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Our pick for the best high tech employer in New York in the under 50 employee category in the month of January 2014. Technology includes Android iOS mobile Redis MongoDB Python Objective-C JAVA Javascript AWS High Volume B - C environment. Great approachable management. Creative and VERY fast-paced. Good benefits. Contact Greg Hutchinson (212) 470-8005
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One of the Top 10Mobile Apps
A MongoDB Ready Partner in New York City
Digital Collectible Card PlatformThree apps that run on the same system
so far…
MLB
NFL BPL
Collectible Card Games
Yes, Trading Cards!
The New Collectible
Live stats and game info
Online trading
Card sales info
Brief History of MLB• 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
• Product Demo
• System Overview
• Specific example of why MongoDB
• One case where another database technology is used with MongoDB
• Scalability issue and resolution
Product Demo
System Overview
Server Technologies• MongoDB
• MySQL
• Redis
• Python
• AWS
• External data feed
MongoDB Basic Structure
Simple View of Server Architecture
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
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
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
Big Piece 1Specific reason we
chose MongoDBProcessing Live Game Data in Realtime
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
Old Way: Processing Live Game Data with MySQL
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 } })
In general, the system with MongoDB is much simpler, faster, more
scalable
Where we use Redis with MongoDB
Leaderboards
Building User Leaderboards
• Leaderboards updated in realtime
• 96 leaderboards in MLB
• 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
Leaderboard Process
Scaling issue and resolution
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
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
Profiles were refactored to include only player IDs
• Finite number of players in the system, so size of player IDs list is limited
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
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
Were Hiring!
MongoDB+Py, Web, iOS, Android, App Producer, Project Management, QA
Greg Hutchinson (212) 470-8005
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