OpenTelemetry integration

The is the recommended OpenTelemetry integration. We have extended the "contrib" OpenTelemetry collector by adding an Elastic exporter so that you can drop this integration into your current OpenTelemetry setup.

The architecture consists of three main components.

OpenTelemetry Collectors can be run as agents or as standalone collectors. They can be deployed as often as necessary and scaled up or out. Deployment planning resources are available in OpenTelemetry’s Getting Started documentation and Collector Performance research.

You can download the latest release of the Collector from the GitHub releases page. The Elastic exporter lives in the opentelemetry-collector-contrib repository.

Docker images are available on dockerhub:

docker pull otel/opentelemetry-collector-contrib

To configure the collector, create a yaml configuration file.

This example configuration file accepts input from an OpenTelemetry Agent, processes the data, and sends it to an Elasticsearch Service instance.

  receivers:
    otlp:
      protocols:
        grpc:
          endpoint: 'localhost:4317'
    hostmetrics: 
      collection_interval: 1m
      scrapers:
        load:
        memory:

  processors:
    batch: null

  exporters:
    elastic:
      apm_server_url: 'https://elasticapm.example.com' 
      secret_token: 'APM_SERVER_SECRET_TOKEN'

  service:
    pipelines:
      metrics:
        receivers:
          - otlp
          - hostmetrics
        exporters:
          - elastic 
      traces:
        receivers:
          - otlp
        processors:
          - batch
        exporters:
          - elastic 

Once a receiver, processor, and exporter are defined, you can configure pipelines in your configuration’s services section. The traces and metrics pipelines represent the path of trace data and metrics through your collector and bring all three of these components together. You can also enable extensions for tasks like monitoring the health of the collector.

To export traces and metrics to the OpenTelemetry Collector, ensure that you have instrumented your services and applications with the OpenTelemetry API, SDK, or both. For example, if you are a Java developer, you need to instrument your Java app using the OpenTelemetry agent for Java.

By defining the following environment variables, you can customize the OTLP endpoint the agent will use to communicate with APM Server.

export OTEL_RESOURCE_ATTRIBUTES=service.name=frontend,service.version=1.1,deployment.environment=staging
export OTEL_EXPORTER_OTLP_ENDPOINT=https://apm_server_url:8200
java -javaagent:/path/to/opentelemetry-javaagent-all.jar \
     -jar target/frontend-1.1.jar

You are now ready to collect traces and metrics, verify metrics, and visualize metrics in Kibana.

The APM Server native support of the OpenTelemetry protocol allows you to send collected telemetry data directly from your applications to APM Server. Trace data collected from your services and the metrics data collected from your applications and infrastructure are sent using the OpenTelemetry protocol.

To export traces and metrics to APM Server, ensure that you have instrumented your services and applications with the OpenTelemetry API, SDK, or both. For example, if you are a Java developer, you need to instrument your Java app using the OpenTelemetry agent for Java.

By defining the following environment variables, you can customize the OTLP endpoint so that the OpenTelemetry agent communicates with APM Server.

export OTEL_RESOURCE_ATTRIBUTES=service.name=checkoutService,service.version=1.1,deployment.environment=production
export OTEL_EXPORTER_OTLP_ENDPOINT=https://apm_server_url:8200
export OTEL_EXPORTER_OTLP_HEADERS="authorization=Bearer apm_secret_token"
java -javaagent:/path/to/opentelemetry-javaagent-all.jar \
     -classpath lib/*:classes/ \
     com.mycompany.checkout.CheckoutServiceServer

You are now ready to collect traces and metrics, verify metrics, and visualize metrics in Kibana.

To export traces and metrics, ensure that you have instrumented your services and applications with the OpenTelemetry API, SDK, or both.

Here is an example of how to capture business metrics from a Java application.

// initialize metric
Meter meter = GlobalMetricsProvider.getMeter("my-frontend");
DoubleCounter orderValueCounter = meter.doubleCounterBuilder("order_value").build();

public void createOrder(HttpServletRequest request) {

   // create order in the database
   ...
   // increment business metrics for monitoring
   orderValueCounter.add(orderPrice);
}

Use Discover to validate that metrics are successfully reported to Kibana.

TSVB within Kibana is the recommended visualization for OpenTelemetry metrics. TSVB is a time series data visualizer that allows you to use the Elasticsearch aggregation framework’s full power. With TSVB, you can combine an infinite number of aggregations to display complex data.

In this example eCommerce OpenTelemetry dashboard, there are four visualizations: sales, order count, product cache, and system load. The dashboard provides us with business KPI metrics, along with performance-related metrics.

Let’s look at how this dashboard was created, specifically the Sales USD and System load visualizations.