A Social blog using MongoDB

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A Social blog using MongoDB

• ITEC-810 Final Presentation
• Lucero Soria - 42403871
• Supervisor Dr. Jian Yang

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Agenda

• Introduction
• Methodology
• Outcomes
• Blog implementation
• MongoDB vs. Relational databases
• Conclusions

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Agenda

• Introduction
• Methodology
• Outcomes
• Blog implementation
• MongoDB vs. Relational databases
• Conclusions

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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

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Project Aim

• Analyse the differences between MongoDB and
  relational databases, especially in supporting
  social media data

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Background Sources

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

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Agenda

• Introduction
• Methodology
• Outcomes
• Blog implementation
• MongoDB vs. Relational databases
• Conclusions

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Project Approach

• This project is a combination of analysis and
  development tasks

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
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Methodology

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

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Agenda

• Introduction
• Methodology
• Outcomes
• Blog implementation
• MongoDB vs. Relational databases
• Conclusions

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A social blog with MongoDB

• Features implemented
• Login with facebook to create users profile in
  MongoDB
• Create, edit and delete posts (text, photos or
  videos)
• Add comments
• Search by tags
• Sort by blogs with more comments

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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

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Relational data model

• Fixed-schema
• Assume well-defined structure data with a fixed
  number of fields (columns) and relationships
• Minimize redundancy and dependency ?
  Normalization

Source http//blog.jruby.org/
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Terminology
RDBMS MongoDB
Table Collection
Rows JSON Document
Index Index
Join Embedding Linking
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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

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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(06-06-2012'),
• title 'Yet another blog post',
• text 'Here is the text...',
• tags 'example', lucero' ,
• comments author 'jim', comment 'I
  disagree' ,
• author 'nancy', comment 'Good
  post'

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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

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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

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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)

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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

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Agenda

• Introduction
• Methodology
• Outcomes
• Blog implementation
• MongoDB vs. Relational databases
• Conclusions

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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

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• Thank You!
• QA