Discover and learn how to build a microservices platform, get a view of the best of breed architecture, solving common challenges, dig into Netflix stack, Yelp PaaSTA, AirBnB SmartStack, Apache Mesos, SoundCloud, Spinnaker experiences. French audience : the JUG live recording is available here, https://www.youtube.com/watch?v=5LnL1HYmLwY&feature=youtu.be

1. BUILDING MICROSERVICES
Cloud Native patterns, Microservices platforms
November 2015@SteveSfartz

2. MICROSERVICES
• Introduction (why, when, how)
• Cloud Native Patterns
– Resilience
– Service Discovery alternatives
– Microservices platforms
– Gateway
• Organizational changes
• Comparing SOA styles
• Sump up

3. 100 - INTRODUCTION TO MICROSERVICES
Why, Monolithic vs MicroServices, scaling the Monolith
/! If you already know about microservices, jump to slide 28

4. Microservices (2014)
In short, the microservice architectural style is an
approach to developing a single application as a
suite of small services, each running in its own
process and communicating with lightweight
mechanisms, often an HTTP resource API.
These services are built around business capabilities
and independently deployable by fully automated
deployment machinery.
There is a bare minimum of centralized management of
these services, which may be written in different
programming languages and use different data
storage technologies.
Martin Fowler, James Lewis

5. THE MONOLITHIC STYLE
• An application built as a single unit, 3 main parts
– client-side UI, a database , and a server-side application
– code specific to each layer (UI, business logic, DB)
– generally a minimum of 3 languages
• This server-side application is a monolith
– a single logical executable
– any change requires building and deploying a new version
• Locally built and tested on devs’ machines
• CI/CD pipeline to secure production

6. SCALING THE MONOLITH
• Several Monolith instances behind a load balancer
+ Quickest way to scale
+ High availability
- Routing traffic complexity
- Very large code base
- Change cycles tied together
- Limited scalability
 lack of modularity
Load Balancer
Monolith
Client
Client
Client

7. MODULARIZING THE MONOLITH
• Specialized instances of a single codebase
– Modular invocation
Load Balancer Real time
business logic
Client
Client
Client
Monolith
MonolithAuthentication
Monolith
Batches
Load Balancer
- Re-engineering alternative to a rewrite
- Generally does the trick
- Prepare for future by introducing “APIs”

8. MONOLITHIC VERSUS MICROSERVICES
http://martinfowler.com/articles/microservices.html
A monolithic application puts all
its functionnality into a single
process…
… and scales by replacing the monolith
on multiple servers
A microservices architecture
puts each element of
functionnality into a separate
service…
… and scales by distributing these services
across servers, replicating as needed

9. GILT TESTIMONIAL
http://www.infoq.com/news/2015/04/scaling-microservices-gilt

10. GILT TESTIMONIAL
http://www.infoq.com/news/2015/04/scaling-microservices-gilt

11. GOOGLE TRENDS

12. STATE OF THE ART
• Pioneers : Amazon, Netflix, Twitter
– Democratized the Microservices architecture style
– Started opensourcing their building blocks in 2010
• Today
– Large corporations for complex systems that need to
evolve frequently
– Digital startups to ensure their core business will scale and
embrace the long tail

13. AMAZON DNA
« Services by design », Jeff Besos, 2002

14. NETFLIX ECOSYSTEM
• 100s of microservices
• 1,000s of daily production changes
• 10,000s of instances
• 100,000s of customer interactions per minute
• 1,000,000s of customers
• 1,000,000,000s of metrics
• 10,000,000,000 hours of streamed
• 10s of operations engineers
http://fr.slideshare.net/AmazonWebServices/
dvo203-the-life-of-a-netflix-engineer-using-37-of-the-internet

15. 200 – DESIGNING FOR MICROSERVICES
Common characteristics, State of the art

16. COMMON CHARACTERISTICS
1. Componentization via Services
2. Organized around Business Capabilities
3. Products not Projects
4. Smart endpoints and dumb pipes
5. Decentralized governance
6. Decentralized data management
7. Infrastructure automation
8. Design for failure
9. Evolutionary design
http://martinfowler.com/articles/microservices.html

17. 1. COMPONENTIZATION VIA SERVICES
• Services as components rather than libraries
• Services avoid tight coupling by using explicit
remote call mechanisms.
• Services are independently deployable and scalable
• Each service also provides a firm module boundary
– allowing for different services to be written in different
languages, and be managed by different teams .
• A microservice may consist of multiple processes
– that will always be developed and deployed together,
– Ex : an application process and a database that's only
used by that service.

18. 2. ORGANIZED AROUND BUSINESS CAPABILITIES
Any organization that
designs a system will
produce a design
whose structure is a
copy of the
organization's
communication
structure.
Melvyn Conway, 1967.

19. 2. ORGANIZED AROUND BUSINESS CAPABILITIES
• Microservices to solve Conway’s anti-pattern
Cross-functional teams…. … organized around capabilities

20. 3. PRODUCTS NOT PROJECTS
• Standard project model
– deliver pieces of software which are then considered to be
completed,
– hand over to a maintenance organization and disband the
project team
• The Microservices style
– a team should own a product over its full lifetime
– Amazon : You build => You run it

21. 4. SMART ENDPOINTS AND DUMB PIPES
• Be as decoupled and as cohesive as possible
– own domain logic,
– act more as filters in the classical Unix sense
– using simple RESTish protocols and lightweight messaging
• Smarts live in the services, not in-between the
endpoints
– No central tool / bus that includes sophisticated routing,
transformations, process, business rules
• Pre-requisite : turn the chatty in-process
communication of the monolith into coarser-grained
network messaging

22. 5. DECENTRALIZED GOVERNANCE
• The common microservices practice is to choose the
best tool for each job
– Tools are shared inside and outside the organization
– Focus on common problems : data storage, IPC,
infrastructure automation
– Example : Netflix opensource libraries
• Favor independent evolution
– Consumer-driven Service contracts,
– Tolerant Reader Pattern
Minimal over-heads create an opportunity to have
teams responsible for all aspects
- Build their microservices and operate with 24/7 SLAs

23. 6. DECENTRALIZED DATA MANAGEMENT
• No unique data model approach
– the conceptual model differs between microservices
– Reinforce the separation of concerns
• Decentralized data storage
– Polyglot persistence is frequent in microservices architectures
• Manage inconsistencies via the business practices in
place throughout the organization
– Common design : reversal processes versus 2PC distributed
transactions

24. 6. DECENTRALIZED DATA MANAGEMENT

25. 7. INFRASTRUCTURE AUTOMATION
• CI/CD, Real-time monitoring, global system and fine
grained resources dashboards,
• Has become a standard practice
– thanks to public cloud providers,
– but also opensource tools
• a QA stake for monoliths
• a pre-requisite for microservices

26. 8. DESIGN FOR FAILURE
• Any service call can fail
– async consumption
• Best practice : 1 to ZERO sync call
– Circuit Breaker pattern
• Ex : Netflix Stack (Hystrix / Eureka / Ribbon)
– /! provide implementations for various languages
• Infrastructure pre-requisites
– monitor and restore at scale
– simulation tools
• Ex : Netflix Simian Army (Chaos Monkey)

27. 9. EVOLUTIONARY DESIGN
• More granular release planning
• Goal : change tolerance
– Identify change impacts quickly (via automation, service
contracts, automated dependency maps)
– Fix rather than revert
– In case of mandatory breaking change, use versioning
• a last resort option in the microservices world

28. 200 – RESILIENCE
Scaling Microservices

29. SOLID COMMUNICATIONS
• Choose a messaging infrastructure adapted to your
business
– RPC / REST style,
– Request/Response, Streaming
– HTTP, HTTP2, TCP, UDP
• Support async consumption / aggregation
– Leverage parallel code structures on your clients
– ES7 (BabelJS, Google Traceur), go, Scala, Java8
• Ease consumption via clients SDKs
– Automated generation from API definitions or IDL
– Web APIS : Swagger (OADF), RAML
– RPC style : Google gRPC, Twitter Finagle, Apache Thrift

30. SOLID COMMUNICATIONS
• Basic REST is not an option for microservices
• Provide a large set of features
– load balancing,
– fault tolerance,
– caching,
– service discovery,
– mutiple transport and protocols (HTTP, TCP, streaming)
• Example : Netflix Ribbon

31. RESILIENCE IN DISTRIBUTED SYSTEMS
• Failures are common
• An application that depends on 30 services
– where each service has 99.99% uptime
– 99.9930 = 99.7% uptime
– 0.3% of 1 billion requests = 3,000,000 failures
– 2+ hours downtime/month even if all dependencies have
excellent uptime.
• Reality is generally worse.

32. WHEN 1 BACKEND BECOMES LATENT

33. AT 50+ REQ/S, ALL REQUEST THREADS
CAN BLOCK IN SECONDS

34. NETFLIX HYSTRIX
• Latency and Fault Tolerance library
– designed to isolate points of access to remote systems,
services and 3rd party libraries,
– stop cascading failure and enable resilience where failure
is inevitable.
– real time monitoring via Dashboard

35. HYSTRIX DASHBOARD

36. HYSTRIX DASHBOARD
• Circuit Breaker : Open

37. HYSTRIX DASHBOARD

38. 300 – SERVICES DISCOVERY
Scaling Microservices

39. SERVICES DISCOVERY
• Typically, a microservice gets created and
destroyed often
– It must be reconfigured on the fly
– In near real-time, others should find it
• Routing messages is somewhat a challenge
– Service discovery (and registration)
– Load balancing (ingress and egress calls)
• Popular configuration services
– Apache ZooKeeper , HashiCorp Consul, CoreOS Etcd,
Netflix Eureka

40. INTRODUCING PATTERNS
• Cloud Native Applications Architecture Patterns
– Core of the solution to build scalable applications with a
microservices architecture style
• Microservices Discovery patterns
1. Discovery via DNS
2. Discovery via a central Load Balancer
3. Discovery via local Load Balancers
4. Discovery via embedded Load Balancers
Cloud Native
PATTERN

41. DISCOVERY VIA A DNS SERVICE
• dynamic DNS (BIND-DLZ)
• DNS service Record (SRV)
• programmatic DNS-aaS
– Amazon Route53, Apache Mesos-DNS
• leverage CNAMEs to abstract traffic routing
• but … propagation delays, random routing, no
priorization, not consumer oriented
 can do the trick for the first 10s microservices
– /! Resilience
– /! Slow upscaling, no 1 to ZERO
Cloud Native
ANTI
PATTERN

42. APACHE MESOS-DNS

43. DISCOVERY VIA A CENTRAL LOAD BALANCER
• either Home-made LB (Nginx, HA-proxy…)
– /! Secure your SPOF
• or levereage a public cloud provider LB
– Rock solid AWS ELB, automation, model driven, autoscaling
– Route53 CNAMEs to ELBs, Private ELB in VPCs
• Limited « Smartness »
– Version, HTTP header, URI path routing
• Limited automation
– ? Health checks customization, no circuit breaker
• fails short for microservices at scale
Cloud Native
ANTI
PATTERN

44. DOCKER INTERLOCK PLUGIN
https://github.com/ehazlett/interlock

45. DISCOVERY VIA EMBEDDED LOAD BALANCERS
• In-process Load Balancer
– Embedded in each service consumer
– Reads information for Service Registry
+ lightweight
+ composable
- language dependency
• ex: Netflix Ribbon
– java glue around Eureka + Hystrix (+ gateway)
Cloud Native
PATTERN

46. NETFLIX RIBBON
• In-Process Fault Tolerant Smart Load Balancer
– DIY from Netflix github repos
– or leverage Spring Cloud http://start.spring.io/
Dynamic Load balancer
(rules library)
Eureka client
HTTP, TCP, UDP Transport
(based on RxNetty)
Hystrix
Netflix Eureka
(service registry)
Microservices
register
reads

47. DISCOVERY VIA LOCAL LOAD BALANCERS
• Load balancer component on localhost
– also referred as « Sidecar » approach
– ex: AirBnB SmartStack
+ leverage HA-proxy / nginx skills
+ easy to integrate (any language, any IT tools)
+ TCP, HTTP,HTTP2 support
- heavier than in-process load balancers (IPC intermediary)
- hasardous load balancing (individual vs sync’ed)
- chatty at scale (numerous health checks)
 Leverage as an intermediary step
Cloud Native
PATTERN

48. AIRBNB SMARTSTACK
http://nerds.airbnb.com/smartstack-service-discovery-cloud/

49. AIRBNB SMARTSTACK
1. MicroS registers to a local Synapse process
2. Local Synapse retreives dependencies locations from
ZooKeeper and registers watches
3. Local Synapse creates the local HAProxy configuration
4. MicroS registers to a local Nerve process
5. Local Nerve registers the MicroS into ZooKeeper
6. ZooKeeper fires events to Synapses through watches
7. Each synapse instances updates the HA-proxy configuration
8. MicroS sends traffic through its local proxy (egress)
Note : Can be extended with a reverse proxy for ingress
(microservice Gateway Pattern)

50. 200 – MICROSERVICES PLATFORMS
Scaling Microservices at Yelp, Netflix

51. MEET PAASTA
• Yelp’s PaaS for Microservices
• integrates existing opensource components
– Docker : code delivery and containment
– Apache Mesos : code execution and scheduling (incl
Docker)
– Marathon for managing long-running services
– Chronos for running things on a timer (nightly batches)
– SmartStack for service registration and discovery
– Sensu for monitoring/alerting
– Jenkins (optionally) for continuous deployment
• opensourced on Nov 2015 10th
– https://github.com/Yelp/paasta/

52. PAASTA DEPLOYMENT PIPELINE
Airbnb
SmartStack
Chronos
Microservices
Microservice
(Docker containers)
Soa-config
(versionned in git)
Jenkins (optional)
devops
package
describe
Apache Mesos + Marathon
Sensu
Monitoring
Facebook
Scribe Docker
PaaSTA CLI
read
updates
- Devs prepare a Docker Image
- DSL to describe how the service is to
be run (cpu, mem, instances, nerve,
bounce, volumes, monitoring,
blacklists, healthchecks)
- git commit the desired state (DSL)
- PaaSTA CLI to provision your
microservices

53. THE NETFLIX MICROSERVICES JOURNEY
• Migration to AWS
AWS re-invent 2015

54. NETFLIX STACK
• Solid communications
– Hystrix : latency and fault tolerance library
– Eureka : registry for resilient mid-tier load balancing and
failover
– Ribbon : client based smart load balancer
– Servo : monitoring library
– EVCache : distributed in-memory data store for AWS EC2
– RxNetty : reactive extension adaptor for netty
– Karyon : blueprint of a cloud ready microservice
– Zuul : edge gateway
– Falcor : js library for efficient data fetching

57. NETFLIX STACK : AUTOMATION TOOLS
• Asgard (transitionning to Spinnaker)
– specialized AWS console (app deployments, management)
• Spinnaker
– microservices console (clusters, pipelines)
• Atlas
– near realtime operational insights
• Vector
– exposes hand picked high resolution metrics from PCP – Performance Co-Pilot
hosts
• SimianArmy
– services (Monkeys) in the cloud for generating various kinds of failures, detecting
abnormal conditions, and testing our ability to survive them
• Dependencies automatically documented from real traffic
analysis

58. DEPENDENCY GRAPH BASED
ON REAL TRAFFIC

60. VECTOR

61. NETFLIX FALCOR
• JSON Virtual Resources to consume Microservices
http://netflix.github.io/falcor/starter/what-is-falcor.html

62. 200 – MICROSERVICES PLATFORMS
Best of breed architecture

63. MICROSERVICES PLATFORM
• Devtime definition
– Resources, Ports
– Dependencies
• Deployment configuration
– System desired state
– Scalability model
• Runtime
– Discovery, Monitoring,
– Fill gap current vs desired state
Requirements for Cloud Native Microservices
– Packager, Deployment pipeline, Scheduler

64. MICROSERVICES PLATFORM
Gateway Load Balancer
MicroServices
Logs Metrics Health chk
Container, VM, AppServer, PaaS
Deployment
configuration
Discovery
Monitoring
Deployment
Devtime
definition
Executable
artifact
MicroServices
MicroServices Platform

65. MEET NETFLIX SPINNAKER
• Multi-cloud continuous delivery platform
– Release changes with high velocity and confidence
– AWS, Google, Cloud Foundry, (Azure coming)
• Opensourced on Nov 2015, 16th
deployment manager cluster manager

66. SPINNAKER DEPLOYMENT PIPELINE

67. SPINNAKER MANUAL STEP

68. SPINNAKER CLUSTER MANAGEMENT

69. SPINNAKER INTERNALS
http://spinnaker.io/

70. 300 – MICROSERVICES GATEWAY

71. MICROSERVICES GATEWAY
• Edge service to access back-end microservices
– Authentication and Security
– Dynamic routing
– Insights and Monitoring
– Stress testing : gradually increasing the traffic to a cluster in
order to gauge performance.
– Load Shedding : allocating capacity for each type of request
and dropping requests that go over the limit.
– Static Response handling : building some responses directly
at the edge instead of forwarding them to an internal cluster
– Multi-region Resiliency : routing requests across regions in
order to move the edge closer to users

72. MICROSERVICES GATEWAY
• Integrate with the other building blocks of your
Microservices communication infrastructure
– Example : Netflix Zuul
• Think also Bridge / Adapter
– REST  RPC : gRPC-gateway, Finatra
• Lots of building blocks
– Embedded gateways (pick your dev language favorite gateway)
– HA-Proxy plugins
– Nginx, Nginx OpenResty plugins
– Dedicated process with central configuration

73. MASHAPE KONG

74. MASHAPE KONG DEMO
a programmatic gateway as nginx plugins

75. 200 – ORGANIZE FOR MICROSERVICES

76. COMMITTED TEAMS
• Ownership core to team organization
– built into the management of the organization
– make sure that teams have sufficient time to truly own the
applications that they are in charge
• “Products versus Projects” principle
• “Functional versus Divisional” organisations
– And give them 360 view on operations

77. SOUNDCLOUD TESTIMONIAL

78. SOUNDCLOUD TESTIMONIAL
• Actual workflow to go live
– 2 months, 11 steps
– Main elephant is all the dance between front-end and
back-end development
– 11 days are doing actual development work

79. SOUNDCLOUD TESTIMONIAL
• Decision : pairing back-end and front-end devs
– pair fully dedicated to a feature until its completion
– Individually, each person ended up spending more time doing
work per feature

80. SOUNDCLOUD TESTIMONIAL
Designer,
Product manager,
and front-end
Developer
working close to
each other

81. SOUNDCLOUD TESTIMONIAL
• The irreducible complexity of the monolith
– Why do we need Pull Requests?
– Why do people make mistakes so often?
– Why is the code base so complex?
– Why do we need a single code base to implement the
many components?
– Why can’t we have economies of scale for multiple,
smaller, systems?

82. SOUNDCLOUD TESTIMONIAL
• Isolated new features in
dedicated microservices,
isolated from the monolith
• New organization with team
of 3 to 4 people
• Each team is responsible for
decided whether parts of the
Monolith are extracted and
rewritten , or kept

83. COMMITTED TEAMS
• Ownership core to team organization
– built into the management of the organization
– make sure that teams have sufficient time to truly own the
applications that they are in charge
• “Products versus Projects” principle
• “Functional versus Divisional” organisations
– And give them 360 view on operations

84. FAQ
How big should be my microservices ?
RESTish microservices ?
Isn’t it SOA ?
The end of the monoliths ?

85. HOW BIG ?
• To ensure ownership, each team encomprises all
traditional product functions
– functional vs Divisional organisations
– Ppoduct Management, Development, QA, Documentation,
Support
• Sizing depends on the system you’re building
– Amazon 2PT principle - Two Pizza Teams
– 6 to 10 people to build/deploy/manitain a microservice
– an average microservice at Gilt consists of 2000 lines of
code, 5 source files, and is run on 3 instances in production

86. RESTISH MICROSERVICES ?
• REST
– Web langa franca, 100% interoprable
– development cost is generally higher
– best practices : provide client sdks, (ex : generated from
Swagger/RAML or other API description languages)
– performance issues if not well-designed (chatiness)
– best practices : experience based and coarser grained APIs
• RPC
– optimized communication via binary formats
– automated generation from IDL, polyglot by default
– integrated support multples scenarios : req/response,
streaming, bi-directional streaming

87. RESTISH MICROSERVICES ?
• RPC or REST style depends on the system you’re
building and existing skillset
• Whatever the style, your microservices architecture
MUST provide
– Services Discovery,
– Reliable Communications,
– Operations insights (logs, monitoring, alerts, real time
analysis)

88. GOT IT, BUT ISN’T IT SOA ?
• SOA so what ?
– Enterprise SOA
– Event-driven architecture (Pub/Sub)
– Streaming Services (Realtime, TimeSeries)
– Container-Services (ala Docker)
– Nano-ervices (ala AWS Lambda)
• Simply stated : Microservices are a SOA style for a
usually large system whose first goal is scalability
 in details, let’s see how microservices differ from…

89. MICROSERVICES VS ENTERPRISE SOA
• Enterprise SOA is often seen as
– multi-year initiatives, costs millions
– complex protocols with productivity and interoperability
challenges
– central governance model that inhibits change
• Enterprise SOA is more about integrating siloed
monoliths
– generally via a smart and centralized service bus
• Microservices is scalable SOA
– an architectural style to design, develop and deploy a large
and complex system, so that it is easier to scale and evolve

90. √ Componentization via Services
√ Organized around Business Capabilities
Products not Projects
Smart endpoints and dumb pipes
Decentralized governance
Decentralized data management
Infrastructure automation
v Design for failure
√ Evolutionary design
VS ENTERPRISE SOA
Microservices
differentiators

91. VS CORBA
• CORBA is a communication middleware, relying on a
common set of
• See microservices as CORBA done right
– Much more lightweight
– Centered on Service Contracts rather than a global
homogeneous message infrastructure
– Much more scalable, flexible and dynamic thanks to
programmatic infrastructure and CI/CD
– Built out of OSS well-know components

92. VS EVENT DRIVEN ARCHITECTURE
• EDA fits well with Document oriented systems
and information flows
• Communication between microservices can be a mix
of RPC (ie, P2P calls) and EDA calls
• See EDA as a communication pattern for your
microservices
• Can address choregraphy, orchestration, pipeline
requirements

93. VS STREAMING SERVICES
• Streaming services fit well
– if you have large volumes (log entries, IoT),
– and/or if you aim at real time analysis
• Data ingestion endpoint of a microService
– Real time
analysis of a
mobile app
usage
aws:reinvent 2015 - Zillow

94. VS CONTAINER SERVICES
• Containers provide infrastructure components to
deploy microservices - independently one from
another
• See Container Services as a building block of your
global microservices platform
– see 200 – Design for Microservices Architectures

95. VS NANOSERVICES
• Nanoservices are small pieces of code, ie functions
– Example : AWS Lambda, Auth0 webtasks
• A icroservice may leverage 1+ nanoservices

96. SUMP UP

97. MICROSERVICES PATTERNS
• Solid communications
– Fault tolerant librairies
– Service discovey
• Committed teams
– Devops culture
– Code/Test/Deploy/Support, 24/7
– Automation
• Ownership
– Organisation aligned with the strategy
– Insights via real time monitoring

98. PREPARE YOUR MONOLITHS FOR CHANGE
• Velocity of innovation for complex systems
– Keep your monolith as is if you don’t need to speed up
features delivery
• To prepare for the journey
• switch from layered architecture to internal APIs,
• automate integration and deployment,
• reorganize from divisional to functional teams committed
to business and owning their code

99. MICROSERVICES IS A LONG JOURNEY
• Several years to implement
– Communications, infrastructure, automation, monitoring,
team organization
• Leverage existing tools and stacks
– Infrastructure as a Service and Containers
– Continuous delivery platform
– Netflix stack, DIY from OSS building blocks

100. LONG IS THE ROAD TO MICROSERVICES
• leverage OSS stacks, programmatic IaaS and containers + continuous delivery
• mandatory CEO and Business teams support
Where to start from ?
• provide multiple glues, ala spring cloud for JVMs…
• leverage « side car » approaches, ex : Netflix Prana
Multiple langages support
• numerous building blocks requiring continuous updates
• automate, automate, automate
« Technical debt » at scale
• keep control of your microservices segmentation
• dynamic cartography
Dependency Hell
• is your organisation ready ?
• change management
Product Ownership

101. THE END OF MONOLITHS ?
http://www.stavros.io/posts/microservices-cargo-cult/

102. TO GO FURTHER
microservices.io

103. REFERENCES
Microservices, Martin Fowler, James Lewis
– http://martinfowler.com/articles/microservices.html
A day in the life of a Netflix Engineer using 37% of the internet
– http://fr.slideshare.net/AmazonWebServices/dvo203-the-life-of-a-
netflix-engineer-using-37-of-the-internet
Dzone – Geting started with Microservices
– https://dzone.com/refcardz/getting-started-with-microservices
SoundCloud – How we ended up with microservices
– http://philcalcado.com/2015/09/08/how_we_ended_up_with_micros
ervices.html
gRPC - boilerplate to high-performance scalable APIs
– http://fr.slideshare.net/AboutYouGmbH/robert-kubis-grpc-boilerplate-
to-highperformance-scalable-apis-codetalks-2015

104. REFERENCES
SmartStack: Service Discovery in the Cloud
– http://nerds.airbnb.com/smartstack-service-discovery-cloud/
Mesosphere advanced course
– https://open.mesosphere.com/advanced-course/
Opensource Service Discovery
– http://jasonwilder.com/blog/2014/02/04/service-discovery-in-the-
cloud/
Why Use a Container Sidecar for Microservices?
– https://www.voxxed.com/blog/2015/01/use-container-sidecar-
microservices/

105. THANK YOU
Stève SFARTZ
• technical program manager
• enterprise architect, web API specialist,
• scalable & interoperable platforms builder
• dev & ops teams lead
Keeping in touch
• steve@sfartz.com
• twitter : @SteveSfartz

106. BACK IN 1981…

107. … THAT WAS MICRO
Sinclair ZX81
introduced in March 1981
US$ 149.95 assembled
500,000 in first 12 months
CPU: NEC Z-80A, 3.25MHz
RAM: 1K, 64K max
OS: ROM BASIC
Display:22 X 32 texthooks to TV
2 ports: memory, cassette
1 peripheral: Sinclair thermal printer