Marketplace-Research OpenSearch + Personalize Caching Best Practices

A reference distillation of caching strategies for two-sided marketplaces running AWS OpenSearch (search) and AWS Personalize (recommendations behind a microservice). Contains 52 rules across 9 categories, ordered by cascade effect — from the upstream decision of whether to cache, through key design, personalisation boundary, strategy selection, TTL design, stampede protection, observability, and the lower-cascade categories of negative caching and tier composition. Each rule explains the WHY (the cost, latency, or correctness mechanism), shows incorrect-vs-correct code (TypeScript/Node for the microservice layer, Python for batch and analytics, OpenSearch JSON for OS-specific queries, YAML for CDN/Kubernetes), and cites the canonical source — AWS Personalize/OpenSearch/ElastiCache documentation, the XFetch paper (Vattani et al. VLDB 2015), RFC 5861 (stale-while-revalidate), and the engineering blogs of cache infrastructure teams (Netflix EVCache, Pinterest Cachelib, Twitter Twemcache, Cloudflare).

This is the complement to opensearch-function-scoring-algorithms — that skill answers "what should the ranking compute?", this skill answers "how do you scale it to production traffic without burning down OpenSearch or Personalize?"

When to Apply

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