Best Practices for Benchmarking ScyllaDB

Benchmarking ScyllaDB requires careful consideration of numerous factors and methodologies to ensure accurate, fair, and reproducible results. This involves understanding the intricacies of system load generation, latency measurement, and the impact of concurrency on performance testing infrastructure. Effective benchmarking also requires addressing coordinated omission, utilizing probability distributions, and applying statistical and queuing theories. Developers need to account for specific workload patterns such as read-heavy or write-heavy operations, and should carefully configure database and system layers to reflect real-world use cases. Additionally, choosing appropriate open-source frameworks like YCSB, and understanding the unique architecture of ScyllaDB, such as its thread-per-core design, are crucial for performance optimization. The goal is to achieve desired throughput and latency targets, such as maintaining a 99th percentile latency within acceptable limits, while ensuring that the system is not overloaded. The article emphasizes the importance of isolating and monitoring the performance of both the target system and the data loaders, and suggests using ScyllaDB's shard-aware drivers for optimal results.