Salvatore Incandela "Loyalty cashback - Scaling with MongoDB"

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Firenze, 25 Settembre

Loyalty cashback -‐ Scaling with MongoDB Salvatore Incandela

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About Me

IT Architecture

Agile Open

Source

NoSql

R&D

Security

Big Data

DDD

CQRS

Electronic Money

Change Management

Continous Improvement

Organization

Paybay - Copyright 2014 - All Right Reserved

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Agenda •  Context

•  Problem Setting

•  Problem Solving

•  Problem Solved

•  MongoDb Focus

•  MongoDb Cluster Config

•  MongoDb Cluster Status

•  Our Loyalty Clients

•  Next Steps


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Context

Loyalty Cashback Program, how it works:

Card Holder Buy Affiliated Merchant

Acquires Transaction

Bank Send

Transaction

Give Rewards

Loyalty Platform


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Card Holder

Context

Big Loyalty Cashback Program

Very Big!!

10Mln 50k+

2+Mln per day

1,5+Mln per day

50k+ per day

1,5+Mln per day

Buy Affiliated Merchant

Acquires Transaction

Bank Send

Transaction

Give Rewards

Loyalty Platform


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Context

Big Loyalty Cashback Program

Very Big!!

Card Holder Affiliated Merchant Buy

Bank Send Transactions Loyalty Platform

Give Rewards Acquires Transactions

10Mln 50k+

2+Mln per day

1,5+Mln per day

50k+ per day

1,5+Mln per day

Just for one Bank but…

….the platform will be multitenant!


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Problem Setting

•  FrontEnd performances

•  BackEnd performances

•  High scalability costs

•  Lack of Smart Business and Operational Analitics

•  Shared DataStores causes poor performances


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Problem Setting

RDBMS

Batch Engine

Core Services

Data Acces Layer

WebApps

Performances Budget Analitics Mobile Web

Services

Is this solution economically sustainable? How to Scale the Data Store?


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Problem Solving

RDBMS RDBMS

Performances Budget Analitics Batch Engine

Core Services

Data Acces Layer

WebApps Mobile Web Services

ß Not for Us


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Problem Solving

CQRS originally introduced by Greg Young

Martin Fowler http://martinfowler.com/bliki/CQRS.html

The change that CQRS introduces is to split the conceptual model into separate models for update and display, which it refers to as Command and Query respectively following the vocabulary of CommandQuerySeparation. The rationale is that for many problems, particularly in more complicated domains, having the same conceptual model for commands and queries leads to a more complex model that does neither well.


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Problem Solving fase 1

MongoDb

Performances Budget Analitics

Document Modeled Fast Reads to more performances Business Analitics collections Operational Analitics Collections

E/R Modeled Reliable Strong Competencies

CQRS Principle transitions

Batch Engine

Data Acces Layer


Core Services Commands Reads

ß Only for Fast Reads RDBMS


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MongoDb

Performances Budget Analitics

CQRS Principle transitions

MongoDb MongoDb

More Reliable configuration

Batch Engine

Core Services

Data Acces Layer


Commands Reads

Problem Solved!

RDBMS Still used

ß Only for Fast Reads RDBMS


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MongoDb Focus

MongoDb MongoDb MongoDb

Noteworthy •  MondoDb is flexible schema •  De-normalized •  MMS: Monitoring, Backup tools •  Database Metrics, HW Statistics •  OOP Oriented no ORM Mappings


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MongoDb Cluster Config


Cluster Configuration •  3 Node •  Replica Set •  9 Collections (the biggest contains 700mln raw data) •  Compound Index •  Background indexes •  TTL Indexes


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MongoDb Cluster Status

Manage Tb of Data •  Up and running about 3 years without fault •  Increase Reads parformances up to 60% •  Decrease costs about 70% •  Self Learned: good documentations



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Our Loyalty Clients


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Next Steps?


We’ll adopt MongoDb as Fast Read DataStore for the entire Blue Tiger Platform



Loyalty Couponing Ticketing Payments Issuing

800Mln Trx per Year

150k Clients

800k Trx per Year

10Mln Trx per Year

1Mln Cards per Year






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Contacts

{ name: “Salvatore Incandela”, twitter: “@sincandela”, email: “[email protected]”, linkedin: “https://www.linkedin.com/in/salvatoreincandela”}


Grazie

Q&A

Date post:	24-Jun-2015
Category:	Software
Upload:	paybay
View:	291 times
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Salvatore Incandela "Loyalty cashback - Scaling with MongoDB"

Software