnings and then presents those errors right at the front. Perhaps a vendor tried to login with the wrong password too many times, or they were assigned an incorrect role on a production. In this new domain, Edgar is solving the same multi-dashboarded problem by tying together information and pointing its users to the next step of resolution.

An example error for a production-related user

What Edgar is and is not

Edgar’s goal is not to be the be-all, end-all of tools or to be the One Tool to Rule Them All. Rather, our goal is to act as a concierge of troubleshooting — Edgar should quickly be able to guide users to an understanding of an issue, as well usher them to the next location, where they can remedy the problem. Let’s say a production vendor is unable to access materials for their production due to an incorrect role/permissions assignment, and this production vendor reaches out to support for assistance troubleshooting. When a support user searches for this vendor, Edgar should be able to indicate that this vendor recently had a role change and summarize what this role change is. Instead of being assigned to Dead To Me Season 2, they were assigned to Season 1! In this case, Edgar’s goal is to help a support user come to this conclusion and direct them quickly to the role management tool where this can be rectified, not to own the full circle of resolution.

Usage at Netflix

While Edgar was created around Netflix’s core streaming video use-case, it has since evolved to cover a wide array of applications. While Netflix streaming video is used by millions of members, some applications using Edgar may measure their volume in requests per minute, rather than requests per second, and may only have tens or hundreds of users rather than millions. While we started with a curated approach to solve a pain point for engineers and support working on streaming video, we found that this pain point is scale agnostic. Getting to the bottom of a problem is costly for all engineers, whether they are building a budget forecasting application used heavily by 30 people or a SVOD application used by millions.

Today, many applications and services at Netflix, covering a wide array of type and scale, publish trace data that is accessible in Edgar, and teams ranging from service owners to customer service operations rely on Edgar’s insights. From streaming to studio, Edgar leverages its wealth of knowledge to speed up troubleshooting across applications with the same fundamental approach of summarizing request tracing, logs, analysis, and metadata.

As you settle into your couch to watch a new episode of Unsolved Mysteries, you may still find yourself with more questions than answers. Why did the victim leave his house so abruptly? How did the suspect disappear into thin air? Hang on, how many people saw that UFO?? Unfortunately, Edgar can’t help you there (trust me, we’re disappointed too). But, if your relaxing evening is interrupted by a production outage, Edgar will be behind the scenes, helping Netflix engineers solve the mystery at hand.

Keeping services up and running allows Netflix to share stories with our members around the globe. Underneath every outage and failure, there is a story to tell, and powerful observability tooling is needed to tell it. If you are passionate about observability then come talk to us.

Edgar: Solving Mysteries Faster with Observability was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Optimized shot-based encodes for 4K: Now streaming!

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/optimized-shot-based-encodes-for-4k-now-streaming-47b516b10bbb

by Aditya Mavlankar, Liwei Guo, Anush Moorthy and Anne Aaron

Netflix has an ever-expanding collection of titles which customers can enjoy in 4K resolution with a suitable device and subscription plan. Netflix creates premium bitstreams for those titles in addition to the catalog-wide 8-bit stream profiles¹. Premium features comprise a title-dependent combination of 10-bit bit-depth, 4K resolution, high frame rate (HFR) and high dynamic range (HDR) and pave the way for an extraordinary viewing experience.

The premium bitstreams, launched several years ago, were rolled out with a fixed-bitrate ladder, with fixed 4K resolution bitrates — 8, 10, 12 and 16 Mbps — regardless of content characteristics. Since then, we’ve developed algorithms such as per-title encode optimizations and per-shot dynamic optimization, but these innovations were not back-ported on these premium bitstreams. Moreover, the encoding group of pictures (GoP) duration (or keyframe period) was constant throughout the stream causing additional inefficiency due to shot boundaries not aligning with GoP boundaries.

As the number of 4K titles in our catalog continues to grow and more devices support the premium features, we expect these video streams to have an increasing impact on our members and the network. We’ve worked hard over the last year to leapfrog to our most advanced encoding innovations — shot-optimized encoding and 4K VMAF model — and applied those to the premium bitstreams. More specifically, we’ve improved the traditional 4K and 10-bit ladder by employing

In this blog post, we present benefits of applying the above-mentioned optimizations to standard dynamic range (SDR) 10-bit and 4K streams (some titles are also HFR). As for HDR, our team is currently developing an HDR extension to VMAF, Netflix’s video quality metric, which will then be used to optimize the HDR streams.

¹ The 8-bit stream profiles go up to 1080p resolution.

Bitrate versus quality comparison

For a sample of titles from the 4K collection, the following plots show the rate-quality comparison of the fixed-bitrate ladder and the optimized ladder. The plots have been arranged in decreasing order of the new highest bitrate — which is now content adaptive and commensurate with the overall complexity of the respective title.

Fig. 1: Example of a thriller-drama episode showing new highest bitrate of 11.8 Mbps
Fig. 2: Example of a sitcom episode with some action showing new highest bitrate of 8.5 Mbps
Fig. 3: Example of a sitcom episode with less action showing new highest bitrate of 6.6 Mbps
Fig. 4: Example of a 4K animation episode showing new highest bitrate of 1.8 Mbps

The bitrate as well as quality shown for any point is the average for the corresponding stream, computed over the duration of the title. The annotation next to the point is the corresponding encoding resolution; it should be noted that video received by the client device is decoded and scaled to the device’s display resolution. As for VMAF score computation, for encoding resolutions less than 4K, we follow the VMAF best practice to upscale to 4K assuming bicubic upsampling. Aside from the encoding resolution, each point is also associated with an appropriate pixel aspect ratio (PAR) to achieve a target 16:9 display aspect ratio (DAR). For example, the 640×480 encoding resolution is paired with a 4:3 PAR to achieve 16:9 DAR, consistent with the DAR for other points on the ladder.

The last example, showing the new highest bitrate to be 1.8 Mbps, is for a 4K animation title episode which can be very efficiently encoded. It serves as an extreme example of content adaptive ladder optimization — it however should not to be interpreted as all animation titles landing on similar low bitrates.

The resolutions and bitrates for the fixed-bitrate ladder are pre-determined; minor deviation in the achieved bitrate is due to rate control in the encoder implementation not hitting the target bitrate precisely. On the other hand, each point on the optimized ladder is associated with optimal bit allocation across all shots with the goal of maximizing a video quality objective function while resulting in the corresponding average bitrate. Consequently, for the optimized encodes, the bitrate varies shot to shot depending on relative complexity and overall bit budget and in theory can reach the respective codec level maximum. Various points are constrained to different codec levels, so receivers with different decoder level capabilities can stream the corresponding subset of points up to the corresponding level.

The fixed-bitrate ladder often appears like steps — since it is not title adaptive it switches “late” to most encoding resolutions and as a result the quality stays flat within that resolution even with increasing bitrate. For example, two 1080p points with identical VMAF score or four 4K points with identical VMAF score, resulting in wasted bits and increased storage footprint.

On the other hand, the optimized ladder appears closer to a monotonically increasing curve — increasing bitrate results in an increasing VMAF score. As a side note, we do have some additional points, not shown in the plots, that are used in resolution limited scenarios — such as a streaming session limited to 720p or 1080p highest encoding resolution. Such points lie under (or to the right of) the convex hull main ladder curve but allow quality to ramp up in resolution limited scenarios.

Challenging-to-encode content

For the optimized ladders we have logic to detect quality saturation at the high end, meaning an increase in bitrate not resulting in material improvement in quality. Once such a bitrate is reached it is a good candidate for the topmost rung of the ladder. An additional limit can be imposed as a safeguard to avoid excessively high bitrates.

Sometimes we ingest a title that would need more bits at the highest end of the quality spectrum — even higher than the 16 Mbps limit of the fixed-bitrate ladder. For example,

  • a rock concert with fast-changing lighting effects and other details or
  • a wildlife documentary with fast action and/or challenging spatial details.

This scenario is generally rare. Nevertheless, below plot highlights such a case where the optimized ladder exceeds the fixed-bitrate ladder in terms of the highest bitrate, thereby achieving an improvement in the highest quality.

As expected, the quality is higher for the same bitrate, even when compared in the low or medium bitrate regions.

Fig. 5: Example of a movie with action and great amount of rich spatial details showing new highest bitrate of 17.2 Mbps

Visual examples

As an example, we compare the 1.75 Mbps encode from the fixed-bitrate ladder with the 1.45 Mbps encode from the optimized ladder for one of the titles from our 4K collection. Since 4K resolution entails a rather large number of pixels, we show 1024×512 pixel cutouts from the two encodes. The encodes are decoded and scaled to a 4K canvas prior to extracting the cutouts. We toggle between the cutouts so it is convenient to spot differences. We also show the corresponding full frame which helps to get a sense of how the cutout fits in the corresponding video frame.

Fig. 6: Pristine full frame — the purpose is to give a sense of how below cutouts fit in the frame
Fig. 7: Toggling between 1024×512 pixel cutouts from two encodes as annotated. Corresponding to pristine frame shown in Figure 6.
Fig. 8: Pristine full frame — the purpose is to give a sense of how below cutouts fit in the frame
Fig. 9: Toggling between 1024×512 pixel cutouts from two encodes as annotated. Corresponding to pristine frame shown in Figure 8.
Fig. 10: Pristine full frame — the purpose is to give a sense of how below cutouts fit in the frame
Fig. 11: Toggling between 1024×512 pixel cutouts from two encodes as annotated. Corresponding to pristine frame shown in Figure 10.
Fig. 12: Pristine full frame — the purpose is to give a sense of how below cutouts fit in the frame
Fig. 13: Toggling between 1024×512 pixel cutouts from two encodes as annotated. Corresponding to pristine frame shown in Figure 12.
Fig. 14: Pristine full frame — the purpose is to give a sense of how below cutouts fit in the frame
Fig. 15: Toggling between 1024×512 pixel cutouts from two encodes as annotated. Corresponding to pristine frame shown in Figure 14.

As can be seen, the encode from the optimized ladder delivers crisper textures and higher detail for less bits. At 1.45 Mbps it is by no means a perfect 4K rendition, but still very commendable for that bitrate. There exist higher bitrate points on the optimized ladder that deliver impeccable 4K quality, also for less bits compared to the fixed-bitrate ladder.

Compression and bitrate ladder improvements

Even before testing the new streams in the field, we observe the following advantages of the optimized ladders vs the fixed ladders, evaluated over 100 sample titles:

  • Computing the Bjøntegaard Delta (BD) rate shows 50% gains on average over the fixed-bitrate ladder. Meaning, on average we need 50% less bitrate to achieve the same quality with the optimized ladder.
  • The highest 4K bitrate on average is 8 Mbps which is also a 50% reduction compared to 16 Mbps of the fixed-bitrate ladder.
  • As mobile devices continue to improve, they adopt premium features (other than 4K resolution) like 10-bit and HFR. These video encodes can be delivered to mobile devices as well. The fixed-bitrate ladder starts at 560 kbps which may be too high for some cellular networks. The optimized ladder, on the other hand, has lower bitrate points that are viable in most cellular scenarios.
  • The optimized ladder entails a smaller storage footprint compared to the fixed-bitrate ladder.
  • The new ladder considers adding 1440p resolution (aka QHD) points if they lie on the convex hull of rate-quality tradeoff and most titles seem to get the 1440p treatment. As a result, when averaged over 100 titles, the bitrate required to jump to a resolution higher than 1080p (meaning either QHD or 4K) is 1.7 Mbps compared to 8 Mbps of the fixed-bitrate ladder. When averaged over 100 titles, the bitrate required to jump to 4K resolution is 3.2 Mbps compared to 8 Mbps of the fixed-bitrate ladder.

Benefits to members

At Netflix we perform A/B testing of encoding optimizations to detect any playback issues on client devices as well as gauge the benefits experienced by our members. One set of streaming sessions receives the default encodes and the other set of streaming sessions receives the new encodes. This in turn allows us to compare error rates as well as various metrics related to quality of experience (QoE). Although our streams are standard compliant, the A/B testing can and does sometimes find device-side implementations with minor gaps; in such cases we work with our device partners to find the best remedy.

Overall, while A/B testing these new encodes, we have seen the following benefits, which are in line with the offline evaluation covered in the previous section:

  • For members with high-bandwidth connections we deliver the same great quality at half the bitrate on average.
  • For members with constrained bandwidth we deliver higher quality at the same (or even lower) bitrate — higher VMAF at the same encoding resolution and bitrate or even higher resolutions than they could stream before. For example, members who were limited by their network to 720p can now be served 1080p or higher resolution instead.
  • Most streaming sessions start with a higher initial quality.
  • The number of rebuffers per hour go down by over 65%; members also experience fewer quality drops while streaming.
  • The reduced bitrate together with some Digital Rights Management (DRM) system improvements (not covered in this blog) result in reducing the initial play delay by about 10%.

Next steps

We have started re-encoding the 4K titles in our catalog to generate the optimized streams and we expect to complete in a couple of months. We continue to work on applying similar optimizations to our HDR streams.


We thank Lishan Zhu for help rendered during A/B testing.

This is a collective effort on the part of our larger team, known as Encoding Technologies, and various other teams that we have crucial partnerships with, such as:

If you are passionate about video compression research and would like to contribute to this field, we have an open position.

Optimized shot-based encodes for 4K: Now streaming! was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Telltale: Netflix Application Monitoring Simplified

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/telltale-netflix-application-monitoring-simplified-5c08bfa780ba

By Andrei U., Seth Katz, Janak Ramachandran, Jeff Butsch, Peter Lau, Ram Vaithilingam, and Greg Burrell

Our Telltale Vision

An alert fires and you get paged in the middle of the night. A metric crossed a threshold. You’re half awake and wondering, “Is there really a problem or is this just an alert that needs tuning? When was the last time somebody adjusted our alert thresholds? Maybe it’s due to an upstream or downstream service?” This is a critical application so you drag yourself out of bed, open your laptop, and start poring through dashboards for more info. You’re not yet convinced there’s a real problem but you’re also aware that the clock is ticking as you dig through a mountain of data looking for clues.

Healthy Netflix services are essential to member joy. When you sit down to watch “Tiger King” you expect it to just play. Over the years we’ve learned from on-call engineers about the pain points of application monitoring: too many alerts, too many dashboards to scroll through, and too much configuration and maintenance. Our streaming teams need a monitoring system that enables them to quickly diagnose and remediate problems; seconds count! Our Node team needs a system that empowers a small group to operate a large fleet.

So we built Telltale.

The Telltale UI shows health over time and across multiple services.
The Telltale timeline.

Telltale combines a variety of data sources to create a holistic view of an application’s health. Telltale learns what constitutes typical health for an application, no alert tuning required. And because we know what’s healthy, we can let application owners know when their services are trending towards unhealthy.

Metrics are a key part of understanding application health. But sometimes you can have too many metrics, too many graphs, and too many dashboards. Telltale shows only the relevant data from the application plus that of upstream and downstream services. We use colors to indicate severity (users can opt to have Telltale display numbers in addition to colors) so users can tell, at a glance, the state of their application’s health. We also highlight interesting broader events such as regional traffic evacuations and nearby deployments, information that is vital to understanding health holistically. Especially during an incident.

That is our Telltale vision. It exists today and monitors the health of over 100 Netflix production-facing applications.

A call graph with four layers of services.
An application lives in an ecosystem

The Application Health Model

A microservice doesn’t live in isolation. It usually has dependencies, talks to other services, and lives in different AWS regions. The call graph above is a relatively simple one, they can be much deeper with dozens of services involved. An application is part of an ecosystem that can be subtly influenced by property changes or radically altered by region-wide events. The launch of a canary can affect an application. As can an upstream or downstream deployments.

Telltale uses a variety of signals from multiple sources to assemble a constantly evolving model of the application’s health:

Different signals have different levels of importance to an application’s health. For example, a latency increase is less critical than error rate increase and some error codes are less critical than others. A canary launch two layers downstream might not be as significant as a deployment immediately upstream. A regional traffic shift means one region ends up with zero traffic while another region has double. You can imagine the impact that has on metrics. A metric’s meaning determines how we should interpret it.

Telltale takes all those factors into consideration when constructing its view of application health.

The application health model is the heart of Telltale.

Intelligent Monitoring

Every service operator knows the difficulty of alert tuning. Set thresholds too low and you get a deluge of spurious alerts. So you overcompensate and relax the tuning to the point of missing important health warnings. The end result is a lack of trust in alerts. Telltale is built on the premise that you shouldn’t have to constantly tune configuration.

We make setup and configuration easy for application owners by providing curated and managed signal packs. These packs are combined into application profiles to address most common service types. Telltale automatically tracks dependencies between services to build the topology used in the application health model. Signal packs and topology detection keep configuration up-to-date with minimal effort. Those who want a more hands-on approach can still do manual configuration and tuning.

No single algorithm can account for the wide variety of signals we use. So, instead, we employ a mix of algorithms including statistical, rule based, and machine learning. We’ll do a future Netflix Tech Blog article focused on our algorithms. Telltale also has analyzers to detect long-term trends or memory leaks. Intelligent monitoring means results our users can trust. It means a faster time to detection and a faster time to resolution during an incident.

Intelligent Alerting

Intelligent monitoring yields intelligent alerting. Telltale creates an issue when it detects a health problem in your application’s ecosystem. Teams can opt in to alerting via Slack, email, or PagerDuty (all powered by our internal alerting system). If the issue is caused by an upstream or downstream system then Telltale’s context-aware routing alerts that team instead. Intelligent alerting also means a team receives a single notification, alert storms are a thing of the past.

An example of a Telltale alert notification in Slack.
An example of a Telltale notification in Slack.

When a problem strikes, it’s essential to have the right information. Our Slack alerts also start a thread containing only the most relevant context about the incident. This includes the signals that Telltale identified as unhealthy and the reasons why. The right context provides a better understanding of the application’s current state so the on-call engineer can return it to health.

Incidents evolve and have their own lifecycle, so updates are essential. Are things getting better or worse? Are there new signals or events to consider? Telltale updates the Slack thread as the current incident unfolds. The thread is marked Resolved upon return to healthy state so users know, at a glance, which incidents are ongoing and which have been successfully remediated.

But these Slack threads aren’t just for Telltale. Teams use them to share additional data, observations, theories, and discussion about the incident. Incident data and discussion all in one thread makes for shared understanding, faster resolution, and easier post-incident analysis.

We strive to improve the quality of Telltale alerts. One way to do that is to learn from our users. So we provide feedback buttons right in the Slack message. Users can tell us to suppress future occurrences of an alert. Or provide a reason for why an alert isn’t actionable. Intelligent alerting means alerts our users can trust.

An example of the details found in a Telltale notification in Slack. Which metrics and which triggers fired.
An example of the details found in a Telltale notification in Slack.

Why Is My Service Unhealthy?

A wide variety of signals, knowledge of the application’s ecosystem, and correlation of signals across multiple services helps Telltale to detect the possible causes of an application’s degraded health. Causes such as an outlier instance, a canary or deployment by a dependent service, an unhealthy database, or just a spike in traffic. Highlighting possible causes saves valuable time during an incident.

Incident Management

An incident summary gathers relevant information for the team.
An example of a Telltale incident summary.

When Telltale sends an alert it also creates a snapshot that has references to the unhealthy signals. As new information arrives, it’s added to this snapshot. This simplifies the post-incident review process for many teams. When it’s time to review past issues, the Application Incident Summary feature shows all aspects of recent issues in a single place including key metrics like total downtime and MTTR (Mean Time To Resolution). We want to help our teams see larger patterns of incidents so they can improve overall service availability.

The cluster view shows groupings of similar incidents so teams can understand the larger patterns.
The cluster view groups similar incidents.

Deployment Monitoring

Telltale’s application health model and intelligent monitoring have proven so powerful that we’re also using it for safer deployments. We start with Spinnaker, our open source delivery platform. As Spinnaker slowly rolls out a new build we use Telltale to continuously monitor the health of the instances running the new build. Continuous monitoring means a deployment stops and rolls back at the first sign of a problem. It means deployment problems have smaller blast radius and a shorter duration.

Continuous Improvement

Operating microservices in a complex ecosystem is challenging. We’re thrilled that Telltale’s intelligent monitoring and alerting helps our service operators improve availability, reduce toil, and sleep better at night. But we’re not done. We’re constantly exploring new algorithms to improve the accuracy of our alerts. We’ll write more about that in a future Netflix Tech Blog post. We’re also evaluating improvements to our application health model. We believe there’s useful information in service log and trace data. And benefits to employing higher resolution metrics. We’re looking forward to collaborating with our platform team on building out those new features. Getting new applications onto Telltale has been a white-glove treatment which doesn’t scale well, we can definitely improve our self-service UI. And we know there’s better heuristics to help pinpoint what’s affecting your service health.

Telltale is application monitoring simplified.

A healthy Netflix service enables us to entertain the world. Correlating disparate signals to model health in realtime is challenging. Add in thousands of streaming device types, an ever-evolving architecture, and a growing content production ecosystem and the problem becomes fascinating. If you’re passionate about observability then come talk to us.

Telltale: Netflix Application Monitoring Simplified was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Improving our video encodes for legacy devices

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/improving-our-video-encodes-for-legacy-devices-2b6b56eec5c9

by Mariana Afonso, Anush Moorthy, Liwei Guo, Lishan Zhu, Anne Aaron

Netflix has been one of the pioneers of streaming video-on-demand content — we announced our intention to stream video over 13 years ago, in January 2007 — and have only increased both our device and content reach since then. Given the global nature of the service and Netflix’s commitment to creating a service that members enjoy, it is not surprising that we support a wide variety of streaming devices, from set-top-boxes and mobile devices to smart TVs. Hence, as the encoding team, we continuously maintain a variety of encode families, stretching back to H.263. In addition, with 193M members and counting, there is a huge diversity in the networks that stream our content as well as in our members’ bandwidth. It is, thus, imperative that we are sensible in the use of the network and of the bandwidth we require.

Together with our partner teams, our endeavor has always been to produce the best bang for the bit, and to that end, we have aggressively moved towards adopting newer codecs — AV1 being a recent example. These efforts allow our members to have the best viewing experience whenever they watch their favorite show or movie. However, not all members have access to the latest and greatest decoders. In fact, many stream Netflix through devices which cannot be upgraded to use the latest decoders owing to memory limitations, device upgrade cycles, etc., and thus fall back to less efficient encode families. One such encode family that has wide decoder support amongst legacy devices is our H.264/AVC Main profile family.

A few years ago, we improved on the H.264/AVC Main profile streams by employing per-title optimizations. Since then, we have applied innovations such as shot-based encoding and newer codecs to deploy more efficient encode families. Yet, given its wide support, our H.264/AVC Main profile family still represents a substantial portion of the members viewing hours and an even larger portion of the traffic. Continuing to innovate on this family has tremendous advantages across the whole delivery infrastructure: reducing footprint at our Content Delivery Network (CDN), Open Connect (OC), the load on our partner ISPs’ networks and the bandwidth usage for our members. In this blog post, we introduce recently implemented changes to our per-title encodes that are expected to lower the bitrate streamed by over 20%, on average, while maintaining a similar level of perceived quality. These changes will be reflected in our product within the next couple of months.

What we have improved on

Keeping in mind our goal to maintain ubiquitous device support, we leveraged what we learned from innovations implemented during the development of newer encode families and have made a number of improvements to our H.264/AVC Main profile per-title encodes. These are summarized below:

  • Instead of relying on other objective metrics, such as PSNR†, VMAF is employed to guide optimization decisions. Given that VMAF is highly correlated with visual quality, this leads to decisions that favor encodes with higher perceived quality.
  • Allowing per-chunk bitrate variations instead of using a fixed per-title bitrate, as in our original complexity-based encoding scheme. This multi-pass strategy, previously employed for our mobile encodes, allows us to avoid over-allocating bits to less complex content, as compared to using a complexity-defined, albeit fixed, bitrate for the entire title. This encoding approach improves the overall bit allocation while keeping a similar average visual quality and requires little added computational complexity.
  • Improving the bitrate ladder that is generated after complexity analysis to choose points with greater intelligence than before.
  • Further tuning of pre-defined encoding parameters.

† which we originally used as a quality measure, before we developed VMAF.

Performance results

In this section, we present an overview of the performance of our new encodes compared to our existing H.264 AVC Main per-title encodes in terms of bitrate reduction, average compression efficiency improvement using Bjontegaard-delta rate (BD-rate) and other relevant metrics. These figures were estimated on 200 full-length titles from our catalog and have been validated through extensive A/B testing. They are representative of the savings we expect our CDN, ISP partners, and members to see once the encodes are live.

It is important to highlight that the expected >20% reduction in average session bitrate for these encodes corresponds to a significant reduction in the overall Netflix traffic as well. These changes also lead to an improvement in Quality-of-Experience (QoE) metrics that affect the end user experience, such as play delays (i.e. how long it takes for the video to start playing), rebuffer rates, etc., as a result of the reduction in average bitrates. In addition, footprint savings will allow more content to be stored in edge caches, thus contributing to an improved experience for our members.


At Netflix, we strive to continuously improve the quality and reliability of our service. Our team is always looking to innovate and to find ways to improve our members’ experiences through more efficient encodes. In this tech blog, we summarized how we made improvements towards optimizing our video encodes for legacy devices with limited decoder support. These changes will result in a number of benefits for our members while maintaining perceived quality. If your preferred device is streaming one of these profiles, you’ll experience the new encodes soon — so, sit back, grab the remote, and stream away, we’ve got your back!

If you are passionate about research and would like to contribute to this field, we have an open position in our team!

Improving our video encodes for legacy devices was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

Hyper Scale VPC Flow Logs enrichment to provide Network Insight

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/hyper-scale-vpc-flow-logs-enrichment-to-provide-network-insight-e5f1db02910d

How Netflix is able to enrich VPC Flow Logs at Hyper Scale to provide Network Insight

By Hariharan Ananthakrishnan and Angela Ho

The Cloud Network Infrastructure that Netflix utilizes today is a large distributed ecosystem that consists of specialized functional tiers and services such as DirectConnect, VPC Peering, Transit Gateways, NAT Gateways, etc. While we strive to keep the ecosystem simple, the inherent nature of leveraging a variety of technologies will lead us to complications and challenges such as:

  • App Dependencies and Data Flow Mappings: Without understanding and having visibility into an application’s dependencies and data flows, it is difficult for both service owners and centralized teams to identify systemic issues.
  • Pathway Validation: Netflix velocity of change within the production streaming environment can result in the inability of services to communicate with other resources.
  • Service Segmentation: The ease of the cloud deployments has led to the organic growth of multiple AWS accounts, deployment practices, interconnection practices, etc. Without having network visibility, it’s not possible to improve our reliability, security and capacity posture.
  • Network Availability: The expected continued growth of our ecosystem makes it difficult to understand our network bottlenecks and potential limits we may be reaching.

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the Cloud Network Infrastructure to address the identified problems. By collecting, accessing and analyzing network data from a variety of sources like VPC Flow Logs, ELB Access Logs, Custom Exporter Agents, etc, we can provide Network Insight to users through multiple data visualization techniques like Lumen, Atlas, etc.

VPC Flow Logs

VPC Flow Logs is an AWS feature that captures information about the IP traffic going to and from network interfaces in a VPC. At Netflix we publish the Flow Log data to Amazon S3. Flow Logs are enabled tactically on either a VPC or subnet or network interface. A flow log record represents a network flow in the VPC. By default, each record captures a network internet protocol (IP) traffic flow (characterized by a 5-tuple on a per network interface basis) that occurs within an aggregation interval.

version vpc-id subnet-id instance-id interface-id account-id type srcaddr dstaddr srcport dstport pkt-srcaddr pkt-dstaddr protocol bytes packets start end action tcp-flags log-status
3 vpc-12345678 subnet-012345678 i-07890123456 eni-23456789 123456789010 IPv4 43416 5001 6 568 8 1566848875 1566848933 ACCEPT 2 OK

The IP addresses within the Cloud can move from one EC2 instance or Titus container to another over time. To understand the attributes of each IP back to an application metadata Netflix uses Sonar. Sonar is an IPv4 and IPv6 address identity tracking service. VPC Flow Logs are enriched using IP Metadata from Sonar as it is ingested.

With a large ecosystem at Netflix, we receive hundreds of thousands of VPC Flow Log files in S3 each hour. And in order to gain visibility into these logs, we need to somehow ingest and enrich this data.

So how do we ingest all these s3 files?

At Netflix, we have the option to use Spark as our distributed computing platform. It is easier to tune a large Spark job for a consistent volume of data. As you may know, S3 can emit messages when events (such as a file creation events) occur which can be directed into an AWS SQS queue. In addition to the s3 object path, these events also conveniently include file size which allows us to intelligently decide how many messages to grab from the SQS queue and when to stop. What we get is a group of messages representing a set of s3 files which we humorously call “Mouthfuls”. In other words, we are able to ensure that our Spark app does not “eat” more data than it was tuned to handle.

We named this library Sqooby. It works well for other pipelines that have thousands of files landing in s3 per day. But how does it hold up to the likes of Netflix VPC Flow Logs that has volumes which are orders of magnitude greater? It didn’t. The primary limitation was that AWS SQS queues have a limit of 120 thousand in-flight messages. We found ourselves needing to hold more than 120 thousand messages in flight at a time in order to keep up with the volumes of files.


There are multiple ways you can solve this problem and many technologies to choose from. As with any sustainable engineering design, focusing on simplicity is very important. This means using existing infrastructure and established patterns within the Netflix ecosystem as much as possible and minimizing the introduction of new technologies.

Equally important is the resilience, recoverability, and supportability of the solution. A malformed file should not hold up or back up the pipeline (resilience). If unexpected environmental factors cause the pipeline to get backed up, it should be able to recover by itself. And excellent logging is needed for debugging purposes and supportability. These characteristics allow for an on-call response time that is relaxed and more in line with traditional big data analytical pipelines.

Hyper Scale

At Netflix, our culture gives us the freedom to decide how we solve problems as well as the responsibility of maintaining our solutions so that we may choose wisely. So how did we solve this scale problem that meets all of the above requirements? By applying existing established patterns in our ecosystem on top of Sqooby. In this case, it’s a pattern which generates events (directed into another AWS SQS queue) whenever data lands in a table in a datastore. These events represent a specific cut of data from the table.

We applied this pattern to the Sqooby log tables which contained information about s3 files for each Mouthful. What we got were events that represented Mouthfuls. Spark could look up and retrieve the data in the s3 files that the Mouthful represented. This intermediate step of persisting Mouthfuls allowed us to easily “eat” through S3 event SQS messages at great speed, converting them to far fewer Mouthful SQS Messages which would each be consumed by a single Spark app instance. Because we ensured that our ingestion pipeline could concurrently write/append to the final VPC Flow Log table, this meant that we could scale out the number of Spark app instances we spin up.

Tuning for Hyper Scale

On this journey of ingesting VPC flow logs, we found ourselves tweaking configurations in order to tune throughput of the pipeline. We modified the size of each Mouthful and tuned the number of Spark executors per Spark app while being mindful of cluster capacity. We also adjusted the frequency in which Spark app instances are spun up such that any backlog would burn off during a trough in traffic.


Providing Network Insight into the Cloud Network Infrastructure using VPC Flow Logs at hyper scale is made possible with the Sqooby architecture. After several iterations of this architecture and some tuning, Sqooby has proven to be able to scale.

We are currently ingesting and enriching hundreds of thousands of VPC Flow Logs S3 files per hour and providing visibility into our cloud ecosystem. The enriched data allows us to analyze networks across a variety of dimensions (e.g. availability, performance, and security), to ensure applications can effectively deliver their data payload across a globally dispersed cloud-based ecosystem.

Special Thanks To

Bryan Keller, Ryan Blue

Hyper Scale VPC Flow Logs enrichment to provide Network Insight was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

How Netflix brings safer and faster streaming experience to the living room on crowded networks…

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/how-netflix-brings-safer-and-faster-streaming-experience-to-the-living-room-on-crowded-networks-78b8de7f758c

How Netflix brings safer and faster streaming experience to the living room on crowded networks using TLS 1.3

By Sekwon Choi

At Netflix, we are obsessed with the best streaming experiences. We want playback to start instantly and to never stop unexpectedly in any network environment. We are also committed to protecting users’ privacy and service security without sacrificing any part of the playback experience.

To achieve that, we are efficiently using ABR (adaptive bitrate streaming) for a better playback experience, DRM (Digital Right Management) to protect our service and TLS (Transport Layer Security) to protect customer privacy and to create a safer streaming experience.

Netflix on consumer electronics devices such as TVs, set-top boxes and streaming sticks was until recently using TLS 1.2 for streaming traffic. Now we support TLS 1.3 for safer and faster experiences.

What is TLS?

For two parties to communicate securely, a secure channel is necessary. This needs to have the following three properties.

  • Authentication: Identity of the communicating party is verified.
  • Confidentiality: Data sent over the channel is only visible to the endpoints.
  • Integrity: Data sent over the channel cannot be modified by attackers without detection.

The TLS protocol is designed to provide a secure channel between two peers by providing tools and methods to achieve the above properties.

TLS 1.3

TLS 1.3 is the latest version of the Transport Layer Security protocol. It is simpler, more secure and more efficient than its predecessor.

Perfect Forward Secrecy

One thing we believe is very important at Netflix is providing PFS (Perfect Forward Secrecy).

PFS is a feature of the key exchange algorithm that assures that session keys will not be compromised, even if the server’s private key is compromised. By generating new keys for each session, PFS protects past sessions against the future compromise of secret keys.

TLS 1.2 supports key exchange algorithms with PFS, but it also allows key exchange algorithms that do not support PFS. Even with the previous version of TLS 1.2, Netflix has always selected a key exchange algorithm that provides PFS such as ECDHE (Elliptic Curve Diffie Hellman Ephemeral). TLS 1.3, however, enforces this concept even more by removing all the key exchange algorithms that do not provide PFS, such as static RSA.

Authenticated Encryption

For encryption, TLS 1.3 removes all weak ciphers and uses only Authenticated Encryption with Associated Data (AEAD). This assures the confidentiality, integrity, and authenticity of the data. We use AES Galois/Counter Mode, as it also provides good performance and high throughput.

Secure Handshake

While the above changes are important, the most important change in TLS 1.3 is perhaps its redesign of the handshake protocol.

The TLS 1.2 handshake was not designed to protect the integrity of the entire handshake. It protected only the part of the handshake after the cipher suite negotiation and this opened up the possibility of downgrade attacks which may allow the attackers to force the use of insecure cipher suites.

With TLS 1.3, the server signs the entire handshake including the cipher suite negotiation and thus prevents the attacker from downgrading the cipher suite.

Also in TLS 1.2, extensions were sent in the clear in the ServerHello. Now with TLS 1.3, even extensions are encrypted and all handshake messages after ServerHello are now encrypted.

Reduced Handshake

TLS 1.2 supports numerous key exchange algorithms, cipher suites and digital signatures, including weak and vulnerable ones. Therefore, it requires more messages to perform a handshake and two network round trips.

In contrast, the handshake in TLS 1.3 now requires only one round trip, with a simplified design and with all weak and vulnerable algorithms removed.

In addition, it has a new feature called 0-RTT, or TLS early data, for the resumed handshake. This allows an application to include application data with its initial handshake message, instead of having to wait until the handshake completes.

At Netflix, by the efficient resumption of the TLS session and careful use of 0-RTT for the streaming data, we can reduce the play delay.

A/B Testing Result

We were pretty confident that TLS 1.3 would bring us better security from the analysis of its protocol composition, but we did not know how it would perform in the context of streaming.

Since TLS 1.3’s performance-related feature is the 0-RTT mode with the resumed handshake, our hypothesis is that TLS 1.3 would reduce play delay, as we are no longer required to wait for the handshake to finish and we can instead issue the HTTP request for media data and receive the HTTP response for media data earlier.

To see the actual performance of TLS 1.3 in the field, we performed an experiment with

  • User accounts: half-million user accounts per cell.
  • Device type: mid-performance device with Quad ARM core @ 1.7GHz.
  • Control cell: TLS 1.2
  • Treatment cell: TLS 1.3

Play Delay

Play Delay is defined by how long it takes for playback to start. Below are the results of the play delay measured in the experiment. The results imply that on slower or congested networks, which can be represented by the quantiles of at least 0.75, TLS 1.3 achieves the largest gains, with improvements across all network conditions.

Below is the time series median play delay graph for this mid-performance device in the field. It also shows that playback starts earlier with TLS 1.3.

Media Rebuffer

At Netflix, we define a media rebuffer as a non-network originated rebuffer. It typically occurs when media data is not processed quickly enough by the device due to the high load on the CPU. Comparing the control cell with TLS 1.2, the experiment cell with TLS 1.3 showed about a 7.4% improvement in media rebuffers. This result implies that using TLS 1.3 with 0-RTT is more efficient and can reduce the CPU load.


From the security analysis, we are confident that TLS 1.3 improves communication security over TLS 1.2. From the field test, we are confident that TLS 1.3 provides us a better streaming experience.

At the time of writing this article, the Internet is experiencing higher than usual traffic and congestion. We believe saving even small amounts of data and round trips can be meaningful and even better if it also provides a more secure and efficient streaming experience.

Therefore, we have started deploying TLS 1.3 on newer consumer electronics devices and we are expecting even more devices to be deployed with TLS 1.3 capability in the near future.

How Netflix brings safer and faster streaming experience to the living room on crowded networks… was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

SVT-AV1: an open-source AV1 encoder and decoder

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/svt-av1-an-open-source-av1-encoder-and-decoder-ad295d9b5ca2

SVT-AV1: open-source AV1 encoder and decoder

by Andrey Norkin, Joel Sole, Mariana Afonso, Kyle Swanson, Agata Opalach, Anush Moorthy, Anne Aaron

SVT-AV1 is an open-source AV1 codec implementation hosted on GitHub https://github.com/OpenVisualCloud/SVT-AV1/ under a BSD + patent license. As mentioned in our earlier blog post, Intel and Netflix have been collaborating on the SVT-AV1 encoder and decoder framework since August 2018. The teams have been working closely on SVT-AV1 development, discussing architectural decisions, implementing new tools, and improving compression efficiency. Since open-sourcing the project, other partner companies and the open-source community have contributed to SVT-AV1. In this tech blog, we will report the current status of the SVT-AV1 project, as well as the characteristics and performance of the encoder and decoder.

SVT-AV1 codebase status

The SVT-AV1 repository includes both an AV1 encoder and decoder, which share a significant amount of the code. The SVT-AV1 decoder is fully functional and compliant with the AV1 specification for all three profiles (Main, High, and Professional).

The SVT-AV1 encoder supports all AV1 tools which contribute to compression efficiency. Compared to the most recent master version of libaom (AV1 reference software), SVT-AV1 is similar in compression efficiency and at the same time achieves significantly lower encoding latency on multi-core platforms when using its inherent parallelization capabilities.

SVT-AV1 is written in C and can be compiled on major platforms, such as Windows, Linux, and macOS. In addition to the pure C function implementations, which allows for more flexible experimentation, the codec features extensive assembly and intrinsic optimizations for the x86 platform. See the next section for an outline of the main SVT-AV1 features that allow high performance at competitive compression efficiency. SVT-AV1 also includes extensive documentation on the encoder design targeted to facilitate the onboarding process for new developers.

Architectural features

One of Intel’s goals for SVT-AV1 development was to create an AV1 encoder that could offer performance and scalability. SVT-AV1 uses parallelization at several stages of the encoding process, which allows it to adapt to the number of available cores, including the newest servers with significant core count. This makes it possible for SVT-AV1 to decrease encoding time while still maintaining compression efficiency.

The SVT-AV1 encoder uses multi-dimensional (process-, picture/tile-, and segment-based) parallelism, multi-stage partitioning decisions, block-based multi-stage and multi-class mode decisions, and RD-optimized classification to achieve attractive trade-offs between compression and performance. Another feature of the SVT architecture is open-loop hierarchical motion estimation, which makes it possible to decouple the first stage of motion estimation from the rest of the encoding process.

Compression efficiency and performance

Encoder performance

SVT-AV1 reaches similar compression efficiency as libaom at the slowest speed settings. During the codec development, we have been tracking the compression and encoding results at the https://videocodectracker.dev/ site. The plot below shows the improvements in the compression efficiency of SVT-AV1 compared to the libaom encoder over time. Note that the libaom compression has also been improving over time, and the plot below represents SVT-AV1 catching up with the moving target. In the plot, the Y-axis shows the additional bitrate in percent needed to achieve similar quality as libaom encoder according to three metrics. The plot shows the results of the 2-pass encoding mode in both codecs. SVT-AV1 uses 4-thread mode, whereas libaom operates in a single-thread mode. The SVT-AV1 results for the 1-pass fixed-QP encoding mode, commonly used in research, are even more competitive, as detailed below.

Reducing BD-rate between SVT-AV1 and libaom in 2-pass encoding mode

The comparison results of the SVT-AV1 against libaom on objective-1-fast test set are presented in the table below. For estimating encoding times, we used Intel(R) Xeon(R) Platinum 8170 CPU @ 2.10GHz machine with 52 physical cores and 96 GB of RAM, with 60 jobs running in parallel. Both codecs use bi-directional hierarchical prediction structure of 16 pictures. The results are presented for 1-pass mode with fixed frame-level QP offsets. A single-threaded compression mode is used. Below, we compute the BD-rates for the various quality metrics: PSNR on all three color planes, VMAF, and MS-SSIM. A negative BD-Rate indicates that the SVT-AV1 encodes produce the same quality with the indicated relative reduction in bitrate. As seen below, SVT-AV1 demonstrates 16.5% decrease in encoding time compared to libaom while being slightly more efficient in compression ability. Note that the encoding times ratio may vary depending on the instruction sets supported by the platform. The results have been obtained on SVT-AV1 cs2 branch (a development branch that is currently being merged into the master, git hash 3a19f29) against the libaom master branch (git hash fe72512). The QP values used to calculate the BD-rates are: 20, 32, 43, 55, 63.

BD-rates of SVT-AV1 vs libaom in 1-pass encoding mode with fixed QP offsets. Negative numbers indicate reduction in bitrate needed to reach the same quality level. The overall encoding time difference is change in total CPU time for all sequences and QPs of SVT-AV1 compared to that of libaom.

*The overall encoding CPU time difference is calculated as change in total CPU time for all sequences and QPs of the test compared to that of the anchor. It is not equal to the average of per sequence values. Per each sequence, the encoding CPU time difference is calculated as change in total CPU time for all QPs for this sequence.

Since all sequences in the objective-1-fast test set have 60 frames, both codecs use one key frame. The following command line parameters have been used to compare the codecs.

libaom parameters:

--passes=1 --lag-in-frames=25 --auto-alt-ref=1 --min-gf-interval=16 --max-gf-interval=16 --gf-min-pyr-height=4 --gf-max-pyr-height=4 --kf-min-dist=65 --kf-max-dist=65 --end-usage=q --use-fixed-qp-offsets=1 --deltaq-mode=0 --enable-tpl-model=0 --cpu-used=0

SVT-AV1 parameters:

--preset 1 --scm 2 --keyint 63 --lookahead 0 --lp 1

The results above demonstrate the excellent objective performance of SVT-AV1. In addition, SVT-AV1 includes implementations of some subjective quality tools, which can be used if the codec is configured for the subjective quality.

Decoder performance

On the objective-1-fast test set, the SVT-AV1 decoder is slightly faster than the libaom in the 1-thread mode, with larger improvements in the 4-thread mode. We observe even larger speed gains over libaom decoder when decoding bitstreams with multiple tiles using the 4-thread mode. The testing has been performed on Windows, Linux, and macOS platforms. We believe the performance is satisfactory for a research decoder, where the trade-offs favor easier experimentation over further optimizations necessary for a production decoder.

Testing framework

To help ensure codec conformance, especially for new code contributions, the code has been comprehensively covered with unit tests and end-to-end tests. The unit tests are built on the Google Test framework. The unit and end-to-end tests are triggered automatically for each pull request to the repository, which is supported by GitHub actions. The tests support sharding, and they run in parallel to speed-up the turn-around time on pull requests.

Unit and e2e test have passed for this pull request

What’s next?

Over the last several months, SVT-AV1 has matured to become a complete encoder/decoder package providing competitive compression efficiency and performance trade-offs. The project is bolstered with extensive unit test coverage and documentation.

Our hope is that the SVT-AV1 codebase helps further adoption of AV1 and encourages more research and development on top of the current AV1 tools. We believe that the demonstrated advantages of SVT-AV1 make it a good platform for experimentation and research. We invite colleagues from industry and academia to check out the project on Github, reach out to the codebase maintainers for questions and comments or join one of the SVT-AV1 Open Dev meetings. We welcome more contributors to the project.

SVT-AV1: an open-source AV1 encoder and decoder was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.