Distributed system such as Kubernetes are designed to be resilient to the failures. More details about Kubernetes High-Availability (HA) may be found at Building High-Availability Clusters
To have a simple view the most of parts of HA will be skipped to describe Kubelet<->Controller Manager communication only.
By default the normal behavior looks like:
-
Kubelet updates it status to apiserver periodically, as specified by
--node-status-update-frequency
. The default value is 10s. -
Kubernetes controller manager checks the statuses of Kubelets every
–-node-monitor-period
. The default value is 5s. -
In case the status is updated within
--node-monitor-grace-period
of time, Kubernetes controller manager considers healthy status of Kubelet. The default value is 40s.
Kubernetes controller manager and Kubelets work asynchronously. It means that the delay may include any network latency, API Server latency, etcd latency, latency caused by load on one's master nodes and so on. So if
--node-status-update-frequency
is set to 5s in reality it may appear in etcd in 6-7 seconds or even longer when etcd cannot commit data to quorum nodes.
Kubelet will try to make nodeStatusUpdateRetry
post attempts. Currently
nodeStatusUpdateRetry
is constantly set to 5 in
kubelet.go.
Kubelet will try to update the status in
tryUpdateNodeStatus
function. Kubelet uses http.Client()
Golang method, but has no specified
timeout. Thus there may be some glitches when API Server is overloaded while
TCP connection is established.
So, there will be nodeStatusUpdateRetry
* --node-status-update-frequency
attempts to set a status of node.
At the same time Kubernetes controller manager will try to check
nodeStatusUpdateRetry
times every --node-monitor-period
of time. After
--node-monitor-grace-period
it will consider node unhealthy. It will remove
its pods based on --pod-eviction-timeout
Kube proxy has a watcher over API. Once pods are evicted, Kube proxy will notice and will update iptables of the node. It will remove endpoints from services so pods from failed node won't be accessible anymore.
If -–node-status-update-frequency
is set to 4s (10s is default).
--node-monitor-period
to 2s (5s is default).
--node-monitor-grace-period
to 20s (40s is default).
--pod-eviction-timeout
is set to 30s (5m is default)
In such scenario, pods will be evicted in 50s because the node will be
considered as down after 20s, and --pod-eviction-timeout
occurs after
30s more. However, this scenario creates an overhead on etcd as every node
will try to update its status every 2 seconds.
If the environment has 1000 nodes, there will be 15000 node updates per minute which may require large etcd containers or even dedicated nodes for etcd.
If we calculate the number of tries, the division will give 5, but in reality it will be from 3 to 5 with
nodeStatusUpdateRetry
attempts of each try. The total number of attemtps will vary from 15 to 25 due to latency of all components.
Let's set -–node-status-update-frequency
to 20s
--node-monitor-grace-period
to 2m and --pod-eviction-timeout
to 1m.
In that case, Kubelet will try to update status every 20s. So, it will be 6 * 5
= 30 attempts before Kubernetes controller manager will consider unhealthy
status of node. After 1m it will evict all pods. The total time will be 3m
before eviction process.
Such scenario is good for medium environments as 1000 nodes will require 3000 etcd updates per minute.
In reality, there will be from 4 to 6 node update tries. The total number of of attempts will vary from 20 to 30.
Let's set -–node-status-update-frequency
to 1m.
--node-monitor-grace-period
will set to 5m and --pod-eviction-timeout
to 1m. In this scenario, every kubelet will try to update the status every
minute. There will be 5 * 5 = 25 attempts before unhealty status. After 5m,
Kubernetes controller manager will set unhealthy status. This means that pods
will be evicted after 1m after being marked unhealthy. (6m in total).
In reality, there will be from 3 to 5 tries. The total number of attempt will vary from 15 to 25.
There can be different combinations such as Fast Update with Slow reaction to satisfy specific cases.