MONITOR POSTGRES DOWNTIME WITH PYTHON LANGUAGE

Monitor your Postgres downtime in your Python application

Postgres, a reliable relational database frequently utilized in Python applications for data persistence and retrieval, offers a wide array of features, rendering it a suitable choice for both small and large-scale applications.

Nonetheless, like any other database system, Postgres is vulnerable to downtime triggered by various factors. Possible causes include hardware failures, network issues, misconfigurations, and more. When such incidents occur, it can disrupt the expected functionality of your application, leading to issues like failed requests and sluggish performance, and in severe cases, data loss.

Hence, it is imperative to maintain vigilant monitoring of your database's status and swiftly address any irregularities. Thankfully, Palzin Track simplifies the process of tracking critical events like database downtime and failures. Palzin Track serves as a user-friendly yet potent event tracking tool, designed to log unexpected application behaviors.

For instance, in the context of Postgres, it's common practice to establish periodic checks to ensure the database's operational status, monitoring its performance, disk usage, and memory utilization. Whenever anomalies surface, such as elevated disk usage, performance degradation, or downtime, Palzin Track records these events, immediately notifying your team for prompt action.

Moreover, Palzin Track offers a robust insights dashboard, enabling you to monitor various aspects of your database, including its status, performance, uptime, memory usage, and other customizable metrics. This feature simplifies the task of maintaining your database's overall health and ensures seamless Python application operation. 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

To track your Postgres downtime, 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("palzin.live")  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "Postgres is down",  


 "description": "Postgres has been down for the last 5 minutes",  


 "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": "Postgres is down",  


 "description": "Postgres has been down 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

In addition, Palzin Track provides several powerful features, such as real-time event tracking, push notifications, charts, funnels, and user journey tracking. Furthermore, it works seamlessly with Python and is an excellent tool for monitoring each application part.

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 memory usage in your Python application
9. Monitor MySQL downtime in your Python application
10. Monitor when a new feature is used 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