#Runjs docker install#

Below is the Dockerfile for the backend API:įirst, we define the names of the services that make up our application: backend and frontend.

Similarly, we repeat this process for the backend.

In this context, we run npm start since that is the command that starts the frontend. CMD: Specifies the command that should be executed when the docker container starts.In this context, we expose port 3000 since that is the port that React is running on. EXPOSE: Tells Docker which ports the container should listen on during runtime.

#Runjs docker install#

In this context, we run npm install -production to only install production dependencies. I typically use this command to install dependencies, debug, and execute commands in the container. RUN: Specifies a command that should be run by Docker.The final path specified is the location the files are copied to (in this case, the working directory). On line 9, we list several files to copy and then specify where to copy them. COPY: Copy a file or files into the Docker container.If the working directory does not already exist, it is created by Docker. WORKDIR: Sets the working directory for the application.LABEL: Defines the metadata for our Dockerfile, including information about the version, description, and maintainers.In this context, we use Node Version 12.18.3. FROM: Defines the image we will use as the basis for our container.After this, pending a successful response from both the Twitter and Google NLP APIs, the data is structured and returned to the frontend, where it is then formatted and passed to various React components. When a user submits a valid tweet URL, the tweet ID is parsed from the URL and sent to the backend, where we then request tweet and user information from the Twitter V2 API, parse the response from Twitter to extract the tweet contents, and send that contents to the Google NLP API for sentiment analysis. To accomplish this task, we decided to use an express.js backend with one endpoint that accepts the Tweet ID as a parameter ( POST /analyze/:tweet_id). First, we needed to build a backend that retrieved data from the Twitter V2 API, sent it to the Google NLP API, and structured the responses for easy frontend consumption. After landing on a final design (shown at the very bottom of the embedded Figma document), we structured the work into two buckets: frontend and backend.