Patterns of modeling tree structures. Case study: MongoDB database

This work includes the analysis and evaluation of the use of different patterns to store data structures that are conceptually organized as a tree and generally can be populating database tables not normalized relational data or other sources of unstructured data such as spreadsheets, text files and more. The type of storage that has been chosen is a NoSQL database-oriented documents, specifically MongoDB it is an early and popular alternative within its scope. Our analysis considers five patterns: Parent References, Child References, Array of Ancestors, Materialized Paths and Nested Sets. An experiment was designed to measure the performance of the database regarding the speed of insert operations, query and indexing as well as the required storage capacity according to each standard used. The database was deployed in the cloud provider as a service MongoLab. The results of the experiment along with their analysis are presented separately showing two tables and 3 graphs appropriately to contrast the differences and approaches between the yield between different patterns.