If a CRI error occurs during the terminating phase after a pod is
force deleted (API or static) then the housekeeping loop will not
deliver updates to the pod worker which prevents the pod's state
machine from progressing. The pod will remain in the terminating
phase but no further attempts to terminate or cleanup will occur
until the kubelet is restarted.
The pod worker now maintains a store of the pods state that it is
attempting to reconcile and uses that to resync unknown pods when
SyncKnownPods() is invoked, so that failures in sync methods for
unknown pods no longer hang forever.
The pod worker's store tracks desired updates and the last update
applied on podSyncStatuses. Each goroutine now synchronizes to
acquire the next work item, context, and whether the pod can start.
This synchronization moves the pending update to the stored last
update, which will ensure third parties accessing pod worker state
don't see updates before the pod worker begins synchronizing them.
As a consequence, the update channel becomes a simple notifier
(struct{}) so that SyncKnownPods can coordinate with the pod worker
to create a synthetic pending update for unknown pods (i.e. no one
besides the pod worker has data about those pods). Otherwise the
pending update info would be hidden inside the channel.
In order to properly track pending updates, we have to be very
careful not to mix RunningPods (which are calculated from the
container runtime and are missing all spec info) and config-
sourced pods. Update the pod worker to avoid using ToAPIPod()
and instead require the pod worker to directly use
update.Options.Pod or update.Options.RunningPod for the
correct methods. Add a new SyncTerminatingRuntimePod to prevent
accidental invocations of runtime only pod data.
Finally, fix SyncKnownPods to replay the last valid update for
undesired pods which drives the pod state machine towards
termination, and alter HandlePodCleanups to:
- terminate runtime pods that aren't known to the pod worker
- launch admitted pods that aren't known to the pod worker
Any started pods receive a replay until they reach the finished
state, and then are removed from the pod worker. When a desired
pod is detected as not being in the worker, the usual cause is
that the pod was deleted and recreated with the same UID (almost
always a static pod since API UID reuse is statistically
unlikely). This simplifies the previous restartable pod support.
We are careful to filter for active pods (those not already
terminal or those which have been previously rejected by
admission). We also force a refresh of the runtime cache to
ensure we don't see an older version of the state.
Future changes will allow other components that need to view the
pod worker's actual state (not the desired state the podManager
represents) to retrieve that info from the pod worker.
Several bugs in pod lifecycle have been undetectable at runtime
because the kubelet does not clearly describe the number of pods
in use. To better report, add the following metrics:
kubelet_desired_pods: Pods the pod manager sees
kubelet_active_pods: "Admitted" pods that gate new pods
kubelet_mirror_pods: Mirror pods the kubelet is tracking
kubelet_working_pods: Breakdown of pods from the last sync in
each phase, orphaned state, and static or not
kubelet_restarted_pods_total: A counter for pods that saw a
CREATE before the previous pod with the same UID was finished
kubelet_orphaned_runtime_pods_total: A counter for pods detected
at runtime that were not known to the kubelet. Will be
populated at Kubelet startup and should never be incremented
after.
Add a metric check to our e2e tests that verifies the values are
captured correctly during a serial test, and then verify them in
detail in unit tests.
Adds 23 series to the kubelet /metrics endpoint.
In order to improve the observability of the cpumanager,
add and populate metrics to track if the combination of
the kubelet configuration and podspec would trigger
exclusive core allocation and pinning.
We should avoid leaking any node/machine specific information
(e.g. core ids, even though this is admittedly an extreme example);
tracking these metrics seems to be a good first step, because
it allows us to get feedback without exposing details.
Signed-off-by: Francesco Romani <fromani@redhat.com>
Because of a bug in the commit 1e7bb20c52,
podresources metrics were added, they are updated in the right
places, but they are never exported, so they cannot be consumed.
Fix trivially registering the metrics.
Signed-off-by: Francesco Romani <fromani@redhat.com>
Track how long it takes for pod updates to propagate from detection
to successful change on API server. Will guide future improvements
in pod start and shutdown latency.
Metric is `kubelet_pod_status_sync_duration_seconds` and is ALPHA
stability. Histogram buckets are chosen based on distribution of
observed status delays in practice.
Before the addition of GetAllocatableResources, the
podresources API had just one endpoint `List()`, thus we could just
account for the total of the calls to have a good pulse of the API usage.
Now that we extend the API with more endpoints
(`GetAlloctableResources`), in order to improve the observability we add
per-endpoint counters, in addition to the existing counter of the total
API calls.
Signed-off-by: Francesco Romani <fromani@redhat.com>
This PR changes the buckets of the
kubelet_runtime_operation_duration_seconds metric to be
metrics.ExponentialBuckets(.005, 2.5, 14) in order to
allow debugging image pull times. Right now the biggest bucket is 10
seconds, which is an ordinary time frame to pull an image, making the
metric useless for the aforementioned usecase.
