Reactive architecture, design, and programming
summary
Defines reactive programming as models where A updates automatically when B and C change, then elevates that to reactive architecture which provides resilience, elasticity and self‑healing across nodes. Describes distributed computing patterns: immutable domain models, microservices-as-transformers, and cluster sharding for scale. Recommends adding streaming analytics (Spark and fast‑data) for predictive analytics to anticipate spikes and avoid failures, citing energy‑grid analogies where per‑node prediction prevents transformer/server blowouts. Toolkits at Lightbend implement these patterns and aim to deliver real‑time predictions.
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