py_zipkin provides a context manager/decorator along with some utilities to facilitate the usage of Zipkin in Python applications.
pip install py_zipkin
py_zipkin requires a transport_handler function that handles logging zipkin
messages to a central logging service such as kafka or scribe.
py_zipkin.zipkin.zipkin_span is the main tool for starting zipkin traces or
logging spans inside an ongoing trace. zipkin_span can be used as a context
manager or a decorator.
from py_zipkin.zipkin import zipkin_span
def some_function(a, b):
with zipkin_span(
service_name='my_service',
span_name='my_span_name',
transport_handler=some_handler,
port=42,
sample_rate=0.05, # Value between 0.0 and 100.0
):
do_stuff(a, b)The difference between this and Usage #1 is that the zipkin_attrs are calculated separately and passed in, thus negating the need of the sample_rate param.
# Define a pyramid tween
def tween(request):
zipkin_attrs = some_zipkin_attr_creator(request)
with zipkin_span(
service_name='my_service',
span_name='my_span_name',
zipkin_attrs=zipkin_attrs,
transport_handler=some_handler,
port=22,
) as zipkin_context:
response = handler(request)
zipkin_context.update_binary_annotations_for_root_span(
some_binary_annotations)
return responseThis can be also be used inside itself to produce continuously nested spans.
@zipkin_span(service_name='my_service', span_name='some_function')
def some_function(a, b):
return do_stuff(a, b)zipkin_span.update_binary_annotations_for_root_span() can be used inside a zipkin trace
to add to the existing set of binary annotations for the root span.
def some_function(a, b):
with zipkin_span(
service_name='my_service',
span_name='some_function',
transport_handler=some_handler,
port=42,
sample_rate=0.05,
) as zipkin_context:
result = do_stuff(a, b)
zipkin_context.update_binary_annotations_for_root_span({'result': result})create_http_headers_for_new_span() creates a set of HTTP headers that can be forwarded
in a request to another service.
headers = {}
headers.update(create_http_headers_for_new_span())
http_client.get(
path='some_url',
headers=headers,
)py_zipkin (for the moment) thrift-encodes spans. The actual transport layer is
pluggable, though. The transport_handler is a function that takes a single
argument - the thrift-encoded bytes.
The simplest way to get spans to the collector is via HTTP POST. Here's an
example of a simple HTTP transport using the requests library. This assumes
your Zipkin collector is running at localhost:9411.
import requests
def http_transport(encoded_span):
# The collector expects a thrift-encoded list of spans. Instead of
# decoding and re-encoding the already thrift-encoded message, we can just
# add header bytes that specify that what follows is a list of length 1.
body = '\x0c\x00\x00\x00\x01' + encoded_span
requests.post(
'http://localhost:9411/api/v1/spans',
data=body,
headers={'Content-Type': 'application/x-thrift'},
)If you have the ability to send spans over Kafka (more like what you might do in production), you'd do something like the following, using the kafka-python package:
from kafka import SimpleProducer, KafkaClient
def transport_handler(message):
kafka_client = KafkaClient('{}:{}'.format('localhost', 9092))
producer = SimpleProducer(kafka_client)
producer.send_messages('kafka_topic_name', message)Copyright (c) 2016, Yelp, Inc. All Rights reserved. Apache v2