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K8s Service No Endpoints — Pod Not Ready

Diagnose a Kubernetes Service with no endpoints because pods are not ready or readiness probe fails.

node -e "require('./repro')"

Set NODE_OPTIONS=--stack-trace-limit=100 and run with verbose logging

Add console.log / debugger / breakpoint at the line before the throw

Symptom and Error Message

When running the Kubernetes code, the runtime raises the following error:

(No error — Service has 0 endpoints; requests to the Service fail with connection refused)

This error indicates that requests to a service fail because it has no ready endpoints. The error is reproducible and appears consistently once the triggering condition is met. In production, you may see this error sporadically because the triggering condition depends on runtime state that is not always present.

Root Cause Analysis

The fundamental cause is: a service routes only to ready pods matching its selector; if none are ready or matched, it has no endpoints. Two factors converge to produce the visible error. First, pods exist but fail their readiness probe. Second, the service selector does not match any pod labels. Either factor alone might not trigger the error, but together they create the conditions for failure.

Diagnosis Walkthrough

Start by reproducing the error in isolation. Strip away everything except the minimum code path that triggers the error. Once you have a minimal reproduction, inspect every variable at the failure point. Look for: undefined where an object is expected, wrong types, stale references, and off-by-one indices. The error message tells you what went wrong; the surrounding state tells you why.

Fix and Code Example

// Before (buggy)
// ... code that triggers the error ...

// After (fixed)
// ... corrected code with proper guard or type check ...

The fix addresses the root cause directly: fix the readiness probe, or correct the service selector to match pod labels. After applying the fix, the error no longer occurs because the triggering condition is either prevented or handled gracefully.

Verification

Confirm kubectl get endpoints shows the pods; send a request and confirm it succeeds. Run the reproduction case again and confirm the error is gone. Then run the full test suite to check for regressions. In production, monitor error logs for 24 hours to confirm the fix is effective under real load.

Common Pitfalls

A frequent mistake is to suppress the error with a try/catch or conditional without addressing the underlying cause. This masks the symptom but the defective logic remains, and the error will resurface in a different form. Always fix the root cause, not just the error message. Another pitfall is fixing the symptom only in the happy path while ignoring edge cases like empty input, null values, or concurrent access.