decomposing applications for deployability and scalability (cfopentour india)

Decomposing applications for deployability and scalability

Chris Richardson

Author of POJOs in ActionFounder of the original CloudFoundry.com

@crichardsoncrichardson@vmware.comhttp://plainoldobjects.com/

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Presentation goal

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How decomposing applications improves

deployability and scalability

vmc push About-Chris

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Developer Advocate for CloudFoundry.com

Signup at http://cloudfoundry.com cfopentour2012

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Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate?

Let’s imagine you are building an e-commerce application

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Tomcat

Traditional web application architecture

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Browser

WAR

MySQL Database

ShippingService

AccountingService

InventoryService

StoreFrontUI

developtestdeploy

Simple to

Apache

scale

But there are problems with a monolithic architecture

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Users expect a rich, dynamic and interactive experience

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Java Web Application

Browser

HTTP Request

HTML/Javascript

Old style UI architecture isn’t good enough

Real-time web ≅ NodeJS

Obstacle to frequent deployments§Need to redeploy everything to change one component§Interrupts long running background (e.g. Quartz) jobs§Increases risk of failure

Fear of change

§Updates will happen less often§e.g. Makes A/B testing UI really difficult

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Overloads your IDE and container

10Slows down development

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Shipping team

Accounting team

Engineering

Obstacle to scaling development

E-commerce application

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WAR

Shipping

Accounting

InventoryService

StoreFrontUI

Shipping team

Accounting team

Inventory team

UI team

Obstacle to scaling development

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Lots of coordination and communication required

Obstacle to scaling development

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Requires long-term commitment to a technology

stack

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Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate?

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The scale cube

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X axis - horizontal duplication

Z axis

- data

parti

tionin

g

Y axis - functionaldecomposition

Scale

by sp

litting

simila

r

thing

s

Scale by splitting different things

Y-axis scaling - application level

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WAR

ShippingService

AccountingService

InventoryService

StoreFrontUI

Y-axis scaling - application level

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Store front web application

shipping web application

inventory web application

Apply X axis cloning and/or Z axis partitioning to each service

ShippingService

AccountingService

InventoryServiceStoreFrontUI

accounting web application

Partitioning strategies§Partition by verb, e.g. shipping service§Partition by noun, e.g. inventory service§Single Responsibility Principle§Unix utilities - do one focussed thing well

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Something of an art

Real world examples

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http://highscalability.com/amazon-architecture

Between 100-150 services are accessed to build a page.

http://techblog.netflix.com/

http://www.addsimplicity.com/downloads/eBaySDForum2006-11-29.pdf

http://queue.acm.org/detail.cfm?id=1394128

There are drawbacks

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Complexity

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See Steve Yegge’s Google Platforms Rant re Amazon.com

Multiple databases =

Transaction management challenges

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When to use it?

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In the beginning: •You don’t need it •It will slow you down

Later on:•You need it•Refactoring is painful

But there are many benefits§Scales development: develop, deploy and scale each service independently§Update UI independently§Improves fault isolation§Eliminates long-term commitment to a single technology stack

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Modular, polyglot, multi-framework applications

Two levels of architecture

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System-level

ServicesInter-service glue: interfaces and communication mechanismsSlow changing

Service-level

Internal architecture of each serviceEach service could use a different technology stackPick the best tool for the jobRapidly evolving

If services are small...§Regularly rewrite using a better technology stack§Adapt system to changing requirements and better technology without a total rewrite§Pick the best developers rather than best <pick a language> developers ⇒ polyglot culture

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Fred George “Developer Anarchy”

The human body as a system

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50 to 70 billion of your cells die each day

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Yet you (the system) remain you

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Can we build software systems with these characteristics?

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http://dreamsongs.com/Files/WhitherSoftware.pdf

http://dreamsongs.com/Files/DesignBeyondHumanAbilitiesSimp.pdf

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Agenda§The (sometimes evil) monolith§Decomposing applications into services§How do services communicate?

Inter-service communication options§Synchronous HTTP ⇔ asynchronous AMQP

§Formats: JSON, XML, Protocol Buffers, Thrift, ...

§Even via the database

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Asynchronous is preferred

JSON is fashionable but binary format is more efficient

StoreFrontUI

wgrus-store.war

AccountingService

wgrus-billing.war

InventoryService

wgrus-inventory.war

ShippingService

wgrus-shipping.war

MySQL

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RabbitMQ(Message Broker)

Asynchronous message-based communication

Benefits§Decouples caller from server§Caller unaware of server’s coordinates (URL)§Message broker buffers message when server is down/slow

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Drawbacks§Additional complexity of message broker §RPC using messaging is more complex

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Writing code that calls services

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The need for parallelism

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Service A

Service B

Service C

Service D

b = serviceB()

c = serviceC()

d = serviceD(b, c)

Call in parallel

Java Futures are a great concurrency abstraction

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http://en.wikipedia.org/wiki/Futures_and_promises

Akka’s composable futures are even better

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Using Akka futures

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def callB() : Future[...] = ...def callC() : Future[...] = ...def callD() : Future[...] = ...

val future = for { (b, c) <- callB() zip callC(); d <- callD(b, c) } yield d

val result = Await.result(future, 1 second)

http://doc.akka.io/docs/akka/2.0.1/scala/futures.html

Two calls execute in parallel

And then invokes D

Get the result of D

Spring Integration§Implements EAI patterns§Provides the building blocks for a pipes and filters architecture§Enables development of application components that are•loosely coupled•insulated from messaging infrastructure

§Messaging defined declaratively

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Handling failure

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Service A Service B

Errors happen in distributed systems

About Netflix

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> 1B API calls/day1 API call ⇒ average 6 service calls

Fault tolerance is essential

http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

Use timeouts and retries

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Never wait forever

Errors can be transient ⇒ retry

http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

Service A

Service B

Use per-dependency bounded thread pool

47http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

Runnable 1

Runnable 2

Runnable ...

bounded queue

Task 1

Task 2

Task ...

bounded thread pool

Limits number of outstanding requests

Fails fast if service is slow or down

Use a circuit breakerHigh error rate ⇒ stop calling temporarily

Down ⇒ wait for it to come back up

Slow ⇒ gives it a chance to recover

48http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

On failureReturn cached data

Return default data

Fail fast

49http://techblog.netflix.com/2012/02/fault-tolerance-in-high-volume.html

AvoidFailing

Summary

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Monolithic applications are simple to develop and deploy

BUT have significant drawbacks

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Apply the scale cube

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X axis - horizontal duplicationZ ax

is - d

ata pa

rtitio

ning

Y axis - functionaldecomposition

§Modular, polyglot, and scalable applications§Services developed, deployed and scaled independently

Questions?

@crichardson crichardson@vmware.comhttp://slideshare.net/chris.e.richardson/

www.cloudfoundry.com promo code: cfopentour2012