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Translating Developer Productivity to Netflix Customer Delight
- 1. Translating Developer Productivity to Netflix Customer Delight Edge Engineering Open House June 9, 2016 @ Netflix Vasanth Asokan Edge Developer Experience @vasanthasokan
- 3. DEVELOP (rapidly) DEPLOY (reliably) OPERATE (effectively) Experimentation driven innovation ~700 apps, dozens of pushes a day 15+ client teams, ~200 developers ~50 direct services, 100s of AB tests, dozens of new features The Innovation Funnel API Devices Netflix Services Client Adaptor Applications
- 4. Why care about DevEx? Developer Productivity Product Innovation Tools Automation Insights Customer Satisfaction
- 5. App Development and Management DEVELOP (rapidly) DEPLOY (reliably) OPERATE (effectively)
- 6. SERVICELAYER Netflix Microservices app WAN Boundary API SERVER JVM js java Developer Ergonomics app ... app app CLIENTLIBRARIES Large / Complex SERVICELAYER
- 7. REMOTESERVICELAYER app API SERVER JVM Developer Ergonomics ... app ... app app CLIENTLIBRARIES js javajs DOCKER CONTAINERS WAN Boundary Netflix Microservices
- 8. Setup Canary SupportProd Push Pre-Prod Metrics Tracing Lifecycle Alerts Build Bootstrap API Discovery REPL Unit Test SDK Debug Logging Profiling Audits Security Custom Routing Dependency Management Client Application Development Critical Component! Dx Developer Experience
- 9. $ newt init Just bring your Javascript business logic NeWT: Netflix Workflow Toolkit Continuous Integration Deployment Pipelines Autoscaling Dashboards Alerting Logging Lifecycle Management Audits and Analytics Container tooling Canaries Dependency Management
- 10. Titus ATLAS NeWT: Netflix Workflow Toolkit
- 11. Edge PaaS UI
- 12. $ newt auto-deploy -d nodeJS project Docker Machine node-inspector Debugger File watcher / live reload trigger File watcher agent NeWT: Local Container Development Local Container docker build / run
- 13. $ newt auto-deploy -d Docker Machine NeWT: Local Container Development Local Container Cloud Microservices Cloud Proxy Terminate security DiscoveryAgent Service Discovery Local System Cloud
- 14. App Operations and Insights DEVELOP (rapidly) DEPLOY (reliably) OPERATE (effectively)
- 15. • Low Latency, High throughput, Highly Efficient • Handle bursty or large scale loads • Extensible programming model 600 jobs in production, 8M messages/sec at peak, 100Gbps network throughput Mantis - Stream Processing Platform
- 16. Monitoring facets of aggregate application health, globally Aggregate Insights
- 18. Analyze in real-time, requests matching a precise set of conditions Surgical Insights
- 19. Surgical Insights - Real-time Stream Queries
- 20. Surgical Insights - Real-time Stream Queries
- 21. Surgical Insights - Real-time Stream Queries
- 22. Monitoring server side calling pattern and internal application profile Session Tracing
- 23. Session Tracing
- 24. Session Tracing - Request Profile
- 25. Session Tracing - Per Node Profile
- 26. Automatic monitoring of high cardinality data across multiple dimensions Real-time Anomaly Detection
- 28. • Scaling developer productivity with business growth •Provide fully managed PaaS experience to client developers • Shift Left Insights to power smart development • Curated, blended visualizations that simplify devops In conclusion...
- 29. Tech Soup
Notes de l'éditeur
- At the Netflix Edge Developer Experience team, we are all about translating developer productivity into Netflix customer delight.
- Wait, What developer experience? Let’s get a show of hands -- how many of you are developers who write code and ship applications and in your daily life? Good, so you know how important developer experience is to being productive at your work.
- But who are these developers that we are talking about? The Netflix Edge is all about an experience based API -- Netflix client application developers write an API that creates the best experience possible for their device. Check out http://techblog.netflix.com/2013/01/optimizing-netflix-api.html, http://techblog.netflix.com/2014/03/the-netflix-dynamic-scripting-platform.html We are talking about these internal Netflix client application developers The innovation velocity for these client applications is very high - there are nearly 700 client adaptor applications deployed today, deploying dozens of times a day, They are authored by nearly 15+ client teams, totalling ~200 developers For a service tier that is funnelling billions of requests a day, there is a large appetite for high velocity changes We would like those developers to be able to develop rapidly, deploy reliably and operate their application effectively. And given Netflix’s experimentation driven culture, those applications are constantly evolving based on AB tests, from which they learn and do further development.
- While a client developer does get to have a lot of fun creating cool new UI experiences for customers, they are also the final feature integration point, for both client and server code. The slightest friction causes a lot of pain, missed deadlines and suboptimal features So developer productivity at Edge leads to faster, more reliable innovation of product, which in turns helps keep our 81m subscriber base happy and growing. Our strategy to achieve developer productivity is to invest in tools, insights and automation and grow their value as our service grows.
- Let’s dive a little deeper. Let’s take a look at innovations we are making in the areas of app development and management
- This is today’s awesome dynamic scripting API server, where apps run on the JVM At the demo stations you will get to learn about Primer, our dynamic app delivery and deployment system. With primer a developer can push one of these apps to production globally effecting change for customers within five minutes However, given our future scale, there is a developer ergonomics challenge with this architecture, that we would like to solve. First, there is a tech stack mismatch -- most Netflix UIs are JS, and the groovy stack at API makes for an unnatural fit The API JVM is large and complex which means that devs cannot run debug their apps by running the server locally Also a complex, overloaded and changing application profile makes it hard to provide guarantees about performance of any individual script in production
- With edge rearchitecture, we are separating client app scripts into own process isolated services implemented as docker containers The ergonomics story improves tremendously. But remember, UI developers typically do not operate services. Just want to write JS, not operate a service tier.
- Stakes are very high - the criticality of the component means that developers have to manage a lot of concerns. What starts out as developmental concerns, quickly grows into various aspects of managing a server application at scale. What developers need is a Platform as a Service Solution
- We are excited about something we are working on towards this, called Newt or the Netflix workflow toolkit. Newt brings docker container based, managed application development concepts to a developer’s hands NeWT itself is a command line tool, but it represents wrapping all of the platform facilities underneath to simplify app development and operations. A newt project gets all of these subsystems initialized, wrapped. Its also about the backend systems and maintaining them on behalf of the application developer Our goal is that developers have to just bring their javascript code!
- Here’s a different view of the various systems that NeWT abstracts You might see some familiar open source systems there, and a few others that are Netflix specific telemetry and container cloud systems.
- The PaaS experience is not just about a CLI but also about the corresponding UI experience This is a preview of our Edge PaaS UI which provides user / team personalized access to apps with integrations into other platform systems much like its CLI counter part. It also had deep integration into operational insights systems which we will talk about shortly.
- Newt is also our main container tooling wrapper Recall that today’s API platform prevents effective debug of client application code With NeWT and containers we are looking to change that around How many of you love live reload debugging? Lots - oh cool, so you will love this None - well, I hope I can entice you towards using live reload debugging by the end of the day <walk through> newt auto-deploy takes your nodeJS project (or pull an image from production) Provisions a docker machine, and builds and runs your app inside a container on the docker machine Installs a file watcher agent that monitors your code And as you make edits, pushes the changes to your container, respins the node process A debugger connection is also established seamlessly via the docker machine allowing you to debug as you edit.
- Our client apps typically expect to terminate security at our proxy layer, so when doing local development, it would be cumbersome to run the proxy locally too. Instead, newt will launch a network agent that creates a cloud like service discovery and registration setup And traffic can flow seamlessly from the proxy in the cloud, to the local container, and then onto downstream cloud systems
- Let’s switch gears a little bit. Now that client application developers run services, we need to extend devops workflows to them They need to be able to operate their deployed code effectively and/or understand client application behavior quickly. We have numerous curated insights tools, we don’t have time to cover them all, but let’s look at a few of them
- Before we look at our actual solutions, I would like to tip my hat towards Mantis which my colleagues in the Edge Realtime Events team work on So, what is Mantis? Low latency, high throughput, stream processing platform Because it is sharded and auto-scaleable Because queries are evaluated at source, and you stream only wha ct matches a query Can chain jobs, a variety of sources and sinks And the numbers speak for themselves… Mantis powers a lot of the insights tools you will be seeing next
- Once an app is deployed, “How does one know the aggregate health of the application, say, globally?”
- For this purpose, we created application specific dashboards, with critical health metrics laid out together so that an engineer can draw correlations. Blends historical metric data with real-time visualizations Also blends contextual information such as API pushes or server pushes to point to source of problems
- Let’s say you have found a latency problem How do you surgically analyze those slow requests? You will want to collect samples
- Enter our queriable real-time data explorer A user can pick a streaming data source, enter a set of conditions, in this specific case, say requests that take > 5 seconds Hit Submit
- And they get aggregated results from all the cloud instances that matched their query Here you see all the slow requests being listed
- We are talking about JS developers here, they can use our real-time javascript mapper to turn that stream and filter, map, reduce on it to further get to an actionable dataset. In this example, they choose to further ignore slow requests from mexico... They can also turn this data stream into a numeric metric, and then plot graphs or create alerts on deviations of that metric’s value This creates a nimble system for transient, on demand metrics. You no longer have to code up all metrics ahead of time into your source
- Maybe you have identified a few devices exhibiting those slow requests At this point a developer can use our session tracing tools to get a view of their device session
- Here is an example Here you see the the client’s view of requests over time, plotted on a timeline that you can zoom in and zoom out of
- Maybe you spotted one or two specific slow requests in that session You can drill down and get a server side call graph / trace for that specific request We try to highlight hotspots in the call graph, as well as annotate each node with rich node specific insights data when available
- And let’s say you identify that the hotspot is within your service You can then get a method level execution profile within your request
- Surgical insights and alerting is possible, when you know the specific dimension that has issues and are trying to debug an issue after it has happened. But in reality, our applications have numerous dimensions and long tail characteristics -- a device could be having issues only in a certain country, and only for a given title. But the cardinality of the data can be really high for each of those dimensions What if we could automatically analyze all combinations of a set of known dimensions - say country / title, or device / title, or uiversion / title, and alert about anomalies in real-time. Not only that, but provide relevant debug data right next to the alert
- We have just the system based on Mantis Here you see a specific title starting to show an increase in errors relative to historical values Future work here is looking towards auto-triaging and enriching the alert signal with a set of correlated data Thus you can send a more targeted alert
- In conclusion you have gotten a flavor for ideas around Providing a managed PaaS experience Shift left insights for powering smart development Curated, blended insights for simplifying devops
- For the tech fans out here, here is a short but by no means comprehensive set of technologies we employ in these solutions Come talk to us at the demo stations to learn more or if you have a great idea, come tell us what we could be doing differently!