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MongoDB Bangalore Conference
Oct 26th 2012

Building a Location-based platform

with MongoDB from Zero

Raviteja Dodda
Co-Founder & CTO, Pipal Tech Ventures

DelightCircle

• Offers, New Arrivals & Rewards
from 250 + brands and 12,000 +
stores across the country.

• Discover what’s nearby, Engage
with your favorite brands, and Earn
real rewards.

SMS: ‘@delight’ to 92431-92431

Website: DelightCircle.com

Some highlights
“DelightCircle is an app to help you pick out the best deals in the shops around
you, and also hand you points for just walking into them. A must-have for all
ye Android and iOS shopaholics.”

ThinkDigit, Sep 2012

“DelightCircle is featured as one of the top 6 mobile apps across the country.”

NASSCOM Emerge AppFame Aug 2012

“DelightCircle is featured by Samsung App Store & Blackberry App World.”

Sep, Oct 2012 - present

MongoDB & MongoEngine power the
data storage & processing parts of
DelightCircle backend platform

What is Geo-spatial/Location
data?

Any data which has a location
(2D – lat/lng)
associated with it.

Some Examples

• The data can be of users.
User = {‘loc’:[12.97,72.023], ‘name’:’xyz’, ‘email’:’..’, ..}

• The data can be of local businesses.
Place = {‘loc’:[12.97,72.023], ‘name’:’mnz’, ‘categories’:’..’, ..}

• The data can be of Photos.
Photo = {‘loc’:[12.97,72.023], ‘file’: File(), ‘taken_by’:’User()’, ..}

• and can be many more types,
leading to the next generation of location-based applications.

Let’s Dive in !
Build the backend for a check-in based rewards app

Requirements

• Users can check-in to places using the app, and earn
rewards based on check-ins.

Ex: GET 10% OFF on every 2nd check-in.

• Rewards can be created by the merchant, and can be assigned
to specific places of his business.

• Rewards should be searchable by users based on the
location and some keywords.

Why MongoDB ?
• Fast, Easy and Schema-less.

• Multiple Language Support & Faster development cycle.

• Document Object store – maps much more cleanly to OOP.

• Replica sets/Automatic failover of failed nodes.

• Support for Geo-spatial Indexes and Queries.

It cheats to make the math easier/faster by assuming a flat earth
(where 1 degree of latitude or longitude is always the same).

Has methods to handle curvature of Earth (Spherical – if needed)

MongoDB – a database with real
world solutions for real world
problems

Simple to use & flexible.

MongoEngine

Python MongoDB

MongoEngine
• Document Object Mapper for Python and MongoDB

•Built on top of PyMongo &Very similar to popular Django
ORM

• Add all your validation stuff in MongoEngine.
$easy_install mongoengine

Structuring & indexing your data
depends on the way you want to
query your data

Data modeling differs between use-
cases.

Place Reward

Id
Id
[Place-ref] title
name
[{Place-ref,
location (2d- 1 to n
Loc}, {Place-ref,
index)
Loc}, ….. ]
tags, etc.
tags, etc.
Place-ref

1 to 1 Check-in

User
Id
Id User-ref
name User-ref Place-ref
email (index) time (index)
mobile 1 to 1 action
Gender
age, etc.

Schema Design

Let’s start with Places
Place = {
_id: ObjectId(),
loc: [12.97,72.23], //suggested option
name: ‘A’,
tags: [‘food’, ‘restaurant’]
}
Place= {
_id: ObjectId(),
loc: {x:12.97, y:72.23},
name: ‘A’,
tags: [‘electronics’, ‘mobiles’]
}
loc: {lon: 40.739, lat:73.992}, ensure that you follow
the same order for consistency.

Place Model - MongoEngine
import mongoengine
connect(‘my_database’) // connecting to DB

Class Place(Document):
loc= GeoPointField() // Geo2D index is
automatically created
name = StringField(max_length=50)
tags = ListField(StringField(max_length=300))

P = Place(name=‘Pizza Hut’, loc=[12.97,77.9],
tags=[‘pizza’, ‘restaurant’, ..])

p.save() // saves the document to Mongo

Some Geospatial Queries
Find the closest 20 places to a given location.

JavaScript Shell

> db.places.find({‘loc’: {‘$near’: [12.62, 72.323]}}).limit(20)

Python (MongoEngine)

Place.objects(loc__near = [12.62, 72.323]).limit(20)

Results are sorted by distance, no need of an additional
sort.

Bounds Queries
Find the closest 20 places to a given location that are
within a distance of 5 km.

JavaScript Shell

> db.places.find( { loc : { $near : [12.62,72.323] ,
$maxDistance : 5/earth_radius } } ).limit(20)


Place.objects(loc__within__distance =[ [12.62, 72.323],
5/earth_radius]).limit(20)

Results are not sorted by distance, hence faster

User & Check-ins data model
User = {
_id: ObjectId(),
name: ‘A’,
email: ‘xyz@domain.com’, etc.
}

Check-in = {
_id: ObjectId(),
place: { "$ref" : "Places", "$id" : ObjectId()},
user: { "$ref" : ”Users", "$id" : ObjectId()},
time: ISODate(),
etc.
}

Check-ins - MongoEngine
Class Check-in(Document):
user = ReferenceField(‘Users’, required=True),
place = ReferenceField(‘Places’, required=True),
time = DateTimeField(required = True)

meta = {
‘ordering’: [‘-time’],
‘indexes’: [(‘user’, ‘place’)]
}

A Compound Index is created on user and place, since
most of our queries will contain both.

Rewards data model
Reward = {
_id: ObjectId(),
name: ‘GET 10% OFF .. ‘,
places: [{ "$ref" : "Places", "$id" : ObjectId()}, { "$ref"
: “Places", "$id" : ObjectId()}, .. ]
etc.
}
If you want to query the rewards based on location
Reward = {
_id: ObjectId(),
places: [ { place: { "$ref" : "Places", "$id" :
ObjectId()}, loc: {x:0, y:0}} , .. ]
etc.
}

Rewards - MongoEngine
Class Reward(Document):
name = StringField(required=True),
places =
ListField(EmbeddedDocumentField(PlaceLocation)
)
tags = ListField(StringField())
num_checkins_required = IntField(default=1)

Class PlaceLocation(EmbeddedDocument):
place = ReferenceField(‘Places’)
loc = GeoPointField()

Find the rewards nearalocation which have tags –
‘food’ and ‘pizza’.

JavaScript Shell

db.Rewards.find({‘places.loc’:{‘near’:[12.62, 72.3]}, ‘tags’:
{‘$all’: [‘food’, ‘pizza’]}}).limit(20);


Reward.objects(places__loc__near =[12.62,72.323],
tags__all = [‘food’, ‘pizza’]).limit(20)

You need to have a compound geo-spatial index defined on
both keys together for better performance of the query.

Conclusion
1. Spatial is easy and fun on MongoDB !!
2. You can now build your own check-in application (or)
also build your own field data entry system.
3. Running MongoDB on any cloud infrastructure is quite
easy.

PS: WE ARE HIRING – looking for smart hackers
Roles: Backend, iOS Developer, UI/UX Designer, etc.

Mail me at (or) come talk to
me.

Questions?

Thank You 

Stay Hungry, Stay Foolish !!!
- Steve Jobs

raviteja@delightcircle.com
Twitter, Facebook - @raviteja2007
www.DelightCircle.com

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Building a Location-based platform with MongoDB from Zero.

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