MongoDB at Agilysys: A Case Study

TECHNOLOGY | INNOVATION | SOLUTIONS

Larry Steinberg (CTO) & Alessandro Muti (EA)


Premier Hospitality Software Provider

• Nasdaq: AGYS

• Agilysys is a leading pure-play provider of software-enabled solutions for the hospitality industry

• Offering includes a full suite of hospitality products, including point-of-sale (POS) and property management systems (PMS)

• Base of 3,000+ customers with 95% retention rate

• 490+ employees world wide

• Engineering headquarters in Bellevue WA with 75 engineers; growing to 100+

• Aggressively investing in next generation solutions


Guest Lifecycle Driving Vision

Discover

Customer Lifecycle

Reserve

Pay

Suggest

Order

Analyze

Procure

Staff

Market

Arrive

Current Offering Future Offering

Experience

Pre-Visit

Reserve

Discover

Book

Staff

Procure

Staff Order Pay

OrderStaff

Analyze Procure Order Pay

Arrive Experience

Reserve

Analyze Procure

ExperienceArrive

Order Pay Order Pay

Staff Suggest

Suggest

Suggest

Pay

Market

SPABACK OFFICE RESTAURANT

CASINOFRONT DESKBACK OFFICE


Hospitality Technology Drives the Guest Experience

Integrated Guest Experience

Point-of-SaleProperty

ManagementInventory and Procurement

Workforce Management

Next Gen Tenets

SaaS delivery

Multi tenant &

High density

Common platform

Native Product

Integration

Centralized data store

Technology Stack

Linux

Java/Spring

Scala

MongoDB

Apache CXF

OptaPlanner

Drools


Why MongoDB – the Business view

The Business Problem • Building a Platform

• Requirements coming at different points in the lifecycle

• Schema less approach better enables change over ti

Flexibility • Licensing

• Deployment – on premise, cloud, appliance

• Supported with our standard devops toolset – ansible

Distributed architecture

• Hybrid – Cloud offering + On Site Appliance

• Geographically dispersed

• Scale


Why MongoDB – the Technical View

• Document based repository• The document paradigm fits well with the logical aggregation of our

modular components• Great integration between user interface and services

• JSON is a great data interchange format • For services that don't have business logic allows us to go directly

from the backend to the front end with minimal time spent converting into intermediate objects

• De-normalization of data allows us to group relevant information in document that require little manipulation

• High Performance and High Availability• Replica sets and automatic failover

• Scalability• Sharding provide easy way to scale data load


The simple case

• Using Spring Data Framework to write to MongoDB• 1 Class = 1 document

• MongoTemplate is the Spring wrapper to the Mongo Java driver• Awesome Helper Classes

• Aggregation, Criteria, Query, Update• Lots of pre-build repositories implementations

• CRUD Repositories, PagingAndSortingRepository, …• Annotations to decorate your class

• Indexed, Field, Value, Id, DBRef,…• Lifecycle management• Automatic Query Builder


The simple case - 1 Class = 1 Document

The Flexible Document ModelMany Classes = 1 document

?


The Flexible Document ModelMany Classes = 1 document


Agilysys Data Access Layer

• Extended Spring Data MongoTemplate• added methods to deal with updates to arrays• wrapped multiple calls to the database into a single atomic method• Helper functions to map Model to Domain

• Most calls to the database show a pattern• Query to find the document with the data• Morph the document and transform to match the domain object• Map the resulting JSON back to the appropriate Class

• Use JSONPath to find values in subDocuments


Lesson Learned

• Understanding the pattern for data access is very important• Look at your data and don't think about it in terms of relations,

de-normalization and duplication are not as bad of an issue as generally thought

• In the flexible document model the hardest objects to deal with are arrays, at least for now, use them carefully

• TextSearch is not yet strong enough for flexible documents, JSONPath can help

• For the cases where a data redesign was not enough to ensure that all the atomic elements of an update are in the same collection, then a strategy like 2 phase commit and application managed states are necessary. Think hard about the problem before deciding on a document design


Questions?

?

MongoDB at Agilysys: A Case Study

Technology

Transcript of MongoDB at Agilysys: A Case Study