Andrew Spyker presented on Netflix's cloud platform and open source projects. Some key points included: - Netflix has migrated from monolithic architectures to microservices and continuous delivery enabled by their open source libraries and services. - Their platform focuses on elasticity, high availability through automation, and operational visibility. - Netflix uses technologies like Eureka, Ribbon, Hystrix, and Servo to enable scalability, resilience, and monitoring across their distributed systems. - They contribute over 50 open source projects to help others adopt their cloud-native approaches and are working on data and UI related projects.

1. Andrew Spyker
@aspyker
Netflix Cloud Platform
and Open Source

2. Introduction
Big enterprise/datacenter to consumer/cloud
Netflix Cloud Platform
Elastic and Web Scale
High Availability and Auto Recovery
Continuous Delivery
Operational Visibility
Security
Agenda

3. About me, road to Netflix
● Working for IBM on Java/Middleware performance
○ Cloud & mobile deemed Enterprise Java benchmarks non-interesting
○ Monolithic DB’s, resiliency and code updates not required
● Acme Air (Benchmark) Example App
○ Showed web/cloud scale
■ 4B+ per day mobile requests end to end, hundreds of nodes
■ But, wasn’t operable
○ Rewrote using NetflixOSS libraries & services
■ Now operable, with same levels of scale
■ Also enabled Microservices and CI/CD
■ Won Netflix Cloud Prize

4. About me, road to Netflix
● Now that NetflixOSS was understood
○ Ported libraries & services to IBM middleware and cloud
■ POC’s for Open Stack, Docker, Mesos, Kubernetes
○ Started to onboard and operate IBM SaaS businesses
■ Most interestingly … IBM Watson
● 2014 - “Should I work on applying this platform to more
systems or help build the next cloud platform?”
● Joined Netflix in the cloud platform team
○ Focusing on performance/scalability
○ Also helping with architecture, containers, open source
@aspyker
ispyker.
blogspot.
com

5. Elastic, Web and Hyper Scale
Doing this
Not doing that

6. Elastic, Web and Hyper Scale
Front end API
(browser and mobile)
Authentication
Service
Booking
Service Temporal
caching
Durable
Storage
Load
Balancers
…
Strategy Benefit
Make deployments automated Without automation impossible
Expose well designed API to users Offloads presentation complexity to clients
Remove state for mid tier services Allows easy elastic scale out
Push temporal state to client and caching tier Leverage clients, avoids data tier overload
Use partitioned data storage Data design and storage scales with HA
…
…
…
…
…

7. HA and Automatic Recovery
Feeling This
Not Feeling That

8. Micro service
Implementation
Call “Auth Service”
Highly Available Service Runtime Recipe
Ribbon REST client
with Eureka
Web App
Front End
(REST services)
App Service
(auth-service)
Execute
auth-service
call
Hystrix
Eureka
Server(s)
Eureka
Server(s)
Eureka
Server(s)
Karyon
Fallback
Implementation
Implementation Detail Benefits
Decompose into micro services
• Key user path always available
• Failure does not propagate across service boundaries
Karyon /w automatic Eureka registration
• New instances are quickly found
• Failing individual instances disappear
Ribbon client with Eureka awareness
• Load balances & retries across instances with “smarts”
• Handles temporal instance failure
Hystrix as dependency circuit breaker
• Allows for fast failure
• Provides graceful cross service degradation/recovery

9. IaaS High Availability
Region (us-east-1)
us-east-1e
us-east-1c
Eureka
Web App Service1 Service2
Cluster Auto Recovery and Scaling Services (Auto Scaling Groups)
…
Global Load
Balancers
Rule Why?
Always > 2 of everything 1 is SPOF, 2 doesn’t web scale and slow DR recovery
Including IaaS and cloud services You’re only as strong as your weakest dependency
Use auto scaler/recovery monitoring Clusters guarantee availability and service latency
Use application level health checks Instance on the network != healthy
Worldwide availability Data replication, global front-end routing, cross region traffic
us-east-1d

10. Testing is only way to prove HA
● Chaos Monkey
○ Kill instances in production - runs regularly
● Chaos Gorilla
○ Kills availability zones (single datacenter)
○ Also testing for split brain important
● Chaos Kong
○ Kill entire region and shift traffic globally
○ Run frequently but with prior scheduling

11. Continuous Delivery
Reading This
Not This

12. v
Continuous Delivery
Cluster v1 Canary v2 Cluster V2
Step Technology
Developers test locally Unit test frameworks
Continuous build Continuous build server based on gradle builds
Build “bakes” full instance image Aminator and deployment pipeline bake images from build artifacts
Developer work across dev and test Archaius allows for environment based context
Developers do canary tests, red/black
deployments in prod
Asgard console provides app cluster common devops approach,
security patterns, and visibility
Continuous
Build Server
Baked to images
(AMI’s)
… …

13. Operational Visibility
If you can’t see it, you can’t improve it

14. Operational Visibility
Web App Auth Service
Visibility Point Technology
Basic IaaS instance monitoring Not enough (not scalable, not app specific)
User like external monitoring SaaS offerings or OSS like Uptime
Targeted performance, sampling Vector performance and app level metrics
Service to service interconnects Hystrix streams ➔Turbine aggregation ➔Hystrix dashboard
Application centric metrics Servo gauges, counters, timers sent to metrics store like Atlas
Remote logging Logstash/Kibana or similar log aggregation and analysis frameworks
Threshold monitoring and alerts Services like Atlas and PagerDuty for incident management
Servo
Hystrix/Turbine
External
Uptime
Monitoring Metric/Event
Repositories
LogStash/Elastic
Search/Kibana
Incidents
……
…
…
Atlas
Vector

15. Security
Solid Security
Done in new ways
NOT

16. Security
Security must consider fluid environment
Security must be automated!
Security Monkey
● Monitors security policies, tracks changes, alerts on situations
Scumblr
● Searches the web, social media for security “nuggets” (credentials, hacking
discussions, etc.). Collect via Sketchy.
Sketchy
● A safe way to collect text and screenshots from websites

17. What did we not cover?
Over 50 github projects
● “Technical indigestion as a service”
Big Data and User Interface Engineering
● Both deserve their own sections
● Extensive Open Source existing and coming
projects (Falcor)

18. How do I get started?
● All of the previous slides shows NetflixOSS components
○ Code: http://netflix.github.io
○ Announcements: http://techblog.netflix.com/
● Want to get running a bit faster?
● ZeroToCloud
○ Workshop for getting started with build/bake/deploy in Amazon EC2
● ZeroToDocker
○ Docker images that containing running Netflix technologies (not
production ready, but easy to understand)