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What readiness and liveness mean
Kubernetes assigns different actions to its probes. A failed liveness probe can trigger a container restart; a failed readiness probe makes the Pod unavailable for Service traffic. A startup probe delays both liveness and readiness checks until initialization succeeds. These are per-instance signals, not release-level rollback instructions. Kubernetes documents the probe behaviors and configuration.
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| Signal | Question it answers | Typical consequence |
|---|---|---|
| Liveness | Is this process still making progress, or is it stuck in a condition a restart may fix? | The orchestrator may restart the container. |
| Readiness | Should this instance receive its assigned traffic or work now? | The Pod is removed from Service traffic while unready. |
| Startup | Has initialization completed enough to begin ordinary health checks? | Liveness and readiness checks wait until startup succeeds. |
| Rollout health | Is this release causing unacceptable service-level regression? | The deployment controller may halt or reverse rollout if configured to do so. |
The meanings of the first three rows describe Kubernetes probe behavior. Other platforms have their own mechanisms and policies; verify the behavior of the orchestrator and deployment controller you actually use.
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Only include a dependency in readiness when its absence means that particular instance cannot safely perform its assigned role. The decision depends on the service architecture, not simply on whether a dependency exists. Spring Boot’s 4.2 Actuator guidance leaves readiness dependency checks to application developers because dependencies differ in importance and scope, and warns against including external systems in liveness.
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Keep shared dependency outages out of liveness
If a shared notification provider, broker, or database becomes unavailable, checking it from liveness can cause all affected instances to be restarted together. Those restarts will not repair the external service and can add load or reduce capacity. Liveness should instead detect a local, unrecoverable condition such as a deadlocked process. A temporary remote failure is generally not evidence that restarting this process will help.
Decide readiness by the work the instance accepts
For an HTTP API that durably enqueues notifications, a downstream provider outage may delay delivery without preventing the API from accepting requests safely. Provider reachability may then belong in delivery monitoring rather than API readiness. This depends on the system’s actual persistence, acknowledgement, retry, and durability guarantees.
A dedicated worker may need a different readiness rule. If it cannot consume or persist assigned work, it may be unable to serve its role. If it can accept work and safely retry it during a provider outage, marking it unready solely because that provider is down could remove useful capacity. Queue-consumer assignment and draining are separate application choices; Kubernetes Service readiness does not implement them automatically.
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- Ask whether the dependency is instance-local or shared across replicas.
- Determine whether the service can use a fallback, buffer work durably, or retry safely.
- Consider whether marking every replica unready would prevent useful work or merely hide a genuine inability to serve.
- Keep checks bounded and inexpensive so the health path does not add material load during an incident.
Make the endpoint represent the real serving path
A health route can return success while the application’s actual listener is broken. This can happen when a management endpoint runs on a separate context or port with different web infrastructure from the main server. Spring Boot recommends exposing liveness and readiness groups on the main server port when a separate management context could miss failures in the serving path. The probe should test enough of that path to establish that the instance can accept the traffic it represents, without turning the endpoint into an expensive dependency audit.
For Kubernetes, a low-cost endpoint can serve both readiness and liveness, with liveness configured more conservatively so a temporary problem can first make the Pod unready rather than immediately trigger a restart. Set timeout, period, and failure threshold to suit the application’s startup and response behavior; Kubernetes example values are illustrations, not universal recommendations. Incorrectly aggressive liveness checks can create restart loops and cascading failures. See the Kubernetes probe guidance.
Use startup probes for slow initialization
If initialization may take longer than the ordinary liveness window, configure a startup probe. Kubernetes does not begin liveness or readiness checks until that startup probe succeeds. This avoids treating expected initialization time as proof that the process is dead.
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Do failed probes roll back a deployment?
Not automatically in every platform. A liveness failure can lead to a container restart, and a readiness failure can withdraw a Pod from Service traffic; neither behavior alone defines what happens to the release. Rollback depends on a deployment controller or release policy configured to evaluate rollout health. Microsoft describes health checks being used by orchestrators to halt rolling deployments or restart containers, while the exact response depends on platform configuration. See Microsoft’s ASP.NET Core health-check documentation; its cited URL is versioned for ASP.NET Core 6.0, so verify API details against the framework version you deploy.
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For a notification rollout, pair instance probes with release-level measurements that reveal user impact. Useful signal categories include delivery success and failure, queue age or backlog, provider errors, and request errors or latency. These are design recommendations, not universal thresholds. Establish stop and rollback criteria for your own baseline and delivery guarantees, and account for retry delays so that a real regression is not concealed by deferred outcomes.
A recent Meta preprint describes rollout checks built from metric queries, thresholds, workflow predicates, and phased deployment integration, including automatic rollback. It is an account of one system, not a general guarantee that a particular orchestrator will reverse a release or that one threshold fits every service. The authors also discuss operational challenges such as noisy signals, alert fatigue, drift, and missed regressions. Read the August 2026 preprint.
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Framework and endpoint naming details
Kubernetes application probes
Configure liveness, readiness, and, when initialization warrants it, startup as distinct signals with actions appropriate to each. A failed readiness probe affects Service traffic; a failed liveness probe can cause a restart. Kubernetes cautions that an incorrect liveness probe can make an incident worse through repeated restarts, failed requests, and extra load on remaining Pods.
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 guidance concerns the API server’s endpoints, not mandatory names for application routes. For machine checks, use HTTP status codes. Details are in the Kubernetes API health endpoints documentation.
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Spring Boot Actuator
Spring Boot exposes liveness and readiness as health groups. It does not add extra health indicators to readiness by default; developers can choose checks based on whether dependencies are essential, shared, and covered by fallbacks or retries. Keep external systems out of liveness. The linked reference is for Spring Boot 4.2, so match configuration details to the version in use: Spring Boot Actuator endpoints.
ASP.NET Core
ASP.NET Core’s MapHealthChecks can map health endpoints, and checks can be filtered to separate readiness from liveness. Microsoft notes that dependency checks are not registered by default. Health checks can also participate in orchestrator decisions to halt rolling deployments or restart containers, but those actions depend on the orchestrator and its configuration. Confirm API details for the target framework version in Microsoft’s documentation.
Quick Recap
A practical design review
- Define the action first. Decide whether each signal should restart a process, withdraw traffic or work, or only alert. Do not reuse one ambiguous “healthy” result for all three purposes.
- Specify the instance’s role. Distinguish an API that accepts and durably enqueues a notification from a worker responsible for consuming and delivering it.
- Classify dependencies. For each readiness check, document whether the dependency is local or shared, what happens during its outage, and whether fallback, retry, or buffering keeps the instance useful.
- Protect initialization. Use startup behavior appropriate to the service’s initialization time, rather than letting ordinary liveness checks misclassify startup as a deadlock.
- Test the serving path. Ensure the chosen endpoint reflects the listener and infrastructure handling the traffic it is meant to represent.
- Set release policy separately. Choose outcome metrics and explicit rollout stop or rollback criteria, then verify what the actual deployment controller does when a criterion fails.
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