MongoDB and NoSQL Databases

            In our practice with REST API backends, multiple times we ran into MongoDB usage, as the database we were using for the API was MongoDB. My only previous experience had been SQL/SQLite, so I wanted to know what the difference was. I came to find out there is a world of difference, in fact MongoDB is a NoSQL database (NoSQL meaning… well exactly what you would expect it to mean). I found an article – part of a MongoDB tutorial – from Guru99 called Types of NoSQL Databases, What is & Example which dove into the differences and usefulness of MongoDB and other NoSQL databases. Chiefly, NoSQL DB’s do not require fixed schemas, avoid joins, and are easy to scale, which is perfect for distributed data storage and “scaling out”, which is becoming increasingly essential in the CS world. The lack of schema is convenient too, as it allows for “heterogeneous structures of data in the same domain,” providing for great versatility. The other essential parts of NoSQL include it being non-relational and having a relatively simple API. The four kinds are Key-value pair based, column-oriented graph, graph based, and document oriented. Key-value pair based means data is stored in pairs (with a key and a value), which is optimal for large datasets and heavy loads. Column-based means that work is done on separated columns with values in single columns being stored contiguously, performing well with aggregation queries. Document-oriented DB’s (MongoDB is one) also function as a key/value pair, but in this, a value is stored as a document, which is stored in JSON or XML. It is not optimal for high performance or aggregate structures, like Key Value or Column Based. Graph based stores entities and relationships between them. Each entity is a node and each relationships is an edge, defining the relationship. This is different from relational DB’s inn that Graph DB’s are “multi-relational.” NoSQL DB’s commonly query with a key and a GET request. The CAP theorem is also important to NoSQL because it guarantees at most two out of the following three: consistency, availability, and partition tolerance. This is a key limitation of NoSQL DB’s. The last key to NoSQL falls on the concept of eventual consistency, meaning that changes to data on one machine must be reflected on other replicas eventually. Where the standard for RDBMS is ACID, NoSQL is BASE: Basically Available, Soft state, Eventual consistency. The article goes on the list advantages, disadvantages, and summarize itself before coming to a close. I thought this was very interesting to learn, as I can see how different DB styles can reflect the needs of a project or client story. I wasn’t sure why we were using MongoDB; I kind of took it as a given. But this reading broadened knowledge, so now, if I am able to build a REST API of my own over the upcoming break, perhaps I can find the optimal DB for my needs, or help me better understand future projects once I join the workforce.

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