Monitoring the performance of a web scraper, such as a Glassdoor scraper, is essential for ensuring that it continues to function correctly and efficiently. Here are several aspects you may want to monitor and some methods you can employ to do so.
1. Response Times
Monitoring how long each request takes can give you insight into potential performance issues or whether you are being rate-limited.
Python Example:
import requests
import time
start_time = time.time()
response = requests.get('https://www.glassdoor.com')
end_time = time.time()
print(f"Request took {end_time - start_time} seconds")
2. Success Rates
Keep track of HTTP status codes to monitor the rate of successful requests versus failed ones (e.g., 200 OK vs. 404 Not Found or 403 Forbidden).
Python Example:
status_code = response.status_code
if status_code == 200:
print("Success")
else:
print(f"Failed with status code: {status_code}")
3. Data Extraction Accuracy
Periodically verify that the data extracted is accurate and complete. This can involve checksums, data validation scripts, or manual checks.
Python Example:
from bs4 import BeautifulSoup
# Assume 'response' holds the HTTP response from earlier
soup = BeautifulSoup(response.text, 'html.parser')
job_titles = soup.find_all('a', class_='job-title')
for job in job_titles:
print(job.text) # Print each job title to manually verify accuracy
4. IP Bans and Rate Limits
If your scraper's IP gets banned, you'll need to handle it by rotating IPs or slowing down your requests.
Python Example:
if status_code == 429: # HTTP 429 Too Many Requests
print("Rate limited! Slowing down...")
time.sleep(10)
elif status_code == 403:
print("IP might be banned, consider using proxies.")
5. Resource Usage
Monitor your CPU and memory usage to ensure the scraper is not consuming too many resources.
Console Commands:
# For Unix-like systems
top
htop # More user-friendly interface
Python Example:
import psutil
process = psutil.Process()
print(f"CPU Percent: {process.cpu_percent()}")
print(f"Memory Usage: {process.memory_info().rss}") # in bytes
6. Logger Setup
Set up a logging system to record events, errors, and performance metrics.
Python Example:
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
try:
response = requests.get('https://www.glassdoor.com')
response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
except requests.RequestException as e:
logger.error(f"Request failed: {e}")
7. Alerts and Notifications
Implement alerts to notify you of any issues with the scraper, such as continuous failures or high response times.
Python Example:
# This could be integrated with an external service like email, Slack, etc.
def send_alert(message):
# Code to send an alert (e.g., through email or Slack)
pass
if status_code != 200 or (end_time - start_time) > acceptable_response_time:
send_alert(f"Scraper encountered an issue: {status_code}, took too long to respond")
8. Dashboard and Visualization Tools
Use monitoring tools and dashboards to visualize the performance of your scraper. Some popular tools include Grafana, Kibana, and Prometheus.
To set up a monitoring system, you can use a combination of these methods along with third-party services and tools to provide real-time insights into the health and performance of your Glassdoor scraper. Remember, however, to always comply with Glassdoor's Terms of Service when scraping their site.
