Deploying workloads in GCP - Deep Dive

Overview - Deploying workloads

What is it?

Deploying workloads means putting your applications or services onto cloud computers so they can run and be used by people. It involves moving your code and resources from your own computer to the cloud platform, like Google Cloud Platform (GCP). This process makes your app available on the internet or within your organization. It also includes managing how your app runs, scales, and stays healthy.

Why it matters

Without deploying workloads, your applications would only run on your own computer, making them unavailable to users everywhere. Deploying workloads to the cloud solves this by using powerful, always-on computers that anyone can access. This allows businesses to serve customers globally, handle many users at once, and update apps quickly without downtime. It also saves money by using resources only when needed.

Where it fits

Before learning to deploy workloads, you should understand basic cloud concepts like virtual machines, containers, and storage. After mastering deployment, you can learn about advanced topics like scaling, monitoring, and security for cloud applications. This topic is a key step between writing code and running it reliably in the cloud.

Mental Model

Core Idea

Deploying workloads is like moving your app from your home computer to a powerful, shared computer in the cloud that runs it for everyone.

Think of it like...

Imagine you baked a cake at home (your computer), but you want to sell slices to many people. Deploying workloads is like placing your cake in a bakery (the cloud) where many customers can buy and enjoy it anytime.

┌─────────────────────────────┐
│       Your Computer         │
│  (Develop & Test Locally)   │
└──────────────┬──────────────┘
               │
               │ Deploy
               ▼
┌─────────────────────────────┐
│        Cloud Platform       │
│  (Run & Manage Workloads)   │
│ ┌──────────────┐            │
│ │ App Instance │            │
│ └──────────────┘            │
└─────────────────────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding workloads and cloud basics

Concept: Learn what workloads are and the basic cloud resources used to run them.

A workload is any application or service you want to run. In the cloud, workloads run on resources like virtual machines (VMs), containers, or serverless functions. These resources provide computing power, memory, and storage. Google Cloud Platform offers many ways to run workloads, such as Compute Engine for VMs, Google Kubernetes Engine for containers, and Cloud Run for serverless apps.

Result

You can identify what a workload is and the basic cloud resources that can run it.

Understanding what a workload is and the cloud resources available is the foundation for knowing how to deploy and manage applications in the cloud.

2

Foundation

Preparing your application for deployment

Concept: Learn how to package and prepare your app so it can run in the cloud environment.

Before deploying, your app must be ready to run outside your computer. This means packaging it with all needed files and dependencies. For example, containerizing your app means putting it and everything it needs into a container image. Alternatively, you might prepare code for serverless deployment by ensuring it fits the platform's requirements. Testing locally helps catch errors early.

Result

Your app is packaged and ready to be moved to the cloud.

Preparing your app properly prevents deployment failures and ensures it runs smoothly once deployed.

3

Intermediate

Deploying to Google Compute Engine

🤔Before reading on: do you think deploying to a VM requires managing the operating system yourself or is it fully managed by Google? Commit to your answer.

Concept: Learn how to deploy workloads on virtual machines using Google Compute Engine and what responsibilities you have.

Google Compute Engine lets you create virtual machines (VMs) that act like real computers in the cloud. You deploy your app by connecting to a VM, installing software, and running your app there. You are responsible for managing the VM's operating system, updates, and scaling. This approach gives you full control but requires more management.

Result

Your app runs on a VM in the cloud, accessible to users.

Knowing that Compute Engine gives full control but requires managing the VM helps you choose when this approach fits your needs.

4

Intermediate

Deploying containerized workloads with Kubernetes

🤔Before reading on: do you think Kubernetes automatically handles scaling your app or do you need to configure it manually? Commit to your answer.

Concept: Learn how to deploy containerized apps using Google Kubernetes Engine (GKE) and how Kubernetes manages workloads.

Kubernetes is a system that runs containers across many machines, managing deployment, scaling, and health. Google Kubernetes Engine (GKE) is a managed service that runs Kubernetes for you. You create container images of your app, upload them to a registry, then tell GKE to run them. Kubernetes can automatically restart failed containers and scale your app based on demand if configured.

Result

Your containerized app runs reliably and can scale in the cloud.

Understanding Kubernetes' role in automating deployment and scaling helps you manage complex workloads efficiently.

5

Intermediate

Using serverless deployment with Cloud Run

🤔Before reading on: do you think serverless means you write no code or that the cloud manages servers for you? Commit to your answer.

Concept: Learn how to deploy workloads without managing servers using Cloud Run, a serverless platform for containers.

Cloud Run lets you deploy containerized apps without worrying about servers. You upload your container image, and Cloud Run runs it on demand, scaling automatically to zero when not used. This means you pay only when your app is running. It handles all infrastructure, so you focus on your code.

Result

Your app runs in a fully managed environment that scales automatically.

Knowing serverless deployment frees you from infrastructure management and reduces costs for variable workloads.

6

Advanced

Managing deployment configurations and updates

🤔Before reading on: do you think updating a deployed workload always requires downtime? Commit to your answer.

Concept: Learn how to configure deployments for smooth updates and manage versions without interrupting users.

Deployments often need updates for new features or fixes. Google Cloud services support strategies like rolling updates, where new versions replace old ones gradually without downtime. You can configure deployment settings to control traffic shifting, rollback on failure, and versioning. Proper configuration ensures users experience no interruption during updates.

Result

You can update workloads safely and keep them available.

Understanding deployment strategies prevents downtime and improves user experience during updates.

7

Expert

Optimizing workload deployment for cost and performance

🤔Before reading on: do you think deploying more instances always improves performance without extra cost? Commit to your answer.

Concept: Learn advanced techniques to balance cost, performance, and reliability when deploying workloads in GCP.

Experts optimize deployments by choosing the right resource types, sizes, and scaling policies. For example, using preemptible VMs can reduce costs but may interrupt workloads. Autoscaling adjusts instances based on demand to save money. Monitoring tools help identify bottlenecks and optimize resource use. Combining these techniques leads to efficient, cost-effective deployments.

Result

Your workloads run efficiently with balanced cost and performance.

Knowing how to optimize deployments prevents waste and ensures your app meets user needs reliably.

Under the Hood

When you deploy a workload, your code and resources are transferred to cloud servers. These servers run your app inside isolated environments like VMs or containers. The cloud platform manages networking, storage, and compute resources, routing user requests to your app. Services like Kubernetes orchestrate containers by scheduling them on nodes, monitoring health, and scaling. Serverless platforms abstract servers entirely, running your code only when needed.

Why designed this way?

Cloud deployment was designed to separate application logic from physical hardware, allowing flexible, scalable, and reliable operation. Early cloud models required manual server management, which was complex and error-prone. Managed services and orchestration tools evolved to automate these tasks, reduce human error, and optimize resource use. This design balances control and convenience for different user needs.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│   Developer   │──────▶│   Deployment  │──────▶│ Cloud Platform│
│  (Your Code)  │       │  Process/API  │       │ (VMs, K8s, SR)│
└───────────────┘       └───────────────┘       └───────┬───────┘
                                                      │
                                                      ▼
                                             ┌─────────────────┐
                                             │ Running Workload │
                                             │ (App Instance)   │
                                             └─────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do you think deploying to the cloud means your app is instantly scalable without any setup? Commit to yes or no.

Common Belief: Deploying to the cloud automatically makes your app scale perfectly without extra configuration.

Reality: Cloud deployment provides tools for scaling, but you must configure scaling policies and design your app to handle scaling properly.

Why it matters: Assuming automatic scaling leads to performance issues or crashes when demand grows, causing poor user experience.

Quick: Do you think serverless means you don't need to write any code? Commit to yes or no.

Common Belief: Serverless means no coding is needed; the cloud does everything for you.

Reality: Serverless means the cloud manages servers, but you still write and deploy your application code.

Why it matters: Misunderstanding serverless can cause confusion about responsibilities and lead to poor application design.

Quick: Do you think deploying on virtual machines means you don't have to manage the operating system? Commit to yes or no.

Common Belief: Using virtual machines in the cloud means the provider manages the OS for you.

Reality: When using VMs, you are responsible for managing the operating system, updates, and security patches.

Why it matters: Ignoring OS management can lead to security vulnerabilities and unstable applications.

Quick: Do you think container orchestration like Kubernetes is only for very large companies? Commit to yes or no.

Common Belief: Kubernetes is too complex and only useful for huge enterprises.

Reality: Kubernetes can benefit many sizes of projects by automating deployment and scaling, and managed services simplify its use.

Why it matters: Avoiding Kubernetes due to this belief can limit scalability and automation benefits even for smaller teams.

Expert Zone

1

Deploying workloads often involves trade-offs between control and convenience; choosing the right service depends on workload characteristics and team expertise.

2

Infrastructure as Code (IaC) tools like Terraform or Deployment Manager are essential for repeatable, version-controlled deployments but require learning new syntax and practices.

3

Understanding the cloud provider's shared responsibility model is critical; some security and maintenance tasks remain your responsibility even in managed services.

When NOT to use

Deploying workloads directly on VMs is not ideal when you want rapid scaling or minimal management; instead, use managed container or serverless platforms. Serverless is not suitable for long-running or stateful applications; use Kubernetes or VMs instead. Avoid complex orchestration if your app is simple and can run on managed services to reduce overhead.

Production Patterns

In production, teams use CI/CD pipelines to automate deployment, ensuring consistent and fast updates. Blue-green or canary deployments minimize downtime and risk. Autoscaling policies adjust resources dynamically based on real user traffic. Monitoring and logging are integrated to detect issues early and maintain reliability.

Connections

Continuous Integration and Continuous Deployment (CI/CD)

Builds-on

Understanding deployment is essential before automating it with CI/CD pipelines, which speed up and improve the reliability of releasing new app versions.

Load Balancing

Complementary

Deploying workloads often requires load balancers to distribute user traffic evenly, ensuring performance and availability.

Supply Chain Management

Analogy in logistics

Deploying workloads is like managing a supply chain where goods (apps) move from factories (developers) to stores (users) efficiently and reliably.

Common Pitfalls

#1 Deploying without testing the app in a cloud-like environment.

Wrong approach:

Deploying code directly from local machine without containerization or environment checks.

Correct approach:

Build and test container images locally or in staging environments before deploying to production.

Root cause:

Assuming local environment matches cloud environment perfectly, leading to runtime errors.

#2 Ignoring scaling configuration after deployment.

Wrong approach:

Deploying workloads with fixed resource allocation and no autoscaling setup.

Correct approach:

Configure autoscaling policies based on CPU, memory, or request metrics to handle variable load.

Root cause:

Believing cloud automatically scales without explicit configuration.

#3 Updating workloads by stopping all instances at once.

Wrong approach:

Manually shutting down all app instances before deploying new version, causing downtime.

Correct approach:

Use rolling updates or blue-green deployment strategies to update without downtime.

Root cause:

Not understanding deployment strategies that maintain availability.

Key Takeaways

Deploying workloads means moving your app to cloud computers so it can run and be accessed by users anywhere.

Different cloud services offer various ways to deploy, from full control with virtual machines to fully managed serverless platforms.

Preparing your app properly and choosing the right deployment method affects reliability, scalability, and cost.

Advanced deployment techniques like rolling updates and autoscaling improve user experience and resource efficiency.

Understanding deployment deeply helps you build, run, and maintain cloud applications that meet real-world needs.

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