Optimizing PostgreSQL & Multi-Tier Redis Caching for 200k+ SKUs: Reducing Latency from ~40s to <6s
How we diagnosed severe enterprise search query bottlenecks using EXPLAIN ANALYZE, restructured composite B-Tree/GIN indexes, and implemented a multi-tier Redis cache invalidation strategy for 200,000+ store-specific SKUs.
The Challenge: 40-Second Multi-Facet Queries
In enterprise marketplaces with 200,000+ active product variants across multi-tenant stores, queries with dynamic attribute filtering (price brackets, vendor availability, custom metadata) were taking up to 40 seconds to resolve.
1. Profiling and Identifying Query Bottlenecks
Using PostgreSQL EXPLAIN (ANALYZE, BUFFERS), we discovered sequential table scans caused by JSONB metadata filtering and missing composite index coverage on tenant and status fields.
2. Multi-Tier Redis Caching Strategy
We introduced partitioned Redis cache keys with short TTLs and tag-based pub/sub invalidation on catalog updates, reducing p99 search latency to sub-6 seconds and delivering a 10x throughput boost.
Jaykumar Kanjia
Sr. Software Engineer specializing in scalable backend architectures, high-performance Redis caching, and autonomous AI agent workflows.