GCP: Serverless stack guide (Cloud Run, AppEngine, Cloud Function)
Azure serverless, AKS, as well as AWS Serverless guides.
This guide will serve as a foundational resource for professionals looking to leverage GCP’s serverless offerings for scalable, cost-effective applications. Here’s an overview of how we can structure the article, divided into chapters for detailed exploration:
Overview
Chapter 1: Introduction to Serverless on GCP
- Overview of serverless computing and its benefits.
- Brief introduction to GCP’s serverless services: Cloud Run, App Engine, and Cloud Functions.
- General use cases for serverless computing in modern applications.
Chapter 2: Cloud Run
- Detailed overview of Cloud Run, including its features and how it operates.
- Use cases specific to Cloud Run.
- Integration examples with GCP databases, messaging, data lakes, and AI services.
- Terraform examples for deploying Cloud Run applications, including CI/CD integration and using Google Cloud Storage for Terraform state files.
Chapter 3: App Engine
- Comprehensive guide on App Engine, highlighting its key features and operational model.
- App Engine-specific use cases.
- Integration with GCP’s ecosystem including databases, messaging systems, etc.
- Terraform examples for deploying applications on App Engine, focusing on infrastructure as code practices.
Chapter 4: Cloud Functions
- In-depth explanation of Cloud Functions, their use cases, and how it fit into event-driven architectures.
- How Cloud Functions integrate with other GCP services.
- Terraform examples for creating and deploying Cloud Functions, with a focus on event triggers and resource management.
Chapter 5: Integrating Serverless Solutions with GCP Services
- Detailed strategies for integrating serverless applications with GCP databases (Firestore, Cloud SQL), messaging (Pub/Sub), data lakes (BigQuery, Dataflow), and AI services (AI Platform, AutoML).
- Practical examples and best practices for leveraging GCP services to enhance serverless applications.
Chapter 6: Infrastructure as Code with Terraform
- Introduction to using Terraform for managing GCP resources, with a focus on serverless technologies.
- Best practices for Terraform configurations, including secure management of state files in Google Cloud Storage and utilizing Artifact Registry for artifacts.
- Comprehensive Terraform code samples for deploying serverless infrastructure on GCP.
Chapter 7: Security in Serverless Applications on GCP
- Identification of common security risks associated with serverless applications.
- Best practices for securing serverless applications, including the use of GCP’s security services like Cloud Armor, Identity-Aware Proxy, and Security Command Center.
- Detailed examples of implementing security measures, such as network security, IAM policies, and encryption, within serverless deployments using Terraform.
Chapter 8: Conclusion and Best Practices
- Summary of key points covered in the guide.
- Compilation of best practices for developing, deploying, managing, and securing serverless applications on GCP.
- Guidance on further resources for deepening knowledge in GCP serverless technologies.
This structure aims to provide a thorough understanding of GCP’s serverless offerings, from operational insights to practical deployment scenarios, integrating with GCP’s broader ecosystem, and addressing security concerns with actionable solutions. Let’s begin with any chapter of your choice to delve into the specifics.
Chapter 1: Introduction to Serverless on GCP
Overview of Serverless Computing
Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers. A serverless architecture allows developers to build and run applications and services without having to manage infrastructure. This model is event-driven, automatically scaling computing resources up or down as needed, ensuring that you only pay for the resources you use. The key benefits of serverless computing include scalability, reduced operational complexity, and cost efficiency.
GCP’s Serverless Offerings
Google Cloud Platform (GCP) provides a robust set of serverless services designed to help developers build and deploy scalable, highly available, and cost-effective applications. The primary serverless offerings in GCP include:
- Cloud Run: A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. Cloud Run is built on Knative, providing a portable and open API and runtime environment for your services.
- App Engine: A Platform as a Service (PaaS) offering that abstracts away infrastructure so you can focus on code. App Engine automatically scales your app up and down while balancing the load. It supports popular languages such as Node.js, Python, Java, Ruby, C, Go, PHP, and more.
- Cloud Functions: A lightweight compute solution for developers to create single-purpose, stand-alone functions that respond to cloud events without the need for managing a server or runtime environment. Cloud Functions can be triggered by events from within GCP services like Cloud Storage, Pub/Sub, and Firestore, or from HTTP requests.
Use Cases for Serverless Computing
Serverless computing is particularly well-suited for applications and services that need to scale dynamically in response to demand or events. Common use cases include:
- Web Applications: Building scalable and fully managed web applications without the complexity of infrastructure management.
- APIs: Creating and deploying scalable APIs that automatically scale with demand.
- Data Processing: Implementing data processing tasks that respond to cloud events, such as file uploads, database changes, or IoT sensor messages.
- Real-time Analytics: Analyzing and processing real-time data streams for applications like IoT, analytics dashboards, and more.
- Machine Learning: Deploying machine learning models as APIs, allowing for scalable inference serving without managing servers.
Conclusion
The introduction of serverless computing has revolutionized the way developers deploy and manage applications, offering unprecedented levels of efficiency and scalability. GCP’s serverless offerings — Cloud Run, App Engine, and Cloud Functions — provide a range of options to build and deploy applications that automatically scale with demand, allowing developers to focus on writing code rather than managing infrastructure. In the following chapters, we’ll dive deeper into each of these services, exploring their features, use cases, and how to effectively deploy and integrate them within the GCP ecosystem.
Chapter 2: Cloud Run
What is Cloud Run?
Cloud Run is a managed compute platform that enables you to run stateless containers that can be invoked via HTTP requests or Pub/Sub events. It abstracts away all infrastructure management, so you only need to focus on your application code. Cloud Run is built on the Knative open standard, which ensures the portability of your applications across hybrid and multi-cloud environments.
Use Cases for Cloud Run
Cloud Run is particularly well-suited for applications and services that need to be scalable and highly available but do not maintain an internal state. Common use cases include:
- HTTP APIs and Microservices: Cloud Run is ideal for deploying RESTful APIs and microservices that can scale automatically with the number of requests.
- Event-driven Applications: Using Pub/Sub, Cloud Run can process events in real-time, making it perfect for applications that respond to Cloud Events, such as notifications, file uploads, or updates in a database.
- Automated Tasks: Cloud Run can execute background tasks, such as image processing or data transformation, triggered by HTTP requests or cloud events.
How Cloud Run Works
- Container-based Deployment: You package your application in containers, which are then deployed to Cloud Run. The service supports any language or library that can be containerized, offering a high degree of flexibility.
- Event-driven Scaling: Cloud Run automatically scales your application up or down based on the number of incoming requests or events, even down to zero when there are no requests, ensuring you only pay for the compute resources you use.
- Fully Managed Infrastructure: Cloud Run manages all aspects of infrastructure, including provisioning, scaling, and networking, freeing you from infrastructure management tasks.
Integration with GCP Services
Cloud Run can be integrated with various GCP services to build complex, scalable applications. Some key integrations include:
- Cloud Storage: Trigger Cloud Run services in response to file uploads or changes in Cloud Storage buckets.
- Cloud Pub/Sub: Process messages or events in real-time by triggering Cloud Run services from Pub/Sub messages.
- Cloud Firestore/Cloud SQL: Serve dynamic content or perform operations by connecting Cloud Run services to Firestore or Cloud SQL databases.
- BigQuery: Analyze large datasets by invoking Cloud Run services to process query results from BigQuery for real-time analytics.
Terraform Example: Deploying Cloud Run
To deploy a Cloud Run service using Terraform, you will define the infrastructure as code. This example includes creating a new Cloud Run service and deploying a container image to it. Note: Ensure you have a Docker container image hosted on the Google Container Registry (GCR) or Artifact Registry.
resource "google_cloud_run_service" "default" {
name = "example-service"
location = "us-central1"
template {
spec {
containers {
image = "gcr.io/your-project-id/your-image"
}
}
}
traffic {
percent = 100
latest_revision = true
}
}
resource "google_project_iam_member" "cloud_run_invoker" {
project = var.project_id
role = "roles/run.invoker"
member = "serviceAccount:your-service-account@your-project-id.iam.gserviceaccount.com"
}This code snippet creates a Cloud Run service named `example-service` in the `us-central1` location, deploying a container from the specified image. It also sets up IAM permissions to allow the service account to invoke the service.
Conclusion
Cloud Run offers a powerful platform for running stateless, containerized applications with automatic scaling and a pay-for-what-you-use pricing model. Its integration with other GCP services enables the development of complex, scalable applications that respond to real-time events or user requests. In the next chapter, we will explore App Engine, another core component of GCP’s serverless offerings, comparing its features and use cases with Cloud Run to help you choose the right tool for your project.
Chapter 3: App Engine
What is App Engine?
Google App Engine is a fully managed, serverless platform for developing and hosting web applications at scale. It offers automatic scaling, high availability, and zero server management, allowing developers to focus on code while Google handles the infrastructure. App Engine supports popular development languages and offers two environments: Standard and Flexible.
Use Cases for App Engine
App Engine is ideal for a wide range of applications, from simple websites to complex enterprise applications. Key use cases include:
- Web Applications: Hosting scalable web applications that automatically scale based on traffic.
- API Backends: Building and deploying API backends that scale automatically with demand.
- Application Ecosystems: Developing and hosting microservices as part of a larger application ecosystem, with each service independently scalable.
How App Engine Works
App Engine provides a platform where applications are automatically managed and scaled by Google’s infrastructure. It differentiates between two environments:
- Standard Environment: Offers automatic scaling with instances that are started and stopped dynamically. It’s optimized for applications that can run in sandboxed environments with specific runtime versions.
- Flexible Environment: Provides more control over the underlying infrastructure, supporting custom runtimes and the ability to access Google Cloud resources directly. It’s suitable for applications that need access to the operating system’s capabilities.
Integration with GCP Services
App Engine integrates seamlessly with GCP services, enabling powerful and scalable applications:
- Cloud Datastore/Firestore: For NoSQL databases that automatically scale with your application.
- Cloud SQL: To use fully managed relational databases.
- Cloud Pub/Sub: For integrating event-driven architectures or handling asynchronous tasks.
- Cloud Endpoints: To develop, deploy, and manage APIs securely.
Terraform Example: Deploying App Engine
Deploying an App Engine application with Terraform involves configuring your application and its environment. The following example demonstrates deploying a simple App Engine application in the Standard environment.
resource "google_app_engine_application" "app" {
project = var.project_id
location_id = "us-central"
// Optional settings: Specify if using App Engine Flexible Environment
// serving_status = "SERVING"
// feature_settings {
// split_health_checks = true
// }
}
resource "google_app_engine_standard_app_version" "myapp_v1" {
version_id = "v1"
service = "default"
runtime = "python39"
entrypoint {
shell = "gunicorn -b :$PORT main:app"
}
deployment {
zip {
source_url = "https://storage.googleapis.com/${google_storage_bucket.app_source.name}/${google_storage_bucket_object.app_version.source}"
}
}
env_variables = {
"ENVIRONMENT" = "production"
}
// Automatically scale your application
automatic_scaling {
max_instances = 5
}
project = google_app_engine_application.app.project
location = google_app_engine_application.app.location_id
}This example configures an App Engine application in the `us-central` location, specifying the runtime and the source code’s location. It sets environment variables and configures automatic scaling with a maximum of 5 instances.
Conclusion
Google App Engine provides a powerful platform for building scalable web applications and APIs with minimal infrastructure management. Whether you choose the Standard or Flexible environment depends on your specific needs regarding runtime support, access to the underlying infrastructure, and specific Google Cloud integrations. Up next, we’ll delve into Cloud Functions, GCP’s event-driven compute solution, to complete our overview of GCP’s serverless offerings.
Chapter 4: Cloud Functions
What is Cloud Functions?
Cloud Functions is a scalable, event-driven compute solution that allows you to run your code in response to specific events triggered by Google Cloud Platform (GCP) services or direct HTTP requests. This serverless architecture enables you to build and deploy single-purpose functions that operate in a fully managed environment, eliminating the need to provision or manage servers. Cloud Functions supports Node.js, Python, Go, Java, .NET, Ruby, and PHP, allowing you to write functions in your preferred programming language.
Use Cases for Cloud Functions
Cloud Functions is designed for a variety of use cases that benefit from event-driven, lightweight compute tasks:
- Data Processing: Automatically process data as it arrives in Cloud Storage, Firestore, Firebase Realtime Database, or Pub/Sub, making it ideal for ETL tasks, data validation, or transformation.
- Webhooks: Implement webhook receivers to handle HTTP callbacks from third-party services, such as GitHub or Stripe.
- IoT: Process and respond to IoT device data messages sent through Pub/Sub, enabling real-time analytics and device management.
- Integration and Orchestration: Create glue code to connect and extend GCP services or third-party APIs, facilitating complex workflows and data processing pipelines.
How Cloud Functions Works
- Event-driven Execution: Functions are triggered by specific events from GCP services like Cloud Storage, Firestore, or Pub/Sub, or by HTTP requests. Each event automatically initiates the execution of your function with the event data provided as input.
- Stateless: Functions are stateless, with each invocation running in its instance. This allows for high scalability, as Cloud Functions can automatically scale from a few invocations to millions, depending on the workload.
- Fully Managed: Cloud Functions provides a fully managed environment, handling all aspects of function execution, including provisioning resources, scaling, monitoring, and logging.
Integration with GCP Services
Cloud Functions seamlessly integrates with various GCP services, enabling you to build sophisticated, serverless workflows:
- Cloud Storage: Trigger functions in response to file uploads, deletions, or other changes in your Cloud Storage buckets.
- Cloud Pub/Sub: Execute functions in response to messages published to Pub/Sub topics, ideal for event-driven processing and data streaming applications.
- Firestore: Run functions in response to changes in Firestore documents, enabling real-time data synchronization and backend workflows.
Terraform Example: Deploying Cloud Functions
Deploying Cloud Functions with Terraform involves defining the `google_cloudfunctions_function` resource, specifying the trigger, and providing the function code. Here’s an example of deploying a Cloud Function triggered by HTTP requests:
resource "google_cloudfunctions_function" "example_function" {
name = "example-function"
runtime = "nodejs10" // Specify the runtime environment
entry_point = "helloWorld" // Function within the source to execute
timeout = 60 // Execution timeout in seconds
available_memory_mb = 256
source_archive_bucket = google_storage_bucket.function_source.name
source_archive_object = google_storage_bucket_object.function_archive.name
trigger_http = true // Enable HTTP trigger
project = var.project_id
region = "us-central1"
// Optional: Set environment variables
environment_variables = {
FOO = "bar"
}
}
// IAM policy to allow unauthenticated access to the function
resource "google_cloudfunctions_function_iam_binding" "example_function_invoker" {
project = var.project_id
region = "us-central1"
cloud_function = google_cloudfunctions_function.example_function.name
role = "roles/cloudfunctions.invoker"
members = ["allUsers"]
}This example deploys a Node.js Cloud Function that can be triggered via HTTP, making it accessible over the internet. It specifies the function’s source code location in a Google Cloud Storage bucket and allows unauthenticated access by setting the appropriate IAM policy.
Conclusion
Cloud Functions offers a powerful, event-driven computing solution for running small, single-purpose functions that respond to cloud events. It’s particularly well-suited for lightweight data processing, APIs, webhooks, and integrating cloud services. By leveraging Cloud Functions, developers can focus on writing code that adds value, without worrying about the underlying infrastructure. Next, we will explore how to integrate these serverless solutions with GCP’s broader ecosystem of database, messaging, data lake, and AI services to build comprehensive, scalable applications.
Chapter 5: Integrating Serverless Solutions with GCP Services
Serverless computing on Google Cloud Platform (GCP) offers powerful, scalable solutions for building modern applications. By integrating serverless solutions like Cloud Run, App Engine, and Cloud Functions with other GCP services, you can create sophisticated, highly scalable applications that leverage the best of cloud computing. This chapter explores how to integrate these serverless platforms with GCP’s database, messaging, data lake, and AI services.
Integrating with GCP Databases
Cloud Firestore and Firebase Realtime Database
Both databases are fully managed, NoSQL databases that scale automatically. Cloud Functions can listen to changes in these databases in real-time, making them ideal for applications requiring real-time updates, like chat apps or live dashboards. Cloud Run and App Engine can connect to these databases to serve dynamic content or perform CRUD operations.
Integrating App Engine with Firestore
To use Firestore as a database for an App Engine application, ensure your GCP project has Firestore in Datastore mode enabled. Here, the focus is more on application code to use Firestore SDKs rather than specific Terraform configurations, as Firestore’s serverless nature allows direct integration with App Engine without specific infrastructure setup. However, you can define your App Engine application in Terraform as follows:
resource "google_app_engine_application" "app" {
project = var.project_id
location_id = "us-central"
}Your App Engine application can use Firestore client libraries to interact with Firestore, leveraging GCP’s IAM for secure access.
Cloud SQL
A fully managed relational database that supports PostgreSQL, MySQL, and SQL Server. You can connect your serverless applications to Cloud SQL to handle traditional relational database workloads. Use the Cloud SQL Auth proxy with Cloud Run and App Engine for secure connections without having to manage database credentials within your application.
Integrating Cloud Run with Cloud SQL
To integrate Cloud Run with Cloud SQL, you can use the Cloud SQL Proxy to securely connect to your database instance without having to manage SSL certificates manually. Below is an example of how you might configure a Cloud Run service to connect to a Cloud SQL instance using Terraform.
First, ensure you have a Cloud SQL instance:
resource "google_sql_database_instance" "default" {
name = "example-instance"
database_version = "POSTGRES_12"
region = "us-central1" settings {
tier = "db-f1-micro"
}
}Next, deploy a Cloud Run service that connects to the Cloud SQL instance:
resource "google_cloud_run_service" "default" {
name = "cloudrun-sql-service"
location = "us-central1"template {
spec {
containers {
image = "gcr.io/your-project-id/your-image" env {
name = "DATABASE_URL"
value = "postgres://user:password@/dbname?host=/cloudsql/instance-connection-name"
}
}
}
} traffic {
percent = 100
latest_revision = true
}
}
Ensure to replace `your-project-id`, `your-image`, and the `DATABASE_URL` with your specific details. The `instance-connection-name` is typically in the format `project:region: instance`.
Integrating with Messaging and Streaming Services
Cloud Pub/Sub
A fully managed real-time messaging service that allows you to send and receive messages between independent applications. You can use Cloud Functions to process Pub/Sub messages, enabling event-driven architectures and workflows. Cloud Run can also process events by subscribing to a Pub/Sub topic, making it suitable for microservices that need to communicate asynchronously.
Integrating Cloud Functions with Cloud Pub/Sub
To trigger a Cloud Function with messages from a Cloud Pub/Sub topic, you first need a Pub/Sub topic:
resource "google_pubsub_topic" "default" {
name = "example-topic"
}Then, deploy a Cloud Function triggered by this topic:
resource "google_cloudfunctions_function" "subscriber" {
name = "pubsub-subscriber-function"
runtime = "nodejs10"
entry_point = "subscribe"
trigger_http = falseevent_trigger {
event_type = "google.pubsub.topic.publish"
resource = google_pubsub_topic.default.id
}source_archive_bucket = google_storage_bucket.function_source.name
source_archive_object = google_storage_bucket_object.function_archive.name
}Replace the `runtime`, `entry_point`, and source archive details with your specific function configuration.
Integrating with Data Lakes and Analytics
BigQuery
A serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. You can use Cloud Functions to trigger data processing or ETL jobs in response to events, such as new data uploads to Cloud Storage. App Engine and Cloud Run can query BigQuery to provide analytics and insights to users, powering dashboards, or generating reports.
Integrating serverless solutions with data lakes and analytics services in GCP, such as BigQuery, can unlock powerful data processing and analytical capabilities. Here, we’ll focus on how to use Terraform to set up integrations that allow serverless applications to interact with BigQuery for analytics purposes.
Triggering Cloud Functions from BigQuery Events
BigQuery does not directly trigger Cloud Functions based on events like inserts. However, you can architect a solution using Pub/Sub and scheduled queries in BigQuery to simulate event-driven triggers. For example, a scheduled query can write results to a Pub/Sub topic, which then triggers a Cloud Function.
First, create a Pub/Sub topic for the BigQuery events:
resource "google_pubsub_topic" "bigquery_events" {
name = "bigquery-events"
}Next, assuming you have a Cloud Function that processes messages from this Pub/Sub topic, you would set it up like so:
resource "google_cloudfunctions_function" "process_bigquery_event" {
name = "process-bigquery-event"
runtime = "nodejs14"
entry_point = "processEvent"
event_trigger {
event_type = "google.pubsub.topic.publish"
resource = google_pubsub_topic.bigquery_events.id
}
source_archive_bucket = "your-source-bucket"
source_archive_object = "your-source-object"
}Replace `”your-source-bucket”` and `”your-source-object”` with your Cloud Storage bucket and object where the Cloud Function source code is stored.
To trigger this function with BigQuery, you would create a scheduled query in BigQuery that publishes to the Pub/Sub topic. This setup is more manual and would involve using the BigQuery and Pub/Sub APIs or the Google Cloud Console, as Terraform does not currently support BigQuery scheduled queries directly.
Streaming Data into BigQuery from Cloud Run
Cloud Run can stream data into BigQuery using the BigQuery client libraries. The Terraform setup involves creating a BigQuery dataset and table, and ensuring Cloud Run has the necessary permissions to access BigQuery.
First, define a BigQuery dataset:
resource "google_bigquery_dataset" "dataset" {
dataset_id = "your_dataset"
location = "US"
description = "Dataset for storing application data"
default_table_expiration_ms = 3600000
}Next, create a BigQuery table within the dataset:
resource "google_bigquery_table" "table" {
dataset_id = google_bigquery_dataset.dataset.dataset_id
table_id = "your_table"
schema = <<EOF
[
{
"name": "name",
"type": "STRING",
"mode": "REQUIRED"
},
{
"name": "value",
"type": "FLOAT",
"mode": "NULLABLE"
}
]
EOF
}For Cloud Run to access BigQuery, you’ll need to assign the appropriate IAM role to the Cloud Run service account:
resource "google_project_iam_member" "cloud_run_bq" {
project = var.project_id
role = "roles/bigquery.dataEditor"
member = "serviceAccount:${google_cloud_run_service.default.template.spec.service_account}"
}Ensure your Cloud Run application uses BigQuery client libraries to stream data into BigQuery, leveraging the service account with the `bigquery.dataEditor` role for authentication.
Integrating with AI and Machine Learning Services
AI Platform and AutoML
GCP offers a range of AI and machine learning services that can be integrated with serverless applications. Use Cloud Functions to process data and make predictions with AI Platform models or to dynamically train models based on incoming data. Cloud Run and App Engine can serve as interfaces to these models, offering scalable endpoints for machine learning applications.
Integrating serverless solutions with Google Cloud Platform’s AI and Machine Learning services can greatly enhance the capabilities of your applications, from adding image recognition features to leveraging machine learning models for predictive analytics. Below, we’ll explore how to use Terraform to integrate Cloud Functions and Cloud Run with AI Platform (Vertex AI) and AutoML services, providing coded examples for each.
Invoking AI Platform Predictions from Cloud Functions
Suppose you want to use Cloud Functions to process data and then invoke a machine learning model hosted on AI Platform for predictions. The following Terraform configuration sets up a Cloud Function that has permissions to access the AI Platform.
First, ensure you have a machine learning model deployed on the AI Platform. Then, use Terraform to deploy a Cloud Function:
resource "google_cloudfunctions_function" "ai_invoker" {
name = "invoke-ai-model"
runtime = "python39"
entry_point = "predict"
available_memory_mb = 256
source_archive_bucket = "your-source-bucket"
source_archive_object = "your-source-code.zip"
event_trigger {
event_type = "providers/cloud.pubsub/eventTypes/topic.publish"
resource = "projects/your-project/topics/your-topic"
}
}In this setup, `your-source-bucket` and `your-source-code.zip` should be replaced with your actual Cloud Storage bucket and the ZIP file containing your Cloud Function’s code. The function is triggered by messages published to a specific Pub/Sub topic, making it ideal for asynchronous processing.
To allow the Cloud Function to invoke AI Platform models, assign the necessary IAM role:
resource "google_project_iam_member" "function_ai_invoker" {
project = var.project_id
role = "roles/aiplatform.user"
member = "serviceAccount:${google_cloudfunctions_function.ai_invoker.service_account_email}"
}Your Cloud Function’s code should use the AI Platform client libraries to make predictions by sending data to your deployed model.
Serving Machine Learning Models with Cloud Run
For scenarios where you want to serve machine learning models directly and manage the inference code yourself, you can use Cloud Run. This allows for greater flexibility in how requests are handled and responses are formatted.
Deploy a container to Cloud Run that includes your machine learning model inference code. Ensure your containerized application can access AI Platform or AutoML models if needed:
resource "google_cloud_run_service" "ml_service" {
name = "ml-model-service"
location = "us-central1"
template {
spec {
containers {
image = "gcr.io/your-project-id/ml-inference-image"
resources {
limits = {
cpu = "1000m"
memory = "512Mi"
}
}
}
}
}
traffic {
percent = 100
latest_revision = true
}
}In this example, `ml-inference-image` should be replaced with your container image stored in the Container Registry or Artifact Registry that is capable of serving your machine learning model.
To allow Cloud Run to access AI Platform models or AutoML, attach the necessary IAM roles to the service account used by the Cloud Run instance:
resource "google_project_iam_member" "cloud_run_ml_user" {
project = var.project_id
role = "roles/aiplatform.user"
member = "serviceAccount:${google_cloud_run_service.ml_service.template.spec.service_account}"
}This configuration grants the Cloud Run service the permissions needed to interact with the AI Platform, enabling it to serve predictions based on the model.
Conclusion
These examples illustrate how to integrate serverless solutions with various GCP services using Terraform. By leveraging Terraform, you can automate the deployment and management of these integrations, ensuring a scalable, manageable, and consistent infrastructure. Remember, the specific configurations will vary based on your project’s needs and the details of your GCP environment.
Integrating serverless solutions with GCP’s powerful suite of services allows you to build scalable, efficient, and highly available applications. Whether you’re building web applications, processing data, or creating machine learning models, leveraging the serverless ecosystem with GCP’s databases, messaging services, data lakes, and AI services can significantly enhance your applications’ capabilities and performance. The next chapter will delve into using Terraform to manage and deploy these serverless architectures, ensuring infrastructure as code best practices and streamlined workflows.
Chapter 6: Infrastructure as Code with Terraform
Infrastructure as Code (IaC) is a key practice in the modern development lifecycle, allowing teams to manage and provision their infrastructure using code, rather than through manual processes. Terraform, by HashiCorp, is a popular IaC tool that enables you to define both cloud and on-premises resources in human-readable configuration files that can be versioned, reused, and shared. This chapter focuses on leveraging Terraform to deploy and manage GCP serverless architectures, emphasizing best practices and providing examples.
Terraform with GCP
To use Terraform with GCP, you’ll need to set up the Google Cloud provider. This involves specifying your GCP project and optionally configuring your credentials. Here’s a basic setup:
provider "google" {
project = "your-gcp-project-id"
region = "your-default-region"
zone = "your-default-zone"
}
terraform {
required_providers {
google = {
source = "hashicorp/google"
version = "~> 3.5"
}
}
backend "gcs" {
bucket = "your-terraform-state-bucket"
prefix = "terraform/state"
}
}In this example, replace `”your-gcp-project-id”`, `”your-default-region”`, and `”your-default-zone”` with your project’s details. The `backend “gcs”` block configures Terraform to store its state in a Google Cloud Storage (GCS) bucket, enabling team collaboration and state locking.
Deploying Serverless Applications
Let’s explore deploying serverless applications across Cloud Run, App Engine, and Cloud Functions using Terraform.
Cloud Run
Deploy a containerized application on Cloud Run:
resource "google_cloud_run_service" "default" {
name = "example-service"
location = "us-central1"
template {
spec {
containers {
image = "gcr.io/your-project-id/hello-world"
}
}
}
traffic {
percent = 100
latest_revision = true
}
}Ensure your container image (`”gcr.io/your-project-id/hello-world”`) is stored in the Google Container Registry or Artifact Registry.
App Engine
Deploy an application to App Engine:
resource "google_app_engine_application" "app" {
project = "your-gcp-project-id"
location_id = "us-central"
}
resource "google_app_engine_standard_app_version" "v1" {
version_id = "v1"
service = "default"
runtime = "nodejs14"
entrypoint {
shell = "node app.js"
}
deployment {
zip {
source_url = "gs://your-source-bucket/app.zip"
}
}
env_variables = {
"KEY" = "value"
}
// Ensures the App Engine application exists before deployment
depends_on = [google_app_engine_application.app]
}This code deploys a Node.js application to App Engine, specifying the runtime and environment variables.
Cloud Functions
Deploy a Cloud Function that responds to HTTP requests:
resource "google_cloudfunctions_function" "default" {
name = "example-function"
description = "A simple example."
runtime = "nodejs14"
available_memory_mb = 128
source_archive_bucket = "your-source-bucket"
source_archive_object = "function-source.zip"
trigger_http = true
entry_point = "helloWorld"
environment_variables = {
"EXAMPLE_VAR" = "example-value"
}
}This function is triggered by HTTP requests and uses Node.js 14 as its runtime environment.
Best Practices
- Version Control: Keep your Terraform configurations in version control to track changes and collaborate with your team.
- State Management: Use GCS as a backend for Terraform state to ensure the state is shared and locked correctly among team members.
- Modularize: Break down your Terraform configurations into modules to reuse code and manage resources more efficiently.
- Secure Secrets: Avoid hardcoding sensitive information in Terraform files. Use GCP’s Secret Manager and reference secrets in your Terraform configurations.
- Review Plans: Always review Terraform’s execution plan before applying changes to understand the impact on your infrastructure.
Conclusion
Using Terraform to manage your GCP serverless infrastructure brings consistency, repeatability, and scalability to your cloud resources. By following best practices and leveraging Terraform’s capabilities, you can efficiently deploy and manage complex serverless applications. This approach not only simplifies infrastructure management but also integrates seamlessly into CI/CD pipelines, enhancing automation and collaboration.
Chapter 7: Security in Serverless Applications on GCP
Securing serverless applications on Google Cloud Platform (GCP) involves understanding the unique security model of serverless computing and applying best practices to protect your applications and data. This chapter outlines key security considerations and guides leveraging GCP’s security features to safeguard your serverless architectures.
Understanding the Security Model
Serverless applications are hosted in a multi-tenant environment, where the cloud provider (GCP, in this case) manages the infrastructure, runtime, and operating system. While this model offers significant operational benefits, it also shifts some security responsibilities to the cloud provider, allowing developers to focus more on application-level security.
Key Security Considerations
1. Least Privilege Access: Ensure that your serverless functions and services have only the permissions they need to perform their tasks. Overly permissive roles can expose your application to unnecessary risks.
2. Data Encryption: Encrypt sensitive data at rest and in transit. GCP automatically encrypts data in transit within its network, but you should ensure encryption in transit to and from the internet and encrypt sensitive data before storing it.
3. Dependency Management: Regularly update your serverless function’s dependencies to mitigate vulnerabilities. Consider using tools to automate vulnerability scanning and patch management.
4. Secure Application Secrets: Use GCP’s Secret Manager to store and manage access to secrets, such as API keys and database credentials, securely. Access to these secrets should be controlled using IAM roles.
5. Input Validation: Validate input to your serverless functions to avoid injection attacks and other common web vulnerabilities. Proper input validation can prevent attackers from exploiting your application.
6. Logging and Monitoring: Enable logging for your serverless applications and use GCP’s operations suite to monitor logs and metrics. This can help detect and respond to security incidents more effectively.
Implementing Security Best Practices
Least Privilege Access
Use Terraform to create and assign a custom IAM role to a Cloud Function:
resource "google_project_iam_custom_role" "function_minimal_role" {
role_id = "function_minimal_role"
title = "Minimal Role for Function"
description = "A minimal role for a Cloud Function"
permissions = [
"logging.logEntries.create",
"pubsub.topics.publish",
]
}
resource "google_cloudfunctions_function_iam_member" "function_member" {
project = var.project_id
region = "us-central1"
cloud_function = google_cloudfunctions_function.example_function.name
role = google_project_iam_custom_role.function_minimal_role.id
member = "serviceAccount:${google_service_account.function_account.email}"
}Data Encryption
GCP encrypts data at rest by default. For data in transit, ensure your functions and services use HTTPS endpoints and SSL/TLS if connecting to databases or other resources.
Secure Application Secrets
Use Secret Manager with a Cloud Function:
resource "google_secret_manager_secret" "api_key" {
secret_id = "api-key"
replication {
automatic = true
}
}
resource "google_secret_manager_secret_version" "api_key_version" {
secret = google_secret_manager_secret.api_key.id
secret_data = "your-api-key"
}
resource "google_cloudfunctions_function" "example_function" {
// Other configuration...
environment_variables = {
API_KEY_SECRET = google_secret_manager_secret.api_key.id
}
}Ensure your serverless application retrieves the secret at runtime using the appropriate SDK.
Conclusion
Security in serverless applications on GCP requires a comprehensive approach, focusing on least privilege access, data protection, dependency management, and monitoring. By leveraging GCP’s built-in security features and following best practices, you can build secure, resilient serverless applications. As serverless architectures continue to evolve, staying informed about the latest security trends and features is crucial for maintaining a robust security posture.
Chapter 8: Conclusion and Best Practices
As we conclude our guide on serverless technologies in Google Cloud Platform (GCP), it’s important to recap the key points we’ve covered and highlight best practices that can help you effectively design, deploy, and manage serverless applications. Serverless computing offers significant benefits in terms of scalability, cost, and operational efficiency, making it an attractive option for many types of applications.
Key Takeaways
- Serverless Computing on GCP: We explored GCP’s primary serverless offerings: Cloud Run, App Engine, and Cloud Functions. Each service has its unique strengths and use cases, from running containerized applications with Cloud Run to building highly scalable apps with App Engine and creating event-driven functions with Cloud Functions.
- Integrating Serverless Solutions with GCP Services: Effective integration with GCP’s database, messaging, data lake, and AI services enables the development of sophisticated, highly scalable applications. We discussed how to leverage these integrations to enhance application capabilities and performance.
- Infrastructure as Code with Terraform: Managing serverless infrastructure using Terraform allows for consistent, repeatable deployments. We provided examples of how to deploy serverless applications and integrate them with other GCP services using Terraform, emphasizing the importance of version control and modularization.
- Security in Serverless Applications: Security remains a top priority in serverless architectures. We covered best practices for securing serverless applications, including implementing least privilege access, managing dependencies, encrypting data, and using GCP’s Secret Manager for secure storage of application secrets.
Best Practices for Serverless Applications on GCP
1. Design for Scalability and Statelessness: Maximize the scalability benefits of serverless by designing stateless applications that can handle variable loads efficiently.
2. Optimize for Cold Start Performance: Minimize dependencies and use lightweight frameworks to reduce cold start times, especially for Cloud Functions and Cloud Run services.
3. Monitor and Optimize Costs: Keep track of your serverless application’s resource usage and request patterns to optimize costs. Use GCP’s pricing calculator and monitoring tools to identify and address any inefficiencies.
4. Embrace DevOps and CI/CD: Automate testing, deployments, and infrastructure management using CI/CD pipelines. This ensures consistency, reduces manual errors, and accelerates development cycles.
5. Stay Informed and Engaged with the Community: Serverless technologies and best practices evolve rapidly. Engage with the GCP community, follow GCP updates, and continuously explore new features and services.
Looking Forward
The landscape of serverless computing is continually evolving, with new patterns, practices, and services emerging. As you build and deploy serverless applications on GCP, remain adaptable and open to exploring innovative approaches that can enhance your applications’ performance, security, and reliability.
Serverless computing on GCP offers a powerful set of tools for developers to build scalable, cost-effective applications. By following the practices and principles outlined in this guide, you can maximize the benefits of serverless computing and ensure your applications are robust, secure, and maintainable.
Thank you for following this guide. I hope it serves as a valuable resource as you embark on or continue your journey with serverless computing on GCP.
To work with app deployment in GCP you would need a guide for the Google Cloud Build.
