how does aurora dsql manage its indexes?

Aurora DSQL manages its indexes in a way that is designed to provide high performance and scalability, but there are some important considerations to keep in mind when designing your data model and queries.

For primary keys, Aurora DSQL uses an approach similar to clustered indexes in other database systems. When you define a primary key, it creates an index-organized table where the data is always ordered according to the primary key. This structure allows for efficient lookups and maintains the order of the data. The primary key is used to construct a cluster-wide unique key for each row, which plays a central role in Aurora DSQL's automatic scaling and concurrency control mechanisms.

Regarding the choice between k-sorted UUIDs and randomly distributed UUIDs like v4, it's generally recommended to avoid using monotonically increasing values (like sequential integers or timestamps) as primary keys for tables with high write volumes. This is because such keys can lead to performance issues due to uneven distribution of new writes. Instead, using randomness in primary keys (like UUID v4) ensures a more even distribution of data, which can help with scaling and performance.

For secondary indexes, Aurora DSQL supports asynchronous index creation using the CREATE INDEX ASYNC command. This allows you to create indexes without blocking other transactions, which can be particularly useful for large tables or in production environments where you can't afford downtime.

As for joins and query performance as table size grows, Aurora DSQL is designed to scale horizontally, allowing it to adapt to workloads of various shapes, including different read/write ratios, data sizes, and query complexities. The system can independently scale its query processing, commit, and storage layers. This means that in theory, the performance degradation for joins should not necessarily be linear as the number of records grows, as the system can distribute join operations across its scaled resources.

However, it's important to note that the actual performance characteristics will depend on your specific workload, data distribution, and query patterns. While Aurora DSQL aims to provide predictable performance at scale, it uses optimistic concurrency control (OCC), which may have different performance characteristics compared to traditional locking mechanisms.

To make informed decisions about workloads to test, consider the following:

1. Design your primary keys with randomness to ensure even distribution of writes.
2. Use asynchronous index creation for large tables to minimize impact on ongoing operations.
3. Be mindful of join complexity and try to optimize queries where possible.
4. Test with realistic data volumes and query patterns to understand how Aurora DSQL performs with your specific workload.

Remember that while Aurora DSQL offers powerful scaling capabilities, optimal performance still relies on good database design principles and query optimization. It's recommended to thoroughly test your specific use cases to understand how Aurora DSQL behaves with your particular workload and data patterns.
Sources
Primary keys in Aurora DSQL - Amazon Aurora DSQL
Creating async indexes in Aurora DSQL - Amazon Aurora DSQL
Whats the impact on latency for DSQL? | AWS re:Post
Introducing Amazon Aurora DSQL | AWS Database Blog