Firebase Serverless Development

This section will create a frontend solution using a Rest API and Firestore database. Cloud Firestore is a NoSQL document database that is part of the Firebase platform where we can store, sync, and query data for our mobile and web apps at scale. The content is based on resolving a real-world scenario using Google Cloud serverless infrastructure.

We will build the following architecture:

Step 1: Create a Firestore database

In this scenario, we create a Firestore Database in Google Cloud. The high-level architecture diagram below summarizes the general architecture.

Requirements:

Create a Firestore database

To complete this section successfully, we are required to implement the following:

• Cloud Firestore Database
• Use Firestore Native Mode
• Add location Nam5 (United States)

Step 2: Populate the Database

In this scenario, populate the database using test data.

A high-level architecture diagram below summarizes the general architecture.

Populate the Database

Example Firestore schema

Netflix Shows Dataset includes the following information

To complete this section successfully, we are required to implement the following tasks:

• Use the sample code from pet-theory/lab06/firebase-import-csv/solutionnpm install content_copy
• To import CSV use the node pet-theory/lab06/firebase-import-csv/solution/index.jsnode index.js netflix_titles_original.csvcontent_copy

Verify the Firestore Database has been updated by viewing the data in the Firestore UI.

Click Check my progress to verify that we performed the above task.

Populate the Firestore DatabaseCheck my progress

Step 3: Create a REST API

In this scenario, create an example REST API.

A high-level architecture diagram below summarizes the general architecture.

Cloud Run Development

To complete this section successfully, we are required to implement the following tasks:

• Access pet-theory/lab06/firebase-rest-api/solution-01
• Build and Deploy the code to Google Container Registry
• Deploy the image as a Cloud Run Service
• curl -X GET $SERVICE_URL should respond with:{"status":"Netflix Dataset! Make a query."}

Step 4: Firestore API access

In this scenario, deploy an updated revision of the code to access the Firestore DB.

A high-level architecture diagram below summarizes the general architecture.

Deploy Cloud Run revision 0.2

To complete this section successfully, we are required to implement the following tasks:

• Access pet-theory/lab06/firebase-rest-api/solution-02
• Build the updated application
• Use Cloud Build to tag and deploy image revision to Container Registry
• Deploy the new image as Cloud Run service
• curl -X GET $SERVICE_URL/2019 should respond with json dataset

Step 5: Deploy the Staging Frontend

In this scenario, deploy the Staging Frontend.

A high-level architecture diagram below summarizes the general architecture.

Deploy Frontend

To complete this section successfully, we are required to implement the following tasks:

• Access pet-theory/lab06/firebase-frontend
• Build the frontend staging application
• Use Cloud Build to tag and deploy image revision to Container Registry
• Deploy the new image as Cloud Run service
• Frontend access to Rest API and Firestore Database

Access the Frontend Service URL.

Note: It’s using a demo dataset to provide the onscreen entries

Step 6: Deploy the Production Frontend

In this scenario, update the Staging Frontend to use the Firestore database.

A high-level architecture diagram below summarizes the general architecture.

Deploy Frontend

To complete this section successfully, we are required to implement the following tasks:

• Access pet-theory/lab06/firebase-frontend/public
• Update the frontend application i.e. app.js to use the REST API
• Dont forget to append the year to the SERVICE_URL
• Use Cloud Build to tag and deploy image revision to Container Registry
• Deploy the new image as Cloud Run service
• Frontend access to Rest API and Firestore Database

Now that the services have been deployed, we can see the contents of the Firestore database using the frontend service.

Thank you and happy Deploying

Originally published at https://prajwol-kc.com.np on July 27, 2022.