MongoDB

Icon SVG MongoDB

OUR UPS IN DATABASE MANAGEMENT WITH MONGODB

– Flexibility/Schema-less – If you have a flexible schema, this is ideal for a document store like MongoDB. This is difficult to implement in a performant manner in RDBMS
– Ease of scale-out – Scale reads by using replica sets. Scale writes by using sharding (auto balancing).
– Expressive Query Language – simple and much easier to understand than SQL.
– Easy Configuration – Installation, setup, and execution are rather simple and can take very little time to initiate.
– Cost – Depends on which RDBMS of course, but MongoDB is free and can run on Linux, ideal for running on a cheaper commodity kit.
– Has a large cache of precise documentation to help both seasoned and new users better understand the technology.

Icon SVG Onomatopea UPs
Icon SVG Onomatopea oUPs

OUR OUPS IN DATABASE MANAGEMENT WITH MONGODB

– Data size in MongoDB is typically higher due to e.g. each document has field names stored it
– less flexibility with querying (e.g. no JOINs)
– Data Redundancy – The relationships in MongoDB are not typically well-defined and the resulting duplicate data sets can be hard to handle.
– no support for transactions – certain atomic operations are supported, at a single document level
– at the moment Map/Reduce (e.g. to do aggregations/data analysis) is OK, but not blisteringly fast.
– Indexing – quick speeds and high performance is only possible with the right indexes. With shoddily implemented indexes and out of order composite indexes, MongoDB will operate at a shockingly slow speed.