Prometheus Distributed Tracing: An Easy-to-Follow Guide for Engineers

When your microservices architecture starts growing, tracking requests as they bounce between services becomes a real headache. You know the feeling—a user reports a slow checkout process, and you're left wondering which of your twenty services is the bottleneck. That's where distributed tracing with Prometheus comes in.

This guide unpacks everything DevOps engineers need to know about implementing Prometheus distributed tracing—from the basics to getting started quickly and solving common issues.

The Fundamentals of Prometheus Distributed Tracing

Prometheus distributed tracing combines the power of Prometheus (a time-series monitoring system) with distributed tracing capabilities to track requests across multiple services. Unlike traditional monitoring that focuses on individual service metrics, distributed tracing follows the complete journey of a request through your system.

Think of it as the difference between knowing individual highway traffic speeds versus tracking a specific car's entire route from origin to destination—complete with every stop, delay, and detour along the way.

Core Components of Distributed Tracing

Distributed tracing consists of several key components working together:

• Spans: Individual operations within a trace (like database queries or API calls)
• Traces: Collections of spans forming a complete request path
• Context Propagation: The mechanism for passing trace information between services
• Collectors: Systems that receive, process, and store trace data
• Visualization Tools: Interfaces for analyzing and troubleshooting traces

Prometheus itself doesn't natively handle distributed tracing, as it's primarily designed for metrics collection. However, it can be integrated with tracing tools like Jaeger or Last9 to create a comprehensive observability solution.

How to Configure Prometheus for Distributed Tracing

Getting Prometheus ready for distributed tracing involves more than just installing the basic Prometheus server. Here's how to set it up:

Basic Prometheus Installation

To get started with Prometheus, you'll need to download and install the Prometheus server. This can be done through package managers, Docker, or direct binary downloads from the Prometheus website. For a basic setup, you'll need to configure the Prometheus YAML file to scrape your services.

Integrating with Tracing Solutions

Since Prometheus itself doesn't handle traces, you'll need to integrate it with a dedicated tracing backend. The two most common approaches are:

Option 1: Prometheus + Jaeger

Jaeger is a popular open-source tracing system that works well with Prometheus. You'll need to:

• Install Jaeger (via Docker or binary)
• Configure Prometheus to scrape Jaeger metrics
• Set up your applications to send traces to Jaeger
• Connect the Prometheus metrics with Jaeger traces

The beauty of this setup is that you can correlate metric anomalies with specific traces to pinpoint issues quickly.

Option 2: Prometheus + OpenTelemetry

OpenTelemetry provides a vendor-neutral way to collect traces and metrics:

• Install the OpenTelemetry Collector
• Configure it to receive traces and send metrics to Prometheus
• Instrument your applications with OpenTelemetry libraries

With this configuration, your services can send traces to the OpenTelemetry Collector, which forwards metrics to Prometheus and traces to your backend of choice.

Option 3: Prometheus + Last9

Last9 offers a robust solution for managing high-cardinality observability data. Here's how you can integrate it with Prometheus:

1. Install the Last9 agent (via Docker or binary)
2. Configure Prometheus to scrape Last9 metrics
3. Set up your applications to send traces and logs to Last9

Use Last9’s advanced features, like streaming aggregation, to optimize data processing and reduce noise. This setup enables you to correlate Prometheus metrics with Last9’s rich observability data, providing real-time insights and faster issue resolution without sacrificing scalability or performance.

Essential Application Instrumentation for Distributed Tracing

The magic happens when your applications are properly instrumented. Here's how to add distributed tracing to your services:

Key Instrumentation Concepts

To instrument your applications for distributed tracing, you need to understand these core concepts:

1. Spans: The building blocks of traces that represent individual operations
2. Context Propagation: How trace information is passed between services
3. Automatic vs. Manual Instrumentation: Libraries can handle much of the work for you

Instrumenting Different Languages

Different programming languages have their tracing libraries:

Go Applications

• Use OpenTracing or OpenTelemetry libraries
• Integrate with the Prometheus Go client for metrics
• Set up middleware to automatically trace HTTP requests

Java Applications

• For Spring Boot: Use Spring Cloud Sleuth with Micrometer
• For other frameworks: Use OpenTelemetry Java SDK
• Configure Prometheus Java client for metrics collection

Python Applications

• Use OpenTelemetry Python or Jaeger Python client
• Add the Prometheus Python client for metrics
• Use middleware in Flask/Django for automatic tracing

The Minimum You Need to Add

At the very minimum, your instrumentation should:

• Generate a unique trace ID for each request
• Create spans for important operations
• Propagate context between services
• Export traces to your chosen backend
• Send related metrics to Prometheus

Beyond Basics: Optimizing Your Prometheus Tracing Implementation

Once you have the basics working, you can take your distributed tracing setup to the next level with these optimizations:

Enhanced Span Context for Better Troubleshooting

Add business-relevant information to your spans to make troubleshooting easier:

• Include order IDs, customer types, and transaction amounts
• Tag spans with feature flags or experiment identifiers
• Add environment and deployment information

This extra context makes it much easier to understand what was happening during an incident.

Smart Sampling Strategies for Production Traffic

In high-volume environments, tracing every request isn't practical. Implement smart sampling:

• Use probabilistic sampling (e.g., 10% of all requests)
• Implement rule-based sampling (100% of errors, 100% of critical paths)
• Consider adaptive sampling based on system load

The right sampling strategy balances observability with performance and storage costs.

Powerful Metrics-Traces Correlation Techniques

One of the most powerful techniques is connecting Prometheus metrics with trace information:

• Include trace IDs in your metrics as labels (be careful of cardinality)
• Add service-level tags to both metrics and traces
• Create dashboards that can pivot from metrics to relevant traces

This correlation allows you to start with a metric anomaly and drill down to the specific traces that show the problem in detail.

Solving Common Distributed Tracing Problems in Prometheus

Let's tackle some real-world problems you might encounter in your tracing journey:

Diagnosing Missing Spans in Your Distributed Traces

Problem: Your traces show gaps where spans should be, creating fragmented request views.

Solution:

1. Check that context propagation is working between services
2. Verify that all services use compatible tracing libraries
3. Ensure HTTP headers are properly passed (especially in API gateways)
4. Look for timing issues—spans that finish after the parent trace is sent

Remember that proper context propagation is the most common root cause. Make sure your services are correctly passing trace and span IDs between them.

Managing High Cardinality Issues in Prometheus Storage

Problem: Your Prometheus storage is exploding due to too many unique time series from trace-related metrics.

Solution: Limit the cardinality of your labels:

• Use service names, endpoint categories, and status code ranges instead of individual values
• Never use highly unique values like user IDs, session IDs, or trace IDs as labels
• Create separate histograms for different label dimensions

A good rule of thumb: if a label can have more than 100 possible values, think twice about using it.

Improving Performance of Trace Queries in Production

Problem: Trace queries take too long to return results, slowing down troubleshooting.

Solution:

1. Implement a more aggressive sampling strategy
2. Add indexes to your trace storage backend
3. Use time range filtering to narrow the search space
4. Optimize your storage layer (use specialized trace storage)
5. Pre-aggregate common queries

Essential Production Deployment Guidelines for Distributed Tracing

To run Prometheus distributed tracing successfully in production:

Resource Planning and Capacity Management

When planning your tracing infrastructure, consider these recommended resources:

• Prometheus server: 2+ CPU cores, 8GB+ RAM, 100GB+ storage (15-day retention)
• Tracing collector: 2+ CPU cores, 4GB+ RAM
• Query service: 1+ CPU core, 2GB+ RAM
• Storage backend: 4+ CPU cores, 16GB+ RAM, 500GB+ storage

Scale these recommendations based on your traffic volume and retention needs.

Critical Security Considerations for Tracing Data

Tracing data often contains sensitive information, so security is crucial:

1. Authentication: Implement basic auth or OAuth for all tracing endpoints
2. TLS: Enable encryption for all traffic between components
3. Sensitive Data: Filter out PII and sensitive info from spans before storage
4. Access Controls: Limit who can query sensitive traces

Optimizing Retention Policies for Cost Efficiency

Balance storage costs with troubleshooting needs:

• Keep high-resolution metrics for 15 days, and downsampled data for longer
• Store complete traces for 7 days in hot storage
• Archive important traces to cold storage for longer retention
• Implement tiered storage strategies for cost optimization

Conclusion

Prometheus distributed tracing gives you X-ray vision into your microservices architecture. With the right setup and instrumentation, you can trace requests as they move through your system, pinpoint bottlenecks, and resolve issues faster.

If you're looking for a managed observability solution that's both budget-friendly and high-performing, give Last9 a try. We’ve handled high-cardinality observability for industry leaders like Probo, CleverTap, and Replit. As a telemetry data platform, we’ve monitored 11 of the 20 largest live-streaming events in history, proving our ability to scale.

By integrating with OpenTelemetry and Prometheus, Last9 unifies metrics, logs, and traces to optimize performance, cost, and real-time insights with correlated monitoring and alerting.

Talk to us to know more about our platform capabilities!

FAQs

How does Prometheus distributed tracing differ from traditional monitoring?

Traditional monitoring focuses on system-level metrics (CPU, memory, request rates) or service-level metrics. Distributed tracing follows the path of individual requests across multiple services, giving you visibility into the entire request lifecycle and making it easier to pinpoint performance bottlenecks.

Can I use Prometheus alone for distributed tracing?

No, Prometheus alone isn't enough. Prometheus is excellent for metrics collection but doesn't support distributed tracing natively. You need to pair it with a tracing system like Jaeger, Zipkin, or OpenTelemetry to get full distributed tracing capabilities.

How much overhead does distributed tracing add?

When implemented correctly, distributed tracing typically adds less than 5% overhead to your application performance. By using sampling strategies (tracing only a percentage of requests), you can reduce this overhead even further in high-traffic systems.

How do I decide which requests to sample?

For most systems, a combination of probabilistic sampling (e.g., 10% of all traffic) and rule-based sampling (100% of errors, 100% of certain critical paths) works well. Adjust your sampling strategy based on your traffic patterns and troubleshooting needs.

How can I correlate logs with traces?

Include the trace ID in your log messages:

// Example log entry
[2025-04-28 13:45:22] [INFO] [TraceID: 7ad6b061a6281fe] Processing payment for order #12345

This allows you to search logs by trace ID and vice versa.

Which tracing backend should I choose?

The best tracing backend depends on your specific needs:

• Jaeger: Great for Kubernetes environments and has good Prometheus integration
• Zipkin: Simpler to set up and has a large ecosystem
• OpenTelemetry: The most flexible and future-proof option
• Last9: Excellent if you want a managed solution that works well with Prometheus

How do I prevent sensitive data from appearing in traces?

Configure your tracing library to filter sensitive information:

• Set up regular expressions to redact patterns like credit card numbers or emails
• Use sampling to avoid capturing certain types of requests
• Implement server-side filtering in your tracing backend