Assume API SLA breach (>100ms) has happened. What are the steps to investigate, diagnose, and mitigate.

Let's dissect it further

Steps to Investigate, Diagnose, and Mitigate API SLA Breach (>100ms)

When an API SLA breach occurs (latency > 100ms), follow these steps systematically:

1️⃣ Detect & Acknowledge the SLA Breach

✅ Check Monitoring & Alerts

• Dashboards (Datadog, Prometheus, New Relic, Grafana)

• APM Tracing (Jaeger, OpenTelemetry)

• Logs & Metrics (ELK Stack, CloudWatch)

• Alerting Systems (PagerDuty, Opsgenie)

✅ Confirm the Issue

• Verify if the latency spike is consistent or temporary.

• Check API response times across different endpoints.

• Validate if it's only a few users or system-wide.

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2️⃣ Diagnose the Root Cause

A. Check API Gateway & Load Balancer

• Logs: Are there 502, 504 Gateway Timeout errors?

• Rate Limits: Check if API limits were exceeded.

• Auto-Scaling Issues: Are new instances not spawning fast enough?

B. Investigate Backend Latency

• Database Slow Queries?

  • Run: EXPLAIN ANALYZE on slow queries.

  • Check DB CPU, locks, long-running transactions.

• Cache Misses?

  • Inspect Redis/Memcached for high miss rates.

• Upstream Service Delays?

  • Use distributed tracing (Jaeger, Zipkin) to identify slow services.

C. Resource Bottlenecks

• CPU/Memory Spikes?

  • Run: top, htop, kubectl top pods

• Thread/Connection Pool Saturation?

  • Run: SHOW PROCESSLIST; (MySQL)

  • Check DB connection pool exhaustion.

D. Network Latency Issues

• Packet Loss / High RTT?

  • Run: ping, traceroute, netstat

• Cloud Provider Issues?

  • Check AWS/GCP/Azure status pages.

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3️⃣ Mitigation Strategies

A. Immediate Fixes (Quick Recovery)

• Restart high-latency service pods/instances.

• Scale up backend services dynamically (increase replica count).

• Bypass DB for frequently used data using caching (Redis, CDN).

• Kill long-running DB queries using KILL QUERY <id>.

B. Short-Term Fixes (Within Hours)

• Optimize slow queries by adding indexes.

• Increase DB connection pool size.

• Enable asynchronous processing for expensive operations.

C. Long-Term Fixes (Prevent Future SLA Breaches)

• Implement Circuit Breakers (Resilience4J, Hystrix) to prevent cascading failures.

• Introduce Read Replicas to distribute DB load.

• Optimize API response payloads (use pagination, compress JSON).

• Implement Auto-Scaling for peak loads.

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4️⃣ Post-Mortem & Prevention

✅ Document Findings

• What caused the issue?

• How was it resolved?

• What should be improved?

✅ Set Up Better Monitoring

• Latency heatmaps to detect patterns.

• Alert thresholds based on P95, P99 latencies.

✅ Simulate Failures (Chaos Engineering)

• Test API under high load (k6, Locust).

• Simulate DB failure, high network latency.

🚀 Real-World Example: Debugging an API SLA Breach (>100ms)

📌 Scenario:

A payment API that usually responds in 50ms is now taking 500ms+, leading to an SLA breach. Customers report slow transactions and timeouts.

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1️⃣ Detection: Identifying the Issue

✅ Monitoring & Alerts Triggered

• Datadog alerts: P99 latency increased to 500ms+.

• Grafana dashboard: API response time spiked suddenly.

• Logs (ELK Stack): Multiple slow requests seen.

✅ Checking API Gateway & Load Balancer

• AWS ALB logs: No 502/504 errors.

• Rate limits: API traffic within expected thresholds.

• Auto-scaling events: No new instances spun up.

🛑 Conclusion: API is receiving traffic normally, but responses are slow.

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2️⃣ Diagnosis: Finding the Root Cause

✅ Step 1: Check Backend Service Performance

• Enable Distributed Tracing (Jaeger)

  • API call → Auth Service: 30ms

  • API call → Payment Processor: 450ms (⚠️ high latency)

• CPU/Mem Usage (k8s metrics): Normal

🔍 Findings:

• Payment Processor latency is the culprit!

✅ Step 2: Investigate Payment Service Slowdown

• Database Slow Queries?

  SELECT * FROM pg_stat_activity WHERE state='active';
  

  Found a long-running transaction.

• Cache Misses?

  • Redis cache hit rate dropped to 40% (normal: 95%+).

🔍 Findings:

• The Redis cache is ineffective, causing DB overload.

• A bad DB query is causing transaction locks.

3️⃣ Mitigation: Fixing the SLA Breach

Immediate Fixes (Quick Recovery)

✅ Restart the Payment Processor service to clear up hanging connections.
✅ Increase Redis cache TTL to reduce DB hits.
✅ Kill long-running DB queries and optimize indexing.

SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE state='active';

✅ Manually reintroduce a missing Redis key to improve cache performance.

💡 Result: Latency drops from 500ms → 120ms immediately.

Short-Term Fixes (Within Hours)

✅ Index missing DB columns for slow queries.
✅ Increase DB connection pool to prevent future locks.
✅ Adjust Redis eviction policy to retain important cache entries.

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Long-Term Fixes (Future Prevention)

✅ Enable Circuit Breakers (Resilience4J) to prevent cascading failures.
✅ Add DB Read Replicas to offload queries.
✅ Implement Auto-Scaling for Redis instances.

✅ Set up Alerts for Cache Hit Rate drops.

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4️⃣ Post-Mortem & Key Takeaways

📌 Issue: Cache miss caused excessive DB queries, leading to transaction locks.
📌 Root Cause: Expired Redis key + Unoptimized DB query.
📌 Fix: Restart, cache optimization, DB indexing, circuit breakers.
📌 Prevention: Monitoring, autoscaling, read replicas.

💡 Lessons Learned

1. Always have monitoring on cache hit rates.

2. DB indexing can prevent major slowdowns.

3. Circuit breakers should be in place to fail fast.