Master Go instrumentation with OpenTelemetry in SigNoz tutorial

Table of Contents:

1. Introduction
2. Installing Signals
3. Getting the Sample Go App
4. Declaring Environment Variables
5. Instrumenting the Web Application with Open Telemetry
6. Initializing the Tracer in main.o5
7. Adding the Open Telemetry Middleware
8. Setting Environment Variables
9. Running the Go Application
10. Interacting with the App
11. Visualizing Trace Data with Signals
12. Analyzing Traces
13. Conclusion

1. Introduction

In this article, we will discuss the implementation of Open Telemetry in a Go application. Open Telemetry is a powerful tool that allows You to Collect and analyze telemetry data from your applications. We will cover the steps required to install Signals, get the sample Go app, instrument the app with Open Telemetry, and analyze the metrics and traces using the Signals dashboard.

2. Installing Signals

To begin, we need to install Signals on our local machine. Signals is the backend that will receive the telemetry data from our Go application. We will follow the instructions provided in the Open Telemetry blog to clone the Signals repository and run the install script. Once installed, Signals will be up and running on localhost:3301.

3. Getting the Sample Go App

Next, we need to get the sample Go app from GitHub. The sample app is provided in two branches: with instrumentation and without instrumentation. For this tutorial, we will use the without instrumentation branch. We will clone the branch and make the necessary code changes to instrument the app with Open Telemetry.

4. Declaring Environment Variables

To configure Open Telemetry, we need to declare some environment variables. These variables will be used to specify the exporter endpoint and other configuration settings. We will add the necessary code to our Go app to declare these variables using the os Package.

5. Instrumenting the Web Application with Open Telemetry

In this step, we will instrument the web application with Open Telemetry. This involves importing the necessary statements and initializing the tracer function. We will add the code provided in the Open Telemetry blog to our app to instrument it.

6. Initializing the Tracer in main.o5

To complete the instrumentation process, we need to initialize the tracer in our main function. We will add the code provided in the blog to our main function to initialize the tracer.

7. Adding the Open Telemetry Middleware

The next step is to add the Open Telemetry middleware to our app. This middleware will capture and propagate spans and trace Context. We will import the necessary package and add the middleware to our app.

8. Setting Environment Variables

To send data to the Signals backend, we need to set some environment variables in our Go application. These variables will specify the exporter, endpoint, and other configuration settings. We will add the necessary code to set the environment variables.

9. Running the Go Application

Now that our Go application is instrumented with Open Telemetry and the environment variables are set, we can run the application. We will use the command provided in the blog to run the application and start sending telemetry data to Signals.

10. Interacting with the App

To generate some user data, we need to Interact with our application. We will access the Books endpoint multiple times to generate some data. The blog provides a minimalistic UI to Visualize the trace data with Signals.

11. Visualizing Trace Data with Signals

The Metrics section in Signals provides an overview of all our applications. We can see the applications that come with the default Hot Rod demo application and the Golgi Map that we just instrumented. Clicking on a service allows us to view detailed trace data.

12. Analyzing Traces

Using distributed tracing, we can retrieve detailed telemetry data and analyze how our applications are performing. The Signals dashboard provides powerful filters and sorting options to narrow down and analyze the traces. We can also view contextual data and drill down into specific events and spans.

13. Conclusion

In conclusion, implementing Open Telemetry in a Go application using Signals allows us to collect and analyze telemetry data. We covered the installation of Signals, getting the sample Go app, instrumenting the app with Open Telemetry, and visualizing and analyzing the trace data using the Signals dashboard. Open Telemetry provides valuable insights into the performance and behavior of our applications.

This article provides a step-by-step guide to implementing Open Telemetry in a Go application. By following the instructions and making the necessary code changes, you can integrate Open Telemetry into your own Go applications and benefit from the powerful telemetry data analysis provided by Signals.

Pros:

• Open Telemetry provides a standardized way to collect telemetry data from applications.
• Signals offers a user-friendly dashboard for visualizing and analyzing telemetry data.
• The step-by-step instructions make it easy to follow along and implement Open Telemetry in a Go application.

Cons:

• Some steps require technical knowledge of Go programming and the use of command-line tools.
• The sample app provided may not cover all use cases, requiring additional customization for specific applications.

Highlights:

• Implementing Open Telemetry in Go applications
• Installing Signals and getting the sample Go app
• Instrumenting the app with Open Telemetry
• Visualizing and analyzing trace data with Signals

FAQ:

Q: What is Open Telemetry? A: Open Telemetry is a framework for collecting, exporting, and analyzing telemetry data from applications. It provides a standardized way to instrument applications and collect data about their performance and behavior.

Q: What is Signals? A: Signals is the backend system that receives and processes telemetry data from applications instrumented with Open Telemetry. It provides a user-friendly dashboard for visualizing and analyzing the telemetry data.

Q: Why is telemetry data important? A: Telemetry data allows developers and operators to understand how their applications are performing, identify bottlenecks and issues, and make informed decisions to improve the overall performance and user experience.

Q: Can Open Telemetry be used with other programming languages? A: Yes, Open Telemetry is language-agnostic and can be used with various programming languages. It provides SDKs and instrumentation libraries for different languages to facilitate the collection and export of telemetry data.

Q: Can I customize the telemetry data collected by Open Telemetry? A: Yes, Open Telemetry allows you to customize the instrumentation and collection of telemetry data according to your specific requirements. You can add additional spans, tags, and context to capture the relevant data for your application.