Set up a classic Application Load Balancer with Cloud Storage buckets

This document shows you how to create an external Application Load Balancer to route requests for static content to Cloud Storage buckets. After you configure a load balancer with the backend buckets, requests to URL paths that begin with /love-to-fetch are sent to the us-east1 Cloud Storage bucket, and all other requests are sent to the europe-north1 Cloud Storage bucket, regardless of the user's region.

If your backends serve dynamic content over HTTP(S), consider using backend services instead of backend buckets.

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To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:

Guide me

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Cloud Storage buckets as load balancer backends

An external Application Load Balancer uses a URL map to direct traffic from specified URL paths to your backends.

In the following diagram, the load balancer sends traffic with a path of /love-to-fetch/ to a Cloud Storage bucket in the us-east1 region. All other requests go to a Cloud Storage bucket in the europe-north1 region.

Distributing traffic to Cloud Storage

By default, Cloud Storage uses the same cache that Cloud CDN uses. If you enable Cloud CDN on the backend bucket, you can use Cloud CDN controls on your content. Cloud CDN controls include, for example, cache modes, signed URLs, and invalidation. Cloud CDN also lets you cache large content (> 10 MB). If you don't enable Cloud CDN on your backend bucket, you can only use origin Cache-Control headers to control caching for smaller content, as set by the Cloud Storage metadata.

Before you begin

Make sure that your setup meets the following prerequisites. If you are using the gcloud storage utility, you can install it by using instructions in Discover object storage with the gcloud tool.

Set a default project

Console

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

5. Make sure that billing is enabled for your Google Cloud project.

gcloud

gcloud config set project PROJECT_ID

Replace PROJECT_ID with the project that you are using for this guide.

Terraform

export GOOGLE_CLOUD_PROJECT=PROJECT_ID

Permissions

To follow this guide, you need to create Cloud Storage buckets and a load balancer in a project. You should be either a project owner or editor, or you should have the following Compute Engine IAM roles:

Task	Required Role
Create load balancer components	Network Admin
Create Cloud Storage buckets	Storage Object Admin

For more information, see the following guides:

• Access control
• IAM Conditions

Set up an SSL certificate resource

For an HTTPS load balancer, create an SSL certificate resource as described in the following documentation:

• Using self-managed SSL certificates
• Using Google-managed SSL certificates

We recommend using a Google-managed certificate.

This example assumes that you already have an SSL certificate resource named www-ssl-cert.

Warning: Don't use a self-signed certificate for production purposes.

Prepare your Cloud Storage buckets and content

The process for preparing your Cloud Storage buckets is as follows:

• Create the buckets.

• Copy content to the buckets.

• Provide public access to the buckets.

Create Cloud Storage buckets

In this example, you create two Cloud Storage buckets for the load balancer to access. For production deployments, we recommend that you choose a multi-region bucket, which automatically replicates objects across multiple Google Cloud regions. This can improve the availability of your content and improve failure tolerance across your application.

Note the names of the Cloud Storage buckets you create, as they're used later. In this guide, they're referred to as BUCKET_1_NAME and BUCKET_2_NAME.

Console

1. In the Google Cloud console, go to the Cloud Storage Buckets page.

   Go to Cloud Storage Buckets

2. Click Create bucket.

3. In the Name your bucket box, enter a globally unique name that follows the naming guidelines.

4. Click Choose where to store your data.

5. Set Location type to Region.

6. Set Location to europe-north1. This is BUCKET_1_NAME in this guide.

7. Click Create.

8. Click Buckets to return to the Cloud Storage Buckets page. Use these instructions to create a second bucket, but set the Location to us-east1. This is BUCKET_2_NAME in this guide.

gcloud

gcloud storage buckets create gs://BUCKET_1_NAME --project=PROJECT_ID --default-storage-class=standard --location=europe-north1 --uniform-bucket-level-access

gcloud storage buckets create gs://BUCKET_2_NAME --project=PROJECT_ID --default-storage-class=standard --location=us-east1 --uniform-bucket-level-access

Replace BUCKET_1_NAME and BUCKET_2_NAME with the names of the buckets that you want to create.

Terraform

To create the buckets, use the google_storage_bucket resource.

# Create Cloud Storage buckets
resource "random_id" "bucket_prefix" {
  byte_length = 8
}

resource "google_storage_bucket" "bucket_1" {
  name                        = "${random_id.bucket_prefix.hex}-bucket-1"
  location                    = "us-east1"
  uniform_bucket_level_access = true
  storage_class               = "STANDARD"
  // delete bucket and contents on destroy.
  force_destroy = true
}

resource "google_storage_bucket" "bucket_2" {
  name                        = "${random_id.bucket_prefix.hex}-bucket-2"
  location                    = "us-east1"
  uniform_bucket_level_access = true
  storage_class               = "STANDARD"
  // delete bucket and contents on destroy.
  force_destroy = true
}

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

Transfer content to your Cloud Storage buckets

So you can test the setup later, copy the following images from a public Cloud Storage bucket to your own Cloud Storage buckets.

gcloud

1. Click Activate Cloud Shell.

2. Run the following commands in Cloud Shell, replacing the bucket name variables with your Cloud Storage bucket names:

gcloud storage cp gs://gcp-external-http-lb-with-bucket/three-cats.jpg gs://BUCKET_1_NAME/never-fetch/

gcloud storage cp gs://gcp-external-http-lb-with-bucket/two-dogs.jpg gs://BUCKET_2_NAME/love-to-fetch/

Terraform

To copy items into the bucket, you can use the google_storage_bucket_object resource.

resource "google_storage_bucket_object" "cat_image" {
  name         = "never-fetch/three-cats.jpg"
  source       = "images/three-cats.jpg"
  content_type = "image/jpeg"

  bucket = google_storage_bucket.bucket_1.name
}

resource "google_storage_bucket_object" "dog_image" {
  name         = "love-to-fetch/two-dogs.jpg"
  source       = "images/two-dogs.jpg"
  content_type = "image/jpeg"

  bucket = google_storage_bucket.bucket_2.name
}

Alternatively, use the null_resource resource.

resource "null_resource" "upload_cat_image" {
provisioner "local-exec" {
  command = "gcloud storage cp gs://gcp-external-http-lb-with-bucket/three-cats.jpg gs://${google_storage_bucket.bucket_1.name}/never-fetch/"
}
}

resource "null_resource" "upload_dog_image" {
provisioner "local-exec" {
  command = "gcloud storage cp gs://gcp-external-http-lb-with-bucket/two-dogs.jpg gs://${google_storage_bucket.bucket_2.name}/love-to-fetch/"
}
}

In the Google Cloud console, click Refresh on each bucket's details page to verify that the file has copied successfully.

Make your Cloud Storage buckets publicly readable

When you make Cloud Storage buckets publicly readable, anyone on the internet can list and view their objects, and view their metadata (excluding ACLs). Don't include sensitive information in your public buckets.

To reduce the likelihood of accidental exposure of sensitive information, don't store public objects and sensitive data in the same bucket.

Console

To grant all users access to view objects in your buckets, repeat the following procedure for each bucket:

1. In the Google Cloud console, go to the Cloud Storage Buckets page.

   Go to Cloud Storage Buckets

2. Click the bucket name, followed by the Permissions tab.

3. Click Add.

4. In the New principals box, enter allUsers.

5. In the Select a role box, select Cloud Storage > Storage Object Viewer.

6. Click Save.

7. Click Allow public access.

gcloud

To grant all users access to view objects in your buckets, run the following commands:

gcloud storage buckets add-iam-policy-binding gs://BUCKET_1_NAME --member=allUsers --role=roles/storage.objectViewer

gcloud storage buckets add-iam-policy-binding gs://BUCKET_2_NAME --member=allUsers --role=roles/storage.objectViewer

Terraform

To grant all users access to view objects in your buckets, use the google_storage_bucket_iam_member resource and specify the allUsers member.

# Make buckets public
resource "google_storage_bucket_iam_member" "bucket_1" {
  bucket = google_storage_bucket.bucket_1.name
  role   = "roles/storage.objectViewer"
  member = "allUsers"
}

resource "google_storage_bucket_iam_member" "bucket_2" {
  bucket = google_storage_bucket.bucket_2.name
  role   = "roles/storage.objectViewer"
  member = "allUsers"
}

Reserve an external IP address

After you've set up your Cloud Storage buckets, you can reserve a global static external IP address that your audience uses to reach your load balancer.

This step is optional but recommended, as a static external IP address provides a single address to point your domain at.

Note: You can skip this step and have Google Cloud associate an ephemeral IP address with your load balancer's forwarding rule. An ephemeral IP address remains constant while the forwarding rule exists. If you need to delete the forwarding rule and re-add it, the forwarding rule might receive a new IP address. If needed, you can make an ephemeral IP address static.

Console

1. In the Google Cloud console, go to the External IP addresses page.

   Go to External IP addresses

2. Click Reserve static address.

3. In the Name box, enter example-ip.

4. Set the Network Service Tier to Premium.

5. Set the IP version to IPv4.

6. Set the Type to Global.

7. Click Reserve.

gcloud

gcloud compute addresses create example-ip \
    --network-tier=PREMIUM \
    --ip-version=IPV4 \
    --global

Note the IPv4 address that was reserved:

gcloud compute addresses describe example-ip \
    --format="get(address)" \
    --global

Terraform

To reserve an external IP address, use the google_compute_global_address resource.

# Reserve IP address
resource "google_compute_global_address" "default" {
  name = "example-ip"
}

Create an external Application Load Balancer with backend buckets

These instructions cover creating either an HTTP or HTTPS load balancer. To create an HTTPS load balancer you must add an SSL certificate resource to the load balancer's frontend. For more information, see the SSL certificates overview.

Console

Start your configuration

1. In the Google Cloud console, go to the Load balancing page.

   Go to Load balancing

2. Click Create load balancer.
3. For Type of load balancer, select Application Load Balancer (HTTP/HTTPS) and click Next.
4. For Public facing or internal, select Public facing (external) and click Next.
5. For Global or single region deployment, select Best for global workloads and click Next.
6. For Load balancer generation, select Classic Application Load Balancer and click Next.
7. Click Configure.

Basic configuration

1. In the Name box, enter http-lb.

Configure the backend

1. Click Backend configuration.

2. Click the Backend services and backend buckets box, and then click Create a backend bucket.

3. In the Backend bucket name box, enter cats.

4. In the Cloud Storage bucket box, click Browse.

5. Select BUCKET_1_NAME, and then click Select. Creating the cats backend bucket first makes it the default, where all unmatched traffic requests are directed. You can't change a default backend bucket's redirect rules in the load balancer.

6. Click Create.

7. Use the same process to create a backend bucket named dogs, and select BUCKET_2_NAME.

8. Click OK.

Configure routing rules

Routing rules determine how your traffic is directed. To configure routing, you'll set up host rules and path matchers, which are configuration components of an external Application Load Balancer's URL map. To set up the rules for this example:

1. Click Host and path rules.
2. For dogs, enter * in the Hosts field, and /love-to-fetch/* in the Paths field.

Configure the frontend

1. Click Frontend configuration.

2. Verify that the following options are configured with these values:

   Property	Value (type a value or select an option as specified)
   Protocol	HTTP
   Network Service Tier	Premium
   IP version	IPv4
   IP address	example-ip
   Port	80

   If you want to create an HTTPS load balancer instead of an HTTP load balancer, you must have an SSL certificate (gcloud compute ssl-certificates list), and you must fill in the fields as follows:

   Property	Value (type a value or select an option as specified)
   Protocol	HTTP(S)
   Network Service Tier	Premium
   IP version	IPv4
   IP address	example-ip
   Port	443
   Certificate	Select the www-ssl-cert certificate you created in the Set up an SSL certificate resource section, or create a new certificate.
   Optional: Enable HTTP to HTTPS Redirect	Use this checkbox to enable redirects. Enabling this checkbox creates an additional partial HTTP load balancer that uses the same IP address as your HTTPS load balancer and redirects HTTP requests to your load balancer's HTTPS frontend. This checkbox can only be selected when the HTTPS protocol is selected and a reserved IP address is used.

3. Click Done.

Review the configuration

1. Click Review and finalize.

2. Review the Frontend, Host and path rules, and Backend buckets.

3. Click Create and wait for the load balancer to be created.

4. Click the name of the load balancer (http-lb).

5. Note the IP address of the load balancer for the next task. In this guide, it's referred to as IP_ADDRESS.

gcloud

Configure the backend

gcloud compute backend-buckets create cats \
  --gcs-bucket-name=BUCKET_1_NAME

gcloud compute backend-buckets create dogs \
  --gcs-bucket-name=BUCKET_2_NAME

Configure the URL map

gcloud compute url-maps create http-lb \
  --default-backend-bucket=cats

gcloud compute url-maps add-path-matcher http-lb \
  --path-matcher-name=path-matcher-2 \
  --new-hosts=* \
  --backend-bucket-path-rules="/love-to-fetch/*=dogs" \
  --default-backend-bucket=cats

Configure the target proxy

gcloud compute target-http-proxies create http-lb-proxy \
  --url-map=http-lb

Configure the forwarding rule

gcloud compute forwarding-rules create http-lb-forwarding-rule \
  --load-balancing-scheme=EXTERNAL \
  --network-tier=PREMIUM \
  --address=example-ip \
  --global \
  --target-http-proxy=http-lb-proxy \
  --ports=80

Terraform

To create the load balancer, use the following Terraform resources.

Configure the backend

To create the backend, use the google_compute_backend_bucket resource.

# Create LB backend buckets
resource "google_compute_backend_bucket" "bucket_1" {
  name        = "cats"
  description = "Contains cat image"
  bucket_name = google_storage_bucket.bucket_1.name
}

resource "google_compute_backend_bucket" "bucket_2" {
  name        = "dogs"
  description = "Contains dog image"
  bucket_name = google_storage_bucket.bucket_2.name
}

Configure the URL map

To create the URL map, use the google_compute_url_map resource.

# Create url map
resource "google_compute_url_map" "default" {
  name = "http-lb"

  default_service = google_compute_backend_bucket.bucket_1.id

  host_rule {
    hosts        = ["*"]
    path_matcher = "path-matcher-2"
  }
  path_matcher {
    name            = "path-matcher-2"
    default_service = google_compute_backend_bucket.bucket_1.id

    path_rule {
      paths   = ["/love-to-fetch/*"]
      service = google_compute_backend_bucket.bucket_2.id
    }
  }
}

Configure the target proxy

To create the target HTTP proxy, use the google_compute_target_http_proxy resource.

# Create HTTP target proxy
resource "google_compute_target_http_proxy" "default" {
  name    = "http-lb-proxy"
  url_map = google_compute_url_map.default.id
}

Configure the forwarding rule

To create the forwarding rule, use the google_compute_global_forwarding_rule resource.

# Create forwarding rule
resource "google_compute_global_forwarding_rule" "default" {
  name                  = "http-lb-forwarding-rule"
  ip_protocol           = "TCP"
  load_balancing_scheme = "EXTERNAL_MANAGED"
  port_range            = "80"
  target                = google_compute_target_http_proxy.default.id
  ip_address            = google_compute_global_address.default.id
}

NOTE: To change the mode to classic Application Load Balancer, set the load_balancing_scheme attribute to "EXTERNAL" instead of "EXTERNAL_MANAGED".

To learn how to apply or remove a Terraform configuration, see Basic Terraform commands.

Send traffic to your load balancer

Several minutes after you have configured your load balancer, you can start sending traffic to the load balancer's IP address.

Console

In a web browser, go to the following addresses to test your load balancer, replacing IP_ADDRESS with the load balancer's IP address:

• http://IP_ADDRESS/love-to-fetch/two-dogs.jpg

• http://IP_ADDRESS/never-fetch/three-cats.jpg

If you've set up an HTTP load balancer, make sure your browser doesn't automatically redirect to HTTPS.

gcloud

Use the curl command to test the response from the following URLs. Replace IP_ADDRESS with the load balancer's IPv4 address:

curl http://IP_ADDRESS/love-to-fetch/two-dogs.jpg

curl http://IP_ADDRESS/never-fetch/three-cats.jpg

Query string parameters for Cloud Storage XML API

When certain query string parameters are included in requests sent to a backend bucket through an Application Load Balancer, the client receives an HTTP 404 response with an "Unsupported query parameter" error. This happens because the Cloud Storage XML API doesn't support these parameters when the request comes from an Application Load Balancer.

The following table summarizes how the Cloud Storage XML API responds to various query parameters when requests are routed through an Application Load Balancer. Parameters are grouped by observed behavior to help identify which are supported, ignored, or rejected in this context.

Parameter type	Parameters	Observed behavior
Supported parameters	generation, prefix, marker, max-keys	Adding these parameters (with appropriate values) works as described in the Cloud Storage XML API documentation. The API returns a standard HTTP response.
Ignored parameters	acl, billing, compose, delimiter, encryption, encryptionConfig, response-content-disposition, response-content-type, tagging, versions, websiteConfig	Adding these parameters has no effect. If the load balancer passes these parameters to Cloud Storage, the Cloud Storage XML API ignores them, and responds as though the parameters don't exist.
Rejected parameters	cors, lifecycle, location, logging, storageClass, versioning	The Cloud Storage XML API returns an "Unsupported query parameter" error.

Limitations

• Backend buckets are only supported with global external Application Load Balancers and classic Application Load Balancer. They aren't supported by the regional external Application Load Balancer or any other load balancer type.
• Backend buckets aren't supported with Identity-Aware Proxy. * The classic Application Load Balancer doesn't fully support uploads to Cloud Storage buckets. In particular, all query parameters on the request are dropped when uploading to Cloud Storage.

  What's next

  • Setting up multi-region load balancing
  • Setting up Cloud CDN with a backend bucket
  • Using Terraform module examples for external Application Load Balancers