A Guide to OpenTelemetry Tracing in Distributed Systems

Understanding what’s happening inside your applications is key to keeping them performing well and reliably. OpenTelemetry tracing is an open-source, flexible solution that lets you monitor your distributed systems without locking you into a specific vendor.
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This guide walks you through everything you need to know about OpenTelemetry tracing, from the basics to more advanced techniques, with practical tips for troubleshooting common issues along the way.

What Is OpenTelemetry Tracing?

OpenTelemetry tracing is a standardized way to collect and export telemetry data from your applications. Unlike older, fragmented approaches, OpenTelemetry offers a vendor-neutral framework that works across different programming languages and environments.

At its core, tracing follows requests as they move through your distributed systems, creating a detailed timeline of what happens and where bottlenecks occur. Consider it as leaving breadcrumbs through your application's journey, making it much easier to find where things went wrong.

Breaking Down the Components of a Trace in OpenTelemetry

A trace in OpenTelemetry consists of:

• Spans: Individual units of work with start and end times
• Context: Information is passed between spans to maintain their relationships
• Attributes: Key-value pairs that add extra details to spans
• Events: Time-stamped logs attached to spans
• Links: Connections between related spans

Here's what this structure looks like in practice:

Why DevOps Teams Need OpenTelemetry Tracing

You've probably been in this situation: a critical service is running slowly, users are complaining, and you're scrambling to figure out what's going wrong. Without proper tracing, you're playing detective with incomplete evidence.

OpenTelemetry tracing solves this by:

• Showing you exactly where time is spent in your application
• Helping identify the root cause of performance issues
• Making it easier to understand how services interact
• Providing data-driven insights for optimization

For DevOps teams specifically, OpenTelemetry tracing means:

OpenTelemetry Reduces Debugging Time

When issues pop up, you don't need to spend hours digging through logs across multiple systems. Tracing shows you the problem's exact location, often reducing debugging time from hours to minutes.

Improves Development and Operations Collaboration

With a standardized approach to observability, development and operations teams speak the same language. When a developer says, "Check span X in service Y," everyone knows exactly what to look for.

Simplifies Cloud Migration and Infrastructure Scaling

As you move workloads to the cloud or scale your infrastructure, tracing helps you understand performance implications and ensure smooth transitions.

Getting Started with OpenTelemetry Tracing

Here's how to get started:

Step 1: Setting Up the OpenTelemetry SDK in Your Application

For most languages, this is as simple as adding a dependency. For example, in Java:

implementation 'io.opentelemetry:opentelemetry-api:1.28.0'
implementation 'io.opentelemetry:opentelemetry-sdk:1.28.0'
implementation 'io.opentelemetry:opentelemetry-exporter-otlp:1.28.0'

Step 2: Configuring and Initializing Your Tracer Provider

You'll need to set up a tracer provider and register it:

SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
    .addSpanProcessor(BatchSpanProcessor.builder(OtlpGrpcSpanExporter.builder().build()).build())
    .build();

OpenTelemetrySdk openTelemetry = OpenTelemetrySdk.builder()
    .setTracerProvider(tracerProvider)
    .buildAndRegisterGlobal();

Tracer tracer = openTelemetry.getTracer("com.example.app");

Step 3: Implementing Spans to Track Operations in Your Code

Now you can start creating spans in your code:

Span span = tracer.spanBuilder("processOrder").startSpan();
try (Scope scope = span.makeCurrent()) {
    // Your business logic here
    span.setAttribute("order.id", orderId);
    
    // You can create child spans for sub-operations
    Span childSpan = tracer.spanBuilder("validatePayment").startSpan();
    try {
        // Payment validation code
    } finally {
        childSpan.end();
    }
} finally {
    span.end();
}

Step 4: Deploying an OpenTelemetry Collector to Process Your Trace Data

The OpenTelemetry Collector receives, processes, and exports your telemetry data. You can run it as a sidecar, agent, or gateway depending on your needs.

Here's a simple docker-compose setup:

version: '3'
services:
  otel-collector:
    image: otel/opentelemetry-collector:latest
    command: ["--config=/etc/otel-collector-config.yaml"]
    volumes:
      - ./otel-collector-config.yaml:/etc/otel-collector-config.yaml
    ports:
      - "4317:4317"  # OTLP gRPC
      - "4318:4318"  # OTLP HTTP

Step 5: Setting Up Your Observability Backend for Trace Analysis

Finally, you'll need a place to send your traces. This is where Last9 comes in. As a telemetry data platform, Last9 can ingest your OpenTelemetry traces and provide a unified view, combining them with your metrics and logs to give you deeper insights into your system’s performance.

Advanced Implementation Techniques

Once you've got the basics down, here are some more advanced ways to use OpenTelemetry tracing:

Adding Tracing to Legacy Systems with OpenTelemetry Auto-Instrumentation

Not every app is easy to instrument manually. OpenTelemetry offers auto-instrumentation libraries for most languages that can add tracing with minimal code changes:

# For Java applications, you can use the Java agent
java -javaagent:opentelemetry-javaagent.jar \
     -Dotel.service.name=your-service-name \
     -jar your-application.jar

Optimizing Trace Collection with Different Sampling Approaches

In high-volume environments, collecting every trace can get expensive. OpenTelemetry lets you implement sampling strategies:

• Always-on: Collect everything (good for low-volume or critical services)
• Probabilistic: Sample a percentage of traces randomly
• Rate-limiting: Cap the number of traces per period
• Tail-based: Focus on slower transactions

Maintaining Trace Context Across Service and Network Boundaries

One of the trickiest parts of distributed tracing is maintaining context across service boundaries. OpenTelemetry handles this with context propagation:

// Extract context from an incoming request
Context extractedContext = OpenTelemetry.getPropagators().getTextMapPropagator()
    .extract(Context.current(), httpRequest, getter);

// Create a span in the same trace
Span span = tracer.spanBuilder("handleRequest")
    .setParent(extractedContext)
    .startSpan();

Enhancing Trace Data with Custom Span Processors

Span processors let you intercept spans before they're exported. This is useful for:

• Adding common attributes to all spans
• Filtering out sensitive information
• Performing real-time analysis

SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
    .addSpanProcessor(new CustomSpanProcessor())
    .build();

Common OpenTelemetry Troubleshooting Scenarios

Let's look at some common issues DevOps engineers face and how OpenTelemetry tracing helps solve them:

Scenario 1: Identifying Performance Bottlenecks in Microservice Architectures

Problem: Your e-commerce checkout process suddenly becomes slow, but you don't know which of your 20+ microservices is causing the issue.

Solution with OpenTelemetry:

1. Look at the trace data for checkout transactions
2. Identify the spans with the longest duration
3. Zero in on the problematic service (in this case, the payment processing service)
4. Drill down further to see exactly which database query is taking too long

Scenario 2: Tracing Error Propagation in System-Wide Cascading Failures

Problem: One service failure triggers a chain reaction that brings down multiple systems.

Solution with OpenTelemetry:

1. Examine traces around the time of failure
2. Identify the original error and how it propagated
3. Use span events to see exception details
4. Implement circuit breakers at the appropriate points based on your findings

Scenario 3: Using Trace Data to Diagnose Gradual Resource Consumption Issues

Problem: A service gradually uses more memory until it crashes, but traditional monitoring doesn't show why.

Solution with OpenTelemetry:

1. Add custom span events that track resource usage
2. Correlate memory growth with specific operations
3. Identify patterns in traces that precede memory spikes
4. Fix the code that's not releasing resources properly

Common Pitfalls and How to Avoid Them

Even with a great tool like OpenTelemetry, there are some common mistakes to watch out for:

Managing Trace Volume and Storage Costs in High-Traffic Systems

Problem: You're collecting so much trace data that it's costing a fortune and slowing down analysis.

Solution: Implement smart sampling strategies, focusing on:

• Critical user journeys
• Error cases
• Unusual behavior patterns

Solving Broken Trace Context Problems in Distributed Applications

Problem: Your traces show separate fragments instead of complete request journeys.

Solution: Ensure proper context propagation between services by:

• Using consistent HTTP headers
• Configuring your frameworks correctly
• Testing trace continuity across service boundaries

Creating Meaningful Trace Data by Reducing Signal-to-Noise Ratio

Problem: Your traces contain so much detail that it's hard to see what matters.

Solution: Be selective about what you trace:

• Use appropriate sampling
• Create spans only for meaningful operations
• Add attributes thoughtfully

Integrating with Your Existing Stack

OpenTelemetry plays well with your existing tools:

Deploying OpenTelemetry in Kubernetes Environments

For container orchestration, you can use the OpenTelemetry Operator to manage collectors and auto-instrumentation:

kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml

CI/CD Pipeline Integration

Add tracing to your deployment pipeline to catch performance regressions before they hit production:

# In your GitHub Actions workflow
- name: Performance Test with OpenTelemetry
  run: |
    export OTEL_EXPORTER_OTLP_ENDPOINT=https://your-collector-endpoint
    export OTEL_SERVICE_NAME=ci-performance-tests
    ./run-performance-tests.sh

Alerting on Trace Data

Set up alerts based on trace metrics to catch issues early:

# Example Prometheus alerting rule
- alert: HighLatencyEndpoint
  expr: histogram_quantile(0.95, sum(rate(http_server_duration_milliseconds_bucket{service="api"}[5m])) by (le, endpoint)) > 500
  for: 5m
  labels:
    severity: warning
  annotations:
    summary: "High latency on {{ $labels.endpoint }}"
    description: "95th percentile latency is above 500ms for {{ $labels.endpoint }}"

Conclusion

OpenTelemetry tracing gives DevOps teams a powerful way to understand, troubleshoot, and optimize applications. When implemented correctly, it lets you spend less time hunting down issues and more time adding value.

If you're ready to take your observability further with OpenTelemetry, check out Last9. Our platform integrates seamlessly with Prometheus and OpenTelemetry, unifying metrics, logs, and traces for a complete view of your system’s health.

Plus, with Last9 MCP, you can bring real-time production context—logs, metrics, and traces—directly into your local environment, helping you fix code faster.

Talk to us to know more about the platform capabilities!

FAQs

How does OpenTelemetry compare to Jaeger or Zipkin?

OpenTelemetry isn't a direct competitor to Jaeger or Zipkin—it's more of a unified standard. You can use OpenTelemetry to collect traces and then send them to Jaeger or Zipkin for visualization. The benefit is that you're not locked into either solution.

Does OpenTelemetry add performance overhead?

Yes, but it's minimal when properly configured. Most implementations add less than 3% overhead. Using sampling strategies can reduce this further in high-volume environments.

Can I use OpenTelemetry with serverless functions?

Absolutely. OpenTelemetry has SDKs for all major serverless platforms. The key challenge is propagating context during cold starts, but there are patterns to handle this.

How do I handle sensitive data in traces?

OpenTelemetry provides span processors that can redact or hash sensitive information before it's exported. You should configure these to comply with your security requirements.

Is OpenTelemetry only for cloud-native applications?

No, OpenTelemetry works great for monoliths too. Auto-instrumentation makes it particularly easy to add tracing to legacy applications without major code changes.

How is OpenTelemetry different from OpenTracing and OpenCensus?

OpenTelemetry is the merger of OpenTracing and OpenCensus. It combines the best of both projects and is now the industry standard, with both older projects in maintenance mode.