Completek8s Classroom notes 04/Nov/2023

Kubernetes Patterns

Foundational Patterns

• The patterns describe here will help as foundational building blocks of distributed container based k8s native applications
  • Predictable Demands
  • Declarative Deployment
  • Health Probe
  • Managed Lifecycle
  • Automated Placement

Predictable Demands

• This pattern indicates how we should declare application requirements (hard runtime dependencies or resource requirements)

Problem

• Kubereneces can manage applications developed in different programming languages as long as they are containerized.
• Different languages have different resource requirements
• Besides resource requirements, application runtimes also have dependencies on platform managed capabilities like data storage or application configuration.

Solution

• Figure out dependencies
  • Dependency on a Persistent Volume
  • Dependency on container port to be mapped to specific port on the host system through hostPort
  • Configurations are another type of dependency
• Resource Profiles:
  • Compute resources in the contenxt of k8s are defined as something that can be request by, allocated to and consumed from a container. The resources are categorized as
    • compressible (such as cpu, network bandwidth) can be throttled
    • incompressible (such as memory) cannot be throttled
  • Ensure we have request limits specified
• In request and limits section we can use the following keys
  • memory:
    • This type is for heap memory demands of your application including volumes of type emptyDir with configuration medium: Memory
  • cpu: It is recommended that you set request for the CPU resource but no limits so that they can benifit from all excess CPU resources that otherwise would be wasted.
  • ephemeral-storage
  • hugepage-size
• Depending on Whether you specify request, the limits or both, the platform offers three types of Quality of Service (QoS)
  • Best-Effort: request or limits not specified
  • Burstable: Pods which have unequal amount of requests and limits are tagged as burstable
  • Guaranteed: Pods which have same request and limit resources belongs to Guranteed Qos
• In the case when node is under incompressible resource pressure. The priority of killing is
  • Best-Effort
  • Burstable
  • Guranteed
• Recommendation
  • For memory always set request equal to limits
  • For CPU set request but no limits
• Refer Here for interesting article on why CPU should not have limits Refer Here for recommended memory settings
• PodPriority allows us to indicate the importance of Pod relative to other pods which affects the order in which pods are schedule
• Refer Here this for official k8s docs
• Project Resources: Use Resource quotas
• Refer Here for Limit range

Declarative Deployment

Problem

• Provisioning isolated environments as namespaces in a self service manner and place the application in these environments with minimal human intervention through scheduler.
• But with a growing number of microservices, continuously updating and replacing them with newer versions becomes an increasing burden.

Solution

• Using concept opf deployment has automated application upgrades.
• A Deployment can be fully managed by updating the k8s resources files howeever kubectl rollout comes in very handy
  • kubectl rollout status
  • kubectl rollout pause
  • kubectl rollout resume
  • kubectl rollout undo
  • kubectl rollout history
  • kubectl rollout restart
• Rolling Deployment: This ensures zero downtime deployment during update
• FixedDeployment: Recreate
• Blue Green: Refer Here
• Canary: Refer Here

Health Probe

Problem

• k8s regularly checks the container porcess status and restarts it if issues are detected.
• This is not enough and we need reliable way to check the health of applications

Solution

• Process Health Checks
• Liveness Probes
• Readiness Probes
• Readiness Gates: Refer Here Refer Here

Managed Lifecycle

• Refer Here here for life cycle and Refer Here

Automated Placements

• This is about where to place new pods
  • nodeSelctor
  • node Affinity
  • Pod Affinitiy and Anti Affinity
  • Taints and Tolerations

Behavioral Patterns

• These patterns are focused on communciations and interactions between Pods and the managing Platoform
  • Batch Job
  • Periodic Job
  • Daemon Service
  • Singleton Service
  • Stateless Service
  • Stateful Service
  • Service Discovery (with service mesh)
  • Self Awareness (with service mesh)

Singleton Service

Problem

• k8s can easily scale applications. In some cases, only one instance of the service is allowed to run at a time

Solution

• This can be implemented by locking
• PodDisruptionBudget

Structural Patterns

• These patterns are focused on structuring and organizing containers in Pod to satisfy different usecases
  • Init Containers
  • Sidecar
  • Adapter
  • Ambassador

Adapter

Problem

• Containers allows us to package and run applications written in different languages in a unified way.
• This heterogenous components can cause difficulties when all components have to treated in a unified way by other systes.

Solution

• Adapter pattern offers a solution by hiding the complexity of system and providing a unified access to it
  

Ambassador

Problem

• Containerized services dont exist in isolation an very often they have to access other services.
• The difficulty in acecssing other services may be due to changing address, unreliable protocol etc

Solution