How do Document Databases Work?

Database Data Storage RDBMS

Databases are like a collection of data in a well-structured and organized form. They are used in a variety of applications, from web applications to large scale systems.

Databases are used to store data in tables, making it easier to access specific information. A table consists of rows and columns, where each row holds data, and the columns represent information based on these rows. Databases can be of two types: relational and non-relational. Relational databases store data in tables using SQL, while non-relational databases are designed to store data in collections, and use NoSQL to access it.

Document databases are a type of non-relational database and are designed to store data using documents instead of tables. Each document is made up of a collection of fields that contain a single value, like a string, integer, or timestamp. This makes the data management system more flexible as compared to that of relational databases. This is what a typical JSON document in Amazon DocumentDB will look like −

{
   "_id": {"$oid": "61778f30a684d4a4e3f47b45"},
   "title": "The Great Gatsby",
   "author": "F. Scott Fitzgerald",
   "publication_date": {"$date": "1925-04-10T00:00:00Z"},
   "genre": ["novel", "drama"],
   "reviews": [
      {
         "username": "bookworm",
         "rating": 4,
         "comment": "Great read!"
      },
      {
         "username": "reader21",
         "rating": 5,
         "comment": "A true masterpiece."
      }
   ]
}

The above document represents information about a book in a library.

_id − A unique identifier for the document, generated by the database automatically
title − Title of the book
author − Author of the book
publication_date − The date a book was published
genre − An array, containing genres of the book
reviews − A sub-document, containing reviews submitted by readers for the book
username − Username of the readers
rating − Ratings given by the readers
comment − Comments submitted by the users.

With the exception of a few minor syntax variations, a document's structure and content are essentially the same as those of other kinds of document databases.

For instance, the similar document representing the information about the book in MongoDB would look as follows −

{
   "_id": ObjectId("61778f30a684d4a4e3f47b45"),
   "title": "The Great Gatsby",
   "author": "F. Scott Fitzgerald",
   "publication_date": ISODate("1925-04-10T00:00:00Z"),
   "genre": ["novel", "drama"],
   "reviews": [
      {
         "username": "bookworm",
         "rating": 4,
         "comment": "Great read!"
      },
      {
         "username": "reader21",
         "rating": 5,
         "comment": "A true masterpiece."
      }
   ]
}

Document databases are frequently used to manage complex data in large-scale industries. Since they avoid complex relationships between tables, document databases can be a better preference than relational ones and offer better scalability.

Advantages

Document-oriented databases offer many advantages as compared to relational databases. Some of them are −

Able to store different data structures, documents are super flexible to use. Plus, users can work with a single document instead of having to juggle between multiple tables.
Document databases are always a better option for data models that need to evolve over time because they are easier to scale up.
Used across a wide range of industries, like technology, e-commerce, finance and healthcare.

Disadvantages

Apart from these features, document databases have their own downsides too −

Because all the data in these databases is stored in a single document, maintaining them as they grow can be a little tiresome. This can subsequently result in slow processing.
They are not that suitable for complex, large queries as compared to relational databases as the later one provides better optimization for complex queries.

Conclusion

In the end, it depends on the use case of the user or an organization to choose the right database based on their requirements. With a more effective and agile data management system, document databases are an excellent replacement for conventional relational databases.
Other alternatives to document databases include relational databases and key-value stores, having their own kind of advantages and disadvantages.
Due to their ease of scalability, document databases are a great choice for developers looking for large ecosystems with higher workloads.

Shreya Purohit

Updated on: 24-Mar-2023

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