Monitor memory usage in your R application

A common problem that we often face with R applications is memory leaks and the overall memory usage of our application. This is a significant problem when building applications that we end up deploying to the cloud either as a serverless function, container, or virtual machine. In such cases, memory usage can become a significant problem by slowing down our application, causing it to crash entirely, or increasing costs.

Therefore, monitoring our R application's memory 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. In cases of a performance issue, for example, when the memory usage goes above a certain threshold, say 80 percent, we can take immediate action and fix the problem before it becomes a significant issue.

To do so, we have created LogSnag, a powerful event tracking tool that works seamlessly with R and allows us to track any event in our application in real-time. For example, with LogSnag, we can track our memory usage in real-time and set up a rule to notify our team and us when the memory usage goes above a certain threshold via push notifications. This way, we will always be aware of the performance of our application, and we 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.

R code snippets

You can use the following code snippets to track memory usage in your R application. Please don't forget to replace the YOUR_API_TOKEN with your API token and update the project and channel names.

library(httr)

headers = c(
  'Content-Type' = 'application/json',
  'Authorization' = 'Bearer YOUR_API_TOKEN'
)

body = '{
  "project": "my-saas",
  "channel": "status",
  "event": "High Memory Usage",
  "description": "Memory usage has exceeded the threshold.",
  "icon": "🚨",
  "notify": true
}';

res <- VERB("POST", url = "https://api.logsnag.com/v1/log", body = body, add_headers(headers))

cat(content(res, 'text'))

library(RCurl)
headers = c(
  "Content-Type" = "application/json",
  "Authorization" = "Bearer YOUR_API_TOKEN"
)
params = "{
  \"project\": \"my-saas\",
  \"channel\": \"status\",
  \"event\": \"High Memory Usage\",
  \"description\": \"Memory usage has exceeded the threshold.\",
  \"icon\": \"🚨\",
  \"notify\": true
}"
res <- postForm("https://api.logsnag.com/v1/log", .opts=list(postfields = params, httpheader = headers, followlocation = TRUE), style = "httppost")
cat(res)

R integration details

LogSnag is a powerful and flexible event tracking tool that works surprisingly well with R 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 LogSnag to your R application in minutes and start tracking events in real-time. 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. 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 MySQL downtime in your R application
9. Monitor when a new feature is used in your R application
10. Monitor your Postgres downtime in your R application
11. Monitor Redis downtime in your R application
12. Monitor suspicious activity in your R application
13. Monitor when a user exceeds the usage limit for your R service
14. Monitor when a user is being rate limited in your R application
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. Track user signup events via R
24. Track waitlist signup events via R