Table Of Content
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.
Know before you go: Amazon DynamoDB sessions at AWS re:Invent 2023 Amazon Web Services - AWS Blog
Know before you go: Amazon DynamoDB sessions at AWS re:Invent 2023 Amazon Web Services.
Posted: Thu, 16 Nov 2023 08:00:00 GMT [source]
Update information and signing up for classes
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.
Limiting the Number of Items in a Set (or List)
The second situation is when using GraphQL due to the way the GraphQL execution flow works. None of this goes to say that you can't use DynamoDB with GraphQL -- you absolutely can. I just think it's a waste to spend time on a single-table design when using GraphQL with DynamoDB. Because GraphQL entities are resolved separately, I think it's fine to model each entity in a separate table. It will allow for more flexibility and make it easier for analytics purposes going forward.
The problem of missing joins in DynamoDB
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.
Access patterns
Items without a sort key are not stored in the index if the sort key is defined. If you combine multiple attributes in the sort key, you can filter by some or all of them, but only in the order that values were combined. It works great if values have a limited set of potential values. You configure streams on the old table that writes to a new one.
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.
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.
Modeling a service using the single table design in DynamoDB
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.
Data Modeling for DynamoDB Single Table Design
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).
Right, now that we’re clear on what we’re working with, let’s start looking at a hypothetical example. Let’s say we want to model the data for a simple blog CMS (Content Management System). The blog will publish articles from multiple authors, articles can contain assets like videos and images and will support comments on each article. In RDBMS databases, while executing transactions, you can access the database or other systems multiple times. DynamoDB transactions are executed as a batch of up to 25 items.
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.
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.
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.
The solution is to split large items into smaller items, and when some part changes, you store and version only those parts. Reads are more complex because you have to assemble the item from different parts and pick only the newest or appropriate version for that part. Times series data typically has a characteristic that only the recent data is accessed most frequently.
No comments:
Post a Comment