Maintaining the Front Door to Netflix презентация

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There are copious notes attached to each slide in this presentation. Please read those notes to get the full context of the presentation

Слайд 1Maintaining the Front Door to Netflix
Daniel Jacobson
@daniel_jacobson
http://www.linkedin.com/in/danieljacobson
http://www.slideshare.net/danieljacobson

Слайд 2There are copious notes attached to each slide in this presentation.

Please read those notes to get the full context of the presentation

Слайд 3Global Streaming Video for TV Shows and Movies

Слайд 4More than 44 Million Subscribers
More than 40 Countries

Слайд 5Netflix Accounts for ~33% of Peak Internet Traffic in North America

Netflix

subscribers are watching more than 1 billion hours a month

Слайд 8Team Focus: Build the Best Global Streaming Product
Three aspects of the Streaming

Product:
Non-Member
Discovery
Streaming

Слайд 9Key Responsibilities
Broker data between services and UIs

Maintain a resilient front-door

Scale the

system vertically and horizontally

Maintain high velocity

Слайд 10But Before Streaming…

Слайд 13Monolithic Application
In Netflix Data Centers

Слайд 14The bigger the ship…
the slower it turns

Слайд 15Distributed Architecture

Слайд 17

1000+ Device Types

Слайд 18
Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
Reviews
A/B Test Engine

Dozens of Dependencies

Слайд 19
Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 20Dependency Relationships

Слайд 212,000,000,000
Requests Per Day to the Netflix API

Слайд 2230
Distinct Dependent Services for the Netflix API

Слайд 23~500
Dependency jars Slurped into the Netflix API

Слайд 2414,000,000,000
Netflix API Calls Per Day to those Dependent Services

Слайд 250
Dependent Services with 100% SLA

Слайд 2699.99% = 99.7%
30
0.3% of 2B = 6M failures per day
2+

Hours of Downtime
Per Month

Слайд 2799.99% = 99.7%
30
0.3% of 2B = 6M failures per day
2+

Hours of Downtime
Per Month


Слайд 2899.9% = 97%
30
3% of 2B = 60M failures per

day

20+ Hours of Downtime
Per Month

Слайд 29Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 30Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 31Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 32Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 33Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 35Circuit Breaker Dashboard





Слайд 37
Call Volume and Health / Last 10 Seconds

Слайд 38
Call Volume / Last 2 Minutes

Слайд 39

Successful Requests

Слайд 40

Successful, But Slower Than Expected

Слайд 41

Short-Circuited Requests, Delivering Fallbacks

Слайд 42

Timeouts, Delivering Fallbacks

Слайд 43

Thread Pool & Task Queue Full, Delivering Fallbacks

Слайд 44

Exceptions, Delivering Fallbacks

Слайд 45

Error Rate
# + # + # + # / (# +

# + # + # + #) = Error Rate


Слайд 46

Status of Fallback Circuit

Слайд 47

Requests per Second, Over Last 10 Seconds

Слайд 48

SLA Information

Слайд 49Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 50Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 51Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine

Слайд 52Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine
Fallback

Слайд 53Personalization Engine
User Info
Movie Metadata
Movie Ratings
Similar Movies
API
Reviews
A/B Test Engine
Fallback

Слайд 54Scaling the Distributed System

Слайд 56AWS Cloud

Слайд 58Autoscaling

Слайд 59Autoscaling

Слайд 60Amazon Auto Scaling Limitations
Hard to fit policies to variable traffic patterns

(weekday vs weekend)
Limited control over capacity adjustments (absolute value or %)

Слайд 61The Impact of AAS Limitations
Traffic drop can lead to scale downs

during outage
Performance degradation between new instance launch and taking traffic
Excess capacity at peak and trough

Слайд 62Scryer : Predictive Auto Scaling
Not yet…

Слайд 63Typical Traffic Patterns Over Five Days

Слайд 64Predicted RPS Compared to Actual RPS

Слайд 65Scaling Plan for Predicted Workload

Слайд 66What is Scryer Doing?
Evaluating needs based on historical data
Week over week,

month over month metrics

Adjusts instance minimums based on algorithms

Relies on Amazon Auto Scaling for unpredicted events

Слайд 67Results

Слайд 68Results : Load Average
Reactive
Predictive

Слайд 69Results : Response Latencies
Reactive
Predictive

Слайд 70Results : Outage Recovery

Слайд 71Results : Outage Recovery

Слайд 72Results : AWS Costs

Слайд 73Scaling Globally

Слайд 74More than 44 Million Subscribers
More than 40 Countries

Слайд 75Zuul Gatekeeper for the Netflix Streaming Application

Слайд 76Zuul *
Multi-Region Resiliency
Insights
Stress Testing
Canary Testing
Dynamic Routing
Load Shedding
Security
Static Response Handling
Authentication
* Most closely

resembles an API proxy

Слайд 77Isthmus

Слайд 79All of these approaches are designed to prevent failures…

Слайд 80But sometimes the best way to prevent failures is to force

them!

Слайд 82I randomly terminate instances in production to identify dormant failures.
Chaos Monkey

Слайд 83Chaos Gorilla
I simulate an outage of an entire Amazon availability zone.

Слайд 84I simulate an outage in an AWS region.
Chaos Kong

Слайд 85I find instances that don’t adhere to best practices.
Conformity Monkey

Слайд 86I extend Conformity Monkey to find security violations.
Security Monkey

Слайд 87I detect unhealthy instances and remove them from service.
Doctor Monkey

Слайд 88I clean up the clutter and waste that runs in the

cloud.

Janitor Monkey

Слайд 89I induce artificial delays and errors into services to determine how

upstream services will respond.

Latency Monkey

Слайд 91Deployments in the Cloud

Слайд 92Dependency Relationships

Слайд 94Testing Philosophy: Act Fast, React Fast

Слайд 95That Doesn’t Mean We Don’t Test

Слайд 96Automated Delivery Pipeline

Слайд 97Cloud-Based Deployment Techniques


Слайд 98Current Code

In Production
API Requests from
the Internet

Слайд 99Single Canary Instance
To Test New Code with Production Traffic
(around 1% or

less of traffic)

Current Code

In Production

API Requests from
the Internet

Слайд 100Canary Analysis Automation

Слайд 101Single Canary Instance
To Test New Code with Production Traffic
(around 1% or

less of traffic)

Current Code

In Production

API Requests from
the Internet

Error!

Слайд 102Current Code

In Production
API Requests from
the Internet

Слайд 103Current Code

In Production
API Requests from
the Internet

Слайд 104Current Code

In Production
API Requests from
the Internet
Perfect!

Слайд 105Stress Test with Zuul

Слайд 106Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 107Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 108Error!
Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 109Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 110Current Code

In Production
API Requests from
the Internet
Perfect!

Слайд 111Stress Test with Zuul

Слайд 112Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 113Current Code

In Production
API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 114API Requests from
the Internet
New Code

Getting Prepared for Production

Слайд 115Brokering Data to 1,000+ Device Types

Слайд 118Screen Real Estate

Слайд 119Controller

Слайд 120Technical Capabilities

Слайд 121One-Size-Fits-All
API
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request
Request

Слайд 122Courtesy of South Florida Classical Review

Слайд 124Resource-Based API vs. Experience-Based API

Слайд 125Resource-Based Requests
/users//ratings/title
/users//queues
/users//queues/instant
/users//recommendations
/catalog/titles/movie
/catalog/titles/series
/catalog/people

Слайд 126
REST API
RECOMMENDATIONS
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS
Network Border
Network Border

Слайд 127RECOMMENDATIONS
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS

OSFA API
Network Border
Network Border
SERVER CODE
CLIENT CODE

Слайд 128RECOMMENDATIONS
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS

OSFA API
Network Border
Network Border
DATA GATHERING,
FORMATTING,
AND DELIVERY
USER INTERFACE
RENDERING

Слайд 131Experience-Based Requests
/ps3/homescreen

Слайд 132
JAVA API

Network Border
Network Border
RECOMMENDATIONS
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS
Groovy Layer

Слайд 134RECOMMENDATIONSAZXSXX C CCC
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS

JAVA API

SERVER CODE

CLIENT CODE

CLIENT ADAPTER CODE
(WRITTEN

BY CLIENT TEAMS, DYNAMICALLY UPLOADED TO SERVER)

Network Border

Network Border

Слайд 135RECOMMENDATIONSAZXSXX C CCC
MOVIE DATA
SIMILAR MOVIES
AUTH
MEMBER
DATA
A/B TESTS
START-UP
RATINGS

JAVA API

DATA GATHERING
DATA FORMATTING
AND DELIVERY
USER INTERFACE
RENDERING
Network

Border

Network Border

Слайд 137https://www.github.com/Netflix

Слайд 138Maintaining the Front Door to Netflix
Daniel Jacobson
@daniel_jacobson
http://www.linkedin.com/in/danieljacobson
http://www.slideshare.net/danieljacobson

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