Deploying workloads in GCP - Step-by-Step Execution

Process Flow - Deploying workloads

Write workload config

↓

Submit deployment command

↓

GCP validates config

↓

GCP provisions resources

↓

Workload starts running

↓

Monitor status & logs

↓

Update or delete workload as needed

This flow shows how you prepare, submit, and manage a workload deployment on GCP step-by-step.

Execution Sample

GCP

gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1

Deploys a container image as a managed service on Google Cloud Run in the specified region.

Process Table

Step	Action	Input/Command	GCP Response	Result
1	Write workload config	Container image URL, service name	Config ready	Deployment prepared
2	Submit deployment command	gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1	Command accepted	Deployment started
3	GCP validates config	Checks image and parameters	Validation successful	Ready to provision
4	GCP provisions resources	Allocates compute, networking	Resources allocated	Workload starting
5	Workload starts running	Container starts	Service is live	Workload available
6	Monitor status & logs	gcloud run services describe my-service	Shows status and logs	Confirm healthy operation
7	Update or delete workload	gcloud run deploy or gcloud run services delete my-service	Updates or deletes service	Workload managed
8	Exit	No further commands	No errors	Deployment complete

💡 Deployment completes when workload is live and monitored or deleted as needed.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
Deployment Status	Not started	Prepared	Started	Validated	Provisioned	Running	Monitored	Complete
Workload State	None	Config ready	Command accepted	Validated	Resources allocated	Live	Healthy	Managed

Key Moments - 3 Insights

Why does the deployment stop if the config validation fails?

Because GCP checks the config before provisioning resources (see Step 3 in execution_table). If validation fails, deployment cannot continue safely.

What happens if the container image URL is incorrect?

During validation (Step 3), GCP will detect the invalid image URL and reject the deployment, preventing resource provisioning.

How do you know the workload is running successfully?

By monitoring status and logs (Step 6), you confirm the service is live and healthy before considering deployment complete.

Visual Quiz - 3 Questions

Look at the execution_table, what is the deployment status after Step 4?

A Running

B Validated

C Provisioned

D Prepared

💡 Hint

Check the 'Deployment Status' column in variable_tracker after Step 4.

At which step does GCP allocate compute and networking resources?

A Step 4

B Step 2

C Step 5

D Step 3

💡 Hint

Look at the 'Action' and 'Result' columns in execution_table for resource allocation.

If the container image URL is wrong, what will happen during deployment?

A Deployment proceeds and workload runs with errors

B Deployment fails at validation step

C Resources are provisioned but workload never starts

D Deployment completes but service is unhealthy

💡 Hint

Refer to the key moment about config validation and Step 3 in execution_table.

Concept Snapshot

Deploying workloads on GCP involves:
- Writing workload config (container image, service name)
- Running deployment command (gcloud run deploy)
- GCP validates config before provisioning
- Resources are allocated and workload starts
- Monitor status and logs to confirm success
- Update or delete workload as needed

Full Transcript

Deploying workloads on Google Cloud Platform starts with preparing your workload configuration, such as specifying the container image and service name. You then submit a deployment command using the gcloud CLI. GCP validates your configuration to ensure it is correct and safe to deploy. Once validated, GCP provisions the necessary resources like compute and networking. The workload container starts running, and you can monitor its status and logs to confirm it is healthy. Finally, you can update or delete the workload as needed to manage your deployment lifecycle.

Practice

1. What does deploying a workload on Google Cloud Run primarily do?

easy

A. Stores your application code without running it

B. Creates a virtual machine that you must manage manually

C. Runs your application in a managed environment that scales automatically

D. Only backs up your application data

Solution

1. Step 1: Understand Cloud Run's purpose

   Cloud Run runs your app in a managed environment that automatically scales based on traffic.

2. Step 2: Compare options

   Options B, C, and D describe other services or actions, not workload deployment on Cloud Run.

3. Final Answer:

   Runs your application in a managed environment that scales automatically -> Option C

4. Quick Check:

   Cloud Run = managed auto-scaling app hosting [OK]

Hint: Cloud Run runs and scales apps automatically [OK]

Common Mistakes:

• Confusing Cloud Run with virtual machines
• Thinking Cloud Run only stores code
• Assuming manual scaling is needed

2. Which of the following is the correct gcloud command to deploy a container image to Cloud Run in region us-central1 with service name my-service?

easy

A. gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1

B. gcloud deploy run --service=my-service --image=my-image --region=us-central1

C. gcloud run create --name my-service --image gcr.io/my-project/my-image --zone us-central1

D. gcloud container deploy my-service --image=gcr.io/my-project/my-image --region=us-central1

Solution

1. Step 1: Recall correct gcloud run deploy syntax

   The correct command uses 'gcloud run deploy SERVICE_NAME --image=IMAGE --region=REGION'.

2. Step 2: Match options to syntax

   gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1 matches the correct syntax exactly. Others have wrong commands or flags.

3. Final Answer:

   gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1 -> Option A

4. Quick Check:

   Correct gcloud run deploy syntax = gcloud run deploy my-service --image=gcr.io/my-project/my-image --region=us-central1 [OK]

Hint: Use 'gcloud run deploy SERVICE --image=IMAGE --region=REGION' [OK]

Common Mistakes:

• Using 'gcloud deploy run' instead of 'gcloud run deploy'
• Confusing --zone with --region
• Using 'gcloud container deploy' which is invalid

3. Given this command: gcloud run deploy my-app --image=gcr.io/project/app:v1 --region=us-east1, what happens after deployment?

medium

A. The app is uploaded but does not run until manually started

B. The app runs on Cloud Run and scales automatically with traffic

C. A virtual machine is created but the app is not deployed

D. The app runs only on your local machine

Solution

1. Step 1: Understand Cloud Run deployment behavior

   Deploying with gcloud run deploy starts the app on Cloud Run immediately with auto-scaling.

2. Step 2: Eliminate incorrect options

   Options B, C, and D describe behaviors not matching Cloud Run's managed service.

3. Final Answer:

   The app runs on Cloud Run and scales automatically with traffic -> Option B

4. Quick Check:

   Cloud Run deploy = app runs and scales automatically [OK]

Hint: Deploy starts app running with auto-scaling on Cloud Run [OK]

Common Mistakes:

• Thinking deployment only uploads code
• Assuming manual start is needed
• Confusing Cloud Run with VM creation

4. You run gcloud run deploy my-service --image=gcr.io/my-project/my-image but get an error about missing region. What is the fix?

medium

A. Remove the --image flag and try again

B. Deploy to Cloud Storage instead

C. Use --zone instead of --region

D. Add the flag --region=REGION with a valid region like us-central1

Solution

1. Step 1: Identify missing required flag

   The error indicates the region flag is missing, which is required for Cloud Run deployment.

2. Step 2: Correct the command by adding --region

   Adding --region=us-central1 (or another valid region) fixes the error.

3. Final Answer:

   Add the flag --region=REGION with a valid region like us-central1 -> Option D

4. Quick Check:

   Missing region error = add --region flag [OK]

Hint: Always specify --region when deploying to Cloud Run [OK]

Common Mistakes:

• Using --zone instead of --region
• Removing --image flag which is required
• Trying to deploy to wrong service like Cloud Storage

5. You want to deploy a new version of your app to Cloud Run but keep the old version running for testing. How can you achieve this?

hard

A. Deploy the new version with the same service name and region

B. Deploy to a different Google Cloud project

C. Use gcloud run update to replace the old version

D. Deploy the new version with a different service name

Solution

1. Step 1: Understand Cloud Run revisions

   Cloud Run automatically creates a new revision when deploying to an existing service, keeping previous revisions available for testing via revision-specific invocations or traffic splitting.

2. Step 2: Deploy new version with the same service name and region

   This leverages Cloud Run's built-in revision management to keep both versions accessible.

3. Final Answer:

   Deploy the new version with the same service name and region -> Option A

4. Quick Check:

   Same service = new revision, old available for testing [OK]

Hint: Deploy to same service name to use revisions for multi-version testing [OK]

Common Mistakes:

• Deploying to a different Google Cloud project unnecessarily
• Using 'gcloud run update' incorrectly for image changes
• Deploying with a different service name when revisions suffice