Book Image

Mastering Distributed Tracing

By : Yuri Shkuro
Book Image

Mastering Distributed Tracing

By: Yuri Shkuro

Overview of this book

Mastering Distributed Tracing will equip you to operate and enhance your own tracing infrastructure. Through practical exercises and code examples, you will learn how end-to-end tracing can be used as a powerful application performance management and comprehension tool. The rise of Internet-scale companies, like Google and Amazon, ushered in a new era of distributed systems operating on thousands of nodes across multiple data centers. Microservices increased that complexity, often exponentially. It is harder to debug these systems, track down failures, detect bottlenecks, or even simply understand what is going on. Distributed tracing focuses on solving these problems for complex distributed systems. Today, tracing standards have developed and we have much faster systems, making instrumentation less intrusive and data more valuable. Yuri Shkuro, the creator of Jaeger, a popular open-source distributed tracing system, delivers end-to-end coverage of the field in Mastering Distributed Tracing. Review the history and theoretical foundations of tracing; solve the data gathering problem through code instrumentation, with open standards like OpenTracing, W3C Trace Context, and OpenCensus; and discuss the benefits and applications of a distributed tracing infrastructure for understanding, and profiling, complex systems.
Table of Contents (21 chapters)
Mastering Distributed Tracing
Contributors
Preface
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15
Afterword
Index

Observing trends


Now that we have our job running and we understand what it is doing, let's run some experiments. The two JSON files with profiles for the microservices simulator model the architecture of the HotROD demo application we covered in Chapter 2, Take Tracing for a HotROD Ride. The second profile file, hotrod-reduced.json, is nearly identical to hotrod-original.json, except that the simulator is instructed to make only five calls to the route service instead of the usual 10 calls. This difference would affect the SpanCountJob. To do the experiment, let the simulator run with the original profile for a few minutes:

$ make microsim-run-original
docker run -v /Users/.../Chapter12:/ch12:ro --net host \
    yurishkuro/microsim:0.2.0 \
    -c /ch12/hotrod-original.json \
    -w 1 -s 500ms -d 5m
 [ . . . ]
2018/12/23 20:34:07 services started
2018/12/23 20:34:10 started 1 test executors
2018/12/23 20:34:10 running for 5m0s

If you built the microsim binary locally, you can run:

$ microsim...