MONITOR MEMORY USAGE WITH PYTHON LANGUAGE

Monitor memory usage in your Python application

In the realm of Python applications, a recurring challenge we encounter relates to memory leaks and overall memory utilization. This issue assumes particular significance when developing applications destined for deployment in the cloud, whether as serverless functions, containers, or virtual machines. In such scenarios, excessive memory consumption can disrupt application performance, potentially leading to crashes and increased operational costs.

To mitigate these concerns, it is imperative to monitor the memory usage of our Python applications and implement a robust tracking system with predefined thresholds. This proactive approach ensures ongoing performance awareness. For instance, if memory usage exceeds a predetermined threshold, such as 80 percent, immediate corrective actions can be taken to preempt more significant issues.

To address this, we introduce Palzin Track, a potent event tracking tool seamlessly compatible with Python. With Palzin Track, you gain the capability to monitor various application events in real-time, including real-time tracking of memory usage. Moreover, you can configure notification rules to promptly alert your team and yourself when memory usage surpasses specified thresholds via push notifications. This vigilance ensures that you maintain a constant pulse on your application's performance, facilitating swift responses when necessary.

Connect Palzin Track to Python

Setting up Palzin Track

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

Python code snippets

You can use the following code snippets to track memory usage in your Python application. 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("palzin.live")  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "High Memory Usage",  


 "description": "Memory usage has exceeded the threshold.",  


 "icon": "🚨",  


 "notify": True  


})  


headers = {  


 'Content-Type': 'application/json',  


 'Authorization': 'Bearer YOUR_API_TOKEN'  


}  


conn.request("POST", "/api/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.palzin.live/v1/log"  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "High Memory Usage",  


 "description": "Memory usage has exceeded the threshold.",  


 "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

Palzin Track is a powerful and flexible event tracking tool that works surprisingly well with Python applications. It provides powerful features such as real-time event tracking, cross-platform push notifications, user and product journeys, charts and analytics, and more.

Connect Palzin Track to your Python application in minutes and start tracking events in real-time. Palzin Track 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 Palzin Track

1. Monitor your CI/CD build status for your Python application
2. Monitor your CPU usage in your Python application
3. Monitor when database goes down in your Python application
4. Monitor high disk usage in your Python application
5. Monitor when a user changes their email address in your Python application
6. Monitor failed logins in your Python application
7. Monitor failed payments for your Python application
8. Monitor MySQL downtime in your Python application
9. Monitor when a new feature is used in your Python application
10. Monitor your Postgres downtime in your Python application
11. Monitor Redis downtime in your Python application
12. Monitor suspicious activity in your Python application
13. Monitor when a user exceeds the usage limit for your Python service
14. Monitor when a user is being rate limited in your Python application
15. Get a notification when your Python code is done executing
16. Send push notifications to your phone or desktop using Python
17. Track canceled subscriptions in your Python application
18. Track your Python cron jobs
19. Track when a file is uploaded to your Python application
20. Track when a form is submitted to your Python application
21. Track payment events via Python
22. Track user sign in events in Python
23. Monitor user signup events via Python
24. Track waitlist signup events via Python