DevOps engineers continuously optimize software delivery, while SRE teams act as the stewards of application reliability, scalability, and top-tier performance. The challenge? These teams require a cutting-edge observability solution, one that encompasses full-stack insights, empowering them to rapidly manage, monitor, and rectify potential disruptions before they culminate into operational challenges.

Observability in our modern distributed software ecosystem goes beyond mere monitoring — it demands limitless data collection, precision in processing, and the correlation of this data into actionable insights. However, the road to achieving this holistic view is paved with obstacles, from navigating version incompatibilities to wrestling with restrictive proprietary code.

Enter OpenTelemetry (OTel), with the following benefits for those who adopt it:

• Escape vendor constraints with OTel, freeing yourself from vendor lock-in and ensuring top-notch observability.
• See the harmony of unified logs, metrics, and traces come together to provide a complete system view.
• Improve your application oversight through richer and enhanced instrumentations.
• Embrace the benefits of backward compatibility to protect your prior instrumentation investments.
• Embark on the OpenTelemetry journey with an easy learning curve, simplifying onboarding and scalability.
• Rely on a proven, future-ready standard to boost your confidence in every investment.

In this blog, we will explore how you can use automatic instrumentation in your Go application using Docker, without the need to refactor any part of your application code. We will use an application called Elastiflix, which helps highlight auto-instrumentation in a simple way.

Application, prerequisites, and config

The application that we use for this blog is called Elastiflix, a movie-streaming application. It consists of several micro-services written in .NET, NodeJS, Go, and Python.

Before we instrument our sample application, we will first need to understand how Elastic can receive the telemetry data.

All of Elastic Observability’s APM capabilities are available with OTel data. Some of these include:

• Service maps
• Service details (latency, throughput, failed transactions)
• Dependencies between services, distributed tracing
• Transactions (traces)
• Machine learning (ML) correlations
• Log correlation

In addition to Elastic’s APM and a unified view of the telemetry data, you will also be able to use Elastic’s powerful machine learning capabilities to reduce the analysis, and alerting to help reduce MTTR.

Prerequisites

• An Elastic Cloud account — sign up now.
• A clone of the Elastiflix demo application, or your own Go application
• Basic understanding of Docker — potentially install Docker Desktop
• Basic understanding of Go

View the example source code

The full source code, including the Dockerfile used in this blog, can be found on GitHub.

The following steps will show you how to instrument this application and run it on the command line or in Docker. If you are interested in a more complete OTel example, take a look at the docker-compose file here, which will bring up the full project.

Step-by-step guide

Step 0. Log in to your Elastic Cloud account

This blog assumes you have an Elastic Cloud account — if not, follow the instructions to get started on Elastic Cloud.

Step 1. Run the Docker Image with auto-instrumentation

We are going to use automatic instrumentation with the Go service from the Elastiflix demo application.

We will be using the following service from Elastiflix:

Elastiflix/go-favorite

Per the OpenTelemetry Automatic Instrumentation for Go documentation, you will configure the application to be auto-instrumented using docker-compose.

As specified in the OTEL Go documentation, we will use environment variables and pass in the configuration values to enable it to connect with Elastic Observability’s APM server.

Because Elastic accepts OTLP natively, we just need to provide the Endpoint and authentication where the OTEL Exporter needs to send the data, as well as some other environment variables.

Getting Elastic Cloud variables You can copy the endpoints and token from Kibana under the path /app/apm/onboarding?agent=openTelemetry.

You will need to copy the following environment variables:

OTEL_EXPORTER_OTLP_ENDPOINT
OTEL_EXPORTER_OTLP_HEADERS

Update the docker-compose.yml file at the top of the Elastiflix repository, adding a go-auto service and updating the favorite-go one:

  favorite-go:
    build: go-favorite/.
    image: docker.elastic.co/demos/workshop/observability/elastiflix-go-favorite:${ELASTIC_VERSION}-${BUILD_NUMBER}
    depends_on:
      - redis
    networks:
      - app-network
    ports:
      - "5001:5000"
    environment:
      - REDIS_HOST=redis
      - TOGGLE_SERVICE_DELAY=${TOGGLE_SERVICE_DELAY:-0}
      - TOGGLE_CANARY_DELAY=${TOGGLE_CANARY_DELAY:-0}
      - TOGGLE_CANARY_FAILURE=${TOGGLE_CANARY_FAILURE:-0}
    volumes:
      - favorite-go:/app
  go-auto:
    image: otel/autoinstrumentation-go
    privileged: true
    pid: "host"
    networks:
      - app-network
    environment:
      OTEL_EXPORTER_OTLP_ENDPOINT: "REPLACE WITH OTEL_EXPORTER_OTLP_ENDPOINT"
      OTEL_EXPORTER_OTLP_HEADERS: "REPLACE WITH OTEL_EXPORTER_OTLP_HEADERS"
      OTEL_GO_AUTO_TARGET_EXE: "/app/main"
      OTEL_SERVICE_NAME: "go-favorite"
      OTEL_PROPAGATORS: "tracecontext,baggage"
    volumes:
      - favorite-go:/app
      - /proc:/host/proc

And, at the bottom of the file:

volumes:
  favorite-go:
networks:
  app-network:
    driver: bridge

Finally, in the configuration for the main node app, you will want to tell Elastiflix to call the Go favorites app by replacing the line:

environment:
  - API_ENDPOINT_FAVORITES=favorite-java:5000

with:

environment:
  - API_ENDPOINT_FAVORITES=favorite-go:5000

Step 3: Explore traces and logs in Elastic APM

Once you have this up and running, you can ping the endpoint for your instrumented service (in our case, this is /favorites), and you should see the app appear in Elastic APM, as shown below:

It will begin by tracking throughput and latency critical metrics for SREs to pay attention to.

Digging in, we can see an overview of all our Transactions.

And look at specific transactions:

This gives you complete visibility across metrics, and traces!

Summary

With this Dockerfile, you've transformed your simple Go application into one that's automatically instrumented with OpenTelemetry. This will aid greatly in understanding application performance, tracing errors, and gaining insights into how users interact with your software.

Remember, observability is a crucial aspect of modern application development, especially in distributed systems. With tools like OpenTelemetry, understanding complex systems becomes a tad bit easier.

In this blog, we discussed the following:

• How to auto-instrument Go with OpenTelemetry.
• Using standard commands in a Docker file, auto-instrumentation was done efficiently and without adding code in multiple places enabling manageability.
• Using OpenTelemetry and its support for multiple languages, DevOps and SRE teams can auto-instrument their applications with ease gaining immediate insights into the health of the entire application stack and reduce mean time to resolution (MTTR).

Since Elastic can support a mix of methods for ingesting data, whether it be using auto-instrumentation of open-source OpenTelemetry or manual instrumentation with its native APM agents, you can plan your migration to OTel by focusing on a few applications first and then using OpenTelemety across your applications later on in a manner that best fits your business needs.

Developer resources:

• Elastiflix application, a guide to instrument different languages with OpenTelemetry

• Python: Auto-instrumentation, Manual-instrumentation

• Java: Auto-instrumentation, Manual-instrumentation

• Node.js: Auto-instrumentation, Manual-instrumentation

• .NET: Auto-instrumentation, Manual-instrumentation

• Go: Auto-instrumentation Manual-instrumentation

• Best practices for instrumenting OpenTelemetry

  General configuration and use case resources:

• Independence with OpenTelemetry on Elastic

• Modern observability and security on Kubernetes with Elastic and OpenTelemetry

• 3 models for logging with OpenTelemetry and Elastic

• Adding free and open Elastic APM as part of your Elastic Observability deployment

• Capturing custom metrics through OpenTelemetry API in code with Elastic

• Future-proof your observability platform with OpenTelemetry and Elastic

• Elastic Observability: Built for open technologies like Kubernetes, OpenTelemetry, Prometheus, Istio, and more

Don’t have an Elastic Cloud account yet? Sign up for Elastic Cloud and try out the auto-instrumentation capabilities that I discussed above. I would be interested in getting your feedback about your experience in gaining visibility into your application stack with Elastic.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all._