Oleg's MongoDB review
January 17, 2022
Oleg's MongoDB review
Score 10 out of 10
Overall Satisfaction with MongoDB
We use it as one of the internal database types in our REST APIs via a Spring/JAP connection in high-load applications. MongoDB is highly scalable, and compared to traditional SQL systems, reads and writes much faster than SQL. What is done on Mongo is as simple and clear as possible, and if there are problems with the amount of data in relational databases, such “bicycles” will have to be invented that will reduce all the advantages of these databases to zero. It’s probably hard to do an initially limited project on ordinary relational databases, that is, not to think about what will happen when everything is slowly covered up ... It’s better to devote more time to the design of the initial data, which will remove all questions in the future.
- MongoDB is highly scalable.
- Reads and writes much faster than SQL.
- What is done on Mongo is as simple and clear as possible.
- Requirement from the application in a highly scalable database.
- I’m at a loss to give numbers right away, but that’s exactly how the pool of threads is much smaller at high load due to the very high write and read speed of Mongo, as a result, we save up to $ 12,000 per month only on server rental.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
Do you think MongoDB delivers good value for the price?
Are you happy with MongoDB's feature set?
Did MongoDB live up to sales and marketing promises?
Did implementation of MongoDB go as expected?
Would you buy MongoDB again?
We have not encountered any unsolvable problems in the process of working with Mongo DB.
MongoDB [is] great at storing JSON data grouped into "collections". In this format, you can store any JSON documents and conveniently categorize them by collections. The JSON document contained in MongoDB is called binary JSON or BSON and, like any other document in this format, is unstructured. Therefore, unlike traditional DBMS, any kind of data can be stored in collections, and this flexibility is combined with the horizontal scalability of the database. It should be noted that MongoDB does not have links between documents and “collections” (this is partially compensated by the Database Reference - links in the DBMS, but this does not completely solve the problem). As a result, a situation arises in which there is a certain set of data that is not related to other information in the database, and there is no way to combine data from different documents. In SQL systems, this would be an elementary task.