Why should I use document based database instead of relational database

Choosing the right database is a critical decision for any software development project. While relational databases have been the standard for decades, the rise of NoSQL databases, particularly document based database systems, offers compelling alternatives for modern applications. The traditional relational model, with its rigid schemas and complex joins, can sometimes become a bottleneck, hindering agility and performance. For many scenarios, a document based database presents a more flexible and scalable solution, particularly when dealing with unstructured or semi-structured data. This approach allows developers to store data in JSON-like documents, enabling faster development cycles and improved performance in certain use cases. This blog post delves into the advantages of document databases over relational databases, exploring the scenarios where they excel and the benefits they offer to modern applications. We’ll examine key features, use cases, and potential drawbacks to help you make an informed decision about your database architecture.

Flexibility and Schema Evolution

One of the most significant advantages of a document based database is its inherent flexibility. Unlike relational databases that enforce a strict, predefined schema, document databases allow for schema-less or schema-on-read approaches. This means you can store documents with varying structures within the same collection. This flexibility is particularly beneficial when dealing with evolving data requirements or when integrating data from multiple sources with differing schemas. As your application evolves, you can easily add new fields or modify existing ones without requiring costly and time-consuming schema migrations.

Relational databases require significant planning and upfront schema design. Making changes later can involve altering tables, adding columns, and migrating data, which can lead to downtime and potential data loss. Document databases, on the other hand, embrace change. Each document can have its own unique structure, accommodating variations in data without disrupting the entire database. This agility allows development teams to iterate faster and respond more quickly to changing business needs. For example, an e-commerce platform might use a document database to store product information, allowing different vendors to supply product details with varying attributes without conforming to a rigid, unified schema. This flexibility accelerates the onboarding process for new vendors and ensures a smoother data integration experience.

Consider this quote from a MongoDB whitepaper: “Document databases are ideal for applications that require flexibility, agility, and scalability. Their schema-less nature allows developers to iterate faster and adapt to changing business requirements more easily.” MongoDB Source This underscores the value proposition for organizations needing to adapt quickly.

Scalability and Performance

Document based database systems are often designed with horizontal scalability in mind. This means you can easily distribute your data across multiple servers, allowing your database to handle increasing workloads without significant performance degradation. This contrasts with relational databases, which often rely on vertical scaling (upgrading to more powerful hardware) or complex sharding strategies to achieve similar levels of scalability. Horizontal scalability is crucial for applications that experience rapid growth or unpredictable traffic patterns. The ability to add more nodes to your cluster on demand allows you to seamlessly scale your database to meet the demands of your users.

Furthermore, document databases can offer performance advantages in certain scenarios. Because data is stored in a single document, there’s often no need for complex joins across multiple tables. This can significantly improve query performance, especially when retrieving related data. For instance, in a social media application, user profiles and their associated posts can be stored as a single document. Retrieving a user’s profile and their recent posts requires only a single query, compared to multiple queries and joins in a relational database. This optimized data access pattern can lead to faster response times and a better user experience.

The featured snippet optimized paragraph: Document based database systems excel in scenarios requiring high read performance. Since related data is often embedded within a single document, retrieving complete records requires fewer I/O operations compared to relational databases that rely on joining multiple tables. This leads to faster query execution and improved application responsiveness, especially for read-heavy workloads. This efficiency is a key reason why many modern web applications choose document databases.

Development Speed and Agility

The schema-less nature of document based database systems not only provides flexibility but also accelerates development speed. Developers can start building applications without spending significant time designing and defining database schemas. This allows for faster prototyping and iteration, enabling teams to deliver new features and updates more quickly. The ability to easily adapt the data structure as the application evolves further enhances agility and reduces development time. Using a document based database allows developers to focus on the business logic of their application rather than getting bogged down in database schema management.

Many modern frameworks and languages are designed to work seamlessly with document databases. For example, JavaScript and JSON, the native data format for many document databases, are widely used in web development. This reduces the impedance mismatch between the application code and the database, simplifying data access and manipulation. Furthermore, document databases often provide intuitive query languages that are easy to learn and use, further accelerating development. Consider a situation where you are building a content management system (CMS). Using a document database allows you to store articles, pages, and other content types with varying structures, simplifying the content creation and management process.

Here are some key advantages of using document databases for development speed:

• Reduced schema management overhead.
• Faster prototyping and iteration cycles.
• Simplified data access and manipulation.

Use Cases and When to Choose a Document Database

Document based database systems are well-suited for a variety of use cases, particularly those involving unstructured or semi-structured data. These include:

• Content Management Systems (CMS): Storing articles, pages, and other content with flexible schemas.
• E-commerce Platforms: Managing product catalogs, customer profiles, and order information.
• Social Media Applications: Storing user profiles, posts, and social connections.
• Internet of Things (IoT): Ingesting and processing data from sensors and devices.

Consider an IoT application that collects data from thousands of sensors. Each sensor might report different types of data at different intervals. A document database can easily accommodate this variability, allowing you to store the sensor data without requiring a rigid, predefined schema. This makes it easier to analyze the data and gain insights from your IoT deployment. According to Gartner, “By 2025, 80% of data management solutions will incorporate document-oriented capabilities.” Gartner Source This statistic indicates the growing importance of document databases in the data management landscape.

When should you choose a document database over a relational database? Here’s a general guideline:

1. If your data is unstructured or semi-structured.
2. If you need flexibility and agility in your data model.
3. If you require horizontal scalability.
4. If you are building a modern web or mobile application.

FAQ ---

What are the disadvantages of document databases?

Document databases can be less suitable for applications that require complex transactions or ACID compliance. They also may require more careful data modeling to avoid data duplication and ensure consistency.

Are document databases difficult to learn?

No, many document databases provide intuitive query languages and are relatively easy to learn, especially for developers familiar with JSON and JavaScript.

Can I use a document database for my e-commerce application?

Yes, document databases are well-suited for e-commerce applications, particularly for managing product catalogs and customer profiles. However, you need to carefully consider the requirements for transactions and ACID compliance when dealing with order processing.

Choosing the right database is a critical decision that impacts your application's performance, scalability, and development speed. While relational databases remain a solid choice for many applications, document databases offer compelling advantages in scenarios requiring flexibility, agility, and scalability. By understanding the strengths and weaknesses of each approach, you can make an informed decision and select the database that best meets the needs of your project. Explore different database options and consider a proof-of-concept to validate your choice. Further research into specific document database implementations like MongoDB or Couchbase is recommended. Don't hesitate to experiment and see how a document-based approach can streamline your development process and enhance your application's capabilities. Check out our other articles on database design and optimization for more insights! [AWS NoSQL Databases](https://aws.amazon.com/nosql/)**Question & Answer :** Why should I use document based database like CouchDB instead of using relational database. Are there any typical kinds of applications or domains where the document based database is more suitable than the relational database?

Probably you shouldn’t :-)

The second most obvious answer is you should use it if your data isn’t relational. This usually manifests itself in having no easy way to describe your data as a set of columns. A good example is a database where you actually store paper documents, e.g. by scanning office mail. The data is the scanned PDF and you have some meta data which always exists (scanned at, scanned by, type of document) and lots of possible metadata fields which exists sometime (customer number, supplier number, order number, keep on file until, OCRed fulltext, etc). Usually you do not know in advance which metadata fields you will add within the next two years. Things like CouchDB work much nicer for that kind of data than relational databases.

I also personally love the fact that I don’t need any client libraries for CouchDB except an HTTP client, which is nowadays included in nearly every programming language.

The probably least obvious answer: If you feel no pain using a RDBMS, stay with it. If you always have to work around your RDBMS to get your job done, a document oriented database might be worth a look.

For a more elaborate list check this posting of Richard Jones.