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Architecting for Scale
Microservices at Netflix-Uber-Spotify Scale
Pooyan	Jamshidi
Foundations	of	
Software	Engineering
Learning	Goals
• Understand	the	value	of	microservices for	building	complex	
applications	that	need	to	operate	at	higher	scale
• Identify	requirements	that	derive	companies	to	migrate	to	
microservices (contrast	of	requirements	between	companies)
• Understand	strategies	for	reliability of microservice architecture	
either	at	micro-level	using	design	patterns	or	at	a	larger	level
• Build	agile	team	structure	that	enable	large	scale	companies	to	move	
fast	(organizational	challenges)
• Understand	challenges	that	Netflix-Uber-Spotify	faced	in	realizing	
microservice based	applications
2
Disclaimer
• I	used	materials	from	
• Netflix	blog
• Spotify,	Uber	and	Netflix’s	architects	GOTO	talks
• And	some	other	sources	referenced	in	the	slides
• I’m	a	postdoc	in	Christian’s	group
• Software	Engineering	+	Machine	Learning
• I	worked	as	a	software	practitioners	for	7	years
• Pre-PhD
• 4	years	as	a	developer
• 3	years	as	an	architect
• Involved	in	migration	to	cloud	and	microservices
2 IEEE SOFTWARE | PUBLISHED BY THE IEEE COMPUTER SOCIETY 0740-7459/16/$33.00 © 2016 IEEE
Microservices
Architecture
Enables DevOps
Migration to a Cloud-Native
Architecture
Armin Balalaie and Abbas Heydarnoori, Sharif University of
Technology
Pooyan Jamshidi, Imperial College London
// This article reports on experiences and lessons learned
during incremental migration and architectural refactoring of
a commercial mobile back end as a service to microservices
architecture. It explains how the researchers adopted
DevOps and how this facilitated a smooth migration. //
A LOOK AT the searches related to
the term “microservices” on Google
Trends revealed that the top searches
are now technology driven. This im-
plies that the time of general search
terms such as “What is microser-
vices?” has now long passed. Not
only are software vendors (for ex-
ample, IBM and Microsoft) using
microservices and DevOps practices,
but also content providers (for exam-
ple, Netflix and the BBC) have ad-
opted and are using them.
In addition, Google Trends re-
veals that both DevOps and mi-
croservices are growing concepts,
with an equal rate of growth after
2014 (see Figure 1). Although Dev-
Ops can also be applied to mono-
lithic software systems, microservices
enable effective implementation of
DevOps by promoting the impor-
tance of small teams.1 (For more on
DevOps and Microservices, see the
related sidebar.)
A microservices architecture is a
cloud-native architecture that aims
to realize software systems as a
package of small services. Each ser-
vice is independently deployable on
a potentially different platform and
technological stack. It can run in
its own process while communicat-
ing through lightweight mechanisms
such as RESTful or RPC-based
APIs—for example, Finagle. (REST
stands for Representational State
Transfer.) In this setting, each ser-
vice is a business capability that can
utilize various programming lan-
guages and data stores and is devel-
oped by a small team.2
Migrating monolithic architec-
tures to microservices brings in
many benefits. In particular, it pro-
vides adaptability to technological
changes to avoid technology lock-in
and, more important, reduced time-
to-market and better development
team structuring around services.3
Here we explain our experiences
and lessons learned during incre-
mental migration of Backtory (www.
backtory.com), a commercial mo-
bile back end as a service (MBaaS),
to microservices in the context of
DevOps. Microservices help Back-
tory in various ways, especially in
shipping new features more fre-
quently and providing scalability for
the collective set of users from differ-
ent mobile-app developers.
Furthermore, we report on migra-
tion patterns we developed on the
basis of our observations in migra-
tion projects. Practitioners can use
these patterns to migrate monolithic
software systems to microservices. In
addition, system consultants can use
FOCUS: DEVOPS
What	is	the	most	interesting	aspect	that	
you	have	learned	from	the	Netflix	talk?
Tradeoff	in	software	architecture
• Everything	is	tradeoff
• Try	to	make	them	intentionally
Organ	Systems
Each	organ	has	a	purpose
Organs	form	systems
Systems	form	an	organism
ELB
and	so	is	taking	traffic
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Largest	Internet	TV	network
86	million	members
~190	countries,	10s	of	languages
125m	hours	content	per	day
Microservices on	AWS
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Netflix	DVD	Data	Center	- 2000
Linux	Host
Apache
Tomcat
Javaweb
STORE
Load	Balancer
BILLING
HTTP
JDBC
DB	Link
HTTP/S
Monolithic	code	base
Monolithic	database
Tightly	coupled	architecture
What	microservices are	not
What	microservices are	not
Enterprise Service Bus (ESB)
Privacy
Service (PS)
Service
Consumer
Service
Consumer
Service
Consumer
Service
Provider
Service
Provider
Service
Provider
Other
Services
Routing
Service Registry Transport
Replication
• Message Routing
• Message Monitoring
• Service Replication
• Language transforma
Edge
ELB
Zuul
NCCP
API
Middle	Tier	&	Platform
Product
• Bucket	testing
• Subscriber
• Recommendations
Platform
• Routing
• Configuration
• Crypto
Persistence
• Cache
• Database
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Architectural	pattern	1:	API	Gateway	
12
Source:	Kasun Indrasiri,	Microservices in	Practice:	From	Architecture	to	Deployment
Architectural	pattern	2:	Inter-process	
Communication	in	a	Microservices Architecture	
13
Architectural	pattern	3:	Service	Discovery	in	a	
Microservices Architecture	
14
Architectural	pattern	4:	Event-Driven	Data	
Management	for	Microservices
15
There are two kinds of channels, point-to-point and publish-subscribe:
• A point-to-point channel delivers a message to exactly one of the consumers that are
reading from the channel. Services use point-to-point channels for the one-to-one
interaction styles described earlier
• A publish-subscribe channel delivers each message to all of the attached consumers.
Services use publish-subscribe channels for the one-to-many interaction styles
described above
Figure 3-4 shows how the taxi-hailing application might use publish-subscribe channels
Figure 3-4. Using publish-subscribe channels in a taxi-hailing application.
DISPATCHER
TRIP CREATED
DRIVER PROPOSED
TRIP
MANAGEMENT
PASSENGER
MANAGEMENT
DRIVER
MANAGEMENT
Chris	Richadson,	Microservices from	Design	to	Deployment
Architectural	pattern	5:	Decentralized	Data	
Management
Christian	Posta,	The	Hardest	Part	About	Microservices:	Your	Data
Architectural	pattern	6:	Choosing	a	
Microservices Deployment	Strategy	
17
Architectural	pattern	7:	Security
18
Source:	Kasun Indrasiri,	Microservices in	Practice:	From	Architecture	to	Deployment
What	architectural	patterns	you	can	
identify	within	these	architectures?
Edge
ELB
Zuul
NCCP
API
Middle	Tier	&	Platform
Product
• Bucket	testing
• Subscriber
• Recommendations
Platform
• Routing
• Configuration
• Crypto
Persistence
• Cache
• Database
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Microservices at	Uber
21
Microservices
at	Spotify
22
Why	reliability	matters	in	
microservices world?
Linux	Host
Linux	Host
Linux	Host
Linux	Host
Intra-service	Requests
Linux	Host
Apache Tomcat
Linux	Host
Apache Tomcat
Network	latency,	congestion,	failure
Logical	or	scaling	failure
Service	A Service	B
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Crossing	the	Chasm
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Cascading	Failure
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Architecting for Scale
Vaccination
Device Service	B	
Service	C
Internet EdgeZuul
Service	A	
ELB
FITSynthetic	transactions
Override	by	device	or	account
%	of	live	traffic	up	to	100%
Fault	Injection	Testing	(FIT)
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Device Service	B	
Service	C
Internet EdgeZuul
Service	A	
ELB
FIT
Fault	Injection	Testing	(FIT)
Enforced	throughout	the	call	path
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
ELB
API
API	
Gateway
App	1
App	2
App	4
App	5
App	6
App	3
App	7
App	8
99.99
99.99
99.99
99.99
99.99
99.99
99.99
99.99
Proxy
99.99 99.99
Combinatorial	Math
99.9910 =	99.9
Critical	Microservices
Persistence
In	the	presence	of	a	network	partition,	you	must	choose	
between	consistency	and	availability
CAP	Theorem
DB
DB
DB
Network	B
Network	C
Network	D
Service
Network	A
X
Zone	A
Zone	B
Zone	C
Zone	B
Zone	C
Clien
t
Zone	A
Local	Quorum
(Typical)
100ms
Eventual	Consistency
Infrastructure
December	24th,	2012
US-East-1
Canada
No	place	to	go
US
Latin	America
US-East-1US-West-2 EU-West-1
Regional	failover
x x
Regional fail-over
Ruslan Meshenberg,	Microservices at	Netflix	Scale:	Principles,	Tradeoffs	&	Lessons	Learned
Regional	failover
Regional fail-over
Ruslan Meshenberg,	Microservices at	Netflix	Scale:	Principles,	Tradeoffs	&	Lessons	Learned
What	is	a	stateless	service?
What	is	a	stateless	service?
• Not	a	cache	or	a	database
• Frequently	accessed	metadata
• No	instance	affinity
• Loss	a	node	is	a	non-event
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
Minimum	size
Desired	capacity
Maximum	size
Scale	out	as	needed
S3AMI	retrieved	on	demand
Compute	efficiency
Node	failure
Traffic	spikes
Performance	bugs
Auto	Scaling	Groups
Cluster	A Cluster	D
Edge	Cluster
Cluster	B
Cluster	C
Surviving Instance Failure
Source:	Josh	Evans,	Mastering	Chaos	- A	Netflix	Guide	to	Microservices
What	is	a	stateful service?
What	is	a	stateless	service?
• Databases	and	caches
• Custom	apps	which	hold	data
• Loss	of	a	node	is	a	notable	event
Dedicated	Shards	– An	Antipattern
Squid	1 Squid	2 Squid	3
Client	Application
Subscriber	Client	Library
Cache	Client Service	Client
S S S S.	.	.
DB DB DB DB.	.	.
Squid	n
HA	Proxy
Set	1 Set	2 Set	3 Set	n
X
Redundancy	is	fundamental
Zone	A Zone	B Zone	C
.	.	..	.	..	.	.
EVCache Writes
Client	Application
Client	Library
EVCache	Client
Client	Application
Client	Library
EVCache	Client
Client	Application
Client	Library
EVCache	Client
.	.	.
Zone	A Zone	B Zone	C
Client	Application
Client	Library
EVCache	Client
.	.	..	.	..	.	.
EVCache Reads
Client	Application
Client	Library
EVCache	Client
Client	Application
Client	Library
EVCache	Client
.	.	.
Why	automation,	in	all	software	
dev/ops	stages,	is	important?
Autonomic	Nervous
System
You	don’t	have	to	think	about	
digestion	or	breathing
Priorities
Our Priorities
1. Innovation
3. Efficiency
2. Reliability
Ruslan Meshenberg,	Microservices at	Netflix	Scale:	Principles,	Tradeoffs	&	Lessons	Learned
Innovation:	tight	coupling	doesn’t	work
Monolithic	vs	Microservice-based	
Applications
59
Lee	Atchison,	Architecting	for	Scale:	High	Availability	for	Your	Growing	Applications
End	to	end	ownership
60
End-end ownership + velocity
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Architect
Design
Develop
ReviewTest
Deploy
Run
Support
Ruslan Meshenberg,	Microservices at	Netflix	Scale:	Principles,	Tradeoffs	&	Lessons	Learned
Kevin	Goldsmith,	Microservices @	Spotify
Server
Core Library
Platform Platform Platform Platform
Infrastructure
Kevin	Goldsmith,	Microservices @	Spotify
Challenges
Synchronization
Client UX implementation
Core Library Implementation
depends on
depends on
depends onServer Implementation
Infrastructure Implementation
Kevin	Goldsmith,	Microservices @	Spotify
Kevin	Goldsmith,	Microservices @	Spotify
Architecting for Scale
Full-stack autonomous
teams
Requires you to structure your
application in loosely coupled parts
Architecture	evolution	of	Spotify
67
Kevin	Goldsmith,	Microservices @	Spotify
Architecture	evolution	of	Spotify
68
69
70
71
LoadBallancer
72
Microservices:	Yay!
• Easier	to	Scale
• Easier	to	test
• Easier	to	deploy
• Easier	to	monitor	
• They	can	versioned	independently
Microservices:	Boo!
• Monitoring	lots	of	services
• Documentations	
• Increased	latency
75
What does this look like at Spotify?
‣ 810activeservices
‣ ~10Systemspersquad
‣ ~1.7Systemsperpersonwithaccessto
productionservers
‣ ~1.15Systemspermemberof
Technology
Microservices
at	Spotify
76
As of April 2016:
Uber Cities Worldwide: 400+
Countries: 70
Employees: 6,000+
Uber
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
Architecting for Scale
Microservices at	Uber
79
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
pre-history PHP (outsourced)
Dispatch Node.JS, moving Go
Core Services Python, moving to Go
Maps Python and Java
Data Python and Java
Metrics Go
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
LANGUAGES
Hard to share code
Hard to move between teams
WIWIK: Fragments the culture
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
Matt	Ranney,	What	I	Wish	I	Had	Known	Before	Scaling	Uber	to	1000	Services
Summary
• Microservices may	be	a	right	solution	for	building	complex	
applications	that	need	to	operate	at	higher	scale
• Tradeoffs	that	companies	made	to	migrate	to	microservices (contrast	
of	requirements	between	companies)
• Making	reliable	microservice architecture	requires	strategies	to	deal	
with	failure	either	at	micro-level	or	at	a	larger	level
• Microservices architecture	help	to	build	agile	team	structure	that	
enable	large	scale	companies	to	move	fast	(organizational	challenges)

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