nw-sd-case-studies
Solid25 real-world system design case studies condensed from Alex Xu's System Design Interview Vol 1 and 2 - requirements, architecture, deep dive insights, key takeaways
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Quality Score: 92/100
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- Author
- nWave-ai
- Repository
- nWave-ai/nWave
- Created
- 3 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
nw-sd-framework
4-step system design framework with back-of-envelope estimation, scaling ladder, and common pitfalls
ddia-principles
Designing Data-Intensive Applications (DDIA) distilled reference guide by Martin Kleppmann. MUST be loaded when: designing database schemas, choosing storage engines, implementing replication or partitioning, handling distributed transactions, building batch/stream processing pipelines, choosing consistency models, implementing consensus, designing data flow architectures, evaluating trade-offs between availability and consistency, encoding/serialization decisions, data modeling (relational vs document vs graph), building fault-tolerant systems, or any system design and architecture discussion involving data-intensive applications. Trigger on: database design, replication, partitioning, sharding, transactions, isolation levels, consistency, consensus, CAP theorem, batch processing, stream processing, MapReduce, Kafka, event sourcing, CDC, OLTP, OLAP, B-tree, LSM-tree, data warehouse, schema evolution, encoding formats, distributed systems, fault tolerance, leader election, quorum.
nw-sd-patterns
Core distributed systems patterns - load balancing, caching, sharding, consistent hashing, message queues, rate limiting, CDN, Bloom filters, ID generation, replication, conflict resolution, CAP theorem
system-design
Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues. Use when the user mentions "system design", "scale this", "high availability", "rate limiter", "design a URL shortener", "system design interview", "capacity planning", or "distributed architecture". Also trigger when estimating infrastructure requirements, choosing between microservices and monoliths, or designing for millions of concurrent users. Covers common system designs and back-of-the-envelope estimation. For data fundamentals, see ddia-systems. For resilience, see release-it.
ddia-systems
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", or "LSM tree vs B-tree". Also trigger when choosing between SQL and NoSQL, 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.