MongoDB is an open-source and document-oriented database program. Its cross-platform abilities enable it to store a large volumes of data as well as work on itvery efficiently. In addition, the MongoDB database is categorized as a NoSQL database program because the storage and retrieval of data in MongoDB is not done in tabular form. Rather, MongoDB uses documents similar to JSON with optional schemas.
MongoDB database has been developed by MongoDB Inc. and is also licensed under the Server Side Public License, which is deemed non-free by several distributions.
Why is it Popular?
MongoDB database is popular for its scalable data model which makes it easy to add or change fields. If a developer requires to quickly modify an app, MongoDB's flexible data model allows him to do so seamlessly. Its flexibility and usage are unparalleled.
Additionally, MongoDB is easy to use. Despite being simple, it still has the capabilities to be an efficient database system that can handle the complexity of modern apps. Another reason behind this app's popularity is that it uses three programming languages, C++, JavaScript, and Python, to set it up.
History of MongoDB Database
In 2007, MongoDB database was founded by the team behind DoubleClick, an internet advertising company now owned by Google. The founders, Kevin Ryan, Dwight Merriman, and Eliot Horowitz, had previously developed and used multiple customized data stores to work around the drawbacks of the then existing databases. Notably, the database was officially named MongoDB because it was a squeezed-up form of the word ‘humongous.’
Features of MongoDB Database
- Schema-less Database: A schema-less database is a collection that stores various types of documents. MongoDB provides excellent flexibility to databases.
- Document-Oriented: All data is stored in documents instead of tables, just like in RDBMS. Further, this data is stored in fields instead of rows and columns. That is why it is more flexible, with each document having its unique object ID.
- Indexing: Every field in MongoDB database documents is indexed with primary and secondary indices. Hence, you can find any data easily.
- Scalability: MongoDB scales horizontally by distributing data on multiple servers. Data is broken down into small chunks using the key of the shard. Notably, the shard key adds new machines to an existing and running database.
- Replication: MongoDB creates multiple copies of a data chunk and transfers it to different servers. Therefore, if one server fails, you can obtain data from the other servers.
- Aggregation: Similar to the SQL GROUP BY command, MongoDB provides a map-reduce function, aggregation pipeline, and single-purpose aggregation methods.
- High Performance: Due to the above-mentioned factors, MongoDB performs highly in all aspects.
Key Concepts of MongoDB
Here are the major concepts related to MongoDB that one must know:
- Documents: These are those records that are stored in a document database.
- Collections: They are the process of grouping documents in a cluster or a group.
- Replica Sets: Duplication of data is done to ensure the high availability of data for faster indexing.
- Sharding: It means scaling up the database operations to handle the massive growth of data.
- Indexes: Due to the availability of duplicate sets of data, indexing improves the speed of the query.
- Aggregation Pipelines: This is again one of the terms we have mentioned above. Due to aggregation, the data flow is faster.
- MongoDB Cloud: It means the availability of the database technology over the cloud with the help of MongoDB Atlas.
Role and Uses of MongoDB Database in Development
MongoDB creators ensure that developers have a pleasant experience while using their programming expertise with MongoDB. It is to be noted that MongoDB is extremely flexible and versatile. It is compatible with various programming languages such as Python, Ruby, Java, JavaScript, Rust, Scala, PHP, Swift, C, C#, .NET, C++, and Go.
MongoDB Atlas converts the database technology into database-as-a-service over the MongoDB database cloud. With a few clicks, you can make a database for your application’s backend from the web UI (user interface) and begin writing code immediately.
MongoDB Atlas is extremely powerful. It has capabilities which are described below -
- MongoDB Atlas Search – It is powered by the Lucene search engine for enabling full search with text.
- Atlas App Services – It provides completely managed backend services for developing both mobile and web apps.
- MongoDB Atlas Data Lake: This feature allows developers to form a query and combine the data stored in Atlas with data stored over the cloud, in an HTTPS store or an Amazon S3.
- Supporting Multi-cloud or Hybrid Apps: MongoDB can be deployed anywhere because of its scalability. You can run it on a desktop, in a public cloud, or a data center. MongoDB supports all present and future configurations.
- Supports Agile Development and Collaboration: MongoDB allows governance and collaboration, which lets two teams control a single document entry in a database at two different points.
Applications
MongoDB has the following applications -
- Balanced Features
MongoDB database can be used to get many balanced features like queuing, map/reduce, and FTS.
Priority of Consistency Over Availability
A developer will find consistency in MongoDB database applications with storage of data.
- Denormalizing the Data
Denormalizing the data again is extremely tough and very expensive.
- Mixing Secondary Indices with Key Lookups
In MongoDB, you can mix secondary indices with key/value lookups. But, do not stretch it over everything, as it might scale poorly.
- Single Server Data
MongoDB database enables sharing on a single server. But, it is also possible with the help of PostgreSQL.
- Best suited database for querying
Mongo is ideal for strong querying because its data structure resembles a DWH (data warehouse) cube.
Best for Document-oriented Storage
MongoDB is the best database for storing loads of documents that a tag or a category can sort.
- Polyglot Database System
MongoDB database can select the best portion of all databases and convert it into a large-scale system with multiple databases.
Frameworks and Tools
There are various frameworks and tools with which the MongoDB database can be used. Here are some examples –
Tools:
- Studio 3T
Studio 3T is a MongoDB tool that can manage users, create indices, and import and export data. It helps in maintenance and frequent updating. A user can migrate from SQL to MongoDB with the help of Studio 3T. It is to be noted that Studio 3T also allows the customization of table-to-JSON relationships, previewing documents, and editing them directly.
- MongoDB Compass
This is a free GUI tool from MongoDB for rich visualization of geo queries, performance charts, database schema analysis, and explain plans.
- NoSQL Booster
NoSQL Booster comes with a shell emulator and IntelliSense. The tool has many snippets and tips that can be opened within the tool. To import single SQL tables from MySQL, SQL Server, and PostgreSQL into MongoDB, NoSQL Booster is a vital tool.
- HumongouS.io
It is a MongoDB GUI accessible online with an intuitive UI that non-tech users can use. It allows you to customize your collections’ display. You can also access your data from anywhere, be it web or mobile. Moreover, you can filter your collections quickly by using its Smart Filter buttons. You can also make charts and dashboards from your MongoDB database.
Frameworks
- Pylons
It is a WSGI-based framework. And, together, they are known as PyMongo. Pylons and MongoDB, when used together, are thread-safe and are used to provide pooling for built-in connection for threaded applications.
- Pyramid
Pyramid can easily perform a query in the MongoDB database without any extraction or complex script writing to flatten the data. Therefore, Pyramid helps in displaying query results in real-time.
- Django
Django is one of the most popular web frameworks in Python. It is an ideal tool for building secure and easy-maintenance apps using MongoDB. Why is using MongoDB advantageous with Django? More unstructured data is generated from chats, surveys, feeds, real-time streams, and surveys. It helps in minimizing that. The migration is also easier when we use MongoDB with Django.
Parting Thoughts
By and large, the demand for MongoDB has shot up because it is a NoSQL database. In recent times, MongoDB has become essential for full-stack developers. We hope this blog has helped you understand more about the MongoDB database and why it is useful for developers to learn.
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