This document summarizes Giant Eagle's use of MongoDB for an expense reimbursement application. It discusses why Giant Eagle chose MongoDB over its traditional .NET/Oracle stack, the technology stack used, and MongoDB's architecture. Some limitations encountered included inability to directly select subdocuments and query across collections. Overall, while MongoDB has some immature areas, it was a good fit for the project's performance needs and flexibility.

1. Mongo DB @ Giant
Eagle Inc.
A Case Study by David Williams
Technical Consultant
David.Williams@gianteagle.com

2. About Giant Eagle
• Regional Grocery Chain
– Pennsylvania, Ohio, West Virginia, Maryland
• $10B in annual sales
• >200 Retail super markets
• >160 GetGo convenience stores
• > 200 Team Members in IS
• > 100 Development/support resources

3. Project
• Expense Reimbursement Application
– Open to all team members (35,000 +)
– Accessible from anywhere
– Integration with LDAP for authentication
– Integration with Google Maps API
– Replacement for existing solution
– Potential to open source result

4. Giant Eagle Standards
• Current Technology Standards
– C#/ASP.NET
– Oracle, SQL Server
– Web Services (SOAP/XML and REST/json)

5. Why not .NET/Oracle?
• Evaluate Alternative Platforms
– Cost reduction
– Multi-platform support
– Easier object mapping
– Rapid development potential
– Performance benefits
– Scalability options

6. Why MongoDB
• Extremely flexible schema
• Excellent scalability
• Easy backup/recovery
• Previous tests showed good performance
• Large community with active support
• Good support from development stack
• GridFS
• One of the lowest barriers to entry for NoSQL
databases

7. Technology Stack
• MongoDB 2.2
• Apache Web Server 2.2
• Python 2.7
• django 1.4.1
• PyMongo 2.3
• mongoengine 0.7.6
• Tastypie 0.9.11
• django Tastypie mongoengine 0.3

8. System Architecture
Client
Web
Templates
API: Public
API: Private
Static
Content Support/
Integration
Jobs
Database HR System
Business Logic
Finance
System
Data Mapping

9. MongoDB Architecture
Non-sharded Replica Set
PrimaryPrimary SecondarySecondary
ArbiterArbiter
Primary Data Center Secondary Data Center
File
Backup
File
Backup

10. Expense Document
class Expense(Document):
title = StringField(required=False)
reportNumber = SequenceField(required=False, unique=True)
businessUnitCode = StringField(required=False)
departmentCode = StringField(required=False)
totalAmount = IntField(required=False, default=0)
disposition = StringField(choices=DISPOSITION,
default="C")
assignedTo = StringField(required=False)
details = ListField(EmbeddedDocumentField(ExpenseDetail),
required=False)
attachments = ListField(ReferenceField(Attachment,
dbref=False))
created = DateTimeField(default=datetime.datetime.now())
ownedBy = StringField(required=False)

11. In the Database
{
"_cls" : "Expense",
"_id" : ObjectId("5160da1db98eef3aacbac356"),
"_types" : ["Expense"],
"attachments" : [],
"businessUnitCode" : "00009",
"created" : ISODate("2013-04-07T02:29:49.87Z"),
"createdBy" : "testuser",
"departmentCode" : "8931",
"details" : [{
"category" : "6",
"subCategory" : "11",
"_types" : ["ExpenseDetail"],
"amount" : 10000,
"_cls" : "ExpenseDetail",
"expenseDate" : ISODate("2013-04-07T00:00:00Z")
}],
"disposition" : "S",
"title" : "YAT - proxy test",
"totalAmount" : 10000
}

12. The Good
• Easy to use
– Both in development and administration
• Flexible
– Create collections on the fly
– Easily grow or migrate schema
• Powerful queries
– Aggregation Framework allows complex
operational pipelines
– Maps well to ad-hoc report capability

13. Limitations Encountered
• Cannot select subdocuments directly
– Returns document where subdocument attributes meet
criteria
– If interested in specific subdocuments, must further reduce
after query
– Use aggregation framework to address
• Without db reference, cannot query across
document collections
– Caused numerous lookups in code
– Even aggregation framework operates on single collection
– Could potentially utilize map-reduce for this task

14. Conclusions
• Still struggling with references
• Tools are maturing, but not what you are used to with SQL
databases
• Inconsistent support for features in our stack
• Would certainly consider using again on the right project
– Must understand query requirements of potential use
case
• Many practical use cases around immediate need for good
performance
• Will likely become a well-used tool in our toolbox

15. Thank You