algolia-observability

# Algolia Observability ## Overview Algolia provides built-in analytics in the dashboard, but production systems need application-level observability: latency histograms, error rate counters, distributed traces, and alerts. This skill instruments the `algoliasearch` v5 client with Prometheus, OpenTelemetry, and structured logging. ## Key Metrics to Track | Metric | Type | Why It Matters | |--------|------|---------------| | Search latency (P50/P95/P99) | Histogram | User experience, SLA compliance | | Search requests/sec | Counter | Capacity planning, cost tracking | | Error rate by type | Counter | Detect API issues before users report | | Index freshness (last updated) | Gauge | Data pipeline health | | Record count | Gauge | Cost monitoring, data integrity | ## Instructions ### Step 1: Instrumented Algolia Client Wrapper ```typescript // src/algolia/instrumented-client.ts import { algoliasearch, ApiError } from 'algoliasearch'; import { Counter, Histogram, Gauge, Registry } from 'prom-client'; const registry = new Registry(); const searchLatency = new Histogram({ name: 'algolia_search_duration_seconds', help: 'Algolia search request duration in seconds', labelNames: ['index', 'status'], buckets: [0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5], registers: [registry], }); const searchTotal = new Counter({ name: 'algolia_search_requests_total', help: 'Total Algolia search requests', labelNames: ['index', 'status'], registers: [registry], }); const sea...