Google Cloud Run: Hands-on Comprehensive Technical Deep Dive

Set up your local system, create the folder structure, and install the required components to begin the lab.

Download lab files from the resources section and copy them to code-root. Rename the .gitignore, extract the zip without changing the folder name, and open code-root in Visual Studio Code.

Install python, gcloud cli, and Visual Studio Code across mac, windows, and linux, using homebrew or links; set up git cli and credential manager, add rest client extension, and verify.

Discover how Google Cloud Run provides a fully managed serverless platform for stateless containers invokable by http requests, deploy from code or images, and automatically scales.

Run code on cloud run as a service or a job. Services run indefinitely for http requests; jobs run to completion for one-time tasks like batch processing or data migrations.

Discover how cloud run service uses a stateless container invokable by HTTP requests and is fully managed by Google, enabling web servers, APIs, streaming, data analytics, and machine learning training.

Explore how Cloud Run provides https endpoints with managed TLS on a run.app domain, supports custom domains, web sockets, http2, gRPC, and auto scales with revisions and traffic routing.

Google Cloud Run offers per-use pricing with two models: request-based and instance-based. Request-based charges CPU time per request; instance-based charges for the service lifetime to handle bursts and cold starts.

Explore how Cloud Run jobs execute containerized code, create tasks, and run one or many parallel instances (array jobs) to complete scheduled tasks efficiently.

Cloud Run services run indefinitely to handle HTTP requests, while Cloud Run jobs run to completion and exit; services auto-scale, jobs require defined parallelism for batch processing and data migration.

Explore how Cloud Run integrates with Cloud SQL, Memorystore, Firestore, Spanner, and Storage, and uses service accounts for authentication while enabling continuous deployment from GitHub, Bitbucket, or Cloud Source Repositories.

Explore the Google Cloud Run resource model, including services, revisions, endpoints, traffic routing, and automatic scaling, with concurrency and parallel tasks powering serverless jobs.

Download lab files from the resources section, set up a git repo on GitHub, extract the zip into the code root, and open Visual Studio Code to review Readme.txt.

We choose GitHub.com over cloud source repositories because GitHub.com offers private repositories for free, avoiding $1 per user per month charges; service accounts will pull code from GitHub.com.

Log in to GitHub.com and create a private repository using the name from readme.txt, then copy the git remote add origin command with the repo URL to push your code.

Generate a local personal access token on GitHub to enable code check-ins from your machine. Create a classic token, set expiration, select repo scopes, and save the token securely.

Initialize a git repository, stage and commit files, set main as the branch, push to origin with an access token, and note credentials stored in plain text as a risk.

Create a new Google Cloud project and optionally install the gcloud CLI to connect to it; download and extract lab files, and set up the workspace in Visual Studio Code.

Sign in to the GCP console, create a new project named Cloud Run Demo, select it, view the Cloud Overview dashboard, and copy the project ID for later use.

Set up the gcloud cli on a local machine (optional), sign in, list projects, and configure the project with gcloud config set; verify with gcloud config get project.

Enable required APIs and services, download the lab files, and prepare the codebase for deploying a Cloud Run service. Extract the lab resources and review the readme to proceed.

Enable the required APIs and services in the GCP console, including Artifact Registry, Cloud Run, IAM, Cloud Build, and Secret Manager, to manage images, deployments, identities, builds, and secrets.

Link your GitHub repository to cloud build repositories and connect your GCP project, then add the repo as a cloud build repository so builds pull code automatically.

Create a second-generation host connection in Google Cloud Build for a GitHub repository, install the Cloud Build app, and enable the selected Udemy Cloud Monkey Cloud Run code repo.

Link your GitHub repository to your Cloud Build connection by selecting the correct connection and repository, optionally rename it, then link and verify the repository under the GitHub connection.

Create an artifact registry to store container images with Cloud Build, download lab five files, place artifact registry.zip in the code minus root directory, and extract it.

Learn how artifact registry centralizes artifacts and dependencies, stores packages and Docker images, deploys to GKE and Cloud Run, and enables vulnerability scanning with artifact analysis and binary authorization.

Create the Cloud Monkey Artifact Registry in GCP by opening Artifact Registry in the console, naming the repository, selecting Docker as the format, and choosing the US central one region.

Download lab six Buildpacks zip from resources, extract it under code root directory, and open Visual Studio Code to build and deploy a cloud run service using Buildpacks per Readme.txt.

Review the cloud run service built with buildpacks, exposing a /get customers endpoint in a microservices folder. Start gunicorn with port from env and app in main.py.

Push your lab files to the GitHub repository by staging changes, committing, and pushing, then prepare to create the build trigger in the next video.

Learn to create a Cloud Build trigger using buildpacks, connect a GitHub repository, specify main, set build directory to lab six/microservices/customers, and push the image to artifact registry.

Analyze build logs to see how a Python project is detected, built with buildpacks, and pushed as a container image to artifact registry, with details on the image and manifest.

Create a Google Cloud Run service from the GCP console by selecting a container image from artifact registry, configuring region, pricing, and min/max instances with ingress and authentication.

Deploying a cloud run service creates an immutable revision that hosts the specified image. Redeploying builds a new revision and enables traffic routing, including blue-green deployments.

Test a Google Cloud Run service by copying the URL, replacing the project hash, sending the request, and reviewing logs for start and response.

Create dedicated service accounts for cloud build and cloud run, assign required permissions, then download and unzip lab seven files into your code directory and review the readme.

Create two service accounts in IAM for cloud build and cloud run, using the given names, note their IDs and emails, and proceed to assign permissions in the next video.

Learn to associate roles with a service account in Google Cloud, including logs writer, Artifact Registry Writer, Cloud Run Admin, and service account user for Cloud Build workflows.