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For a Node.js API, useful health monitoring separates two questions: should this instance receive traffic, and should its process keep running? In Kubernetes, readiness failures remove an instance from service, while liveness failures can trigger a restart. To judge a new release, combine those probe outcomes with API error rate and latency—then compare them with a service-specific baseline rather than applying a universal rollback threshold.
How do I add a health check endpoint to my Node.js API?
Expose lightweight HTTP endpoints that answer distinct operational questions, then configure your orchestrator to probe them. In Kubernetes, the application endpoints you define are separate from the Kubernetes API server’s own /livez and /readyz endpoints; the API server’s older /healthz endpoint is deprecated. See Kubernetes API health endpoints.
Give each endpoint one purpose
- Liveness: confirm that the application process can respond and make progress. A failed liveness probe can cause Kubernetes to restart the container.
- Readiness: indicate whether this instance can safely serve requests. A failed readiness probe removes the instance from traffic while it remains unready; it should not restart the process just because the instance is temporarily unable to serve.
- Startup: use a startup probe when initialization is long or unpredictable. It delays liveness and readiness probing until startup succeeds.
Kubernetes documents these probe behaviors and configuration in Configure Liveness, Readiness and Startup Probes and Liveness, Readiness, and Startup Probes.
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Keep liveness independent of external services
A liveness endpoint should normally establish that the process itself is alive, not require a database, network service, or other dependency to be healthy. If a shared dependency goes down and every replica’s liveness check fails, repeated restarts can compound the incident rather than repair the dependency. AWS recommends keeping liveness and readiness distinct and avoiding external dependencies in liveness checks; see AWS guidance on probes and load balancer health checks.
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Use dependency-aware readiness with care
Readiness can reflect whether an instance is able to handle requests, including relevant dependency state. But a check that marks every replica unready when a shared database is unavailable can remove all application capacity from traffic. Decide whether the endpoint reflects instance-specific ability to serve, and consider how the service behaves during a common dependency outage. Amazon EKS discusses application availability and dependency behavior in Running highly-available applications.
Configure probes to tolerate normal behavior
Set probe timing and failure thresholds to allow for expected startup and brief load variation. Overly aggressive settings can misclassify normal behavior as failure. Probe frequency and exec-based checks can also add CPU overhead, particularly at high pod density. Kubernetes explains probe configuration and its operational effects in its probe documentation.
For Node.js services on Kubernetes, Lightship is one package that describes readiness, liveness, startup checks, and graceful shutdown: npm: lightship. It is an implementation option, not a requirement; the key is to preserve the probe semantics above.
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What is the difference between readiness and liveness?
| Signal | Question it answers | Typical action | External dependency check? |
|---|---|---|---|
| Readiness | Can this instance accept traffic now? | Remove it from traffic while it is unready | Possibly, but avoid taking every replica out for one shared outage |
| Liveness | Is the process stuck or unable to make progress? | Restart the container after configured failures | Normally no; keep it independent of external dependencies |
| Startup | Has this instance completed initialization? | Defer the other probes until startup succeeds | Only as appropriate to startup behavior |
These are Kubernetes probe semantics, not interchangeable names for a generic “healthy” endpoint. AWS also advises using distinct readiness and liveness checks; its guidance is at Configure probes and load balancer health checks.
Which four signals can identify a release that needs attention?
Use four complementary signals during a rollout. The first two are operational recommendations; Kubernetes and AWS define the behavior of the readiness and liveness probes, but do not prescribe universal error-rate, latency, capacity, or restart thresholds.
1. API error rate
Compare the new revision’s error rate with the pre-deployment baseline and the expected traffic profile. A sustained increase that begins with the rollout is a reason to investigate or pause, especially if other signals also worsen. Define which responses count as errors for your API; there is no single threshold established by the cited Kubernetes or AWS guidance.
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2. API latency
Watch for a sustained latency regression against the service’s normal distribution or user-facing objective. Avoid pausing or rolling back because of one slow request. Choose the observation window and acceptable change based on the service’s own baseline; the cited sources do not set a universal latency limit.
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Track the number of instances that remain ready and whether failures cluster in the new revision. Readiness is a traffic-eligibility signal: Kubernetes uses it to decide whether a pod should receive traffic, rather than to restart it solely for being temporarily unable to serve. A falling ready count can therefore reveal reduced capacity even before a liveness failure appears.
4. Liveness failures or rising restarts
Watch for liveness probe failures and a rise in restarts, particularly when they concentrate in the new revision. These can indicate that an application is stuck or cannot make progress. Check whether a shared database, network, or infrastructure issue explains the pattern before attributing it to the release; dependency-driven liveness failures can create restart loops.
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When should I roll back a deployment?
Pause or roll back when the evidence indicates that the new revision is materially degrading service, not simply because one probe or request failed. A practical rollout controller can evaluate all four signals over an observation window chosen for the API’s traffic and recovery characteristics.
- Compare error rate with the pre-deployment baseline and expected traffic profile.
- Compare latency with the service’s normal distribution or objective, using a sustained window rather than an isolated slow request.
- Check ready capacity and determine whether readiness failures are concentrated in the new revision.
- Check liveness failures and restart growth, and look for a shared dependency or infrastructure incident.
- Pause the rollout or roll back when multiple signals worsen together after the new revision starts and the impact is consistent with a release regression.
This is an operational decision framework, not a rollback formula prescribed by Kubernetes or AWS. Set thresholds, severity, and observation windows against your service’s baseline and rollout policy. If a shared external dependency is down, a readiness cascade or restart loop may worsen the impact; diagnose that incident before treating every affected pod as a bad release.
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Correlate probe failures with revision, node conditions, and events. If problems cluster on the new revision across healthy nodes, the release is a stronger suspect. If unrelated workloads on one node are also affected, investigate node health and resource pressure before rolling back the application.
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Kubernetes node status includes readiness and resource-pressure conditions; see Node Status. Amazon EKS documents node signals and monitoring-agent events in Detect node health issues with the EKS node monitoring agent. Its automatic node repair responds to specified node conditions, not every resource-pressure condition, as described in Detect node health issues and enable automatic node repair. Node repair addresses infrastructure health; it is not the same action as rolling back an application release.
For visibility beyond in-cluster probes, combine application and infrastructure monitoring with external uptime checks. External checks can help establish whether users can reach the API, while probe alerts and cluster events help explain why capacity changed.
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