The Evolution of Distributed Systems on Kubernetes Bilgin Ibryam - - PowerPoint PPT Presentation

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the evolution of distributed systems on kubernetes
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The Evolution of Distributed Systems on Kubernetes Bilgin Ibryam - - PowerPoint PPT Presentation

Jan 22, 2023 111 likes •675 views

The Evolution of Distributed Systems on Kubernetes Bilgin Ibryam Product Manager @RedHat @bibryam 1 Bilgin Ibryam Product Manager at Red Hat Former Architect/Consultant Committer at Apache Camel Author of Camel Design

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Bilgin Ibryam Product Manager @RedHat @bibryam

The Evolution of Distributed Systems on Kubernetes

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@bibryam

Bilgin Ibryam

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  • Product Manager at Red Hat
  • Former Architect/Consultant
  • Committer at Apache Camel
  • Author of “Camel Design Patterns” and

“Kubernetes Patterns” books

  • Latest interest: cloud native data

@bibryam

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What comes after Microservices?

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@bibryam

Agenda

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  • Distributed system needs
  • Monolithic architectures
  • Cloud-native technologies

■ Kubernetes, Istio, Knative, Dapr

  • Future architecture trends
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@bibryam

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Modern distributed applications

  • 100s of components and 1000s of instances
  • Polyglot, independent, and automatable components
  • Hybrid workloads on hybrid environments
  • Open source, open standards, and interoperable
  • Based on Kubernetes ecosystem
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What are the needs of distributed applications?

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@bibryam

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Distributed application needs

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@bibryam

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Distributed application needs

Lifecycle management

  • Deployment/rollback
  • Placement/scheduling
  • Configuration management
  • Resource/failure isolation
  • Auto/manual scaling
  • Hybrid workloads (stateless, stateful,

serverless, etc)

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@bibryam

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Distributed application needs

Advanced networking

  • Service discovery and failover
  • Dynamic traffic routing
  • Retry, timeout, circuit breaking
  • Security, rate limiting, encryption
  • Observability and tracing
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@bibryam

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Distributed application needs

Resource bindings

  • Connectors for APIs
  • Protocol conversion
  • Message transformation
  • Filtering, light message routing
  • Point-to-point, pub/sub interactions
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@bibryam

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Distributed application needs

Stateful abstractions

  • Workflow management
  • Temporal scheduling
  • Distributed caching
  • Idempotency
  • Transactionality (SAGA)
  • Application state
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Monolithic architectures

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@bibryam

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Traditional middleware capabilities

  • Stateful primitives
  • Resource bindings
  • Networking
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@bibryam

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Traditional middleware limitations

  • Lifecycle management

○ Single, shared language runtime ○ Manual deployment/rollback ○ Manual placement ○ Manual scaling ○ No resource/failure isolation

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Cloud-native architectures

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@bibryam

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Microservices and Kubernetes

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@bibryam

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Microservices and Kubernetes

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@bibryam

Health probes

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@bibryam

Managed start/stop

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@bibryam

Declarative deployment

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@bibryam

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Demands & placement

Predictable resource demand Automated placement

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@bibryam

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Configuration management

  • ConfigMaps used in Pods as:

○ environment variables ○ volumes

  • Secrets:

○ Minimal Node spread ○ Only stored in memory in a tmpfs ○ Encrypted in the backend store (etcd) ○ Access can be restricted with RBAC

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@bibryam

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Foundational kubernetes capabilities

More Kubernetes Patterns

  • Foundational patterns
  • Structural patterns
  • Configuration patterns
  • Behavioural patterns

(For more Kubernetes Patterns, check out the link at the end of the slides)

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@bibryam

Batch/Periodic Job

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Hybrid workloads

Global Singleton Stateful Service Stateless Service

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@bibryam

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Lifecycle capabilities

  • Deployment/rollback
  • Placement/scheduling
  • Configuration management
  • Resource/failure isolation
  • Auto/manual scaling
  • Hybrid workloads: stateless, stateful,

batch jobs, serverless

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How do we extend Kubernetes?

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@bibryam

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Out-of-process extension mechanism

Deployment guarantees Lifecycle guarantees

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@bibryam

Sidecar

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@bibryam

Controller Pattern

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Default schema

  • ReplicaSet
  • StatefulSet
  • Job, CronJob

Default controllers

  • replicaset
  • statefulset
  • job, cronjob

Managed resources state

  • Pod
  • PVC...

Custom controller -> Custom behaviour

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@bibryam

Operator Pattern

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kind: ConfigWatcher apiVersion: k8spatterns.io/v1 metadata: name: webapp-config-watcher spec: configMap: webapp-config podSelector: app: webapp

Custom operator

  • Go
  • Helm
  • Ansible
  • Java
  • Python

Custom application

  • AI/ML
  • Big Data
  • Storage
  • Streaming
  • Monitoring

CustomResourceDefinition + Controller = Operator

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Kubernetes based platforms

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@bibryam

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What is Service Mesh?

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@bibryam

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What is Service Mesh?

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@bibryam

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What is Service Mesh?

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@bibryam

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What is Service Mesh?

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Networking capabilities

API Gateway Service Mesh

Abstract away details and decouple consumers from implementations

  • Controls what’s allowed in/out
  • Bridging security domains
  • Request / response transformation
  • Protocol, data format transformation
  • API composition
  • Rate limiting

Enhances the reliability and the visibility of the networking interactions

  • Telemetry, tracing collection
  • Service discovery, load balancing
  • TLS termination/origination
  • Request routing, traffic splitting
  • Traffic shadowing
  • Rate limiting
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@bibryam

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What is Knative?

Serving Common infrastructure for request-driven interactions that can "scale to zero". Eventing Common infrastructure for consuming and producing events declaratively.

Kubernetes-based platform to deploy, and manage serverless workloads.

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Knative Serving concepts

  • Scale-to-zero & activation
  • Rapid autoscaling
  • Traffic splitting
  • Callable by Knative eventing
  • Simplified deployment model

○ Single Port ○ No PersistentVolumes ○ Single Container

apiVersion: serving.knative.dev/v1alpha1 kind: Service metadata: name: lotto spec: replicas: 1 selector: matchLabels: app: lotto template: metadata: labels: app: lotto spec: containers:

  • image: cds19/lotto
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@bibryam

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Knative Eventing concepts

  • Sources (Kafka, CronJob, Apache Camel 200+, etc)
  • Broker implementations (In-memory, Kafka, etc)
  • CloudEvents data format
  • Trigger with filters
  • Sequence: chaining multiple steps composed of

containers

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@bibryam

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Lifecycle, networking, binding capabilities

  • Knative Serving

○ Simplified deployment for stateless workloads ○ Traffic based autoscaling including Scale-to-Zero ○ Traffic splitting for custom rollout / rollback scenarios

  • Knative Eventing

○ External triggers for feeding Knative Services ○ Based on CloudEvents ○ Backed by proven messaging systems ○ Declarative messaging infrastructure

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What is Dapr?

Sidecar architecture Developer first, standard APIs used from any programming language or framework. Building blocks Make it easy for developers to create microservice without being an expert in distributed systems.

A portable runtime for building distributed applications.

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Dapr building blocks

Distributed Tracing See and measure the message calls across components and networked services Service Invocation Act as a reverse proxy with built-in service discovery, tracing and error handling Publish & Subscribe Secure, scalable messaging between services Resource Bindings Trigger code through events from input and output bindings to external resources. Actors Encapsulate code and data in reusable actor objects as a common microservices State Management Provides a key/value-based state API with pluggable state stores for persistence

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@bibryam

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Dapr architecture

Source: https://github.com/dapr/docs

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@bibryam

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Dapr on Kubernetes

Source: https://github.com/dapr/docs

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Full circle

  • Centralized control plane
  • Centralized data plane
  • Centralized control plane
  • Decentralized, highly-scalable data plane

Service discovery Dynamic routing Resiliency Observability Deployment Placement Config mgmt Scaling Bindings State abstraction Pub/Sub Observability Connectors Eventing Filtering Serverless

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Future cloud native trends

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@bibryam

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Lifecycle trends

Source: https://operatorhub.io

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@bibryam

  • Introduction of Service Mesh Interface specification
  • Architecture consolidation of Istio with istiod
  • More L7 protocols: MongoDB, DynamoDB, ZooKeeper, MySQL, Redis, Kafka(8188)

○ KIP-559 can enable bridging, validation, encryption, filtering, transformation

  • HTTP Cache filter (eCache)
  • HTTP tap filter (with matcher)
  • WebAssembly (wasm) filters with dynamic loading (C++ -> Rust, Go, etc)

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Networking trends

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@bibryam

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Binding trends

Dev Environment Cloud kamel CLI Camel K Operator Custom Resource Running Pod Fast redeploy! Less than 1 second!

  • from:

uri: "direct:route" steps:

  • split:

tokenize: ","

  • to: "mock:split"

Camel-K Operator: 1. Choose a runtime 2. Scaffold a project 3. Add boilerplate 4. Add dependencies 5. Create container image 6. Create Kubernetes resources for deployment

Source: https://github.com/apache/camel-k Live updates!

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@bibryam

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State trends

Source: https://github.com/cloudstateio/cloudstate

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What does all this mean?

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Multi-runtime microservices are here

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Smart sidecars and dumb pipes

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@bibryam

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What comes after Microservices?

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Thank You

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@bibryam https://k8spatterns.io