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Real Time Ecommerce Analytics at Gilt Groupe Michael Bryzek, CTO & Founder Michael Nutt, Senior Engineer Mongo SF - April 30, 2010 We’re hiring: michael@gilt.com www.gilt.com/invite/michael
What is Gilt Groupe? The world’s best brands at up to 70% off Sales start every day at noon Simple, luxurious online experience Relentless focus on the customer . . .  A fast growing young company
What does noon look like in Tech?
What does noon look like?
MongoDB at Gilt Groupe Real time analytics is a sweet spot for MongoDB Two production examples we’ll share today at Gilt Groupe: Selecting product to sell based on real time data Hummingbird: Real time visualization of site traffic
Using MongoDB for Real Time Analytics Goal: Improve conversion of our gifts section (www.gilt.com/gifts) by ensuring good products are being promoted at the right time Challenge: High traffic makes it hard to collect and analyze data in a scalable and fast way Approach: Capture data in real time in MongoDB Analyze w/ Map Reduce Update txn systems Repeat
Step 1: Data Capture ,[object Object]
Each page view receive a list of every item on the page and its position via AJAX
Purchase data sent by background job post purchase,[object Object]
Step 2: Map Calculate a score for each item based on page views, conversion, inventory, and merchandising input m = function(){  [snip]   if ( hourly.visits > 0 && this.quantity_sold > 0 ) { 	var rate = this.quantity_sold / hourly.visits; 	points = parseInt(100*rate); 	v += points; 	explanation += "Conversion rate of " + points + "% ”;   } else if ( hourly.visits == null || hourly.visits == 0 ) { 	v += 500; 	explanation += "Product has never been seen (500 points). ";   }   [snip]    emit( { gift_product_look_guid : this._id },           { score : v,  	       explanation : explanation}}); }
Step 2: Reduce Reduce is a passthrough r = function( pid , values ){     return values[0]; } Map Reduce run every 15 minutes via CRON – results stored in a collection named “scores” res = db.gift_product_looks.mapReduce( m , r , { out : "scores" } );
Step 3: Update Transactional Systems ,[object Object]
Send “scores” collection back to our primary data center, storing latest scores in our primary relational database
Gift items are always sorted by score – transactional system only needed an “order by score desc” clause,[object Object]
Tracking Pixels /tracking.gif?events=&prop1=women&server=www.gilt.com&products=&pageName=sales%3A+women&channel=sale&prop4=sale+category+page&u=http%3A%2F%2Fwww.gilt.com%2Fsale%2Fwomen&guid=418237ca-2bc6-932e-84c2-d4f02d9fd5bf&gen=f&uid=25423567&cb=443460396
Omniture GILT Data Warehouse Users
Omniture 24 hours later... GILT Data Warehouse Users
Node.js Asynchronous, evented web framework http://nodejs.org
var mongo = require(’lib/mongodb’); var db = new mongo.Db('hummingbird', new mongo.Server('localhost', 27017, {}), {}); db.createCollection('visits', function(err, collection) {   db.collection('visits', function(err, collection) {     collection.insert(env);   }); });
DEMO

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Real time ecommerce analytics with MongoDB at Gilt Groupe (Michael Bryzek & Michael Nutt)

  • 1. Real Time Ecommerce Analytics at Gilt Groupe Michael Bryzek, CTO & Founder Michael Nutt, Senior Engineer Mongo SF - April 30, 2010 We’re hiring: michael@gilt.com www.gilt.com/invite/michael
  • 2. What is Gilt Groupe? The world’s best brands at up to 70% off Sales start every day at noon Simple, luxurious online experience Relentless focus on the customer . . . A fast growing young company
  • 3.
  • 4. What does noon look like in Tech?
  • 5. What does noon look like?
  • 6. MongoDB at Gilt Groupe Real time analytics is a sweet spot for MongoDB Two production examples we’ll share today at Gilt Groupe: Selecting product to sell based on real time data Hummingbird: Real time visualization of site traffic
  • 7. Using MongoDB for Real Time Analytics Goal: Improve conversion of our gifts section (www.gilt.com/gifts) by ensuring good products are being promoted at the right time Challenge: High traffic makes it hard to collect and analyze data in a scalable and fast way Approach: Capture data in real time in MongoDB Analyze w/ Map Reduce Update txn systems Repeat
  • 8.
  • 9. Each page view receive a list of every item on the page and its position via AJAX
  • 10.
  • 11. Step 2: Map Calculate a score for each item based on page views, conversion, inventory, and merchandising input m = function(){ [snip] if ( hourly.visits > 0 && this.quantity_sold > 0 ) { var rate = this.quantity_sold / hourly.visits; points = parseInt(100*rate); v += points; explanation += "Conversion rate of " + points + "% ”; } else if ( hourly.visits == null || hourly.visits == 0 ) { v += 500; explanation += "Product has never been seen (500 points). "; } [snip] emit( { gift_product_look_guid : this._id }, { score : v, explanation : explanation}}); }
  • 12. Step 2: Reduce Reduce is a passthrough r = function( pid , values ){ return values[0]; } Map Reduce run every 15 minutes via CRON – results stored in a collection named “scores” res = db.gift_product_looks.mapReduce( m , r , { out : "scores" } );
  • 13.
  • 14. Send “scores” collection back to our primary data center, storing latest scores in our primary relational database
  • 15.
  • 17. Omniture GILT Data Warehouse Users
  • 18. Omniture 24 hours later... GILT Data Warehouse Users
  • 19. Node.js Asynchronous, evented web framework http://nodejs.org
  • 20. var mongo = require(’lib/mongodb’); var db = new mongo.Db('hummingbird', new mongo.Server('localhost', 27017, {}), {}); db.createCollection('visits', function(err, collection) { db.collection('visits', function(err, collection) { collection.insert(env); }); });
  • 21. DEMO