Benchmarks assess database elasticity by measuring how effectively a database system adapts to changing workloads through scaling resources (like compute, storage, or nodes) while maintaining performance, availability, and consistency. Elasticity focuses on both scaling out (adding resources) and scaling in (removing resources) dynamically. Benchmarks simulate real-world scenarios to evaluate metrics such as response time during scaling, throughput consistency, and recovery time after scaling events. For example, a benchmark might test how a distributed database handles a sudden 10x increase in read/write requests by automatically adding nodes, then measure if latency stays within acceptable thresholds and if data remains consistent across nodes.
To evaluate elasticity, benchmarks often use variable workload patterns. For instance, they might start with a baseline workload, abruptly increase traffic to simulate a spike (e.g., a flash sale), then reduce it to test scaling down. Tools like Yahoo! Cloud Serving Benchmark (YCSB) or custom scripts can generate these patterns. Benchmarks also monitor how the database manages resource allocation—such as whether it provisions new storage or compute instances quickly enough to avoid throttling. For example, a cloud-native database like Amazon Aurora might be tested on how fast it scales read replicas during a query surge, and whether scaling operations disrupt existing transactions. Metrics like time to stabilize (how long the system takes to reach optimal performance after scaling) are critical here.
Another key aspect is evaluating the automation and policies driving elasticity. Benchmarks assess whether scaling decisions are rule-based (e.g., CPU usage thresholds) or predictive (machine learning-driven). For example, a benchmark might test if a database scales out preemptively during periodic traffic spikes (like daily analytics runs) or reacts only after performance degrades. Cost efficiency is often measured alongside elasticity—ensuring the system doesn’t over-provision resources unnecessarily. A benchmark might compare the total cost of running elastic scaling versus static provisioning under the same workload. Tools like TPC-C (for OLTP workloads) or cloud-specific frameworks (e.g., AWS CloudFormation templates with load testing) are often adapted to include elasticity-specific metrics, ensuring the database balances performance, cost, and resilience during scaling.