opensearch-personalize-caching-strategies

opensearch-personalize-caching-strategies is a Backend performance & caching skill for AI agents, published by pproenca in the dot-skills repository on GitHub. Skillspace rates opensearch-personalize-caching-strategies C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Caching strategies in front of AWS OpenSearch (Elasticsearch) or AWS Personalize — search, recommenders, multi-recommender pages, anon vs logged-in traffic. Covers ROI decision (TPS/minProvisionedTPS, Zipf, amplification), key design (canonicalisation, cohort vs user, solution-version pinning, bucketing), personalisation boundary (anon/logged split, fan-out coalescing), strategies (cache-aside, refresh-ahead, write-through, batch precompute, L1+L2), TTL (volatility, soft/hard, jitter, event-driven invalidation), stampede protection (single-flight, XFetch, stale-while-revalidate, circuit breaker), observability (hit-rate, cost-per-1k, cardinality drift, log-replay), defensive caching (negative, Bloom filter), and tier composition (LRU, ElastiCache Redis, CloudFront, OpenSearch request/filter cache). Triggers on cache hit rate, Personalize throttling, stampede, single-flight, L1/L2, ElastiCache sizing. Complements opensearch-function-scoring-algorithms.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

180

Unproven: rated by static analysis only, not yet compared head-to-head.

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