This talk investigates the aftermath of a production issue where performance diverges significantly from expected boundaries. To do that, the talk follows an application team as they debug and instrument to understand why their application behaves significantly worse under certain load parameters.
In this talk, I'll cover false starts during early issue investigation including cold starts, provisioned concurrency, and function memory limits. You'll learn how and why increasing application observability enabled my team to bypass invalid assumptions about root causes of poor application performance, how we moved from guessing to knowing, and how that focus helped us fix application behaviors that most directly impacted our customer experience. This talk also explores how observability sharpen's a team's ability to identify the right resource investments to make and how SLIs and SLOs provide the clarity teams need to prioritize and assess production issues.
Please register for o11ycon+hnycon first, then register for this workshop. Conference registration is required.
Michael is a Platform Engineer at Honeycomb.io. Has worked with various public and private cloud providers over for the past 8 years. Originally was deeply rooted in system administration but has since gained fondness for infrastructure as code and developer tooling. He has been using Kubernetes + Terraform software pairing since 2017. In his spare time he is an avid PC gamer, enjoys cooking and tinkers with mixed reality.
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