role-database:capacity-planning

Estimate database storage growth by calculating current data volume against expected row insertion rates and average row size. This process predicts future storage needs to prevent performance bottlenecks and unexpected infrastructure costs over time.

Determine optimal memory sizing for MongoDB by analyzing workload patterns and data access frequencies. Proper memory allocation prevents performance bottlenecks and ensures efficient query processing across your database systems.

You need read replicas to distribute read-heavy workloads and sharding to partition large datasets across multiple nodes. Choosing the right scaling strategy depends on your specific read-write ratio and data volume growth.

Use pgbench for PostgreSQL and sysbench for MySQL to execute performance benchmarking under simulated load. These load testing methodologies identify performance bottlenecks and validate capacity planning decisions before production deployment.

You can estimate cloud infrastructure costs for AWS RDS and DynamoDB by projecting storage growth and memory sizing requirements. Accurate cost estimation helps optimize cloud database infrastructure spending and prevents unexpected billing surprises.

Vertical scaling limitations include hardware ceiling constraints and high costs for incremental memory upgrades. Once maximum instance size is reached, horizontal approaches like read replicas or sharding become necessary for further scaling.