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CodeSignal

Google Cloud Run: Deploying and Managing Containers

via CodeSignal

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Overview

Stand up a real Kubernetes cluster on Google Kubernetes Engine (GKE), deploy your container image from Artifact Registry using kubectl, and expose it publicly. You'll learn core Kubernetes objects—Deployments, Pods, and Services—to manage scaling, rolling updates, and cluster access for production-ready workloads on GCP.

Syllabus

  • Unit 1: Getting Started with Cloud Run
    • Initialize and Verify Your First Cloud Run Environment
    • Set Up Cloud Run Development Environment and Verify Configuration
    • Managing Multiple Environments with Cloud Run Services
  • Unit 2: Configuring Cloud Run Services
    • Extracting the Service URL from a Deployed Cloud Run Service
    • Deploying a Containerized Web Application to Cloud Run Using Cloud Build and Artifact Registry
    • Deploying a Cost-Optimized Service with CPU and Scaling Controls
    • Update and Inspect Cost-Optimized Cloud Run Service
  • Unit 3: Deploying Cloud Run Services
    • Cloud Run Revision Lifecycle Management
    • Debugging Missing Required Flags in Cloud Run Deployment
    • Understanding and Resolving Cloud Run CPU and Memory Constraints
    • Deploying Cloud Run Service with Environment-Specific Configuration
    • Completing a Production-Ready Deployment Configuration
  • Unit 4: Securing and Networking Cloud Run Services
    • Securing and Publishing Your Web Service with IAM and Custom Domain
    • Scaling a Web Service with Cloud Run
    • Cloud Run Service Lifecycle Management
  • Unit 5: Cloud Run Logging Debugging
    • Managing Log Retention and Real-Time Monitoring for Cloud Run Services
    • Real-Time Log Filtering and Live Debugging for Cloud Services
    • Systematic Debugging and Failure Investigation for Cloud Run Services
    • Service Recovery Workflow and Health Monitoring

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