So for production systems, make sure you secure the MongoDB instance with an admin user and run the mongod process using the --auth option. With the MongoDB server up and running, we'll make a few configurational changes and deploy the application to the App Engine:. GKE is powered by the container management system, Kubernetes. Containers are built to do one specific task, and so we'll separate the application and the database as we did for App Engine.
The data within a container is transient, and so we need an external disk to safely store the MongoDB data. The App Container includes a Node. The container engine cluster runs on top of GCE. For this recipe, we'll create a two-node cluster which will be internally managed by Kubernetes:. The gcloud command automatically generates a kubeconfig entry that enables us to use kubectl on the cluster:.
A custom port of is used for the KeystoneJS application. This port will be mapped to port 80 later in the Kubernetes service configuration. Similarly, mongo will be the name of the load-balanced MongoDB service that will be created later.
After the service is created, the External IP will be unavailable for a short period; you can retry after a few seconds. The Google Cloud Console has a rich interface to view the cluster components, in addition to the Kubernetes dashboard. In case of any errors, you can view the logs and verify the configurations on the Console. Google Cloud Functions is the serverless compute service that runs our code in response to events.
The resources needed to run the code are automatically managed and scaled. At the time of writing this recipe, Google Cloud Functions is in beta. The functions can be written in JavaScript on a Node. We'll use the simple calculator JavaScript code available on the book's GitHub repository and deploy it to Cloud Functions:.
The entry point for the function will be automatically taken as the calculator function. If you choose to use another name, index. First, we'll create a Cloud SQL instance, which will be used by the application servers. The application servers should be designed to be replicated at will depending on any events, such as CPU usage, high utilization, and so on.
So, we'll create an instance template which is a definition of how GCP should create a new application server when it is needed. We feed in the start up script that prepares the instance to our requirements. Then, we create an instance group which is a group of identical instances defined by the instance template.
The instance group also monitors the health of the instances to make sure they maintain the defined number of servers. It automatically identifies unhealthy instances and recreates them as defined by the template. With the load balancer in place, we now have two instances serving traffic to the users under a single endpoint provided by the load balancer.
Finally, to handle any unexpected load, we'll use the autoscaling feature of the instance group. The implementation approach would be to first create the backend service the database , then the instance-related setup, and finally the load balancing setup:.
The root password is set to a simple password for demonstration purposes. We'll create a health check that will poll the instance at specified intervals to verify that they can continue to serve traffic:.
When the user hits the endpoint URL of the load balancer, it transfers the request to one of the available instances under its control. A load balancer constantly checks for the health of the instance under its supervision.
So, irrespective of the request hitting Instance 1 or Instance 2 , the data is dealt from the common Cloud SQL database. Also, the Autoscaler is turned on in the Instance Group governing the two instances. If there is an increase in usage CPU in our example , the Autoscaler will spawn a new instance to handle the increase in traffic:. Legorie Rajan PS has an experience of 12 years in software development, business analysis, and project management.
He has a rich multicultural experience working in India, the United States, and France. He has a good understanding of full-stack development, and has also been a technical reviewer for Packt Publishing. About this book Google Cloud Platform is a cloud computing platform that offers products and services to host applications using state-of-the art infrastructure and technology.
Publication date: April Publisher Packt. Pages ISBN Download code from GitHub. Chapter 1. Hosting a Node. Getting ready. Create or select a GCP project. Install Node. How to do it Running the application on the development machine. Some working experience on other public cloud platforms would help too. Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago.
Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence.
You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect.
Git stats 22 commits. Failed to load latest commit information. View code. About the Book Google Cloud Platform is a cloud computing platform that offers products and services to host applications using a state-of-the art infrastructure and technology. Instructions and Navigation All of the code is organized into folders. For example, Chapter MIT License. Releases No releases published. Broad, deep, and complete, this authoritative book has everything you need. About the Reader Written for intermediate developers.
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