Book Image

Modern Distributed Tracing in .NET

By : Liudmila Molkova
Book Image

Modern Distributed Tracing in .NET

By: Liudmila Molkova

Overview of this book

As distributed systems become more complex and dynamic, their observability needs to grow to aid the development of holistic solutions for performance or usage analysis and debugging. Distributed tracing brings structure, correlation, causation, and consistency to your telemetry, thus allowing you to answer arbitrary questions about your system and creating a foundation for observability vendors to build visualizations and analytics. Modern Distributed Tracing in .NET is your comprehensive guide to observability that focuses on tracing and performance analysis using a combination of telemetry signals and diagnostic tools. You'll begin by learning how to instrument your apps automatically as well as manually in a vendor-neutral way. Next, you’ll explore how to produce useful traces and metrics for typical cloud patterns and get insights into your system and investigate functional, configurational, and performance issues. The book is filled with instrumentation examples that help you grasp how to enrich auto-generated telemetry or produce your own to get the level of detail your system needs, along with controlling your costs with sampling, aggregation, and verbosity. By the end of this book, you'll be ready to adopt and leverage tracing and other observability signals and tools and tailor them to your needs as your system evolves.
Table of Contents (23 chapters)
1
Part 1: Introducing Distributed Tracing
6
Part 2: Instrumenting .NET Applications
11
Part 3: Observability for Common Cloud Scenarios
16
Part 4: Implementing Distributed Tracing in Your Organization

Chapter 7 – Adding Custom Metrics

  1. We should first decide what we need the metric for. For example, if we need it to rank memes in search results or to calculate ad hits, we should separate it from telemetry. Assuming we store the meme download counter in a database for business logic purposes, we could also stamp it on traces or events as an attribute when the counter is updated.

From a telemetry-only standpoint, metric per meme would have high cardinality as we probably have millions of memes in the system and thousands active per minute. With some additional logic (for example, if we can ignore rarely accessed memes), we might even be able to introduce a metric with a meme name as an attribute.

I would start with traces and aggregate spans by meme name in a rich query. Even if traces are sampled, I can still calculate the estimated number of downloads, compare it between memes, and see trends.

  1. Usually, both, but it depends: we need incoming HTTP request...