- Variable values across the entire call stack for each error
- Broad visibility across issues in development and production environments
- Identifies root cause for each error without sifting through logs
- Proactive approach of fixing errors instead of waiting for them to happen
Senior Java Developer
Key challenges and pain points:
It is hard to detect production errors using only logs, and detection generally came from end users and/or the product team. Existing tools also provided limited visibility into why code failed in production.
Example problem that OverOps helped resolve:
In the past, we couldn’t debug production to find user specific issues, and we couldn’t reproduce these issues in our dev environments manually so we had to copy our production database and create a local dev environment pointing to the copy production database, which would take days/weeks to solve application errors.
“Using OverOps, you see the exact variable state during each exception to practically debug your production environment”
A few weeks ago we had a similar problem and were able to detect the production application error in OverOps and immediately see all the user and variable data, our team was able to solve the problem in just a few minutes.
What is so unique and compelling about OverOps?
OverOps allows our team to debug production environments and recreate errors/bugs in minutes without needing logs. We see everything our end users see in production but catch things sooner so we’re more pro-active.
“Our team was able to solve the problem in just a few minutes”
Who uses OverOps at Nielsen?
Our development teams use OverOps in test and production environments, allowing them to immediately spot new errors and regression before releases are pushed to production. Our developers also get to filter and label/tag errors in OverOps so each team gets their own personalized view of their packages and components. This allows us to divide and conquer and ultimately reduce the bug and error rates of our code
“Our developers can filter and label errors so each team gets a personalized view of packages and components”