Spring Boot + OpenTelemetry: Tracing Every Byte in Your Microservice

Unlock deep observability in your Spring Boot applications by harnessing the power of OpenTelemetry. In this article, we’ll walk through why distributed tracing matters, how to instrument your services, and best practices to turn raw telemetry into business insights.

Why Distributed Tracing Matters

In a world of microservices, requests hop across multiple services, networks, and data stores. When something goes wrong—or simply needs optimization—traditional logs and metrics can leave you guessing. Distributed tracing provides:

• End-to-end visibility: Follow a transaction from the moment it enters your gateway to the final database call.
• Performance insights: Identify slow operations down to individual method calls or external HTTP requests.
• Error localization: Quickly spot where exceptions or timeouts occur in a complex call chain.

Imagine you’re seeing intermittent latency spikes in your payment microservice. With distributed traces, you can drill into each individual span—every method invocation or external call—and pinpoint the culprit byte by byte.

What Is OpenTelemetry?

OpenTelemetry is a Cloud Native Computing Foundation (CNCF)–backed, open standard for collecting traces, metrics, and logs. It unifies instrumentation across languages and frameworks. Key components:

1. SDK & API: Exposes interfaces to create spans, record attributes, and emit metrics.
2. Auto-instrumentation agents: Plug-and-play Java agent that hooks into Spring Boot, JDBC, HTTP clients, and more.
3. Collectors: Centralize telemetry data, batch and export to backends like Jaeger, Zipkin, Prometheus, or commercial APMs.

By adopting OpenTelemetry, you avoid vendor lock-in and gain flexibility: switch your backend—Jaeger today, New Relic tomorrow—without changing your instrumentation.

Getting Started: Spring Boot Integration

1. Include Dependencies

Add the OpenTelemetry Java agent to your JVM startup:

java -javaagent:/path/opentelemetry-javaagent.jar \
     -jar target/my-microservice.jar        

Alternatively, for manual instrumentation, include the OpenTelemetry SDK and instrumenters:

<dependency>
  <groupId>io.opentelemetry</groupId>
  <artifactId>opentelemetry-sdk</artifactId>
  <version>1.x.x</version>
</dependency>
<dependency>
  <groupId>io.opentelemetry.instrumentation</groupId>
  <artifactId>opentelemetry-spring-boot-starter</artifactId>
  <version>1.x.x</version>
</dependency>        

(Replace 1.x.x with the latest stable versions.)

2. Configure Exporters

In your application.yml:

otel:
  tracer:
    exporter: otlp
  metrics:
    exporter: prometheus
otel.exporter.otlp.endpoint: http://otel-collector:4317        

This directs both traces and metrics to an OpenTelemetry Collector running as a sidecar or separate service.

3. Auto-Instrumentation

With the Java agent, most Spring components—@RestController, @Repository, HTTP clients—are traced automatically. No code changes required. Simply launch with:

-javaagent:/opt/opentelemetry/opentelemetry-javaagent.jar        

4. Custom Spans and Attributes

For business-critical operations, you’ll want to create custom spans:

@Service
public class OrderService {
  private static final Tracer tracer = GlobalOpenTelemetry.getTracer("com.acme.order");

  public Order createOrder(OrderRequest req) {
    Span span = tracer.spanBuilder("OrderService.createOrder")
                      .setAttribute("order.size", req.getItems().size())
                      .startSpan();
    try (Scope scope = span.makeCurrent()) {
      // business logic...
      return repository.save(new Order(...));
    } catch (Exception ex) {
      span.recordException(ex);
      throw ex;
    } finally {
      span.end();
    }
  }
}        

By tagging spans with attributes (e.g., order.size, payment.method), you enrich trace data, making filtering and analysis far more powerful.

Visualizing Your Traces

1. Jaeger UI: Great open-source tracing dashboard. See flame graphs, span timelines, and operation dependencies.
2. Zipkin: Lightweight UI focused on latency analysis.
3. Commercial APMs: Datadog, New Relic, or Lightstep provide enhanced analytics and anomaly detection.

Deploy an OpenTelemetry Collector to batch, sample, and forward your spans:

receivers:
  otlp:
exporters:
  jaeger:
    endpoint: "jaeger:14250"
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [jaeger]        

Best Practices & Tips

• Sampling strategy: For high-throughput services, use probabilistic sampling (e.g., 10% of requests) to manage volume without sacrificing insights.
• Semantic conventions: Adhere to the OpenTelemetry semantic conventions for HTTP, messaging, and database spans—consistency helps downstream analytics.
• Context propagation: Ensure trace context flows through async calls, thread pools, and messaging systems (Kafka, RabbitMQ) by using the OpenTelemetry instrumentation libraries.
• Metrics + Traces: Combine latency histograms and error counters (via Micrometer) with trace data for a unified observability picture.

Real-World Example: Payment Flow

1. API Gateway span: Records inbound HTTP timing, client IP, and route.
2. Authentication service span: Checks JWT, enriches context with user.id.
3. Order service span: Calculates totals, invokes Inventory and Payment spans.
4. External HTTP span: Calls third-party payment gateway, records HTTP status and payload size.
5. Database span: Persists transaction record, tagging SQL query and row count.

You’ll end up with a tree of spans, each annotated with attributes that let you answer questions like:

• Why did this credit card transaction take 500 ms?
• How often does inventory lookup fail?
• Which customers experience the highest latencies?

Conclusion & Next Steps

Embracing OpenTelemetry in your Spring Boot ecosystem transforms your microservices from black boxes into fully instrumented, self-diagnosing systems. Start by:

1. Adding the Java agent for instant visibility.
2. Customizing spans in business-critical paths.
3. Deploying a Collector to your Kubernetes cluster.
4. Visualizing in Jaeger or your preferred APM.

By tracing every byte and recording every millisecond, you’ll accelerate root-cause analysis, optimize performance, and build more reliable, resilient services. Ready to dive in? 🚀