Mapping Relational Databases to MongoDB
If you are familiar with relational database then it will be difficult for you to understand the terms mongoDB, so to make easy for you we come up with term Mapping Relational Databases to MongoDB.
Difference between Relational Databases to MongoDB :-
The major difference between Relational Databases to MongoDB −
| Relational Databases | MongoDB |
|---|---|
| Relational database | Non-relational and document-oriented database |
| Table based | collection based and key-value pair |
| Row based | Document based |
| Column based | Field based |
| Group By | Aggregation |
| Support for triggers | No Support for triggers |
| SQL injection vulnerability | Unaffected by SQL injection |
| Not suitable for hierarchical data storage | suitable for hierarchical data storage |
| Supports SQL | Supports JSON |
Collections:-Collections in MongoDB is equivalent to the tables in RDBMS.
Documents:-Documents in MongoDB is equivalent to the rows in RDBMS.
Fields:-Fields in MongoDB is equivalent to the columns in RDBMS.
Note:-Fields (key and value pairs) are stored in document, documents are stored in collection and collections are stored in database.
Advantages of using MongoDB over traditional RDBMS
The major Advantages of using MongoDB over traditional RDBMS is −
Table-less flexibility:MongoDB is a non-relational database, therefore, very different from a SQL database. This means that the database is easy to manage and provides a high degree of flexibility to accommodate new data models.
No need to develop a detailed database model: The non-relational nature of MongoDB allows database architects to quickly build databases without the need to develop a detailed (fine-grained) database model. This saves a lot of time for development.
Manageable: The database does not require a database administrator. As it is quite user friendly, it can be used by both developers and administrators for use and maintenance without any special training.
Sharding: MongoDB enables better performance for complex operations by distributing large data sets across multiple machines.
Dynamic Schema: This allows you to develop your data schema without affecting existing data.
Speed: All related data are in personal documents, this eliminates the need to be involved in the operation and improves the speed and performance of queries.
Scalability:It is horizontally scalable, i.e. you can easily add more servers to handle the load while increasing the data. RDBMS requires more hardware with increased processing power to handle the increase in data.
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