LinkedIn Data Extractor: LinkedIn Proxy Data Extraction

When Data Hunter Meets Copper and Iron Wall

Doing market research friends are quite a headache recently, LinkedIn obviously lying on a large number of industry data, but reach out to grab when the system is always cut off. Last week, the old Zhang changed three computers in a row, the results of the account was shut down all the small black house, anxious to the corner of the mouth straight bubbles. This is when you need to understand thatProxy IP is a key piece of equipment to break through the anti-climbing mechanismThe

Traditional single-IP collection is like walking a tightrope in a sequined suit, and the platform can lock your real identity at a glance. We have tested, the same IP continuous request for more than 20 times, the probability of triggering the verification is as high as 78%. If you change to ipipgo's dynamic residential IP, it is equivalent to give the crawler set of ten layers of camouflage clothing, the system sees different regions "real users" in the browsing.

Choose the right tool and get three years less out of the way

There are all kinds of proxy services on the market, but messing with LinkedIn data can be tricky. Here's a focused comparison table for you:

The killer feature of ipipgo is thatReal Residential IP Rotation + Browser Fingerprinting Emulation. Their dynamic IP pool automatically switches every 5 minutes, and with the UA camouflage technology, they can camouflage the capture behavior like a normal user browsing. The last time I helped a customer capture 2000+ enterprise information, it ran continuously for 12 hours without triggering the wind control.

Teach you to build a collection system by hand

Here's a python example that uses ipipgo's proxy service to interface with the Scrapy framework:

import requests
from itertools import cycle

ip_pool = [
    'usw1.ip ipgo.com:8000',
    'eun1.ip ipgo.com:8000', 'asia1.ip ipgo.com:8000'
    'asia1.ipipgo.com:8000'
]
proxy_cycle = cycle(ip_pool)

def make_request(url).
    proxy = next(proxy_cycle)
    proxies = {
        "http": f "http://user:pass@{proxy}",
        "https": f "http://user:pass@{proxy}"
    }
    response = requests.get(url, proxies=proxies, timeout=10)
    return response

 Example of a call
profile_data = make_request('https://linkedin.com/in/example')

Pay special attention to three points:1) Empty cookies before each request 2) Initiate requests at random intervals of 1-3 seconds 3) Use different geographic IPs for weekdays and weekends. this way, the account survival rate can be increased from 30% to more than 85%.

A practical guide to avoiding the pit

Last year, I helped a recruiting platform with data synchronization and stepped into a few bloody potholes:

1. IP purity makes the difference between success and failure: One time I used a second-hand proxy, and as a result, 30%'s IP was labeled as high-risk, and I directly lost 200 high-quality accounts!

2. Traffic rhythms should look like real people: Access patterns must be different at 3pm on Monday and early Saturday morning, and ipipgo's intelligent scheduling automatically matches geographic time zones

3. Anomaly detection cannot be understated: It is recommended to check the response code every 50 crawls, and switch IPs immediately when encountering a CAPTCHA

5 Questions You Definitely Want to Ask

Q: What should I do if the collection speed is slow?
A: Use ipipgo's concurrent proxy function to open 5 IP channels at the same time, and the speed is directly doubled 5 times!

Q: What should I do if I need to verify my business homepage?
A: Add the company mailbox suffix in the request header, with ipipgo's enterprise dedicated IP line, the pass rate is increased 60%

Q: What's wrong with incomplete data capture?
A: 80% of the time, dynamic loading is triggered, remember to set the scroll loading delay and render the full page with a headless browser

Q: Do free proxies work?
A: Never! Public proxy pool 99% IP are blacked out by the platform, professional things still have to ipipgo this kind of professional tools

Q: How often is the data updated?
A: According to the account weight, the new number is recommended 1 time per week, the old account can be collected every day, remember to cooperate with the IP rotation strategy

One last rant, data collection is a constant battle. Just last week, I used ipipgo to take down a hardcore project, helping a client capture 30,000+ HNW users' information. Remember.Good agency service is like oxygen, usually do not feel the presence, but when there is no immediately suffocated!. Choosing the right tool doubles the effort.