MongoDB is a document-oriented NoSQL database used for high volume data storage.
MongoDB is a general-purpose document database designed for modern application development and for the cloud. Because of its scale-out architecture, you can add more nodes to share the load in order to handle your system's rising demand.
Cloud-hosted MongoDB service on AWS, Azure and Google Cloud, deploy, operate, and scale a MongoDB database in just a few clicks
Instead of storing data in tables of rows or columns like SQL databases, each record in a MongoDB database is a document description in BSON, a binary representation of the data. Applications are able to access this data in JSON format.
With MongoDB Atlas, infrastructure deployment, setup, and provisioning are all entirely automated. By using the Cluster Builder or the API, choose a cloud provider, location, instance size, RAM, and other configurations, and you're ready to go.
Simple Configuration Modifications:
MongoDB Atlas makes it simple to modify the database after deployment as needs and workloads change. With a single click, you can scale up, add extra storage, set up cross-region clusters, create read-only or analytics nodes, and more.
You can benefit from the most recent updates and features by taking advantage of the automated patch and minor version upgrades. When you want major version upgrades to take place is up to you. It's simple with Atlas
Your data is secured with predefined security measures for authentication, authorisation, encryption, and more with MongoDB Atlas.
I Integrate MongoDB Atlas with your current security procedures and procedures to give yourself more control over the security of your environment.
Encryption Key Management
Granular Database auditing
Independent audits have determined that MongoDB Atlas complies with regulations governing data security.
How to Monitor MongoDB
With only a few utilities and commands, you can check instance status, cluster operations and connections metrics, hardware metrics, and more to monitor the health and performance of your cluster. Real-time concerns can be found and addressed by monitoring before they become serious.
With the help of the MongoDB Community Edition, MongoDB can still be used as an open-source database, as it was intended to be.
A commercial licence is required to access advanced features in MongoDB Enterprise Edition.
MongoDB Atlas is a Database-as-a-Service version of MongoDB Enterprise Edition that is offered on all public clouds.
With built-in, closely integrated capabilities like MongoDB Atlas Search, MongoDB Atlas Data Lake, and sophisticated features for geo-locating data and creating backups, MongoDB Atlas has been expanded in a number of ways.
MongoDB Cloud is the umbrella term for MongoDB Atlas as well as other recent releases like Realm, a serverless platform for building mobile applications built on MongoDB Atlas.
Key Aspects of MongoDB:
Documents: The Records in a Document Database
MongoDB stores data as JSON documents.
The document data model maps naturally to objects in application code, making it simple for developers to learn and use.
The fields in a JSON document can vary from document to document. Compare that to a traditional relational database table, where adding a field means adding a column to the database table itself and therefore to every record in the database.
Documents can be nested to express hierarchical relationships and to store structures such as arrays.
Collections: Grouping Documents
In MongoDB, a collection is a group of documents.
If you are familiar with relational databases, you can think of a collection as a table. But collections in MongoDB are far more flexible. Collections do not enforce a schema, and documents in the same collection can have different fields.
Each collection is associated with one MongoDB database. To show which collections are in a particular database, use the command listCollection.
Replica Sets: Ensuring High Availability
An important way to ensure high availability is by keeping more than one copy of your data. With MongoDB, high availability is built right into the design.
When you create a database in MongoDB, the system automatically creates at least two more copies of the data, referred to as a replica set. A replica set is a group of at least three MongoDB instances that continuously replicate data between them, offering redundancy and protection against downtime in the face of a system failure or planned maintenance.
Sharding: Scalability to Handle Massive Data Growth
A modern data platform needs to be able to handle very fast queries and massive datasets using ever bigger clusters of small machines. Sharding is the term for distributing data intelligently across multiple machines.
How does sharding work in MongoDB? MongoDB shards data at the collection level, distributing documents in a collection across the shards in a cluster. The result is a scale-out architecture that supports even the largest applications.
Indexes: Improving Query Speed
Indexes support the efficient execution of queries. MongoDB offers a variety of different Indexing strategies, including compound indexes on multiple fields. Chosen carefully, indexes speed up queries because queries scan the index instead of reading every document in the collection.
There is still work to do to analyze which queries could benefit from adding an index. One tool that does this analysis for you is Performance Advisor, which analyzes queries and suggests indexes that would improve query performance.
Aggregation Pipelines: Fast Data Flows
MongoDB offers a flexible framework for creating data processing pipelines called aggregation pipelines. It features dozens of stages and over 150 operators and expressions, enabling you to process, transform, and analyze data of any structure at scale. One recent addition is the Union Stage, which flexibly aggregate results from multiple collections.
Programming Languages: Does MongoDB Speak Your Language?
What languages can you use with MongoDB? The list of supported languages includes Node.js, C, C++, C#, Go, Java, Perl, PHP, Python, Ruby, Rust, Scala, and Swift. The library for each language is actively maintained, which means that it is updated with new features, bug fixes, security patches, and performance enhancements.
Why is using MongoDB better than using MySQL?
Organizations of all sizes are adopting MongoDB, especially as a cloud database, because it enables them to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale.
Development is simplified as MongoDB documents map naturally to modern, object-oriented programming languages. Using MongoDB removes the complex object-relational mapping (ORM) layer that translates objects in code to relational tables. MongoDB’s flexible data model also means that your database schema can evolve with business requirements. MySQL's rigid relational structure adds overhead to applications and slows developers down as they must adapt objects in code to a relational structure.
MongoDB can also be scaled within and across multiple distributed data centers, providing new levels of availability and scalability previously unachievable with relational databases like MySQL. As your deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime, and without changing your application. In contrast, achieving scale with MySQL often requires significant custom engineering work.
Which Query Language Does MongoDB Support?
Both databases support a rich query language. MySQL, like many relational databases, uses structured query language (SQL) for access. MongoDB uses the MongoDB Query Language (MQL), designed for easy use by developers.
Sathish Aravind is a Lead Software Engineer, who started off his career in non-IT industry and later moved on to IT sector. He is passionate about learning and exploring new technologies and always gets updated on the latest trends for future enhancement. At the same time, he is interested in spreading his knowledge and guiding beginners to learn new technologies.
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