As one of the leading grocery delivery services in India, Grofers needed to make massive fulfillment adjustments as demand rocketed due to an entire nation sheltering in place to help control the spread of infection during a global pandemic. For the Grofers team, providing much-needed grocery delivery service is as much of a technical challenge as a human resources one.

On the surface, food delivery service seems straightforward. However, creating a food delivery startup is more like building a transportation and logistics company than pizza delivery. Coordinating web and mobile applications for seamless ordering, tracking, and delivery, all within razor-thin service windows, takes an unprecedented level of complexity (when you get it right). And once you begin to scale service, the technical challenges become even greater.

At Sentry, we often get asked how error monitoring is different from APM or logging. To help answer this, I’ve broken down the different types of monitoring into four major categories.

With a service designed to help remote teams run more efficiently, Tability’s success relies on application reliability and deliverability to help its users keep complicated projects on schedule. This is why Tability has relied on Sentry error monitoring to identify issues in code and scale development work efficiently.

Different development teams exercise different production philosophies. These philosophies usually stem from greater company direction and culture but are guided by the experience of technical leadership. Aumni has built a unique technology solution to unlock data insights into investment auditing and due diligence for VCs. They need to remain nimble and develop great code (areas 1 and 2) but reduce the probability of user-facing errors and the time spent detecting and fixes new issues (area 3).

Big ideas need creative solutions. Creative solutions mean solving out of the box engineering problems. When you’re solving these new problems, strong error monitoring is imperative to the development cycle. You don’t want to spend all your time chasing down issues and struggling to track the source of errors.

While powerful, our first iteration of Discover had some user experience complexities that made it less user-friendly than other potential search mechanisms. We also heard from you that our Events feature was useful for identifying individual events, but finding common patterns within those events was extremely difficult — keyword, ”was”. I want to share some of the features and the top 5 use cases that you can do with the refreshed Discover.

Here at Sentry, we use Travis CI, a continuous integration tool for GitHub that lets us automate our tests and view the results right within each pull request. In this blog post, we’ll walk through a quick example of how to automatically create Sentry Releases with Travis CI when a commit is pushed to your project’s master branch. (Sentry Releases enable some of our best features, like identifying the commits that likely introduced new errors, and much more!)