performance-skill

Resolve N+1 query performance bottlenecks in multi-tenant SaaS by applying targeted database indexing and diagnostic workflows. This isolates tenant-specific impacts and meets defined p95/p99 latency budgets.

Setting performance budgets for API latency involves defining concrete p95 and p99 metrics across tenants. This creates guardrails to quantify user-perceived load and validate that backend optimizations meet scalability goals.

Tune BullMQ and Redis caching for backend optimization by implementing queueing backpressure and targeted caching strategies. This resolves caching inefficiencies and enforces per-tenant rate controls for scalable workloads.

Yes, this performance optimization approach works for frontend bottlenecks by applying end-to-end diagnostic guidance. It measures user-perceived load and implements patterns to resolve dashboard inefficiencies across all layers.

Diagnose slow API paths in multi-tenant architectures by isolating tenant impact and applying performance budgets. Implement a minimal caching and indexing strategy to validate targets and resolve scalability bottlenecks.

Use TimescaleDB for multi-tenant database optimization when addressing query bottlenecks that standard indexing cannot resolve. It supports time-series workloads to help teams meet defined performance budgets across tenants.