Cloud Native Apps on Google Cloud Platform: Use Serverless, Microservices and Containers to Rapidly Build and Deploy Apps on Google Cloud
- Length: 422 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2022-04-12
- ISBN-10: 935551123X
- ISBN-13: 9789355511232
- Sales Rank: #1942338 (See Top 100 Books)
Step-by-step guide for developing cloud native apps on GCP powered by hands-on interactive learning
Key Features
- Cutting-edge coverage on Google Cloud Build, Cloud Run, GKE, Kubectl and Anthos.
- Includes tutorials and exercises to learn designing, deploying and running cloud native apps.
- Covers Service Mesh, Apps Optimization, logs monitoring and cloud IAM access.
Description
The book “Cloud Native Apps on Google Cloud Platform”teaches the readers how to design, construct, and maintain successful cloud-native apps using the Google Cloud Platform. With interactive tutorials, the book reinforces learning and helps to develop practical skills for working in an Agile and DevOps context. The book provides a step-by-step approach to building and managing cloud-native applications on Google Cloud Platform for Google Cloud Users, DevOps teams, and Cloud-Native Developers.
First, you will investigate the advantages and applicability of each Google Serverless Computing option. You’ll learn about Cloud Build and how to use it to prepare code files, create microservices, and build container images. The book walks readers through creating and running Docker image containers on Cloud Run and App Engine. You’ll learn how to use kubectl to create and manage Kubernetes clusters, as well as how to configure the autoscaler for increased resilience and availability. You’ll build a pipeline that uses Cloud Build to automate CI/CD and Pub/Sub to ingest streaming data.
Finally, you’ll have the opportunity to learn about Anthos, which enables you to manage massive GKE clusters in both Cloud and on-premises environments.
What you will learn
- Distinguish between using containers or microservices for cloud native apps.
- Build a streaming data pipeline using BigQuery and Dataflow using Pub/Sub.
- Practice to deploy and optimize cloud native applications on Kubernetes Engine.
- Build continuous integration/continuous delivery pipelines and improve Kubernetes apps.
- Learn to protect apps running on GCP from cyberattacks.
Who this book is for
This book is meant for the Cloud and DevOps professionals and for those who wish to learn about Google Cloud services and incorporate them into end-to-end cloud applications.
Cover Page
Title Page
Copyright Page
About the Author
About the Reviewers
Acknowledgement
Preface
Errata
Table of Contents
1. Introducing Cloud Native Apps
Structure
Objectives
About Cloud Native Apps
Cloud-native application architecture
Features of a cloud-native application
Benefits of Cloud-native applications
The Dawn of Modern DevOps
Cloud Native DevOps
The Cloud Service Model
IaaS vs PaaS vs SaaS vs FaaS
Google Compute Engine (GCE) — IaaS
The Serverless Paradigm
Google Kubernetes Engine (GKE) — (CaaS)
Google App Engine (GAE) — (PaaS)
Google Cloud Functions — (FaaS)
Cloud Run --- (CaaS+)
Advantages of serverless architecture
Advantages of Functions (FaaS)
Advantages of a Microservices architecture
Advantages of working with APIs
What is the best approach?
Interactive tutorials
Interactive Tutorial 1.1: Deploy Node.js code in Cloud Function
Tutorial 1.2: Deploy a Nodejs App in Google App Engine
Tutorial 1.3: Deploy a Nodejs Container in Cloud Run
Conclusion
2. Developing Cloud Native Apps with Cloud Shell
Structure
Objectives
A Cloud native approach to SDLC
Principles for Cloud Native architecture
Principle 1: Automation by design
Principle 2: Be wary of Preserving State
Principle 3: Favor managed services
Principle 4: Practice defense in depth
Principle 5: Always be architecting
What is Agile Development
Understanding the Cloud Native development lifecycle
Cloud native patterns
The four pillars of Cloud native
Best practices for cloud native application development
Tools for cloud-native app development
What is Google Cloud Code?
Selecting a development and operations environment
Google Cloud SDK
The gcloud command-line tool
Client libraries
Product-specific command-line tools
Cloud Shell
Walkthrough 2.1: Cloud Shell setup
Launching Cloud Shell from the Console
Launching a standalone session
Using the Cloud Shell Terminal
Using the Cloud Shell Editor
Ephemeral mode
Walkthrough 2.2: Running VScode with Cloud Shell in the browser
Installing the Cloud SDK locally
Walkthrough 2.3: Installing Cloud SDK Docker Image
Docker image options
Installing a specified Docker image
Interactive Tutorial 2.1: Developing with Cloud Code using the Cloud Shell Editor
Working with Cloud Run
Workspace management in the Cloud Shell Editor
Walkthrough 2.4: Create a new workspace
Debugging with Cloud Shell Editor
Debugging actions
Setting breakpoints
Additional Kubernetes debugging support
Configuration files in your Cloud Shell home directory
tmux support
Container image customization
Customizing Cloud Shell container images
Interactive tutorial 2.2: Creating a custom Cloud Shell Docker Image
Creating the Docker Image
Conclusion
3. Preparing Source Code with Cloud Build
Structure
Objectives
A Cloud native approach to DevOps
DevOps lifecycle
Software Configuration Management (SCM)
Types of Version Control Systems
Version Control Systems – Best Practices
Cloud Source Repositories
Simplified Continuous Integration (CI) with Container Builder
Pre-Installed tools and languages in Cloud Shell and Cloud Shell Editor
Cloud Source Repositories and App Engine
The Software Build process
What is a Build?
Types of Software Build
Automating the Build
Interactive Tutorial 3.1: Creating a Cloud Repository
Clean up
Disable your app
Delete the repository
Cloud Build
Build configuration and the build steps
Starting builds
Viewing build results
How do builds work?
Overview of Artifact Registry
Introduction
Artifact registry and container registry
Cloud Build and Docker
Cloud Build interfaces
Running builds locally
Differences between the local builder and Cloud Build
Interactive Tutorial 3.2
Clean up
Conclusion
4. Create and Deploy Microservices
Structure
Objectives
The case for microservices architecture
Interactive tutorial 4.1: Demonstrating the Monolith and Microservice architectures
Microservices deployed on Cloud functions
Technical walkthrough 4.1: Under the Bonnet with Cloud Function Microservices
Web app
How does it work?
Microservices architecture on Google App Engine
App Engine Services as Microservices
Project isolation
Versions within services
Service isolation
Interactive tutorial 4.2: Build and deploy Microservices in App Engine
Conclusion
5. Building and Deploying Containers in Cloud Build
Structure
Objectives
What are Containers?
Container architecture
DockerHub
Running a Docker Container
Roll your own Container
Containerization – Suitable GCP Engines
Google Artifact Registry (formerly Google Container Registry)
Working with repositories and registries
Exploring artifact registry
Walkthrough 5.1: Exploring the artifact registry repositories
A quick guide to some essential technologies
How does Git work?
Access controls
Configuring permissions
Customizing Cloud Shell containers
Persistent storage for $HOME
Interactive tutorial 5.1: Building a custom Cloud Shell Docker Container
Customizing Cloud Shell container images
Interactive tutorial 5.2: Build and deploy a Container in Cloud Run
Working with APIs
Interactive tutorial 5.3: Running a Google Translate API on Cloud Run
Managing Containers
Conclusion
6. Create a Serverless Pipeline with Pub/Sub, Dataflow, and BigQuery
Structure
Objectives
Cloud Pub/Sub
How does Pub/Sub work?
Common use cases
Limitations
Interactive tutorial 6.1: Getting familiar with Cloud Pub/Sub
An overview
Setting up Pub/Sub
Cloud Task Queues
InteractivetTutorial 6.2: Working with Cloud tasks
Cloud Task Queues
Interactive tutorial 6.3: Working with Cloud tasks
Cloud Scheduler
BigQuery
Interactive tutorial 6.4: Building a Serverless Pipeline with Pub/Sub, Functions, and BigQuery
Clean Up!
Cloud Dataflow
Interactive tutorial 6.5: Stream processing with Cloud Pub/Sub and Dataflow
Clean up!
Conclusion
7. Container Orchestration with Google Kubernetes Engine
Structure
Objectives
Scaling Containers
Clustering basics
Cluster management architecture
Google Kubernetes
Decomposing the Monolith
Kubernetes essentials
Kubernetes architecture
Deployment
Kubernetes Control Plane
Kubernetes on Google Cloud
Cluster orchestration with Google Kubernetes Engine
Interactive tutorial 7.1: Building your first Kubernetes Cluster
Interactive tutorial 7.2: Building your first Kubernetes App
Set default settings for the gcloud tool
Clean up!
Difference between imperative and declarative
Interactive tutorial 7.3: A declarative approach to configuring and deploying to Kubernetes
Troubleshooting Kubernetes
Using the kubectl CLI for debugging issues
Clean Up!
Conclusion
8. Deploying and Managing Kubernetes Applications
Structure
Objectives
Cluster Design patterns
The age-old dilemma
The single cluster model
Multi-cluster model
Managing Kubernetes resources
What is a namespace?
The “default” namespace
Creating namespaces
Viewing namespaces
Creating resources in the namespace
Viewing resources in the namespace
Namespace communication
Namespace granularity
Liveness and readiness checks
Readiness
Liveness
Type of Probes
Exploring some key Kubernetes features
Zero-downtime upgrades
Upgrading nodes with zero downtime
Rolling update
Migration with node pools
Tutorial 8.1: Migrating a Monolith to Microservices on Kubernetes Engine
Introduction
Interactive tutorial 8.1: Running Microservices in Google Kubernetes Engine (GKE)
Overview
Architecture diagram of the Microservices
Conclusion
9. Optimizing Kubernetes Cluster and Apps in GKE
Structure
Objectives
Optimization starts with the application
Running Cloud Native apps on GKE
Understanding apps and resources
Setting resource requests and limit ranges
Autoscaling
Pod disruption budgets
Liveness and readiness probes
Batch and serving applications
Batch apps
Serving apps
Optimizing Kubernetes applications on GKE
A cost-optimized Cluster
GKE cost-optimization features and options
Four Kubernetes scaling dimensions
Horizontal Pod Autoscaler (HPA)
HPA setting the thresholds
Vertical Pod Autoscaler (VPA)
Some best practices common to both HPA And VPA
Cluster Autoscaler
Node auto-provisioning
Preemptible VMs
Serving workloads
Prepare cloud-based Kubernetes applications
Understand your application capacity
Make sure your application can grow vertically and horizontally
Set appropriate resource requests and limits
Make sure your container is as lean as possible
Understand the metrics server
Consider using Anthos Policy Controller
Design your CI/CD pipeline to enforce cost-saving practices
Spread the cost-optimization culture
Interactive tutorial 9.1: Autoscaling deployments with GKE workload metrics
Objectives
Before you begin
Querying workload metrics using the Kubernetes Custom Metrics API
Deploying a HorizontalPodAutoscaler object
Observing HorizontalPodAutoscaler scaling up
Clean up
Summary of optimization's broad steps
Conclusion
10. Deploying a CI/CD Pipeline With Kubernetes and Cloud Build
Structure
Objectives
Why CI/CD?
Why Kubernetes?
Simple health check capability
Rolling updates and native rollback
Resilience
Simplified monitoring
Aligns with business objectives
Let the system engineers implement CD
Kubernetes and CI/CD a divine combo
Complementary tools
The Promise of GitOps
How to implement CI in GitOps?
Objections to CI
Common pitfalls
Measuring CI
GitOps-style continuous delivery with Cloud Build
Implementing an automated deployment process
Common pitfalls in deployment automation
Complexity
Dependencies
Not designed for automation
Poor collaboration between teams
Ways to improve deployment automation
Ways to measure deployment automation
Continuous Delivery vs Continuous Deployment
Implementing continuous delivery
Common pitfalls of implementing continuous delivery
Measuring continuous delivery
Interactive tutorial 10.1: GitOps-style continuous delivery with Cloud Build
Objectives
Prepare
Creating the continuous delivery pipeline
Granting Cloud Build access to GKE
Initializing the hello-cloudbuild-env repository
Creating the trigger for the continuous delivery pipeline
Modifying the continuous integration pipeline to trigger the continuous delivery pipeline
Testing the complete pipeline
Testing the rollback
Clean up!
Deleting the resources
Conclusion
11. Build a Software Delivery Platform with Anthos
Structure
Objectives
Defining Anthos
The way Anthos works
Anthos agent
Anthos hub
Anthos Service Mesh
Anthos operations suite
Anthos's business utility
Today's hybrid and multi-cloud ecosystems
Anthos fleets
Anthos technical drill-down
Computing environment
Multi-cluster management
Connecting across environments
Connecting to Google services
Microservice architecture support
Managed service mesh
Centralized config management
Cloud Run for Anthos
Secure software supply chain
Consolidated logging and monitoring
Unified user interface
Anthos for development
Anthos for operations
Anthos for security
Designing modern CI/CD with Anthos
Software delivery platforms
Software delivery workflow
Code repositories
Application landing zones
Operating model
Governance
Platform as a product
Deploying CI/CD with Anthos
Selecting a pilot application
Developer considerations
Operator considerations
Security team considerations
CI/CD workflow
Architecture
Platform infrastructure
Code repositories
Application landing zones
Technical walkthrough 11.1: Building a CI/CD in Anthos
Overview
Objectives
Before you begin
Part 1
Part 2: Applying the developer workflow
Overview
Objectives
Clean up!
Conclusion
12. Application Management with Anthos
Structure
Objectives
The rise of Kubernetes
The meta control planes
Monitoring
Anthos Cloud
Anthos — Hybrid and multi cloud dexterity
Key components of Anthos
Cloud logging, monitoring, and observability
Anthos application security
Service accounts
Cloud Identity and Access Management
Zero Trust
Technical walkthrough 12.1: Launching Anthos using the sample deployment
Your journey
Objectives
Costs
Before you begin
What’s deployed?
Launch the Anthos sample deployment on Google Cloud
Using the Anthos Dashboard
Explore Anthos clusters resources
Cluster management
Cluster workloads
Services and Ingress
Observing services
Observe the services table view
Observe the services topology view
Exploring Anthos further
Setting up your Cloud Shell environment
Enforcing mTLS in your service mesh
Confirm Anthos Config Management setup
Update a policy to encrypt all service traffic
Push your changes to the repository
Using Policy Controller to set up guardrails
Apply guardrails
Use Policy Controller
Apply a policy constraint for privileged containers
Test your policy
Conclusion
13. Securing Cloud Native Apps in Anthos
Structure
Objectives
Anthos Identity Service (AIS)
Beyond user identity
Kubernetes Engine (GKE) IAM roles
Clarifying the GKE predefined IAM roles
Potential risks and privilege elevation paths
Google Cloud Platform IAM
Group membership over individual access
Role-based Access Control
Anthos: Modernize your security posture
Consistent policies
Software supply chain security
Security of multi-tenant environments across a shared platform
Stage 1: The infrastructure
Stage 2: Guardrails and policy
Stage 3: Securing the workloads
Anthos Service Mesh
Stage 4: Monitoring and detecting flaws
Anthos: Modernizing security for hybrid and multi-cloud
Enforcing consistent policies across environments
A GitOps approach
Deploying only trusted workloads
Isolating workloads with different risk profiles
Anthos under the hood
Anthos Config Controller
Managing Anthos and Google Cloud with your Kubernetes tools
Focus on what you want, Config Controller takes care of the rest
Consistency with GitOps
Policy guardrails and auditing
Config sync benefits
Configuring clusters
Policy Controller overview
Constraints
Constraint templates
Interactive tutorial 13.1: Validating apps against policy
Overview
Resources
Pipeline
CI pipeline for Policy Controller
Objectives
Before you begin
Cleaning up!
Conclusion
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