A custom requests HTTPAdapter that serializes spans to stdout for OpenTelemetry OTLP exporters
Project description
OTLP Stdout Adapter
The otlp-stdout-adapter
library is designed to export OpenTelemetry data to stdout in a formatted JSON structure, suitable for serverless environments like AWS Lambda. It implements a custom HTTP adapter that can be used with OpenTelemetry OTLP exporters to send telemetry data to stdout.
By outputting telemetry data to stdout, this library enables seamless integration with log management systems in serverless environments. For instance, in AWS Lambda, CloudWatch Logs can capture this output, allowing tools like the lambda-otlp-forwarder to efficiently collect and forward the data to centralized OpenTelemetry collectors.
[!IMPORTANT] This package is highly experimental and should not be used in production. Contributions are welcome.
Features
- Implements a custom HTTP adapter for use in OpenTelemetry OTLP pipelines
- Exports OpenTelemetry data to stdout in a structured JSON format
- Designed for serverless environments, especially AWS Lambda
- Configurable through environment variables
- Support for GZIP compression
- Base64 encoding for binary payloads
Installation
You can install the otlp-stdout-adapter
using pip:
pip install otlp-stdout-adapter
Usage
Here's a basic example of how to use the library with OpenTelemetry:
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from otlp_stdout_adapter import StdoutAdapter, get_lambda_resource
# Initialize the StdoutAdapter
adapter = StdoutAdapter()
session = adapter.get_session()
# Create OTLP exporter with custom session
exporter = OTLPSpanExporter(
endpoint="http://your-collector:4318/v1/traces",
session=session
)
# Set up the trace provider
provider = TracerProvider(resource=get_lambda_resource())
processor = BatchSpanProcessor(exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
# Get a tracer
tracer = trace.get_tracer(__name__)
# Create spans as usual
with tracer.start_as_current_span("example_span") as span:
span.set_attribute("example.attribute", "value")
Environment Variables
The adapter can be configured using standard OpenTelemetry environment variables:
OTEL_SERVICE_NAME
: Sets the service name for the logsAWS_LAMBDA_FUNCTION_NAME
: Used as fallback for service name if OTEL_SERVICE_NAME is not setOTEL_EXPORTER_OTLP_ENDPOINT
: Sets the endpoint for the OTLP collectorOTEL_EXPORTER_OTLP_HEADERS
: Sets additional headers for the OTLP exporterOTEL_EXPORTER_OTLP_COMPRESSION
: Specifies the compression algorithm (only gzip is supported)OTEL_EXPORTER_OTLP_TRACES_PROTOCOL
: Specifies the protocol (http/protobuf or http/json)*
[!IMPORTANT]: While the OpenTelemetry specification supports both JSON and Protobuf over HTTP, the Python SDK currently only supports Protobuf (see opentelemetry-python#1003). The environment variable is recognized but JSON format is not yet implemented. All exports will use application/x-protobuf content-type.
Output Format
Each log record includes:
__otel_otlp_stdout
: Record marker and version identifier for the adapter (format: package-name@version)source
: The service name from configurationendpoint
: The configured OTLP endpointmethod
: The HTTP method (always "POST")content-type
: The content type (currently always "application/x-protobuf")payload
: The OTLP data (base64 encoded if compressed)base64
: Boolean indicating if the payload is base64 encoded
Example output:
{
"__otel_otlp_stdout": "otlp-stdout-adapter@0.1.3",
"source": "my-lambda-function",
"headers": {
"content-type": "application/x-protobuf"
},
"endpoint": "http://collector:4318/v1/traces",
"method": "POST",
"content-type": "application/x-protobuf",
"payload": "base64-encoded-content",
"base64": true
}
AWS Lambda Usage
Here's a complete example of using the adapter in an AWS Lambda function with distributed tracing:
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
from otlp_stdout_adapter import StdoutAdapter, get_lambda_resource
# Initialize once per Lambda container
adapter = StdoutAdapter()
session = adapter.get_session()
# Set up the trace provider with Lambda resource detection
provider = TracerProvider(resource=get_lambda_resource())
exporter = OTLPSpanExporter(
endpoint="http://your-collector:4318/v1/traces",
session=session
)
processor = BatchSpanProcessor(exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
# Get a tracer
tracer = trace.get_tracer(__name__)
def process_event(event):
with tracer.start_as_current_span("process_event") as span:
# Your processing logic here
span.set_attribute("event.type", event.get("type"))
return {"processed": True}
def lambda_handler(event, context):
with tracer.start_as_current_span(
"lambda_handler",
kind=trace.SpanKind.SERVER
) as span:
try:
# Add event information to span
span.set_attribute("event.type", event.get("type"))
span.add_event("Processing Lambda event")
result = process_event(event)
return {
"statusCode": 200,
"body": json.dumps(result)
}
except Exception as e:
span.record_exception(e)
span.set_status(trace.Status(trace.StatusCode.ERROR))
raise
finally:
# Ensure spans are exported before Lambda freezes
provider.force_flush()
Lambda Configuration Notes
- Initialize tracing components outside the handler for container reuse
- Always call
provider.force_flush()
before the handler completes - Use environment variables for configuration:
OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf OTEL_EXPORTER_OTLP_COMPRESSION=gzip
Best Practices
-
Initialize Outside Handler
- Create providers, exporters, and tracers outside the handler
- Reuse these components across invocations
-
Resource Detection
- Use
get_lambda_resource()
to automatically capture Lambda metadata - Merge with custom resources as needed
- Use
-
Span Processing
- Use
BatchSpanProcessor
for efficient span processing - Always flush spans before handler completion
- Configure appropriate batch size and timeout for Lambda
- Use
-
Error Handling
- Record exceptions using
span.record_exception()
- Set appropriate span status
- Ensure spans are ended in finally blocks
- Record exceptions using
-
Context Propagation
- Use TraceContext propagator for distributed tracing
- Propagate context in outgoing requests
License
This project is licensed under the MIT License - see the LICENSE file for details.
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