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Proxy IP and crawler anomaly detection make data collection more stable and efficient

Published at

1/8/2025

I. Proxy IP: Break through access restrictions and protect real IP

1.1 Basic concepts of proxy IP

Proxy IP, that is, the IP address provided by the proxy server, acts as an intermediary between the client and the target server. By using a proxy IP, the client's request is first sent to the proxy server, and then forwarded to the target server by the proxy server, thereby hiding the client's real IP address. As a professional proxy IP service provider, 98IP provides proxy IP resources around the world, which are highly anonymized, high-speed and stable, and have wide coverage, making them very suitable for data collection tasks.

1.2 Advantages of 98IP in data collection

Break through geographical restrictions: 98IP provides proxy IPs from all over the world, which can easily bypass the geographical restrictions of the target website.
Prevent IP blocking: 98IP has a huge IP pool and can change IPs regularly to avoid a single IP being blocked due to frequent access.
Increase request speed: 98IP's proxy server network architecture is optimized to reduce request delays and improve data collection efficiency.

1.3 Sample code: Send requests through 98IP using Python and the requests library

import requests

# Proxy IP address and port provided by 98IP (example)
proxy_ip = 'http://your-98ip-proxy:port'  # Please replace with the actual 98IP proxy address and port

# Setting up a proxy
proxies = {
    'http': proxy_ip,
    'https': proxy_ip.replace('http', 'https')  # If an HTTPS proxy is required, make the appropriate substitutions
}

# Target URL
url = 'http://example.com/data'

# Send request
try:
    response = requests.get(url, proxies=proxies)
    response.raise_for_status()  # Check if the request was successful
    print(response.status_code)
    print(response.text)
except requests.RequestException as e:
    print(f"Request Failed: {e}")

2. Crawler anomaly detection: timely detection and handling of anomalies to ensure data quality

2.1 The importance of anomaly detection

During the data collection process, abnormal situations such as network timeouts, HTTP error codes, and data format mismatches often occur. An effective anomaly detection mechanism can detect these problems in a timely manner, avoid invalid requests, and improve the accuracy and efficiency of data collection.

2.2 Abnormal Detection Strategy

Status code check: HTTP status code is the direct basis for judging whether the request is successful, such as 200 for success, 404 for resource not found, and 500 for internal server error.
Content verification: Check whether the returned data conforms to the expected format, such as whether the JSON structure is complete and whether the HTML page contains specific elements.
Retry mechanism: For temporary errors (such as network fluctuations), implement a reasonable retry strategy to avoid abandoning the entire request due to a single failure.
Logging: Record the log of each request in detail, including time, URL, status code, error information, etc., to facilitate subsequent analysis and debugging.

2.3 Example code: Data collection process combined with anomaly detection

import requests
import time
from requests.exceptions import HTTPError, ConnectionError, Timeout

# Target URL List
urls = ['http://example.com/data1', 'http://example.com/data2']

# exception handler
def fetch_data(url, proxies, retries=3, backoff_factor=0.3):
    for attempt in range(retries):
        try:
            response = requests.get(url, proxies=proxies, timeout=10)
            response.raise_for_status()  # Checking for HTTP errors
            if response.headers['Content-Type'].startswith('application/json'):
                data = response.json()  # Assuming that JSON data is expected to be returned
                return data
            else:
                raise ValueError("Unexpected content type")
        except (HTTPError, ValueError) as http_err:
            print(f"HTTP error occurred: {http_err}")
        except (ConnectionError, Timeout) as conn_err:
            print(f"Connection error occurred: {conn_err}")
            time.sleep(backoff_factor * (2 ** attempt))  # Index Exit Strategy
        except Exception as err:
            print(f"Other error occurred: {err}")
    return None

# Proxy IP (example)
proxies = {
    'http': 'http://your-proxy-ip:port',
    'https': 'https://your-proxy-ip:port'
}

# data acquisition
for url in urls:
    data = fetch_data(url, proxies)
    if data:
        print(f"Successfully fetched data from {url}")
        # Processing data...
    else:
        print(f"Failed to fetch data from {url}")

III. Summary

Taking 98IP as an example, we demonstrated the application advantages of proxy IP in data collection, and combined with crawler anomaly detection technology to build a more efficient and stable data collection system. Through reasonable strategies and code implementation, we can make full use of high-quality proxy IP services such as 98IP, as well as effective anomaly detection mechanisms, to provide a solid foundation for data analysis and decision-making. In actual applications, it is also necessary to adjust the proxy IP selection strategy, anomaly detection logic, and retry mechanism according to specific needs to achieve the best effect.

98IP Proxy IP

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