Tempo Traces: A Practical Guide for DevOps Engineers and SREs

Tempo Traces: A Practical Guide for DevOps Engineers and SREs

As distributed systems and microservices architectures become the norm, observability is no longer optional—it’s essential. Among the three pillars of observability (metrics, logs, and traces), Tempo traces have emerged as a powerful solution for storing, querying, and visualizing distributed trace data at scale. In this guide, we’ll dive deep into what Tempo traces are, how they work, and how DevOps engineers and SREs can leverage them to gain end-to-end visibility into their applications.

What Are Tempo Traces?

Tempo traces refer to the distributed trace data collected, stored, and queried using Grafana Tempo—a high-scale, cost-efficient, open-source distributed tracing backend. Unlike traditional tracing solutions that require complex indexing and expensive databases, Tempo traces are stored in object storage (such as AWS S3, GCS, or Azure Blob Storage) without indexes, making them both scalable and affordable.

Tempo is designed to work seamlessly with the Grafana stack, allowing you to correlate Tempo traces with metrics from Prometheus and logs from Loki in a single dashboard. This integration is invaluable for troubleshooting and performance analysis in production environments.

Why Use Tempo Traces?

• Cost-Effective Storage: Tempo traces are stored in object storage, which is much cheaper than indexed databases.
• Massive Scale: Tempo can handle millions of spans per second, making it suitable for large-scale microservices architectures.
• No Indexing Overhead: By not maintaining indexes, Tempo reduces operational complexity and infrastructure costs.
• Strong Grafana Integration: Tempo traces can be visualized directly in Grafana, alongside metrics and logs.
• Open Standards: Tempo supports open tracing protocols like Jaeger, Zipkin, and OpenTelemetry, making it easy to integrate with existing instrumentation.

How Tempo Traces Work

The journey of a Tempo trace typically follows this path:

1. An instrumented application generates trace data (spans) using OpenTelemetry or another tracing SDK.
2. The trace data is sent to a trace receiver (such as OpenTelemetry Collector or Jaeger Agent).
3. The receiver batches and forwards the Tempo traces to the Tempo backend.
4. Tempo stores the traces in object storage, using trace IDs for retrieval.
5. When troubleshooting, you use Grafana to query and visualize the Tempo traces.

Setting Up Tempo Traces: A Practical Example

Let’s walk through a simple setup using OpenTelemetry and Grafana Tempo. We’ll instrument a Python application to generate Tempo traces and send them to Tempo via the OpenTelemetry Collector.

Step 1: Instrument Your Application

First, install the OpenTelemetry SDK for Python:

pip install opentelemetry-api opentelemetry-sdk opentelemetry-exporter-otlp

Next, instrument your application to generate traces:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

# Set up the tracer
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)

# Configure the OTLP exporter to send traces to Tempo
exporter = OTLPSpanExporter(endpoint="http://tempo:4317", insecure=True)
span_processor = BatchSpanProcessor(exporter)
trace.get_tracer_provider().add_span_processor(span_processor)

# Example: Create a span
with tracer.start_as_current_span("example-span"):
    print("Hello, Tempo traces!")

Step 2: Configure the OpenTelemetry Collector

Create a collector-config.yaml file to forward traces to Tempo:

receivers:
  otlp:
    protocols:
      grpc:

exporters:
  otlp/tempo:
    endpoint: "tempo:4317"
    insecure: true

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [otlp/tempo]

Step 3: Run Tempo and Grafana

Use Docker Compose to run Tempo, Grafana, and the OpenTelemetry Collector:

version: '3'
services:
  tempo:
    image: grafana/tempo:latest
    command: [ "-config.file=/etc/tempo.yaml" ]
    volumes:
      - ./tempo-config.yaml:/etc/tempo.yaml
    ports:
      - "3200:3200"
      - "4317:4317"

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_FEATURE_TOGGLES_ENABLE=tempoSearch
    depends_on:
      - tempo

  otel-collector:
    image: otel/opentelemetry-collector:latest
    command: [ "--config=/etc/collector-config.yaml" ]
    volumes:
      - ./collector-config.yaml:/etc/collector-config.yaml
    depends_on:
      - tempo

Querying and Visualizing Tempo Traces

Once your application is generating Tempo traces, you can query and visualize them in Grafana:

• Go to Grafana and add Tempo as a data source.
• Use the Explore view to search for traces by trace ID.
• Correlate Tempo traces with metrics and logs for deeper insights.

For example, if you see a spike in latency in your metrics, you can use the trace ID from your logs (via Loki) to find the corresponding Tempo trace and pinpoint the root cause.

Best Practices for Tempo Traces

• Instrument Early: Add tracing instrumentation as early as possible in your development cycle.
• Use Structured Logging: Include trace IDs in your logs to make it easier to correlate Tempo traces with log events.
• Monitor Trace Volume: Keep an eye on the volume of Tempo traces to avoid overwhelming your storage or collector.
• Leverage Grafana Dashboards: Build dashboards that combine Tempo traces with metrics and logs for a holistic view of your system.
• Secure Your Traces: Ensure that sensitive data is not included in your Tempo traces, and secure access to your Tempo backend.

Conclusion

Tempo traces are a game-changer for observability in distributed systems. By storing trace data in object storage without indexes, Tempo offers a cost-effective, scalable solution for collecting and analyzing Tempo traces at scale. When combined with Grafana, Prometheus, and Loki, Tempo traces provide DevOps engineers and SREs with the tools they need to troubleshoot, optimize, and secure their applications.

Start instrumenting your applications today and unlock the full power of Tempo traces for your observability stack.