Monitoring the performance of your ImmoScout24 scraper involves keeping track of various metrics that can impact the effectiveness and efficiency of your scraping process. Here are some key aspects to monitor and ways to implement performance tracking:
1. Response Time
Track how long it takes for ImmoScout24 to respond to your requests. Longer response times could indicate network issues, server overload, or that your scraper is being throttled.
Python Example:
import requests
import time
url = 'https://www.immoscout24.de/'
start_time = time.time()
response = requests.get(url)
end_time = time.time()
print(f"Response time: {end_time - start_time} seconds")
2. Request Success Rate
Monitor the HTTP status codes of your responses to determine the success rate of your requests. Frequent 4xx or 5xx errors may signal that your scraper is being blocked or encountering other issues.
Python Example:
response = requests.get(url)
if response.ok:
print("Request successful")
else:
print(f"Request failed with status code: {response.status_code}")
3. Data Extraction Success
Ensure that your data extraction logic is working correctly by verifying the parsed data against expected results.
Python Example with BeautifulSoup:
from bs4 import BeautifulSoup
# Suppose you're extracting listing titles
soup = BeautifulSoup(response.content, 'html.parser')
titles = soup.find_all('h2', class_='listing-title') # Adjust selector based on the actual structure
if titles:
print("Data extraction successful")
else:
print("Data extraction failed")
4. Scraping Speed
Keep track of how many pages/items you can scrape within a certain time frame. Be aware that scraping too quickly can lead to being blocked by the website.
Python Example:
# Assuming you have a function that scrapes a single page
def scrape_page(page_url):
# Scrape logic here
pass
start_time = time.time()
for page_url in list_of_page_urls:
scrape_page(page_url)
end_time = time.time()
total_pages = len(list_of_page_urls)
print(f"Scraped {total_pages} pages in {end_time - start_time} seconds")
5. Resource Usage
Monitor the CPU and memory usage of your scraper to ensure it's not consuming excessive resources.
Python Example with psutil:
import psutil
# Get the process ID of your running scraper
process = psutil.Process(pid) # Replace 'pid' with the actual process ID
cpu_usage = process.cpu_percent()
memory_usage = process.memory_percent()
print(f"CPU usage: {cpu_usage}%")
print(f"Memory usage: {memory_usage}%")
6. Error Handling and Logging
Implement robust error handling and maintain logs to record any issues that occur during the scraping process.
Python Example with logging:
import logging
logging.basicConfig(filename='scraper.log', level=logging.INFO)
try:
response = requests.get(url)
# More scraping logic here
except requests.RequestException as e:
logging.error(f"Request error: {e}")
except Exception as e:
logging.error(f"An error occurred: {e}")
7. IP Rotation and User-Agent Spoofing
If you're experiencing frequent blocks or captchas, you might need to use proxy rotation and change user-agents to mimic different users.
Python Example with rotating proxies and user-agents:
import random
proxies = ['http://ip1:port', 'http://ip2:port'] # Replace with actual proxy addresses
user_agents = ['User-Agent 1', 'User-Agent 2'] # Replace with actual user-agent strings
proxy = random.choice(proxies)
user_agent = random.choice(user_agents)
headers = {'User-Agent': user_agent}
response = requests.get(url, proxies={'http': proxy, 'https': proxy}, headers=headers)
8. Monitoring Tools
Consider using monitoring tools like Prometheus, Grafana, or even simpler solutions like Google Sheets to keep track of these metrics over time. This will help you identify trends and potential issues with your scraper.
Remember to always follow the terms of service of ImmoScout24, and use web scraping ethically. Excessive requests to their servers can lead to legal issues or permanent IP bans.
