Creating a Go-Based Data Pipeline for Job Listings Data

Table of Contents

1. Introduction
2. Prerequisites
3. Setting Up the Project
4. Fetching Job Listings
5. Processing Job Listings
6. Storing Job Listings
7. Running the Data Pipeline
8. Conclusion

Introduction

In this tutorial, we will learn how to create a Go-based data pipeline to fetch, process, and store job listings data. We will build a script that retrieves job listings from a remote API, performs some processing on the raw data, and stores the cleaned data in a local file. By the end of this tutorial, you will have a better understanding of how to design and implement a basic data pipeline using Go.

Prerequisites

To follow along with this tutorial, you should have basic knowledge of the Go programming language. You should also have Go installed on your machine. If you haven’t installed Go yet, please visit the official Go website (https://golang.org/) and download the appropriate installer for your operating system.

Setting Up the Project

1. Create a new directory for your project:

    $ mkdir job-listings-pipeline
    $ cd job-listings-pipeline
   

2. Initialize a new Go module:

    $ go mod init github.com/your-username/job-listings-pipeline
   

Fetching Job Listings

To fetch job listings, we will use the popular http package in Go. In this example, we will be fetching job listings from a mock API provided by jsonplaceholder.typicode.com. You can replace this API with any other source of job listings data.

1. Create a new file called fetch.go in your project directory.

2. Open fetch.go in your preferred text editor.

3. Import the required packages:

    package main
       
    import (
    	"fmt"
    	"io/ioutil"
    	"net/http"
    )
   

4. Define a function that retrieves job listings from the remote API:

    func fetchJobListings() ([]byte, error) {
    	resp, err := http.Get("https://jsonplaceholder.typicode.com/posts")
    	if err != nil {
    		return nil, err
    	}
       
    	defer resp.Body.Close()
       
    	body, err := ioutil.ReadAll(resp.Body)
    	if err != nil {
    		return nil, err
    	}
       
    	return body, nil
    }
   

5. Add a main function that calls the fetchJobListings function and prints the result:

    func main() {
    	jobListings, err := fetchJobListings()
    	if err != nil {
    		fmt.Printf("Error fetching job listings: %v\n", err)
    		return
    	}
       
    	fmt.Println(string(jobListings))
    }
   

6. Save the file and exit the text editor.

Processing Job Listings

Now that we have fetched the job listings data, let’s process it by decoding the JSON response.

1. Create a new file called process.go in your project directory.

2. Open process.go in your preferred text editor.

3. Import the required packages:

    package main
       
    import (
    	"encoding/json"
    	"fmt"
    )
   

4. Define a struct to represent a job listing:

    type JobListing struct {
    	ID    int    `json:"id"`
    	Title string `json:"title"`
    	Body  string `json:"body"`
    }
   

5. Define a function that processes the fetched job listings:

    func processJobListings(data []byte) ([]JobListing, error) {
    	var jobListings []JobListing
    	if err := json.Unmarshal(data, &jobListings); err != nil {
    		return nil, err
    	}
       
    	return jobListings, nil
    }
   

6. Add a main function that calls the processJobListings function and prints the processed job listings:

    func main() {
    	jobListings, err := fetchJobListings()
    	if err != nil {
    		fmt.Printf("Error fetching job listings: %v\n", err)
    		return
    	}
       
    	processedListings, err := processJobListings(jobListings)
    	if err != nil {
    		fmt.Printf("Error processing job listings: %v\n", err)
    		return
    	}
       
    	for _, listing := range processedListings {
    		fmt.Printf("ID: %d, Title: %s, Body: %s\n", listing.ID, listing.Title, listing.Body)
    	}
    }
   

7. Save the file and exit the text editor.

Storing Job Listings

Next, we need to store the processed job listings in a local file.

1. Create a new file called store.go in your project directory.

2. Open store.go in your preferred text editor.

3. Import the required packages:

    package main
       
    import (
    	"encoding/json"
    	"fmt"
    	"io/ioutil"
    )
   

4. Define a function that stores the processed job listings in a JSON file:

    func storeJobListings(jobListings []JobListing) error {
    	data, err := json.MarshalIndent(jobListings, "", "  ")
    	if err != nil {
    		return err
    	}
       
    	if err := ioutil.WriteFile("job_listings.json", data, 0644); err != nil {
    		return err
    	}
       
    	return nil
    }
   

5. Add a main function that calls the storeJobListings function to store the processed job listings:

    func main() {
    	jobListings, err := fetchJobListings()
    	if err != nil {
    		fmt.Printf("Error fetching job listings: %v\n", err)
    		return
    	}
       
    	processedListings, err := processJobListings(jobListings)
    	if err != nil {
    		fmt.Printf("Error processing job listings: %v\n", err)
    		return
    	}
       
    	if err := storeJobListings(processedListings); err != nil {
    		fmt.Printf("Error storing job listings: %v\n", err)
    		return
    	}
       
    	fmt.Println("Job listings stored successfully.")
    }
   

6. Save the file and exit the text editor.

Running the Data Pipeline

To run the data pipeline, we will execute the scripts in a specific order: fetch.go, process.go, and store.go.

1. Open a terminal and navigate to your project directory.

2. Run the fetch.go script:

    $ go run fetch.go
   

   You should see the raw job listings data printed to the console.

3. Run the process.go script:

    $ go run process.go
   

   You should see the processed job listings with their IDs, titles, and bodies printed to the console.

4. Run the store.go script:

    $ go run store.go
   

   The processed job listings should now be stored in a file called job_listings.json in the same directory.

Conclusion

Congratulations! You have successfully created a Go-based data pipeline for fetching, processing, and storing job listings data. In this tutorial, you learned how to retrieve job listings from a remote API, process the data by decoding JSON, and store the processed data in a local file. This basic example can serve as a foundation for building more complex data pipelines in Go. Keep experimenting and exploring different data sources and processing techniques to expand your knowledge and skills in Go programming.

Published: 4 August 2020

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