Running the PHP Bookshelf app on Compute Engine

This tutorial shows how to run the PHP Bookshelf app on Compute Engine. Follow this tutorial to deploy an existing PHP web app to Compute Engine. You should work through the Bookshelf app documentation as part of the tutorial for the App Engine standard environment.

Objectives

  • Deploy the Bookshelf sample app to a single Compute Engine instance.
  • Scale the app horizontally by using a managed instance group.
  • Serve traffic by using HTTP load balancing.
  • Respond to traffic changes by using autoscaling.

Costs

This tutorial uses billable components of Google Cloud Platform (GCP), including:

  • Compute Engine
  • Cloud Storage
  • Cloud Datastore
  • Stackdriver Logging
  • Cloud Pub/Sub

Use the Pricing Calculator to generate a cost estimate based on your projected usage. New GCP users might be eligible for a free trial.

Before you begin

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Select or create a Google Cloud Platform project.

    Go to the Manage resources page

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

    Learn how to enable billing

  4. Enable the Cloud Datastore, Cloud Storage, and Cloud Pub/Sub APIs.

    Enable the APIs

  5. Install and initialize the Cloud SDK.
  6. Install PHP and Install Composer.

Initializing Cloud Datastore

The Bookshelf app uses Cloud Datastore to store the books. To initialize Cloud Datastore in your project for the first time:

  1. Open Cloud Datastore on the GCP Console.

  2. Select a region for your datastore and click Continue. When you reach the Create an Entity page, close the window. The Bookshelf app is ready to create entities on Cloud Datastore.

Creating a Cloud Storage bucket

The following instructions show how to create a Cloud Storage bucket. Buckets are the basic containers that hold your data in Cloud Storage.

  1. In a terminal window, enter the following command:

    gsutil mb gs://[YOUR-BUCKET-NAME]

    [YOUR-BUCKET-NAME] represents the name of your Cloud Storage bucket.

  2. To view uploaded images in the bookshelf app, set the bucket's default access control list (ACL) to public-read.

    gsutil defacl set public-read gs://[YOUR-BUCKET-NAME]

Cloning the sample app

The sample app is available on GitHub at GoogleCloudPlatform/getting-started-php.

  1. Clone the repository.

    git clone https://github.com/GoogleCloudPlatform/getting-started-php.git
    
  2. Go to the sample directory.

    cd getting-started-php/optional-compute-engine
    

Configuring the app

  1. Copy the settings.yml.dist file:

    cp config/settings.yml.dist config/settings.yml
    
  2. Open config/settings.yml for editing.

  3. Replace YOUR_PROJECT_ID with your project ID.

  4. Set the value of bookshelf_backend to datastore.

  5. Save and close settings.yml.

Running the app on your local computer

  1. Install dependencies:

    composer install
    
  2. Start a local web server:

    php -S localhost:8000 -t web
    
  3. In your web browser, enter this address:

    http://localhost:8000

To stop the local web server, press Control+C.

Deploying to a single instance

Single-instance deployment

This section walks you through running a single instance of your app on Compute Engine.

Push your code to a repository

You can use Cloud Source Repositories to create a Git repository in your project and upload your app code there. Your instances can then pull the latest version of your app code from the repository during startup. This is convenient because updating your app doesn't require configuring new images or instances; all you need to do is restart an existing instance or create a new one.

If this is your first time using Git, use git config --global to set up your identity.

  1. In your Google Cloud Platform Console, create a repository

    Create Repository

    or use gcloud to create a repository.

    gcloud source repos create [YOUR_REPO]
    

    Where:

    • [YOUR_PROJECT_ID] is your project ID.
    • [YOUR_REPO] is the name of the repository just created.
  2. Push your app's code to your project's repository.

    git commit -am "Updating configuration"
    git config credential.helper gcloud.sh
    git remote add cloud https://source.developers.google.com/p/[YOUR_PROJECT_ID]/r/[YOUR_REPO]
    git push cloud master
    

Use a startup script to initialize an instance

Now that your code is accessible by Compute Engine instances, you need a way to instruct your instance to download and run your code. An instance can have a startup script that is executed whenever the instance is started or restarted.

The entire startup script is defined in gce/startup-script.sh.

# Install PHP and dependencies from apt
apt-get update
apt-get install -y git nginx mongodb-clients php5 php5-fpm php5-mysql php5-dev php-pear pkg-config
pecl install mongodb

# Enable the MongoDB PHP extension
echo "extension=mongodb.so" >> /etc/php5/mods-available/mongodb.ini
php5enmod mongodb

# Install Composer
curl -sS https://getcomposer.org/installer | \
    /usr/bin/php -- \
    --install-dir=/usr/local/bin \
    --filename=composer

# Fetch the project ID from the Metadata server
PROJECTID=$(curl -s "http://metadata.google.internal/computeMetadata/v1/project/project-id" -H "Metadata-Flavor: Google")

# Get the application source code
git config --global credential.helper gcloud.sh
git clone https://source.developers.google.com/p/$PROJECTID /opt/src -b master
ln -s /opt/src/optional-compute-engine /opt/app

# Run Composer
composer install -d /opt/app --no-ansi --no-progress

First, the script installs and configures system libraries required for this app, including PHP, the MongoDB PHP extension, Composer, and NGINX. Then it clones the app's source code from Cloud Source Repositories and installs dependencies.

# Fetch the application config file from the Metadata server and add it to the project
curl -s "http://metadata.google.internal/computeMetadata/v1/instance/attributes/project-config" \
  -H "Metadata-Flavor: Google" >> /opt/app/config/settings.yml

The app's YAML configuration, including project secrets, is fetched from the Compute metadata server. This file is pushed to the metadata server when the compute instance is created.

# Disable the default NGINX configuration
rm /etc/nginx/sites-enabled/default

# Enable our NGINX configuration
cp /opt/app/gce/nginx/bookshelf.conf /etc/nginx/sites-available/bookshelf.conf
ln -s /etc/nginx/sites-available/bookshelf.conf /etc/nginx/sites-enabled/bookshelf.conf
cp /opt/app/gce/nginx/fastcgi_params /etc/nginx/fastcgi_params

# Start NGINX
systemctl restart nginx.service

The NGINX configuration for this app is installed using gce/nginx/bookshelf.conf, and NGINX is started.

# Install Fluentd
curl -s "https://storage.googleapis.com/signals-agents/logging/google-fluentd-install.sh" | bash

# Enable our Fluentd configuration
cp /opt/app/gce/fluentd/bookshelf.conf /etc/google-fluentd/config.d/bookshelf.conf

# Start Fluentd
service google-fluentd restart &

Finally, the script installs and configures the Logging agent. This ensures the logging added earlier in this tutorial works as expected.

Create and configure a Compute Engine instance

  1. Create a Compute Engine instance. The following command creates a new instance, allows it to access GCP services, and runs your startup script. The instance name is my-app-instance.

    Linux/macOS

    gcloud compute instances create my-app-instance \
        --image-family=debian-9 \
        --image-project=debian-cloud \
        --machine-type=g1-small \
        --scopes userinfo-email,cloud-platform \
        --metadata app-location=$BOOKSHELF_DEPLOY_LOCATION \
        --metadata-from-file startup-script=gce/startup-script.sh \
        --zone us-central1-f \
        --tags http-server
    

    Windows

    gcloud compute instances create my-app-instance ^
        --image-family=debian-9 ^
        --image-project=debian-cloud ^
        --machine-type=g1-small ^
        --scopes userinfo-email,cloud-platform ^
        --metadata-from-file startup-script=gce/startup-script.sh ^
        --zone us-central1-f ^
        --tags http-server
    

  2. Check the progress of the instance creation.

    gcloud compute instances get-serial-port-output my-app-instance --zone us-central1-f
    

    If the startup script has completed, Finished running startup script is displayed near the end of the command output.

  3. Create a firewall rule to allow traffic to your instance.

    Linux/macOS

    gcloud compute firewall-rules create default-allow-http-8080 \
        --allow tcp:8080 \
        --source-ranges 0.0.0.0/0 \
        --target-tags http-server \
        --description "Allow port 8080 access to http-server"
    

    Windows

    gcloud compute firewall-rules create default-allow-http-8080 ^
        --allow tcp:8080 ^
        --source-ranges 0.0.0.0/0 ^
        --target-tags http-server ^
        --description "Allow port 8080 access to http-server"
    

  4. Get the external IP address of your instance.

    gcloud compute instances list
    
  5. To see the app running, go to http://[YOUR_INSTANCE_IP]:8080,

    where [YOUR_INSTANCE_IP] is the external IP address of your instance.

Manage and monitor an instance

You can use the Google Cloud Platform Console to monitor and manage your instance.

To view the running instance and connect to it by using ssh, go to Compute > Compute Engine.

To view all of the logs generated by your Compute Engine resources, go to Monitoring > Logs. Stackdriver Logging is automatically configured to gather logs from various common services, including syslog.

Horizontal scaling with multiple instances

Multiple-instance deployment with managed instances

Compute Engine can easily scale horizontally. By using a managed instance group and the Compute Engine Autoscaler, Compute Engine can automatically create new instances of your app when needed and shut down instances when demand is low. You can set up an HTTP load balancer to distribute traffic to the instances in a managed instance group.

Deployment script

The sample app includes a script that automates the following deployment steps. The script named deploy.sh deploys the resources for a complete, autoscaled, load-balanced app as described in Horizontal scaling with multiple instances.

You can run each of the following steps yourself, or run gce/deploy.sh from the gce directory.

Create a managed instance group

A managed instance group is a group of homogeneous instances based on the same instance template. An instance template defines the configuration of your instance, including source image, disk size, scopes, and metadata, including startup scripts.

  1. First, create a template.

    gcloud compute instance-templates create my-app-tmpl \
        --machine-type=g1-small \
        --scopes logging-write,storage-ro,https://www.googleapis.com/auth/projecthosting \
        --metadata-from-file startup-script=gce_deployment/startup-script.sh,project-config=config/settings.yml \
        --image ubuntu-15-04 \
        --tags http-server
    
  2. Create an instance group.

    gcloud compute instance-groups managed create my-app-group \
        --base-instance-name my-app \
        --size 2 \
        --template my-app-tmpl \
        --zone us-central1-f
    

    The --size parameter species the number of instances in the group. After all of the instances have finished running their startup scripts, the instances can be accessed individually by using their external IP addresses and port 8080. To find the external IP addresses of the instances, enter gcloud compute instances list. The managed instances have names that start with the same prefix, my-app, which you specified in the --base-instance-name parameter.

Create a load balancer

An individual instance is fine for testing or debugging, but for serving web traffic it's better to use a load balancer to automatically direct traffic to available instances. To create a load balancer, follow these steps.

  1. Create a health check. The load balancer uses a health check to determine which instances are capable of serving traffic.

    gcloud compute http-health-checks create ah-health-check \
        --request-path /_ah/health
    
  2. Create a named port. The HTTP load balancer looks for the http service to know which port to direct traffic to. In your existing instance group, give port 8080 the name http.

    gcloud compute instance-groups managed set-named-ports my-app-group \
        --named-ports http:8080 \
        --zone us-central1-f
    
  3. Create a backend service. The backend service is the target for load-balanced traffic. It defines which instance group the traffic should be directed to and which health check to use.

    gcloud compute backend-services create my-app-service \
        --http-health-check ah-health-check
    
  4. Add the backend service.

    gcloud compute backend-services add-backend my-app-service \
        --group my-app-group \
        --zone us-central1-f
    
  5. Create a URL map and proxy. The URL map defines which URLs should be directed to which backend services. In this sample, all traffic is served by one backend service. If you want to load balance requests between multiple regions or groups, you can create multiple backend services. A proxy receives traffic and forwards it to backend services using URL maps.

    1. Create the URL map.

      gcloud compute url-maps create my-app-service-map \
          --default-service my-app-service
      
    2. Create the proxy.

      gcloud compute target-http-proxies create my-app-service-proxy \
          --url-map my-app-service-map
      
  6. Create a global forwarding rule. The global forwarding rule ties a public IP address and port to a proxy.

    gcloud compute forwarding-rules create my-app-service-http-rule \
        --global \
        --target-http-proxy my-app-service-proxy \
        --port-range 80
    

Configure the autoscaler

The load balancer ensures that traffic is distributed across all of your healthy instances. But what happens if there is too much traffic for your instances to handle? You could manually add more instances. But a better solution is to configure a Compute Engine autoscaler to automatically create and delete instances in response to traffic demands.

  1. Create an autoscaler.

    gcloud compute instance-groups managed set-autoscaling my-app-group \
        --max-num-replicas 10 \
        --target-load-balancing-utilization 0.5 \
        --zone us-central1-f
    

    The preceding command creates an autoscaler on the managed instance group that automatically scales up to 10 instances. New instances are added when the load balancer is above 50% utilization and are removed when utilization falls below 50%.

  2. Create a firewall rule.

    Linux/macOS

    gcloud compute firewall-rules create default-allow-http-8080 \
        --allow tcp:8080 \
        --source-ranges 0.0.0.0/0 \
        --target-tags http-server \
        --description "Allow port 8080 access to http-server"
    

    Windows

    gcloud compute firewall-rules create default-allow-http-8080 ^
        --allow tcp:8080 ^
        --source-ranges 0.0.0.0/0 ^
        --target-tags http-server ^
        --description "Allow port 8080 access to http-server"
    

  3. Check progress until at least one of your instances reports HEALTHY.

    gcloud compute backend-services get-health frontend-web-service --global
    

View your app

  1. Get the forwarding IP address for the load balancer.

    gcloud compute forwarding-rules list --global
    

    Your forwarding-rules IP address is in the IP_ADDRESS column.

  2. In a browser, enter the IP address from the list. Your load-balanced and autoscaled app is now running on Compute Engine!

Manage and monitor your deployment

Managing multiple instances is as easy as managing a single instance. You can use the GCP Console to monitor load balancing, autoscaling, and your managed instance group.

You can manage your instance group and autoscaling configuration by using the Compute Engine > Instance groups section.

You can manage your load balancing configuration, including URL maps and backend services, by using the Network services > Load balancing section.

Cleaning up

To avoid incurring charges to your Google Cloud Platform account for the resources used in this tutorial:

Run the teardown script

If you ran the deploy.sh script, run the teardown.sh script to remove all resources created by the deploy.sh script. This returns your project to the state before running deploy.sh script and helps to avoid further billing. To remove the single instance and the storage bucket created at the beginning of the tutorial, follow the instructions in the next section.

Delete resources manually

If you followed the steps in this tutorial manually, you can manually delete the cloud resources you created.

Delete your load balancer

  1. In the GCP Console, go to the Load Balancing page.

    Go to the Load Balancing page

  2. Click the checkbox next to the load balancer you want to delete.

  3. Click the Delete button at the top of the page to delete the load balancer.

  4. In the Delete load balancer dialog, select the associated backend service and health check resources.

  5. Click the Delete Load Balancer button to delete the load balancer and its associated resources.

Delete your Compute Engine managed instance group

To delete a Compute Engine instance group:

  1. In the GCP Console, go to the Instances Groups page.

    Go to the Instances Groups page

  2. Click the checkbox next to the instance group you want to delete.
  3. Click Delete delete at the top of the page to delete the instance group.

Delete your single Compute Engine instance

To delete a Compute Engine instance:

  1. In the GCP Console, go to the VM Instances page.

    Go to the VM Instances page

  2. Click the checkbox next to the instance you want to delete.
  3. Click Delete delete at the top of the page to delete the instance.

Delete your Cloud Storage bucket

To delete a Cloud Storage bucket:

  1. In the GCP Console, go to the Cloud Storage Browser page.

    Go to the Cloud Storage Browser page

  2. Click the checkbox next to the bucket you want to delete.
  3. Click Delete delete at the top of the page to delete the bucket.

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