Scalability Approach: RDBMS vs. DynamoDB

Traditional RDBMS

RDBMS face scalability challenges due to the unpredictability of traffic spikes. Scaling horizontally can be complex and costly, often requiring significant effort. Additionally, SQL joins can become time-consuming and expensive as the database grows, impacting performance and scalability. Relational databases also have maximum sizes for files and tables, imposing upper limits on scalability. Scaling up typically requires faster hardware or distributed systems, leading to additional investments.

DynamoDB

DynamoDB is designed for seamless scalability, shifting scalability considerations to the table design phase. It can automatically scale to handle varying workloads without manual intervention, ensuring consistent performance regardless of data size. DynamoDB bounds queries to prevent scalability issues, efficiently handling large item collections by limiting the number of items accessed. This ensures quick data retrieval even for substantial datasets. DynamoDB scales out using distributed clusters of hardware, allowing increased throughput without latency increases. Customers specify their throughput requirements, and DynamoDB allocates resources accordingly, with no upper limits on table size or item count.