Manual Instrumentation

Libraries that want to export telemetry data using OpenTelemetry MUST only depend on the opentelemetry-api package and should never configure or depend on the OpenTelemetry SDK. The SDK configuration must be provided by Applications which should also depend on the opentelemetry-sdk package, or any other implementation of the OpenTelemetry API. This way, libraries will obtain a real implementation only if the user application is configured for it. For more details, check out the Library Guidelines.

Set up

The first step is to get a handle to an instance of the OpenTelemetry interface.

If you are an application developer, you need to configure an instance of the OpenTelemetrySdk as early as possible in your application. This can be done using the OpenTelemetrySdk.builder() method.

For example:

    SdkTracerProvider sdkTracerProvider = SdkTracerProvider.builder()

    OpenTelemetry openTelemetry = OpenTelemetrySdk.builder()

As an aside, if you are writing library instrumentation, it is strongly recommended that you provide your users the ability to inject an instance of OpenTelemetry into your instrumentation code. If this is not possible for some reason, you can fall back to using an instance from the GlobalOpenTelemetry class. Note that you can’t force end-users to configure the global, so this is the most brittle option for library instrumentation.


In the following, we present how to trace code using the OpenTelemetry API. Note: Methods of the OpenTelemetry SDK should never be called.

First, a Tracer must be acquired, which is responsible for creating spans and interacting with the Context. A tracer is acquired by using the OpenTelemetry API specifying the name and version of the library instrumenting the instrumented library or application to be monitored. More information is available in the specification chapter Obtaining a Tracer.

Tracer tracer =
    openTelemetry.getTracer("instrumentation-library-name", "1.0.0");

Important: the “name” and optional version of the tracer are purely informational. All Tracers that are created by a single OpenTelemetry instance will interoperate, regardless of name.

Create a basic Span

To create a basic span, you only need to specify the name of the span. The start and end time of the span is automatically set by the OpenTelemetry SDK.

Span span = tracer.spanBuilder("my span").startSpan();
// put the span into the current Context
try (Scope scope = span.makeCurrent()) {
	// your use case
} catch (Throwable t) {
    span.setStatus(StatusCode.ERROR, "Change it to your error message");
} finally {
    span.end(); // closing the scope does not end the span, this has to be done manually

Create nested Spans

Most of the time, we want to correlate spans for nested operations. OpenTelemetry supports tracing within processes and across remote processes. For more details how to share context between remote processes, see Context Propagation.

For a method a calling a method b, the spans could be manually linked in the following way:

void parentOne() {
  Span parentSpan = tracer.spanBuilder("parent").startSpan();
  try {
  } finally {

void childOne(Span parentSpan) {
  Span childSpan = tracer.spanBuilder("child")
  // do stuff

The OpenTelemetry API offers also an automated way to propagate the parent span on the current thread:

void parentTwo() {
  Span parentSpan = tracer.spanBuilder("parent").startSpan();
  try(Scope scope = parentSpan.makeCurrent()) {
  } finally {
void childTwo() {
  Span childSpan = tracer.spanBuilder("child")
    // NOTE: setParent(...) is not required; 
    // `Span.current()` is automatically added as the parent
  try(Scope scope = childSpan.makeCurrent()) {
    // do stuff
  } finally {

To link spans from remote processes, it is sufficient to set the Remote Context as parent.

Span childRemoteParent = tracer.spanBuilder("Child").setParent(remoteContext).startSpan();

Span Attributes

In OpenTelemetry spans can be created freely and it’s up to the implementor to annotate them with attributes specific to the represented operation. Attributes provide additional context on a span about the specific operation it tracks, such as results or operation properties.

Span span = tracer.spanBuilder("/resource/path").setSpanKind(SpanKind.CLIENT).startSpan();
span.setAttribute("http.method", "GET");
span.setAttribute("http.url", url.toString());

Some of these operations represent calls that use well-known protocols like HTTP or database calls. For these, OpenTelemetry requires specific attributes to be set. The full attribute list is available in the Semantic Conventions in the cross-language specification.

Create Spans with events

Spans can be annotated with named events that can carry zero or more Span Attributes, each of which is itself a key:value map paired automatically with a timestamp.

Attributes eventAttributes = Attributes.of(
    AttributeKey.stringKey("key"), "value",
    AttributeKey.longKey("result"), 0L);

span.addEvent("End Computation", eventAttributes);

A Span may be linked to zero or more other Spans that are causally related. Links can be used to represent batched operations where a Span was initiated by multiple initiating Spans, each representing a single incoming item being processed in the batch.

Span child = tracer.spanBuilder("childWithLink")

For more details how to read context from remote processes, see Context Propagation.

Context Propagation

OpenTelemetry provides a text-based approach to propagate context to remote services using the W3C Trace Context HTTP headers.

The following presents an example of an outgoing HTTP request using HttpURLConnection.

// Tell OpenTelemetry to inject the context in the HTTP headers
TextMapSetter<HttpURLConnection> setter =
  new TextMapSetter<HttpURLConnection>() {
    public void set(HttpURLConnection carrier, String key, String value) {
        // Insert the context as Header
        carrier.setRequestProperty(key, value);

URL url = new URL("");
Span outGoing = tracer.spanBuilder("/resource").setSpanKind(SpanKind.CLIENT).startSpan();
try (Scope scope = outGoing.makeCurrent()) {
  // Use the Semantic Conventions.
  // (Note that to set these, Span does not *need* to be the current instance in Context or Scope.)
  outGoing.setAttribute(SemanticAttributes.HTTP_METHOD, "GET");
  outGoing.setAttribute(SemanticAttributes.HTTP_URL, url.toString());
  HttpURLConnection transportLayer = (HttpURLConnection) url.openConnection();
  // Inject the request with the *current*  Context, which contains our current Span.
  openTelemetry.getPropagators().getTextMapPropagator().inject(Context.current(), transportLayer, setter);
  // Make outgoing call
} finally {

Similarly, the text-based approach can be used to read the W3C Trace Context from incoming requests. The following presents an example of processing an incoming HTTP request using HttpExchange.

TextMapGetter<HttpExchange> getter =
  new TextMapGetter<>() {
    public String get(HttpExchange carrier, String key) {
      if (carrier.getRequestHeaders().containsKey(key)) {
        return carrier.getRequestHeaders().get(key).get(0);
      return null;

   public Iterable<String> keys(HttpExchange carrier) {
     return carrier.getRequestHeaders().keySet();
public void handle(HttpExchange httpExchange) {
  // Extract the SpanContext and other elements from the request.
  Context extractedContext = openTelemetry.getPropagators().getTextMapPropagator()
        .extract(Context.current(), httpExchange, getter);
  try (Scope scope = extractedContext.makeCurrent()) {
    // Automatically use the extracted SpanContext as parent.
    Span serverSpan = tracer.spanBuilder("GET /resource")
    try {
      // Add the attributes defined in the Semantic Conventions
      serverSpan.setAttribute(SemanticAttributes.HTTP_METHOD, "GET");
      serverSpan.setAttribute(SemanticAttributes.HTTP_SCHEME, "http");
      serverSpan.setAttribute(SemanticAttributes.HTTP_HOST, "localhost:8080");
      serverSpan.setAttribute(SemanticAttributes.HTTP_TARGET, "/resource");
      // Serve the request
    } finally {

Metrics (alpha only!)

Spans are a great way to get detailed information about what your application is doing, but what about a more aggregated perspective? OpenTelemetry provides supports for metrics, a time series of numbers that might express things such as CPU utilization, request count for an HTTP server, or a business metric such as transactions.

In order to access the alpha metrics library, you will need to explicitly depend on the opentelemetry-api-metrics and opentelemetry-sdk-metrics modules, which are not included in the opentelemetry-bom until they are stable and ready for long-term-support.

All metrics can be annotated with labels: additional qualifiers that help describe what subdivision of the measurements the metric represents.

First, you’ll need to get access to a MeterProvider. Note the APIs for this are in flux, so no example code is provided here for that.

The following is an example of counter usage:

// Gets or creates a named meter instance
Meter meter = meterProvider.get("instrumentation-library-name", "1.0.0");

// Build counter e.g. LongCounter 
LongCounter counter = meter
        .setDescription("Processed jobs")

// It is recommended that the API user keep a reference to a Bound Counter for the entire time or 
// call unbind when no-longer needed.
BoundLongCounter someWorkCounter = counter.bind(Labels.of("Key", "SomeWork"));

// Record data

// Alternatively, the user can use the unbounded counter and explicitly
// specify the labels set at call-time:
counter.add(123, Labels.of("Key", "SomeWork"));

Observer is an additional instrument supporting an asynchronous API and collecting metric data on demand, once per collection interval.

The following is an example of observer usage:

// Build observer e.g. LongSumObserver
    LongSumObserver observer = meter
        .setDescription("CPU Usage")
        .setUpdater(result -> {
        result.observe(getCpuUsage(), Labels.of("Key", "SomeWork"));

Tracing SDK Configuration

The configuration examples reported in this document only apply to the SDK provided by opentelemetry-sdk. Other implementation of the API might provide different configuration mechanisms.

The application has to install a span processor with an exporter and may customize the behavior of the OpenTelemetry SDK.

For example, a basic configuration instantiates the SDK tracer provider and sets to export the traces to a logging stream.

    SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
      .addSpanProcessor(BatchSpanProcessor.builder(new LoggingSpanExporter()).build())


It is not always feasible to trace and export every user request in an application. In order to strike a balance between observability and expenses, traces can be sampled.

The OpenTelemetry SDK offers four samplers out of the box:

  • AlwaysOnSampler which samples every trace regardless of upstream sampling decisions.
  • AlwaysOffSampler which doesn’t sample any trace, regardless of upstream sampling decisions.
  • ParentBased which uses the parent span to make sampling decisions, if present.
  • TraceIdRatioBased which samples a configurable percentage of traces, and additionally samples any trace that was sampled upstream.

Additional samplers can be provided by implementing the io.opentelemetry.sdk.trace.Sampler interface.

    SdkTracerProvider tracerProvider = SdkTracerProvider.builder()

Span Processor

Different Span processors are offered by OpenTelemetry. The SimpleSpanProcessor immediately forwards ended spans to the exporter, while the BatchSpanProcessor batches them and sends them in bulk. Multiple Span processors can be configured to be active at the same time using the MultiSpanProcessor.

    SdkTracerProvider tracerProvider = SdkTracerProvider.builder()
      .addSpanProcessor(SimpleSpanProcessor.create(new LoggingSpanExporter()))
      .addSpanProcessor(BatchSpanProcessor.builder(new LoggingSpanExporter()).build())


Span processors are initialized with an exporter which is responsible for sending the telemetry data a particular backend. OpenTelemetry offers five exporters out of the box:

  • In-Memory Exporter: keeps the data in memory, useful for debugging.
  • Jaeger Exporter: prepares and sends the collected telemetry data to a Jaeger backend via gRPC.
  • Zipkin Exporter: prepares and sends the collected telemetry data to a Zipkin backend via the Zipkin APIs.
  • Logging Exporter: saves the telemetry data into log streams.
  • OpenTelemetry Exporter: sends the data to the OpenTelemetry Collector.

Other exporters can be found in the OpenTelemetry Registry.

    ManagedChannel jaegerChannel = ManagedChannelBuilder.forAddress("localhost", 3336)

    JaegerGrpcSpanExporter jaegerExporter = JaegerGrpcSpanExporter.builder()
      .setTimeout(30, TimeUnit.SECONDS)

    SdkTracerProvider tracerProvider = SdkTracerProvider.builder()

Auto Configuration

To configure the OpenTelemetry SDK based on the standard set of environment variables and system properties, you can use the opentelemetry-sdk-extension-autoconfigure module.

  OpenTelemetrySdk sdk = OpenTelemetrySdkAutoConfiguration.initialize();

See the supported configuration options in the module’s README.

Logging and Error Handling

OpenTelemetry uses java.util.logging to log information about OpenTelemetry, including errors and warnings about misconfigurations or failures exporting data.

By default, log messages are handled by the root handler in your application. If you have not installed a custom root handler for your application, logs of level INFO or higher are sent to the console by default.

You may want to change the behavior of the logger for OpenTelemetry. For example, you can reduce the logging level to output additional information when debugging, increase the level for a particular class to ignore errors coming from that class, or install a custom handler or filter to run custom code whenever OpenTelemetry logs a particular message.


## Turn off all OpenTelemetry logging 
io.opentelemetry.level = OFF
## Turn off logging for just the BatchSpanProcessor 
io.opentelemetry.sdk.trace.export.BatchSpanProcessor.level = OFF
## Log "FINE" messages for help in debugging 
io.opentelemetry.level = FINE

## Sets the default ConsoleHandler's logger's level 
## Note this impacts the logging outside of OpenTelemetry as well 
java.util.logging.ConsoleHandler.level = FINE

For more fine-grained control and special case handling, custom handlers and filters can be specified with code.

// Custom filter which does not log errors that come from the export
public class IgnoreExportErrorsFilter implements Filter {

 public boolean isLoggable(LogRecord record) {
    return !record.getMessage().contains("Exception thrown by the export");
## Registering the custom filter on the BatchSpanProcessor
io.opentelemetry.sdk.trace.export.BatchSpanProcessor = io.opentelemetry.extension.logging.IgnoreExportErrorsFilter