Using AWS to Build a Graph-Based Product Recommendation System (BDT303) | AWS re:Invent 2013

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Andre Fatala & Renato Pedigoni

November 14, 2013

Using AWS to Build a Graph-based Product Recommendation System

Friday, November 15, 13

About Magazine LuizaAbout Magazine Luiza

Magazine Luiza is one of the largest household appliance retail chains in Brazil. Focused on providing durable goods for Brazil's middle and lower-to-middle income classes.

• 731 stores• 8 distribution centers• more than 23.000 workers• 22.8 million customers• multi-channel strategy


Recommendation systems


Graphs


Graph Stack

Distributed Graph Database Distributed database management system


Graph Stack

• Used for OLTP queries



Graph Stack

• Used for OLTP queries• Native integration with Tinkerpop



Graph Stack

• Continuously available with no single point of failure• Used for OLTP queries• Native integration with Tinkerpop



Graph Stack

• Continuously available with no single point of failure• Elastic scalability




Graph Stack

• Continuously available with no single point of failure• Elastic scalability• Caching layer




Graph Stack

• Continuously available with no single point of failure• Elastic scalability• Caching layer• Built-in replication




Storing users data

Cassandra cluster

m2.xlarge m2.xlarge

m2.xlarge m2.xlarge

ElasticLoad Balancing

EC2instance

EC2instance

m2.xlarge m2.xlargeAuto Scaling

API instances


In graph words…

person


In graph words…

person session


In graph words…

person sessioncreated


In graph words…

person

channel

sessioncreated


In graph words…

person

channel

sessioncreated

visited


In graph words…

person

channel

session

item

created

visited


In graph words…

person

channel

session

item

created

visited

viewed


In graph words…

person

channel

session

item

created

visited

viewed +1


In graph words…

person

channel

session

item

created

visited

+1add_to_cart


In graph words…

person

channel

session

item

created

visited

+1add_to_cart +13


In graph words…

person

channel

session

item

created

visited

+1+13bought


In graph words…

person

channel

session

item

created

visited

+1+13bought +21


Base recommendations

Who viewed this item also viewed



Who bought this item also bought



Bought after viewing this item



Upselling


How to query the graph for recs?


Gremlin Graph Language



• Groovy DSL for graph traversals



• Groovy DSL for graph traversals• Easy to learn



• Groovy DSL for graph traversals• Easy to learn• Great community



• Groovy DSL for graph traversals• Easy to learn• Great community• Part of the Tinkerpop stack



• Groovy DSL for graph traversals• Easy to learn• Great community• Part of the Tinkerpop stack• Works with any Blueprints enabled graph database


People who viewed a product

LED TV 42"

Renato

Fatala

LED TV 40"

LCD TV 42"

LED50"

viewed

viewed

viewed

viewed

viewed

viewed


g.v(4).in(‘viewed’)People who viewed a product

LED TV 42"

Renato

Fatala

LED TV 40"

LCD TV 42"

LED50"

viewed

viewed

viewed

viewed

viewed

viewed


Who viewed this product also viewed

LED TV 42"

Renato

Fatala

LED TV 40"

LCD TV 42"

LED50"

viewed

viewed

viewed

viewed

viewed

viewed


Who viewed this product also viewed

g.v(4).in(‘viewed’).out(‘viewed’)

LED TV 42"

Renato

Fatala

LED TV 40"

LCD TV 42"

LED50"

viewed

viewed

viewed

viewed

viewed

viewed


Processing data with Spot Instances


Bob

Simple Queue Service(Amazon SQS)

dispatch a task to Amazon SQS

containing the product id



EC2instance

m1.large

EC2instance

m1.large

EC2instance

m1.large

Spot instances

…

Bob


consume Amazon SQS tasks



process W*A*recommendations



Simple Storage Service (Amazon S3)

EC2instance

m1.large

EC2instance

m1.large

EC2instance

m1.large

Spot instances

…

Bob





process W*A*recommendations

sync logs

sync logs



Personalized e-mails

Abandoned cart Price dropped


Personalized e-mailsUsers receive e-mails when:



• A product has a price drop

Users receive e-mails when:



• A product has a price drop• Abandoned a product on cart




• A product has a price drop• Abandoned a product on cart• Visits many similar products




Bob

Bob API



Bob

Bob API

notifies an

user interactionMailer

Manager

m1.largeSimple Queue Service

(Amazon SQS)


containing the customer id

Bobby Mailer



EC2instance

m1.large

EC2instance

m1.large

EC2instance

m1.large

Spot instances

…

Bob

Bob API

notifies an


Manager


(Amazon SQS)




find the best recommendationfor that user

Bobby Mailer



Simple EmailService (Amazon SES)

EC2instance

m1.large

EC2instance

m1.large

EC2instance

m1.large

Spot instances

…

Bob

Bob API

notifies an


Manager


(Amazon SQS)





Bobby Mailer

send the e-mail



Simple Storage Service (Amazon S3)


EC2instance

m1.large

EC2instance

m1.large

EC2instance

m1.large

Spot instances

…

Bob

Bob API

notifies an


Manager


(Amazon SQS)





Bobby Mailer

send the e-mail

sync logs

sync logs


Analytics with Faunus

Graph Analytics Engine Distributed computing

Amazon EMR



Graph Analytics Engine Distributed computing• Provides graphs input/output formats

Amazon EMR



Graph Analytics Engine Distributed computing• Provides graphs input/output formats and traversal language for graphs

Amazon EMR



Graph Analytics Engine Distributed computing• Distributed processing of large data sets across clusters• Provides graphs input/output formats

and traversal language for graphs

Amazon EMR



Graph Analytics Engine Distributed computing• Distributed processing of large data sets across clusters• Designed to scale

• Provides graphs input/output formats and traversal language for graphs

Amazon EMR



Graph Analytics Engine Distributed computing• Distributed processing of large data sets across clusters• Designed to scale• Detect and handle failures at application layer

• Provides graphs input/output formats and traversal language for graphs

Amazon EMR


Analytics in Graphs with AWS



> g.V.has(‘element_type’, ‘person’).age.mean()34.683232



> g.V.has(‘element_type’, ‘person’).age.mean()34.683232

Amazon EMR


Backup process

nodetool script Amazon S3


AmazonRoute 53

InternetGateway

ElasticLoad Balancing

EC2instance

EC2instance

m2.xlarge m2.xlargeAuto Scaling

API instances

Cassandra cluster

Backups

AmazonS3

Logs

AmazonS3

CACHE

AmazonElastiCache

Amazon EMR

m2.xlarge m2.xlarge

m2.xlarge m2.xlarge

Queue Queue Queue

Amazon SQS

EC2instance

m2.xlarge

EC2instance

m2.xlarge

Spot instances


Infrastructure

Auto Scaling


Metrics


Metrics

• 4.3 million Magazine Luiza identified customers


Metrics

• 4.3 million Magazine Luiza identified customers• 50,000 nodes “products”


Metrics

• 4.3 million Magazine Luiza identified customers• 50,000 nodes “products”• 90 million total nodes


Metrics

• 4.3 million Magazine Luiza identified customers• 50,000 nodes “products”• 90 million total nodes• 350 million total edges


Metrics

• 4.3 million Magazine Luiza identified customers• 50,000 nodes “products”• 90 million total nodes• 350 million total edges• 700 GB of data


Metrics

• 4.3 million Magazine Luiza identified customers• 50,000 nodes “products”• 90 million total nodes• 350 million total edges• 700 GB of data• Peaks with 20,000 reads/sec - Cassandra Cluster


Results matter…

10x faster 60%


Results matter…

January 2013 March 2013 May 2013 July 2013 September 2013


Results matter…

Solution A alone



Results matter…

First Bob testsSolution A alone



Results matter…

First Bob tests

Bob out for 2 weeks

Solution A alone



Results matter…

First Bob tests

Bob out for 2 weeks Bob alone

Solution A alone



Results matter…

First Bob tests

Bob alone



Results matter…

First Bob tests

Bob alone

190%



Next steps


Next steps

• Use Faunus to pre-process all W*A* recommendations


Next steps

• Use Faunus to pre-process all W*A* recommendations• Algorithms to identify communities in graph


Next steps

• Use Faunus to pre-process all W*A* recommendations• Algorithms to identify communities in graph• Cassandra replication between regions


Please give us your feedback on this presentation

As a thank you, we will select prize winners daily for completed surveys!

BDT303 Thank You


Using AWS to Build a Graph-Based Product Recommendation System (BDT303) | AWS re:Invent 2013

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Transcript of Using AWS to Build a Graph-Based Product Recommendation System (BDT303) | AWS re:Invent 2013