This library is a Python platform API for OpenTracing.
In order to understand the Python platform API, one must first be familiar with the OpenTracing project and terminology more specifically.
In the current version, opentracing-python provides only the API and a basic no-op implementation that can be used by instrumentation libraries to collect and propagate distributed tracing context.
Future versions will include a reference implementation utilizing an abstract Recorder interface, as well as a Zipkin-compatible Tracer.
The work of instrumentation libraries generally consists of three steps:
- When a service receives a new request (over HTTP or some other protocol), it uses OpenTracing's inject/extract API to continue an active trace, creating a Span object in the process. If the request does not contain an active trace, the service starts a new trace and a new root Span.
- The service needs to store the current Span in some request-local storage, (called
Spanactivation) where it can be retrieved from when a child Span must be created, e.g. in case of the service making an RPC to another service. - When making outbound calls to another service, the current Span must be retrieved from request-local storage, a child span must be created (e.g., by using the
start_child_span()helper), and that child span must be embedded into the outbound request (e.g., using HTTP headers) via OpenTracing's inject/extract API.
Below are the code examples for the previously mentioned steps. Implementation of request-local storage needed for step 2 is specific to the service and/or frameworks / instrumentation libraries it is using, exposed as a ScopeManager child contained as Tracer.scope_manager. See details below.
Somewhere in your server's request handler code:
def handle_request(request):
span = before_request(request, opentracing.global_tracer())
# store span in some request-local storage using Tracer.scope_manager,
# using the returned `Scope` as Context Manager to ensure
# `Span` will be cleared and (in this case) `Span.finish()` be called.
with tracer.scope_manager.activate(span, True) as scope:
# actual business logic
handle_request_for_real(request)
def before_request(request, tracer):
span_context = tracer.extract(
format=Format.HTTP_HEADERS,
carrier=request.headers,
)
span = tracer.start_span(
operation_name=request.operation,
child_of=span_context)
span.set_tag('http.url', request.full_url)
remote_ip = request.remote_ip
if remote_ip:
span.set_tag(tags.PEER_HOST_IPV4, remote_ip)
caller_name = request.caller_name
if caller_name:
span.set_tag(tags.PEER_SERVICE, caller_name)
remote_port = request.remote_port
if remote_port:
span.set_tag(tags.PEER_PORT, remote_port)
return spanSomewhere in your service that's about to make an outgoing call:
from opentracing import tags
from opentracing.propagation import Format
from opentracing_instrumentation import request_context
# create and serialize a child span and use it as context manager
with before_http_request(
request=out_request,
current_span_extractor=request_context.get_current_span):
# actual call
return urllib2.urlopen(request)
def before_http_request(request, current_span_extractor):
op = request.operation
parent_span = current_span_extractor()
outbound_span = opentracing.global_tracer().start_span(
operation_name=op,
child_of=parent_span
)
outbound_span.set_tag('http.url', request.full_url)
service_name = request.service_name
host, port = request.host_port
if service_name:
outbound_span.set_tag(tags.PEER_SERVICE, service_name)
if host:
outbound_span.set_tag(tags.PEER_HOST_IPV4, host)
if port:
outbound_span.set_tag(tags.PEER_PORT, port)
http_header_carrier = {}
opentracing.global_tracer().inject(
span_context=outbound_span,
format=Format.HTTP_HEADERS,
carrier=http_header_carrier)
for key, value in http_header_carrier.iteritems():
request.add_header(key, value)
return outbound_spanFor getting/setting the current active Span in the used request-local storage, OpenTracing requires that every Tracer contains a ScopeManager that grants access to the active Span through a Scope. Any Span may be transferred to another task or thread, but not Scope.
# Access to the active span is straightforward.
scope = tracer.scope_manager.active()
if scope is not None:
scope.span.set_tag('...', '...')The common case starts a Scope that's automatically registered for intra-process propagation via ScopeManager.
Note that start_active_span('...') automatically finishes the span on Scope.close() (start_active_span('...', finish_on_close=False) does not finish it, in contrast).
# Manual activation of the Span.
span = tracer.start_span(operation_name='someWork')
with tracer.scope_manager.activate(span, True) as scope:
# Do things.
# Automatic activation of the Span.
# finish_on_close is a required parameter.
with tracer.start_active_span('someWork', finish_on_close=True) as scope:
# Do things.
# Handling done through a try construct:
span = tracer.start_span(operation_name='someWork')
scope = tracer.scope_manager.activate(span, True)
try:
# Do things.
except Exception as e:
span.set_tag('error', '...')
finally:
scope.close()If there is a Scope, it will act as the parent to any newly started Span unless the programmer passes ignore_active_span=True at start_span()/start_active_span() time or specified parent context explicitly:
scope = tracer.start_active_span('someWork', ignore_active_span=True)Each service/framework ought to provide a specific ScopeManager implementation that relies on their own request-local storage (thread-local storage, or coroutine-based storage for asynchronous frameworks, for example).
This project includes a set of ScopeManager implementations under the opentracing.scope_managers submodule, which can be imported on demand:
from opentracing.scope_managers import ThreadLocalScopeManagerThere exist implementations for thread-local (the default instance of the submodule opentracing.scope_managers), gevent, Tornado, asyncio and contextvars:
from opentracing.scope_managers.gevent import GeventScopeManager # requires gevent
from opentracing.scope_managers.tornado import TornadoScopeManager # requires tornado<6
from opentracing.scope_managers.asyncio import AsyncioScopeManager # fits for old asyncio applications, requires Python 3.4 or newer.
from opentracing.scope_managers.contextvars import ContextVarsScopeManager # for asyncio applications, requires Python 3.7 or newer.Note that for asyncio applications it's preferable to use ContextVarsScopeManager instead of AsyncioScopeManager because of automatic parent span propagation to children coroutines, tasks or scheduled callbacks.
virtualenv env
. ./env/bin/activate
make bootstrap
make testYou can use tox to run tests as well.
toxA testbed suite designed to test API changes and experimental features is included under the testbed directory. For more information, see the Testbed README.
This project has a working design of interfaces for the OpenTracing API. There is a MockTracer to facilitate unit-testing of OpenTracing Python instrumentation.
from opentracing.mocktracer import MockTracer
tracer = MockTracer()
with tracer.start_span('someWork') as span:
pass
spans = tracer.finished_spans()
someWorkSpan = spans[0]virtualenv env
. ./env/bin/activate
make bootstrap
make docsThe documentation is written to docs/_build/html.
Before new release, add a summary of changes since last version to CHANGELOG.rst
pip install 'zest.releaser[recommended]'
prerelease
release
git push origin master --follow-tags
make docs
python setup.py sdist upload -r pypi upload_docs -r pypi
postrelease
git push
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