- Install dependencies
- 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, orstaging-1
/staging-2
if you are doing QA
- In the provider section, change the profile to match the
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
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.
-
Under the lambda folder there will be 3 folders: publicdash, teamdash,personaldash. In each of these folders is where we will be setting up instructions to querying against mock data from BigQuery.
- view comments in this file to get an understanding of code as it will be repeated for queries we will be making to the actual Impact dataset.
- 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 with the mock data.
- line 7 will both export the query files and set it up to get the query we want
-
navigate to handler.js
- We are importing the each query file into handler.js to utilize those query.
- There will be 3 methods. One for each dashboard.
- This is where the query for each metric 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.
- Create an
.env
file in root directory. - Set
AWS_PROFILE
to the aws profile that you want to use for deployment (should be in.aws/credentials
). - Set
REACT_APP_STAGE
, should only contain text and dashes. - Set
PUBLIC_URL
to be the stage name wrapped in slashes. Format here is important and required for assets to be loaded properly in the built index.html file. - Your
.env
file should now closely match.env.sample
- Run
yarn build:deploy
.
Currently /api/external is protected by checkJwt middleware. To test this from the frontend with the ExternalApi component, go to index.js in the src folder, comment out line 8, and uncomment lines 9 and 41
In the project directory, you can run:
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.
Launches the test runner in the interactive watch mode.
See the section about running tests for more information.
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.
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.
Builds a dev environment. The backend will depend on what your serverless.yml has specified. The frontend will go to http://localhost:3000
You can learn more in the Create React App documentation.
To learn React, check out the React documentation.
This section has moved here: https://facebook.github.io/create-react-app/docs/code-splitting
This section has moved here: https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size
This section has moved here: https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app
This section has moved here: https://facebook.github.io/create-react-app/docs/advanced-configuration
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