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2019

Kubernetes Short Notes(3)

  • Ops

Cluster Maintainance

OS Upgrade

Pod Eviction Timeout

When the nodes was down for more than 5 minute(default) then the pods are terminated; pod will recreate if has replicaset

Drain, Cordon, Uncordon

We’re not sure the node will come back online in 5 minutes, therefore we can drain the node.

After the drained node upgraded and come back, it still unschedulable, uncordon the node to make it schedulable.

Note that the previouse pods won’t be automatically reschedule back to the nodes.

Cluster Upgrade

The core control plane components’s version can be different, but should follow certain rules:

  • the kube-api is the primary component, none of the other components’s version must not be higher than the kube-api
  • the components can be lower in 1-2 versions
    • kube-api: x
    • Controlloer-manager, kube-scheduler: x, x-1
    • kubelet, kube-proxy: x, x-1, x-2
  • the kubectl can be one version higher than kube-api: x+1, x, x-1

The kubernetes support only up to the recent 3 minor versions. The recommanded approch is to update one minor version at a time.

Update the cluster depend on how you deploy them:

  • cloud provider: few clicks at the UI
  • kubeadm: using upgrade argument (you should upgrade the kubeadm first!)
  • the hard way from scratch: manually upgrade components by yourself

Two major steps:

  1. upgrade master node, the control plane componets goes down, all management function are down, only the applications deploy on worker nodes keeps serving
  2. update worker nodes, with strategies:
    • upgrade all at once with downtimes
    • upgrade one at a time
    • create new nodes and remove the workloads, then finally remove old nodes

When you run command like kubectl get nodes, the VERSION is indicat the version of the kubelet

Backup and Restore

Master / Node DR

  • Cordon & drain
  • Provision replacement master / node

ETCD DR

Option: Backup resources

Saving objects as a copy by query the kube-api

Option: Backup ETCD

Making copies of the ETCD data directory

Or use the etcd command line tool

  1. Make a snap shot

    Remember to specify the certification files for authentication
  2. Stop kube-api
  3. Restore snapshot

    When ETCD restore from a backup, it initialize a new cluster configuration and configures the members of ETCD as new members to a new cluster. This is to prevent a new member from accidentally joining an existing cluster.
    For example, using a snapshot to provision a new etcd-cluster from testing purpose. You don’t want the members in the new test cluster to accidentally join the production cluster.

  4. Configure the etcd.service with new data directory and new cluster token

    During a restore, you must provide a new cluster token and the same initial cluster configuration

  5. Restart ETCD service
  6. Start kube-api

Persistant Volume DR

You can’t relay on kubernetes to for backing up and restore persistant volumes.

If you’re using cloud provider specific persistant volumes like EBS volumes, Azure managed disks or GCE persistent disks, you should use cloud provider snapshot APIs

Read More »Kubernetes Short Notes(3)

無處可逃

  • Quote

意思是，當達碧還在年高中時，癌細胞就在她媽媽體內生長了。當她說謊解釋皮包裡為什麼會有一張破損的西爾斯百貨標籤時，癌細胞就在那了；當她嗑了劣質搖頭丸，手腕上戴著綠色螢光手環，在凌晨三點昏昏沉沉開車回家，噙著淚水的媽媽在門廊上大罵她是賤貨時，癌細胞就在那了。那個看不見的傢伙始終棲息在媽媽肩膀上，偷聽她們說話。媽媽一在慢慢死去，但兩人都毫無所覺。

珊蒂似乎很喜歡把他當孩子照顧，彷彿這樣才能給自己恨他的理由。

Kubernetes Short Notes (2)

  • Ops

Scheduling

Manual Scheduling

  • Bind the pod to node by nodeName property, before that, the pod stays in the Pending state

  • Manutal ways to bind:

    • specify the spec.nodeName, not updatable

    • create the Binding object

Labeling

Use to group and select the objects, for example a ReplicaSet object configs:

  • metadata.labels sets the ReplicaSet itself
  • spec.template.metadata.lables sets the Pod
  • spec.selector.matchLabels defines how ReplicaSet to discover the Pod

Annotations

Use to record other details for intergration purpose e.g. build info, contact details

Restriction

Taint/Toleration

Limit pods without tolerations cannot get scheduled to a tainted node

  • Taint the nodes

  • Set the pods’ tolerance, three behavior are avaliable if not tolerant:

    • NoSchedule

  • PreferNoSchedule: not guaranteed

  • NoExecute: new pods=NoSchedule, existed pods=evicted

Note the value in tolerations keys must use double quotes

Node Selector

Limit the pod to get scheduled to one kind of node only

  • Lable the node
  • Set the nodeSelector

Note there is no OR or NOT conditions, use node affinity instead

Node Affinity

Limit the pod to get scheduled to one or more particular nodes

  • Lable the node
  • Set the nodeAffinity
  • operators: In, NotIn, Exists, DoesNotExist, Gt, Lt
  • 3 types

Combines the Taint/Toleration with NodeSelector or NodeAffinity to cover the scenarios

Resources

Request

  • The scheduling base on the resource requests
  • By default, k8s assumes a pod requires 0.5 cpu and 256Mi memory

Limit

  • By default, k8s limit a pod to 1 cpu and 512Mi memory
  • When a pod try to exceed resources beyond the limit
    • cpu: k8s throttles the cpu won’t kill
    • memory: k8s kill the pod with OOM

Static Pods

Use in creating control plane components (kube admin tools)

Without the intervention from the kube-api server, the kubelet can manage a node independently by monitor config files in the file system, and be able to create, recreate, update and delete the POD only object

  • –pod-manifest-path=/etc/Kubernetes/manifest
  • –config=kubeconfig.yaml (staticPodPath)

While the static pod created, the kube-api only get a readable mirror and not have the ability to update/delete it

Multiple Scheduler

  • copy the kube-scheduler configs from /etc/kubernetes/manifests
  • rename the scheduler --scheduler-name
  • if one master nodes with multiple scheduler:
    • set the --leader-elect=false
  • if multiple masters with multiple scheduler, only one scheduler can active at a time
    • set the --leader-elect=true
    • set the --lock-object-name to differentiate the custom scheduler from default if multiple master
  • specify the scheduler for pod by schedulerName
Read More »Kubernetes Short Notes (2)

Kubernetes Short Notes (1)

  • Ops

Cluster Architecture

Master Node

  • ETCD cluster
  • kube-scheduler
  • kube-controller-manager

These components communicate via kube-api server

Worker Node

  • container runtime engine, e.g. Docker, Rocket, ContainerD
  • kubelet: agent that runs and listen for instructions from kube-api
  • containers

The services deploy within worker nodes communicate with each other via kube-proxy

Objectives

ETCD

  • a distributed reliable key-value store
  • client commuications on port 2379
  • server to server on port 2380

kube-api

  • primary management component

  • setup:

    1. using kube-admin tools

      • deploy kube-api as a pod in kube-system namespace

      • the manifests is at /etc/kubernetes/manifests/kube-apiserver.yaml

      • the options is at /etc/systemd/system/kube-apiserver.service

      • search for kube-apiserver process on master node

  • example: apply deployment using kubectl

    1. authenticates user

  • validate the HTTP requests

  • the kube-scheduler monitored the changes from the kube-api, then:

    • retrieve the node information from kube-api

  • schedule the pod to some node through kube-api to kubelet

  • update the pod info to ETCD

    • kube-controller-manager

    • continuously monitors the state of components
    • the controllers packages into a single process called Kube-Controller-Manager, which includes:
      1. deployment-controller, cronjob, service-account-controller …
      2. namespace-controller, job-contorller, node-controller …
      3. endpoint-controller, replicaset, replication-controller(replica set) …
    • remediate situation

    kube-scheduler

    • decide which pod goes to which node
      1. filter nodes
      2. rank nodes

    kubelet

    • follow the instruction from kube-scheduler to controll the container runtime engine (e.g. docker) that run or remove a container
    • using kube-admin tools to deploy cluster, the kubelet are not installed by default in worker nodes, need intstall manually

    kube-proxy

    • runs on each nodes in the cluster
    • create iptables rules on each nodes to forward traffic heading to the IP of the services to the IP of the actual pods
    • kube-admin tool deploy kube-proxy as daemonset in each nodes

    pod

    • the container are encapsulated into a pod
    • is a single instance of an application, the smallest object in k8s
    • containers in same pod shares storages and network namespaces, created and removed in the same time
    • multi-container pod is rare use case

    ReplicationController

    • apiVersion support in v1
    • the process to monitor the pods
    • maintain the HA and specified number of pods that running on all nodes
    • only care about the pod which RestartPolicy is set to Always
    • scalable and replacable application should be managed by the controller
    • use cases: rolling updates, multiple release tracks (multiple replication controller replica the same pod but using different labels)

    ReplicaSets

    • next generation of ReplicationController
    • api version support in apps/v1
    • enhance the filtering in .spec.selector (the major difference)
    • be aware of the non-template pod that has same lables
    • using Deployment as a replacement is recommended, it own and manage its ReplicaSets

    Deployment

    • provide replication vis replicaset and other:
      • rolling update
      • rollout
      • pause and resume

    Namespace

    • namespaces created at cluster creation

      1. kube-system

    • kube-public

    • default

    • each namespace can be assigned quota of resources

    • a DNS entry with SERVICE_NAME.NAMESPACE.svc.cluster.local format is automatically created when at service creation

      1. the cluster.local is the default domain name of the cluster

    • permanently config the namespace

    Read More »Kubernetes Short Notes (1)

    Generator as Coroutines

    • Python

    Generator as Coroutines

    • cooperative multitasking (cooperative routines)
    • concurrent not parallel (python program execute on a single thread)

    The way to create coroutines:

    • generators (asyncio)
    • native coroutines (using async /await)

    Concepts

    • concurrency: tasks start, run and complete in overlapping time periods
    • parallelism: tasks run simultaneousely

    image

    • cooperative: control relinquished to other task voluntarily, control by application(developer)
    • preemptive: control relinquished to other task involuntarily, control by the OS.

      some sort of scheduler involved

    image

    • Global Interpreter Lock(GIL)

      Only one native thread excutes at a time.

      Use Process based parallelism to avoid GIL. Not Thread based.

      The Python threading module uses threads instead of processes. Threads uniquely run in the same unique memory heap. Whereas Processes run in separate memory heaps. This makes sharing information harder with processes and object instances. One problem arises because threads use the same memory heap, multiple threads can write to the same location in the memory heap which is why the global interpreter lock(GIL) in CPython was created as a mutex to prevent it from happening.

    Make the right choice

    • CPU Bound => Multi processing
    • I/O Bound, Fast I/O, Limit Connections => Muilti Threading
    • I/O Bound, Slow I/O, Many Connections => Concurrency

    Use deque

    Much more efficient way to implement the stack and queue.

    Operate 10,000 items take 1,000 times average:

    (times in seconds) list deque
    append(right) 0.87 0.87
    pop(right) 0.002 0.0005
    insert(left) 20.8 0.84
    pop(left) 0.012 0.0005

    Use unlimited deque with deque() or deque(iterable)
    Use limited deque with deque(maxlen=n). If full, a corresponding number of items are discarded from the opposite end.

    Implement producer / consumer coroutine using deque

    Implement simple event loop

    Read More »Generator as Coroutines

    Context Manager

    • Python

    Context Manager

    what is context

    the state surrounding a section of code

    why we need a context manager

    • writing try/finally every time can get cumbersom
    • easy to forget closing the file

    use cases

    Useful for program that needs Enter / Exit handeling

    • create / releasing resources
    • database transaction
    • set and reset decimal context

    Common patterns

    • open / close
    • lock / release
    • change / reset
    • start / stop
    • enter / exit

    protocal

    implement these two dunder methods:

    • __enter__

      perform the setup, optionally return an object

    • __exit__

      receives error (silence or propagate)

      • need arguments exc_type, exc_value, exc_trace to handle exception
      • return True to silence exception

      perform clean up

    examples

    contextlib

    nested contexts