OpenTracing vs OpenTelemetry

1. Introduction

In the realm of observability, OpenTracing and OpenTelemetry play crucial roles in providing insights into the performance of software systems. This lesson explores the differences, use cases, and best practices for using these tools.

2. Key Concepts

• Observability: The ability to measure the internal states of a system based on its external outputs.
• Distributed Tracing: A method to trace requests as they propagate through various services in a distributed system.
• Instrumentation: The process of adding code to track the performance and behavior of a system.

3. OpenTracing

OpenTracing is an API specification designed to provide a vendor-neutral framework for distributed tracing.

# Example of OpenTracing in Python
from opentracing import global_tracer

def some_function():
    with global_tracer().start_active_span('some_function') as scope:
        # Your function logic
        pass
        

4. OpenTelemetry

OpenTelemetry is a unified standard for observability that encompasses tracing, metrics, and logging.

# Example of OpenTelemetry in Python
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

def some_function():
    with tracer.start_as_current_span('some_function'):
        # Your function logic
        pass
        

5. Comparison

Feature	OpenTracing	OpenTelemetry
Scope	Tracing only	Tracing, Metrics, Logging
Standardization	Vendor-neutral API	CNCF backed, more standardized
Adoption	Widely used but being phased out	Rapidly gaining adoption

6. Best Practices

• Choose OpenTelemetry for new projects due to its comprehensive approach.
• Ensure consistent instrumentation across services to maintain observability.
• Leverage automatic instrumentation where possible to reduce manual overhead.

7. FAQ