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

The Hello application


The Hello application we will use in this section is very similar to the one we used with the service mesh in Chapter 7, Tracing with Service Mesh. It consists of only two services: hello and formatter. The following diagram describes the overall architecture of this exercise:

Figure 11.2: Architecture of the Hello application and its monitoring components and backends

All components of the Hello application are configured with a jaeger client, a prom client, and the logback logging framework with a LogstashTcpSocketAppender plugin that sends the logs directly to Logstash, which saves them to Elasticsearch. Kibana is the web UI used to query logs from storage. The Prometheus client accumulates the metrics in memory, until the Prometheus server pulls them via an HTTP endpoint. Since the Prometheus server runs inside the networking namespace created by docker-compose, it is not configured to scrape metrics from the two clients that run on the host network.

As before, we...