impact-dashboard

Getting started:

1. Install dependencies
2. On the root directory, there should be a file named serverless.yml. Open that up.
   • In the provider section, change the profile to match the credentials file in your .aws folder
   • Change the stage to dev-${your name} if you're developing, or staging-1/staging-2 if you are doing QA

Although we won't be using the queryShakespeare.js and /shakespeareQuotes endpoint, Nisa has the necessary credentials. Put them in a secrets.js file as a json object, and export it. For example:

const bigQueryCredentials = {
    type: "service_account",  
    project_id: "",  
    private_key_id: "",  
    private_key: "",  
    client_email: "",  
    client_id: "",  
    auth_uri: "",  
    token_uri: "",  
    auth_provider_x509_cert_url: "",  
    client_x509_cert_url: ""  
}  

module.exports = bigQueryCredentials  

Use the command serverless deploy to deploy onto AWS. You should then see your functions under Lambda. If not, make sure your region is set to North Virginia

Connecting BigQuery to AWS

required dependency:

• @google-cloud/bigquery (should already be installed and in the root package.json)

Take note of the secrets.js file, which is in the gitignore file so that keys aren't exposed. The credentials in secrets.js are what allows us to query against the Shakespeare public dataset.

• queryShakespeare.js: where will be setting up instructions to querying against BigQuery public shakespeare dataset.

  • view comments in this file to get an understanding of code as it will be repeated for queries we will be making to impact bigquery db.
  • notice that where we are creating the client, we initially hard coded credentials to be able to query against the dataset. For securities sake, we moved over the credentials into a secrets file that is then imported and used in place of hard coding the credentials.
  • look at sqlQuery. That will be the sql needed to get what information we are looking for which is looking at word count.
  • line 7 will both export the queryShakespeare file and set it up to get the query we want( for the time being)

• navigate to handler.js

  • We are importing the queryShakespeare.js file as shakespeare to utilize that query.
  • The first two methods are just joke examples but enjoy.
  • The last method is the shakespeareQuotes
    • This is where the query will take place
    • when serverless deploy is sent to work its magic, it should create a lambda function that will query bigquery and send back the data we requested ( as long as the credentials are valid, which the key will be valid by the time this is a necessary read)

• once serverless deploy is working its magic, ideally it should deploy successfully. This being said we should receive urls that we can use for the GET requests later once we have the correct dataset to query against and the corresponding credentials. If you navigate to the in place GET urls, you'll see corresponding messages and details.

• I would suggest following this set of steps/files to successfully query against the impact dataset.

This project was bootstrapped with Create React App.

Available Scripts

In the project directory, you can run:

yarn start

Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.

The page will reload if you make edits.
You will also see any lint errors in the console.

yarn test

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

yarn build

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about deployment for more information.

yarn eject

Note: this is a one-way operation. Once you eject, you can’t go back!

If you aren’t satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.

Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.

You don’t have to ever use eject. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.

yarn build:dev

Builds a dev environment. The backend will depend on what your serverless.yml has specified. The frontend will go to http://localhost:3000

Learn More

You can learn more in the Create React App documentation.

To learn React, check out the React documentation.

Code Splitting

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Analyzing the Bundle Size

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Making a Progressive Web App

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Advanced Configuration

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Deployment

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