Advanced Tracing (OTEL)

This documentation provides advanced use cases and examples, such as manual context propagation, custom decorators, and sampling , filtering methods. These examples cater to real-world requirements like asynchronous execution, multi-service interactions, and specialized exporters or decorators for observability platforms like Future AGI.

1. Manual Context Propagation

In OpenTelemetry, context propagation ensures that the current tracing context (i.e., the active span and its metadata) is maintained across thread, task, or process switches. This is crucial when your code involves asynchronous tasks or spans multiple microservices. Typically, OTEL instrumentation libraries manage context propagation automatically. However, manual intervention is sometimes necessary, especially in asynchronous workflows or custom instrumentation scenarios.

Handling Propagation in Async Functions

For Python async/await code, you might need to manually pass context if automated instrumentation doesn’t suffice or if custom logic is involved. The process involves:

Extracting the current context (e.g., from an incoming HTTP request)
Creating a new span as a child of that context
Embedding the context into the async function for reuse

import asyncio
from opentelemetry import trace
from opentelemetry.context import attach, detach, get_current

tracer = trace.get_tracer(__name__)

async def async_func(ctx):
    token = attach(ctx)
    try:
        current_span = trace.get_current_span()
        current_span.set_attribute("input.value", "User Input") # Corrected attribute key
        await asyncio.sleep(1)  # Simulate async work
    finally:
        detach(token)

def sync_func():
    with tracer.start_as_current_span("sync_span") as span:
        # Capture the current context 
        context = get_current()
        # Run the async function, passing the context
        asyncio.run(async_func(context))

if __name__ == "__main__":
    sync_func()

Propagation Between Microservices

When making HTTP or gRPC calls to another microservice, the current tracing context is usually propagated through HTTP headers. Built-in instrumentation (like opentelemetry-instrumentation-requests or opentelemetry-instrumentation-httpx) handles this automatically. For a custom approach, follow these steps:

Inject the current span context into HTTP headers before sending the request
Extract the context from incoming headers on the receiving microservice

Example: Service A sends a request to Service B. Service A:

import requests
from opentelemetry import trace
# from opentelemetry.context import Context # Not strictly needed for inject but good for awareness
from opentelemetry.propagators.textmap import DefaultTextMapPropagator

tracer = trace.get_tracer(__name__)

def make_request_to_service_b():
    # Start a new span for this operation
    with tracer.start_as_current_span("llm_service_a") as span:
        # Prepare headers
        headers = {}
        DefaultTextMapPropagator().inject(carrier=headers)  # Inject the current context

        # Make the request with the injected headers
        response = requests.get("http://localhost:5001/endpoint", headers=headers) # Assuming Python Service B runs on 5001
        return response.text

# Example usage (ensure Service B is running and OTel SDK is configured for console output):
# if __name__ == "__main__":
#     from opentelemetry.sdk.trace import TracerProvider
#     from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
#     trace.set_tracer_provider(TracerProvider())
#     trace.get_tracer_provider().add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
#     print(make_request_to_service_b())

Service B:

from flask import Flask, request
from opentelemetry import trace
from opentelemetry.propagators.textmap import DefaultTextMapPropagator
# Minimal OTel setup for console output if not already configured globally
# from opentelemetry.sdk.trace import TracerProvider
# from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
# trace.set_tracer_provider(TracerProvider())
# trace.get_tracer_provider().add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

app = Flask(__name__)
tracer = trace.get_tracer("my-service-b-tracer") # Corrected tracer name from __name__ for clarity

@app.route("/endpoint")
def endpoint():
    # Extract the context from incoming request
    context_from_propagator = DefaultTextMapPropagator().extract(carrier=dict(request.headers))

    # Create a new span as child
    with tracer.start_as_current_span("python_service_b_processing", context=context_from_propagator) as span:
        span.add_event("Received request in Python Service B")
        # ... do some processing ...
        return "Hello from Python Service B"

# if __name__ == "__main__":
#     app.run(port=5001) # Assuming Python Service B runs on 5001

Propagation with Concurrent Threads

When tasks are submitted to a ThreadPoolExecutor or any concurrency mechanism, each task runs in a separate thread. The tracer’s current context (which stores the active span or baggage) doesn’t automatically follow tasks to worker threads. By capturing the context in the main thread and attaching it in each worker thread, you maintain the association between tasks and the original trace context. Example: Below is a detailed, annotated example to show how you can:

Capture the current context before submitting tasks to the executor.
Attach that context within each worker thread (using attach).
Run your task logic (e.g., processing questions).
Detach the context when the task is complete (using detach).

import concurrent.futures
from opentelemetry import trace
from opentelemetry.context import attach, detach, get_current
from typing import Callable # Added for type hint

tracer = trace.get_tracer(__name__)

def func1():
    # Some example work done in a thread.
    current_span = trace.get_current_span()
    current_span.set_attribute("input.value", "User Input from func1") # Corrected attribute value
    return "func1 result"

def func2():
    # Another example function that logs an event to the current span.
    current_span = trace.get_current_span()
    current_span.set_attribute("input.value", "User Input from func2") # Corrected attribute value
    return "func2 result"
    
def wrapped_func(func: Callable, main_context):
    # Wraps the original function to attach/detach the captured context
    # so the worker thread has the correct span context.
    def wrapper():
        token = attach(main_context)  # Attach context to this thread
        try:
            return func()
        finally:
            detach(token)              # Detach after finishing
    return wrapper

# Example main execution logic:
# def main_concurrent_execution():
#     with tracer.start_as_current_span("main_operation") as parent_span:
#         parent_span.set_attribute("orchestrator", "ThreadPoolExecutor")
#         # Capture the context from the current thread (main_operation's context)
#         main_context_to_propagate = get_current()

#         # Create a list of functions to be executed in parallel
#         funcs_to_run = [func1, func2, func1, func2]
#         results = []

#         with concurrent.futures.ThreadPoolExecutor() as executor:
#             # Map each function to its wrapped version, passing the captured context
#             futures = [executor.submit(wrapped_func(f, main_context_to_propagate)) for f in funcs_to_run]
#             for future in concurrent.futures.as_completed(futures):
#                 results.append(future.result())
#         parent_span.set_attribute("results.count", len(results))
#     return results

# if __name__ == "__main__":
#     # Minimal OTel SDK setup for console output
#     from opentelemetry.sdk.trace import TracerProvider
#     from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
#     trace.set_tracer_provider(TracerProvider())
#     trace.get_tracer_provider().add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
#     final_results = main_concurrent_execution()
#     print(f"Concurrent execution results: {final_results}")

# The original return results was outside a function, wrapped it in main_concurrent_execution for clarity

2. Creating Custom Decorators

Decorators offer a convenient way to instrument functions and methods across your codebase without repeatedly inserting tracing calls. A custom decorator can:

Initiate a new span before the function call
Add attributes/events with function arguments (inputs)
Return the function’s result (outputs) and annotate or log it in the span
Conclude the span

Example Decorator Implementation:

from opentelemetry import trace
import functools # Import functools for functools.wraps

def trace_function(span_kind=None, additional_attributes=None):
    def decorator(func):
        @functools.wraps(func) # Preserve function metadata
        def wrapper(*args, **kwargs):
            tracer = trace.get_tracer(__name__, "0.1.0") # Added version for tracer
            with tracer.start_as_current_span(func.__name__) as span:
                if span_kind:
                    span.set_attribute("fi.span.kind", span_kind)

                # Securely convert args and kwargs to string for attributes
                try:
                    span.set_attribute("function.arguments", str(args))
                    span.set_attribute("function.keyword_arguments", str(kwargs))
                except Exception as e:
                    span.set_attribute("function.arguments.error", str(e))

                if additional_attributes:
                    for key, value in additional_attributes.items():
                        span.set_attribute(key, value)
                
                result = func(*args, **kwargs)
                try:
                    span.set_attribute("function.return_value", str(result))
                except Exception as e:
                    span.set_attribute("function.return_value.error", str(e))
                return result
        return wrapper
    return decorator

# Example Implementation
@trace_function(span_kind="LLM", additional_attributes={"llm.model_name": "gpt-4o"})
def process_text(text: str, verbose: bool = False):
    if verbose:
        print(f"Processing text: {text}")
    return text.upper()

# if __name__ == "__main__":
#     # Minimal OTel SDK setup for console output
#     from opentelemetry.sdk.trace import TracerProvider
#     from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
#     trace.set_tracer_provider(TracerProvider())
#     trace.get_tracer_provider().add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
#     print(process_text("hello world", verbose=True))

3. Selective Span Filtering Based on Attributes

In large-scale applications, recording every span may not be necessary. Instead, you might want to selectively sample:

Spans from a specific service or component
Spans meeting certain business criteria (e.g., user.id in a specific subset)
Only error or slow spans

Creating a custom sampler allows you to dynamically control which spans are recorded/exported based on their attributes or names. This approach helps manage telemetry volume and cost while ensuring you capture the most relevant traces for debugging or analysis.

Basics of Custom Sampling

Sampler Interface

In OTEL Python, create a custom sampler by subclassing the Sampler interface from opentelemetry.sdk.trace.sampling. Implement:

should_sample(...)
- Determines whether the span is recorded (Sampled) or dropped (NotSampled)
- You can examine attributes, span name, span kind, parent context, etc.

Sampling Result

When implementing should_sample, you must return a SamplingResult, which indicates:

Sampling Decision: Decision.RECORD_AND_SAMPLE, Decision.RECORD_ONLY, or Decision.DROP
Attributes: Optionally modify or add attributes in the returned SamplingResult (e.g., a reason for sampling)

Example:

from opentelemetry.context import Context
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter
from opentelemetry.sdk.trace.sampling import Sampler, SamplingResult, Decision, ParentBasedTraceIdRatio
from opentelemetry import trace
from opentelemetry.util.types import Attributes # For type hinting

USER_ID_TO_DROP = "user_to_skip_tracing"

class UserBasedSampler(Sampler):
    # A custom sampler that drops any span having a `user.id` attribute matching
    # a specified user ID. For other cases, it delegates to a root sampler.

    def __init__(self, root_sampler: Sampler = ParentBasedTraceIdRatio(0.5)):
        self._root_sampler = root_sampler

    def should_sample(
        self,
        parent_context: Context,
        trace_id: int,
        name: str,
        kind, # SpanKind is implicitly an int here
        attributes: Attributes,
        links
    ) -> SamplingResult:
        user_id = attributes.get("user.id") if attributes else None
        if user_id == USER_ID_TO_DROP:
            return SamplingResult(
                decision=Decision.DROP,
                attributes={"sampler.reason": f"Dropping span for user.id={user_id}"}
            )
        else:
            return self._root_sampler.should_sample(parent_context, trace_id, name, kind, attributes, links)
    
    def get_description(self) -> str:
        return f"UserBasedSampler(root_sampler={self._root_sampler.get_description()})"

# Example usage:
# if __name__ == "__main__":
#     custom_sampler = UserBasedSampler(root_sampler=ParentBasedTraceIdRatio(1.0))
#     provider = TracerProvider(sampler=custom_sampler)
#     trace.set_tracer_provider(provider)
#     provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
#     tracer = trace.get_tracer(__name__, "0.1.0")
#     with tracer.start_as_current_span("op_for_dropped_user", attributes={"user.id": USER_ID_TO_DROP}): pass
#     with tracer.start_as_current_span("op_for_sampled_user", attributes={"user.id": "another_user"}): pass
#     with tracer.start_as_current_span("op_without_user_id"): pass

You then pass your custom sampler into your tracer provider.

Get Started

Guides

1. Manual Context Propagation

Handling Propagation in Async Functions

Propagation Between Microservices

Propagation with Concurrent Threads

2. Creating Custom Decorators

3. Selective Span Filtering Based on Attributes

Basics of Custom Sampling

Sampler Interface

Sampling Result

Get Started

Guides

​1. Manual Context Propagation

​Handling Propagation in Async Functions

​Propagation Between Microservices

​Propagation with Concurrent Threads

​2. Creating Custom Decorators

​3. Selective Span Filtering Based on Attributes

​Basics of Custom Sampling

​Sampler Interface

​Sampling Result

1. Manual Context Propagation

Handling Propagation in Async Functions

Propagation Between Microservices

Propagation with Concurrent Threads

2. Creating Custom Decorators

3. Selective Span Filtering Based on Attributes

Basics of Custom Sampling

Sampler Interface

Sampling Result