# 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.
import abc
from typing import Dict, Mapping, Optional, Sequence
# pylint: disable=unused-import
from opentelemetry.trace import Link, SpanContext
from opentelemetry.util.types import Attributes, AttributeValue
[docs]class Decision:
"""A sampling decision as applied to a newly-created Span.
Args:
sampled: Whether the `opentelemetry.trace.Span` should be sampled.
attributes: Attributes to add to the `opentelemetry.trace.Span`.
"""
def __repr__(self) -> str:
return "{}({}, attributes={})".format(
type(self).__name__, str(self.sampled), str(self.attributes)
)
def __init__(
self,
sampled: bool = False,
attributes: Optional[Mapping[str, "AttributeValue"]] = None,
) -> None:
self.sampled = sampled # type: bool
if attributes is None:
self.attributes = {} # type: Dict[str, "AttributeValue"]
else:
self.attributes = dict(attributes)
[docs]class Sampler(abc.ABC):
[docs] @abc.abstractmethod
def should_sample(
self,
parent_context: Optional["SpanContext"],
trace_id: int,
span_id: int,
name: str,
attributes: Optional[Attributes] = None,
links: Sequence["Link"] = (),
) -> "Decision":
pass
[docs]class StaticSampler(Sampler):
"""Sampler that always returns the same decision."""
def __init__(self, decision: "Decision"):
self._decision = decision
[docs] def should_sample(
self,
parent_context: Optional["SpanContext"],
trace_id: int,
span_id: int,
name: str,
attributes: Optional[Attributes] = None,
links: Sequence["Link"] = (),
) -> "Decision":
return self._decision
[docs]class ProbabilitySampler(Sampler):
def __init__(self, rate: float):
self._rate = rate
self._bound = self.get_bound_for_rate(self._rate)
# For compatibility with 64 bit trace IDs, the sampler checks the 64
# low-order bits of the trace ID to decide whether to sample a given trace.
TRACE_ID_LIMIT = (1 << 64) - 1
[docs] @classmethod
def get_bound_for_rate(cls, rate: float) -> int:
return round(rate * (cls.TRACE_ID_LIMIT + 1))
@property
def rate(self) -> float:
return self._rate
@rate.setter
def rate(self, new_rate: float) -> None:
self._rate = new_rate
self._bound = self.get_bound_for_rate(self._rate)
@property
def bound(self) -> int:
return self._bound
[docs] def should_sample(
self,
parent_context: Optional["SpanContext"],
trace_id: int,
span_id: int,
name: str,
attributes: Optional[Attributes] = None, # TODO
links: Sequence["Link"] = (),
) -> "Decision":
if parent_context is not None:
return Decision(parent_context.trace_flags.sampled)
return Decision(trace_id & self.TRACE_ID_LIMIT < self.bound)
# Samplers that ignore the parent sampling decision and never/always sample.
ALWAYS_OFF = StaticSampler(Decision(False))
ALWAYS_ON = StaticSampler(Decision(True))
# Samplers that respect the parent sampling decision, but otherwise
# never/always sample.
DEFAULT_OFF = ProbabilitySampler(0.0)
DEFAULT_ON = ProbabilitySampler(1.0)