9 Developer observability

 

This chapter covers

  • What developer observability means
  • What continuous profiling is and why you should care
  • How cloud and developer observability drive developer productivity
  • What tooling is available in the developer observability space

In chapter 1, we talked about different observability use cases and target audiences. In the past couple of chapters, we often had a rather operations-biased view—that is, the topics focused on cloud-native applications running in production. Now, we are changing gears to focus in this chapter on developers as the main audience for observability. We will talk about their natural habitats (IDEs and the command line) and how observability for developers is useful. So this chapter is about observability for developers, not observability on or about developers (such as time tracking or commits created or LOC produced per day).

Before we get to the core of the challenge, we will first define a concept pertinent to our conversation: shift left. This concept started out in the context of testing (http://mng.bz/Zq1P) and CI/CD and then was extended to security (http://mng.bz/RxdK). Nowadays, this term is also something marketing departments like to apply in the context of observability.

9.1 Continuous profiling

9.1.1 The humble beginnings

9.1.2 Common technologies

9.1.3 Open source CP tooling

9.1.4 Commercial continuous profiling offerings

9.1.5 Using continuous profiling to assess continuous profiling

9.2 Developer productivity

9.2.1 Challenges

9.2.2 Tooling

9.3 Tooling considerations

9.3.1 Symbolization

9.3.2 Storing profiles

9.3.3 Querying profiles

9.3.4 Correlation