OTA Platform with MongoDB
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This document summarizes an OTA (Online Travel Agency) platform that uses MongoDB as its database. The platform allows one stop shopping for hotels, flights, tours and other travel products. It also facilitates B2B travel. MongoDB is used because it is fast, allows flexible schema design, and saves time on database tasks like handling dynamic data through embedded documents. Some data is stored in referenced collections to handle large amounts of data like room rates and media files.
OTA Platform with MongoDB
- 1. OTA Platform with MongoDB with MongoDB talks 19 Jan 2013
- 2. About Our Application • One stop shopping OTA (Online Travel Agency) • Regular • Flash Deals • Various type of products • eg: hotels, flights, tours/activities, campaigns (flash deals), etc • Facilitate B2B travel
- 4. IS THAT IT?
- 5. About Our Application Travel Ideas • Travel News, up-to-date info all around the world • Travel Tips • based on places (countries, cities, areas) • covers various aspects, such as overview, sightseeing, eating/restaurants, shopping and events • Travel Stories, submitted by members • Photos and Videos • Blog, monthly theme posts based on event calendar
- 7. SOON ON YOUR BROWSER
- 8. Technology Overview These are stuff that we use behind our apps Web Server : Apache Application : PHP CodeIgniter (PHP MVC Framework) Backbone.js (Javascript Framework) with its friends Require.js, Underscore.js, Mustache.js, jQuery Database : MongoDB
- 9. Why MongoDB? • It’s fast • Development speed time faster • No join, no relation • No sql injection worries (you bet) • Reduced database requests • Super flexible schema design • Fewer tables/collections used
- 10. What do we store in MongoDB? All datas are stored in MongoDB. Including important data such as bookings, inventory, etc.
- 11. Building the Apps... We will tell you guys how MongoDB helps us a lot
- 12. Saving huge amount of time on database stuff spreadsheet is more than enough db structure alters and changes straight on the code
- 13. With RDBMS approach, dynamic data is pain in the a*s Embedded documents to the rescue This is how we handle dynamic data on our apps
- 14. Use Case • Hotels may have multiple contracts • One hotel contracts may have multiple bank accounts • Hotels may have multiple facilities • Hotel facilities have several categories
- 15. Schema Design MongoDB approach RDBMS approach more or less like this... plus two/three tables for reference data
- 16. And now the query... “get me hotels with its facilities which has BCA account in Jakarta only” RDBMS approach SELECT * FROM hotels INNER JOIN facilities .... INNER JOIN photos .... AND THEN JOIN the_banks .... AND OF COURSE JOIN the_locations .... AND JOIN the_city .... WHERE the_city IS “Jakarta” AND the_banks IS “Bank BCA” We all know JOINs are expensive. Use EXPLAIN on SQL to see it
- 17. And now the query... “get me hotels with its facilities which has BCA account in Jakarta only” MongoDB approach db.hotel.find( { “locations.city” : “Jakarta” , “contracts.banks.name” : “Bank BCA” } ) One single query to fetch all the data we need
- 18. Be wise when choosing which Data Models are used Embedded documents not applied to all data model We use referenced model when it comes to very large record sets / documents
- 19. Model Referenced One-to-Many Relationships between Documents http://docs.mongodb.org/manual/tutorial/model-referenced-one-to-many-relationships-between- data-modeling-publisher-and-books Normalize the data, just like RDBMS approach but using “fake” relationship Use key/id to store references between two documents
- 20. Use Case • Each hotel may have multiple room types • One room type may have up to 10 photos/videos • Each room type have their rate/price • Room rates based on contract period (one or more years) • Room rates stored daily
- 21. Use Case Example : A Hotel has 5 room types Each Room type has 10 photos Hotel contract period is one year 5 room types x 10 photos = 50 documents 5 room types x 365 room rates = 1825 documents Total documents for 1 hotel : 1 + 5 + 50 + 1825 = 1881 documents
- 22. Use Case Example : Total documents for 1 hotel : 1 + 5 + 50 + 1825 = 1881 documents If we have 1500 hotels then 1881 x 1500 = 2.821.500 What if we have more than 1500 hotels? What if a hotel have more than 5 room types? What if a room type has more than 15 photos?
- 23. And then we decided to separate the collections that potentially has huge data example : room_rates, hotel_media, etc
- 24. More to see... • Other slides • http://www.slideshare.net/h.ariawan/okezonecom-inline-voting-system • http://www.slideshare.net/h.ariawan/sekilas-php-mongodb • Codes and Articles • https://github.com/hadiariawan/monode-crud • http://hadiariawan.web.id/2012/06/27/simple-crud-web-using-nodejs-and- mongodb/
- 25. @hadiariawan hadi.ariawan http://about.me/hadiariawan