migrating to microservices - goto conference native high rate of change code pushes can cause floods...
TRANSCRIPT
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Migrating to MicroservicesAdrian Cockcroft @adrianco
Technology Fellow - Battery Ventures GOTO Berlin - November 2014
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Typical reactions to my Netflix talks…
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Typical reactions to my Netflix talks…
“You guys are crazy! Can’t believe it”
– 2009
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Typical reactions to my Netflix talks…
“You guys are crazy! Can’t believe it”
– 2009
“What Netflix is doing won’t work”
– 2010
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Typical reactions to my Netflix talks…
“You guys are crazy! Can’t believe it”
– 2009
“What Netflix is doing won’t work”
– 2010 It only works for ‘Unicorns’ like
Netflix” – 2011
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Typical reactions to my Netflix talks…
“You guys are crazy! Can’t believe it”
– 2009
“What Netflix is doing won’t work”
– 2010 It only works for ‘Unicorns’ like
Netflix” – 2011
“We’d like to do that but can’t”
– 2012
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Typical reactions to my Netflix talks…
“You guys are crazy! Can’t believe it”
– 2009
“What Netflix is doing won’t work”
– 2010 It only works for ‘Unicorns’ like
Netflix” – 2011
“We’d like to do that but can’t”
– 2012
“We’re on our way using Netflix OSS code”
– 2013
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What I learned from my time at Netflix
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What I learned from my time at Netflix
•Speed wins in the marketplace
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development•High trust, low process, no hand-offs between teams
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development•High trust, low process, no hand-offs between teams•Freedom and responsibility culture
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development•High trust, low process, no hand-offs between teams•Freedom and responsibility culture•Don’t do your own undifferentiated heavy lifting
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development•High trust, low process, no hand-offs between teams•Freedom and responsibility culture•Don’t do your own undifferentiated heavy lifting•Use simple patterns automated by tooling
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What I learned from my time at Netflix
•Speed wins in the marketplace•Remove friction from product development•High trust, low process, no hand-offs between teams•Freedom and responsibility culture•Don’t do your own undifferentiated heavy lifting•Use simple patterns automated by tooling•Self service cloud makes impossible things instant
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Cloud Adoption
%*&!”
By Simon Wardley http://enterpriseitadoption.com/
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Cloud Adoption
%*&!”
By Simon Wardley http://enterpriseitadoption.com/
2009
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Cloud Adoption
%*&!”
By Simon Wardley http://enterpriseitadoption.com/
2009
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Cloud Adoption
@adrianco’s new job at the intersection of cloud and Enterprise IT
%*&!”
By Simon Wardley http://enterpriseitadoption.com/
20142009
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This is the year that Enterprises finally embraced cloud.
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This is the year that Enterprises finally embraced cloud.
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This is the year that Enterprises finally embraced cloud.
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This is the year that Enterprises finally embraced cloud.
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What separates incumbents from
disruptors?
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“It isn't what we don't know that gives us trouble, it's what we
know that ain't so.” !
Will Rogers
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Assumptions
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Optimizations
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Assumption: Process prevents
problems
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Organizations build up slow complex “Scar
tissue” processes
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"This is the IT swamp draining manual for anyone who is neck deep in alligators.”
1984 2014
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Product Development
Processes
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Observe
Orient
Decide
Act Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
INNOVATION
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
Model Hypotheses
INNOVATION
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
Model Hypotheses
BIG DATA
INNOVATION
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Model Hypotheses
BIG DATA
INNOVATION
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Model Hypotheses
BIG DATA
INNOVATION
CULTURE
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Incremental Features
Automatic Deploy
Launch AB Test
Model Hypotheses
BIG DATA
INNOVATION
CULTURE
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Incremental Features
Automatic Deploy
Launch AB Test
Model Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Incremental Features
Automatic Deploy
Launch AB Test
Model Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure Customers
Continuous Delivery
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Observe
Orient
Decide
Act
Land grab opportunity Competitive
Move
Customer Pain Point
Analysis
JFDI
Plan Response
Share Plans
Incremental Features
Automatic Deploy
Launch AB Test
Model Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure Customers
Continuous Delivery
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Breaking Down the SILOs
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
Mgr
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
Mgr
Product Team Using Monolithic DeliveryProduct Team Using Monolithic Delivery
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
MgrProduct Team Using Microservices
Product Team Using Monolithic Delivery
Product Team Using MicroservicesProduct Team Using Microservices
Product Team Using Monolithic Delivery
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
MgrProduct Team Using Microservices
Product Team Using Monolithic Delivery
Platform TeamProduct Team Using MicroservicesProduct Team Using Microservices
Product Team Using Monolithic Delivery
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
MgrProduct Team Using Microservices
Product Team Using Monolithic Delivery
Platform TeamA P I
Product Team Using MicroservicesProduct Team Using Microservices
Product Team Using Monolithic Delivery
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Breaking Down the SILOs
QA DBA Sys Adm
Net Adm
SAN AdmDevUXProd
MgrProduct Team Using Microservices
Product Team Using Monolithic Delivery
Platform Team
DevOps is a Re-Org
A P I
Product Team Using MicroservicesProduct Team Using Microservices
Product Team Using Monolithic Delivery
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Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release Integration
Ops Replace Old With New
Release
Monolithic service updates
Works well with a small number of developers and a single language like php, java or ruby
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Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release Integration
Ops Replace Old With New
Release
Bugs
Monolithic service updates
Works well with a small number of developers and a single language like php, java or ruby
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Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release Integration
Ops Replace Old With New
Release
Bugs
Bugs
Monolithic service updates
Works well with a small number of developers and a single language like php, java or ruby
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Developer
Developer
Developer
Developer
Developer
Old Release Still Running
Release Plan
Release Plan
Release Plan
Release Plan
Immutable microservice deployment is faster, scales with large teams and diverse platform components
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Developer
Developer
Developer
Developer
Developer
Old Release Still Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Immutable microservice deployment is faster, scales with large teams and diverse platform components
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Developer
Developer
Developer
Developer
Developer
Old Release Still Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Bugs
Immutable microservice deployment is faster, scales with large teams and diverse platform components
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Developer
Developer
Developer
Developer
Developer
Old Release Still Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Deploy Feature to Production
Bugs
Deploy Feature to Production
Immutable microservice deployment is faster, scales with large teams and diverse platform components
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Non-Destructive Production Updates
● “Immutable Code” Service Pattern
● Existing services are unchanged, old code remains in service
● New code deploys as a new service group
● No impact to production until traffic routing changes
● A|B Tests, Feature Flags and Version Routing control traffic
● First users in the test cell are the developer and test engineers
● A cohort of users is added looking for measurable improvement
● Finally make default for everyone, keeping old code for a while
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Developing at the Speed of Docker
Developers • Compile/Build • Seconds
Extend container • Package dependencies • Seconds
PaaS deploy Container • Docker startup • Seconds
etc…
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Developing at the Speed of Docker
Emerging market for Docker runtime orchestration options
Developers • Compile/Build • Seconds
Extend container • Package dependencies • Seconds
PaaS deploy Container • Docker startup • Seconds
etc…
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What Happened?Rate of change
increased
Cost and size and risk of change
reduced
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Disruptor: Continuous Delivery with Microservices
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A Microservice Definition !
Loosely coupled service oriented architecture with bounded contexts
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A Microservice Definition !
Loosely coupled service oriented architecture with bounded contexts
If every service has to be updated at the same time it’s not loosely coupled
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A Microservice Definition !
Loosely coupled service oriented architecture with bounded contexts
If every service has to be updated at the same time it’s not loosely coupled
If you have to know too much about surrounding services you don’t have a bounded context. See the Domain Driven Design book by Eric Evans.
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Separate Concerns with Microservices
http://en.wikipedia.org/wiki/Conway's_law
● Invert Conway’s Law – teams own service groups and backend stores
● One “verb” per single function micro-service, size doesn’t matter
● One developer independently produces a micro-service
● Each micro-service is it’s own build, avoids trunk conflicts
● Deploy in a container: Tomcat, AMI or Docker, whatever…
● Stateless business logic. Cattle, not pets.
● Stateful cached data access layer using replicated ephemeral instances
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High Availability Patterns
● Business logic isolation in stateless micro-services
● Immutable code with instant rollback
● Auto-scaled capacity and deployment updates
● Distributed across availability zones and regions
● De-normalized single function NoSQL data stores
● See over 40 NetflixOSS projects at netflix.github.com
● Get “Technical Indigestion” trying to keep up with techblog.netflix.com
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US Bandwidth April 2014
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US Bandwidth April 2014
ELB
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US Bandwidth April 2014
ELB
OpenConnect
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Microservices Development ● Client libraries
Even if you start with a raw protocol, a client side driver is the end-state Best strategy is to own your own client libraries from the start
● Multithreading and Non-blocking Calls Reactive model RxJava uses Observable to hide concurrency cleanly Netty can be used to get non-blocking I/O speedup over Tomcat container
● Circuit Breakers – See Fluxcapacitor.com for code NetflixOSS Hystrix, Turbine, Latency Monkey, Ribbon/Karyon Also look at Finagle/Zipkin from Twitter
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Microservice Datastores● Book: Refactoring Databases
SchemaSpy to examine schema structure Denormalization into one datasource per table or materialized view
● Polyglot Persistence Use a mixture of database technologies, behind REST data access layers See NetflixOSS Storage Tier as a Service HTTP (staash.com) for MySQL and C*
● CAP – Consistent or Available when Partitioned Look at Jepsen torture tests for common systems aphyr.com/tags/jepsen There is no such thing as a consistent distributed system, get over it…
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Cloud Native Monitoring and Microservices
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Cloud Native● High rate of change
Code pushes can cause floods of new instances and metrics Short baseline for alert threshold analysis – everything looks unusual
● Ephemeral Configurations Short lifetimes make it hard to aggregate historical views Hand tweaked monitoring tools take too much work to keep running
● Microservices with complex calling patterns End-to-end request flow measurements are very important Request flow visualizations get overwhelmed
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Microservice Based Architectures
See http://www.slideshare.net/LappleApple/gilt-from-monolith-ruby-app-to-micro-service-scala-service-architecture
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“Death Star” Architecture Diagrams
As visualized by Appdynamics, Boundary.com and Twitter internal tools
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“Death Star” Architecture Diagrams
Netflix Gilt Groupe (12 of 450) Twitter
As visualized by Appdynamics, Boundary.com and Twitter internal tools
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Continuous Delivery and DevOps
●Changes are smaller but more frequent
● Individual changes are more likely to be broken
●Changes are normally deployed by developers
●Feature flags are used to enable new code
● Instant detection and rollback matters much more
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Whoops! I didn’t mean that! Reverting…
Not cool if it takes 5 minutes to see it failed and 5 more to see a fix No-one notices if it only takes 5 seconds to detect and 5 to see a fix
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NetflixOSS Hystrix/Turbine Circuit Breaker
http://techblog.netflix.com/2012/12/hystrix-dashboard-and-turbine.html
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NetflixOSS Hystrix/Turbine Circuit Breaker
http://techblog.netflix.com/2012/12/hystrix-dashboard-and-turbine.html
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Low Latency SaaS Based Monitors
www.vividcortex.com and www.boundary.com
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Metric to display latency needs to be less than human attention span (~10s)
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Prototyping Ideas
Model and visualize microservices !See github.com/adrianco/spigo Simulate Protocol Interactions in Go !See github.com/adrianco/d3grow Dynamic visualization concept
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Separation of Concerns
Bounded Contexts
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Forward Thinking
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Forward Thinking
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Forward Thinking
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Forward Thinking
http://eugenedvorkin.com/seven-micro-services-architecture-advantages/
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Any Questions?
Disclosure: some of the companies mentioned are Battery Ventures Portfolio Companies See www.battery.com for a list of portfolio investments
● Battery Ventures http://www.battery.com ● Adrian’s Blog http://perfcap.blogspot.com ● Slideshare http://slideshare.com/adriancockcroft !
● Monitorama Opening Keynote Portland OR - May 7th, 2014 - Video available ● GOTO Chicago Opening Keynote May 20th, 2014 - Video available ● Qcon New York – Speed and Scale - June 11th, 2014 - Video available ● Structure - Cloud Trends - San Francisco - June 19th, 2014 - Video available ● GOTO Copenhagen/Aarhus – Denmark – Sept 25th, 2014 ● DevOps Enterprise Summit - San Francisco - Oct 21-23rd, 2014 #DOES14 - Videos available ● GOTO Berlin - Germany - Nov 6th, 2014 ● AWS Re:Invent - Cloud Native Cost Optimization - Las Vegas - November 14th, 2014 ● Dockercon Europe - Amsterdam - December 4th, 2014