As of 2021, a survey conducted on software developers shows that MongoDB is the second-most wanted database skill, just after PostgreSQL. MongoDB is an in-demand skill among developers, and the best way to learn it is through a MongoDB tutorial.
MongoDB is a document-oriented database used to build scalable Internet applications. Due to its schema approach of database designing, MongoDB is especially popular with developers and software professionals working with agile methodologies. MongoDB is ideal for building dynamic and modern big data applications with its flexible schema design offerings. With MongoDB, you can store, manage, and retrieve data and experience seamless application building without application downtime.
This MongoDB tutorial for beginners will walk you through the fundamentals of MongoDB, including a brief MongoDB query tutorial.
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What is MongoDB?
MongoDB is an open-source documented-oriented database. It falls under the NoSQL database category because, unlike traditional databases, MongoDB does not use tables and rows for data storage and retrieval. Instead, each record in a MongoDB database is a binary representation of the data (BSON) that applications can retrieve in a JSON format. Below is an example of a simple JSON document format:
Essential Components in the MongoDB Architecture
Next, in this MongoDB tutorial for beginners, we’ll look at some of the fundamental concepts of the NoSQL database.
MongoDB documents are a collection equivalent to a table in RDBMS (Relational Database Management System). A single database comprises a collection and does not enforce any schema. In addition, a collection includes documents with different fields with related purposes.
A database is a container for collections, with each database receiving its own set of files on the file system. A MongoDB server can typically store multiple databases.
A MongoDB is the basic building block of data and consists of field and value pairs. While the value of a field is typically BSON data type, field names are strings. A field in MongoDB is what a column is in a relational database. Documents are grouped into a collection, and documents within a collection may have different fields. Also, BSON documents may have multiple fields with the same name.
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The _id field represents a unique value and is mandatory in every MongoDB document. It is a 12 bytes hexadecimal number that serves as a document’s primary key. Thus, MongoDB automatically provides the unique _id for every document even if you create a new document lacking the _id field.
Below is an example of a MongoDB document with values of different types:
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Top Technical Features of MongoDB
MongoDB addresses several limitations of relational databases and other NoSQL solutions to ensure scalability and flexibility. In this section of the MongoDB tutorial, we will discuss some of the technical features of MongoDB.
Indexing is a crucial part of databases and, if done right, enhances performance and search speed. Improper indexing often leads to accessibility issues. It is possible to index any field or key in a MongoDB document. The extensive indices and features on MongoDB support even the most complicated access patterns to datasets. Moreover, you can create MongoDB indices as per dynamic and real-time query patterns and requirements.
Ad hoc query support
Ad hoc queries are temporary commands whose values are variable-dependent. Thus, the variable in question decides the result every time an ad hoc query is executed. Ad hoc queries become significant when numerous variables come into the picture. MongoDB offers ad hoc query support, allowing developers to make real-time updates in ad hoc queries. Optimizing ad hoc in this way improves performance and enables scalability.
Sharding means splitting large datasets across multiple collections or “shards.” It allows better execution since working with massive datasets can cause unexpected problems. MongoDB allows for horizontal scalability with the sharding feature. In horizontal scaling, the collection of distributed shards makes it easier to handle a dynamic application and ensures zero downtime. In MongoDB, instances called mongos manage all the operations in the sharding environment.
Replication means distributing data across multiple servers for backup and disaster recovery. It helps avoid potential failures such as hardware malfunctions, service interruptions, or service crashes that usually result when data is kept in a single database. MongoDB uses replica sets to achieve replication – a primary server accepts write operations and replicates them across secondary servers.
Optimal and efficient load balancing is critical for database management, and MongoDB ensures large-scale load balancing through features like sharding and replication. MongoDB simultaneously manages multiple read and write requests for the same data through locking protocols and concurrency control. The platform achieves all this without any external load balancer.
Why use MongoDB?
MongoDB is one of the most sought-after databases among developers due to the ease and convenience of data storage, management, and retrieval. At the same time, it is simple to learn and use and can be used to create robust applications using most programming languages. Let’s look at some of the advantages of MongoDB that make it so popular.
- Data in MongoDB is stored in flexible, JSON-like documents, giving ample scope to change data structure over time.
- MongoDB’s document model makes data easy to work with by mapping to the objects in your application code.
- At its core, MongoDB is a distributed database. As a result, you get easy-to-use in-built features like horizontal scaling, high availability, and geographic distribution.
- Ad hoc queries, real-time aggregation, and indexing allow convenient data access and analysis.
- MongoDB has a comprehensive platform and a mature ecosystem further reinforced by its large and thriving community of developers.
- Most importantly, MongoDB is open source and free to use.
Typical Use Cases of MongoDB
MongoDB is a general-purpose database with use cases spanning different businesses and industries, including finances, telecommunications, gaming, retail, and healthcare. This section of the MongoDB tutorial looks at some of the specific use cases of MongoDB.
- The scale-out architecture of MongoDB supports voluminous transactions on large databases, and its scalable feature enables data delivery in high-performance applications.
- MongoDB’s document model can be used to integrate vast amounts of diverse data, even where other database approaches fail.
- MongoDB is the solution for describing complex and evolving data structures. Document databases enable document embedding to describe nested structures resilient to data variations.
- Document databases like MongoDB support agile development and collaboration. Its dynamic schema seamlessly adapts and evolves with the application and is perfectly compatible with modern object-oriented programming.
- MongoDB supports hybrid and multi-cloud applications, and it can be deployed and run in a public cloud, in a data center, or on a desktop. In addition, MongoDB can also be deployed and run through its database-as-a-service product, MongoDB Atlas.
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MongoDB Query is a way to retrieve data from the MongoDB database. It is similar to SQL queries in SQL databases and simplifies getting the data from the database. Moreover, query operations in MongoDB also allow using conditions or criteria for fetching specific information from the database. The find() method is used to perform query operations in MongoDB, and its basic syntax is:
MongoDB is beneficial for working with vast sets of distributed data due to its schema approach’s flexibility and scalability. Most importantly, MongoDB has official driver support for some of the most widely used programming languages, including Java, C, C++, C#, Python, PHP, Pearl, and more. Google, Adobe, AstraZeneca, Forbes, Toyota, Thermo Fisher Scientific, KPMG, Barclays, and eBay are a few prominent companies using MongoDB.
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1. Can we MongoDB with Python?
Yes, you can use MongoDB with Python. MongoDB’s native Python driver PyMongo and its dedicated driver engineers ensure that Python and MongoDB work together seamlessly.
2. Is MongoDB better than SQL?
MongoDB overcomes several limitations of SQL databases to offer faster query processing. However, MongoDB has higher load and system requirements. Thus, while SQL is suitable for simple use cases with limited system offerings, MongoDB is more reliable where optimized querying is needed.
3. Does MongoDB support SQL?
Unlike MySQL, MongoDB does not directly support traditional SQL queries. However, MongoDB supports a rich query language of its own which is limited compared to SQL. Despite the limitation, MongoDB offers several options, such as MongoDB BI Connectors and PyMango to support analytics on MongoDB data.