1. The Necessity of Custom Databases in Today's Tech Landscape

In recent years, the development community has witnessed an explosion of front-end frameworks. The options seem endless, with new frameworks emerging regularly, each promising better performance, more features, or an enhanced developer experience. This surge underscores the industry's focus on creating dynamic and responsive user interfaces.

But what about the backend? While there are notable backend frameworks like Express.js—a tried-and-true option—Fastify, and Hono (a personal favorite), the variety doesn't seem as overwhelming as on the front end. This disparity leads us to consider databases, a critical component of backend development that often operates behind the scenes.

Surprisingly, there are more databases on the market than front-end frameworks. This abundance isn't immediately apparent but becomes clear when we consider the unique needs of different organizations. Many companies have developed their own databases to address specific challenges that existing solutions couldn't efficiently solve.

For example:

• Facebook with Apache Cassandra: Developed to handle massive amounts of data across multiple servers without a single point of failure.
• Google with Bigtable: Designed for petabyte-scale data storage and rapid access, underpinning services like Google Search and Google Analytics.
• Amazon with DynamoDB: A scalable NoSQL database service optimized for high-throughput workloads and low-latency performance.
• LinkedIn with Voldemort: A distributed key-value storage system aimed at high scalability and fault tolerance.
• Apple with FoundationDB: A distributed database focusing on ACID transactions and horizontal scalability, providing a solid foundation for services like iCloud.

These tech giants created custom databases to meet unique performance requirements, scalability needs, and to gain a competitive edge by optimizing their data storage solutions.

Even if you're not running a company operating at such a massive scale, understanding why and how these companies built their own databases can be incredibly insightful. It highlights the importance of having a data storage solution tailored to your application's specific needs.

If you're intrigued by the idea of architecting your own database, the first step is to understand how databases store data on a machine. At a fundamental level, databases manage how data is written to and read from storage media, how it's organized internally, and how it can be efficiently retrieved and manipulated.

There are two main types of database storage approaches:

• Native Databases: Standalone systems like MySQL and PostgreSQL. They run as separate server processes, and applications connect to them over a network. Native databases are designed to handle multiple simultaneous connections and large volumes of data. They offer extensive features for transaction management, concurrency control, and data security.
• Embedded Databases: Examples include SQLite, LevelDB, and RocksDB. These databases are embedded directly within the application, running in the same process space. They are lightweight, require minimal setup, and are ideal for applications needing a simple, fast, and reliable way to store data without the overhead of a separate database server.

By exploring these concepts, you begin to understand the building blocks of database architecture. Whether you're aiming to build a database for a large-scale application or simply satisfying your curiosity, delving into how databases work can significantly enhance your development skills and open up new possibilities for optimizing your applications.

2. Choosing Between Native and Embedded Databases: Building on Existing Libraries

Now, the question is: Which type of database should you use—native or embedded?

Writing code at the hardware level by implementing data structures like B-Trees, Hash Tables, or LSM-Trees is an enormous undertaking and beyond the scope of most projects. Instead, the goal is to build on top of existing libraries to create a solution tailored to your needs.

Given this approach, embedded databases emerge as the optimal choice. Native databases like MySQL, PostgreSQL, or managed services such as AWS RDS, PlanetScale, or Neon DB are robust and designed to solve large-scale scaling problems. However, they come with complexities like setup, maintenance, and network configurations that might be unnecessary for your application.

By choosing an embedded database, you can:

• Leverage Existing Libraries: Utilize well-established database libraries without reinventing the wheel.
• Simplify Deployment: Embed the database directly within your application, eliminating the need for separate servers and complex configurations.
• Customize Efficiently: Focus on building features specific to your application without dealing with the overhead of a full-fledged native database.
• Enhance Performance: Reduce latency by eliminating network communication between your application and the database.
• Avoid Unnecessary Complexity: Bypass the complexities of scaling solutions that native databases address, which may be overkill for your current needs.

This approach allows you to develop a database solution that is efficient, easier to manage, and perfectly aligned with the specific requirements of your application.

Conclusion and What's Next in Part 2

In this first part, we've established why you might want to build a custom database and explored the differences between native and embedded databases. By choosing to build upon embedded databases and existing libraries, you can create a tailored solution without delving into low-level data structure implementations.

In Part 2, we'll dive into selecting the specific database libraries to use and define the scope of what we're building. We'll explore options like SQLite, LevelDB, and RocksDB, discussing their strengths and suitability for different use cases. Additionally, we'll outline how to integrate these libraries into your application and customize them to meet your unique requirements.

Stay tuned as we transition from conceptual understanding to practical implementation, setting the stage for architecting a database that not only meets your current needs but is also adaptable for future challenges.

Next Steps in Part 2:

• Selecting a Database Library: We'll evaluate different embedded database libraries to find the best fit.
• Defining the Project Scope: Clearly outline what we aim to build and the features we need.
• Integration Strategies: Discuss how to integrate the chosen database into your application seamlessly.
• Customization Techniques: Explore ways to customize the database library to better suit your application's needs.
• Performance Considerations: Look at how to optimize for speed and efficiency within your specific context.

By the end of Part 2, you'll have a solid foundation for implementing your custom database solution, empowering you to take control of your data storage and management strategies.