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OpenTelemetry Python uses a Global Interpreter Lock (GIL) that can cause performance issues when using a web server that spawns multiple processes to serve requests in parallel.

To avoid this, you have to register a post_fork hook that will be called after each worker process is forked.

Note

Other web servers, such as uWSGI, can be instrumented in the same way (@postfork instead of post_fork).

Use this gunicorn.conf.py file (gunicorn app -c gunicorn.conf.py) in addition to the Python production instrumentation guide.

python 
import logging
from uuid import uuid4

from opentelemetry import metrics, trace
from opentelemetry.exporter.otlp.proto.grpc._log_exporter import (
    OTLPLogExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import (
    OTLPMetricExporter,
)
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import (
    OTLPSpanExporter,
)
# support for logs is currently experimental
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.resources import SERVICE_INSTANCE_ID
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# your gunicorn config here
# bind = "127.0.0.1:8000"

collector_endpoint = "http://localhost:4317"


def post_fork(server, worker):
    server.log.info("Worker spawned (pid: %s)", worker.pid)

    resource = Resource.create(
        attributes={
            # each worker needs a unique service.instance.id to distinguish the created metrics in prometheus
            SERVICE_INSTANCE_ID: str(uuid4()),
            "worker": worker.pid,
        }
    )

    tracer_provider = TracerProvider(resource=resource)
    tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(endpoint=collector_endpoint)))
    trace.set_tracer_provider(tracer_provider)

    metrics.set_meter_provider(
        MeterProvider(
            resource=resource,
            metric_readers=[(PeriodicExportingMetricReader(
                OTLPMetricExporter(endpoint=collector_endpoint)
            ))],
        )
    )

    logger_provider = LoggerProvider(resource=resource)
    logger_provider.add_log_record_processor(BatchLogRecordProcessor(OTLPLogExporter(endpoint=collector_endpoint)))
    logging.getLogger().addHandler(LoggingHandler(level=logging.NOTSET, logger_provider=logger_provider))