Amazon DynamoDB single-table design using DynamoDBMapper and Spring Boot AWS Database Blog

Table Of Content

• Designing the DynamoDB table around our access patterns
• One-To-Many Relationships
• Getting Started with TypeDORM
• Use single-table design to reduce your operational burden
• Supercharge ⚡️ your DynamoDB single-table design pattern with TypeDORM
• AWS Compute Blog
• Limiting the Number of Items in a Set (or List)
• Mastering DynamoDB: Essential Techniques for Modern Data Modeling

The access patterns should describe the query to implement and the entity attributes required to retrieve the item. The results of this query can contain items of different types of objects, both LiftDynamicStats and LiftStaticStats objects. The DynamoDBMapper class isn’t suited to implement this query because its typed methods don’t allow for a query result that contains different types of objects. However, for this access pattern it is important to retrieve the data set containing different types of objects with just one query to DynamoDB.

Designing the DynamoDB table around our access patterns

These are reversible scripts that can quickly and easily make changes to the structure and data of your database. You are now finally ready to actually create your DynamoDB database. Use Cloud Formation, the Serverless Framework, CDK or equivalent to specify and create your database. Understanding how to build an entity schema would be a different article on its own — our main focus is how we can reliably work with the above entities using TypeDORM. In our model, we have just a few survey items per target, so it should be ok to just use a between expression in the FilterExpression as well. Here we need to query all active surveys to a specific app (Target).

One-To-Many Relationships

It is crucial to consider common data access patterns when designing our data in DynamoDB. Within each table, you must have a partition key, which is a string, numeric, or binary value. This key is a hash value used to locate items in constant time regardless of table size. It is conceptually different to an ID or primary key field in a SQL-based database and does not relate to data in other tables. When there is only a partition key, these values must be unique across items in a table.

Getting Started with TypeDORM

Note that we can have more than one gsi index, here PK has order_id . Before designing, we need to determine the application entities and their relationships. So normalizing the logical structure is not really a prudent decision here. That’s why a new best practice - A Single Table Design Pattern - has evolved for DynamoDB where one table, constituted of multiple different entities, serves the entire application. This is information that will rarely change and we don’t care that much if an older article is not updated with the most recent author profile picture.

And one of the big benefits of joins is the ability to get multiple, heterogenous items from your database in a single request. Additionally, to maximize the storage per row, choose short attribute names. You can also compress data in attributes by storing as GZIP output instead of raw JSON, and using a binary data type for the attribute. This increases processing for the producing and consuming applications, which must compress and decompress the items.

Specifically, we saw how single-table design can make it harder to evolve your access patterns and complicates your analytics work. In this situation, you may decide that the performance characteristics of a single-table design are not worth the loss of flexibility and more difficult analytics. You may opt for a Faux-SQL approach where you use DynamoDB but in a relational way by normalizing your data across multiple tables. A second complaint about DynamoDB is the difficulty of accommodating new access patterns in a single-table design.

AWS Compute Blog

The advantage of doing this is that we can fetch listings and individual articles in one query quickly. Because the list of classes for a semester is fairly static, there is no need to always retrieve the list from the DynamoDB table. Instead, we can activate per-resolver caching on the AppSync GraphQL API, and then enable caching on the allClasses resolver.

Limiting the Number of Items in a Set (or List)

Here the timestamp will come in handy and we will be able to get them from most recent to least recent without the need to sort them. This realtime solution means that we do not have to implement polling from the website that would put additional load on the DynamoDB table. Furthermore, we can activate caching on this resolver with a $ctx.identity.sub caching key, and choose an appropriate TTL (e.g., 10 minutes instead of one hour). Then, run a quick test on the API to evaluate the performance. Utilize vegeta to query the API at 50 transactions per second for 60 seconds.

OneTable uses the value property to specify the template which operates just like a JavaScript string template. This post is about a simple serverless service created using AWS Lambda, AWS API Gateway, and DynamoDB. Here I will focus on the DynamoDB part, which was created using the single table design. This app is already running in production and it’s working very well. Here I will bring a simplistic version to explain how we have modeled the table using the single table design.

With dynamic and static data in a single table, we can construct queries that return all needed data in a single interaction with the database. This is important for speeding up the performance of the application for these specific access patterns. However, there is a potential downside, the design of your data model is tailored towards supporting these specific access patterns. Which could conflict with other access patterns, making those less efficient.

However, because of scalability requirements, like all NoSQL databases, it lacks features common in RDBMS databases. Designing data models is quite difficult and goes against established principles of designing RDBMS databases. RDBMS databases (SQL databases) were designed at a time when data storage was expensive but processing power was relatively cheap. You save costs with data normalization (removing duplicated data, etc.). This normalization and denormalization require computing power.

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The DynamoDB database provides an easy to configure, high-performance, NoSQL database with low operational overhead and extreme scalability. It appeals to developers with OLTP applications requiring a simple serverless database or those needing the utmost in scalability. Now it’s very easy to identify the access patterns required by the application and how we solve each one. The application was created to get responses from our users to some specific surveys, that are exhibited in our web applications.

Projected attributes are attributes copied over from the base table into the index. This is configurable, but the primary key of the base table will always be projected. New GSI s can be added on the fly and queries can be run against them to support use cases that were not known in the design phase. To fetch a list of classes for a specific semester, we utilize the allClasses query.

A good practice when modeling the tables is to separate the keys from the other attributes. In this case, the id, type, and createdAt attributes are separate from the pk and sk of the item, giving us more flexibility in the future should we need to alter them. To get the most out of DynamoDB, you need to understand how the application you’re building is going to consume/produce data aka the application access patterns. It’s essential to start by listing all the entities and patterns, these will drive the discussion of data modeling. Since filtering data by only using the partition and sort key can be limited, DynamoDB supports indexes. A Global Secondary Index is essentially a secondary table, that can have other attributes as a primary key, where data is being replicated from the base table.