Deploying robotic process automation (RPA) applications with Docker and Javascript

21 Sep 2023

Docker

In recent years, Robotic Process Automation (RPA) has become increasingly popular among businesses looking to automate repetitive tasks and streamline their operations. RPA uses software robots or “bots” to mimic human actions and interact with different systems and applications.

To deploy RPA applications efficiently, developers can leverage Docker and Javascript. Docker allows for containerization of applications, making it easier to manage dependencies and ensure consistent deployment across different environments. Javascript, on the other hand, provides a versatile and widely supported language for developing RPA bots.

Setting up the Environment

Before diving into the details, let’s start by setting up our development environment. Make sure you have Docker installed and running on your machine. Additionally, install Node.js and NPM to work with Javascript and its libraries effectively.

Creating the RPA Application

Initialize the project: Start by creating a new directory for your project and navigating to it using the command line. Run the following command to initialize a new Node.js project:
```
npm init -y
```
Install dependencies: To enable RPA capabilities in our application, we can use the puppeteer library, which provides a high-level API for controlling headless Chrome or Chromium. Install it by running the following command:
```
npm install puppeteer
```

Write the application code: Create a new Javascript file, e.g., rpa.js, and add the following code snippet as an example:

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();
  await page.goto('https://www.example.com');
  await page.screenshot({ path: 'example.png' });
  await browser.close();
})();

The above code uses puppeteer to launch a headless browser, navigate to a web page, take a screenshot, and then close the browser instance.

Test the application: Run the script using the following command:
```
node rpa.js
```
If everything works as expected, you should see a screenshot saved as example.png.

Containerizing the RPA Application using Docker

Now that we have our RPA application working locally, let’s containerize it using Docker to ensure consistent deployment across different environments.

Create a Dockerfile: In your project directory, create a new file named Dockerfile (without any extension) and add the following lines:
```
FROM node:14

WORKDIR /app

COPY package.json .
RUN npm install

COPY . .

CMD ["node", "rpa.js"]
```
The Dockerfile defines the base image, sets the working directory, copies the package.json file, installs dependencies, copies the entire project directory, and specifies the command to run the application.
Build the Docker image: Open the command line, navigate to your project directory, and run the following command to build the Docker image:
```
docker build -t rpa-app .
```
This command builds the Docker image using the Dockerfile and tags it with the name rpa-app.
Run the Docker container: After the Docker image is built, you can run a container using the following command:
```
docker run rpa-app
```
The command starts a container based on the rpa-app image, running the RPA application inside it.

Conclusion

In this blog post, we explored how to deploy RPA applications using Docker and Javascript. By combining Docker’s containerization capabilities with the versatility of Javascript, developers can easily package and deploy RPA applications across different environments.

By using Docker, you ensure consistent deployments and eliminate potential dependency issues. And with Javascript and libraries like puppeteer, you can create powerful bots capable of automating a wide range of tasks.

#RPA #Docker #Javascript