One Approach to AWS DynamoDB Single-Table Design

Think of a database as a well-organized wardrobe, with each sort of data neatly folded into its own drawer for easy access. Labels (or “keys”) allow you to simply find and get the specific item you’re looking for, like picking out your favorite shirt, without fuss. 

The way we arrange data has evolved as modern applications require faster performance and the ability to handle more data. Amazon DynamoDB, AWS’s robust NoSQL database, has evolved to support highly scalable, high-performance applications. One of its standout features is the single-table design approach, an innovative method that efficiently organizes data within a single table, maximizing speed and cost-effectiveness. This approach was championed by Rick Houlihan, a principal technologist at AWS, who wanted to help developers harness DynamoDB’s full potential by designing data models that prioritize efficient access patterns.

In this article, we explore the fundamentals of DynamoDB single-table design and provide valuable insights into its benefits and potential drawbacks while also sharing best practices for implementation. For developers and architects who want to leverage DynamoDB to its fullest, understanding single-table design can be a transformative step in building streamlined, performant applications that align with the evolving needs of modern, data-driven businesses.

​​The suite of AWS purpose-built databases allows organizations to effectively tackle a wide range of data challenges — from high-velocity transaction processing to complex analytical workloads — ensuring that each application can achieve optimal performance while maintaining flexibility and scalability in an ever-changing data landscape.

At AWS re:Invent 2021, Las Vegas, Nevada, USA.
Svyatoslav Lavryk – Account Manager (AM) at DevCom and
Rick Houlihan – Sr. Practice Manager at AWS and
Inventor of Single-Table DynamoDB Design

In databases, a “table” is a way to organize data in a grid with rows and columns. Each column represents a type of information (like “Name” or “Age”), and each row is a record (like a person’s information). Tables help us keep data organized and easy to find. Each table usually has a “primary key” that uniquely identifies each record, and different tables can be linked to keep related data together.

In Amazon DynamoDB, single-table design is a method of putting different types of data into one single table instead of using separate tables for each type. For example, instead of having one table for users, another for orders and another for products, we can put all this information into a single table. This makes it quicker to run searches and get the data we need.

In a typical database, we’d keep users, orders and products in their own separate tables. Each entire table would have its own keys and indexes to manage the data and connect it to other tables. However, single-table design combines these into one table, which simplifies data retrieval and speeds up performance for applications.

However, the DynamoDB single-table design consolidates these entities into a single table, which requires a more strategic approach to structuring data relationships.

In a single-table design, we use specially designed keys to keep different types of items organized within one table. These keys (called partition and sort keys) help separate data types, make retrieving information faster and allow us to avoid creating multiple tables.

Each item in the table has attributes, which are the fields or pieces of information for that item. These attributes help identify the type of item (such as a user or an order) and make it easier to search for specific details across different types of data all within one unified structure.

DynamoDB Keys Recap

In DynamoDB, keys are crucial for data organization and retrieval. There are two main types of keys used in single-table design:

1. Partition Key: This is a unique identifier for items within a table. When data is stored, DynamoDB uses the partition key to determine which partition (physical storage location) the item will be placed in. Properly choosing the partition key is essential for data distribution and access efficiency.
2. Sort Key: This key allows for multiple items to have the same partition key but differ based on the sort key value. This creates a composite primary key, enabling efficient querying and sorting of related items. 

Understanding how to effectively use these keys is fundamental for implementing a successful single-table design, as they directly influence the performance and cost-effectiveness of your data operations.

Single-table design is becoming a popular choice in application development because of its ability to optimize high-volume, low-latency operations. This approach is especially beneficial in DynamoDB setups, where related data entities are frequently queried together. Developers can significantly reduce read and write capacity usage by organizing multiple entities — such as users, orders and products — within a single table, leading to performance gains and cost savings.

In practical DynamoDB single-table design examples, composite keys, which comprise partition keys and sort keys, play a critical role in supporting complex, multi-entity queries within a single table. These keys enable DynamoDB to efficiently locate and retrieve data without the need for additional tables or joins, which are common in traditional database models. By designing data with access patterns in mind, developers can create a model that serves high-volume single query demands with minimal latency.

The decision to use the single-table design DynamoDB is largely driven by its potential to streamline operational costs and enhance efficiency. Applications that involve interrelated data, such as e-commerce platforms or content management systems, benefit greatly from this approach. By consolidating related data into one table, businesses can achieve a more unified and cohesive data structure, reducing the complexity and overhead associated with managing multiple tables while ensuring that data access is fast and scalable.

When implemented thoughtfully, single-table design in DynamoDB offers a range of advantages that can improve database performance, streamline scaling and reduce operational costs. Here are some key benefits of this approach:

• Enhanced Query Efficiency

  One of the primary advantages of single-table design is its ability to reduce the need for complex joins by structuring data according to access patterns. This design leverages partition and sort keys, allowing DynamoDB to retrieve related data items efficiently. By structuring data within a single table, developers ensure that frequently accessed data is optimized for fast queries, minimizing latency and low querying costs.

• Cost Savings

  With AWS DynamoDB’s single-table design, fewer tables mean a reduction in storage and capacity usage. This can translate to substantial cost savings, particularly for applications with high storage and query requirements. By consolidating data into one table, organizations reduce not only storage costs but also the maintenance expenses associated with managing multiple tables, which can lower the overall operational overhead.

• Simplified Scaling

  A single-table design also makes it easier to scale an entire application as data volumes increase. Amazon DynamoDB’s managed scaling takes on much of the work involved in handling large datasets, adjusting automatically to meet query demands and ensuring high availability. When using a single-table design, applications benefit from DynamoDB’s seamless scalability features without managing multiple tables and complex schemas.

• Unified Data Structure

  Developers create a consistent data model that supports all data relationships and access patterns by organizing multiple entity types within a single table. This unified approach makes it easier to manage entities and their relationships, simplifying data management and reducing the need for data transfers between tables. With a single-table design, executing application logic across related data items is more straightforward, enabling a cohesive structure that aligns with DynamoDB’s best practices for efficient data retrieval.

While the single-table design philosophy in DynamoDB has many benefits, there are notable drawbacks that organizations should consider before implementing this approach. Below are some of the key challenges associated with DynamoDB single-table design:

• Increased Complexity

  Planning and executing a single-table design pattern can be a complex and detailed process. Unlike traditional relational databases, DynamoDB lacks strict schema constraints, which means the responsibility of enforcing data structure consistency falls on the application layer. This requires developers to have a deep understanding of the application’s access patterns and usage requirements, as well as DynamoDB’s limitations. Crafting a well-optimized single-table design demands careful planning and can be time-consuming.

• Data Duplication

  In a single-table setup, developers may need to duplicate data across items to ensure efficient querying. For example, certain attributes may appear multiple times in various items to support multiple query patterns, which can increase storage usage. Without careful management, this data duplication can lead to higher storage costs and potentially redundant information, requiring consistent maintenance to avoid errors.

• Learning Curve

  Adopting DynamoDB’s single-table design presents a learning curve for developers familiar with relational databases. The design model demands a strong understanding of NoSQL data modeling principles, particularly in leveraging partition and sort keys to enable effective querying and data retrieval. Developers must adjust to DynamoDB’s unique approach to data modeling, as it lacks familiar relational features like joins, which can take time and adjustment.

• Limited Flexibility

  A single-table design can impose limitations on future adaptability. If access patterns change or expand significantly over time, restructuring the DDB single-table design to accommodate new requirements can be difficult. Unlike multi-table designs, which are often more modular, a single-table setup is less flexible, and extensive modifications may be required to support evolving application requirements, potentially making it less suitable for rapidly changing use cases.

When evaluating DynamoDB single-table design vs. multiple tables, it’s essential to understand how each approach aligns with an application’s unique needs. Both have distinct advantages and limitations, and choosing the appropriate design often depends on data relationships, query patterns and scalability requirements.

• Single-Table Design

  Single-table design is often preferred for applications where entities are closely related and require frequent querying based on their relationships. This approach leverages a single, consolidated structure, allowing related entities to be accessed more efficiently. With proper use of partition and sort keys, single-table design can optimize for high query efficiency and cost-effective scaling, particularly in scenarios where multiple entity types are often accessed together.

• Multi-Table Design

  A DynamoDB multi-table design might be more suitable for applications where datasets are relatively independent and don’t require inter-entity interactions. In this key structure, each entity type can be housed in a dedicated table, simplifying the data model and reducing the complexity of the initial setup. This approach offers greater flexibility and allows for independent scaling for each table, which can be advantageous if different data entities grow at varying rates or need different levels of access.

Below is a comparison of key aspects of single-table vs. multi-table design:

Understanding these trade-offs helps developers make informed choices between single-table and multi-table designs. By carefully assessing project requirements and access patterns, teams can select a DynamoDB table design approach that best suits their application’s data structure and scalability needs.

Designing an efficient single-table structure in DynamoDB requires a thoughtful approach to accommodate the unique needs of an application. Here are some essential steps to follow when setting up a single-table design:

• 1. Define Access Patterns

  The first step in single-table design is identifying key access patterns for your application. This means understanding the various ways in which data will be queried, filtered, and accessed. Defining these access patterns early helps guide decisions around partition keys, sort keys and any potential secondary indexes that might be necessary to support efficient data retrieval.

  For example, an e-commerce application may need access patterns to display user order histories, view product inventory or manage user preferences. These patterns directly inform the design of keys and indexes that optimize DynamoDB’s read and write efficiency.

• 2. Design Primary Keys

  With access patterns established, the next step is to create an effective composite primary key structure. A composite key consists of both a partition key and a sort key. These keys not only organize data within a single table but also enable efficient querying by aligning with the access patterns.

  For example, if the goal is to retrieve a user’s order history by date, an ideal primary key design might involve using UserID as the partition key and OrderDate as the sort key. This approach groups orders by a user and enables efficient sorting and retrieval based on date, making it suitable for applications that require chronological data display.

• 3. Add Secondary Indexes (If Needed)

  Global secondary indexes (GSIs) and local secondary indexes (LSIs) are optional but useful for supporting additional access patterns that the primary key alone may not cover. GSIs allow for querying on attributes other than the primary key, while LSIs provide an alternative sort key on the partitioned data.

  However, secondary indexes incur additional costs, so they should be used wisely. Consider adding them only when absolutely necessary to support specific query needs within the single-table design. Each index in the DynamoDB table design should be carefully evaluated to balance functionality and cost.

• 4. Avoid Duplication with Attribute Mapping

  In single-table design, careful planning around attribute mapping is crucial for minimizing redundancy and maintaining efficiency. Duplicate data can increase storage costs, so it’s essential to map attributes thoughtfully to avoid unnecessary duplication.

  When designing attribute mappings, store only essential data in each item. For example, instead of duplicating customer data on each order record, consider referencing it or storing it in a separate part of the single table that serves general customer queries. Thoughtful attribute mapping helps keep the design streamlined and cost-effective, which is a core principle of DynamoDB single-table design.

• 5. Use Denormalization Strategies

  To maximize efficiency in single-table design, denormalization strategies can be employed to reduce the need for complex joins and allow for faster data retrieval. In DynamoDB, denormalization involves embedding related data within items rather than storing it separately, which simplifies queries and minimizes latency. However, because denormalization can increase storage needs, it’s essential to apply this strategy selectively, focusing on high-priority access patterns and carefully managing potential data redundancy. Thoughtful denormalization enables DynamoDB to handle high-performance workloads more effectively, aligning with the goals of single-table design.

Implementing a robust DynamoDB table design example for single-table design requires adherence to several best practices:

• Use Efficient Composite Keys: Select the partition and sort keys that correspond directly to access patterns.
• Optimize Secondary Indexes: Keep indexes minimal to prevent unnecessary storage and retrieval costs.
• Plan for Scalability: Design the single table with scalability in mind. Partition keys should distribute data evenly to prevent “hot” partitions.
• Document Access Patterns Thoroughly: Documenting access patterns helps in maintaining the data model and understanding how to accommodate future changes.
• Leverage DynamoDB Streams: Use DynamoDB Streams to handle changes and trigger events, which can facilitate reactive programming and event-driven architectures.

By following these best practices, developers can implement a DynamoDB single table design best practices model that is both cost-effective and scalable.

While single-table design presents various advantages, it is not a one-size-fits-all solution. Certain scenarios may indicate that a multi-table design would be more appropriate. Here are specific situations where single-table design might not be ideal:

• 1. Complex, Unpredictable Access Patterns

  When access patterns are highly variable and difficult to predict, structuring all entities within a single table can lead to excessive complexity. In such cases, managing the relationships and querying can become cumbersome, diminishing the benefits of the single-table approach. If the application’s data access needs are inconsistent or frequently changing, a multi-table design may provide clearer separation and simpler management.

• 2. Low Data Volume with Minimal Inter-Entity Relationships

  For smaller applications with limited data and few inter-entity relationships, adopting a single table design may introduce unnecessary complexity. In these cases, the overhead associated with managing a single table could outweigh the benefits. A multi-table setup can be more straightforward and efficient for simple use cases, enabling quicker development and easier maintenance.

• 3. Frequent Schema Changes

  Applications that require frequent structural adjustments to the database may find multi-table designs more beneficial. Single-table designs can become rigid, making it challenging to accommodate changes without impacting other entities. In contrast, multi-table designs offer greater flexibility, allowing developers to adapt to new requirements with less disruption. If schema evolution is a common occurrence in the application’s lifecycle, it may be wiser to avoid a single-table approach.

In summary, while single-table design can enhance performance and efficiency for certain applications, it’s crucial to evaluate the specific requirements and characteristics of your project. In situations marked by complex access patterns, low data volume or frequent schema changes, a multi-table design may provide a more effective and manageable solution.

DynamoDB Modeling

Effective data modeling in DynamoDB requires careful consideration of how data will be accessed and related. Here are some key modeling techniques to keep in mind:

• Domain Understanding: Identify key entities and their relationships
• Logical Model: Organize entities with a focus on access patterns instead of traditional joins
• Access Patterns: Structure composite keys (partition and sort keys) based on common queries to optimize single-table design
• Physical Model: Define partitions, indexes, and sample queries for efficient data retrieval, leveraging DynamoDB’s NoSQL design
• App Development: Implement a Data Access Layer (DAL) to manage interactions, optimizing queries for high-performance

AWS single table design in DynamoDB represents a powerful approach for data modeling, particularly suited for high-performance applications with predictable access patterns. While it requires upfront planning and in-depth knowledge of DynamoDB’s indexing and key structure, it can yield substantial benefits in terms of cost efficiency, speed, and scalability.

However, developers should carefully evaluate their application’s needs and access patterns to determine whether single-table design or DynamoDB multi-table design is the best choice. By following best practices and adhering to the principles outlined here, teams can make informed decisions and leverage DynamoDB’s full potential to build robust and efficient applications. Our DynamoDB development services are customized to meet your unique data needs, offering optimized data models, seamless scalability, and enhanced performance for your applications.