To get started, use npx to generate a new React app using create-react-app:
npx create-react-app <app-name>
Once npx has generated all the necessary files for your React app, let’s make sure it runs fine by doing:
cd <app-name> npm start
In your favorite browser, navigate to http://localhost:3000
You should see a screen similar to this one:
Now that your app is up and running, let’s create a production build out of it. To do so, simply run:
npm run build
At this point, there is nothing left to do from a React perspective. The next step is to configure a new project in the App Engine. Then, all that is left to do is to use the Google Cloud SDK to deploy our freshly built React app to GCP.
Here is a series of screenshots that will walk you through how to do the first part: configure a new project in the GCP App Engine.
If you followed the steps successfully, you should be able to see your Google App Engine dashboard like the last picture above. That was the last thing we had to do with the App Engine web console. Now, our focus will be on using the gcloud CLI. Verify that you have it by executing:
If you are having trouble installing it, here is your official guide.
Now navigate to your React app folder. We need to create a new app.yaml file in the root of our project folder that the gcloud CLI will use to deploy our app to the App Engine. After you create the file, add this content to it (official source):
runtime: nodejs env: flex # This sample incurs costs to run on the App Engine flexible environment. # The settings below are to reduce costs during testing and are not appropriate # for production use. For more information, see: # https://cloud.google.com/appengine/docs/flexible/nodejs/configuring-your-app-with-app-yaml manual_scaling: instances: 1 resources: cpu: 1 memory_gb: 0.5 disk_size_gb: 10
Make sure to save the file, and now we are finally ready to use the CLI:
Follow the steps to add your account, region, and make sure to pick the project you just created.
Once that has been initialized successfully, we can run the final and ultimate deploy command:
gcloud app deploy
After a few minutes, you should see a link in “Deployed service [default] to”:
Visit it in your browser to find your deployed React App. Here is mine.
Congrats Champ! You did it! 🎉🎉🎉
Here is a link to the git repo that contains the React app and the YAML file that was used for this article. Thank you for reading! 💟📖
If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?
In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.
Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.
Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.
The popularity of React Native comes from its advantages. Some of its advantages are as follows:
Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.
React Native is very close to native. Consider the following aspects as described on the React Native website:
Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.
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A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
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If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.
Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
Let’s see what was the Quest Outline:
A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.
**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.
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