Social Media Counter Using MongoDB And Spring Data
About a month ago, I created a simple project to better manage reading list (fed by an ever growing number of RSS feeds) by prioritizing which articles to read based on attributes such as URLs’ popularity, age, tags, etc..
The popularity of an URL can be determined based on its social media counters (Facebook likes, Tweet count, Google +1′s, LinkedIn shares, etc.), number of comments, etc.
Information stored for one URL is likely to be very different from that stored for another. For example,
- URL1 only has a Tweet count and the number of LinkedIn shares,
- URL2 has Facebook likes, Tweet count, LinkedIn shares and a set of tags
- URL3 has Facebook likes, Tweet count, LinkedIn shares and Google +1′s (introduced later in the project),
MongoDB is excellent for handling schemaless data similar to the simple example above. Spring Data makes it very easy to integrate MongoDB into a new/existing project. In this article, I describe the project’s setup, the relevant sections of the project’s pom.xml, part of the DAO code and a simple aggregation done by making use of MongoDB’s Map Reduce feature. Specifics of trending algorithms, the UIs will be covered in later articles.
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