Five DynamoDB limitations you should know before using it

Amazon DynamoDB is a NoSQL database known for its scalability, but it comes with several limitations that can hinder developers and applications in certain scenarios. In this article, we’ll dive into five specific areas where DynamoDB falls short and explain how Fauna solves these challenges.

The trouble with transactions

One of the more frustrating limitations of DynamoDB’s transactions is that you can’t read a value and then use that value in a subsequent write or update within the same transaction. This can be a major hindrance when implementing workflows or business logic that requires reading data and then immediately performing updates based on that data all while keeping data strongly consistent.

Furthermore, DynamoDB transactions lack support for complex logic, such as branching conditions or multi-step decision processes. You’re limited to basic read, write, and update operations, without the ability to incorporate logic based on prior reads. This forces developers to handle workflows outside the database, bloating the application code and increasing the risk of data consistency issues, performance impacts, and maintenance overhead. This is especially problematic for serverless and edge applications.

Contrast that with Fauna, where transactions are far more powerful and flexible. Fauna allows you to read a set of documents based on a query, apply complex logic to those results, and update multiple documents with each operation, all within the same transaction. This seamless read-modify-write capability ensures that all operations are executed atomically and consistently, with no need for complex client-side logic. Additionally, Fauna’s transactions maintain strong consistency across all regions by default, ensuring that data is always in sync, regardless of where it’s accessed. For example, after migrating from DynamoDB to Fauna, Skylark reduced its application code by 80%, thanks to Fauna’s ability to handle complex operations natively within the database. This global, distributed consistency model eliminates the need to worry about data conflicts or eventual consistency problems, making Fauna the better choice for applications that demand reliable, multi-region operations.

Multi-region misfortunes

While DynamoDB is built for extreme scale, high availability, and low-latency operations, it cannot do multi-region replication with strong consistency. DynamoDB’s replication model when using the Global Tables feature, is eventually consistent across AWS regions. This works for many use cases but introduces challenges when strong consistency is required. For global applications where data integrity must be maintained across multiple regions, DynamoDB’s lack of strong consistency guarantees across regions becomes a significant drawback.

Fauna, on the other hand, offers strong consistency across all regions by default. Thanks to Fauna’s unique, globally distributed, multi-region architecture, you get strong consistency without sacrificing performance. This means that no matter where your application reads or writes data, Fauna ensures that every transaction is immediately visible everywhere. Unlike DynamoDB, Fauna handles all the complexity of synchronizing data across multiple regions automatically as part of every transaction and still performs well. Developers don’t need to worry about data consistency issues or working around complex replication mechanisms.

With Fauna, you can confidently build globally distributed applications that require real-time access to strongly consistent data without the headaches of managing eventual consistency. This advantage translates into simpler code, fewer bugs, and better user experiences.

Sub-optimal API/query language

DynamoDB’s API and query language can be difficult to work with, particularly for developers who are used to the power and flexibility of relational databases. DynamoDB uses a basic query model with limited functionality, requiring you to plan your application’s data access patterns up front and model your data in convoluted ways. Queries are limited to primary key lookups or specific secondary indexes, and more complex filtering must often be done client-side, which can lead to inefficiencies and higher costs. If you get access patterns or data models wrong, it can quickly lead to higher costs, lower performance, or worse yet, limit how you can evolve your application.

Fauna offers a more flexible and powerful approach to querying data. Fauna uses FQL (Fauna Query Language), a fully expressive, server-side query language that supports various operations, including joins, filtering, and complex data relationships. Unlike DynamoDB, Fauna doesn’t limit you to predefined access patterns. Instead, you can query your data dynamically, based on whatever criteria you need, with support for relational and document-based data structures. FQL also allows you to compose complex, nested queries that run efficiently on the server side, eliminating the need for additional client-side logic, especially when made into server-side functions. Fauna customer Insights.gg reduced their home page rendering time from 4 seconds to 100 milliseconds, going from 50 database round trips to 1. In addition, they didn't have to aggregate the response data into a JSON document, as Fauna’s server-side functions did all the heavy lifting.

This flexibility not only simplifies development but also enables more sophisticated use cases that would be hard to implement in DynamoDB. Whether it’s querying across relationships or performing complex transformations, Fauna’s query language provides the power and versatility that DynamoDB’s API lacks. Developers can write concise, powerful queries that reduce application complexity and improve performance.

Schema definitions and enforcement snafus

DynamoDB’s schemaless design offers flexibility, but it also introduces risks. Without schema enforcement, developers can easily introduce inconsistencies into the data model, particularly in large teams, multiple applications/functions, or rapidly evolving projects. Over time, this lack of structure and control can lead to messy data, unexpected application errors, and additional maintenance headaches. Furthermore, DynamoDB offers no built-in tools to help with table migrations or evolving your schema over time. If you need to change something as fundamental as the primary key of your base table, you’re often left managing a complex and error-prone migration process entirely on your own, which can be disruptive and costly.

Fauna strikes a balance between flexibility and control, allowing you to start schemaless while gradually introducing schema definitions as your application evolves. With Fauna’s built-in zero-downtime migration functionality, you can safely evolve your schema over time without disrupting your application. This means you can iterate quickly in the early stages, and as your data model grows more complex, you can enforce constraints and relationships without the risk of downtime or performance degradation. This ability to quickly iterate and migrate data models to add new application features was a key challenge for Skylark when they used DynamoDB. By migrating to Fauna, they can adapt their data model as they add new customer features without operational disruptions.

Fauna’s document-relational approach enforces rules and relationships within your data model, ensuring data integrity while retaining the agility of a NoSQL approach. The ability to evolve your schema over time gives you the flexibility to adapt to changing requirements without facing the operational challenges that come with DynamoDB’s primary key + schemaless design. This combination of control and adaptability in Fauna helps developers maintain long-term data consistency, manage complex data models, and scale their applications more easily.

Indexing impediments

One of DynamoDB’s significant limitations is its inability to index nested values within documents. While you can create secondary indexes on top-level attributes of a base table, you’re out of luck regarding nested data fields. This makes efficient querying or filtering based on nested data impossible inside the database and forces developers to flatten their data models, duplicate data, or add additional logic at the application layer to work around this impediment. The lack of support for indexing nested values can make DynamoDB a poor choice for applications that rely on complex, hierarchical data structures.

Fauna handles this more effectively by allowing you to index any field, including nested values within documents. Fauna’s flexible, document-relational-based model supports deeply nested structures, and you can create indexes on any level of the data hierarchy. This means you can query and filter your data efficiently, no matter how complex or nested. Unlike DynamoDB, Fauna enables you to build sophisticated queries and maintain performance without needing to restructure your data or write custom code to handle nested data.

Fauna’s powerful indexing system also integrates with its relational and document-based data model, allowing you to work with rich, structured data without sacrificing query performance. This capability makes Fauna an ideal choice for use cases where you need to store and query deeply nested data—such as in applications handling complex documents, configurations, or user profiles—without the limitations that come with DynamoDB.

Conclusion

While DynamoDB is a powerful NoSQL database for handling large-scale workloads, its limitations in transactional logic, multi-region consistency, querying, schema evolution, and indexing make it less suitable for complex, dynamic applications. Fauna, by comparison, addresses these challenges with ease. With Fauna’s support for flexible, powerful transactions, globally distributed strong consistency, expressive query language, seamless schema evolution, and robust indexing of nested data, developers can build sophisticated applications without the operational headaches that come with DynamoDB.

By migrating from DynamoDB to Fauna, many companies have not only simplified their codebases but also unlocked higher performance and scalability, all while reducing the complexity of their data management. If your application requires advanced data handling, real-time consistency, or more efficient querying, Fauna offers a more robust, developer-friendly alternative that can help you innovate faster and with fewer constraints.

Interested in trying Fauna to see for yourself? Explore the Fauna docs and get started for free. Don't hesitate to contact myself or Fauna with any questions!