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Add true auto-instrumentation support to opentelemetry-instrument
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This commit extends the instrument command so it automatically
configures tracing with a provider, span processor and exporter. Most of
the component used can be customized with env vars or CLI arguments.

Details can be found on opentelemetry-instrumentation's README package.

Fixes open-telemetry#663
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owais committed Oct 4, 2020
1 parent 14fad78 commit 15d2d19
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class _DjangoMiddleware(MiddlewareMixin):
"""Django Middleware for OpenTelemetry
"""
"""Django Middleware for OpenTelemetry"""

_environ_activation_key = (
"opentelemetry-instrumentor-django.activation_key"
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3 changes: 2 additions & 1 deletion opentelemetry-instrumentation/CHANGELOG.md
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## Unreleased

- Fixed boostrap command to correctly install opentelemetry-instrumentation-falcon instead of opentelemetry-instrumentation-flask
- Fixed boostrap command to correctly install opentelemetry-instrumentation-falcon instead of opentelemetry-instrumentation-flask. ([#1138](https://github.com/open-telemetry/opentelemetry-python/pull/1138))
- Added support for `OTEL_EXPORTER` to the `opentelemetry-instrument` command ([#1036](https://github.com/open-telemetry/opentelemetry-python/pull/1036))

## Version 0.13b0

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80 changes: 72 additions & 8 deletions opentelemetry-instrumentation/README.rst
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This package provides a couple of commands that help automatically instruments a program:


opentelemetry-bootstrap
-----------------------

::

opentelemetry-bootstrap --action=install|requirements

This commands inspects the active Python site-packages and figures out which instrumentation
packages the user might want to install. By default it prints out a list of the suggested
instrumentation packages which can be added to a requirements.txt file. It also supports
installing the suggested packages when run with :code:`--action=install` flag.

The command also installs the OTLP exporter by default for both spans and metrics. This can
be overriden by specifying another exporter using the `--exporter` or `-e` CLI flag. The flag
accepts multiple values to install multiple exporters. Run `opentelemetry-bootstrap --help`
to list down all supported exporters.

Manually specifying exporters to install:

::

opentelemetry-bootstrap -e otlp zipkin


opentelemetry-instrument
------------------------

::

opentelemetry-instrument python program.py

The instrument command will try to automatically detect packages used by your python program
and when possible, apply automatic tracing instrumentation on them. This means your program
will get automatic distrubuted tracing for free without having to make any code changes
at all. This will also configure a global tracer and tracing exporter without you having to
make any code changes. By default, the instrument command will use the OTLP exporter but
this can be overrided when needed.

The command supports the following configuration options as CLI arguments and environments vars:


* ``--exporter`` or ``OTEL_EXPORTER``

Used to specify which trace exporter to use. Can be set to one or more
of the well-known exporter names (see below) or a fully
qualified Python import path to a span exporter implementation.

- Defaults to `otlp`.
- Can be set to `none` to disbale automatic tracer initialization.

You can pass multiple values to configure multiple exporters e.g, ``zipkin,prometheus``

Well known trace exporter names:

- datadog
- jaeger
- opencensus
- otlp
- otlp_span
- otlp_metric
- zipkin

``otlp`` is an alias for ``otlp_span,otlp_metric``.

* ``--service-name`` or ``OTEL_SERVICE_NAME``

When present the value is passed on to the relevant exporter initializer as ``service_name`` argument.

The code in ``program.py`` needs to use one of the packages for which there is
an OpenTelemetry integration. For a list of the available integrations please
check `here <https://opentelemetry-python.readthedocs.io/en/stable/index.html#integrations>`_

Examples
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

opentelemetry-bootstrap
-----------------------
::

opentelemetry-instrument -e otlp flask run --port=3000

The above command will pass ``-e otlp`` to the instrument command and ``--port=3000`` to ``flask run``.

::

opentelemetry-bootstrap --action=install|requirements
opentelemetry-instrument -e zipkin,otlp celery -A tasks worker --loglevel=info

This commands inspects the active Python site-packages and figures out which
instrumentation packages the user might want to install. By default it prints out
a list of the suggested instrumentation packages which can be added to a requirements.txt
file. It also supports installing the suggested packages when run with :code:`--action=install`
flag.
The above command will configure global trace provider, attach zipkin and otlp exporters to it and then
start celery with the rest of the arguments.

References
----------
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# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
from logging import getLogger
from os import environ, execl, getcwd
from os.path import abspath, dirname, pathsep
from shutil import which
from sys import argv

from opentelemetry.instrumentation import symbols

logger = getLogger(__file__)


def parse_args():
parser = argparse.ArgumentParser(
description="""
opentelemetry-instrument automatically instruments a Python
program and it's dependencies and then runs the program.
"""
)

parser.add_argument(
"-e",
"--exporter",
required=False,
help="""
Uses the specified exporter to export spans.
Must be one of the following:
- Name of a well-known trace exporter. Choices are:
{0}
- A fully qualified python import path to a trace exporter
implementation or a callable that returns a new instance
of a trace exporter.
""".format(
symbols.trace_exporters
),
)

parser.add_argument(
"-s",
"--service-name",
required=False,
help="""
The service name that should be passed to a trace exporter.
""",
)

parser.add_argument("command", help="Your Python application.")
parser.add_argument(
"command_args",
help="Arguments for your application.",
nargs=argparse.REMAINDER,
)
return parser.parse_args()


def load_config_from_cli_args(args):
if args.exporter:
environ["OTEL_EXPORTER"] = args.exporter
if args.service_name:
environ["OTEL_SERVICE_NAME"] = args.service_name


def run() -> None:
args = parse_args()
load_config_from_cli_args(args)

python_path = environ.get("PYTHONPATH")

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environ["PYTHONPATH"] = pathsep.join(python_path)

executable = which(argv[1])

execl(executable, executable, *argv[2:])
executable = which(args.command)
execl(executable, executable, *args.command_args)
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# Copyright The OpenTelemetry Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from logging import getLogger
from typing import Sequence, Tuple

from opentelemetry import trace
from opentelemetry.configuration import Configuration
from opentelemetry.instrumentation import symbols
from opentelemetry.sdk.metrics.export import MetricsExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
BatchExportSpanProcessor,
SpanExporter,
SpanProcessor,
)

logger = getLogger(__file__)

_DEFAULT_EXPORTER = symbols.exporter_otlp

known_exporters = {
symbols.exporter_otlp: (
"opentelemetry.exporter.otlp.trace_exporter.OTLPSpanExporter",
"opentelemetry.exporter.otlp.metrics_exporter.OTLPMetricsExporter",
),
symbols.exporter_dd: (
"opentelemetry.exporter.datadog.DatadogSpanExporter",
),
symbols.exporter_oc: (
"opentelemetry.exporter.opencensus.trace_exporter.OpenCensusSpanExporter",
),
symbols.exporter_otlp_span: (
"opentelemetry.exporter.otlp.trace_exporter.OTLPSpanExporter",
),
symbols.exporter_otlp_metric: (
"opentelemetry.exporter.otlp.metrics_exporter.OTLPMetricsExporter"
),
symbols.exporter_jaeger: (
"opentelemetry.exporter.jaeger.JaegerSpanExporter",
),
symbols.exporter_zipkin: (
"opentelemetry.exporter.zipkin.ZipkinSpanExporter",
),
symbols.exporter_prometheus: (
"opentelemetry.exporter.prometheus.PrometheusMetricsExporter",
),
}


def _import(import_path: str) -> any:
split_path = import_path.rsplit(".", 1)
if len(split_path) < 2:
raise ImportError(
"could not import module or class: {0}".format(import_path)
)
module, class_name = split_path
mod = __import__(module, fromlist=[class_name])
return getattr(mod, class_name)


def get_service_name() -> str:
return Configuration().SERVICE_NAME or ""


def get_exporter_names() -> Sequence[str]:
exporter = Configuration().EXPORTER or _DEFAULT_EXPORTER
if exporter.lower().strip() == "none":
return []

return [e.strip() for e in exporter.split(",")]


def get_tracer_provider_class() -> trace.TracerProvider:
return TracerProvider


def get_processor_class_for_exporter(exporter_name: str) -> SpanProcessor:
if exporter_name == symbols.exporter_dd:
return _import(
"opentelemetry.exporter.datadog.DatadogExportSpanProcessor"
)
return BatchExportSpanProcessor


def init_tracing(exporters: Sequence[SpanExporter]):
service_name = get_service_name()
provider = get_tracer_provider_class()(
resource=Resource.create({"service.name": service_name}),
)
trace.set_tracer_provider(provider)

for exporter_name, exporter_class in exporters.items():
processor_class = get_processor_class_for_exporter(exporter_name)

exporter_args = {}
if exporter_name == symbols.exporter_dd:
exporter_args["service"] = service_name
elif exporter_name not in [
symbols.exporter_otlp,
symbols.exporter_otlp_span,
]:
exporter_args["service_name"] = service_name

provider.add_span_processor(
processor_class(exporter_class(**exporter_args))
)


def init_metrics(exporters: Sequence[MetricsExporter]):
if exporters:
logger.warning("automatic metric initialization is not supported yet.")


def import_exporters(
exporter_names: Sequence[str],
) -> Tuple[Sequence[SpanExporter], Sequence[MetricsExporter]]:
trace_exporters, metric_exporters = {}, {}
for exporter_name in exporter_names:
print(">> ", exporter_name)
for exporter_path in known_exporters.get(
exporter_name, [exporter_name]
):
exporter_impl = _import(exporter_path)
if issubclass(exporter_impl, SpanExporter):
trace_exporters[exporter_name] = exporter_impl
elif issubclass(exporter_impl, MetricsExporter):
metric_exporters[exporter_name] = exporter_impl
else:
raise RuntimeError(
"{0} ({1}) is neither a trace exporter nor a metric exporter".format(
exporter_name, exporter_path
)
)
return trace_exporters, metric_exporters


def initialize_components():
exporter_names = get_exporter_names()
trace_exporters, metric_exporters = import_exporters(exporter_names)
init_tracing(trace_exporters)

# We don't support automatic initialization for metric yet but have added
# some boilerplate in order to make sure current implementation does not
# lock us out of supporting metrics later without major surgery.
init_metrics(metric_exporters)
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