A Social blog using MongoDB ITEC-810 Final Presentation Lucero Soria Supervisor: Dr. Jian Yang

Agenda Introduction Methodology Outcomes Blog implementation MongoDB vs. Relational databases Conclusions 2

Agenda Introduction Methodology Outcomes Blog implementation MongoDB vs. Relational databases Conclusions 3

Problem Specification Relational Databases Management Systems (RDBMS), such as MySQL, do not provide the flexibility and scalability needed to manage social media data NoSQL databases, such as MongoDB, emerged to provide the features that modern applications demand such as flexibility, scalability and productivity 4

Project Aim Analyse the differences between MongoDB and relational databases, especially in supporting social media data 5

Background Sources MongoDB MongoDB Online Manual Online articles Relational databases MySQL 5.5 reference manual Social Media Management Handbook by Robert Wollan Online articles 6

Agenda Introduction Methodology Outcomes Blog implementation MongoDB vs. Relational databases Conclusions 7

Project Approach This project is a combination of analysis and development tasks 8 Research  MongoDB, social media data and relational databases Implement a social blog using MongoDB Based on the implementation and research: Analyse the differences between MongoDB and relational databases

Methodology Incremental methodology was used to implement the social blog Combines waterfall model with iterations 9

Agenda Introduction Methodology Outcomes Blog implementation MongoDB vs. Relational databases Conclusions 10

A social blog with MongoDB 11 Features implemented: Login with facebook to create user’s profile in MongoDB Create, edit and delete posts (text, photos or videos) Add comments Search by tags Sort by blogs with more comments

Analysis Based on our experience implementing the social blog, the most relevant features to manage social media data are: Handle irregular data Handle large binary objects (videos, photos) Operations Metadata Manage huge volume of data Handle geospatial queries 12

Relational data model Fixed-schema Assume well-defined structure data with a fixed number of fields (columns) and relationships Minimize redundancy and dependency  Normalization 13 Source:

Terminology RDBMSMongoDB TableCollection RowsJSON Document Index JoinEmbedding & Linking 14

Document-oriented data model MongoDB uses a document-oriented model using collections Main characteristics: Schema-less Collections can be created on-the-fly when first referenced Capped collections: Fixed size, older records dropped after limit reached Collections store documents 15

MongoDB Document Main characteristics: Are represented in a format called BSON (Binary JSON) Data is de-normalized No joins  Embedding & Linking { author: ‘Lucero', created: Date(‘ '), title: 'Yet another blog post', text: 'Here is the text...', tags: [ 'example', ‘lucero' ], comments: [ { author: 'jim', comment: 'I disagree' }, { author: 'nancy', comment: 'Good post' }] } 16

Storing irregular data Example: Different information in user profiles MongoDB Each document can have different information doc1 = {name: “Joe”, age: ”20”, interest: ”football” } doc2 = {name : “Michele”} Relational database Tables with all attributes NULL value in columns where data was not provided Results: Special queries to handle NULL values  Expensive 17

Managing large binary data MongoDB Divide a large file among multiples documents (GridFS) Include metadata to large files Search files base on its content Retrieve only the first N bytes of a video Relational database Use BLOB (Binary large objects) Inefficient manipulating rich media BLOB cannot be searched or manipulated using standard database command 18

Geospatial Indexes Queries to find the nearest N point to a current location MongoDB Embedded Geospatial features Relational database Spatial extensions MySQL implements a subset of the SQL with Geometry Types environment proposed by Open Geospatial Consortium (OGC) 19

Managing huge volume of data MongoDB High performance No joins and embedding makes reads and writes fast Indexes including indexing of keys from embedded documents and arrays Horizontal scalability Automatic sharding (auto-partitioning of data across servers) Relational database Have shown poor performance on certain data-intensive applications and delivering streaming media  Case study: Foursquare Difficult to scale to multiple servers 20

Agenda Introduction Methodology Outcomes Blog implementation MongoDB vs. Relational databases Conclusions 21

Conclusions Benefits that MongoDB offers over relational database: Flexible schema High performance Manipulation of large object files out of the box Embedded geospatial features However, MongoDB does not replace relational databases MongoDB and relational databases can coexist 22

Thank You! Q&A 23