Yelp Review Grabber: Merchant Rating Collection System

Why does Yelp review crawl always get blocked?

Friends who have engaged in data crawling know that Yelp's anti-crawler mechanism is particularly difficult to deal with. Last week there is a milk tea store old brother to find me complaining, said he used Python to write a script to capture the ratings of competing stores, the results just run half an hour IP was blocked. This problem is, to put it bluntlyHigh Frequency Visits Trigger Risk ControlIt's as if you've been back and forth to get a cupcake a dozen times in the sampling section of the supermarket, and it's a wonder the clerk doesn't stop you.

The real-world value of proxy IPs

This is where a proxy IP is needed toDecentralization of request pressure. The principle is like opening a chain of stores - each branch sends a different clerk to try the food, and each store is visited only once a day. Specifically, there are three core points to keep in mind when it comes to the technical implementation:

Hands-on configuration of the agent system

Here's a demo of the basic configuration in Python, focusing on the proxy settings section. Note that you have to choose to supportResidential Agentsservice provider, the IPs of the server rooms on the market have long been flagged by Yelp:

import requests
from random import choice

 Proxy pool from ipipgo
proxies = [
    "203.34.56.78:8800",
    "198.23.189.102:3128",
    "45.76.203.91:8080"
]

headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}

def scrape_yelp(url).
    try: response = requests.get(url).
        response = requests.get(
            response = requests.get(
            proxies={"http": choice(proxies)},
            headers=headers,
            timeout=15
        )
        return response.text
    except Exception as e.
        print(f "Request Exception: {str(e)}")

Guide to avoiding pitfalls (real-life examples)

Last year a client used a free proxy to grab data and ended up with three rollover scenarios:

• IP repetition rate exceeds 60%
• Response time fluctuations from 0.5 to 15 seconds
• 20%'s agent can't connect at all.

Then I switched to ipipgo.Dynamic Residential AgentsThe success rate is directly pulling up to 92%. their IP pool is updated daily with more than 20% addresses, which is especially suitable for scenarios that require long-term data running.

Frequently Asked Questions QA

Q: Why is it still blocked after using a proxy?
A: Check three points: 1. Whether the random delay is set 2. Whether User-Agent is random 3. Whether a single IP is used more than 10 times

Q: What should I do if my proxy IP responds slowly?
A: It is recommended to turn on ipipgo'sIntelligent RoutingFunction that automatically selects the node with the lowest latency. It is measured to be more than 3 times faster than manual node selection.

Q: How much IP volume is needed to be sufficient?
A: According to the daily crawl 10,000 pieces of data calculation, it is recommended to prepare 500 + dynamic IP. ipipgo's package just have a899/month program, contains 600 high quality residential IPs and is top value for money.

Upgraded Solutions

For enterprise-level users, a distributed crawler architecture is recommended. Deploy the crawler nodes in different regions of the server, each node configured with an independent ipipgo proxy account. This not only improves the collection speed, but also realizesGeographical data collection(e.g., obtaining merchant data specifically for the New York area).

In a recent program to help a restaurant chain, they used 10 servers + ipipgo's enterprise version of the proxy to grab 2.7 million comments in three months. The key is that you don't have to maintain your own IP pool, saving the labor costs of at least two programmers.