The case for logging & metrics

Good software can be written with great coding practices. But maintaining software has its own share of challenges independent of the code quality. We talk about improving our coding skills, but it is time we improve our software health! Logs & metrics help debug but also inform us about the status of the product. Be it a small user base or large, your users will run into errors occasionally and it is up to you, to figure out what went wrong and how to fix it. How to avoid it from happening again? We will look at how logging can drastically improve such a situation, and how by using efficient tools, we can scan logs to our advantage. Now, logging will be incomplete without understanding metrics. How do you rank the urgency of errors floating in your logs? Do 90% of your users face this problem? Or it the 90 percentile? What is the difference anyway? HUGE! We will look at metric math: how can we efficiently tell how big a problem at hand is. It will also allow us to be proactive towards setting alarms to prevent big fires in the future.



Ritika Kanade


Software Engineer @ Apple

Ritika is a Software Engineer at the iCloud Content team at Apple in Seattle. Before she started at Apple, she was a Software Engineer at Rakuten in Fukuoka, Japan for ~3