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This article explains why database capacity planning is challenging and presents a systematic approach—including workload analysis, performance modeling, consistency considerations, and node scaling decisions—using the open‑source NoSQL database ScyllaDB to guide accurate capacity estimation.
ScyllaDB, a high‑performance C++ implementation of Apache Cassandra, introduces row‑level repair to replace the traditional partition‑level repair, reducing data transfer and I/O by operating on individual rows; the presentation details its architecture, multi‑stage process, experimental results, and the resulting six‑fold speedup.
ScyllaDB, a Cassandra‑compatible NoSQL database, achieves over tenfold performance improvements through a thread‑per‑core design, asynchronous I/O, custom caching, self‑tuning schedulers, a user‑space TCP/IP stack, and LLVM‑JIT query execution, making it a compelling study for high‑performance database engineering.