Kubernetes Patterns receives mostly positive reviews, with readers praising its engaging content, clear structure, and practical examples. Many find it valuable for understanding Kubernetes concepts and best practices. Some reviewers appreciate the book's approach to explaining patterns, while others feel certain "patterns" are just basic Kubernetes features. The book is recommended for developers and system administrators seeking deeper Kubernetes knowledge, though it may not be as useful for experienced practitioners. Overall, it's considered a helpful resource for building cloud-native applications.
Kubernetes: The Foundation for Cloud-Native Applications
Foundational Patterns: Building Blocks for Containerized Apps
Behavioral Patterns: Pod Management and Service Discovery
Structural Patterns: Organizing Containers within Pods
Configuration Patterns: Adapting Applications for Various Environments
Advanced Patterns: Extending Kubernetes and Managing Complex Workloads
Scaling and Building: Elastic Applications and In-Cluster Image Creation
Kubernetes is a container orchestration platform that forms the foundation of other platforms built on top of it.
Distributed primitives. Kubernetes introduces a new set of distributed primitives for building cloud-native applications. These include Pods (groups of containers), Services (for networking and load balancing), and various controllers for managing application lifecycle. These primitives provide a higher level of abstraction compared to traditional in-process building blocks, allowing developers to focus on application logic rather than infrastructure concerns.
Declarative approach. Kubernetes adopts a declarative model where developers specify the desired state of their applications, and the platform continuously works to maintain that state. This approach simplifies application management and enables automated healing and scaling. Key Kubernetes concepts include:
Containers: Packaged, isolated units of application code and dependencies
Pods: The smallest deployable units, consisting of one or more containers
Services: Stable network endpoints for accessing groups of Pods
Labels and Annotations: Metadata for organizing and selecting resources
Namespaces: Virtual clusters for resource isolation and multi-tenancy
To be fully automatable, a cloud-native application must be highly observable by allowing its state to be inferred so that Kubernetes can detect whether the application is up and whether it is ready to serve requests.
Predictable Demands. Applications should declare their resource requirements and runtime dependencies. This enables Kubernetes to make intelligent decisions about placement and scaling. Key aspects include:
Resource Profiles: Specifying CPU and memory requests and limits
Quality of Service (QoS) classes: Best-Effort, Burstable, and Guaranteed
Pod Priority: Indicating the relative importance of Pods
Declarative Deployment. Kubernetes provides mechanisms for updating applications with minimal downtime:
Rolling updates: Gradually replacing old Pods with new ones
Blue-Green deployments: Switching traffic between two versions
Canary releases: Gradually increasing traffic to a new version
Health Probes and Managed Lifecycle. Applications should implement health checks and respond to lifecycle events:
Liveness probes: Detecting if an application is running
Readiness probes: Determining if an application is ready to serve traffic
Lifecycle hooks: Responding to start and stop events
The Singleton Service pattern ensures only one instance of an application is active at a time and yet is highly available.
Job Management. Kubernetes provides abstractions for managing different types of workloads:
Batch Jobs: For running finite, completable tasks
Periodic Jobs (CronJobs): For scheduled, recurring tasks
Daemon Services: For running system-level services on every node
Stateful Services. Kubernetes offers StatefulSets for managing applications that require stable network identities and persistent storage:
Ordered deployment and scaling
Stable network identities
Persistent storage per Pod
Service Discovery. Kubernetes provides multiple mechanisms for service discovery:
ClusterIP Services: For internal communication
NodePort and LoadBalancer Services: For external access
Ingress: For HTTP-based routing and load balancing
A Sidecar container extends and enhances the functionality of a preexisting container without changing it.
Multi-Container Pods. Kubernetes allows multiple containers to be grouped into a single Pod, enabling various patterns:
Init Containers: For initialization tasks before the main container starts
Sidecars: For adding functionality to the main container
Adapters: For standardizing output from heterogeneous applications
Ambassadors: For proxying communication with external services
These patterns promote separation of concerns, modularity, and reusability in application design. They allow developers to compose complex applications from simpler, single-purpose containers while leveraging the shared context and resources provided by the Pod abstraction.
ConfigMaps and Secrets allow the storage of configuration information in dedicated resource objects that are easy to manage with the Kubernetes API.
Externalized Configuration. Kubernetes provides several mechanisms for managing application configuration:
Environment Variables: For simple key-value pairs
ConfigMaps: For non-sensitive configuration data
Secrets: For sensitive information (e.g., passwords, API keys)
Immutable Configuration. To ensure consistency across environments, configuration can be packaged into immutable container images:
Configuration containers: Dedicated images for storing configuration data
Init containers: For copying configuration into shared volumes
Configuration Templates. For complex configurations that differ slightly between environments:
Template processing: Using tools like Gomplate to generate configuration files
Init containers: For processing templates during Pod initialization
These patterns enable developers to separate configuration from application code, promoting portability and reducing the risk of environment-specific issues.
An operator is a Kubernetes controller that understands two domains: Kubernetes and something else. By combining knowledge of both areas, it can automate tasks that usually require a human operator that understands both domains.
Controllers and Operators. Kubernetes can be extended to manage complex applications:
Controllers: For implementing custom behavior based on resource changes
Operators: For encoding domain-specific knowledge and automating complex operations
Custom Resource Definitions (CRDs): For defining new resource types
Frameworks and Tools. Several projects facilitate the development of controllers and operators:
Operator Framework: For building Go-based operators
Kubebuilder: For scaffolding and managing operator projects
Metacontroller: For implementing operators in any language
These patterns enable developers to extend Kubernetes' functionality and automate complex, application-specific operations within the cluster.
Autoscaling in Kubernetes allows us to define a varying application capacity that is not fixed but instead ensures just enough capacity to handle a different load.
Elastic Scale. Kubernetes provides multiple mechanisms for scaling applications:
Horizontal Pod Autoscaling (HPA): Adjusting the number of Pod replicas
Vertical Pod Autoscaling (VPA): Modifying resource requests and limits
Cluster Autoscaling: Adding or removing nodes based on resource demand
Image Building. Kubernetes can be used to build container images within the cluster:
OpenShift Build: An integrated system for building images using various strategies
Knative Build: A Kubernetes-native building framework
Daemonless builds: Tools like img, buildah, and Kaniko for rootless image creation
These patterns enable developers to create elastic, self-adjusting applications and streamline the image building process by leveraging Kubernetes' scheduling and resource management capabilities.