How do I monitor dependencies behind my APIs?

To monitor dependencies behind your APIs, focus on visibility across the full request path: API gateway → service → database/cache/queue/external APIs. The goal is to know what each request called, how long it took, whether it failed, and which dependency caused it.

1) Instrument distributed tracing

Use distributed tracing so every incoming API request gets a trace ID and every downstream dependency call becomes a span.

What to capture:

• Request ID / trace ID
• Service name and endpoint
• Downstream dependency name
• Latency per dependency
• Status code / error
• Retries, timeouts, circuit breaker events

Tools:

• OpenTelemetry for instrumentation
• Trace backends like Jaeger, Grafana Tempo, Datadog, New Relic, Honeycomb

Example flow:

• GET /orders/123
  • API span
  • orders-service span
  • payment-service span
  • postgres query span
  • inventory-api span

2) Collect dependency-specific metrics

Track metrics for each dependency, not just the API itself.

Core metrics:

• Latency: p50 / p95 / p99
• Error rate: 4xx/5xx, timeouts, connection errors
• Throughput: requests per second
• Saturation: connection pool usage, queue depth, thread usage
• Retries/fallbacks
• Timeout counts

Useful breakdowns:

• By dependency
• By endpoint
• By environment
• By region/tenant/customer if relevant

3) Add structured logs with correlation IDs

Logs should be searchable and tied to traces.

Include:

• trace_id / span_id
• dependency name
• operation/query name
• latency
• error message / stack trace
• retry count

This lets you jump from an alert to the exact failing downstream call.

4) Set dependency-level SLIs/SLOs

Define service objectives for critical dependencies.

Examples:

• 99.9% of calls to payment gateway complete under 300 ms
• DB query error rate stays below 0.1%
• Cache hit ratio above 95%
• Queue lag under 30 seconds

Alert on burn rate or sustained SLO violations, not single spikes.

5) Monitor external and internal dependencies separately

Classify dependencies:

• Internal services: other microservices
• Datastores: Postgres, Redis, Elasticsearch
• Messaging: Kafka, SQS, RabbitMQ
• External APIs: Stripe, Twilio, shipping providers
• Infrastructure: DNS, load balancers, service mesh

For external APIs, monitor:

• DNS resolution
• TLS handshake failures
• Response time by region
• Rate limits / quota exhaustion
• Retry and fallback behavior

6) Use synthetic checks for critical paths

Run scheduled tests that mimic real requests:

• Login
• Checkout
• Search
• Payment authorization

This helps you detect dependency failures even when traffic is low.

7) Add health checks, but don’t rely on them alone

Health checks should verify real dependency reachability, not just process liveness.

Types:

• Liveness: is the app running?
• Readiness: can it serve traffic?
• Dependency health: can it reach DB/cache/queue/external service?

Avoid making readiness too strict if it can cause cascading outages.

8) Create dependency maps / service graphs

Maintain a dependency inventory:

• Which APIs call which services
• Which services call which DBs and queues
• Which external providers are critical

A service map helps you answer:

• “If this API is slow, what downstream system is likely responsible?”
• “What breaks if Redis is down?”

9) Alert on symptoms and root causes

Alert examples:

• API latency high
• Downstream dependency timeout rate increased
• Cache hit ratio dropped
• DB pool exhausted
• External API 429s spiked
• Circuit breaker opened

Best practice:

• One alert for customer impact
• One alert for the suspected dependency
• Don’t create noisy alerts for every single failed call

10) Correlate with releases and config changes

Many dependency issues are caused by deploys or config changes.

Track:

• version/deploy ID
• feature flags
• config changes
• dependency client version changes

This makes it easier to identify whether a spike started after a release.

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Recommended stack

A common setup:

• OpenTelemetry for tracing/metrics/log correlation
• Prometheus + Grafana for metrics and dashboards
• Tempo/Jaeger for traces
• Loki/ELK for logs
• Alertmanager/PagerDuty/Opsgenie for alerts

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Practical dashboard checklist

For each API, show:

• Request rate
• Latency p50/p95/p99
• Error rate
• Top downstream dependencies
• Latency/error breakdown per dependency
• Retry counts
• Timeout counts
• Circuit breaker state
• Cache hit ratio
• DB pool utilization / queue lag

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Quick implementation plan

1. Add trace propagation across all services
2. Instrument all outbound calls, DB queries, and queue operations
3. Standardize structured logs with trace IDs
4. Build dashboards by dependency
5. Define alerts on latency, errors, and saturation
6. Add synthetic checks for critical flows
7. Review service maps regularly

If you want, I can also give you:

• a Kubernetes/microservices monitoring architecture
• an OpenTelemetry example
• or a dashboard/alerting template for APIs and dependencies.