What is MongoDB?
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MongoDB is a document-oriented NoSQL database for storing large amounts of data.
MongoDB uses collections and documents rather than the tables and rows found in
conventional relational databases. The fundamental unit of data in MongoDB is
a pair of key-value pairs, which make up documents. Collections are the equivalent
of relational database tables in that they store sets of documents and perform
specific functions. A database called MongoDB first became popular in the middle of the 2000s.
Records in MongoDB are built of documents that have a data structure made up of field and value pairs.
In MongoDB, documents are the fundamental type of data. The papers employ a format called Binary JSON,
supporting more data kinds. These documents' fields resemble relational databases' columns in structure.
According to the MongoDB user handbook, values included can be a wide range of data formats,
including other documents, arrays, and arrays of documents. A main key will be used as a primary
identifier for documents.
Collections, which serve as the equivalent of relational database tables, are groups of documents.
Data in a collection cannot be dispersed across multiple databases, even though collections can
contain any kind of data.
The open-source MongoDB distributions come bundled with the mongo shell. Users connect the mongo shell to their
active MongoDB instances after installing MongoDB. Users can query and update data, perform administrative tasks,
The BSON document storage and data interchange standard offers a binary representation of JSON-like documents.
As data volumes and throughput demands rise, automatic sharing is another crucial feature that enables data
in a MongoDB collection to be distributed across several computers for horizontal scalability.
For data consistency, the NoSQL DBMS employs a single master architecture, with backups of the primary
database kept in subsidiary databases. For automated failover, operations are automatically
replicated to those backup databases.
The vendor MongoDB Inc. offers both free and paid versions of MongoDB. MongoDB Enterprise
Server adds more security features, an in-memory storage engine, administrative and
authentication facilities, and monitoring capabilities with Ops Manager over the
open-source MongoDB Community Edition.
Users have access to the MongoDB Compass graphical user interface (GUI), which allows
them to deal with document structure, run queries, index data, and more. Users can
connect the NoSQL database to their business intelligence software to see data and
generate reports using SQL queries using the MongoDB Connector for BI.
In 2016, MongoDB Inc. released a cloud database as a service called MongoDB Atlas,
following other NoSQL database vendors' lead. AWS, Microsoft Azure, and Google
Cloud Platform are used to power Atlas. With intentions to expand it to on-premises
databases, MongoDB later created a platform called Stitch for application development
on MongoDB Atlas.
As part of MongoDB 4.0 in 2018, the business also included support for multi-document
ACID transactions. By adhering to the ACID principles of atomicity, consistency,
isolation, and durability across many documents, MongoDB can handle a wider range
of transactional workloads with assurance of precision and dependability.
Dwight Merriman and Eliot Horowitz, who were designing web applications at DoubleClick,
an online advertising firm that is now owned by Google Inc., ran into development and
scalability problems with conventional relational database systems. The word "humongous"
was used as the basis for the database's name to signify its ability to handle massive
volumes of data.
Merriman and Horowitz participated in the establishment of 10Gen Inc. in 2007 to market
MongoDB and associated products. In 2013, the business changed its name to MongoDB Inc.,
and in October 2017, it began trading publicly under the ticker code MDB.
In addition to the commercial licenses provided by MongoDB Inc., the DBMS was made available
as open-source software in 2009 and is now accessible under the conditions of Version 3.0 of
the GNU Affero General Public License from the Free Software Foundation.
Each database has collections, and those collections have documents in them.
Each document can differ from another and have a variable number of fields.
Each document may vary in size and content from the others.
The document's structure more closely resembles how programmers create classes and objects in their chosen programming languages.
Developers frequently claim that their classes have a clear structure with key-value pairs rather than being rows and columns.
There is no requirement that a schema be defined before the rows (or documents as MongoDB calls them) are created. The fields might instead be made up as needed.
You can more easily depict hierarchical connections, store arrays, and other more complicated structures thanks to MongoDB's data model.
Scalability – The MongoDB environments are very scalable. Companies across the world have defined clusters with some of them running 100+ nodes with around millions of documents within the database.
Key Components of MongoDB Architecture
Below are a few of the common terms used in MongoDB.
_id - The field "_id" is a need for all MongoDB documents. A distinct value is
represented by the _id field of a MongoDB document. Like the document's primary key is
the _id field. Without a _id field, MongoDB will automatically create one whenever you
create a new document. Therefore, if we take the customer table example from above as
an example, Mongo DB will give each document in the collection a 24-digit unique identifier.
Collection - This is a grouping of MongoDB documents. In any other RDMS, like Oracle or MS SQL, a collection is
the equivalent of a table. A single database has a collection. As the introduction shown, collections do not
impose any kind of organization.
Cursor – This is a pointer to the result set of a query. Clients can iterate through a cursor to retrieve results.
Database – This is a container for collections like in RDMS wherein it is a container for tables. Each database
gets its own set of files on the file system. A MongoDB server can store multiple databases.
Document – A record in a MongoDB collection is basically called a document. The document, in turn, will consist
of field name and values.
Field – A name-value pair in a document. A document has zero or more fields. Fields are analogous to columns in
relational databases. The following diagram shows an example of Fields with Key value pairs.
structured data. JSON is currently supported in many programming languages.
Below are the few of the reasons as to why one should use MongoDB.
Document-oriented - Because MongoDB is a NoSQL database, it stores data in
documents rather than in a relational style. Because of this, MongoDB is particularly
adaptable to the conditions and needs of the real business world.
Ad hoc queries - Field searching, range queries, and regular expression searches
are all supported by MongoDB. Within documents, queries can be constructed to return
Indexing - Indexes can be created to improve the performance of searches within
MongoDB. Any field in a MongoDB document can be indexed.
Replication - With replica sets, MongoDB can achieve high availability. At least two
Mongo DB instances make up a replica set. At any point, any member of the replica set may
take on the role of the primary or secondary replica. The main server, which communicates
with the client and handles all read/write activities, is the primary replica. Using built-in
replication, the secondary replicas keep a duplicate of the primary's data on hand. When a
primary replica fails, the replica set automatically moves over to the secondary and then it becomes the primary server.
Load Balancing - By dividing data among several MongoDB instances, MongoDB can scale horizontally.
This is accomplished through the concept of sharding. In the event of hardware failure, MongoDB can
run across numerous servers, balancing the demand and/or replicating data to keep the system operational.
Where can you use the MongoDB NoSQL database?
Big Data and Hadoop applications can make heavy use of the MongoDB NoSQL database to work with the
enormous volumes of NoSQL data that make up a large percentage of Big Data. Although MongoDB and SQL
are both database systems, their effectiveness in the modern world distinguishes them from one another.
For the successful parsing of all flowing unstructured data, MongoDB can also be used in social media and
mobile applications. The MongoDB NoSQL database is also widely used for content management and delivery.
User data management and data hubs are other domains.
Today, a sizable number of IT companies employ MongoDB as a database service for applications
or data storage systems. Over 4000 businesses have confirmed using MongoDB as a database, according
to a Siftery survey on the subject. The organisations that use MongoDB include the ones listed below.
What are the Applications of MongoDB?
Content Management -
Years ago, static text alone could be used to create webpages.
However, given how quickly technology is developing, making a website in this way will
be viewed as archaic. To attract users today, a website needs to have social media, videos,
audio, and a variety of texts.
It can be difficult to create high-quality websites using a relational database. Thank goodness,
MongoDB offers accessibility to such content on a single database and interoperability with it.
This is because it accommodates both structured and unstructured data.
Internet of things (IoT) -
IoT connects billions of devices worldwide and is one of the most
admired technology developments in use today. Businesses may utilize operational efficiency,
reinvent their business models, and increase productivity with IoT. MongoDB enables IoT devices
to reach their full potential. The distribution and operation of IoT devices are accelerated by
its sophisticated Data Platform.
Real-Time Analytics -
Analytics and transactional databases were typically kept apart while using
RDBMS databases. It would be necessary to transfer data from the transactional database to the
analytics environment, which needed a massive daily data load. Companies may now analyze data in
real-time while saving money thanks to NoSQL databases like MongoDB.
Customers now favor using their smartphones for business transactions. As a result, a sizable
population uses mobile applications on a daily basis. RDBMS often struggle to handle such a high
volume of concurrent transactions. But with MongoDB, you can develop apps more cheaply and attract more users.
Due to this, numerous financial institutions, healthcare organizations, and industry titans have
started using MongoDB smartphone apps for their operations. MongoDB's strong query capabilities
and flexible data format enable enterprises to build outstanding mobile applications and help
clients expand their businesses. Even organizations like Automatic Data Processing, Inc.
(ADP) and The Weather Channel have gushed over how well this technology for developing smartphone
apps has worked for them.
Schema less − MongoDB is a document database in which one collection holds different documents. Number of fields, content and size of the document can differ from one document to another.
Structure of a single object is clear.
No complex joins.
Deep query-ability. MongoDB supports dynamic queries on documents using a document-based query language that’s nearly as powerful as SQL.
Ease of scale-out − MongoDB is easy to scale.
Conversion/mapping of application objects to database objects not needed.
Uses internal memory for storing the (windowed) working set, enabling faster access of data.
MongoDB might not be the best fit for every organization or project for a few reasons.
Joins are uneasy -
MongoDB does not support joins like a relational database does. In other words, connecting your saved papers is a challenging task. Although manual coding is an option, it takes time and might have an impact on performance.
Heavy reliance on reliable indexes -
The speed and performance of MongoDB will suffer if your indexing is implemented poorly or contains any discrepancies or other issues. As a result, you will have to put in some effort correcting and studying the indexes you apply.
Duplicate data is a frequent issue -
Duplicate data can be problematic, according to common criticism of MongoDB. In other words, you acquire it and it may make it challenging to handle your info effectively.
Excessive memory use -
MongoDB frequently requires high memory utilization, in addition to the memory inefficiency of duplicate data and the limited capacity to implement joins. which more and more could be thought to be unneeded.
Restricted data size and nesting -
The restrictions on document sizes and document nesting are the last drawback on our list of MongoDB's benefits and drawbacks. MongoDB has a 16MB maximum document size restriction. Additionally, you are limited to 100 levels of nesting.