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When working with a NoSQL database, it's important to plan your application's data access patterns and design your tables around them. With relational databases, you do not need to consider access patterns ahead of time. You can instead use tools like SQL joins to query for whatever combination of data your application needs. With flexible access patterns, though, can come a performance impact, as these complex queries can be slow to run (read more here).
Replica with GSI
We use the higher-level programming interface for DynamoDB called DynamoDBMapper to demonstrate an example implementation based on Spring Boot. This is slightly slower than making a single request, as you’ll be waiting for the slowest of the two requests to return. And you’ll pay a bit more, as you’re not getting the aggregation benefits of the Query operation in calculating your RCUs. But it’s likely you’ll need to implement something like this anyway, even in single-table design, for pagination instances where a client is fetching beyond the first page. If these tradeoffs are acceptable to you and your application, then you can opt for a multi-table design.
Implement serverless FIFO queues with filtering capabilities using Amazon DynamoDB transactions Amazon Web ... - AWS Blog
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NoSQL design for DynamoDB
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One of the downsides of DynamoDB Streams is the limitation on the number of concurrent consumers. DynamoDB limits you to no more than two concurrent consumers on a DynamoDB stream. If you have additional consumers, your stream processing requests will get throttled.
Our access patterns
A global secondary index is an index which allows us to usea completely different set of partition and sort keys to access our data. Also an important thing to point out is that theglobal secondary index needs to be provisioned separately (meaning it has its own RCUs and WCUs). Also compared to the DynamoDB table,the global secondary index allows only eventually consistent reads. Data modeling in DynamoDB is tied to the data’s access patterns. This makes DynamoDB unusable for analytics, but it’s not an issuesince DynamoDB was designed to be an OLTP database.
Enhancing Amazon DynamoDB single-table design with AWS AppSync access and security features
Even though DynamoDB is fully-managed and pretty hands-off compared to a relational database, you still need to configure alarms, monitor metrics, etc. If you have one table with all items in it rather than eight separate tables, you reduce the number of alarms and metrics to watch. This is what single-table design is all about -- tuning your table so that your access patterns can be handled with as few requests to DynamoDB as possible, ideally one.
Version History Pattern with Manual Transactions
Every NoSQL database uses some form of partitioning to horizontally scale, and every database under the sun uses B-trees (or close relatives) in indexing operations. For different types in your query, such as User and Order in our example, you would usually have a resolver that would make a database request to resolve the value. The resolver would be given some arguments to indicate which instances of that type should be fetched, and then the resolver will fetch and return the data. First, there's a request to fetch the User, then there's a follow up request to fetch the Orders for the given User. Because multiple requests must be made and these requests must be made serially, there's going to be a slower response time for clients of your backend application. But first I want to emphasize that these are exceptions, not general guidance.
Collection of reference DynamoDB single-table models helping you design your models and access patterns
This is a tiny attribute—a few bits of data—but it might increment thousands of times per day. If you stored this counter on the same item as the video metadata, you might be paying multiple WCUs every time you want to increment the view count. If I have a one-to-many or many-to-many relationship, I might have an access pattern where I’m fetching one item but also need some information about a related parent item.
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Condition Expression on the Same Item
While the single-table pattern is powerful and ridiculously scalable, it doesn't come without costs. In this section, we'll review some of the downsides of a single-table design. The operations burden on DynamoDB is quite low, and the pricing will only save you a bit of money on the margins.
You should create a single table with a generic primary key and any additional secondary indexes. Here is the list of all access patterns and what attributes we will be querying on. In case you didn’t follow this article and came up with different access patterns, that’s perfectly okay too.
With Version History pattern, you can do multiple access to the database, prepare data for the new state, then mark data as current and in a sort of committing the transaction. The principal of optimistic locking is that you update the item only if it was not changed since you read it from the database. This is simplify achieved with a special attribute that holds the version number, which we increase when we execute an update. With the condition, we guarantee that the item was not updated.
Afterwards, you normalize your data by creating a table for each entity type in your ERD design. So many new DynamoDB users do what I did—lift and shift their relational data model into a bunch of DynamoDB tables. They then write a bad version of a query processor in their application by doing in-memory joins and aggregations.
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