Backend development is intricate, and that’s only getting more noticeable as the digital landscape evolves. One of the most pivotal elements in backend development is the choice of a database. In the early days of web development, the backend primarily revolved around a few select databases. Fast forward to today, and the backend ecosystem has a lot of database options, each vying for the attention of developers.

Take, for instance, the relational stalwart, MySQL. For years, it has been a cornerstone in the world of web applications, lauded for its reliability and open-source nature. Alongside it, PostgreSQL emerged, offering a more extensible approach and often being the go-to for those looking for powerful querying capabilities. Then there's Microsoft's SQL Server, integrating seamlessly with the broader Microsoft ecosystem, making it a favourite among businesses deeply embedded in that realm.

But as the digital landscape evolved, so did the needs of developers and businesses. Enter the NoSQL movement, a testament to the changing tides. MongoDB became the poster child of this revolution, with its document-oriented approach offering flexibility that rigid relational schemas often couldn't. It catered to developers building applications with rapidly evolving data structures. Simultaneously, Cassandra carved its niche, inspired by Google's Bigtable, and became the darling of those looking for a solution that scaled seamlessly across multiple nodes.

However, the innovation didn't stop there. As the importance of interconnected data became evident, graph databases like Neo4j emerged. They allowed developers to traverse complex relationships in data, a feat challenging for traditional databases. Think of social networks, where understanding the depth and breadth of connections can offer invaluable insights.

Yet, the world of databases wasn't just expanding horizontally across different types but also vertically within specific niches. Time-series databases like InfluxDB gained traction, especially in an age where chronological data from sources like IoT devices began pouring in at unprecedented rates. These databases, optimised for time-stamped data, provided tools for developers grappling with real-time analytics challenges.

Then there's the realm of NewSQL databases, like Google Spanner and CockroachDB. These systems aimed to bring together the structured world of SQL with the scalability and flexibility of NoSQL. They promised developers the best of both worlds, catering to those who wanted the familiarity of SQL without sacrificing the benefits of modern database architectures.

In essence, the backend ecosystem today is a vibrant tapestry of options, each database bringing its unique colors to the canvas. For developers, this means a world of opportunities but also the challenge of navigating this intricate maze to find the perfect fit for their applications.

The Problems of Choice

The backend of any application is its unsung hero. It quietly works in the background, ensuring that the frontend has all the resources it needs. The choice of a database, therefore, significantly impacts the overall efficiency and effectiveness of the backend. When we delve deeper into backend processes, we understand that each database, be it relational like MySQL or NoSQL like MongoDB, is designed with specific strengths and weaknesses. The challenge for backend developers lies in discerning which of these databases aligns perfectly with their application's requirements.

Moreover, in the backend development ecosystem, scalability is a pending issue. The unpredictable nature of the web means that an application, once launched, can experience varying levels of traffic. A sudden surge can strain the backend, and if the database isn't equipped to handle such spikes, the entire application can falter. Hence, the choice of database isn't just about its current capabilities; it's about anticipating future growth and ensuring the backend can scale accordingly.

Beyond scalability, the complexity of modern databases also presents a steep learning curve for backend developers, especially those who are just starting out or transitioning from simpler systems. Here are some examples that shed light on these challenges:

1. Query Language Variations: While SQL (Structured Query Language) has been the standard for relational databases, many modern databases introduce their own query languages or variations.

• MongoDB, for instance, does not use SQL. Instead, it employs a method-based query language. This means that instead of writing a SQL statement like SELECT * FROM users WHERE age > 21, in MongoDB, you would use something like db.users.find({ age: { $gt: 21 } }).

• Cypher is the query language for the graph database Neo4j. It's specifically designed to handle graph patterns. Instead of tables, you're often dealing with nodes and relationships, which demands a different mindset.

2. Data Modeling Paradigms: The way data is structured and related can differ vastly between databases.

• Traditional relational databases rely on tables, rows, and columns. Developers need to understand normalisation and relationships, such as one-to-many or many-to-many.

• Document-oriented databases like MongoDB or CouchDB store data in documents, often in JSON format. Here, the challenge is to design data without the rigid structure of tables, which can be both freeing and perplexing.

• Graph databases, such as Neo4j, require developers to think in terms of nodes, edges, and properties. This is a significant shift from tabular data and requires understanding graph theory concepts.

3. Consistency, Availability, and Partition Tolerance (CAP Theorem): Modern distributed databases introduce the challenges of the CAP theorem. Understanding the trade-offs between consistency, availability, and partition tolerance is crucial. For instance, Cassandra might prioritise availability over strict consistency, while databases like CockroachDB might aim for strong consistency. Backend developers must understand these nuances to make informed architectural decisions.

4. Scaling Mechanisms: Different databases handle scaling in various ways. While some databases are designed to scale up (add more power to the current machine), others are designed to scale out (add more machines). Understanding the implications of each is crucial.

5. Concurrency and Transactions: Managing concurrent access to data and ensuring data integrity through transactions can vary. While relational databases often offer ACID (Atomicity, Consistency, Isolation, Durability) guarantees, many NoSQL databases prioritise BASE (Basically Available, Soft state, Eventually consistent) semantics. Databases like FaunaDB aim to bring ACID guarantees to a distributed system, introducing concepts like distributed transactions, which can be complex to grasp.

6. Advanced Features: Many modern databases offer advanced features that require deep understanding. For example:

• Full-text search: Databases like Elasticsearch specialise in providing search capabilities, demanding knowledge of indexing, tokenisation, and search algorithms.

• Real-time analytics: In-memory databases like Redis or SAP HANA can process data in real-time, requiring developers to understand in-memory data processing nuances.

While the plethora of modern databases empowers developers with vast capabilities, it simultaneously demands a deeper understanding and continuous learning. As the database landscape evolves, backend developers are continually challenged to climb the steep learning curves presented by these advanced systems.

To add to this, each database system introduces its own set of quirks and nuances. The backend team must invest time and resources to understand these intricacies to harness the full potential of the database. This learning phase can sometimes deter developers from exploring newer, potentially more efficient databases, causing them to stick with familiar but possibly outdated options.

Financial considerations also play a significant role in database selection. Backend decisions, while largely technical, cannot ignore the cost implications. Certain databases, while free or cheap initially, can escalate in costs as the application grows, either due to licensing or operational expenses. This aspect further complicates the database selection process, requiring backend developers to strike a balance between technical prowess and budgetary constraints.

The Right Priorities

The modern backend developer is faced with the monumental task of selecting the right database. This decision, while technical at its core, is influenced by a myriad of factors ranging from scalability and complexity to cost and community support. As the backbone of any application, the backend, and by extension, the database, holds the key to the application's long-term viability and success.

Oftentimes, teams are crippled by analysis paralysis, taking months if not entire quarters to make such decisions—and rightfully so! These decisions can be extremely costly if made uninformed. The right database can be the difference between a successful application and a failed one. Hence, it's crucial to make the right choice.

At Keel, we're making this high-stakes decision easier for all developers. Our backend platform is entirely declarative: as a developer, you'd simply describe the data that you plan to work with and any operations you intend to perform on the data, and Keel handles the rest—including authentication, authorisation, and monitoring with unprecedented uptime. Since Keel abstracts away the complexities of the database, you can focus on rapidly building your applications without worrying about the backend. Give it a try today!