1. Dog on a Horse
Mobile Apps
A MongoDB Ready Partner

2. Woody’s Great Grandfather

3. Digital Trading Card Platform with Topps
Three apps that run on the same system
so far…
BUNT
HUDDLE
KICK
MLB
NFL
BPL
Collectible Card Games

4. Yes, That Topps

5. The New Trading Card
Card sales info
Live stats and
game info
Online trading

6. Brief History of BUNT
•
2012: Introduced product on MySQL
•
•
•
Revenue model fine-tuned
Thought of new features and scaling considerations
Migrate to MongoDB for MLB 2013 opening day
•
Learn MongoDB
•
Re-design the schema
•
Write & Run migration scripts
•
Total: 4-5 months, 1-2 people

7. •
Product Demo
•
System Overview
•
Specific example of why MongoDB
•
One case where another database technology is
used with MongoDB
•
Scalability issue and resolution

8. Product Demo

9. System Overview

10. Server Technologies
•
MongoDB
•
MySQL
•
Redis
•
Python
•
AWS
•
External data feed

11. MongoDB Basic Structure

12. Simple View of Server Architecture

13. Big Piece 1: Processing Live
Data
• Live data is received and stored into MySQL
•
•
A heartbeat picks up the event and stores player stats into MongoDB
It then pulls from MongoDB and updates leaderboard data in Redis

14. Big Piece 2: Formatting Leaderboards for
Users
• API servers combine fan data from MongoDB with points data from Redis
•
The result is a richly-detailed set of leaderboards

15. All other
processes
• All other processes are handled by the API servers and MongoDB
Sign in, Trading, Commenting, Content Management, Purchases, Playing Cards
Storage is used for fan and player photos as well as other simple files
•
•

16. Big Piece 1
Specific reason we chose
MongoDB
Processing Live Game Data in Realtime

17. Realtime Live Game Updates
•
Game play requires up-to-the-minute stats from live
events for user scoring
•
These data are stored in JSON format for the app
•
The JSON data has to be updated frequently with
stats and player points
•
Support multiple live games for multiple apps on the
same platform

18. Old Way: Processing Live
Game Data with MySQL

19. New Way: With MongoDB, we can simply
update the JSON data in the player’s document
db.players.update(
{
_id: ObjectId(“52be0717978ca03fc1984069"),
‘games.g':'2013-e.39141
},
{
$set:{
'games.$.b': "1-for-2: Ground out, Walk, Home run"
},
$inc:{
'points': 36
}
}
)

20. In general, the system with
MongoDB is much simpler,
faster, more scalable

21. Where we use Redis
with MongoDB
Leaderboards

22. Building User Leaderboards
•
Leaderboards updated in realtime
•
96 leaderboards in BUNT
•
Final output is constructed on-demand, no cache
•
User scores are stored in sorted sets in Redis
(ranking is automatic)
•
Redis is an in-RAM key-value data store

23. Leaderboard Process

24. Scaling issue and
resolution

25. Scaling Example: Buying
Packs of Cards
•
In order to support complex trading algorithms,
each user profile needs to contain a reference to
the owner’s card collection

26. Initial Structure of User Profile with
Embedded Card Summary Documents
•
Profile contains an embedded
document of card summaries
•
When users buy cards, the
profile can grow out of its
allocated space
•
MongoDB creates a new,
bigger allocation for the user
profile

27. Profiles were refactored to
include only player IDs
•
Finite number of players in the system, so size of
player IDs list is limited

28. Basic Metrics
•
On average, 1 pack sold per second
•
Consistently top 10 grossing sports app
•
Up to 30,000 requests per minute
•
Up to 2,000 OPS

29. Conclusions
•
MongoDB great for apps, especially social
•
JSON-ready data
•
Normal NoSQL arguments
•
For realtime leaderboards, Redis provides simple and
fast “automatic sorting” of user scores
•
Don’t embed documents if you hope for them to grow
•
Easy to learn

30. We’re
Hiring!
MongoDB+Py, Web, iOS, Android, App Producer, Project Management,
QA