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A Basic Introduction to OpenTelemetry Python

By: HitSubscribe
  |  March 11, 2024
A Basic Introduction to OpenTelemetry Python

Think of a tool that simplifies application monitoring and helps developers and staff trace, collect logs and measure performance metrics. That is what OpenTelemetry Python provides. OpenTelemetry (OTel) Python acts as a guiding light, offering insights into the behaviors and interactions of complex, distributed systems and enabling a deeper understanding of performance bottlenecks and system dependencies. The significance of OTel lies in its pivotal role in modern software development. OTel is crafted to address the challenges microservices architectures and cloud-native environments pose. Providing a standardized approach to observability facilitates the collection and analysis of crucial data points, which paves a way for continuous performance enhancement.

Understanding how software works behind the scenes is key in today’s tech world. OpenTelemetry helps you see into this complex world, showing how your apps perform and behave. By using OTel, developers can quickly find and fix issues, making their software stronger and better.

This post will guide you through the fundamentals of OpenTelemetry Python. You will gain a comprehensive understanding of key concepts, practical applications and real use cases, enabling you to harness the power of OTel to simplify monitoring of cloud-native software applications.

Key Concepts of OpenTelemetry Python

OpenTelemetry helps analyze any software application data in the form of traces, logs and metrics. Providing a unified approach to instrumenting applications and collecting telemetry data across diverse environments, OTel is an invaluable tool for understanding and optimizing distributed systems. Now, let’s unravel some of its pivotal concepts.

Distributed Tracing

Distributed tracing tracks the flow of requests and operations across multiple services or components in a distributed system. By creating traces, which are essentially timelines of events, developers can identify performance bottlenecks, pinpoint errors and gain insights into the overall behavior of their system.

OpenTelemetry Python provides a powerful distributed tracing API that allows developers to instrument their code and generate traces easily. Traces are exportable to various back ends for analysis and visualization, providing a comprehensive view of the system’s performance and health.


Logs are a fundamental part of monitoring and debugging applications. Providing a stream of events and messages that record the behavior of the system, logs make it easier to identify errors, track user activity and understand system behavior.

OpenTelemetry Python integrates seamlessly with logging frameworks, enabling developers to collect and export logs in a standardized format. This integration allows for centralized logging and analysis, providing a holistic view of the system’s activity and ensuring that crucial data about an application’s behavior is readily available for analysis, troubleshooting, and debugging.


Metrics are numerical values representing the state or behavior of a system over time. They provide a quantitative measure of system performance, resource utilization, and other key indicators like response times, error rates or resource usage.

OpenTelemetry Python supports the collection and export of metrics, enabling developers to monitor key performance indicators (KPIs) and identify trends or anomalies in system behavior. This information is crucial for optimizing resource allocation, identifying performance bottlenecks and ensuring overall system health.

Getting Started With OpenTelemetry Python

OpenTelemetry Python serves as a potent tool for collecting essential data to monitor and observe applications in action. Here, we’ll walk through the initial setup and demonstrate how to harness OTel capabilities for effective instrumentation within a basic Python application.

Installation & Setup

Installing the OpenTelemetry Python Package

The first step involves installing the OpenTelemetry Python package. Utilize Python’s package manager, PIP, to install the necessary components. Execute the following command in your terminal or command prompt:

$ pip install opentelemetry-api
$ pip install opentelemetry-sdk

Configuration Setup for Basic Instrumentation

After installing the OpenTelemetry Python package, the next crucial step involves setting up the basic configuration for instrumentation within your Python application. This configuration defines how OpenTelemetry collects and exports data.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import ConsoleSpanExporter
from opentelemetry.sdk.trace.export import BatchSpanProcessor

We have imported the necessary modules from OTel. These imports serve the following purpose:

  • TracerProvider: Represents the core component responsible for managing tracing resources and providing tracers to the application.
  • Resource: Defines attributes (like service name) describing the application, aiding in the identification and categorization of traces.
  • ConsoleSpanExporter: An exporter directing trace spans to the console, facilitating visibility and demonstration of traced data for testing or learning purposes.
  • BatchSpanExporter(ConsoleSpanExporter): Sets up a span processor that processes spans before exporting, linking to the console exporter for handling trace spans.
  • trace.get_tracer(name): Retrieves a tracer for use within the application, allowing the creation of spans to encapsulate specific operations or blocks of code for tracing purposes.

Basic Instrumentation

Adding Instrument to a Sample Python Application

In this section, you’ll learn how you can export the trace spans to the console for a sample Python application. Below is a simple Python application of a loop printing the numbers from 0 to 5. Now you have to trace the working of each iteration and get the metrics. Below demonstrates how OpenTelemetry helps you.

# simple application
def main():
    print("Starting the application...")
    for i in range(5):
    print("Application finished.")

def process_data(num):
    print(f"Processing data: {num}")

if __name__ == "__main__":

Integrating Tracing

Now let’s instrument the basic application with OTel to trace the process_data() function.

# let's apply tracing
def main():
    # setup OpenTelemetry tracing
    resource = Resource.create({"": "my-service"})
    span_processor = BatchSpanProcessor(ConsoleSpanExporter())
    tracer = trace.get_tracer(__name__)

    print("Starting the application...")
    with tracer.start_as_current_span("Main-Span"):
        for i in range(5):
            process_data(tracer, i)
    print("Application finished.")

def process_data(tracer, num):
    with tracer.start_as_current_span(f"processing-Span-{num}"):
        print(f"Processing data: {num}")

if __name__ == "__main__":

The process_data() function is now wrapped in a span created by the tracer, which represents each iteration of the data processing. This simple instrumentation showcases how OTel can be applied to monitor and trace specific operations within a Python application. Adjust and expand this instrumentation as needed to capture additional details or specific segments of interest within your application’s codebase.

  • tracer.start_as_current_span(…): Context manager that creates a new span for a block of code. Spans represent individual operations or sections of code being traced.
  • process_data(tracer, i): Calls the process_data() function, which starts a new span for each iteration to trace the processing of data.

Integrating Logging

Begin by integrating logging into the application alongside tracing. Modify the existing code to include logging statements using Python’s logging module or any preferred logging framework.

import logging
# configure logging

def main():
    # ... (existing code remains the same)"Starting the application...")
    with tracer.start_as_current_span("main"):
        for i in range(5):
            process_data(tracer, i)"Application finished.")

Here, statements are added at the start and end of the application and can be inserted at relevant points within the code to capture information during execution.

Integrating Metrics

Next, integrate metrics to capture relevant data points about the application’s performance. Use OTel metrics functionalities or other metric collection libraries to define and record metrics.

from opentelemetry import metrics

# create a meter to register and manage metrics
meter = metrics.get_meter(__name__)

# define a counter metric
requests_count = meter.create_counter(name="requests_count", description="Number of requests processed")

def main():
    # ... (existing code remains the same)
    with tracer.start_as_current_span("main"):
        for i in range(5):
            process_data(tracer, i)
            requests_count.add(1)  # Increment the counter on each iteration

In this example, a metric named requests_count is defined, and within the application’s main loop, the metric is incremented for each iteration of the loop.

By incrementally integrating logging and metrics alongside tracing in the same application, developers can enhance observability by capturing logs, tracing spans, and monitoring important metrics related to the application’s behavior and performance.

Common Challenges & Solutions

Addressing common issues during setup and offering troubleshooting tips for smooth integration is crucial for ensuring a successful implementation of instrumentation within a Python application.

Addressing Common Issues During Setup

  1. Dependency version conflicts: Check for any conflicting versions or dependencies between OpenTelemetry and other libraries or frameworks used within the application. Resolve version conflicts to ensure compatibility and smooth operation.
  2. Misconfigured exporters: Incorrect configuration of exporters or span processors might lead to spans not being exported or displayed correctly. Verify the exporter setup and ensure it’s the correct configuration to handle and export spans.
  3. Missing instrumentation libraries: Ensure all necessary instrumentation libraries or modules for tracing, logging, or metrics are installed and correctly configured within the application. Missing or improperly configured instrumentation might lead to incomplete or inaccurate data collection.

Troubleshooting Tips for a Smooth Integration

  1. Logging and debugging setup: Configure robust logging and debugging mechanisms alongside tracing to effectively capture and analyze issues or errors encountered during the integration process.
  2. Incremental integration approach: Implement instrumentation incrementally, focusing on one aspect at a time (e.g., tracing first, then logging, followed by metrics). This step-by-step approach allows for better isolation and debugging of issues that might arise during integration.
  3. Testing and validation: Perform thorough testing of the instrumentation setup by generating test scenarios and verifying if the expected traces, logs, or metrics are being captured accurately. Use test cases to validate the functionality and correctness of the integrated instrumentation.
  4. Community support and resources: Leverage community forums, documentation, and resources provided by the instrumentation libraries or tools (e.g., OpenTelemetry documentation, forums, GitHub repositories). Seek assistance and advice from the community to troubleshoot any integration challenges encountered.

Practical Use Cases of OpenTelemetry Python

OpenTelemetry is a very useful tool that helps monitor the performance of various applications and application development workflows. In this section, you’ll learn about some real use cases of OpenTelemetry Python in performance monitoring.

Microservice Architecture

In a microservices-based application, numerous services interact to handle various functionalities. OpenTelemetry Python can effectively trace and monitor requests as they traverse through these distributed services. It helps in understanding the entire flow of a request, identifying performance bottlenecks, and diagnosing issues within and across services. Tracing capabilities makes it easier to pinpoint which microservice might be causing delays or errors, allowing for quick resolution and optimization.

Cloud-Native Deployments

When you’re deploying applications in cloud-native environments, understanding performance across diverse cloud services, platforms, or containers becomes vital. OpenTelemetry Python provides insights into how an application utilizes these cloud resources. It enables monitoring and observing the application’s behavior within cloud environments, allowing for better resource management, cost optimization, and performance enhancement.

Benefits of Using OpenTelemetry Python

  1. Unified observability: Standardized framework for collecting traces, logs, and metrics.
  2. Distributed tracing: Visibility into complex, distributed architectures for issue identification.
  3. Performance optimization: Proactive identification and resolution of performance issues.
  4. Troubleshooting & debugging: Contextual insights for faster issue resolution.
  5. Cost optimization: Resource consumption insights for better cost management.
  6. Compatibility & flexibility: Integration across diverse tech stacks and platforms.
  7. Community support & growth: Continual evolution and support through a thriving community.

Impacts of OpenTelemetry on Observability & Troubleshooting Capabilities

  1. Enhanced observability: Provides comprehensive insights into application behavior, interactions, and performance across distributed systems.
  2. Real-time monitoring: Facilitates real-time monitoring of system health, enabling proactive identification and resolution of issues.
  3. Efficient issue resolution: Accelerates issue resolution by providing detailed, contextual information for root cause analysis.
  4. Improved resource utilization: Assists in optimizing resource allocation and usage, leading to better cost management in cloud-native environments.
  5. Streamlined debugging: Simplifies debugging processes by providing visibility into code execution and service interactions.


After reading this article, you’ve gained a solid understanding of OpenTelemetry Python. You have explored its pivotal role in modern software observability. You’ve learned about fundamental concepts like tracing, logging, and metrics. You understand how OTel offers a unified framework to capture and analyze these crucial aspects of application behavior. 

From the setup to practical integration, you’ve seen how OTel empowers developers with unparalleled insights into distributed systems, which enables efficient troubleshooting and performance optimization.

This post was written by Gourav Bais. Gourav is an applied machine learning engineer skilled in computer vision/deep learning pipeline development, creating machine learning models, retraining systems, and transforming data science prototypes into production-grade solutions.

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