scaling runa inc big data e-commerce service with aws

Runa on AWSBig Data & Machine Intelligence for a SaaS

Startup

Runa

a

SaaS

converts Shoppers to Buyers

for

Online Commerce Sites

by presenting

Dynamic Personalized Promotions

on the

Merchant’s Website

in Real-Time

in the

Shopping Flow

Tech Challenges

Big Data

JavaScript client collects activity on

every Merchant page for every Shopper

One or more Ajax call & Event Store to Runa per

Merchant page view

Step function increase of calls

and stores as each new

Merchant added

We capture everything we

can and store it forever

Expecting to grow to

thousands of merchants

That’s a lot of Data

Processing Data with

Machine Intelligence

Batch Processing for Statistical Analysis

and Reports

Real-Time Rule based inserts of

Promotions

Why AWS for Runa?

At First (a couple years ago)

Not Much Money in the Bank

Didn’t Know exactly

what were making

Or exactly how we were going to do it

Prototyped with Ruby / Rails / MySQL

Then

Prototype became

Production

EC2 & AWS let us scale the prototype to

Beta Production

Flexibility to incrementally

refine service & infrastructure

Confidence we could scale

as we added Merchants

More Recently

Incrementally added next-gen

Tech & Full Production

Goal: Everything

Horizontally Scalable

Batch Processing & Infinite Storage

Map / Reduce & BigTable

via

Hadoop & HBase

Flexible Real-Time parallel processing

via

Clojure / Swarmiji

Opscode ChefManagement & Monitoring

Consumers onMerchant Websites

InternetAdmin & Merchant

Dashboard(Rails)

Runtime Rules

Merchant Info

Merchants

Internet

AnalyticsReporting

Monitor & Recovery

Data Collectors

Hadoop / HBaseMap / ReducePetabyte Store

Load Balancer

HTTP

SharedSessionMemory

HTTPDispatchers

RedisMem

CacheRedisMem

CacheRedisMem

CacheRedisMem

CacheRedisMem

Cache

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

HBaseHBase

Amazon S3Data Backup

9+ Amazon EC2 Instances

Amazon ElasticLoad Balancer

3+ Amazon EC2Instances

Cheshire / Swarmiji

Dynamic Runtime

Queue

AWS Elastic Load Balancer

Rails App ServersNginx / Unicorn EC2 m1.xlarge

MySQL Master / Slave

EC2 m1.xlarge EBS

Legacy Runtime

Rails App Nginx/UnicornMySQL Master/Slave EC2 m1.xlarge / EBS

Merchant Dashboard

EC2 m1.xlarge

HBase / Hadoop

EC2 m1.xlargeEBSRabbitMQ

Cheshire / SwarmijiRedis

EC2 m1.xlarge

Clojure Based RuntimeAWS Elastic

Load Balancer

EC2 m1.large

Opscode Chef

Monitoring

EC2 m1.large

All Deployed on

Deployment & Configuration Management

via Opscode Chef

Good Things

Able to Start Small

Then

GROW BIGGER

Having the flexibility to throw

“Hardware” at our Prototype got

us to market faster

Ability to launch test and staging

environments almost at will

“Hardware” as

“Software”

Living in “interesting”

times

Managing Complexity

lots of moving parts

Easy to launch a few instances

Impossible to manage

horizontal stacks “by hand”

Must have tool like

Opscode Chef

Chef automates deployment & puts it under

Revision Control

There’s going to be some blood

when using cutting edge tech

Lots of Learning Curves to

climb

Useful Monitoring is

hard but Critical

HBase on AWS may be dangerous

because of Hadoop namenode SPOF

EC2 bill can surprise you if you cavalierly

deploy multiple versions of

horizontally scalable environments

Could not do our startup without

AWS or lots more VC Funding