Getting Started with Grafana Tempo: The Ultimate Guide for Developers and Operators

Distributed tracing has become an invaluable tool for untangling the ever more complex architectures that underpin modern applications. By shedding light on request flows across services and infrastructure, tracing empowers developers and operators alike to quickly identify and resolve performance issues. Grafana Tempo is an exciting new open source distributed tracing backend that is purpose-built for the scale and flexibility the cloud-native world demands.

In this comprehensive guide, we‘ll cover everything you need to know to get started with Tempo for monitoring your systems.

The Growing Complexity of Modern Architecture

Applications have radically transformed in the past decade. Monoliths have given way to sophisticated microservice architectures composed of dozens or even hundreds of discrete services. Containers and orchestrators like Kubernetes have exploded in popularity, dynamically managing application resources across clusters of commodity hardware. The result is a tangled web of services, containers, hosts, and networks.

While this architectural shift enables unprecedented agility and scalability, it also makes monitoring exponentially more difficult. Legacy approaches like log aggregation provide incomplete visibility. The ephemeral nature of containers means metrics and events are constantly in flux. Making sense of what is happening at any given moment becomes impossibly challenging.

Distributed tracing provides a lifeline for gaining insight into these vast and complex environments. By tracing requests end-to-end across all services, and analyzing the volumes of telemetry data in context, we can begin to untangle the web.

Why Distributed Tracing Matters

At its core, distributed tracing enables developers and operators to analyze the path of a request through a complex distributed system. Unlike logs or metrics, tracing data provides context by tying related events together into a cohesive visualization.

Concretely, tracing achieves two primary objectives:

Observability – The ability to understand the internal state of the system and the overall flow of requests is critical for monitoring health and performance. Tracing provides this end-to-end viewpoint across the full architecture.

Debugging – When issues arise, tracing provides immediate insight into the chain of events preceding a problem. Analyzing traces accelerates root cause identification and remediation.

As architectures scale in complexity, tracing becomes exponentially more valuable. It provides a unified viewpoint across dynamically shifting applications and infrastructure.

Core Concepts of Distributed Tracing

Implementing an effective tracing strategy requires understanding some key concepts:

Instrumentation – Code must be added within services to generate trace data and propagate context between requests. Popular frameworks like OpenTelemetry automate instrumentation.

Spans – A span represents a unit of work within a trace, like an RPC request. Spans contain timing data, metadata, attributes and relationships.

Traces – A trace encompasses the full lifecycle of a request across all involved services, composed of many correlated spans.

Backend – The tracing backend ingests, processes and queries trace data at scale. Popular options include Jaeger, Zipkin and Grafana Tempo.

Visualization – Traces are analyzed and understood through visualization. Grafana provides out-of-the-box support for Tempo‘s tracing data.

By instrumenting services and sending trace data to a robust backend, we gain end-to-end observability and accelerated debugging abilities.

Introducing Grafana Tempo

Grafana Tempo is an open source distributed tracing backend focused on overcoming scaling challenges and operational complexity. Created by Grafana Labs, Tempo integrates tightly with Loki and Prometheus for logs and metrics respectively. The project has seen rapid adoption by organizations with demanding cloud-native environments.

Tempo‘s architecture is designed for cloud scale:

• Uses object storage like S3 for durability and unlimited retention of trace data
• Leverages streaming processing with ingesters and compactors for high write throughput
• Horizontally scalable queriers to support demands of large-scale query loads
• Support for multi-tenant operation via foundation in Cortex

By building on battle-tested systems like Cortex, Tempo combines proven scalability with usability. For organizations already using Grafana, Loki and Prometheus, Tempo is a natural fit.

Hands-on with Tempo

Now that we‘ve introduced the key concepts and motivation behind Tempo, let‘s walk through a hands-on example to see it in action. We‘ll deploy Tempo locally via Docker, send tracing data to it from a sample application, and visualize traces in Grafana.

Deploying Tempo with Docker

Thanks to public images on DockerHub, deploying Tempo on your local workstation takes just a few commands:

# Create Docker network 
docker network create docker-tempo

# Initialize config
curl -o tempo.yaml https://raw.githubusercontent.com/grafana/tempo/master/example/docker-compose/etc/tempo-local.yaml

# Start Tempo server
docker run -d --rm --name tempo -v $(pwd)/tempo-local.yaml:/etc/tempo-local.yaml --network docker-tempo grafana/tempo:latest -config.file=/etc/tempo-local.yaml

# Start Tempo query server
curl -o tempo-query.yaml https://raw.githubusercontent.com/grafana/tempo/master/example/docker-compose/etc/tempo-query.yaml

docker run -d --rm -p 16686:16686 -v $(pwd)/tempo-query.yaml:/etc/tempo-query.yaml --network docker-tempo grafana/tempo-query:latest -grpc-storage-plugin.configuration-file=/etc/tempo-query.yaml

After a few seconds, Tempo will be up and running! The query service is exposed on port 16686.

Sending Trace Data

Now we need to send tracing data into Tempo. As a sample application, we‘ll use a simple Node.js web service instrumented with OpenTelemetry:

const { NodeTracerProvider } = require("@opentelemetry/node");
const { SimpleSpanProcessor } = require("@opentelemetry/tracing");
const { TempoExporter } = require("@opentelemetry/exporter-trace-otlp-http");

const provider = new NodeTracerProvider();

const exporter = new TempoExporter({
  url: "http://localhost:55680/v1/traces",
});

provider.addSpanProcessor(new SimpleSpanProcessor(exporter));

provider.register();

This instruments the Node.js runtime and sends spans over HTTP to the default Tempo port. Many other languages and frameworks are similarly straightforward to integrate.

When requests are sent to this application, tracing data will automatically flow into the Tempo backend!

Visualizing in Grafana

With traces flowing into Tempo, we can now visualize them in the Grafana UI. Simply add a Tempo data source, then search for a trace ID in the Explore view. The integration provides built-in support for querying Tempo data and visualizing beautiful trace waterfalls.

The Grafana integration with Tempo provides a seamless monitoring experience. Operators can pivot from metrics and logs to a detailed analysis of related traces with a single click. This unlock unprecedented visibility into the performance of distributed systems.

Operating Tempo In Production

In production scenarios, Tempo is deployed across multiple nodes for scale and redundancy. The core Tempo Docker images provide a simple starting point, but orchestration via Kubernetes is recommended for managing clusters.

For storage, S3 or GCS buckets should be used. Local disk is not suitable for production workloads. When deploying on Kubernetes, using something like MinIO for persistent shared storage simplifies management.

For resiliency, ingesters and distributors should be run with replication. Queriers and compactors can be scaled horizontally to meet workload demands. Runs of compactor jobs should be scheduled periodically to optimize storage.

There are many tuning knobs and configuration options to ensure Tempo runs smoothly at scale. Refer to the operational guide for detailed recommendations.

Conclusion

As complexity continues to grow, distributed tracing provides invaluable visibility into the behaviors of modern applications and infrastructure. Grafana Tempo makes operating tracing backends approachable for organizations of any size. Combined with complementary tools like Prometheus and Loki, Tempo unlocks end-to-end observability.

We‘ve only scratched the surface of Tempo‘s capabilities. To learn more, refer to the official Grafana Tempo site. The documentation provides in-depth setup guides, configuration details, API references and more. Tempo is truly driving trace-based monitoring into the future, and we‘ve just gotten started uncovering the possibilities.