Date of Submission
Project Advisor 1
Robert W. McGrail
For decades now relational databases, namely SQL, have been the industry standard. However, recently MongoDB and other NoSQL databases have been growing in popularity due to their flexibility and scalability. SQL still has the upper hand in a variety of areas, including data consistency, advanced and established analytics functions, and efficient “JOIN” functions. This project focuses on MongoDB’s shortcomings when it comes to replicating “JOIN” operations using MongoDB’s aggregate functions. “JOIN” operations refer to the action of comparing data from one or more collections of data and joining similar data together in order to analyze and draw statistics from the combined data pool. MongoDB’s performance in regards to these expensive aggregate operations can be accelerated with the help of Postgres, an SQL database management system. By caching, or storing, the results of these MongoDB aggregate operations along with information about the aggregate queries themselves into Postgres, users can greatly improve the performance of MongoDB “JOIN” equivalents. Thus, users can enjoy the benefits of a NoSQL database system without sacrificing the ability to efficiently gather insightful statistics and analytics from the database.
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Porter-Brown, Mason Q., "Accelerating Aggregation Efficiency: Using Postgres as a Cache with MongoDB" (2021). Senior Projects Fall 2021. 24.
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