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Adopting continuous-profiling: Understand how your code utilizes cpu/memory

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Adopting continuous-profiling: Understand how your code utilizes cpu/memory
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Introduction into continuous-profiling and how it can help you writing more efficient code
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542
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CC Attribution 2.0 Belgium:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
With the popularity of observability tooling to analyze Logs, Metrics and Traces, it has become easier than ever to find the bottleneck in your software stack. Once you have identified the particular system introducing a user facing performance degradation, as its developer you need to understand which part (ideally down to the function and line of code) is slowing it down. With that insight you are able to effectively optimize your application. In this talk I will show how profiles are collected, how they can be aggregated and visualized. And then how those insights can be used to optimize your code. While there is a focus on the Go ecosystem, most of the content of the talk should be transferable to other languages