Kubernetes¶

servicewright's shutdown ordering was designed against how Kubernetes actually terminates pods. This page is the configuration that matches it.

What happens on a rollout¶

When the kubelet terminates a pod, two things happen concurrently:

1. the endpoint is removed from Services (eventually — it propagates asynchronously);
2. SIGTERM is sent to your process.

That race is why a service must keep serving after it has been told to stop. servicewright does: readiness flips to false first, so the endpoint controller starts removing you, and only then does the drain begin.

sequenceDiagram
    participant K as kubelet
    participant EC as Endpoint controller
    participant P as Pod
    participant LB as Load balancer

    K->>P: SIGTERM
    K-->>EC: pod marked Terminating
    P->>P: readiness = false
    EC->>LB: endpoint removed
    Note over P,LB: the pod is still accepting —<br/>this overlap is the point
    P->>P: drain(grace): finish in-flight work
    P->>P: stop(), pre_shutdown hooks
    P->>P: app scope closes, pools disposed
    P-->>K: exit 0

Probes¶

livenessProbe:
  httpGet:
    path: /system/health/livez
    port: 8000
  periodSeconds: 10
  failureThreshold: 3

readinessProbe:
  httpGet:
    path: /system/health/readyz
    port: 8000
  periodSeconds: 5
  failureThreshold: 2

Liveness is independent of your dependencies — it only says the event loop is alive. Pointing it at readyz would restart every pod in the fleet during a database blip instead of just routing around them. See Health checks.

A startupProbe is usually unnecessary: readiness stays false through warmup, so a slow start is already handled. Add one if your warmup can exceed the liveness failure budget.

gRPC¶

readinessProbe:
  grpc:
    port: 50051
  periodSeconds: 5

The standard health service is registered automatically and is driven by the same registry as the HTTP probes.

Litestar¶

The Litestar adapter uses shorter paths: /system/livez and /system/readyz.

Termination grace period¶

Get this arithmetic right or the kubelet will SIGKILL you mid-drain:

terminationGracePeriodSeconds  >  drain_grace_seconds + cleanup_timeout_seconds + slack

spec = AppSpec(
    service_name="orders",
    create_container=build_container,
    drain_grace_seconds=30.0,
    cleanup_timeout_seconds=10.0,
)

spec:
  terminationGracePeriodSeconds: 60   # 30 + 10 + slack

Exit codes¶

A crash never looks like a clean stop, which is what makes restartPolicy and Job retries behave.

Deployment¶

apiVersion: apps/v1
kind: Deployment
metadata:
  name: orders-service
spec:
  replicas: 3
  strategy:
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    metadata:
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8000"
        prometheus.io/path: /system/metrics
    spec:
      terminationGracePeriodSeconds: 60
      containers:
        - name: api
          image: registry.example.com/orders-service:1.4.0
          ports:
            - containerPort: 8000
          env:
            - name: LOGGING__LEVEL
              value: INFO
            - name: TRACING__COLLECTOR_URL
              value: http://otel-collector:4317
          livenessProbe:
            httpGet: { path: /system/health/livez, port: 8000 }
            periodSeconds: 10
          readinessProbe:
            httpGet: { path: /system/health/readyz, port: 8000 }
            periodSeconds: 5

With maxUnavailable: 0 and correct readiness behaviour, a rolling update drops no requests.

Batch jobs¶

An OneShotEntrypoint is a Kubernetes Job. It runs its function once, returns, and the whole service shuts down.

apiVersion: batch/v1
kind: Job
metadata:
  name: orders-migration
spec:
  backoffLimit: 3
  template:
    spec:
      restartPolicy: Never
      containers:
        - name: migrate
          image: registry.example.com/orders-service:1.4.0
          command: ["python", "-m", "orders.jobs.migrate"]

The function raising means a non-zero exit, so backoffLimit retries do what you expect. You still get warmup, observability and ordered cleanup — the same code path as the API.

Scaling workers separately¶

Same image, same AppSpec, different entrypoint lists:

# api.py
Service(build_spec(), entrypoints=[http])

# worker.py
Service(build_spec(), entrypoints=[cron, consumer])

containers:
  - name: api
    command: ["python", "-m", "orders.api"]
---
containers:
  - name: worker
    command: ["python", "-m", "orders.worker"]

Two Deployments with independent replica counts, resource requests and HPAs. Warmup, health, observability and shutdown stay identical because they come from the same spec.

Logging¶

Ship JSON to stdout and let your collector do the rest:

LoggingSettings(level="INFO", use_json=True)

Every line carries request_id (or job_id / run_id) automatically, so a single grep spans the API request and the work it triggered. See Request context.