Event-Driven Architecture with Knative

Ship resilient event driven microservices on Kubernetes with Knative, clear contracts, safe rollouts, and observability you can act on.

Synchronous chains amplify latency and failures. Teams need a platform that scales on demand, routes events predictably, and survives retries without double effects. This book gives you a complete, practical system built on Knative Serving and Eventing, so services stay fast, routing stays transparent, and upgrades stop being risky.

You will learn how to model CloudEvents, compose routes with Brokers, Triggers, Channels, Sequences, and Parallels, and run production traffic with delivery guarantees, security boundaries, and clear runbooks. Every concept is backed by manifests, CLI steps, and small working services.

stand up knative serving and eventing, pick ingress classes kourier contour istio and switch safely
design stable cloudevents with subject and type discipline, write cesql filters, reshape events with jsonata eventtransform
set deliveryspec for retries backoff timeouts and dead letter sinks, build a dlq service for triage and replay
tune kpa and hpa, set soft and hard concurrency, reduce cold starts with minscale image and startup tuning
wire event sources apiserversource pingsource githubstyle webhooks and compose sinks with sinkbinding
integrate kafka and rabbitmq, choose broker versus channel, manage ordering consumer groups and hot partitions
secure transport and identity with tls gateway api cluster local routing oidc sender tokens and authorization policies
observe with opentelemetry, prometheus metrics, jaeger or otlp traces across broker to subscriber, and consistent json logs
use kn and func for the inner loop, hot reload and local fixtures, choose dockerfiles buildpacks or ko for images
build ci cd with tekton or github actions, sign images with cosign, promote with kustomize and argo cd
operate with ha controllers and webhooks, leader election pdbs topology spread, plus version aware upgrade and rollback plans
apply production patterns idempotency and outbox sagas and compensation schema versioning and filter stability with cesql

This is a code heavy guide, you get working YAML Bash Go Python JavaScript SQL Dockerfile and JSON examples that drop into real clusters and pipelines.

Grab your copy today and run event driven systems that scale, stay observable, and keep shipping.