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  • Book Overview & Buying Mastering Distributed Observability in Rust
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Mastering Distributed Observability in Rust

Mastering Distributed Observability in Rust

By : Manjunath Gangappa, Rajkumar Rangaraj
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Mastering Distributed Observability in Rust

Mastering Distributed Observability in Rust

By: Manjunath Gangappa, Rajkumar Rangaraj

Overview of this book

Gain the skills to build, monitor, and debug distributed systems in Rust with this hands-on guide to observability using OpenTelemetry. As Rust adoption grows in backend services, developers face fragmented documentation and limited tooling for telemetry. This book fills that gap by presenting a unified, end-to-end solution to implement distributed observability in modern Rust systems. You’ll explore the foundations of observability and Rust’s ownership model before learning how to collect, export, and correlate logs, metrics, and traces. Discover how to instrument applications using OpenTelemetry crates and bridge them with the tracing ecosystem. Learn to deploy the OpenTelemetry Collector, integrate with Prometheus, Grafana, and Jaeger, and tackle challenges like sampling, context propagation, and async tracing. Written by two seasoned engineers with over 35 years of combined experience in large-scale systems and open-source observability leadership, this book balances theory with real-world implementations. From debugging async bottlenecks to configuring cost-effective telemetry pipelines, you’ll finish with the confidence to operate reliable, observable Rust systems at scale.
Table of Contents (23 chapters)
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Lock Free Chapter
1
Part 1: Foundation
5
Part 2: Instrumentation
10
Part 3: Advanced Observability
14
Part 4: Real-World Problems
18
Part 5: AI-Augmented Observability
21
Other Books You May Enjoy
22
Index

Optimizing Async Performance

Once these signals are visible, optimization becomes a diagnosis workflow, not a guessing exercise. The goal is not "make everything faster," but "remove the specific runtime behavior causing contention."

At this stage, ask one concrete question: Is checkout slow because business logic is expensive, or because the executor cannot schedule ready work quickly enough? The dashboard from Section 10.3 answers this. If the queue wait is high and the downstream spans are normal, the executor is the bottleneck. The two fixes below address the two most common causes of executor contention.

Avoiding Blocking in Async Code

This is the single most important rule for Tokio-based services: never perform blocking work on an async worker thread.

Blocking means any operation that holds the OS thread without yielding to the Tokio scheduler. Common examples include std::thread::sleep(), synchronous file I/O (std::fs::read), CPU-heavy computation that runs...

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