Debugging at Scale Using Elastic and Machine Learning

Friday, November 03, 2017 - 3:00 pm3:30 pm

Mohit Suley, Microsoft


Engineers are well-tuned with debugging issues on a single machine. However, when the architecture scales out to possibly hundreds or thousands of machines with components 10+ layers deep, debugging doesn't look the same anymore. The concept of looking at logs becomes 'collective' in nature and looking for patterns in logs is the only viable way of associating them with the problems you are trying to solve.

We will walk through motivation for building such a system and how it differs from traditional monitoring and debugging. A system designed this way collects all needed artifacts, identifies known/unknown patterns in error messages, correlates with infrastructure serving these errors, and allows outlier service components to be exposed within 10-15 minutes of a developing problem trend.

Mohit Suley, Microsoft

Mohit is an Availability Engineer on Bing's Live Site Engineering team. By day, he investigates all the issues that subtly affect Bing’s availability and performance. Designing systems to proactively improve availability, route around problems, is a core mission of the team. He loves long walks, talking about end-user availability and how network-level data can tell interesting stories about customer experience in aggregate. R is his go-to data analysis tool these days. Opportunities to dive into network flows, architecture issues or scaling problems never go ignored.

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@conference {207259,
author = {Mohit Suley},
title = {Debugging at Scale Using Elastic and Machine Learning},
year = {2017},
address = {San Francisco, CA},
publisher = {USENIX Association},
month = oct

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