SlideShare uma empresa Scribd logo
1 de 92
Scaling the
Netflix API
Daniel Jacobson
@daniel_jacobson
http://www.linkedin.com/in/danieljacobson
http://www.slideshare.net/danieljacobson
Please read the notes
associated with each slide for
the full context of the
presentation
Scaling the Netflix API
What do I mean by “scale”?
Netflix API : Requests Per Month
-
5
10
15
20
25
30
35
RequestsinBillions
Scaling the Netflix API
Netflix API : Requests Per Month
-
5
10
15
20
25
30
35
RequestsinBillions
But There Are Many Ways to Scale!
Organization
Systems
Devices
Development
Testing
Scaling…
Organization
Systems
Devices
Development
Testing
Streaming
More than 36 Million Subscribers
More than 40 Countries
Netflix Accounts for ~33% of Peak
Internet Traffic in North America
Netflix subscribers are watching more than 1 billion hours a month
Scaling the Netflix API
In other words,
pretty big scale…
Organization Structure
Distributed Architecture
Scaling the Netflix API
1000+ Device Types
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
Reviews
A/B Test
Engine
Dozens of Dependencies
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
http://www.slideshare.net/reed2001/culture-1798664
Scaling…
Organization
Systems
Devices
Development
Testing
Scaling the System
Growth of Netflix API Requests
0.6
20.7
41.7
-
5
10
15
20
25
30
35
40
45
Jan-10 Jan-11 Jan-12
RequestinBillions
70x growth in two years!
Growth of Netflix API Requests
2+ billion requests per day
Exploding out to 14 billion dependency calls per day
AWS Cloud
Scaling the Netflix API
Scaling the Netflix API
Autoscaling
Autoscaling
System Resiliency
Dependency Relationships
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Scaling the Netflix API
Circuit Breaker Dashboard
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Fallback
Personaliz
ation
Engine
User Info
Movie
Metadata
Movie
Ratings
Similar
Movies
API
Reviews
A/B Test
Engine
Fallback
Forced Failure
Scaling the Netflix API
Global System
More than 36 Million Subscribers
More than 40 Countries
Zuul
Gatekeeper for the Netflix Streaming Application
Zuul
• Multi-Region
Resiliency
• Insights
• Stress Testing
• Canary Testing
• Dynamic Routing
• Load Shedding
• Security
• Static Response
Handling
• Authentication
Isthmus
Scaling…
Organization
Systems
Devices
Development
Testing
Scaling the Netflix API
Screen Real Estate
Controller
Technical Capabilities
One-Size-Fits-All
API
Request
Request
Request
Scaling…
Organization
Systems
Devices
Development
Testing
Courtesy of South Florida Classical Review
Scaling the Netflix API
Resource-Based API
vs.
Experience-Based API
Resource-Based Requests
• /users/<id>/ratings/title
• /users/<id>/queues
• /users/<id>/queues/instant
• /users/<id>/recommendations
• /catalog/titles/movie
• /catalog/titles/series
• /catalog/people
REST API
RECOMME
NDATIONS
MOVIE
DATA
SIMILAR
MOVIES
AUTH
MEMBER
DATA
A/B
TESTS
START-
UP
RATINGS
Network Border Network Border
RECOMME
NDATIONS
MOVIE
DATA
SIMILAR
MOVIES
AUTH
MEMBER
DATA
A/B
TESTS
START-
UP
RATINGS
OSFA API
Network Border Network Border
SERVER CODE
CLIENT CODE
RECOMME
NDATIONS
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
Scaling the Netflix API
Scaling the Netflix API
Experience-Based Requests
• /ps3/homescreen
JAVA API
Network Border Network Border
RECOMME
NDATIONS
MOVIE
DATA
SIMILAR
MOVIES
AUTH
MEMBER
DATA
A/B
TESTS
START-
UP
RATINGS
Groovy Layer
Scaling the Netflix API
RECOMME
NDATIONSA
ZXSXX 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
RECOMME
NDATIONSA
ZXSXX 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
Scaling the Netflix API
Scaling…
Organization
Systems
Devices
Development
Testing
Dependency Relationships
API alone includes more than 500 client jars
Testing Philosophy:
Act Fast, React Fast
That Doesn’t Mean We Don’t Test
• Unit tests
• Functional tests
• Regression scripts
• Continuous integration
• Capacity planning
• Load / Performance tests
Cloud-Based Deployment Techniques
Current Code
In Production
API Requests from
the Internet
Single 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!
Current Code
In Production
API Requests from
the Internet
Current Code
In Production
API Requests from
the Internet
Perfect!
Current Code
In Production
API Requests from
the Internet
New Code
Getting Prepared for Production
Error!
Current Code
In Production
API Requests from
the Internet
New Code
Getting Prepared for Production
Current Code
In Production
API Requests from
the Internet
New Code
Getting Prepared for Production
Current Code
In Production
API Requests from
the Internet
Perfect!
Current Code
In Production
API Requests from
the Internet
New Code
Getting Prepared for Production
Current Code
In Production
API Requests from
the Internet
New Code
Getting Prepared for Production
API Requests from
the Internet
New Code
Getting Prepared for Production
https://www.github.com/Netflix
Scaling the
Netflix API
Daniel Jacobson
@daniel_jacobson
http://www.linkedin.com/in/danieljacobson
http://www.slideshare.net/danieljacobson

Mais conteúdo relacionado

Mais procurados

Netflix API - Presentation to PayPal
Netflix API - Presentation to PayPalNetflix API - Presentation to PayPal
Netflix API - Presentation to PayPalDaniel Jacobson
 
Netflix API : BAPI 2011 Presentation : SF
Netflix API : BAPI 2011 Presentation : SFNetflix API : BAPI 2011 Presentation : SF
Netflix API : BAPI 2011 Presentation : SFDaniel Jacobson
 
Scaling the Netflix API - From Atlassian Dev Den
Scaling the Netflix API - From Atlassian Dev DenScaling the Netflix API - From Atlassian Dev Den
Scaling the Netflix API - From Atlassian Dev DenDaniel Jacobson
 
Maintaining the Netflix Front Door - Presentation at Intuit Meetup
Maintaining the Netflix Front Door - Presentation at Intuit MeetupMaintaining the Netflix Front Door - Presentation at Intuit Meetup
Maintaining the Netflix Front Door - Presentation at Intuit MeetupDaniel Jacobson
 
Netflix API - Separation of Concerns
Netflix API - Separation of ConcernsNetflix API - Separation of Concerns
Netflix API - Separation of ConcernsDaniel Jacobson
 
The future-of-netflix-api
The future-of-netflix-apiThe future-of-netflix-api
The future-of-netflix-apiDaniel Jacobson
 
Top 10 Lessons Learned from the Netflix API - OSCON 2014
Top 10 Lessons Learned from the Netflix API - OSCON 2014Top 10 Lessons Learned from the Netflix API - OSCON 2014
Top 10 Lessons Learned from the Netflix API - OSCON 2014Daniel Jacobson
 
History and Future of the Netflix API - Mashery Evolution of Distribution
History and Future of the Netflix API - Mashery Evolution of DistributionHistory and Future of the Netflix API - Mashery Evolution of Distribution
History and Future of the Netflix API - Mashery Evolution of DistributionDaniel Jacobson
 
Maintaining the Front Door to Netflix
Maintaining the Front Door to NetflixMaintaining the Front Door to Netflix
Maintaining the Front Door to NetflixBenjamin Schmaus
 
Why API? - Business of APIs Conference
Why API? - Business of APIs ConferenceWhy API? - Business of APIs Conference
Why API? - Business of APIs ConferenceDaniel Jacobson
 
API Design - When to buck the trend (Webcast)
API Design - When to buck the trend (Webcast)API Design - When to buck the trend (Webcast)
API Design - When to buck the trend (Webcast)Apigee | Google Cloud
 
The API Facade Pattern: Overview - Episode 1
The API Facade Pattern: Overview - Episode 1The API Facade Pattern: Overview - Episode 1
The API Facade Pattern: Overview - Episode 1Apigee | Google Cloud
 
The API Facade Pattern: Technology - Episode 3
The API Facade Pattern: Technology - Episode 3The API Facade Pattern: Technology - Episode 3
The API Facade Pattern: Technology - Episode 3Apigee | Google Cloud
 
Essential API Facade Patterns - Composition (Episode 1)
Essential API Facade Patterns - Composition (Episode 1)Essential API Facade Patterns - Composition (Episode 1)
Essential API Facade Patterns - Composition (Episode 1)Apigee | Google Cloud
 
Your API is So 2006 - MoDevEast 2011
Your API is So 2006 - MoDevEast 2011Your API is So 2006 - MoDevEast 2011
Your API is So 2006 - MoDevEast 2011Delyn Simons
 
Migrating Automation Tests to Postman Monitors and ROI
Migrating Automation Tests to Postman Monitors and ROIMigrating Automation Tests to Postman Monitors and ROI
Migrating Automation Tests to Postman Monitors and ROIPostman
 
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...CA API Management
 

Mais procurados (20)

Netflix API - Presentation to PayPal
Netflix API - Presentation to PayPalNetflix API - Presentation to PayPal
Netflix API - Presentation to PayPal
 
Netflix API : BAPI 2011 Presentation : SF
Netflix API : BAPI 2011 Presentation : SFNetflix API : BAPI 2011 Presentation : SF
Netflix API : BAPI 2011 Presentation : SF
 
Scaling the Netflix API - From Atlassian Dev Den
Scaling the Netflix API - From Atlassian Dev DenScaling the Netflix API - From Atlassian Dev Den
Scaling the Netflix API - From Atlassian Dev Den
 
Maintaining the Netflix Front Door - Presentation at Intuit Meetup
Maintaining the Netflix Front Door - Presentation at Intuit MeetupMaintaining the Netflix Front Door - Presentation at Intuit Meetup
Maintaining the Netflix Front Door - Presentation at Intuit Meetup
 
Netflix API
Netflix APINetflix API
Netflix API
 
Netflix API - Separation of Concerns
Netflix API - Separation of ConcernsNetflix API - Separation of Concerns
Netflix API - Separation of Concerns
 
The future-of-netflix-api
The future-of-netflix-apiThe future-of-netflix-api
The future-of-netflix-api
 
Top 10 Lessons Learned from the Netflix API - OSCON 2014
Top 10 Lessons Learned from the Netflix API - OSCON 2014Top 10 Lessons Learned from the Netflix API - OSCON 2014
Top 10 Lessons Learned from the Netflix API - OSCON 2014
 
History and Future of the Netflix API - Mashery Evolution of Distribution
History and Future of the Netflix API - Mashery Evolution of DistributionHistory and Future of the Netflix API - Mashery Evolution of Distribution
History and Future of the Netflix API - Mashery Evolution of Distribution
 
Maintaining the Front Door to Netflix
Maintaining the Front Door to NetflixMaintaining the Front Door to Netflix
Maintaining the Front Door to Netflix
 
Why API? - Business of APIs Conference
Why API? - Business of APIs ConferenceWhy API? - Business of APIs Conference
Why API? - Business of APIs Conference
 
Huge: Running an API at Scale
Huge: Running an API at ScaleHuge: Running an API at Scale
Huge: Running an API at Scale
 
API Trends: What to expect in 2012
API Trends: What to expect in 2012API Trends: What to expect in 2012
API Trends: What to expect in 2012
 
API Design - When to buck the trend (Webcast)
API Design - When to buck the trend (Webcast)API Design - When to buck the trend (Webcast)
API Design - When to buck the trend (Webcast)
 
The API Facade Pattern: Overview - Episode 1
The API Facade Pattern: Overview - Episode 1The API Facade Pattern: Overview - Episode 1
The API Facade Pattern: Overview - Episode 1
 
The API Facade Pattern: Technology - Episode 3
The API Facade Pattern: Technology - Episode 3The API Facade Pattern: Technology - Episode 3
The API Facade Pattern: Technology - Episode 3
 
Essential API Facade Patterns - Composition (Episode 1)
Essential API Facade Patterns - Composition (Episode 1)Essential API Facade Patterns - Composition (Episode 1)
Essential API Facade Patterns - Composition (Episode 1)
 
Your API is So 2006 - MoDevEast 2011
Your API is So 2006 - MoDevEast 2011Your API is So 2006 - MoDevEast 2011
Your API is So 2006 - MoDevEast 2011
 
Migrating Automation Tests to Postman Monitors and ROI
Migrating Automation Tests to Postman Monitors and ROIMigrating Automation Tests to Postman Monitors and ROI
Migrating Automation Tests to Postman Monitors and ROI
 
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...
Developers are People Too! Building a DX-Based API Strategy Ronnie Mitra, Pri...
 

Destaque

Netflix Edge Engineering Open House Presentations - June 9, 2016
Netflix Edge Engineering Open House Presentations - June 9, 2016Netflix Edge Engineering Open House Presentations - June 9, 2016
Netflix Edge Engineering Open House Presentations - June 9, 2016Daniel Jacobson
 
Maintaining the Front Door to Netflix : The Netflix API
Maintaining the Front Door to Netflix : The Netflix APIMaintaining the Front Door to Netflix : The Netflix API
Maintaining the Front Door to Netflix : The Netflix APIDaniel Jacobson
 
Scaling Twitter - Railsconf 2007
Scaling Twitter - Railsconf 2007Scaling Twitter - Railsconf 2007
Scaling Twitter - Railsconf 2007Alex Payne
 
PyData NYC 2015 - Automatically Detecting Outliers with Datadog
PyData NYC 2015 - Automatically Detecting Outliers with Datadog PyData NYC 2015 - Automatically Detecting Outliers with Datadog
PyData NYC 2015 - Automatically Detecting Outliers with Datadog Datadog
 
Canary Analyze All the Things
Canary Analyze All the ThingsCanary Analyze All the Things
Canary Analyze All the Thingsroyrapoport
 
Scaling Twitter
Scaling TwitterScaling Twitter
Scaling TwitterBlaine
 
MicroServices at Netflix - challenges of scale
MicroServices at Netflix - challenges of scaleMicroServices at Netflix - challenges of scale
MicroServices at Netflix - challenges of scaleSudhir Tonse
 
Scaling LinkedIn - A Brief History
Scaling LinkedIn - A Brief HistoryScaling LinkedIn - A Brief History
Scaling LinkedIn - A Brief HistoryJosh Clemm
 

Destaque (10)

Netflix Edge Engineering Open House Presentations - June 9, 2016
Netflix Edge Engineering Open House Presentations - June 9, 2016Netflix Edge Engineering Open House Presentations - June 9, 2016
Netflix Edge Engineering Open House Presentations - June 9, 2016
 
Maintaining the Front Door to Netflix : The Netflix API
Maintaining the Front Door to Netflix : The Netflix APIMaintaining the Front Door to Netflix : The Netflix API
Maintaining the Front Door to Netflix : The Netflix API
 
Culture
CultureCulture
Culture
 
Scaling Twitter - Railsconf 2007
Scaling Twitter - Railsconf 2007Scaling Twitter - Railsconf 2007
Scaling Twitter - Railsconf 2007
 
PyData NYC 2015 - Automatically Detecting Outliers with Datadog
PyData NYC 2015 - Automatically Detecting Outliers with Datadog PyData NYC 2015 - Automatically Detecting Outliers with Datadog
PyData NYC 2015 - Automatically Detecting Outliers with Datadog
 
Canary Analyze All the Things
Canary Analyze All the ThingsCanary Analyze All the Things
Canary Analyze All the Things
 
Scaling Twitter
Scaling TwitterScaling Twitter
Scaling Twitter
 
From SOA to MSA
From SOA to MSAFrom SOA to MSA
From SOA to MSA
 
MicroServices at Netflix - challenges of scale
MicroServices at Netflix - challenges of scaleMicroServices at Netflix - challenges of scale
MicroServices at Netflix - challenges of scale
 
Scaling LinkedIn - A Brief History
Scaling LinkedIn - A Brief HistoryScaling LinkedIn - A Brief History
Scaling LinkedIn - A Brief History
 

Semelhante a Scaling the Netflix API

API Strategy Evolution at Netflix
API Strategy Evolution at NetflixAPI Strategy Evolution at Netflix
API Strategy Evolution at NetflixMichael Hart
 
Oscon2014 Netflix API - Top 10 Lessons Learned
Oscon2014 Netflix API - Top 10 Lessons LearnedOscon2014 Netflix API - Top 10 Lessons Learned
Oscon2014 Netflix API - Top 10 Lessons LearnedSangeeta Narayanan
 
API World 2013 - Transforming the Netflix API
API World 2013 - Transforming the Netflix APIAPI World 2013 - Transforming the Netflix API
API World 2013 - Transforming the Netflix APIBenjamin Schmaus
 
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Atlassian
 
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Atlassian
 
Gluecon 2013 netflix api crash course
Gluecon 2013   netflix api crash courseGluecon 2013   netflix api crash course
Gluecon 2013 netflix api crash courseBenjamin Schmaus
 
Stranger Things: The Forces that Disrupt Netflix
Stranger Things: The Forces that Disrupt NetflixStranger Things: The Forces that Disrupt Netflix
Stranger Things: The Forces that Disrupt NetflixC4Media
 
Netapp Michael Galpin
Netapp Michael GalpinNetapp Michael Galpin
Netapp Michael Galpinrajivmordani
 
Move Fast;Stay Safe:Developing & Deploying the Netflix API
Move Fast;Stay Safe:Developing & Deploying the Netflix APIMove Fast;Stay Safe:Developing & Deploying the Netflix API
Move Fast;Stay Safe:Developing & Deploying the Netflix APISangeeta Narayanan
 
Why your next serverless project should use AWS AppSync
Why your next serverless project should use AWS AppSyncWhy your next serverless project should use AWS AppSync
Why your next serverless project should use AWS AppSyncYan Cui
 
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventPros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventSudhir Tonse
 
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
 
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017Amazon Web Services
 
Evolution of the Netflix API
Evolution of the Netflix APIEvolution of the Netflix API
Evolution of the Netflix APIC4Media
 
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...CA Technologies
 
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech Talks
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech TalksDeep Dive - Amazon Kinesis Video Streams - AWS Online Tech Talks
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech TalksAmazon Web Services
 
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014Amazon Web Services
 
Microservices, Events, and Breaking the Data Monolith with Kafka
Microservices, Events, and Breaking the Data Monolith with KafkaMicroservices, Events, and Breaking the Data Monolith with Kafka
Microservices, Events, and Breaking the Data Monolith with KafkaVMware Tanzu
 
Evaluating and Testing Web APIs
Evaluating and Testing Web APIsEvaluating and Testing Web APIs
Evaluating and Testing Web APIsSmartBear
 
Future of SOA & Modern APIs
Future of SOA & Modern APIsFuture of SOA & Modern APIs
Future of SOA & Modern APIsRam Lakshmanan
 

Semelhante a Scaling the Netflix API (20)

API Strategy Evolution at Netflix
API Strategy Evolution at NetflixAPI Strategy Evolution at Netflix
API Strategy Evolution at Netflix
 
Oscon2014 Netflix API - Top 10 Lessons Learned
Oscon2014 Netflix API - Top 10 Lessons LearnedOscon2014 Netflix API - Top 10 Lessons Learned
Oscon2014 Netflix API - Top 10 Lessons Learned
 
API World 2013 - Transforming the Netflix API
API World 2013 - Transforming the Netflix APIAPI World 2013 - Transforming the Netflix API
API World 2013 - Transforming the Netflix API
 
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
 
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
Extend Your Use of JIRA by Solving Your Unique Concerns: An Exposé of the New...
 
Gluecon 2013 netflix api crash course
Gluecon 2013   netflix api crash courseGluecon 2013   netflix api crash course
Gluecon 2013 netflix api crash course
 
Stranger Things: The Forces that Disrupt Netflix
Stranger Things: The Forces that Disrupt NetflixStranger Things: The Forces that Disrupt Netflix
Stranger Things: The Forces that Disrupt Netflix
 
Netapp Michael Galpin
Netapp Michael GalpinNetapp Michael Galpin
Netapp Michael Galpin
 
Move Fast;Stay Safe:Developing & Deploying the Netflix API
Move Fast;Stay Safe:Developing & Deploying the Netflix APIMove Fast;Stay Safe:Developing & Deploying the Netflix API
Move Fast;Stay Safe:Developing & Deploying the Netflix API
 
Why your next serverless project should use AWS AppSync
Why your next serverless project should use AWS AppSyncWhy your next serverless project should use AWS AppSync
Why your next serverless project should use AWS AppSync
 
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInventPros and Cons of a MicroServices Architecture talk at AWS ReInvent
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
 
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...
 
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017
NEW LAUNCH! Introducing Amazon Kinesis Video Streams - ABD216 - re:Invent 2017
 
Evolution of the Netflix API
Evolution of the Netflix APIEvolution of the Netflix API
Evolution of the Netflix API
 
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...
End-to-End Continuous Delivery with CA Automic Release Automation and CA Serv...
 
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech Talks
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech TalksDeep Dive - Amazon Kinesis Video Streams - AWS Online Tech Talks
Deep Dive - Amazon Kinesis Video Streams - AWS Online Tech Talks
 
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014
(ARC303) Panning for Gold: Analyzing Unstructured Data | AWS re:Invent 2014
 
Microservices, Events, and Breaking the Data Monolith with Kafka
Microservices, Events, and Breaking the Data Monolith with KafkaMicroservices, Events, and Breaking the Data Monolith with Kafka
Microservices, Events, and Breaking the Data Monolith with Kafka
 
Evaluating and Testing Web APIs
Evaluating and Testing Web APIsEvaluating and Testing Web APIs
Evaluating and Testing Web APIs
 
Future of SOA & Modern APIs
Future of SOA & Modern APIsFuture of SOA & Modern APIs
Future of SOA & Modern APIs
 

Mais de Daniel Jacobson

NPR Presentation at Wolfram Data Summit 2010
NPR Presentation at Wolfram Data Summit 2010NPR Presentation at Wolfram Data Summit 2010
NPR Presentation at Wolfram Data Summit 2010Daniel Jacobson
 
NPR: Digital Distribution Strategy: OSCON2010
NPR: Digital Distribution Strategy: OSCON2010NPR: Digital Distribution Strategy: OSCON2010
NPR: Digital Distribution Strategy: OSCON2010Daniel Jacobson
 
NPR's Digital Distribution and Mobile Strategy
NPR's Digital Distribution and Mobile StrategyNPR's Digital Distribution and Mobile Strategy
NPR's Digital Distribution and Mobile StrategyDaniel Jacobson
 
NPR API Usage and Metrics
NPR API Usage and MetricsNPR API Usage and Metrics
NPR API Usage and MetricsDaniel Jacobson
 
OpenID Adoption UX Summit
OpenID Adoption UX SummitOpenID Adoption UX Summit
OpenID Adoption UX SummitDaniel Jacobson
 

Mais de Daniel Jacobson (6)

NPR Presentation at Wolfram Data Summit 2010
NPR Presentation at Wolfram Data Summit 2010NPR Presentation at Wolfram Data Summit 2010
NPR Presentation at Wolfram Data Summit 2010
 
NPR: Digital Distribution Strategy: OSCON2010
NPR: Digital Distribution Strategy: OSCON2010NPR: Digital Distribution Strategy: OSCON2010
NPR: Digital Distribution Strategy: OSCON2010
 
NPR's Digital Distribution and Mobile Strategy
NPR's Digital Distribution and Mobile StrategyNPR's Digital Distribution and Mobile Strategy
NPR's Digital Distribution and Mobile Strategy
 
NPR API Usage and Metrics
NPR API Usage and MetricsNPR API Usage and Metrics
NPR API Usage and Metrics
 
OpenID Adoption UX Summit
OpenID Adoption UX SummitOpenID Adoption UX Summit
OpenID Adoption UX Summit
 
NPR : Examples of COPE
NPR : Examples of COPENPR : Examples of COPE
NPR : Examples of COPE
 

Último

TrustArc Webinar - How to Live in a Post Third-Party Cookie World
TrustArc Webinar - How to Live in a Post Third-Party Cookie WorldTrustArc Webinar - How to Live in a Post Third-Party Cookie World
TrustArc Webinar - How to Live in a Post Third-Party Cookie WorldTrustArc
 
LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0DanBrown980551
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNeo4j
 
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxGraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxNeo4j
 
Automation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsAutomation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsDianaGray10
 
Flow Control | Block Size | ST Min | First Frame
Flow Control | Block Size | ST Min | First FrameFlow Control | Block Size | ST Min | First Frame
Flow Control | Block Size | ST Min | First FrameKapil Thakar
 
My key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIMy key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIVijayananda Mohire
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024Brian Pichman
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarThousandEyes
 
Introduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationIntroduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationKnoldus Inc.
 
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTSIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTxtailishbaloch
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
 
Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.IPLOOK Networks
 
IT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingIT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingMAGNIntelligence
 
Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...DianaGray10
 
.NET 8 ChatBot with Azure OpenAI Services.pptx
.NET 8 ChatBot with Azure OpenAI Services.pptx.NET 8 ChatBot with Azure OpenAI Services.pptx
.NET 8 ChatBot with Azure OpenAI Services.pptxHansamali Gamage
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)IES VE
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfInfopole1
 

Último (20)

TrustArc Webinar - How to Live in a Post Third-Party Cookie World
TrustArc Webinar - How to Live in a Post Third-Party Cookie WorldTrustArc Webinar - How to Live in a Post Third-Party Cookie World
TrustArc Webinar - How to Live in a Post Third-Party Cookie World
 
LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4j
 
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxGraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
 
Automation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsAutomation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projects
 
Flow Control | Block Size | ST Min | First Frame
Flow Control | Block Size | ST Min | First FrameFlow Control | Block Size | ST Min | First Frame
Flow Control | Block Size | ST Min | First Frame
 
My key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIMy key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAI
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? Webinar
 
Introduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationIntroduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its application
 
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENTSIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
SIM INFORMATION SYSTEM: REVOLUTIONIZING DATA MANAGEMENT
 
Key Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdfKey Trends Shaping the Future of Infrastructure.pdf
Key Trends Shaping the Future of Infrastructure.pdf
 
Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.
 
SheDev 2024
SheDev 2024SheDev 2024
SheDev 2024
 
IT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingIT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced Computing
 
Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...
 
.NET 8 ChatBot with Azure OpenAI Services.pptx
.NET 8 ChatBot with Azure OpenAI Services.pptx.NET 8 ChatBot with Azure OpenAI Services.pptx
.NET 8 ChatBot with Azure OpenAI Services.pptx
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdf
 

Scaling the Netflix API

Notas do Editor

  1. For Netflix, our API strategy can be discussed in the form of an iceberg. The public API strategy is the prominent, highly visible part of the iceberg that is above water. It is also the smallest part of the iceberg, in terms of mass. Meanwhile, the large mass of ice underwater that you cannot see is the most critical and biggest part of the iceberg. The visible part of the iceberg represents the public API and the part underwater is the internal strategy. Netflix’s strategy over the years has shifted from public to internal.
  2. There are many ways to think of “scale”…
  3. People generally think of scaling as growth in traffic…
  4. And traffic growth typically equals server growth. System scaling requires a balance between need and capacity.
  5. As a result, as traffic grows over time, scaling effectively requires adding capacity to support it.
  6. To have an effective engineering organization, you need to scale in a variety of ways, not just in your systems. This presentation discusses these scaling needs. Of course, I will focus a bit on systems, but that is not the only area that requires focus to be successful.
  7. Netflix’s focus is on providing the best global streaming video experience for TV shows and movies.
  8. We now have more than 36 million global subscribers in more than 40 countries.
  9. Those subscribers consume more than a billion hours of streaming video a month which accounts for about 33% of the peak Internet traffic in North America.
  10. We are also on more than 1,000 different device types.
  11. With that scale, it is critical to have the right organizational structure to support it.
  12. Our organization, and therefore our system, is set up to support a distributed architecture.
  13. I like to think of the Netflix product engineering teams that support development and innovation as being shaped like an hourglass…
  14. In the top end of the hourglass, we have our device and UI teams who build out great user experiences on Netflix-branded devices. To put that into perspective, there are a few hundred more device types that we support than engineers at Netflix.
  15. At the bottom end of the hourglass, there are several dozen dependency teams who focus on things like metadata, algorithms, authentication services, A/B test engines, etc.
  16. The API is at the center of the hourglass, acting as a broker of data.
  17. This hourglass architecture allows us to scale horizontal easily with our device integrations and our backend dependencies.
  18. The glue that helps all of this work as effectively as it does is our engineering culture. We hire great, seasoned engineers with excellent judgment and engineering acumen and enable them to build systems quickly by giving them the right context and helping them work together in a highly aligned way.
  19. With the adoption of the devices, API traffic took off! We went from about 600 million requests per month to about 42 BILLION requests in just two years.
  20. Fast forward, those numbers have grown to more than 2B incoming requests per day, which translates into more than 14B outbound calls to our dependencies per day.
  21. Rather than relying on data centers, we have moved everything to the cloud! Enables rapid scaling with relative ease. Adding new servers, in new locations, take minutes. And this is critical when the service needs to grow from 1B requests a month to 2B requests a day in a relatively short period of time.
  22. That is much more preferable for us than spending our time, money and energy in data centers, adding servers, dealing with power supplies, etc.
  23. Instead, we spend time in tools such as Asgard, created by Netflix staff, to help us manage our instance types and counts in AWS. Asgard is available in our open source repository at github.
  24. Another feature afforded to us through AWS to help us scale is Autoscaling. This is the Netflix API request rates over a span of time. The red line represents a potential capacity needed in a data center to ensure that the spikes could be handled without spending a ton more than is needed for the really unlikely scenarios.
  25. Through autoscaling, instead of buying new servers based on projected spikes in traffic and having systems administrators add them to the farm, the cloud can dynamically and automatically add and remove servers based on need.
  26. Our distributed architecture, with the number of systems involved, can get quite complicated. Each of these systems talks to a large number of other systems within our architecture.
  27. Because of this complexity, the API can play a key role in ensuring overall system resiliency. None of our dependency services have SLAs of 100%. Given our unique position in the hourglass as being the last point just before delivery to our users, the API can serve a critical role in protecting our customers from various failures throughout the system.
  28. In the old world, the system was vulnerable to such failures. For example, if one of our dependency services fails…
  29. Such a failure could have resulted in an outage in the API.
  30. And that outage likely would have cascaded to have some kind of substantive impact on the devices.
  31. The challenge for the API team is to be resilient against dependency outages, to ultimately insulate Netflix customers from low level system problems and to keep them happy.
  32. To solve this problem, we created Hystrix, as wrapping technology that provides fault tolerance in a distributed environment. Hystrix is also open source and available at our github repository.
  33. To achieve this, we implemented a series of circuit breakers for each library that we depend on. Each circuit breaker controls the interaction between the API and that dependency. This image is a view of the dependency monitor that allows us to view the health and activity of each dependency. This dashboard is designed to give a real-time view of what is happening with these dependencies (over the last two minutes). We have other dashboards that provide insight into longer-term trends, day-over-day views, etc.
  34. So, going back to the engineering diagram…
  35. If that same service fails today…
  36. We simply disconnect from that service.
  37. And replace it with an appropriate fallback. The fallback, ideally is a slightly degrade, but useful offering. If we cannot get that, however, we will quickly provide a 5xx response which will help the systems shed load rather than queue things up (which could eventually cause the system as a whole to tip over).
  38. This will keep ourcustomers happy, even if the experience may be slightly degraded. It is important to note that different dependency libraries have different fallback scenarios. And some are more resilient than others. But the overall sentiment here is accurate at a high level.
  39. Hystrix and other techniques throughout our engineering organization help keep things resilient. We also have an army of tools that introduce failures to the system which will help us identify problems before they become really big problems.
  40. The army is the Simian Army, which is a fleet of monkeys who are designed to do a variety of things, in an automated way, in our cloud implementation. Chaos Monkey, for example, periodically terminates AWS instances in production to see how the system as a whole will respond once that server disappears. Latency Monkey introduces latencies and errors into a system to see how it responds. The system is too complex to know how things will respond in various circumstances, so the monkeys expose that information to us in a variety of ways. The monkeys are also available in our open source github repository.
  41. Going global has a different set of scaling challenges. AWS enables us to add instances in new regions that are closer to our customers.
  42. To help us manage our traffic across regions, as well as within given regions, we created Zuul. Zuul is open source in our github repository.
  43. Zuul does a variety of things for us. Zuul fronts our entire streaming application as well as a range of other services within our system.
  44. Moreover, Zuul is the routing engine that we use for Isthmus, which is designed to marshall traffic between regions, for failover, performance or other reasons.
  45. Again, we have more than 1,000 different device types that we support. Across those devices, there is a high degree of variability. As a result, we have seen inefficiencies and problems emerge across our implementations. Those issues also translate into issues with the API interaction.
  46. For example, screen size could significantly affect what the API should deliver to the UI. TVs with bigger screens that can potentially fit more titles and more metadata per title than a mobile phone. Do we need to send all of the extra bits for fields or items that are not needed, requiring the device itself to drop items on the floor? Or can we optimize the deliver of those bits on a per-device basis?
  47. Different devices have different controlling functions as well. For devices with swipe technologies, such as the iPad, do we need to pre-load a lot of extra titles in case a user swipes the row quickly to see the last of 500 titles in their queue? Or for up-down-left-right controllers, would devices be more optimized by fetching a few items at a time when they are needed? Other devices support voice or hand gestures or pointer technologies. How might those impact the user experience and therefore the metadata needed to support them?
  48. The technical specs on these devices differ greatly. Some have significant memory space while others do not, impacting how much data can be handled at a given time. Processing power and hard-drive space could also play a role in how the UI performs, in turn potentially influencing the optimal way for fetching content from the API. All of these differences could result in different potential optimizations across these devices.
  49. Many UI teams needing metadata means many requests to the API team. In the one-size-fits-all API world, we essentially needed to funnel these requests and then prioritize them. That means that some teams would need to wait for API work to be done. It also meant that, because they all shared the same endpoints, we were often adding variations to the endpoints resulting in a more complex system as well as a lot of spaghetti code. Make teams wait due to prioritization was exacerbated by the fact that tasks took longer because the technical debt was increasing, causing time to build and test to increase. Moreover, many of the incoming requests were asking us to do more of the same kinds of customizations. This created a spiral that would be very difficult to break out of…
  50. That variability ultimately caused us to do some introspection on our API layer.
  51. Many other companies have seen similar issues and have introduced orchestration layers that enable more flexible interaction models.
  52. Odata, HYQL, ql.io, rest.li and others are examples of orchestration layers. They address the same problems that we have seen, but we have approached the solution in a very different way.
  53. We evolved our discussion towards what ultimately became a discussion between resource-based APIs and experience-based APIs.
  54. The original OSFA API was very resource oriented with granular requests for specific data, delivering specific documents in specific formats.
  55. The interaction model looked basically like this, with (in this example) the PS3 making many calls across the network to the OSFA API. The API ultimately called back to dependent services to get the corresponding data needed to satisfy the requests.
  56. In this mode, there is a very clear divide between the Client Code and the Server Code. That divide is the network border.
  57. And the responsibilities have the same distribution as well. The Client Code handles the rendering of the interface (as well as asking the server for data). The Server Code is responsible of gathering, formatting and delivering the data to the UIs.
  58. And ultimately, it works. The PS3 interface looks like this and was populated by this interaction model.
  59. But we believe this is not the optimal way to handle it. In fact, assembling a UI through many resource-based API calls is akin to pointillism paintings. The picture looks great when fully assembled, but it is done by assembling many points put together in the right way.
  60. We have decided to pursue an experience-based approach instead. Rather than making many API requests to assemble the PS3 home screen, the PS3 will potentially make a single request to a custom, optimized endpoint.
  61. In an experience-based interaction, the PS3 can potentially make asingle request across the network border to a scripting layer (currently Groovy), in this example to provide the data for the PS3 home screen. The call goes to a very specific, custom endpoint for the PS3 or for a shared UI. The Groovy script then interprets what is needed for the PS3 home screen and triggers a series of calls to the Java API running in the same JVM as the Groovy scripts. The Java API is essentially a series of methods that individually know how to gather the corresponding data from the dependent services. The Java API then returns the data to the Groovy script who then formats and delivers the very specific data back to the PS3.
  62. We also introduced RxJava into this layer to improve our ability to handle concurrency and callbacks. RxJava is open source in our github repository.
  63. In this model, the border between Client Code and Server Code is no longer the network border. It is now back on the server. The Groovy is essentially a client adapter written by the client teams.
  64. And the distribution of work changes as well. The client teams continue to handle UI rendering, but now are also responsible for the formatting and delivery of content. The API team, in terms of the data side of things, is responsible for the data gathering and hand-off to the client adapters. Of course, the API team does many other things, including resiliency, scaling, dependency interactions, etc. This model is essentially a platform for API development.
  65. If resource-based APIs assemble data like pointillism, experience-based APIs assemble data like a photograph. The experience-based approach captures and delivers it all at once.
  66. Again, the dependency chains in our system are quite complicated. The API alone includes more than 500 client jars from dependencies when building the application. That complexity makes it virtually impossible to maintain a high degree of nimbleness while having a very high confidence in testing and the code that will be deployed.
  67. As a result, our philosophy is to act fast (ie. get code into production as quickly as possible), then react fast (ie. response to issues quickly as they arise).
  68. That said, we do spend a lot of time testing. We just don’t intend to make the system bullet-proof before deploying. Instead, we have employed some techniques to help us learn more about what the new code will look like in production.
  69. Two such examples are canary deployments and what we call red/black deployments.
  70. The canary deployments are comparable to canaries in coal mines. We have many servers in production running the current codebase. We will then introduce a single (or perhaps a few) new server(s) into production running new code. Monitoring the canary servers will show what the new code will look like in production.
  71. If the canary encounters problems, it will register in any number of ways. The problems will be determined based on a comprehensive set of tools that will automatically perform health analysis on the canary.
  72. If the canary shows errors, we pull it/them down, re-evaluate the new code, debug it, etc.
  73. We will then repeat the process until the analysis of canary servers look good.
  74. If the new code looks good in the canary, we can then use a technique that we call red/black deployments to launch the code. Start with red, where production code is running. Fire up a new set of servers (black) equal to the count in red with the new code.
  75. Then switch the pointer to have external requests point to the black servers. Sometimes, however, we may find an error in the black cluster that was not detected by the canary. For example, some issues can only be seen with full load.
  76. If a problem is encountered from the black servers, it is easy to rollback quickly by switching the pointer back to red. We will then re-evaluate the new code, debug it, etc.
  77. Once we have debugged the code, we will put another canary up to evaluate the new changes in production.
  78. If the new code looks good in the canary, we can then bring up another set of servers with the new code.
  79. Then we will switch production traffic to the new code.
  80. If everything still looks good, we disable the red servers and the new code becomes the new red servers.
  81. All of the open source components discussed here, as well as many others, can be found at the Netflix github repository.