Use Cases: What is MongoDB and Why To Use It With Case Study
MongoDB is the leading modern, general purpose database platform, designed to unleash the power of software
In this article I am going to share some information about: Use Case and Case Study of MongoDB What is it and Why To Use MongoDB.
There are many things that would be extremely difficult, if not impossible, to do without databases… The Internet, GPS, electronic banking, and a variety of other technologies.
They eliminate the need for a human to go through millions of file cabinets in order to locate a certain record. They provide a standardised and efficient method for programmes to store, retrieve, and modify data.
Databases are all around you, even if you don’t recognise it. Whether or whether you know anything about them, or care to, their impact on our everyday lives is significant. Databases power many of the services we use on a daily basis, from weather programmes to online movies.
A database is a collection of data, usually stored in electronic form. A database is typically designed so that it is easy to store and access information.
What is MongoDB?
MongoDB stores data as flexible, JSON-like documents, which means that fields can differ from document to document and the data structure can change over time.
MongoDB is a free and open-source database management system (DBMS) with a document-oriented database paradigm. MongoDB was created in C++. MongoDB can store a variety of data types.
- The document model corresponds to the objects in your application code, making it simple to interact with data.
- Ad hoc searches, indexing, and real-time aggregation are all useful methods for accessing and analysing data.
- MongoDB is, at its heart, a distributed database, thus high availability, horizontal scaling, and global dispersion are all built in and simple to use.
- MongoDB is completely free to use.
- MongoDB is a type of NoSQL database.
What is NoSQL Database?
NoSQL is a new type of database management system that differs fundamentally from relational database systems. NoSQL databases are a type of database management system that is highly scalable and adaptable.
NoSQL databases enable users to store and process unstructured and semi-structured data, which is not feasible with RDBMS technologies. NoSQL is a database design style that may support a broad range of data structures, including key-value, document, columnar, and graph formats.
In general, NoSQL systems do not provide the same level of data consistency as SQL databases. NoSQL databases guarantee high speed and scalability performance. NoSQL systems have the architecture in such a way to operate at high speed and wider flexibility towards the developer side.
Characteristics of NoSQL
Below are the characteristics of NoSQL:
- Multi-Model: This feature of NoSQL databases makes them extremely flexible when it comes to handling data.
- Easily Scalable: This feature of NoSQL databases easy scales to adapt to huge volumes and complexity of cloud applications. This scalability also improves performance, allowing for continuous availability and very high read/write speeds.
- Flexible: This feature of NoSQL databases allows you to process all varieties of data. It can process structured, semi-structured and unstructured data. It works on many processors — NoSQL systems allow you to store your database on multiple processors and maintain high-speed performance.
- Less Downtime: The elastic nature of NoSQL allows for the workload to automatically be spread across any number of servers.
Difference between SQL and NoSQL
Difference Between MongoDB and NoSQL
- A MongoDB “database” is the top-level container, consists of one or more collections while NoSQL data stores provide a top-level namespace or container for storing data.
- MongoDB is based on the document store data model in which a document is stored as BSON format. BSON format is a binary JSON format. NoSQL is an open-source, document database that provides high performance and scalability and data modelling and data management of huge data sets in an enterprise application.
- MongoDB supports advanced features for searching any field or range of queries or regular expression while NoSQL databases are more flexible in data storage and processing.
- MongoDB uses the features of sharding to scale horizontally. Simultaneously, NoSQL systems allow you to drag-and-drop your data into a folder and then query it without creating an entity-relational model.
Why use MongoDB?
MongoDB was created for developers of internet and commercial applications that need to change swiftly and grow gracefully. If you’re going to accomplish that, you should think about MongoDB.
Companies and development teams of all sizes use MongoDB because:
- The document data model is a powerful way to store and retrieve data that allows developers to move fast.
- MongoDB’s horizontal, scale-out architecture can support huge volumes of both data and traffic.
- MongoDB has a great user experience for developers who can install MongoDB and start writing code immediately.
- MongoDB can be used everywhere by anyone:
- For free through the open source community edition
- In the largest data centers through the enterprise edition
- In any of the major public clouds through MongoDB Atlas
2. MongoDB has developed a large and mature platform ecosystem, which means:
- MongoDB has a worldwide community of developers and consultants, so it is easy to get help.
- MongoDB works on all types of computing platforms, both on-premise and in the cloud (both private, and public clouds such as AWS, Azure, and Google Cloud)
- MongoDB can be used from all major languages.
- MongoDB can be accessed from all major ETL and data management systems.
- MongoDB has enterprise-grade support.
When to use MongoDB?
MongoDB is used in a wide variety of ways to support applications in IoT, Gaming, Logistics, Banking, e-Commerce, and Content Management.
MongoDB is a great choice if you need to:
- Represent data with natural clusters and variability over time or in its structure
- Support rapid iterative development.
- Enable collaboration of a large number of teams
- Scale to high levels of read and write traffic.
- Scale your data repository to a massive size.
- Evolve the type of deployment as the business changes.
- Store, manage, and search data with text, geospatial, or time series dimensions.
MongoDB has found a home in so many different businesses and functions because it does a great job solving many longstanding problems in data management and software development.
Here are a few of the problems that MongoDB solves:
- Integrating large amounts of diverse data:
If you are bringing together tens or hundreds of data sources, the flexibility and power of the document model can create a unified single view in ways that other databases cannot. MongoDB has succeeded in bringing such projects to life when approaches using other databases failed.
- Describing complex data structures that evolve:
Document databases allow embedding of documents to describe nested structures and easily tolerate variations in data in generations of documents. Specialized data formats like geospatial are efficiently supported. This results in a repository that is resilient and doesn’t break or need to be redesigned every time something changes.
- Delivering data in high-performance applications:
MongoDB’s scale-out architecture can support huge numbers of transactions on humongous databases. Unlike other databases that either cannot support such scale or can only do so with massive amounts of engineering and additional components, MongoDB has a clear path to scalability because of the way it was designed. MongoDB is scalable out of the box.
- Supporting hybrid and multi-cloud applications:
MongoDB can be deployed and run on a desktop, a huge cluster of computers in a data center, or in a public cloud, either as installed software or through MongoDB Atlas, a database as a service product. If you have applications that need to run wherever they make sense, MongoDB supports any configuration now and in the future.
- Supporting agile development and collaboration: Document databases put developers in charge of the data. Data becomes like code that is friendly to developers. This is far different from making developers use a strange system that requires a specialist. Document databases also allow evolution of the structure of the data as needs are better understood.
Features of MongoDB
These are some important features of MongoDB:
1. Support ad hoc queries
In MongoDB, you can search by field, range query and it also supports regular expression searches.
You can index any field in a document.
MongoDB supports Master Slave replication.
A master can perform Reads and Writes and a Slave copies data from the master and can only be used for reads or back up (not writes)
4. Duplication of data
MongoDB can run over multiple servers. The data is duplicated to keep the system up and also keep its running condition in case of hardware failure.
5. Load balancing
It has an automatic load balancing configuration because of data placed in shards.
6. Supports map reduce and aggregation tools.
8. It is a schema-less database written in C++.
9. Provides high performance.
10. Stores files of any size easily without complicating your stack.
11. Easy to administer in the case of failures.
12. It also supports:
- JSON data model with dynamic schemas
- Auto-sharding for horizontal scalability
- Built in replication for high availability
Pros and Cons of Using MongoDB
Pros of MongoDB
- By storing a majority of the data in RAM, query performance in MongoDB is much quicker. Instead of grabbing the data from the hard disk during a query, it grabs the data from the RAM and query returns come back much quicker. That being said, in order to take advantage of that enhanced performance, there’s a need for significant RAM and precise indexes.
- Because of its expressive query language, many users find MongoDB query syntax to be rather simple and much easier to understand than SQL. Additionally, installation, setup, and execution are rather simple and can take very little time to initiate. This has led more people to embrace the NoSQL structure over SQL databases for quick learning and fast training.
- MongoDB has no predefined schema and thus has very dynamic schematics architecture for unstructured data and storage options. Setting values for a variety of keys is vast, offering a lot of potential. In today’s ever-changing data-centric environment, having a flexible data model can be beneficial.
- One of the most attractive features of MongoDB is its reliable scalability. As opposed to SQL databases that utilize vertical scalability, MongoDB (like other NoSQL databases) uses shards for horizontal scalability, which makes it easier to increase storage capacity.
- Anyone that takes one look at MongoDB’s resources will see a large cache of precise documentation. For every edition and version, MongoDB doesn’t skimp on the details; they provide excellent documentation to help both seasoned and new users better understand the technology.
Cons of MongoDB
On the other hand, there are some aspects of MongoDB that are entirely favorable. Though in many ways the pros outweigh the cons, there are still areas where MongoDB doesn’t perform as well as other databases. Here are some of the less great parts of MongoDB:
- One of the downsides of MongoDB is that it doesn’t support transactions. Though fewer and fewer applications are requiring transactions, there are still some that need transactions in order to update multiple documents/collections. If that’s a necessary function for your team, MongoDB should not be used. There’s potential for data corruption.
- Joining documents in MongoDB is no easy task and though 3.2 introduced left-outer joins, developers are still working on the function and it’s not quite mature yet. Pulling data from several collections requires a number of queries, which will inevitably lead to messy code and long turn-around times.
- As mentioned earlier, enjoying MongoDB’s quick speeds and high performance is only possible with the right indexes. With shoddily implemented indexes and out of order composite indexes, MongoDB will operate at a shockingly slow speed.
- Some of these downsides could ultimately lead to duplicate data (as has happened for many users in the past). The relationships in MongoDB are not typically well-defined and the resulting duplicate data sets can be hard to handle. That, along with not being ACID compliant, would lead to corrupted data as well.
Case Study of MongoDB: How it solved Cisco’s Problems
Cisco is the global networking leader, redefining how people connect, communicate, and collaborate. Cisco released WebEx Social (formerly known as Quad) in November 2011, a business collaboration platform geared for today’s social, mobile, visual, and virtual workforce.
WebEx Social, dubbed “Facebook for Enterprises,” links individuals to the knowledge and knowledge they require, when they want it. Users communicate information and ideas across the company, as well as across geographic and organisational barriers.
Cisco chose MongoDB as their NoSOL database of choice when their old relational database required a NoSOL expansion to support the collaborative workspace.
WebEx Social is a true Enterprise 2.0 platform, “a place where people ‘live’ at work,” according to Biren Gandhi, principal architect for Cisco’s Cloud Collaboration Applications Technology Group (CCATG) The comprehensive collaboration platform combines the four core Enterprise 2.0 pillars — business processes, content, communication and social features -that enable users to work together effortlessly.
With their existing relational database, complex SOL queries against highly normalized schema were time consuming and Cisco had little room to scale horizontally. Additionally, it was difficult to manage schema upgrades and migrate data from release to release.
WebEx Social’s rapid evolution required expanded data store capabilities and the ability to build successful new social features. Cisco sought an open source, NoSOL solution that was scalable, flexible and met the product’s hardcore enterprise needs: secure, mature commercial support, able to deliver consistent back up and disaster recovery, and easy to integrate with legacy systems.
After an extensive evaluation and successful proof of concept, Cisco adopted MongoDB in mid-2011.
MongoDB now serves as the primary real-time data store for WebEx Social’s social features that are write-heavy in nature. For example, a social activity feed that notifies thousands of users depending on some complex privacy and visibility rules. Additionally, Cisco uses MongoDB to perform some social network analytics.
With MongoDB’s lightweight MapReduce feature, Cisco developers are able to automatically generate, with ease, recommendations such as users to connect with, communities to join and interesting content to view, as well as statistics, including top contributors, most popular colleagues and most downloaded docs.
“MongoDB is a very active, open source project with a vibrant community. It matches WebEx Social’s feature set and fits in line exactly with our needs,” said Gandhi.
EASY TO IMPLEMENT DOCUMENT MODEL :
MongoDB’s document-based data model offers Cisco the right balance between complex relational databases and the pure key value store of most NoSOL solutions.
Intuitive mapping from the domain model to JSON docs and complex querying capabilities enable Cisco to crossreference users and query embedded lists, functions that were either not possible in their existing database or were costly to perform.
In contrast with a relational model, which requires a three-step process of fetch-update-commit, MongoDB’s in-place atomic updates allow for “one-stop operations,” according to Gandhi.
MILLISECOND RESPONSE TIME:
With MongoDB, Cisco sped up reads from 30 seconds in some extreme cases to tens of milliseconds per object and eliminated caching needs in certain cases. As the WebEx Social platform evolves, Cisco plans to migrate more write-heavy features to MongoDB.
MongoDB meets the demands of WebEx Social’s high-volume write activity. MongoDB offers a fast, easy way to denormalize, index and extract information from data stores and its unique compound and geospatial indexing are a big plus.
Cisco uses built-in sharding and replica sets, which are easy to use and enable high availability through automatic failover of nodes and recovery of member nodes.
As part of various innovation initiatives at Cisco, many developers participate in voluntary projects where people can collaborate and share results on their ideas. Many have taken up MongoDB as part of their project development, a testament to the usability, flexibility and innovation that the database offers.
The Cisco development team is constantly evaluating the most effective product roadmap to drive customer success on WebEx Social. MongoDB provides Cisco with a user friendly platform for customers and an easy, powerful technology for developers. Cisco is focused on rolling out new features quickly and offering flexibility to users, and “MongoDB is a big piece of that,” said Gandhi.
In recent years, the need for NoSQL databases like MongoDB has increased.
Here are several real-world MongoDB use cases, as well as several firms that use MongoDB in their day-to-day operations.
I hope this essay has given you a better understanding of MongoDB and its applications…