Probes and graceful shutdown

How a k8s deployment of iterion answers is this pod alive, should it receive traffic, and what happens when it is asked to leave.

Read this when:

• a rolling deploy or an autoscale event produces 502s, or a forge webhook is delivered but no run starts;
• a pod CrashLoops at boot with nothing in the logs but a startup sequence;
• a runner pod shows Ready while the queue is not moving;
• you need to size terminationGracePeriodSeconds after changing a delay.

The four endpoints

Surface	Endpoint	Answers 503 when	Never fails on
server	GET /healthz	never	anything — it is a mux-liveness signal only
server	GET /readyz	draining, superseded rollout epoch, or a critical dependency is down	a non-critical dependency (reported as degraded, still 200)
runner	GET /healthz (metrics port)	the idle consume loop stopped cycling	a run being in flight, however long
runner	GET /readyz (metrics port)	starting, draining, or superseded rollout epoch	—

Both server endpoints ride the API port; both runner endpoints ride the metrics port (9090), which the chart already exposed. All four return a JSON envelope carrying version/commit, so a probe response also tells you which build answered.

Why /readyz does not gate on every dependency

Every replica pings the same Mongo, the same NATS, the same S3. If all of them gate readiness, a 15-second Mongo failover removes every pod from the Service at once and the ingress 503s everything — including the routes that never touch Mongo. A partial degradation becomes a total outage, and the pods take an extra readiness period to come back after the backend does.

So a dependency is marked Critical only when the pod genuinely cannot serve without it. Today that is Mongo alone (cmd/iterion/server.go, the ReadinessChecks map). NATS, S3 and Valkey are pinged, reported, and not fatal:

{"status":"degraded","mode":"cloud","version":"v3.52.0","commit":"a984b1066",
 "usage_cap":"usage caps off",
 "checks":{"mongo":"ok","s3":"error: connection refused","nats":"ok","valkey":"ok"}}

(version / commit / usage_cap ride every health response, so a probe also tells you which build answered and whether the usage cap reached the deployment. "valkey":"ok" means reachable or not configured — the check returns nil when no Redis client is wired.)

That body comes back with 200. Alerting is what should page on it — docs/observability.md — because a degraded backend no longer trips a /readyz 503 alert.

The dependencies are pinged concurrently, each under its own 1s deadline, so the probe answers in max(ping) rather than sum(ping). That is why probes.readiness.timeoutSeconds is 3 and not 1: the kubelet must not abort a probe that was about to report a merely-degraded backend, which would fail readiness on exactly the case the split exists to survive.

A ping that panics (a driver bug, a malformed response) is reported as "panic: <value>" in the same map and counts as a failure for its own Critical flag — it is caught rather than allowed to exit the process, which would be an un-drained shutdown, the very thing this page is about. The panic is also sent to the error tracker.

A ping that never returns at all (a driver ignoring its context) is reported as "timeout: no answer within …", and the next probes say "stalled: an earlier probe's ping has not returned" — the wedged check is not launched a second time. That matters more than it looks: re-launching it every five seconds strands two goroutines each time (~100 MB/day, measured), and the pod eventually dies by OOMKill — a SIGKILL, so no drain, no lame-duck window. One captive goroutine per dependency is the price; a growing pile of them is not.

The flip side is that overlapping probes share one ping rather than each starting their own: a kubelet tick next to an operator's curl, or an LB health check, all read the same answer. Reporting the second caller as stalled would 503 a perfectly healthy pod — and in a mechanism that evicts, a false positive costs as much as a missed failure.

Each check is reported from a single atomic observation, so a ping that completed is never published as a timeout. What no design removes is the boundary itself: a dependency answering in the same microsecond the deadline expires is a coin flip, like any timeout. With a 1.2s budget against a Mongo answering in milliseconds that window is negligible, and failureThreshold: 3 means it would have to land three probes in a row.

Why the runner's liveness is not a TCP check

A tcpSocket probe on the metrics port proves a socket is listening, which the process does from boot to exit. A runner whose JetStream consumer died stayed Ready forever, consuming nothing.

/healthz reports the consume loop instead (pkg/runner/health.go):

• a run in flight → alive (the loop is blocked inside processOne by design, for hours; killing that pod throws away checkpointable work — the drain ceiling and the budget are what bound a run, not the kubelet);
• idle and cycling → alive. The threshold is 2 minutes or 20× the loop's FetchWait, whichever is longer, so raising the fetch cadence never turns a healthy long-poll into a restart;
• idle and not cycling → stalled, and a restart genuinely fixes it.

/readyz is the drain signal: a runner in its lame-duck can hold a pod for up to config.runner.drainTimeout (8h), and kubectl get pods should distinguish it from a fresh one.

The shutdown sequence (server)

pod marked Terminating
├─ endpoints controller starts removing the pod   ← asynchronous, 1–10s
├─ preStop hook (probes.preStopDelaySeconds, default 0 = disabled)
└─ SIGTERM
   ├─ /readyz → 503 {"status":"draining"}         ← immediately
   ├─ wait config.server.shutdownDelay (5s)       ← the lame-duck window
   ├─ run-console drain      ⎫ config.server.shutdownTeardown (30s),
   └─ http.Server.Shutdown   ⎭ split ⅔ / ⅓ so in-flight requests keep a window

The lame-duck window is the whole point. Endpoint removal is asynchronous: without the pause the listener closes while traffic is still being routed to the pod, which is a connection-refused — a 502 for a studio user, a dropped delivery for a forge webhook. With server.hpa.enabled (the default) this happens on every scale-down, not just on deploys.

/healthz deliberately stays 200 through the drain: a liveness failure would make the kubelet kill the pod in the middle of it.

Once the sequence starts it runs to completion or to its own deadline — a second SIGTERM (or a second Ctrl-C) does not shorten it. Locally that is why ITERION_SHUTDOWN_DELAY defaults to 0: a studio you interrupt must stop now. A zero delay still flips /readyz; it only removes the pause, so the probe stays honest for however long the teardown takes.

Known limitation. http.Server.Shutdown does not close hijacked connections, and the run-console / shell / browser WebSockets (pkg/server/runs_ws.go and siblings) do not watch the shutdown signal — only the studio's /api/ws hub does. Those sockets therefore get a TCP reset at process exit rather than a close frame; clients reconnect, so it is cosmetic, but a deploy is visible in a watching browser.

Sizing terminationGracePeriodSeconds

probes.preStopDelaySeconds
  + config.server.shutdownDelay
  + config.server.shutdownTeardown
        ≤ server.terminationGracePeriodSeconds

Defaults: 0 + 5 + 30 = 35 ≤ 60. Widen any of the three and raise the grace period by as much, or the kubelet SIGKILLs the pod mid-teardown. Raise shutdownTeardown when the deployment serves long requests (a big artifact upload, a streamed response) that a 30s cut would truncate on every deploy.

The runner has its own, much larger arithmetic — it waits out a whole run: runner.terminationGracePeriodSeconds (29100s) ≥ config.runner.drainTimeout (8h) + checkpoint margin. See docs/cloud-deployment.md.

When to enable the preStop hook

probes.preStopDelaySeconds is 0 (disabled) by default: the in-process window already covers the standard endpoints-based path, and a preStop sleep runs before SIGTERM, so its seconds add to the delay rather than overlapping it.

Turn it on for an ingress controller that routes to the Service ClusterIP instead of to endpoints (nginx's service-upstream annotation), where a readiness flip alone does not divert traffic.

The startup probe

A cloud server connects NATS + S3 + Mongo, ensures the schema of ~28 collections, seeds the marketplace and reconciles the bootstrap admin — before it binds its listener. On a cold or contended Mongo that can outlast the liveness budget (10s + 3×10s), and the kubelet kills the pod into a CrashLoop that reads like an application bug.

probes.startup (enabled, 30 × 5s = 150s) suspends liveness and readiness until the listener answers once. Raise failureThreshold if your Mongo is slow to accept connections; it is the only bound on boot time.

Configuration

Key	Env	Default	Effect
config.server.shutdownDelay	ITERION_SHUTDOWN_DELAY	5s cloud / 0 local	Lame-duck window before teardown. 0s disables it — an empty chart value does not: it just omits the variable and the binary's own default applies.
config.server.shutdownTeardown	ITERION_SHUTDOWN_TEARDOWN	30s cloud / 60s local	Budget for the run drain + in-flight HTTP requests, after the window. Must be > 0: zero yields an already-expired shutdown context, so in-flight runs never flip to failed_resumable and every request is cut. Refused at startup on every surface.
probes.startup.enabled	—	true	Suspends liveness/readiness during boot.
probes.startup.failureThreshold	—	30	× periodSeconds (5) = the boot budget.
probes.preStopDelaySeconds	—	0	Extra pre-SIGTERM sleep, for ingresses that bypass endpoints.
server.terminationGracePeriodSeconds	—	60	Hard bound before SIGKILL.
config.runner.drainMode	ITERION_RUNNER_DRAIN_MODE	complete	complete finishes the in-flight run; interrupt checkpoints it.
config.runner.drainTimeout	ITERION_RUNNER_DRAIN_TIMEOUT	8h	Lame-duck ceiling for a run.
config.rollout.runnerEpoch	ITERION_RUNNER_EPOCH	0	Monotonic generation, rendered literally into both PodTemplates.
config.rollout.epochMismatchDelay	ITERION_RUNNER_EPOCH_MISMATCH_DELAY	2m	Delayed redelivery while a newer runner fleet becomes ready.

Both variables are honoured by iterion server in either mode and by iterion studio. The defaults differ, though, because iterion server without ITERION_MODE=cloud routes to the studio: locally the delay is 0 (Ctrl-C stops now) and the teardown is 60s; only cloud mode gets the 5s / 30s pair. A malformed value (5 instead of 5s) is a startup error on every surface, not a silent fallback to zero.

Diagnosing

502s during a deploy. Confirm the window is actually configured:

kubectl -n <ns> exec deploy/iterion -- env | grep ITERION_SHUTDOWN_DELAY
kubectl -n <ns> logs deploy/iterion | grep 'server: draining'

The log line server: draining — /readyz now 503, waiting 5s for endpoint removal is emitted once per shutdown. No line ⇒ the delay is 0 or the binary predates it.

Pod CrashLoops at boot. kubectl describe pod shows whether the startup or the liveness probe failed. A liveness kill during boot means the startup probe is disabled or its threshold is too low.

Runner Ready but the queue is not moving. Ask the pod:

kubectl -n <ns> exec deploy/iterion-runner -- curl -sf localhost:9090/readyz
kubectl -n <ns> exec deploy/iterion-runner -- curl -sf localhost:9090/healthz

(The image ships curl, not wget — a custom runner.image may ship neither, in which case kubectl port-forward and probe from your machine.)

{"status":"stalled","idle_for":"7m12s"} means the consume loop is wedged and the liveness probe is about to restart the pod. {"status":"draining", "busy":true} means it is finishing a run and will not take new work.

A run keeps resuming after every deploy. That is the runner's drain ceiling, not a probe: see config.runner.drainMode above and docs/resume.md.

Tests

• pkg/server/health_test.go — the critical/degraded split, the draining flip, the budget split, and the concurrency of the dependency pings.
• e2e/cli_server_boot_test.go — assertLameDuck drives the real binary: after SIGTERM and before exit, /readyz says draining while /healthz still answers 200 on the same open listener.
• pkg/runner/health_test.go — busy vs wedged, and the pre-Set "starting" state.