System Design: Data Store — Document-Oriented Database Profile

Published in

AyoKoding

7 min readJul 4, 2023

Introduction

Document-oriented databases are a type of NoSQL database that store data in a document format, typically using JSON or BSON. These databases are becoming increasingly popular due to their flexible data models and ability to handle unstructured or semi-structured data. This contrasts traditional relational databases, which require a predefined schema and are less flexible.

Document-oriented databases are particularly useful in web applications, content management systems, and other modern applications that require the ability to store and retrieve large amounts of structured and unstructured data. They are also well-suited for applications that require frequent updates and changes to the data schema, as they can easily accommodate modifications without requiring changes to the underlying structure.

One key feature of document-oriented databases is their ability to scale horizontally, meaning they can efficiently distribute data across multiple nodes in a cluster. This makes them ideal for high availability and performance applications, as they can easily handle large amounts of data and traffic.

Document-oriented databases offer a powerful and flexible solution for managing complex data models in modern applications. As more and more organizations adopt cloud-based architectures and modern application development practices, document-oriented databases are likely to become increasingly popular in the years to come.

Key Characteristics

When it comes to the key characteristics of this system, there are several important factors to consider. Let’s take a closer look at each one of them:

Document-oriented data model: One of the most critical aspects of this system is its document-oriented data model. This means that data is stored in a way that is similar to how documents are stored, making it easy to manage and work with.
No fixed schema: Another critical characteristic of this system is that it does not have a fixed schema. This means you can easily add or modify new fields without worrying about breaking anything.
Support for nested data structures: This system also supports nested data structures, meaning you can store complex data types within each document.
High scalability and performance: Scalability and performance are two critical aspects of any system, and this one delivers on both. With its high scalability, you can easily add more nodes to your cluster as your needs grow. And with its strong performance, you can be sure your system can handle even the most demanding workloads.
Eventual consistency: Another vital aspect of this system is its eventual consistency. This means that while updates may not be immediately reflected across all nodes in the cluster, they will eventually catch up.
Automatic sharding and replication: This system offers automatic sharding and replication, which means you can efficiently distribute data across multiple nodes for increased redundancy and reliability.

CAP Theorem

General CAP theorem handling

Document-oriented databases are designed to prioritize availability and partition tolerance over consistency, making them a good fit for applications that require high availability and scalability. In other words, they are optimized to ensure that the system remains operational even when some of its nodes fail and can handle a large amount of data across multiple nodes.

Guarantee of consistency

Document-oriented databases typically provide eventual consistency, which means that updates to the database may take some time to propagate to all nodes in the system. This can be understood as a trade-off between consistency and availability, as the system prioritizes the latter. However, some databases also offer strict consistency, ensuring all nodes see the same data simultaneously. This means the system can ensure data integrity and users always see the most up-to-date information.

Guarantee of availability

Document-oriented databases are designed to prioritize availability, which means they can continue operating even if some nodes in the system fail. This is achieved through redundancy and automatic failover mechanisms, which ensure that the system can continue to serve users even if some of its components fail.

Guarantee of partition tolerance

Document-oriented databases are designed to be highly scalable and handle large amounts of data across multiple nodes. They use automatic sharding and replication to ensure that data is distributed evenly across the system and that it can continue operating even if some nodes fail. This means that the system can handle a large number of requests and that it can scale horizontally as the data volume grows.

Usage

Best usage

Document-oriented databases are highly recommended for applications that require flexible data models and high scalability, such as content management systems, web applications, and real-time analytics. They can help improve such applications’ performance by providing a more efficient way of handling unstructured and semi-structured data. Additionally, they can reduce the complexity of the application’s architecture, making it easier to maintain and scale.

Neutral usage

Document-oriented databases can also be used for applications that require structured data but may not be the best choice for applications that require strict consistency or complex transactions. However, they can offer advantages over traditional relational databases, such as better performance and scalability.

Some developers prefer document-oriented databases for their simplicity and ease of use. They can be more intuitive, especially for those unfamiliar with SQL or other database-specific languages.

Worst usage

While document-oriented databases may not be the best choice for applications that require complex joins or transactions or for applications that require strict consistency, they can still be used in such scenarios with some trade-offs. For example, they may require additional programming effort to achieve the desired consistency level, and performance may be affected due to the lack of indexing and other optimization techniques used in relational databases.

System Design Role

Document-oriented databases are well-suited for systems that require high scalability and availability, such as distributed systems and cloud-based applications. They can help improve such systems’ performance and reliability by enabling them to handle large volumes of data and traffic. Additionally, they can help simplify the system’s architecture by reducing the components needed to handle data storage and retrieval.

Data Model

Document-oriented data model
Non-relational database
Advantages: flexible data model, easy to scale, good performance with large datasets
Disadvantages: may not be suitable for applications that require strict consistency or complex transactions

Query Language

NoSQL query language (e.g., MongoDB Query Language)
Advantages: flexible, easy to use, good performance with large datasets
Disadvantages: it may not be as powerful as SQL for complex queries

Scalability

How to make it performant

To make document-oriented databases performant, several optimization techniques can be implemented. Apart from indexing, query optimization can also be done by selecting the proper data structure and optimizing queries for better performance.

High traffic handling

While document-oriented databases are perfect for high-read workloads, they may not be well-suited for high-write workloads. To handle high traffic, it is essential to monitor the performance of the database and use techniques such as load balancing, caching, and sharding to distribute the load.

How to scale it

Scaling document-oriented databases horizontally can be done through automatic sharding and replication. However, it is essential to note that scaling may require additional hardware resources, which can be costly.

Usage in distributed systems

Document-oriented databases can be used in distributed systems, but additional considerations must be remembered. Data partitioning and replication should be carefully planned and implemented to ensure optimal performance in distributed systems.

Replication

To ensure data availability and durability, document-oriented databases typically use automatic replication. Best practices for replication include using a replication factor of at least three, ensuring that replicas are distributed across multiple data centers, and implementing a backup and recovery strategy to prevent data loss in case of hardware or software failure.

In Practice

Best Practices

To ensure that your system is performing at its best, it is essential to consider the following practices:

Use indexing and other performance optimization techniques to improve query performance. This includes optimizing your queries based on the data type you are working with and the queries you will run. By doing this, you can improve the speed and efficiency of your queries, ultimately improving your entire system’s performance.
Use a replication factor of at least three to ensure data availability and durability. This means you should have at least three copies of your data spread across different nodes in your system. By doing this, you can ensure that your data is always available and that you can recover from any possible failures.
Monitor the system for performance issues and adjust as necessary. This includes monitoring your queries, system resources, and network traffic. By doing this, you can identify any performance bottlenecks and take steps to address them before they become a problem.

Additionally, consider other best practices such as optimizing your data model, using compression to reduce storage requirements, and using caching to improve query performance. By following these practices, you can ensure that your system runs smoothly and efficiently.

Common Pitfalls

Regarding database management, several common pitfalls can negatively impact performance and overall efficiency. Here are some additional details about each one:

Not understanding the data model and how it affects query performance: Understanding the data model is crucial for optimal query performance. It is essential to know how data is structured and how tables relate. This knowledge can help identify inefficiencies and areas for improvement.
Not properly configuring replication and sharding: Replication and sharding are essential for scaling databases and ensuring high availability. However, many people overlook the importance of adequately configuring these features. Without proper configuration, replication and sharding can hinder performance and cause problems.
Not monitoring the system for performance issues is essential for promptly identifying and addressing performance issues. Without proper monitoring, it can be challenging to diagnose and troubleshoot problems. Regular monitoring can help detect issues before they become significant problems and can help ensure that the system runs smoothly and efficiently.

Examples

MongoDB, a popular NoSQL database management system, is used by large organizations.
Couchbase, another NoSQL database, offers advanced features such as in-memory caching and geo-replication.
Amazon DocumentDB is a fully managed NoSQL database service compatible with MongoDB workloads, making migrating from MongoDB to Amazon’s cloud platform easy.