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Fernando DoglioJanuary 4, 2024

Moving from Relational to Key-Value Databases

Technical articles and news about Memurai.

It's been years since most of the data generated by applications and different user interactions is no longer structured. We've shifted to capturing as much data as possible in various formats, including free text (like social media posts) because we now have the computation power to process this data properly.

And because of this, databases have started to migrate away from structured data as well. There are plenty of different types of NoSQL databases that deal with unstructured data, such as document-based databases, columnar databases, and even key-value databases.

Now, the key for any architect is to properly pick the right database based on their data needs. Choosing the right one will work towards improving your app's performance and making the life of your devs a lot easier.

So, let's take a look at what it means for you to move away from relational databases into a key-based one, such as Memurai.

Understanding Key-Value Databases

Key-value databases, like Memurai, form a crucial part of the evolving landscape of data management. So, let's dive into what makes key-value databases unique and explore their operational principles.

Key-Value Databases: Unveiling the Basics

In essence, a key-value database stores data as a collection of key-value pairs. Each piece of data is assigned a unique identifier, the "key," and is associated with a corresponding value. This basic structure simplifies the storage and retrieval process, enabling swift access to information without the constraints of rigid, tabular formats. Operational simplicity is a hallmark of key-value databases. They leverage the principle of direct key-based access, allowing for rapid information retrieval. Unlike relational databases that rely on intricate joins and complex schemas, key-value databases excel in their ability to manage data in a straightforward manner.

That simplicity can be deceiving, though, considering that if you're coming from the relational world, you'll be used to having the ability to shift through data, perform searches, and mass update your records (to name a few examples). Key-based databases, on the other hand, are great when it comes to accessing single records (they're amazing at that), but reading and dealing with multiple records at the same time is not always an option.

That said, this doesn't mean they're limited; it just means developers need to change the way they think about data with key-based databases.

Effectiveness in Handling Large-Scale, Unstructured Data

The shift towards capturing vast amounts of unstructured data, such as free text from social media posts, aligns seamlessly with the strengths of key-value databases. Their adaptability to handle diverse data formats, coupled with the speed of key-based access, makes them particularly effective in scenarios where the volume and variety of data are substantial.

Consider scenarios dealing with large-scale, dynamic datasets where the structure may evolve over time. Key-value databases shine in these environments, offering flexibility and scalability. Their ability to accommodate unstructured data without sacrificing performance positions them as a compelling choice for applications dealing with real-time, ever-expanding datasets.

In essence, the adoption of key-value databases reflects a strategic move towards a more streamlined and efficient data management paradigm. As architects navigate the landscape of database choices, understanding the fundamental principles and effectiveness of key-value databases becomes pivotal in making informed decisions that enhance both application performance and developer workflows.

Challenges of Transitioning

Embarking on the journey from relational databases to key-value databases is a strategic move, but it comes with its set of challenges. Let's delve into the common hurdles encountered during this transition and explore effective strategies to overcome them.

Data Modeling Dilemmas

One of the primary challenges lies in adapting to a different data modeling approach (that whole "changing the way your devs think about data" comment I made earlier).

Relational data modeling thrives on structured, tabular data, often requiring intricate normalization to maintain data integrity. In some situations, even using very intricate entity relationships to model real-world scenarios. This approach makes dealing with relational databases very intuitive.

In contrast, key-value databases embrace a more flexible, schema-less design. There is no preset structure modeled, thus requiring a higher level of abstraction to "visualize" the use of the data. This is one of the major blockers when it comes to adoption, however, it's also what makes these databases so powerful and flexible.

To address this challenge, architects should embrace the schema flexibility offered by key-value databases. Instead of predefined structures, the focus shifts to designing data models that cater to specific application needs. This flexibility empowers developers to iterate swiftly, accommodating evolving data requirements seamlessly.

Query Adjustments for Key-Based Access

Another significant hurdle emerges when transitioning from SQL queries, commonly used in relational databases, to key-based access in key-value databases. SQL queries often involve complex joins and aggregations, which may not align with the simplicity of key-value storage. The key to overcoming this challenge is to rethink and rework query patterns. Instead of relying on intricate SQL queries to access their data, developers should leverage the direct access provided by keys and rethink their querying logic to work that way.

This involves a shift in mindset towards designing queries that align with the decentralized nature of key-value databases. Additionally, caching mechanisms and indexing strategies can further optimize query performance.

Best Practices for a Seamless Transition

To navigate these challenges effectively, consider implementing the following best practices:

  • Thorough Assessment: Conduct a comprehensive analysis of the existing relational database structure and data access patterns before initiating the transition. This is key to identifying the problematic areas that will suffer the most from the transition.
  • Incremental Migration: Break down the migration process into incremental steps, allowing for continuous testing and adjustment.
  • Developer Training: Provide training sessions for developers to familiarize them with the new data modeling and query paradigms. Make sure every developer who should deal with the new data paradigm is properly trained before moving forward with a full migration.
  • Testing Environments: Establish robust testing environments to validate the performance and functionality of the key-value database implementation.

In the end, the key to transitioning from relational to key-value databases is a proper strategic approach and a clear commitment to best practices (especially the ones listed above).

Benefits of Key-Value Databases

Let's now delve into the key benefits and examine real-world scenarios where these types of databases really shine.

Scalability Beyond Boundaries

One of the standout features of key-value databases is their unparalleled scalability. Traditional relational databases often encounter bottlenecks when handling massive datasets or accommodating a growing user base. Key-value databases, on the other hand, excel in distributing data across clusters, allowing seamless horizontal scalability. This is mainly because of the lack of data schema, a simpler overall structure, and in the case of some of them, such as Redis (or their Windows counterpart, Memurai), the fact that they're in-memory databases makes them quite easy to scale.

Consider an e-commerce platform experiencing rapid growth in product listings and user interactions. Key-value databases enable effortless scaling to handle the increasing volume of transactions, ensuring a smooth and responsive user experience during peak shopping seasons.

Flexibility in Data Modeling

Key-value databases embrace a schema-less design, providing a level of flexibility that is instrumental in adapting to evolving data requirements. Unlike rigid table structures in relational databases, key-value stores accommodate dynamic data models, allowing developers to iterate swiftly without the constraints of predefined schemas. This particular point greatly reduces development time, even after the application is in production. This is because, for structured databases, every single change in the schema requires a migration (along with the corresponding code changes). In the case of key-value databases, the code is the only aspect that needs to be changed.

Imagine a content management system where content types may vary widely; key-value databases empower content creators to define and modify data structures on the fly (imagine being able to create simple posts, image carousels, or perhaps even custom social media posts). This flexibility is definitely possible with a relational database, but only after crafting an intricate data structure to support it. Through the use of a key-value database such as Redis, this becomes trivial to implement.

Performance Improvements Through Direct Key-Based Access

The efficiency of key-value databases stems from their direct key-based access, eliminating the need for complex join operations commonly associated with relational databases. This streamlined approach translates into faster data retrieval times and improved overall performance. This is one of the major reasons why architects go for these types of databases. Adding on top of this, the fact that the most common key-value databases (such as Memurai and Redis) store their information in memory (primarily), making data reads and writes incredibly faster than traditional databases that suffer from traditional I/O delays.

In scenarios demanding real-time analytics, key-value databases shine. By swiftly retrieving specific data points using keys, these databases excel in powering applications that require instantaneous insights. This is particularly evident in financial systems processing high-frequency trading data or social media platforms delivering personalized content in real-time.

Harnessing the Power for Future Innovations

The benefits of key-value databases extend beyond immediate gains by future-proofing the applications they serve.

As data continues to grow in volume and complexity, the scalability, flexibility, and performance advantages offered by key-value databases position them as key players in shaping the future of data management.


In summary, the migration from a structured database environment into one dominated by key-value databases can definitely be beneficial for the product and the development team as well. By providing easy scalability, incredible data modeling flexibility, and unparalleled performance, key-value databases are the missing piece of any growing architecture.

Of course, like with any new piece of technology that you integrate into an existing project or team, the migration process needs to be properly structured, and best practices need to be followed before any big changes hit production.

If you'd like to understand how an in-memory key-value database can improve your application, give Memurai's free developer edition a try and check it out for yourself.