Monitor your CPU usage in your Python application

LogSnag makes it easy to track your Python application and monitor your CPU usage.

When building a Python application, we often have to think and deal with performance issues. Performance is a critical aspect of any application and can significantly affect the user experience. One of the most common performance issues for Python applications is when the workload is CPU bound. This means that the application spends most of its time waiting for the CPU to complete a task.

In such cases, monitoring our Python application's CPU usage and setting up a system to track when use goes above a certain threshold is essential. This way, we can always be aware of the performance of our application. Furthermore, in cases of a performance issue, for example, when the CPU usage goes above a certain threshold, we can take immediate action and fix the problem before it becomes a significant issue.

Fortunately, here at LogSnag, we have created a powerful solution for this problem. LogSnag is a powerful, real-time event tracking tool that works seamlessly with Python. With LogSnag, you can set up event tracking for anything you want and monitor things like your CPU usage in real-time. In addition, you can set up a rule to notify you and your team when the CPU usage goes above a certain threshold. This way, you will always be aware of the performance of your application, and you can take immediate action if needed.


Setting up LogSnag

  1. Sign up for a free LogSnag account.
  2. Create your first project from the dashboard.
  3. Head to settings and copy your API token.

Python code snippets

To track your CPU usage, you can use the following code snippet Please don't forget to replace the YOUR_API_TOKEN with your API token and update the project and channel names.

Using Python with http.client
import http.client
import json

conn = http.client.HTTPSConnection("api.logsnag.com")
payload = json.dumps({
  "project": "my-saas",
  "channel": "status",
  "event": "High CPU Usage",
  "description": "CPI usage has been over 90% for the last 5 minutes",
  "icon": "🚨",
  "notify": True
})
headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer YOUR_API_TOKEN'
}
conn.request("POST", "/v1/log", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
Using Python with Requests
import requests
import json

url = "https://api.logsnag.com/v1/log"

payload = json.dumps({
  "project": "my-saas",
  "channel": "status",
  "event": "High CPU Usage",
  "description": "CPI usage has been over 90% for the last 5 minutes",
  "icon": "🚨",
  "notify": True
})
headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer YOUR_API_TOKEN'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

Python integration details

We believe that event tracking should be simple and accessible to every developer and team. Therefore, we have worked hard to create the next generation of event tracking tools. As a result, LogSnag is flexible and easy to use, making it a great companion for your Python applications.

In addition to real-time event tracking, LogSnag provides powerful features such as cross-platform push notifications, event filtering, user and product journeys, charts, insights, and more.

LogSnag provides a generous free plan to get you started with event tracking. You can also check out our pricing page to see our paid plans. So please give us a try and let us know what you think!

Other use-cases for LogSnag

  1. Track payment events via Python

  2. Track user signup events via Python

  3. Track your Python cron jobs

  4. Track waitlist signup events via Python

  5. Track user sign in events in Python

  6. Get a notification when your Python code is done executing

  7. Monitor when a user exceeds the usage limit for your Python service

  8. Monitor when a new feature is used in your Python application

  9. Monitor suspicious activity in your Python application

  10. Monitor when a user is being rate limited in your Python application

  11. Monitor when database goes down in your Python application

  12. Monitor memory usage in your Python application

  13. Monitor high disk usage in your Python application

  14. Track when a file is uploaded to your Python application

  15. Track when a form is submitted to your Python application

  16. Track canceled subscriptions in your Python application

  17. Monitor failed payments for your Python application

  18. Monitor your CI/CD build status for your Python application

  19. Monitor when a user changes their email address in your Python application

  20. Monitor Redis downtime in your Python application

    View all common use-cases with Python