Content Comparison

The Next API Lead…

dun dun dun

• Josh is graduating next semester

• Reach out to Josh if you’re even a bit interested

  • Involves some one-on-one mentoring about running the API and API team

Overview

• Relational databases. Eg. SQL

  • This is most of what you learn at UTD

  • Fairly rigid data structure, strict adherence to column-based schemas

• Document databases

  • Both have been around, but this is more modern

  • Stored in basically JSON

  • Still have primary and foreign keys but it’s less of a focus

  • Looser data structure, can still support schemas but more adaptable

Diving in

• Looking at MongoDB Go Driver and the mongo Go package

• MongoDB Compass app helps visualize the database

• Structure: multiple databases with collections (folders)

  • In this case the folders are courses, degrees, exams, professors, and sections

• Working with course collection

  • Find method: find items in collection based on query

    • uses filters can use bson.D or bson.M (documentation): what to find by

    • uses supports various options such as optionLimit: defined in configs, mainly used to Limit results. supports pagination with Skip

  • Cursor: iterate over stream of documents

    • kind of works like reading a file: you can decode it all at once, or document by document

    • has various options to set such as setting MongoDB server timeout, find

    • can allow finding errors in specific documents while salvaging the rest

    • most importantly allows for continuous streaming of documents

• Object IDs are auto generated by MongoDB but can be customized

• More specific queries return less data and allow the more efficient database server to do the work for you

• Aggregations let you set up data pipelines and can produce queryable collections like regular collections