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For a notification-delivery service, liveness and readiness should answer different questions: liveness asks whether a process needs restarting; readiness asks whether an instance should receive new traffic or work. Neither signal, by itself, decides whether a release should roll back. That decision belongs to the rollout system and its configured release-health policy.
What liveness, readiness, and startup checks mean
These checks may all report health, but they drive different operational actions. Kubernetes uses liveness to decide whether to restart a container, readiness to determine whether a Pod should receive Service traffic, and startup to hold off on the other probes until initialization succeeds. See the Kubernetes probe documentation.
| Check | Question it answers | What a failure means |
|---|---|---|
| Liveness | Is this process making progress, or is it stuck in a condition it cannot recover from on its own? | The container may be restarted. |
| Readiness | Should this instance receive its assigned traffic or work right now? | The Pod is not eligible for Service traffic while unready. Queue assignment, consumer pause, and work draining may need separate application or platform logic. |
| Startup | Has initialization finished enough for the other probes to be meaningful? | Liveness and readiness checks do not begin until startup succeeds. |
Think of these as instance-level signals, not release verdicts. A liveness failure can restart an instance; a readiness failure can withdraw it from traffic. A release rollback requires a separate rollout policy.
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What should a notification service check?
Choose checks according to the role of the instance and the failure action the check will trigger. A request API that durably accepts and queues notifications may still be able to serve clients while a downstream delivery provider is unavailable. In that design, provider reachability may be better tracked as an operational or delivery signal than as API readiness. A worker that cannot consume or persist work may need a different readiness condition.
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Acceptance is not successful delivery. Before treating a dependency as optional, confirm the service’s actual durability, retry, acknowledgement, and fallback behavior. A queue that accepts work safely changes the meaning of a provider outage; it does not make delivery failures irrelevant.
Keep liveness local and recoverability-focused
Use liveness to detect an unrecoverable local failure, such as a deadlocked process—not every failure that makes work slower or temporarily impossible. Kubernetes cautions that a badly configured liveness probe can cause cascading failures: repeated restarts and failed requests can add load to the instances that remain. Spring Boot likewise warns against making liveness depend on external systems, because a shared dependency outage can cause all instances to restart. Its guidance is in the Spring Boot 4.2 Actuator reference.
Do not fail liveness solely because a shared broker, database, notification provider, or remote API is unreachable. If an external dependency is down, restarting every otherwise healthy process is unlikely to restore that dependency and may make recovery harder.
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Make readiness specific to the instance’s role
Readiness should indicate whether this particular replica or worker can safely take on new assigned work. For an HTTP API, that might depend on completed initialization and essential local prerequisites. For a dedicated worker, it may depend on whether it can consume and persist work. Kubernetes readiness does not, on its own, implement a queue consumer’s assignment or drain policy; design those behaviors separately.
Assess each dependency rather than adding every available check. Ask whether its failure makes this instance unable to serve its role, whether the dependency is shared across replicas, and whether retries, a circuit breaker, or a fallback let the service continue safely. If all replicas depend on the same unavailable service, including it in readiness can make them all unready at once. Excluding an essential dependency can have the opposite problem: the instance accepts work it cannot handle. Spring Boot leaves readiness indicators to the application because dependencies differ in importance and scope.
Make the health route represent the real serving path
A health endpoint is useful only if it exercises enough of the path it is meant to represent. A management endpoint on a separate port or context can respond successfully while the main application listener or its connection path is broken. Spring Boot warns about this difference and recommends exposing liveness and readiness groups on the main server port when the separate management context uses different infrastructure.
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Keep probe work bounded and inexpensive. A slow or costly check can create its own load, and an aggressive timeout or failure threshold can turn temporary latency into repeated restarts. Tune the probe period, timeout, and failure threshold to the application’s behavior and startup characteristics rather than treating example values as universal guidance. Kubernetes documents a common pattern: use a low-cost endpoint for both liveness and readiness, with a higher liveness failure threshold so the Pod can first be observed as unready.
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Separate probe failures from release rollback
A failed health response does not universally roll back a deployment. Probe semantics govern an instance’s restart or traffic eligibility; a rollout controller’s configured policy governs whether a release halts or reverses. Microsoft documents health checks being used by orchestrators to halt rolling deployments or restart containers, but the exact action depends on the platform and its configuration. See Microsoft’s ASP.NET Core health-check guidance; that page is versioned for ASP.NET Core 6.0, so check API details against the framework version actually deployed.
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Choose release signals that reveal delivery regressions
Pair per-instance probes with release-level measurements tied to notification outcomes. Useful signal categories include delivery success and failure rates, queue age or backlog, provider error rates, and request error or latency indicators where relevant. These are candidate categories, not universal thresholds. Establish explicit stop and rollback criteria against an appropriate baseline, and use a canary or phased rollout where the platform supports it.
Account for retries and delayed delivery when interpreting those signals: they can obscure a regression if the release is judged only on immediate request success. The Meta authors’ August 2026 preprint, “Making Deployments Safe at Meta: Health Checks for Continuous Change-Safety”, describes rollout checks using metric queries, thresholds, workflow predicates, and phased rollout integration. It also discusses operational challenges such as noise, alert fatigue, drift, and regressions that checks do not cover. This is one system’s account, not a universal rollout guarantee or a ready-made threshold set.
Verify the actual deployment controller’s behavior before relying on an automatic rollback. A /ready or /live route name has no inherent rollback meaning.
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Framework and endpoint details to keep straight
Kubernetes application probes
Configure liveness, readiness, and—when initialization time warrants it—startup as distinct probe roles. A failed readiness probe affects Service traffic eligibility; a failed liveness probe can cause a restart. Avoid making the restart signal depend on shared external services.
Kubernetes API server health routes
The Kubernetes API server’s /healthz endpoint has been deprecated since Kubernetes v1.16; its documentation recommends /livez and /readyz instead. This applies to API-server endpoints, not to route names an application chooses for its own health checks. For machine checks of those API endpoints, use HTTP status codes. Details are in the Kubernetes API health endpoint documentation.
Spring Boot Actuator
Spring Boot exposes liveness and readiness as health groups. It does not include extra health indicators in readiness by default; developers can add them deliberately. Keep external-system checks out of liveness, and decide whether each dependency belongs in readiness based on the application’s role, dependency scope, and fallback behavior. The linked reference is for Spring Boot 4.2; match endpoint configuration details to the deployed version.
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ASP.NET Core health checks
ASP.NET Core provides MapHealthChecks for mapping health endpoints, and checks can be filtered to separate ready and live behavior. The cited Microsoft guidance says dependency checks are not registered by default and describes health checks as inputs that orchestrators can use to halt rolling deployments or restart containers. Verify APIs and hosting behavior for the target ASP.NET Core release.
Quick Recap
A practical design review before rollout
- Failure action: Decide whether a failed check should restart an instance, withdraw it from traffic or work, or only raise an alert.
- Dependency scope: Identify whether a dependency is local to one instance or shared across replicas.
- Recoverability: Distinguish a stuck process from a transient outage that retries, fallback, or a circuit breaker can contain.
- Work acceptance: Confirm whether the API can safely enqueue work during provider disruption and what a worker must have available to accept or process work.
- Startup behavior: Allow initialization enough time before judging liveness or readiness.
- Endpoint path: Ensure the route checks the main serving path when it is meant to represent that path, not only a separate management context.
- Release policy: Define release-level regression signals and the controller action they trigger; do not assume a probe failure reverses a release.
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