control theory + declarative API = Kubernetes

Presented at Automaatiopäivät 2023: https://www.automaatioseura.fi/automaatiopaivat2023/

Transcript

1. control theory + declarative API = Kubernetes
   Lucas Käldström - CNCF Ambassador
   March 28, 2023 – Helsinki
   Image credit: CNCF
   

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2. © 2023 Lucas Käldström
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   $ whoami
   Lucas Käldström, 1st-year MSc student at Aalto University, Finland
   CNCF Ambassador, Certified Kubernetes Administrator
   and Former Kubernetes maintainer
   KubeCon Speaker in Berlin, Austin, Copenhagen,
   Shanghai, Seattle, San Diego & Valencia
   KubeCon Keynote Speaker in Barcelona
   kubeadm, Weave Ignite & libgitops co-author, Entrepreneur
   Cloud Native Nordics co-founder & meetup organizer
   Guild of Automation and Systems Technology Head of Corporate Relations
   

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   est. 2014
   

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   Based on decades of experience at Google
   

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   Fast-forward 7 years, has 75000+ contributors
   Cloud Native Computing Foundation is a non-profit under Linux Foundation
   CNCF hosts 153 “cloud native” projects under it’s vendor-neutral umbrella
   Kubernetes was the first project donated to CNCF by Google in 2015
   It is one of the top 10 most actively developed open source projects
   

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   Let’s start by defining it
   

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   A Container Orchestrator? Yes
   

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   A Container Orchestrator? Yes
   But in fact, even more than that
   

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   Kubernetes: A Control Plane for (any) infrastructure
   

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    Kubernetes: A Control Plane for (any) infrastructure
    = A set of automated controllers with operational
    knowledge of how to control a target system
    

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11. Not: Humans Operating Servers
    

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12. Instead: Humans Operating Automation,
    that in turn Operate Servers
    

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13. © 2023 Lucas Käldström
    13 Credits to Simon Sinek
    

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    Run anywhere
    Self-healing
    Scalable
    workload
    scheduling
    Service discovery
    + config mgmt
    What?
    

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    Specify once; Kubernetes makes your dream true
    JSON
    container
    workload
    specification
    REST API
    server
    HTTP POST JSON object
    Container
    Workload
    Controller
    read
    desired
    state
    *The process doesn’t look exactly like this, it is a simplified mental model for now
    pull
    start
    re-start
    monitor
    

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16. © 2023 Lucas Käldström
    16 Credits to Simon Sinek
    

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    Run anywhere
    Self-healing
    Scalable
    workload
    scheduling
    Service discovery
    + config mgmt
    How?
    Closed-loop controllers Uniform, declarative and extensible API
    

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    Sysadmin Servers/Applications
    controls
    business value
    (e.g. webservice)
    business
    objective
    narrative
    Classic system administration (Imperative)
    e.g. “start 5 virtual machines through GUI”
    

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    Sysadmin Servers/Applications
    controls
    business value
    (e.g. webservice)
    Monitoring dashboard
    -
    business
    objective
    narrative
    (desired state
    in PDF)
    +
    human
    diff
    actual state
    Classic system administration (Declarative)
    e.g. “start 2 static web server processes per VM, send alerts via email if CPU exhausted”
    

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    Controller Servers/Applications
    controls
    business value
    (e.g. webservice)
    Monitoring data
    -
    declarative
    business
    objective
    (desired state
    in JSON)
    +
    programmatic
    diff
    actual state
    System Administration by Kubernetes Operators
    REST API
    desired state
    REST API
    actual state
    e.g. “I want at least 10 web servers always running, scale up to 20 if load is high. I
    don’t care where the web servers run as long as they are reachable in this subnet”
    

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    declarative
    business
    objective
    (desired state
    in JSON)
    Notice that desired and actual state are separated
    REST API
    desired state
    REST API
    actual state
    REST API is
    uniform, i.e.
    all resources
    has the same
    structure:
    kind,
    apiVersion,
    metadata,
    spec,
    Status
    REST API is
    extensible,
    you can add
    your own
    objects freely
    kind: Pod # What kind of object is this?
    apiVersion: v1 # What schema version is used?
    metadata: # Metadata about this object
    name: my-pod
    labels:
    app: web
    spec: # Desired state, set by user
    containers:
    - image: nginx:1.23
    ports:
    - containerPort: 80
    status: # Actual, observed state, set by controller
    conditions:
    - type: Ready
    status: "True"
    lastTransitionTime: 2018-01-01T00:00:00Z
    

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    controllers + extensible API = abstraction layer
    

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    Kubernetes is a “platform for platforms”
    Platform A Platform B
    Platform C
    Platform D
    

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    Kubernetes is a “platform for platforms”
    Platform A Platform B
    Platform C
    Platform D
    

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    Let’s say I create a “Workload” controller
    with the following API:
    kind: Workload # What kind of object is this?
    apiVersion: luxas.dev/v1 # What schema version is used?
    metadata: # Metadata about this object
    name: my-workload
    spec: # Desired state, set by user
    type: VM
    osImage: https://luxas.dev/ubuntu-2204
    status: # Actual, observed state, set by controller
    phase: Running
    bootTime: 2023-03-28T00:00:00Z
    

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    Workload
    Controller
    Target Servers
    controls
    business value,
    running workloads
    Inspect server state
    -
    +
    programmatic
    diff
    actual state
    Controller implemented as:
    Workload
    spec
    Workload
    status
    

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    I need to make two new controllers:
    1) a replicated Workload, which creates multiple
    workloads based on a template
    2) a “Job” workload, which runs until completion
    

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    But I don’t want to duplicate the implementation of
    the Workload controller!
    

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    Workload
    Controller
    Target
    Servers
    running
    workloads
    Inspect server state
    -
    +
    Workload
    API object
    spec+status
    ReplicatedWorkload
    Controller
    Observe
    status
    -
    +
    ReplicatedWorkload
    spec
    ReplicatedWorkload
    status
    JobWorkload
    Controller
    Observe
    status
    -
    +
    JobWorkload
    spec
    JobWorkload
    status
    create “child” Workload object
    create “child” Workload object
    

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    Now the declarative implementation of controlling
    the Workload can be re-used and built upon!
    

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    This forms a loosely coupled microservice
    architecture!
    REST API server
    JobWorkload Workload ReplicatedWorkload
    JobWorkload
    controller
    ReplicatedWorkload
    controller
    Workload
    controller
    declare intent declare intent
    

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    Run anywhere
    Self-healing
    Scalable
    workload
    scheduling
    Service discovery
    + config mgmt
    How?
    Closed-loop controllers Uniform, declarative and extensible API
    

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    Kubernetes: A Control Plane for (any) infrastructure
    

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34. Not: Humans Operating Servers
    

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35. Instead: Humans Operating Automation,
    that in turn Operate Servers
    

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36. Summary
    Baim Hanif on Unsplash
    Thank you!
    @luxas on Github
    @luxas on LinkedIn
    @luxas on SpeakerDeck
    @kubernetesonarm on Twitter
    lucas@luxaslabs.com
    

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