API and Integration Troubleshooting Questions

Diagnostic checklist — quick steps to triage intermittent 403s

1. Reproduce & scope

• Confirm frequency/timestamps, regions, user segments, ad zones. Correlate with load/peak-hour windows.
• Verify whether 403s come from specific endpoints, client tokens, or IP ranges.

2. Authentication checks

• Check client credentials used (API key, OAuth token): expiry, rotation events, clock skew for JWTs.
• Inspect request headers in logs for Authorization, API-Key, or x-client-id mismatches.
• Validate token issuance logs (auth server) and recent deployments that might change header format.

3. Rate-limit / quota checks

• Review third-party rate-limit headers (X-RateLimit-*, Retry-After) and our request rate during peaks.
• Query metrics for 429 vs 403; sometimes vendor returns 403 when quota exceeded or invalid plan.
• Check per-API-key and per-IP usage and sudden spikes (bursts from a worker/job).

4. Vendor-side and contract checks

• Check vendor status pages, incident notices, and API change logs for recent policy changes (geo-blocking, content rules).
• Confirm account standing (billing, plan limits, TOS enforcement) with vendor support.
• Verify whether vendor is rate-limiting or blocking specific creatives/ads (content moderation).

5. Network & infra checks

• Inspect WAF, CDN, or reverse-proxy rules for false positives blocking requests during peak traffic.
• Confirm IP allowlists/deny rules and outbound NAT IP churn that might trigger vendor blocks.
• Check TLS negotiation errors that might surface as 403 in vendor logs.

6. Application & code checks

• Audit recent deploys that touch auth header construction, retry logic, or header sanitization.
• Ensure no malformed URLs/headers or incorrect content-type that vendor rejects.

7. Logs & telemetry

• Correlate app logs, vendor response body, and vendor request-id headers for specific failures.
• Capture full request/response (sanitized) for sample 403 events and forward to vendor for investigation.

Temporary mitigations to reduce customer impact

1. Short-term fallbacks

• Implement a circuit breaker for the failing vendor endpoint with fast failover to a secondary provider or cached creatives.
• Serve cached ads or lower-risk default creatives for affected zones during the outage window.

2. Throttling & retry strategy

• Add adaptive client-side throttling: detect spikes and smooth requests (token bucket) to avoid bursts that trigger blocks.
• Implement exponential backoff with jitter and honor Retry-After. Cap retries to avoid escalating failures.

3. Auth resilience

• Pre-warm and refresh tokens before peak hours; add graceful fallback when token refresh fails (use a read-only cached token until recovered).
• Temporarily route requests through a stable set of NAT IPs if vendor blocks by IP.

4. Visibility & communication

• Add temporary monitoring/alerting for 403 rate and notify SRE/PM/vendor on-call.
• Communicate an incident status to stakeholders/customers and provide ETA and workaround guidance.

When escalating to vendor

• Provide sanitized request/response examples including timestamps, request-id headers, client IPs, tokens (masked), and traffic patterns.
• Ask vendor to confirm whether 403 is due to auth, quota, content policy, or WAF on their side and request logs correlating their decision.

Why this approach

• Systematic isolation (auth vs rate vs vendor policy vs infra) narrows root cause quickly.
• Temporary mitigations reduce user impact without masking the underlying issue; they also provide time for vendor coordination and a safe rollback if a deploy introduced the problem.

Situation: A deployment introduced a breaking change causing many clients to fail (authentication schema/API contract change).

Immediate mitigation (triage and choices):

• First 10–15 min: Triage to confirm scope (which services, percentage of clients, errors in logs/metrics). I’d check alerts, error traces, and recent release diff.
• Preferred immediate mitigation: Rollback the release if it’s fast and low-risk — this restores service quickly and buys time.
• If rollback is risky (database migrations or long-running jobs), use a compatibility shim or feature-flagged toggle to restore the old behavior for clients. Implement a quick adapter layer that maps new payloads to the old shape.
• If failures are due to traffic spikes or partially degraded downstreams, apply targeted rate-limiting / throttling to preserve availability for most clients while investigating.
• For public APIs, enable a canary/traffic split to route new traffic to the fixed version while isolating impacted clients.

Communication to clients and stakeholders:

• Immediately post a concise incident banner/status: what happened, who’s affected, and ETA for a fix. Use established channels: status page, email to major clients, Slack/PagerDuty for internal teams.
• Provide actionable guidance for clients (temporary workarounds, which endpoints are safe).
• Commit to regular updates every 30–60 minutes until resolved, then a clear “resolved” message with root-cause promised in a postmortem.

Patch and validate:

• Create a minimal, well-tested patch: either revert commits or add a compatibility shim. Keep the change small and review it quickly.
• Run local and CI unit tests, contract tests (consumer-driven contracts), and smoke tests against a staging environment that mirrors production.
• Deploy to a canary segment (e.g., 1–5% traffic) and monitor key metrics (error rate, latency, success rate) for a short period before full rollout.
• After deployment, run automated end-to-end tests that cover the client-facing flows that previously failed, and verify logs/traces are clean.

CI/process changes to prevent recurrence:

• Add or strengthen automated contract tests (PACT/contract test suite) between services and external clients; enforce them in CI.
• Expand integration and end-to-end smoke tests that run against a staging environment as part of the PR gating.
• Require feature flags for risky changes and a clear migration plan for schema or API changes (backwards-compatible first, with deprecation windows).
• Improve deployment safety: mandatory canary deployments + automated rollback on error thresholds.
• Add a pre-deploy checklist: migrations strategy (backward/forward compatible), consumer impact review, API versioning where applicable.
• Post-incident: run a blameless postmortem with timeline, root cause, action items (owners and deadlines), and add the improvements above to the definition of done.

This approach minimizes customer impact quickly, communicates transparently, ensures fixes are validated, and closes the loop with process and CI improvements to reduce future regressions.

Start by measuring: collect P50/P95/P99 latencies, tail latency heatmap, request traces (distributed tracing), CPU/memory/Goroutine/thread counts, GC, socket counts, DB query times, and error/retry rates. Only with data can you prioritize.

Evaluate optimizations (what they do, when effective, trade-offs, signals to watch):

• Caching (edge, CDN, app-level, DB result cache)

  • Benefit: cuts downstream calls => large, immediate P95/P99 reduction for cacheable responses.
  • Trade-offs: staleness, cache invalidation complexity, memory cost.
  • Measure: cache hit rate, latency delta on cache vs miss, consistency requirements.

• Connection reuse (keepalives/pooling)

  • Benefit: avoids TCP/TLS handshake and connection setup jitter; reduces tail from many short calls.
  • Trade-offs: pool sizing, resource exhaustion if misconfigured.
  • Measure: connection churn, SYN/retry rates, per-request connect time.

• Parallelizing backend calls (fan-out)

  • Benefit: reduces critical-path time when service composes independent calls.
  • Trade-offs: increased load on downstream services, complexity, coordination (first-success vs join).
  • Measure: downstream QPS increase, amplified error/latency propagation.

• Circuit breakers

  • Benefit: prevent cascading failures and cap tail latency by failing fast.
  • Trade-offs: potential increased user-visible errors; tuning thresholds matters.
  • Measure: circuit-open events, fallback success rate, reduced waiting threads.

• Asynchronous responses (deferred, polling, webhooks)

  • Benefit: moves work off critical path, great for long-running ops.
  • Trade-offs: UX changes, increased system complexity, retry/in-order guarantees.
  • Measure: request acceptance latency vs completion latency, backlog lengths.

• HTTP/2

  • Benefit: multiplexing reduces head-of-line blocking and expensive connection setup for many small requests.
  • Trade-offs: server/proxy compatibility, complexity with middleboxes, potential single-connection saturation.
  • Measure: per-stream latency, number of connections per client, TLS handshake reduction.

• Payload compression

  • Benefit: reduces transfer time for large payloads; helps high-latency networks.
  • Trade-offs: CPU cost for compression/decompression; not useful for small JSONs.
  • Measure: bytes transferred, CPU usage, end-to-end transfer time.

• Database query tuning

  • Benefit: eliminates slow tail from bad queries—often highest ROI for dynamic data paths.
  • Trade-offs: schema/index changes, maintenance, potential write amplification.
  • Measure: slow query log, P99 query time, index hit ratios, lock/IO waits.

Prioritization framework:

1. Instrumentation & baseline (tracing, metrics) — required first.
2. Quick wins with low risk: caching hot responses, connection reuse, payload compression for large responses.
3. Medium work: DB tuning (profile queries), enable HTTP/2 where safe.
4. Higher effort/impact: parallelization with throttling, async patterns, circuit breakers with sensible fallbacks.
5. Validate with canary/A-B and load tests; monitor P99 while rolling.

Final note: always measure before/after (P50/P95/P99, errors, CPU), and avoid optimistic fixes that move latency elsewhere (e.g., fan-out without throttling).