NoSQL design for DynamoDB

• In RDBMS, data can be queried flexibly, but queries are relatively expensive and don't scale well in high-traffic situations (see First steps for modeling relational data in DynamoDB).

• In a NoSQL database such as DynamoDB, data can be queried efficiently in a limited number of ways, outside of which queries can be expensive and slow.

• In RDBMS, you design for flexibility without worrying about implementation details or performance. Query optimization generally doesn't affect schema design, but normalization is important.

• In DynamoDB, you design your schema specifically to make the most common and important queries as fast and as inexpensive as possible. Your data structures are tailored to the specific requirements of your business use cases.

How NoSQL design is different

• By contrast, you shouldn't start designing your schema for DynamoDB until you know the questions it will need to answer. Understanding the business problems and the application use cases up front is essential.

• You should maintain as few tables as possible in a DynamoDB application. Having fewer tables keeps things more scalable, requires less permissions management, and reduces overhead for your DynamoDB application. It can also help keep backup costs lower overall.

• Data size: Knowing how much data will be stored and requested at one time will help determine the most effective way to partition the data.

• Data shape: Instead of reshaping data when a query is processed (as an RDBMS system does), a NoSQL database organizes data so that its shape in the database corresponds with what will be queried. This is a key factor in increasing speed and scalability.

• Data velocity: DynamoDB scales by increasing the number of physical partitions that are available to process queries, and by efficiently distributing data across those partitions. Knowing in advance what the peak query loads will be might help determine how to partition data to best use I/O capacity.

• Keep related data together.   Research has shown that the principle of 'locality of reference', keeping related data together in one place, is a key factor in improving performance and response times in NoSQL systems, just as it was found to be important for optimizing routing tables many years ago.

  As a general rule, you should maintain as few tables as possible in a DynamoDB application.

  Exceptions are cases where high-volume time series data are involved, or datasets that have very different access patterns. A single table with inverted indexes can usually enable simple queries to create and retrieve the complex hierarchical data structures required by your application.

• Use sort order.   Related items can be grouped together and queried efficiently if their key design causes them to sort together. This is an important NoSQL design strategy.

• Distribute queries.   It's also important that a high volume of queries not be focused on one part of the database, where they can exceed I/O capacity. Instead, you should design data keys to distribute traffic evenly across partitions as much as possible, avoiding hot spots.

• Use global secondary indexes.   By creating specific global secondary indexes, you can enable different queries than your main table can support, and that are still fast and relatively inexpensive.