What problem does it solve?
It solves data-layer design indecision and reliability gaps by turning Kleppmann’s distributed-systems fundamentals into concrete, decision-ready rules for storage, replication, transactions, consistency, and partitioning.
Core Features & Use Cases
- Storage engine selection guidance: choose between log-structured (LSM-tree) and update-in-place (B-tree) engines based on workload write/read characteristics.
- Replication and consistency decisioning: select replication topology (single-leader, multi-leader, leaderless) and map it to consistency requirements like linearizability vs eventual consistency.
- Concurrency control and sharding discipline: choose isolation levels to prevent anomalies (lost updates, write skew) and design sharding/partitioning to avoid hot partitions and unsafe rebalancing.
- Schema evolution and operational guardrails: apply encoding choices (JSON/Avro/Protobuf) and “never do this” anti-patterns that prevent silent correctness failures.
Quick Start
Use data-systems-craft when planning a system’s storage and replication strategy so you can pick the right consistency and isolation guarantees before implementation starts.
