Python Scrapy spider that scrapes Jobs data from Indeed.com. There are two versions:

1. Scrapes Job Summary Data: The scraper will query the Indeed search page with your query parameters and extract the job data directly from the search results.
2. Scrapes Full Job Data: The scraper will crawl the Indeed search pages with your query parameters, then send a request to each individual job page and scrape all the job data from the page.

Both of these scrapers only scrape some of the available data, however, you can easily expand them to scrape other data that is available in the response.

These scrapers extract the following fields from Indeed jobs pages:

• Company Name
• Company Location
• Job Title
• Job Description
• Job Salary
• Job Location
• Etc.

The following article goes through in detail how these Indeed spiders were developed, which you can use to understand the spiders and edit them for your own use case.

Python Scrapy: Build A Indeed Scraper

This Indeed spider uses ScrapeOps Proxy as the proxy solution. ScrapeOps has a free plan that allows you to make up to 1,000 requests per month which makes it ideal for the development phase, but can be easily scaled up to millions of pages per month if needs be.

You can sign up for a free API key here.

To monitor our scraper, this spider uses the ScrapeOps Monitor, a free monitoring tool specifically designed for web scraping.

Live demo here: ScrapeOps Demo

To make sure the required modules are installed into your Python virtual environment. From the top level of the project run:

pip install -r requirements.txt

If you have issues running scrapy crawl after installing the above, try deactivating your virtual environment and then reactivating it.

deactivate

Followed by

source venv/bin/activate

To run the Indeed spiders you should first set the Job query parameters you want to search by updating the keyword_list and location_list lists in the spiders:

def start_requests(self):
    keyword_list = ['software engineer']
    location_list = ['California']
    for keyword in keyword_list:
        for location in location_list:
            indeed_jobs_url = self.get_indeed_search_url(keyword, location)
            yield scrapy.Request(url=indeed_jobs_url, callback=self.parse_search_results, meta={'keyword': keyword, 'location': location, 'offset': 0})

Then to run the spiders, enter one of the following commands:

The following are instructions on how to modify the Indeed scrapers for your particular use case.

Check out this guide to building a Indeed.com Scrapy spider if you need any more information.

To change the query parameters for the job search just change the keywords and locations in the keyword_list and location_list lists in each spider.

For example:

def start_requests(self):
    keyword_list = ['software engineer', 'devops engineer', 'product manager']
    location_list = ['California', 'texas']
    for keyword in keyword_list:
        for location in location_list:
            indeed_jobs_url = self.get_indeed_search_url(keyword, location)
            yield scrapy.Request(url=indeed_jobs_url, callback=self.parse_search_results, meta={'keyword': keyword, 'location': location, 'offset': 0})

The JSON blobs the spiders extract the job data from are pretty big so the spiders are configured to only parse some of the data.

You can expand or change the data that gets extract by changing the yield statements:

yield {
        'keyword': keyword,
        'location': location,
        'page': round(offset / 10) + 1 if offset > 0 else 1,
        'position': index,
        'company': job.get('company'),
        'companyRating': job.get('companyRating'),
        'companyReviewCount': job.get('companyReviewCount'),
        'companyRating': job.get('companyRating'),
        'highlyRatedEmployer': job.get('highlyRatedEmployer'),
        'jobkey': job.get('jobkey'),
        'jobTitle': job.get('title'),
        'jobLocationCity': job.get('jobLocationCity'),
        'jobLocationPostal': job.get('jobLocationPostal'),
        'jobLocationState': job.get('jobLocationState'),
        'maxSalary': job.get('estimatedSalary').get('max') if job.get('estimatedSalary') is not None else 0,
        'minSalary': job.get('estimatedSalary').get('min') if job.get('estimatedSalary') is not None else 0,
        'salaryType': job.get('estimatedSalary').get('max') if job.get('estimatedSalary') is not None else 'none',
        'pubDate': job.get('pubDate'),
    }

The spiders are set to only use 1 concurrent thread in the settings.py file as the ScrapeOps Free Proxy Plan only gives you 1 concurrent thread.

However, if you upgrade to a paid ScrapeOps Proxy plan you will have more concurrent threads. Then you can increase the concurrency limit in your scraper by updating the CONCURRENT_REQUESTS value in your settings.py file.

# settings.py

CONCURRENT_REQUESTS = 10

The spiders are set to save the scraped data into a CSV file and store it in a data folder using Scrapy's Feed Export functionality.

custom_settings = {
        'FEEDS': { 'data/%(name)s_%(time)s.csv': { 'format': 'csv',}}
        }

If you would like to save your CSV files to a AWS S3 bucket then check out our Saving CSV/JSON Files to Amazon AWS S3 Bucket guide here

Or if you would like to save your data to another type of database then be sure to check out these guides:

• Saving Data to JSON
• Saving Data to SQLite Database
• Saving Data to MySQL Database
• Saving Data to Postgres Database

To deactivate the ScrapeOps Proxy & Monitor simply comment out the follow code in your settings.py file:

# settings.py

# ## Enable ScrapeOps Proxy
# SCRAPEOPS_PROXY_ENABLED = True

# # Add In The ScrapeOps Monitoring Extension
# EXTENSIONS = {
# 'scrapeops_scrapy.extension.ScrapeOpsMonitor': 500, 
# }


# DOWNLOADER_MIDDLEWARES = {

#     ## ScrapeOps Monitor
#     'scrapeops_scrapy.middleware.retry.RetryMiddleware': 550,
#     'scrapy.downloadermiddlewares.retry.RetryMiddleware': None,
    
#     ## Proxy Middleware
#     'indeed.middlewares.ScrapeOpsProxyMiddleware': 725,
# }