Book Image

Observability with Grafana

By : Rob Chapman, Peter Holmes
Book Image

Observability with Grafana

By: Rob Chapman, Peter Holmes

Overview of this book

To overcome application monitoring and observability challenges, Grafana Labs offers a modern, highly scalable, cost-effective Loki, Grafana, Tempo, and Mimir (LGTM) stack along with Prometheus for the collection, visualization, and storage of telemetry data. Beginning with an overview of observability concepts, this book teaches you how to instrument code and monitor systems in practice using standard protocols and Grafana libraries. As you progress, you’ll create a free Grafana cloud instance and deploy a demo application to a Kubernetes cluster to delve into the implementation of the LGTM stack. You’ll learn how to connect Grafana Cloud to AWS, GCP, and Azure to collect infrastructure data, build interactive dashboards, make use of service level indicators and objectives to produce great alerts, and leverage the AI & ML capabilities to keep your systems healthy. You’ll also explore real user monitoring with Faro and performance monitoring with Pyroscope and k6. Advanced concepts like architecting a Grafana installation, using automation and infrastructure as code tools for DevOps processes, troubleshooting strategies, and best practices to avoid common pitfalls will also be covered. After reading this book, you’ll be able to use the Grafana stack to deliver amazing operational results for the systems your organization uses.
Table of Contents (22 chapters)
1
Part 1: Get Started with Grafana and Observability
5
Part 2: Implement Telemetry in Grafana
10
Part 3: Grafana in Practice
15
Part 4: Advanced Applications and Best Practices of Grafana

Understanding the Tempo architecture

Like Loki and Mimir, Tempo leverages object stores such as Amazon S3, Google Cloud Storage, and Microsoft Azure Blob Storage. With the horizontal scalability of components in both the read and write pathways, Tempo has a fantastic ability to scale as data volumes increase.

The following diagram shows the architecture used by Tempo:

Figure 6.7 – The Tempo architecture

Figure 6.7 – The Tempo architecture

The write pathway for Tempo consists of the following:

  • Distributor: The distributor is responsible for accepting spans and routing them to the correct instance of the ingester service, based on the trace ID of the span.
  • Ingester: The ingester is responsible for grouping spans into traces, batching multiple traces into blocks, and writing bloom filters and indexes for querying. Once a block is complete, the ingester also flushes the data to the backend.
  • Metrics generator: The metrics generator is an optional component; it receives...