class zipkin_span(object):
"""Context manager/decorator for all of your zipkin tracing needs.
Usage #1: Start a trace with a given sampling rate
This begins the zipkin trace and also records the root span. The required
params are service_name, transport_handler, and sample_rate.
# Start a trace with do_stuff() as the root span
def some_batch_job(a, b):
with zipkin_span(
service_name='my_service',
span_name='my_span_name',
transport_handler=some_handler,
port=22,
sample_rate=0.05,
):
do_stuff()
Usage #2: Trace a service call.
The typical use case is instrumenting a framework like Pyramid or Django. Only
ss and sr times are recorded for the root span. Required params are
service_name, zipkin_attrs, transport_handler, and port.
# Used in 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 response
Usage #3: Log a span within the context of a zipkin trace
If you're already in a zipkin trace, you can use this to log a span inside. The
only required param is service_name. If you're not in a zipkin trace, this
won't do anything.
# As a decorator
@zipkin_span(service_name='my_service', span_name='my_function')
def my_function():
do_stuff()
# As a context manager
def my_function():
with zipkin_span(service_name='my_service', span_name='do_stuff'):
do_stuff()
"""
def __init__(
self,
service_name,
span_name='span',
zipkin_attrs=None,
transport_handler=None,
annotations=None,
binary_annotations=None,
port=0,
sample_rate=None,
include=('client', 'server'),
add_logging_annotation=False,
):
"""Logs a zipkin span. If this is the root span, then a zipkin
trace is started as well.
:param service_name: The name of the called service
:type service_name: string
:param span_name: Optional name of span, defaults to 'span'
:type span_name: string
:param zipkin_attrs: Optional set of zipkin attributes to be used
:type zipkin_attrs: ZipkinAttrs
:param transport_handler: Callback function that takes a message parameter
and handles logging it
:type transport_handler: function
:param annotations: Optional dict of str -> timestamp annotations
:type annotations: dict of str -> int
:param binary_annotations: Optional dict of str -> str span attrs
:type binary_annotations: dict of str -> str
:param port: The port number of the service. Defaults to 0.
:type port: int
:param sample_rate: Rate at which to sample; 0.0 - 100.0. If passed-in
zipkin_attrs have is_sampled=False and the sample_rate param is > 0,
a new span will be generated at this rate. This means that if you
propagate sampling decisions to downstream services, but still have
sample_rate > 0 in those services, the actual rate of generated
spans for those services will be > sampling_rate.
:type sample_rate: float
:param include: which annotations to include
can be one of {'client', 'server'}
corresponding to ('cs', 'cr') and ('ss', 'sr') respectively
:type include: iterable
:param add_logging_annotation: Whether to add a 'start_logging'
annotation when py_zipkin starts logging spans
:type add_logging_annotation: boolean
"""
self.service_name = service_name
self.span_name = span_name
self.zipkin_attrs = zipkin_attrs
self.transport_handler = transport_handler
self.annotations = annotations or {}
self.binary_annotations = binary_annotations or {}
self.port = port
self.logging_context = None
self.sample_rate = sample_rate
self.include = include
self.add_logging_annotation = add_logging_annotation
# Validation checks
if self.zipkin_attrs or self.sample_rate is not None:
if self.transport_handler is None:
raise ZipkinError(
'Root spans require a transport handler to be given')
if self.sample_rate is not None and not (0.0 <= self.sample_rate <=
100.0):
raise ZipkinError('Sample rate must be between 0.0 and 100.0')
if not set(include).issubset(STANDARD_ANNOTATIONS_KEYS):
raise ZipkinError('Only %s are supported as annotations' %
STANDARD_ANNOTATIONS_KEYS)
else:
# get a list of all of the mapped annotations
self.annotation_filter = set()
for include_name in include:
self.annotation_filter.update(
STANDARD_ANNOTATIONS[include_name])
def __call__(self, f):
@functools.wraps(f)
def decorated(*args, **kwargs):
with zipkin_span(
service_name=self.service_name,
span_name=self.span_name,
zipkin_attrs=self.zipkin_attrs,
transport_handler=self.transport_handler,
annotations=self.annotations,
binary_annotations=self.binary_annotations,
port=self.port,
sample_rate=self.sample_rate,
include=self.include,
):
return f(*args, **kwargs)
return decorated
def __enter__(self):
return self.start()
def start(self):
"""Enter the new span context. All annotations logged inside this
context will be attributed to this span. All new spans generated
inside this context will have this span as their parent.
In the unsampled case, this context still generates new span IDs and
pushes them onto the threadlocal stack, so downstream services calls
made will pass the correct headers. However, the logging handler is
never attached in the unsampled case, so the spans are never logged.
"""
self.do_pop_attrs = False
# If zipkin_attrs are passed in or this span is doing its own sampling,
# it will need to actually log spans at __exit__.
self.perform_logging = self.zipkin_attrs or self.sample_rate is not None
if self.sample_rate is not None:
# This clause allows for sampling this service independently
# of the passed-in zipkin_attrs.
if self.zipkin_attrs and not self.zipkin_attrs.is_sampled:
self.zipkin_attrs = create_attrs_for_span(
sample_rate=self.sample_rate,
trace_id=self.zipkin_attrs.trace_id,
)
elif not self.zipkin_attrs:
self.zipkin_attrs = create_attrs_for_span(
sample_rate=self.sample_rate, )
if not self.zipkin_attrs:
# This span is inside the context of an existing trace
existing_zipkin_attrs = get_zipkin_attrs()
if existing_zipkin_attrs:
self.zipkin_attrs = ZipkinAttrs(
trace_id=existing_zipkin_attrs.trace_id,
span_id=generate_random_64bit_string(),
parent_span_id=existing_zipkin_attrs.span_id,
flags=existing_zipkin_attrs.flags,
is_sampled=existing_zipkin_attrs.is_sampled,
)
# If zipkin_attrs are not set up by now, that means this span is not
# configured to perform logging itself, and it's not in an existing
# Zipkin trace. That means there's nothing else to do and it can exit
# early.
if not self.zipkin_attrs:
return self
push_zipkin_attrs(self.zipkin_attrs)
self.do_pop_attrs = True
self.start_timestamp = time.time()
# Set up logging if this is the root span
if self.perform_logging:
# Don't set up any logging if we're not sampling
if not self.zipkin_attrs.is_sampled:
return self
endpoint = create_endpoint(self.port, self.service_name)
self.log_handler = ZipkinLoggerHandler(self.zipkin_attrs)
self.logging_context = ZipkinLoggingContext(
self.zipkin_attrs,
endpoint,
self.log_handler,
self.span_name,
self.transport_handler,
self.binary_annotations,
add_logging_annotation=self.add_logging_annotation,
)
self.logging_context.start()
return self
else:
# In the sampled case, patch the ZipkinLoggerHandler.
if self.zipkin_attrs.is_sampled:
# Be defensive about logging setup. Since ZipkinAttrs are local to
# the thread, multithreaded frameworks can get in strange states.
# The logging is not going to be correct in these cases, so we set
# a flag that turns off logging on __exit__.
if len(zipkin_logger.handlers) > 0:
# Put span ID on logging handler. Assume there's only a single
# handler, since all logging should be set up in this package.
self.log_handler = zipkin_logger.handlers[0]
# Store the old parent_span_id, probably None, in case we have
# nested zipkin_spans
self.old_parent_span_id = self.log_handler.parent_span_id
self.log_handler.parent_span_id = self.zipkin_attrs.span_id
return self
def __exit__(self, _exc_type, _exc_value, _exc_traceback):
self.stop(_exc_type, _exc_value, _exc_traceback)
def stop(self, _exc_type=None, _exc_value=None, _exc_traceback=None):
"""Exit the span context. Zipkin attrs are pushed onto the
threadlocal stack regardless of sampling, so they always need to be
popped off. The actual logging of spans depends on sampling and that
the logging was correctly set up.
"""
# Always remove the stored zipkin_attrs
if self.do_pop_attrs:
pop_zipkin_attrs()
# Exit early if this request is not being sampled
if not self.zipkin_attrs or not self.zipkin_attrs.is_sampled:
return
# If this is the root span, exit the context (which will handle logging)
if self.logging_context:
self.logging_context.stop()
self.logging_context = None
return
end_timestamp = time.time()
# Put the old parent_span_id back on the handler
self.log_handler.parent_span_id = self.old_parent_span_id
# To get a full span we just set cs=sr and ss=cr.
full_annotations = {
'cs': self.start_timestamp,
'sr': self.start_timestamp,
'ss': end_timestamp,
'cr': end_timestamp,
}
# But we filter down if we only want to emit some of the annotations
filtered_annotations = {
k: v
for k, v in full_annotations.items() if k in self.annotation_filter
}
self.annotations.update(filtered_annotations)
# Store this span on the logging handler object.
self.log_handler.store_client_span(
span_name=self.span_name,
service_name=self.service_name,
annotations=self.annotations,
binary_annotations=self.binary_annotations,
span_id=self.zipkin_attrs.span_id,
)
def update_binary_annotations_for_root_span(self, extra_annotations):
"""Updates the binary annotations for the root span of the trace.
If this trace is not being sampled then this is a no-op.
"""
if not self.zipkin_attrs.is_sampled:
return
if not self.logging_context:
raise ZipkinError('No logging context available')
self.logging_context.binary_annotations_dict.update(extra_annotations)
class zipkin_span(object):
"""Context manager/decorator for all of your zipkin tracing needs.
Usage #1: Start a trace with a given sampling rate
This begins the zipkin trace and also records the root span. The required
params are service_name, transport_handler, and sample_rate.
# Start a trace with do_stuff() as the root span
def some_batch_job(a, b):
with zipkin_span(
service_name='my_service',
span_name='my_span_name',
transport_handler=some_handler,
port=22,
sample_rate=0.05,
):
do_stuff()
Usage #2: Trace a service call.
The typical use case is instrumenting a framework like Pyramid or Django. Only
ss and sr times are recorded for the root span. Required params are
service_name, zipkin_attrs, transport_handler, and port.
# Used in 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(
some_binary_annotations)
return response
Usage #3: Log a span within the context of a zipkin trace
If you're already in a zipkin trace, you can use this to log a span inside. The
only required param is service_name. If you're not in a zipkin trace, this
won't do anything.
# As a decorator
@zipkin_span(service_name='my_service', span_name='my_function')
def my_function():
do_stuff()
# As a context manager
def my_function():
with zipkin_span(service_name='my_service', span_name='do_stuff'):
do_stuff()
"""
def __init__(
self,
service_name,
span_name="span",
zipkin_attrs=None,
transport_handler=None,
max_span_batch_size=None,
annotations=None,
binary_annotations=None,
port=0,
sample_rate=None,
include=None,
add_logging_annotation=False,
report_root_timestamp=False,
use_128bit_trace_id=False,
host=None,
context_stack=None,
span_storage=None,
firehose_handler=None,
kind=None,
timestamp=None,
duration=None,
encoding=Encoding.V2_JSON,
_tracer=None,
):
"""Logs a zipkin span. If this is the root span, then a zipkin
trace is started as well.
:param service_name: The name of the called service
:type service_name: string
:param span_name: Optional name of span, defaults to 'span'
:type span_name: string
:param zipkin_attrs: Optional set of zipkin attributes to be used
:type zipkin_attrs: ZipkinAttrs
:param transport_handler: Callback function that takes a message parameter
and handles logging it
:type transport_handler: BaseTransportHandler
:param max_span_batch_size: Spans in a trace are sent in batches,
max_span_batch_size defines max size of one batch
:type max_span_batch_size: int
:param annotations: Optional dict of str -> timestamp annotations
:type annotations: dict of str -> int
:param binary_annotations: Optional dict of str -> str span attrs
:type binary_annotations: dict of str -> str
:param port: The port number of the service. Defaults to 0.
:type port: int
:param sample_rate: Rate at which to sample; 0.0 - 100.0. If passed-in
zipkin_attrs have is_sampled=False and the sample_rate param is > 0,
a new span will be generated at this rate. This means that if you
propagate sampling decisions to downstream services, but still have
sample_rate > 0 in those services, the actual rate of generated
spans for those services will be > sampling_rate.
:type sample_rate: float
:param include: which annotations to include
can be one of {'client', 'server'}
corresponding to ('cs', 'cr') and ('ss', 'sr') respectively.
DEPRECATED: use kind instead. `include` will be removed in 1.0.
:type include: iterable
:param add_logging_annotation: Whether to add a 'logging_end'
annotation when py_zipkin finishes logging spans
:type add_logging_annotation: boolean
:param report_root_timestamp: Whether the span should report timestamp
and duration. Only applies to "root" spans in this local context,
so spans created inside other span contexts will always log
timestamp/duration. Note that this is only an override for spans
that have zipkin_attrs passed in. Spans that make their own
sampling decisions (i.e. are the root spans of entire traces) will
always report timestamp/duration.
:type report_root_timestamp: boolean
:param use_128bit_trace_id: If true, generate 128-bit trace_ids.
:type use_128bit_trace_id: boolean
:param host: Contains the ipv4 or ipv6 value of the host. The ip value
isn't automatically determined in a docker environment.
:type host: string
:param context_stack: explicit context stack for storing
zipkin attributes
:type context_stack: object
:param span_storage: explicit Span storage for storing zipkin spans
before they're emitted.
:type span_storage: py_zipkin.storage.SpanStorage
:param firehose_handler: [EXPERIMENTAL] Similar to transport_handler,
except that it will receive 100% of the spans regardless of trace
sampling rate.
:type firehose_handler: BaseTransportHandler
:param kind: Span type (client, server, local, etc...).
:type kind: Kind
:param timestamp: Timestamp in seconds, defaults to `time.time()`.
Set this if you want to use a custom timestamp.
:type timestamp: float
:param duration: Duration in seconds, defaults to the time spent in the
context. Set this if you want to use a custom duration.
:type duration: float
:param encoding: Output encoding format, defaults to V1 thrift spans.
:type encoding: Encoding
:param _tracer: Current tracer object. This argument is passed in
automatically when you create a zipkin_span from a Tracer.
:type _tracer: Tracer
"""
self.service_name = service_name
self.span_name = span_name
self.zipkin_attrs_override = zipkin_attrs
self.transport_handler = transport_handler
self.max_span_batch_size = max_span_batch_size
self.annotations = annotations or {}
self.binary_annotations = binary_annotations or {}
self.port = port
self.sample_rate = sample_rate
self.add_logging_annotation = add_logging_annotation
self.report_root_timestamp_override = report_root_timestamp
self.use_128bit_trace_id = use_128bit_trace_id
self.host = host
self._context_stack = context_stack
self._span_storage = span_storage
self.firehose_handler = firehose_handler
self.kind = self._generate_kind(kind, include)
self.timestamp = timestamp
self.duration = duration
self.encoding = encoding
self._tracer = _tracer
self._is_local_root_span = False
self.logging_context = None
self.do_pop_attrs = False
# Spans that log a 'cs' timestamp can additionally record a
# 'sa' binary annotation that shows where the request is going.
self.remote_endpoint = None
self.zipkin_attrs = None
# It used to be possible to override timestamp and duration by passing
# in the cs/cr or sr/ss annotations. We want to keep backward compatibility
# for now, so this logic overrides self.timestamp and self.duration in the
# same way.
# This doesn't fit well with v2 spans since those annotations are gone, so
# we also log a deprecation warning.
if "sr" in self.annotations and "ss" in self.annotations:
self.duration = self.annotations["ss"] - self.annotations["sr"]
self.timestamp = self.annotations["sr"]
log.warning(
"Manually setting 'sr'/'ss' annotations is deprecated. Please "
"use the timestamp and duration parameters.")
if "cr" in self.annotations and "cs" in self.annotations:
self.duration = self.annotations["cr"] - self.annotations["cs"]
self.timestamp = self.annotations["cs"]
log.warning(
"Manually setting 'cr'/'cs' annotations is deprecated. Please "
"use the timestamp and duration parameters.")
# Root spans have transport_handler and at least one of
# zipkin_attrs_override or sample_rate.
if self.zipkin_attrs_override or self.sample_rate is not None:
# transport_handler is mandatory for root spans
if self.transport_handler is None:
raise ZipkinError(
"Root spans require a transport handler to be given")
self._is_local_root_span = True
# If firehose_handler than this is a local root span.
if self.firehose_handler:
self._is_local_root_span = True
if self.sample_rate is not None and not (0.0 <= self.sample_rate <=
100.0):
raise ZipkinError("Sample rate must be between 0.0 and 100.0")
if self._span_storage is not None and not isinstance(
self._span_storage, storage.SpanStorage):
raise ZipkinError(
"span_storage should be an instance of py_zipkin.storage.SpanStorage"
)
if self._span_storage is not None:
log.warning(
"span_storage is deprecated. Set local_storage instead.")
self.get_tracer()._span_storage = self._span_storage
if self._context_stack is not None:
log.warning(
"context_stack is deprecated. Set local_storage instead.")
self.get_tracer()._context_stack = self._context_stack
def __call__(self, f):
@functools.wraps(f)
def decorated(*args, **kwargs):
with zipkin_span(
service_name=self.service_name,
span_name=self.span_name,
zipkin_attrs=self.zipkin_attrs,
transport_handler=self.transport_handler,
max_span_batch_size=self.max_span_batch_size,
annotations=self.annotations,
binary_annotations=self.binary_annotations,
port=self.port,
sample_rate=self.sample_rate,
include=None,
add_logging_annotation=self.add_logging_annotation,
report_root_timestamp=self.report_root_timestamp_override,
use_128bit_trace_id=self.use_128bit_trace_id,
host=self.host,
context_stack=self._context_stack,
span_storage=self._span_storage,
firehose_handler=self.firehose_handler,
kind=self.kind,
timestamp=self.timestamp,
duration=self.duration,
encoding=self.encoding,
_tracer=self._tracer,
):
return f(*args, **kwargs)
return decorated
def get_tracer(self):
if self._tracer is not None:
return self._tracer
else:
return get_default_tracer()
def __enter__(self):
return self.start()
def _generate_kind(self, kind, include):
# If `kind` is not set, then we generate it from `include`.
# This code maintains backward compatibility with old versions of py_zipkin
# which used include rather than kind to identify client / server spans.
if kind:
return kind
else:
if include:
# If `include` contains only one of `client` or `server`
# than it's a client or server span respectively.
# If neither or both are present, then it's a local span
# which is represented by kind = None.
log.warning(
"The include argument is deprecated. Please use kind.")
if "client" in include and "server" not in include:
return Kind.CLIENT
elif "client" not in include and "server" in include:
return Kind.SERVER
else:
return Kind.LOCAL
# If both kind and include are unset, then it's a local span.
return Kind.LOCAL
def _get_current_context(self):
"""Returns the current ZipkinAttrs and generates new ones if needed.
:returns: (report_root_timestamp, zipkin_attrs)
:rtype: (bool, ZipkinAttrs)
"""
# This check is technically not necessary since only root spans will have
# sample_rate, zipkin_attrs or a transport set. But it helps making the
# code clearer by separating the logic for a root span from the one for a
# child span.
if self._is_local_root_span:
# If sample_rate is set, we need to (re)generate a trace context.
# If zipkin_attrs (trace context) were passed in as argument there are
# 2 possibilities:
# is_sampled = False --> we keep the same trace_id but re-roll the dice
# for is_sampled.
# is_sampled = True --> we don't want to stop sampling halfway through
# a sampled trace, so we do nothing.
# If no zipkin_attrs were passed in, we generate new ones and start a
# new trace.
if self.sample_rate is not None:
# If this trace is not sampled, we re-roll the dice.
if (self.zipkin_attrs_override
and not self.zipkin_attrs_override.is_sampled):
# This will be the root span of the trace, so we should
# set timestamp and duration.
return (
True,
create_attrs_for_span(
sample_rate=self.sample_rate,
trace_id=self.zipkin_attrs_override.trace_id,
),
)
# If zipkin_attrs_override was not passed in, we simply generate
# new zipkin_attrs to start a new trace.
elif not self.zipkin_attrs_override:
return (
True,
create_attrs_for_span(
sample_rate=self.sample_rate,
use_128bit_trace_id=self.use_128bit_trace_id,
),
)
if self.firehose_handler and not self.zipkin_attrs_override:
# If it has gotten here, the only thing that is
# causing a trace is the firehose. So we force a trace
# with sample rate of 0
return (
True,
create_attrs_for_span(
sample_rate=0.0,
use_128bit_trace_id=self.use_128bit_trace_id,
),
)
# If we arrive here it means the sample_rate was not set while
# zipkin_attrs_override was, so let's simply return that.
return False, self.zipkin_attrs_override
else:
# Check if there's already a trace context in _context_stack.
existing_zipkin_attrs = self.get_tracer().get_zipkin_attrs()
# If there's an existing context, let's create new zipkin_attrs
# with that context as parent.
if existing_zipkin_attrs:
return (
False,
ZipkinAttrs(
trace_id=existing_zipkin_attrs.trace_id,
span_id=generate_random_64bit_string(),
parent_span_id=existing_zipkin_attrs.span_id,
flags=existing_zipkin_attrs.flags,
is_sampled=existing_zipkin_attrs.is_sampled,
),
)
return False, None
def start(self):
"""Enter the new span context. All annotations logged inside this
context will be attributed to this span. All new spans generated
inside this context will have this span as their parent.
In the unsampled case, this context still generates new span IDs and
pushes them onto the threadlocal stack, so downstream services calls
made will pass the correct headers. However, the logging handler is
never attached in the unsampled case, so the spans are never logged.
"""
self.do_pop_attrs = False
report_root_timestamp, self.zipkin_attrs = self._get_current_context()
# If zipkin_attrs are not set up by now, that means this span is not
# configured to perform logging itself, and it's not in an existing
# Zipkin trace. That means there's nothing else to do and it can exit
# early.
if not self.zipkin_attrs:
return self
self.get_tracer().push_zipkin_attrs(self.zipkin_attrs)
self.do_pop_attrs = True
self.start_timestamp = time.time()
if self._is_local_root_span:
# Don't set up any logging if we're not sampling
if not self.zipkin_attrs.is_sampled and not self.firehose_handler:
return self
# If transport is already configured don't override it. Doing so would
# cause all previously recorded spans to never be emitted as exiting
# the inner logging context will reset transport_configured to False.
if self.get_tracer().is_transport_configured():
log.info("Transport was already configured, ignoring override "
"from span {}".format(self.span_name))
return self
endpoint = create_endpoint(self.port, self.service_name, self.host)
self.logging_context = ZipkinLoggingContext(
self.zipkin_attrs,
endpoint,
self.span_name,
self.transport_handler,
report_root_timestamp or self.report_root_timestamp_override,
self.get_tracer,
self.service_name,
binary_annotations=self.binary_annotations,
add_logging_annotation=self.add_logging_annotation,
client_context=self.kind == Kind.CLIENT,
max_span_batch_size=self.max_span_batch_size,
firehose_handler=self.firehose_handler,
encoding=self.encoding,
annotations=self.annotations,
)
self.logging_context.start()
self.get_tracer().set_transport_configured(configured=True)
return self
def __exit__(self, _exc_type, _exc_value, _exc_traceback):
self.stop(_exc_type, _exc_value, _exc_traceback)
def stop(self, _exc_type=None, _exc_value=None, _exc_traceback=None):
"""Exit the span context. Zipkin attrs are pushed onto the
threadlocal stack regardless of sampling, so they always need to be
popped off. The actual logging of spans depends on sampling and that
the logging was correctly set up.
"""
if self.do_pop_attrs:
self.get_tracer().pop_zipkin_attrs()
# If no transport is configured, there's no reason to create a new Span.
# This also helps avoiding memory leaks since without a transport nothing
# would pull spans out of get_tracer().
if not self.get_tracer().is_transport_configured():
return
# Add the error annotation if an exception occurred
if any((_exc_type, _exc_value, _exc_traceback)):
try:
error_msg = u"{0}: {1}".format(_exc_type.__name__, _exc_value)
except TypeError:
# This sometimes happens when an exception raises when calling
# __str__ on it.
error_msg = u"{0}: {1!r}".format(_exc_type.__name__,
_exc_value)
self.update_binary_annotations({ERROR_KEY: error_msg})
# Logging context is only initialized for "root" spans of the local
# process (i.e. this zipkin_span not inside of any other local
# zipkin_spans)
if self.logging_context:
try:
self.logging_context.stop()
except Exception as ex:
err_msg = "Error emitting zipkin trace. {}".format(repr(ex))
log.error(err_msg)
finally:
self.logging_context = None
self.get_tracer().clear()
self.get_tracer().set_transport_configured(configured=False)
return
# If we've gotten here, that means that this span is a child span of
# this context's root span (i.e. it's a zipkin_span inside another
# zipkin_span).
end_timestamp = time.time()
# If self.duration is set, it means the user wants to override it
if self.duration:
duration = self.duration
else:
duration = end_timestamp - self.start_timestamp
endpoint = create_endpoint(self.port, self.service_name, self.host)
self.get_tracer().add_span(
Span(
trace_id=self.zipkin_attrs.trace_id,
name=self.span_name,
parent_id=self.zipkin_attrs.parent_span_id,
span_id=self.zipkin_attrs.span_id,
kind=self.kind,
timestamp=self.timestamp
if self.timestamp else self.start_timestamp,
duration=duration,
annotations=self.annotations,
local_endpoint=endpoint,
remote_endpoint=self.remote_endpoint,
tags=self.binary_annotations,
))
def update_binary_annotations(self, extra_annotations):
"""Updates the binary annotations for the current span."""
if not self.logging_context:
# This is not the root span, so binary annotations will be added
# to the log handler when this span context exits.
self.binary_annotations.update(extra_annotations)
else:
# Otherwise, we're in the context of the root span, so just update
# the binary annotations for the logging context directly.
self.logging_context.tags.update(extra_annotations)
def add_annotation(self, value, timestamp=None):
"""Add an annotation for the current span
The timestamp defaults to "now", but may be specified.
:param value: The annotation string
:type value: str
:param timestamp: Timestamp for the annotation
:type timestamp: float
"""
timestamp = timestamp or time.time()
if not self.logging_context:
# This is not the root span, so annotations will be added
# to the log handler when this span context exits.
self.annotations[value] = timestamp
else:
# Otherwise, we're in the context of the root span, so just update
# the annotations for the logging context directly.
self.logging_context.annotations[value] = timestamp
def add_sa_binary_annotation(
self,
port=0,
service_name="unknown",
host="127.0.0.1",
):
"""Adds a 'sa' binary annotation to the current span.
'sa' binary annotations are useful for situations where you need to log
where a request is going but the destination doesn't support zipkin.
Note that the span must have 'cs'/'cr' annotations.
:param port: The port number of the destination
:type port: int
:param service_name: The name of the destination service
:type service_name: str
:param host: Host address of the destination
:type host: str
"""
if self.kind != Kind.CLIENT:
# TODO: trying to set a sa binary annotation for a non-client span
# should result in a logged error
return
remote_endpoint = create_endpoint(
port=port,
service_name=service_name,
host=host,
)
if not self.logging_context:
if self.remote_endpoint is not None:
raise ValueError("SA annotation already set.")
self.remote_endpoint = remote_endpoint
else:
if self.logging_context.remote_endpoint is not None:
raise ValueError("SA annotation already set.")
self.logging_context.remote_endpoint = remote_endpoint
def override_span_name(self, name):
"""Overrides the current span name.
This is useful if you don't know the span name yet when you create the
zipkin_span object. i.e. pyramid_zipkin doesn't know which route the
request matched until the function wrapped by the context manager
completes.
:param name: New span name
:type name: str
"""
self.span_name = name
if self.logging_context:
self.logging_context.span_name = name
class zipkin_span(object):
"""Context manager/decorator for all of your zipkin tracing needs.
Usage #1: Start a trace with a given sampling rate
This begins the zipkin trace and also records the root span. The required
params are service_name, transport_handler, and sample_rate.
# Start a trace with do_stuff() as the root span
def some_batch_job(a, b):
with zipkin_span(
service_name='my_service',
span_name='my_span_name',
transport_handler=some_handler,
port=22,
sample_rate=0.05,
):
do_stuff()
Usage #2: Trace a service call.
The typical use case is instrumenting a framework like Pyramid or Django. Only
ss and sr times are recorded for the root span. Required params are
service_name, zipkin_attrs, transport_handler, and port.
# Used in 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(
some_binary_annotations)
return response
Usage #3: Log a span within the context of a zipkin trace
If you're already in a zipkin trace, you can use this to log a span inside. The
only required param is service_name. If you're not in a zipkin trace, this
won't do anything.
# As a decorator
@zipkin_span(service_name='my_service', span_name='my_function')
def my_function():
do_stuff()
# As a context manager
def my_function():
with zipkin_span(service_name='my_service', span_name='do_stuff'):
do_stuff()
"""
def __init__(self,
service_name,
span_name='span',
zipkin_attrs=None,
transport_handler=None,
annotations=None,
binary_annotations=None,
port=0,
sample_rate=None,
include=('client', 'server'),
add_logging_annotation=False,
report_root_timestamp=False,
use_128bit_trace_id=False,
host=None):
"""Logs a zipkin span. If this is the root span, then a zipkin
trace is started as well.
:param service_name: The name of the called service
:type service_name: string
:param span_name: Optional name of span, defaults to 'span'
:type span_name: string
:param zipkin_attrs: Optional set of zipkin attributes to be used
:type zipkin_attrs: ZipkinAttrs
:param transport_handler: Callback function that takes a message parameter
and handles logging it
:type transport_handler: function
:param annotations: Optional dict of str -> timestamp annotations
:type annotations: dict of str -> int
:param binary_annotations: Optional dict of str -> str span attrs
:type binary_annotations: dict of str -> str
:param port: The port number of the service. Defaults to 0.
:type port: int
:param sample_rate: Rate at which to sample; 0.0 - 100.0. If passed-in
zipkin_attrs have is_sampled=False and the sample_rate param is > 0,
a new span will be generated at this rate. This means that if you
propagate sampling decisions to downstream services, but still have
sample_rate > 0 in those services, the actual rate of generated
spans for those services will be > sampling_rate.
:type sample_rate: float
:param include: which annotations to include
can be one of {'client', 'server'}
corresponding to ('cs', 'cr') and ('ss', 'sr') respectively
:type include: iterable
:param add_logging_annotation: Whether to add a 'logging_end'
annotation when py_zipkin finishes logging spans
:type add_logging_annotation: boolean
:param report_root_timestamp: Whether the span should report timestamp
and duration. Only applies to "root" spans in this local context,
so spans created inside other span contexts will always log
timestamp/duration. Note that this is only an override for spans
that have zipkin_attrs passed in. Spans that make their own
sampling decisions (i.e. are the root spans of entire traces) will
always report timestamp/duration.
:type report_root_timestamp: boolean
:param use_128bit_trace_id: If true, generate 128-bit trace_ids
:type use_128bit_trace_id: boolean
:param host: Contains the ipv4 value of the host. The ipv4 value isn't
automatically determined in a docker environment
:type host: string
"""
self.service_name = service_name
self.span_name = span_name
self.zipkin_attrs = zipkin_attrs
self.transport_handler = transport_handler
self.annotations = annotations or {}
self.binary_annotations = binary_annotations or {}
self.port = port
self.logging_context = None
self.sample_rate = sample_rate
self.include = include
self.add_logging_annotation = add_logging_annotation
self.report_root_timestamp_override = report_root_timestamp
self.use_128bit_trace_id = use_128bit_trace_id
self.host = host
# Spans that log a 'cs' timestamp can additionally record
# 'sa' binary annotations that show where the request is going.
# This holds a list of 'sa' binary annotations.
self.sa_binary_annotations = []
# Validation checks
if self.zipkin_attrs or self.sample_rate is not None:
if self.transport_handler is None:
raise ZipkinError(
'Root spans require a transport handler to be given')
if self.sample_rate is not None and not (0.0 <= self.sample_rate <=
100.0):
raise ZipkinError('Sample rate must be between 0.0 and 100.0')
if not set(include).issubset(STANDARD_ANNOTATIONS_KEYS):
raise ZipkinError('Only %s are supported as annotations' %
STANDARD_ANNOTATIONS_KEYS)
else:
# get a list of all of the mapped annotations
self.annotation_filter = set()
for include_name in include:
self.annotation_filter.update(
STANDARD_ANNOTATIONS[include_name])
def __call__(self, f):
@functools.wraps(f)
def decorated(*args, **kwargs):
with zipkin_span(service_name=self.service_name,
span_name=self.span_name,
zipkin_attrs=self.zipkin_attrs,
transport_handler=self.transport_handler,
annotations=self.annotations,
binary_annotations=self.binary_annotations,
port=self.port,
sample_rate=self.sample_rate,
include=self.include,
host=self.host):
return f(*args, **kwargs)
return decorated
def __enter__(self):
return self.start()
def start(self):
"""Enter the new span context. All annotations logged inside this
context will be attributed to this span. All new spans generated
inside this context will have this span as their parent.
In the unsampled case, this context still generates new span IDs and
pushes them onto the threadlocal stack, so downstream services calls
made will pass the correct headers. However, the logging handler is
never attached in the unsampled case, so the spans are never logged.
"""
self.do_pop_attrs = False
# If zipkin_attrs are passed in or this span is doing its own sampling,
# it will need to actually log spans at __exit__.
self.perform_logging = self.zipkin_attrs or self.sample_rate is not None
report_root_timestamp = False
if self.sample_rate is not None:
if self.zipkin_attrs and not self.zipkin_attrs.is_sampled:
report_root_timestamp = True
self.zipkin_attrs = create_attrs_for_span(
sample_rate=self.sample_rate,
trace_id=self.zipkin_attrs.trace_id,
use_128bit_trace_id=self.use_128bit_trace_id,
)
elif not self.zipkin_attrs:
report_root_timestamp = True
self.zipkin_attrs = create_attrs_for_span(
sample_rate=self.sample_rate,
use_128bit_trace_id=self.use_128bit_trace_id,
)
if not self.zipkin_attrs:
# This span is inside the context of an existing trace
existing_zipkin_attrs = get_zipkin_attrs()
if existing_zipkin_attrs:
self.zipkin_attrs = ZipkinAttrs(
trace_id=existing_zipkin_attrs.trace_id,
span_id=generate_random_64bit_string(),
parent_span_id=existing_zipkin_attrs.span_id,
flags=existing_zipkin_attrs.flags,
is_sampled=existing_zipkin_attrs.is_sampled,
)
# If zipkin_attrs are not set up by now, that means this span is not
# configured to perform logging itself, and it's not in an existing
# Zipkin trace. That means there's nothing else to do and it can exit
# early.
if not self.zipkin_attrs:
return self
push_zipkin_attrs(self.zipkin_attrs)
self.do_pop_attrs = True
self.start_timestamp = time.time()
if self.perform_logging:
# Don't set up any logging if we're not sampling
if not self.zipkin_attrs.is_sampled:
return self
endpoint = create_endpoint(self.port, self.service_name, self.host)
client_context = set(self.include) == {'client'}
self.log_handler = ZipkinLoggerHandler(self.zipkin_attrs)
self.logging_context = ZipkinLoggingContext(
self.zipkin_attrs,
endpoint,
self.log_handler,
self.span_name,
self.transport_handler,
report_root_timestamp or self.report_root_timestamp_override,
binary_annotations=self.binary_annotations,
add_logging_annotation=self.add_logging_annotation,
client_context=client_context)
self.logging_context.start()
return self
else:
# In the sampled case, patch the ZipkinLoggerHandler.
if self.zipkin_attrs.is_sampled:
# Be defensive about logging setup. Since ZipkinAttrs are local to
# the thread, multithreaded frameworks can get in strange states.
# The logging is not going to be correct in these cases, so we set
# a flag that turns off logging on __exit__.
if len(zipkin_logger.handlers) > 0:
# Put span ID on logging handler. Assume there's only a single
# handler, since all logging should be set up in this package.
self.log_handler = zipkin_logger.handlers[0]
# Store the old parent_span_id, probably None, in case we have
# nested zipkin_spans
self.old_parent_span_id = self.log_handler.parent_span_id
self.log_handler.parent_span_id = self.zipkin_attrs.span_id
return self
def __exit__(self, _exc_type, _exc_value, _exc_traceback):
self.stop(_exc_type, _exc_value, _exc_traceback)
def stop(self, _exc_type=None, _exc_value=None, _exc_traceback=None):
"""Exit the span context. Zipkin attrs are pushed onto the
threadlocal stack regardless of sampling, so they always need to be
popped off. The actual logging of spans depends on sampling and that
the logging was correctly set up.
"""
if self.do_pop_attrs:
pop_zipkin_attrs()
if not self.zipkin_attrs or not self.zipkin_attrs.is_sampled:
return
# Logging context is only initialized for "root" spans of the local
# process (i.e. this zipkin_span not inside of any other local
# zipkin_spans)
if self.logging_context:
self.logging_context.stop()
self.logging_context = None
return
# If we've gotten here, that means that this span is a child span of
# this context's root span (i.e. it's a zipkin_span inside another
# zipkin_span).
end_timestamp = time.time()
self.log_handler.parent_span_id = self.old_parent_span_id
# We are simulating a full two-part span locally, so set cs=sr and ss=cr
full_annotations = {
'cs': self.start_timestamp,
'sr': self.start_timestamp,
'ss': end_timestamp,
'cr': end_timestamp,
}
# But we filter down if we only want to emit some of the annotations
filtered_annotations = {
k: v
for k, v in full_annotations.items() if k in self.annotation_filter
}
self.annotations.update(filtered_annotations)
self.log_handler.store_local_span(
span_name=self.span_name,
service_name=self.service_name,
annotations=self.annotations,
binary_annotations=self.binary_annotations,
sa_binary_annotations=self.sa_binary_annotations,
span_id=self.zipkin_attrs.span_id,
)
def update_binary_annotations(self, extra_annotations):
"""Updates the binary annotations for the current span.
If this trace is not being sampled then this is a no-op.
"""
if not self.zipkin_attrs:
return
if not self.zipkin_attrs.is_sampled:
return
if not self.logging_context:
# This is not the root span, so binary annotations will be added
# to the log handler when this span context exits.
self.binary_annotations.update(extra_annotations)
else:
# Otherwise, we're in the context of the root span, so just update
# the binary annotations for the logging context directly.
self.logging_context.binary_annotations_dict.update(
extra_annotations)
def add_sa_binary_annotation(
self,
port=0,
service_name='unknown',
host='127.0.0.1',
):
"""Adds a 'sa' binary annotation to the current span.
'sa' binary annotations are useful for situations where you need to log
where a request is going but the destination doesn't support zipkin.
Note that the span must have 'cs'/'cr' annotations.
:param port: The port number of the destination
:type port: int
:param service_name: The name of the destination service
:type service_name: str
:param host: Host address of the destination
:type host: str
"""
if not self.zipkin_attrs or not self.zipkin_attrs.is_sampled:
return
if 'client' not in self.include:
# TODO: trying to set a sa binary annotation for a non-client span
# should result in a logged error
return
sa_endpoint = create_endpoint(
port=port,
service_name=service_name,
host=host,
)
sa_binary_annotation = create_binary_annotation(
key=zipkin_core.SERVER_ADDR,
value=SERVER_ADDR_VAL,
annotation_type=zipkin_core.AnnotationType.BOOL,
host=sa_endpoint,
)
if not self.logging_context:
self.sa_binary_annotations.append(sa_binary_annotation)
else:
self.logging_context.sa_binary_annotations.append(
sa_binary_annotation)