Database
The cloud offers purpose-built database services that address different problems presented by your workload. You can choose from many purpose-built database engines including relational, key-value, document, in-memory, graph, time series, and ledger databases. By selecting the most effective database to solve a specific problem (or a group of problems), you can break away from restrictive one-size-fits-all monolithic databases and focus on building applications to meet the performance needs of your customers.
In AWS you can choose from multiple purpose-built database engines including relational, key-value, document, in-memory, graph, time series, and ledger databases. With AWS databases, you don’t need to worry about database management tasks such as server provisioning, patching, setup, configuration, backups, or recovery. AWS continuously monitors your clusters to keep your workloads up and running with self-healing storage and automated scaling, so that you can focus on higher value application development.
The following question focuses on these considerations for performance efficiency.
| PERF 4: How do you select your database solution? |
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| The most effective database solution for a system varies based on requirements for availability, consistency, partition tolerance, latency, durability, scalability, and query capability. Many systems use different database solutions for various subsystems and turn on different features to improve performance. Selecting the wrong database solution and features for a system can lead to lower performance efficiency. |
Your workload's database approach has a significant impact on performance efficiency. It's often an area that is chosen according to organizational defaults rather than through a data-driven approach. As with storage, it is critical to consider the access patterns of your workload, and also to consider if other non-database solutions could solve the problem more efficiently (such as using graph, time series, or in-memory storage database).
