ddia-systems
覆盖存储、复制、分区与一致性等核心设计决策，适合工程团队。
构建可靠可扩展数据系统的设计原则与架构考量指南。
从存储引擎、复制和分区到事务、一致性模型与流/批处理，提供构建可靠可扩展数据系统的实践与权衡。适用于选库、设计 schema、处理复制延迟或查询在大规模下变慢、以及构建数据管道时。核心思想是把数据生命周期放在中心，帮助你在容错与性能之间做出工程化决策。
▸ 展开 SKILL.md 英文原文
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
帮我安装这个 skill：https://raw.githubusercontent.com/wondelai/skills/main/ddia-systems/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/wondelai/skills/main/ddia-systems/SKILL.md"# Designing Data-Intensive Applications Framework A principled approach to building reliable, scalable, and maintainable data systems. Apply these principles when choosing databases, designing schemas, architecting distributed systems, or reasoning about consistency and fault tolerance. ## Core Principle **Data outlives code.** Applications are rewritten and frameworks come and go, but data persists for decades -- prioritize the long-term correctness, durability, and evolvability of the data layer. Most applications are data-intensive, not compute-intensive: the hard problems are data volume, complexity, and rate of change, and explicit consistency/availability/latency trade-offs separate