1.Methodology

A repeatable system-design method: clarify requirements, quantify scale, define data and invariants, identify bottlenecks, choose components, and validate failure modes.

Strategies for large-scale data: partitioning, external sorting, hashing, Bloom filters, streaming aggregation, distributed storage, and choosing algorithms from access patterns.

Availability engineering through redundancy, failure domains, timeouts, retries, load shedding, replication, failover, graceful degradation, observability, and recovery testing.

A practical path from reducing work per request to caching, horizontal scaling, partitioning, asynchronous processing, backpressure, and hotspot control.

Choosing data models from invariants and access patterns: relational, document, key-value, search/index, event log, graph, denormalization, and derived views.

Refactoring as behavior-preserving design improvement, with test protection, incremental changes, observability, and boundaries between cleanup and architectural rewrite.

Vertical vs. horizontal scaling, statelessness, load balancing, autoscaling signals, warm-up, downstream capacity, and stateful partitioning.

A methodology for technical planning: business goals, current bottlenecks, target architecture, risk reduction, migration stages, observability, and measurable outcomes.