Microservice # Debugging End To End

 Here is a full live example of debugging a microservices failure — exactly how it happens in real companies (Netflix, Uber, Amazon style).

This example will make the entire debugging process crystal clear. 👇

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🎯 Scenario: Order Placement Failure in a Microservices System

User tries to place an order:

/placeOrder

But the user gets:

500 Internal Server Error

We have these microservices:

• API Gateway

• Order Service

• Payment Service

• Inventory Service

• Notification Service

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🔥 Step 1: Check Logs in Centralized Logging (ELK)

We search for Correlation ID:

X-Correlation-ID: 8ab12c

We find the logs:

Order Service → Calling Payment Service...
Payment Service → Timeout when calling Bank API
Order Service → Payment Failed

🔍 Insight

Payment Service timed out while calling external Bank API.

But we need more clarity → go to distributed tracing.

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🔥 Step 2: Open Distributed Tracing (Jaeger / Zipkin)

Trace ID: 8ab12c

You see this:

Timeline diagram shows:

API Gateway → Order Service → Payment Service → Bank API (timeout)

👉 The slowest span is Payment → Bank API
👉 Bank API took 12 seconds
👉 But our timeout is 3 seconds

🔍 Root Cause

Bank API is responding too slowly → causing Payment Service to fail → causing Order failure.

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🔥 Step 3: Check Metrics (Prometheus + Grafana)

Graphs show:

• Payment Service latency (p99) increased to 10 seconds

• Error rate for Payment Service spiked to 65%

• CPU & memory are normal (so not resource issue)

This confirms:

👉 The bottleneck is external Bank API, not our services.

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🔥 Step 4: Check Kubernetes Pod Health

Run commands:

kubectl get pods -n prod
kubectl describe pod payment-service-xyz
kubectl logs payment-service-xyz

Logs show:

java.net.SocketTimeoutException: Read timed out
Retrying...
CircuitBreaker: OPEN

🔍 Meaning

The circuit breaker opened, so Payment Service started failing immediately to avoid cascading failures.

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🔥 Step 5: Reproduce the Issue Manually

We call Bank API directly:

curl https://bankapi.example.com/authorize?amount=500

Result:

Response took 11,230 ms

🔍 Conclusion

Bank API is definitely slow.

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🔥 Step 6: Check Recent Deployments / Changes

Use Git or CI/CD logs:

Payment Service v2.3 deployed 2 hours ago
Changes: Added encryption & validation → increased payload size by 40%

Possible reasons:

• New payload size causes Bank API bottleneck

• New encryption increased processing time

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🔥 Step 7: Apply Temporary Fix

Quick mitigation:

• Increase Payment Service → Bank API timeout from 3s to 8s

• Increase retry delay

• Reduce payload size (remove unwanted fields)

Apply config change:

timeout = 8000ms
retry = 1

Redeploy:

kubectl rollout restart deployment payment-service

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🔥 Step 8: Observe the System After Fix

Metrics after fix:

• Latency drops from 10s → 2s

• Error rate drops from 65% → 1%

• Circuit breaker closes

• Order flow is successful again

User can place orders now!

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🧠 End-to-End Debugging Summary (LIVE EXAMPLE)

Step	What We Found	Tool
1	Payment timeout in logs	ELK
2	Bank API span slow	Jaeger
3	Payment latency spike	Grafana
4	Timeout exception in pods	kubectl logs
5	Bank API slow manually	curl
6	Change in deployment	Git / CI-CD
7	Applied fix	Config update
8	System stable	Grafana

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🏆 Final Real-World Root Cause

Bank API became slow due to increased payload size introduced in the latest deployment.

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🎁 Want an even better example?

I can give you:

✅ Debugging example for Kafka failure
✅ Debugging example for Database failure
✅ Debugging example for Service Mesh (Istio) routing failure
Just tell me which one you want!