04, Aug 2023

MONITOR USAGE THROTTLING WITH R LANGUAGE

Palzin Track R Use Cases

Monitor when a user is being rate limited in your R application

When working on a R application, managing throttling is a common necessity. Throttling involves restricting the number of requests a user can make to your application, a crucial measure when dealing with high user volumes. For instance, consider a R application enabling users to upload files; it's prudent to set a limit, like allowing users to upload a maximum of 10 files per minute. Such limitations serve to prevent potential misuse of your service.

Monitoring when throttling is triggered in your R application is of utmost importance. It can indicate issues in your implementation or even hint at users attempting to exploit your service. Thus, it's essential to establish a robust monitoring system to detect throttle triggers and promptly notify you and your team when irregularities occur.

Palzin Track proves to be an invaluable tool for addressing this challenge. It simplifies the process of tracking events within your R application, making it effortless to monitor throttle occurrences. As an example, you can employ Palzin Track to track the event of a user uploading a file and set up alert rules to notify you when a user reaches the ten-file limit. This approach empowers you to stay informed about user activity and respond effectively when necessary.

Connect Palzin Track to R


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.

R code snippets

Copy and paste the following code into your R project. You are required to replace the YOUR_API_TOKEN with your API token and update the project and channel names.

Using R with httr


library(httr)  


headers = c(  


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


 'Authorization' = 'Bearer YOUR_API_TOKEN'  


)  


body = '{  


 "project": "my-project",  


 "channel": "limits",  


 "event": "User is being rate limited",  


 "description": "User has uploaded more than the allowed amount of files",  


 "icon": "⏱",  


 "notify": true  


}';  


res VERB("POST", url = "https://api.palzin.live/v1/log", body = body, add_headers(headers))  


cat(content(res, 'text'))

Using R with RCurl


library(RCurl)  


headers = c(  


 "Content-Type" = "application/json",  


 "Authorization" = "Bearer YOUR_API_TOKEN"  


)  


params = "{  


 \"project\": \"my-project\",  


 \"channel\": \"limits\",  


 \"event\": \"User is being rate limited\",  


 \"description\": \"User has uploaded more than the allowed amount of files\",  


 \"icon\": \"⏱\",  


 \"notify\": true  


}"  


res postForm("https://api.palzin.live/v1/log", .opts=list(postfields = params, httpheader = headers, followlocation = TRUE), style = "httppost")  


cat(res)

R integration details

When designing Palzin Track, we aimed to create the most simple yet flexible event tracking tool possible. We wanted to make it easy for developers to integrate with their R applications and to start tracking events in no time.

Today, Palzin Track is what we believe to be the next generation of event tracking. It works excellent with R and provides powerful features such as real-time event tracking, cross-platform push notifications, event filtering, user and product journeys, charts and analytics, and much more.

Palzin Track provides a free plan to get you started with event tracking, and we can't wait to see how you use it. So please give it a try, and don't hesitate to reach out to us if you have any questions or feedback!

Other use-cases for Palzin Track

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

Go Beyond the Metrics. Understand the Why.

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