Why MongoDB is used including for Big Data and more

Why MongoDB is Used

One of the top NoSQL databases is MongoDB, an open-source document-oriented database. Written in C++, it is intended for easy scalability, high availability, and excellent performance. Its fundamental idea is to build a quick, flexible, scalable, and feature-rich data storage. Because of MongoDB’s architecture, developers can focus on creating applications rather than worrying about database scalability.

The following explains why it is preferred for particular domains:

1. Big Data: MongoDB excels at handling massive data problems. Large volumes of varied data can be stored thanks to its flexible schema, which eliminates the need for a strict, predetermined structure. This flexibility is essential when working with unstructured, quickly changing data, which is common in big data contexts. Large datasets may be distributed over numerous machines thanks to its capacity to scale out horizontally through sharding, which effectively manages demand and storage.
2. Content Management and Delivery: MongoDB’s document model is especially beneficial for applications that require a lot of material. Every piece of material, such as an image, video, or text, can be saved as a single document that contains all of its tags, metadata, and even comments. This “structure of a single object is clear” method does away with the necessity of intricate multi-table joins, which are frequently necessary in RDBMSs for hierarchical content.
3. Mobile and Social Infrastructure: A database that provides high availability and quick in-place updates is necessary for mobile and social applications, which frequently entail diversified content, heavy traffic, and quickly changing user data. Dynamic content updates and different user profiles are easily supported by MongoDB’s adaptable schema. For always-on mobile and social platforms, features like replication guarantee high availability, which means data is still available even in the event of a server failure.
4. User Data Management: MongoDB’s document-oriented storage is useful for managing user data, particularly when there are a variety of user profiles and preferences. Because user profiles can be saved as separate documents, dynamic fields can be added without changing the global structure. A ‘null’ entry for all other users is not necessary if, for example, one user document has a ‘preferences’ field with particular settings but another does not. Because application objects can frequently be stored directly as documents, this flexibility lessens the need for object-relational mapping.

RDBMS Terminology Compared with MongoDB

Although the terminology used by MongoDB may appear different from that of RDBMS, many of the ideas are directly equivalent. The divergence frequently draws attention to MongoDB’s schema-less architecture and relationship management methodology.

This is an analogy:

• Database (RDBMS) vs. Database (MongoDB): This is a clear conceptual comparison. A database in MongoDB is a physical container for collections, each of which has an own file system. Multiple databases can be hosted on a single MongoDB server.
• Table (RDBMS) vs. Collection (MongoDB): An RDBMS table is comparable to a MongoDB collection. It is a collection of MongoDB documents housed in a single database. One significant distinction is that collections do not impose a schema; documents in a collection may have diverse fields and structures, albeit they usually have related or comparable purposes.
• Tuple/Row (RDBMS) vs. Document (MongoDB): In MongoDB, a document is the same as a tuple or row in an RDBMS. It is a collection of key-value pairs with a dynamic schema, which means that different data types can be stored in common fields and that documents in the same collection do not necessarily need to have the same fields or structures.
• An example of a JSON-style document structure is:
• A blog post document with an array of comments inserted is displayed in this example.
• Column (RDBMS) vs. Field (MongoDB): In MongoDB, a field is equivalent to a column in an RDBMS. A document’s key-value pairs are called fields.
• Table Join (RDBMS) vs. Embedded Documents (MongoDB): Table joins are frequently used in RDBMS data interactions. However, complicated joins are typically not supported by MongoDB. Rather, references or embedded documents are used to model relationships. By eliminating the need for repeated enquiries, embedded documents make it possible to keep related data directly within a single document, which helps streamline data retrieval. For instance, a blog post’s comments can be included right in the post document. Manual references, which save the _id of one document in another, or DBRefs can be utilised if data duplication is an issue or if related data is retrieved independently. In these cases, a second query is necessary to clarify the relationship. The main tactic is still embedding or referring, even though MongoDB versions 3.2 and later added the $lookup aggregation operator providing limited join-like capability.
• Primary Key (_id supplied by MongoDB) against Primary Key (RDBMS): A primary key is used in both systems to provide unique identity. Every document in MongoDB needs to have a unique _id key within its collection. When you insert a document, MongoDB automatically assigns a unique ObjectId if you don’t supply a _id. This ObjectId is a 12-byte hexadecimal integer that includes the machine ID, process ID, current timestamp, and an incremental value to guarantee uniqueness.

Code Examples for Basic Operations:

To insert a document:

> db.mycollection.insert({
   title: 'My First MongoDB Document',
   author: 'A Developer',
   date: new Date()
})

If ‘mycollection’ doesn’t exist, MongoDB will create it automatically before inserting the document.

To query documents, for instance, to find documents with “Govindhtech” as the author:

> db.mycol.find({"by": "Govindhtech"}).pretty()

This command finds documents where the ‘by’ field matches ‘Govindhtech’ and outputs them in a readable format.

To limit the number of records returned by a query:

> db.COLLECTION_NAME.find().limit(5)

This would display only the first 5 documents found in COLLECTION_NAME

In conclusion, MongoDB uses a document-oriented data format to provide a scalable, high-performance, and adaptable solution, especially for huge, dynamic datasets and contemporary online applications. Although it uses different language and approaches data relationships than RDBMS, knowing these distinctions shows how effective it is at handling intricate and dynamic data structures.