linkedin-jobs-scraper 1.8.4

Scrape public available jobs on Linkedin using headless browser

linkedin-jobs-scraper

Scrape public available jobs on Linkedin using headless browser. For each job, the following fields are extracted: job_id, link, apply_link, title, company, place, description, description_html, date, seniority_level, job_function, employment_type, industries.

It's also available an equivalent npm package.

Table of Contents

• Requirements
• Installation
• Usage
• Anonymous vs authenticated session
• Rate limiting
• Proxy mode
• Filters
• Company filter
• Logging
• License

Requirements

• Chrome or Chromium
• Chromedriver
• Python >= 3.6

Installation

Install package:

pip install linkedin-jobs-scraper

Usage

import logging
from linkedin_jobs_scraper import LinkedinScraper
from linkedin_jobs_scraper.events import Events, EventData
from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters, RemoteFilters

# Change root logger level (default is WARN)
logging.basicConfig(level = logging.INFO)


def on_data(data: EventData):
    print('[ON_DATA]', data.title, data.company, data.date, data.link, len(data.description))


def on_error(error):
    print('[ON_ERROR]', error)


def on_end():
    print('[ON_END]')


scraper = LinkedinScraper(
    chrome_executable_path=None, # Custom Chrome executable path (e.g. /foo/bar/bin/chromedriver) 
    chrome_options=None,  # Custom Chrome options here
    headless=True,  # Overrides headless mode only if chrome_options is None
    max_workers=1,  # How many threads will be spawned to run queries concurrently (one Chrome driver for each thread)
    slow_mo=1.3,  # Slow down the scraper to avoid 'Too many requests (429)' errors
)

# Add event listeners
scraper.on(Events.DATA, on_data)
scraper.on(Events.ERROR, on_error)
scraper.on(Events.END, on_end)

queries = [
    Query(
        options=QueryOptions(
            optimize=True,  # Blocks requests for resources like images and stylesheet
            limit=27  # Limit the number of jobs to scrape
        )
    ),
    Query(
        query='Engineer',
        options=QueryOptions(
            locations=['United States'],
            optimize=False,
            limit=5,
            filters=QueryFilters(
                company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',  # Filter by companies
                relevance=RelevanceFilters.RECENT,
                time=TimeFilters.MONTH,
                type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
                experience=None,                
            )
        )
    ),
]

scraper.run(queries)

Anonymous vs authenticated session

By default the scraper will run in anonymous mode (no authentication required). In some environments (e.g. AWS or Heroku) this may be not possible though. You may face the following error message:

Scraper failed to run in anonymous mode, authentication may be necessary for this environment.

In that case the only option available is to run using an authenticated session. These are the steps required:

1. Login to LinkedIn using an account of your choice.
2. Open Chrome developer tools:

3. Go to tab Application, then from left panel select Storage -> Cookies -> https://www.linkedin.com. In the main view locate row with name li_at and copy content from the column Value.

4. Set the environment variable LI_AT_COOKIE with the value obtained in step 3, then run your application as normal. Example:

LI_AT_COOKIE=<your li_at cookie value here> python your_app.py

Rate limiting

You may experience the following rate limiting warning during execution:

[429] Too many requests. You should probably increase scraper "slow_mo" value or reduce concurrency.

This means you are exceeding the number of requests per second allowed by the server (this is especially true when using authenticated sessions where the rate limits are much more strict). You can overcome this by:

• Trying a higher value for slow_mo parameter (this will slow down scraper execution).
• Reducing the value of max_workers to limit concurrency. I recommend to use no more than one worker in authenticated mode.
• If you are using anonymous mode, you can try proxy mode.

The right value for slow_mo parameter largely depends on rate-limiting settings on Linkedin servers (and this can vary over time). For the time being, I suggest a value of at least 1.3 in anonymous mode and 0.4 in authenticated mode.

Proxy mode [experimental]

It is also possible to pass a list of proxies to the scraper:

scraper = LinkedinScraper(
    chrome_executable_path=None,
    chrome_options=None,
    headless=True,
    max_workers=1,
    slow_mo=1,
    proxies=[
        'http://localhost:6666',
        'http://localhost:7777',        
    ]
)

How it works? Basically every request from the browser is intercepted and executed from a python library instead, using one of the provided proxies in a round-robin fashion. The response is then returned back to the browser. In case of a proxy error, the request will be executed from the browser (a warning will be logged to stdout).

WARNING: proxy mode is currently not supported when using an authenticated session.

Filters

It is possible to customize queries with the following filters:

• RELEVANCE:
  • RELEVANT
  • RECENT
• TIME:
  • DAY
  • WEEK
  • MONTH
  • ANY
• TYPE:
  • FULL_TIME
  • PART_TIME
  • TEMPORARY
  • CONTRACT
• EXPERIENCE LEVEL:
  • INTERNSHIP
  • ENTRY_LEVEL
  • ASSOCIATE
  • MID_SENIOR
  • DIRECTOR
• REMOTE:
  • REMOTE (supported only with authenticated session)

See the following example for more details:

from linkedin_jobs_scraper.query import Query, QueryOptions, QueryFilters
from linkedin_jobs_scraper.filters import RelevanceFilters, TimeFilters, TypeFilters, ExperienceLevelFilters, RemoteFilters


query = Query(
    query='Engineer',
    options=QueryOptions(
        locations=['United States'],
        optimize=False,
        limit=5,
        filters=QueryFilters(            
            relevance=RelevanceFilters.RECENT,
            time=TimeFilters.MONTH,
            type=[TypeFilters.FULL_TIME, TypeFilters.INTERNSHIP],
            experience=[ExperienceLevelFilters.INTERNSHIP, ExperienceLevelFilters.MID_SENIOR],
            remote=RemoteFilters.REMOTE, # supported only with authenticated session
        )
    )
)

Company Filter

It is also possible to filter by company using the public company jobs url on LinkedIn. To find this url you have to:

1. Login to LinkedIn using an account of your choice.
2. Go to the LinkedIn page of the company you are interested in (e.g. https://www.linkedin.com/company/google).
3. Click on jobs from the left menu.

4. Scroll down and locate See all jobs or See jobs button.

5. Right click and copy link address (or navigate the link and copy it from the address bar).
6. Paste the link address in code as follows:

query = Query(    
    options=QueryOptions(        
        filters=QueryFilters(
            # Paste link below
            company_jobs_url='https://www.linkedin.com/jobs/search/?f_C=1441%2C17876832%2C791962%2C2374003%2C18950635%2C16140%2C10440912&geoId=92000000',        
        )
    )
)

Logging

Package logger can be retrieved using namespace li:scraper. Default level is INFO. It is possible to change logger level using environment variable LOG_LEVEL or in code:

import logging

# Change root logger level (default is WARN)
logging.basicConfig(level = logging.DEBUG)

# Change package logger level
logging.getLogger('li:scraper').setLevel(logging.DEBUG)

# Optional: change level to other loggers
logging.getLogger('urllib3').setLevel(logging.WARN)
logging.getLogger('selenium').setLevel(logging.WARN)

License

MIT License

If you like the project and want to contribute you can donate something here!