MONITOR DB OUTAGES WITH PYTHON LANGUAGE

Monitor when database goes down in your Python application

In the context of Python applications, data persistence is a fundamental requirement. While simple data storage options like JSON, CSV, or plain text files suffice in some scenarios, more complex applications often demand a robust solution capable of managing vast datasets, handling numerous requests, and executing intricate queries.

This is where databases come into play, offering a structured approach to data storage and retrieval. Databases empower applications to perform complex queries and scale efficiently. However, delving into databases can be a challenging endeavor, involving intricate setup and maintenance.

One prevalent challenge associated with databases is the possibility of downtime, which can result from various factors. When a database becomes unavailable, it adversely impacts the functionality of your Python application, hindering data retrieval and storage.

To mitigate such issues, it is crucial to establish a monitoring system for your database's activity. Proactive monitoring ensures that anomalies are promptly detected and brings them to your attention, allowing for immediate action to rectify problems before they escalate.

Fortunately, Palzin Track is an ideal solution for addressing this concern, simplifying the process of tracking events within your Python application and monitoring database outages. With Palzin Track, real-time monitoring of database outages is made effortless, and it provides the capability to notify both you and your entire team whenever issues arise.

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

Use the following code snippet to track your database outages with Palzin Track. 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": "Database is Down",  


 "description": "PostgresSQL is down in Oregon",  


 "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": "Database is Down",  


 "description": "PostgresSQL is down in Oregon",  


 "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 real-time event tracking tool that works seamlessly with Python applications. It provides a number of features such as real-time event tracking, cross-platform push notifications, event filtering, user and product journeys, charts and analytics, and much more.

By being a use-case agnostic event tracking tool, Palzin Track allows you to track any event in your Python applications in any way you want. You can track your database outages, system status, and even user activity in real-time.

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 high disk usage in your Python application
4. Monitor when a user changes their email address in your Python application
5. Monitor failed logins in your Python application
6. Monitor failed payments for your Python application
7. Monitor memory usage in 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