AlkantarClanX12
Your IP : 3.144.123.24
import io
import os
import random
import re
import sys
import threading
import time
import warnings
import zlib
from abc import ABC, abstractmethod
from contextlib import contextmanager
from datetime import datetime, timezone
from functools import wraps, partial
import sentry_sdk
from sentry_sdk.utils import (
ContextVar,
now,
nanosecond_time,
to_timestamp,
serialize_frame,
json_dumps,
)
from sentry_sdk.envelope import Envelope, Item
from sentry_sdk.tracing import (
TRANSACTION_SOURCE_ROUTE,
TRANSACTION_SOURCE_VIEW,
TRANSACTION_SOURCE_COMPONENT,
TRANSACTION_SOURCE_TASK,
)
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from typing import Any
from typing import Callable
from typing import Dict
from typing import Generator
from typing import Iterable
from typing import List
from typing import Optional
from typing import Set
from typing import Tuple
from typing import Union
from sentry_sdk._types import BucketKey
from sentry_sdk._types import DurationUnit
from sentry_sdk._types import FlushedMetricValue
from sentry_sdk._types import MeasurementUnit
from sentry_sdk._types import MetricMetaKey
from sentry_sdk._types import MetricTagValue
from sentry_sdk._types import MetricTags
from sentry_sdk._types import MetricTagsInternal
from sentry_sdk._types import MetricType
from sentry_sdk._types import MetricValue
warnings.warn(
"The sentry_sdk.metrics module is deprecated and will be removed in the next major release. "
"Sentry will reject all metrics sent after October 7, 2024. "
"Learn more: https://sentry.zendesk.com/hc/en-us/articles/26369339769883-Upcoming-API-Changes-to-Metrics",
DeprecationWarning,
stacklevel=2,
)
_in_metrics = ContextVar("in_metrics", default=False)
_set = set # set is shadowed below
GOOD_TRANSACTION_SOURCES = frozenset(
[
TRANSACTION_SOURCE_ROUTE,
TRANSACTION_SOURCE_VIEW,
TRANSACTION_SOURCE_COMPONENT,
TRANSACTION_SOURCE_TASK,
]
)
_sanitize_unit = partial(re.compile(r"[^a-zA-Z0-9_]+").sub, "")
_sanitize_metric_key = partial(re.compile(r"[^a-zA-Z0-9_\-.]+").sub, "_")
_sanitize_tag_key = partial(re.compile(r"[^a-zA-Z0-9_\-.\/]+").sub, "")
def _sanitize_tag_value(value):
# type: (str) -> str
table = str.maketrans(
{
"\n": "\\n",
"\r": "\\r",
"\t": "\\t",
"\\": "\\\\",
"|": "\\u{7c}",
",": "\\u{2c}",
}
)
return value.translate(table)
def get_code_location(stacklevel):
# type: (int) -> Optional[Dict[str, Any]]
try:
frm = sys._getframe(stacklevel)
except Exception:
return None
return serialize_frame(
frm, include_local_variables=False, include_source_context=True
)
@contextmanager
def recursion_protection():
# type: () -> Generator[bool, None, None]
"""Enters recursion protection and returns the old flag."""
old_in_metrics = _in_metrics.get()
_in_metrics.set(True)
try:
yield old_in_metrics
finally:
_in_metrics.set(old_in_metrics)
def metrics_noop(func):
# type: (Any) -> Any
"""Convenient decorator that uses `recursion_protection` to
make a function a noop.
"""
@wraps(func)
def new_func(*args, **kwargs):
# type: (*Any, **Any) -> Any
with recursion_protection() as in_metrics:
if not in_metrics:
return func(*args, **kwargs)
return new_func
class Metric(ABC):
__slots__ = ()
@abstractmethod
def __init__(self, first):
# type: (MetricValue) -> None
pass
@property
@abstractmethod
def weight(self):
# type: () -> int
pass
@abstractmethod
def add(self, value):
# type: (MetricValue) -> None
pass
@abstractmethod
def serialize_value(self):
# type: () -> Iterable[FlushedMetricValue]
pass
class CounterMetric(Metric):
__slots__ = ("value",)
def __init__(
self, first # type: MetricValue
):
# type: (...) -> None
self.value = float(first)
@property
def weight(self):
# type: (...) -> int
return 1
def add(
self, value # type: MetricValue
):
# type: (...) -> None
self.value += float(value)
def serialize_value(self):
# type: (...) -> Iterable[FlushedMetricValue]
return (self.value,)
class GaugeMetric(Metric):
__slots__ = (
"last",
"min",
"max",
"sum",
"count",
)
def __init__(
self, first # type: MetricValue
):
# type: (...) -> None
first = float(first)
self.last = first
self.min = first
self.max = first
self.sum = first
self.count = 1
@property
def weight(self):
# type: (...) -> int
# Number of elements.
return 5
def add(
self, value # type: MetricValue
):
# type: (...) -> None
value = float(value)
self.last = value
self.min = min(self.min, value)
self.max = max(self.max, value)
self.sum += value
self.count += 1
def serialize_value(self):
# type: (...) -> Iterable[FlushedMetricValue]
return (
self.last,
self.min,
self.max,
self.sum,
self.count,
)
class DistributionMetric(Metric):
__slots__ = ("value",)
def __init__(
self, first # type: MetricValue
):
# type(...) -> None
self.value = [float(first)]
@property
def weight(self):
# type: (...) -> int
return len(self.value)
def add(
self, value # type: MetricValue
):
# type: (...) -> None
self.value.append(float(value))
def serialize_value(self):
# type: (...) -> Iterable[FlushedMetricValue]
return self.value
class SetMetric(Metric):
__slots__ = ("value",)
def __init__(
self, first # type: MetricValue
):
# type: (...) -> None
self.value = {first}
@property
def weight(self):
# type: (...) -> int
return len(self.value)
def add(
self, value # type: MetricValue
):
# type: (...) -> None
self.value.add(value)
def serialize_value(self):
# type: (...) -> Iterable[FlushedMetricValue]
def _hash(x):
# type: (MetricValue) -> int
if isinstance(x, str):
return zlib.crc32(x.encode("utf-8")) & 0xFFFFFFFF
return int(x)
return (_hash(value) for value in self.value)
def _encode_metrics(flushable_buckets):
# type: (Iterable[Tuple[int, Dict[BucketKey, Metric]]]) -> bytes
out = io.BytesIO()
_write = out.write
# Note on sanitization: we intentionally sanitize in emission (serialization)
# and not during aggregation for performance reasons. This means that the
# envelope can in fact have duplicate buckets stored. This is acceptable for
# relay side emission and should not happen commonly.
for timestamp, buckets in flushable_buckets:
for bucket_key, metric in buckets.items():
metric_type, metric_name, metric_unit, metric_tags = bucket_key
metric_name = _sanitize_metric_key(metric_name)
metric_unit = _sanitize_unit(metric_unit)
_write(metric_name.encode("utf-8"))
_write(b"@")
_write(metric_unit.encode("utf-8"))
for serialized_value in metric.serialize_value():
_write(b":")
_write(str(serialized_value).encode("utf-8"))
_write(b"|")
_write(metric_type.encode("ascii"))
if metric_tags:
_write(b"|#")
first = True
for tag_key, tag_value in metric_tags:
tag_key = _sanitize_tag_key(tag_key)
if not tag_key:
continue
if first:
first = False
else:
_write(b",")
_write(tag_key.encode("utf-8"))
_write(b":")
_write(_sanitize_tag_value(tag_value).encode("utf-8"))
_write(b"|T")
_write(str(timestamp).encode("ascii"))
_write(b"\n")
return out.getvalue()
def _encode_locations(timestamp, code_locations):
# type: (int, Iterable[Tuple[MetricMetaKey, Dict[str, Any]]]) -> bytes
mapping = {} # type: Dict[str, List[Any]]
for key, loc in code_locations:
metric_type, name, unit = key
mri = "{}:{}@{}".format(
metric_type, _sanitize_metric_key(name), _sanitize_unit(unit)
)
loc["type"] = "location"
mapping.setdefault(mri, []).append(loc)
return json_dumps({"timestamp": timestamp, "mapping": mapping})
METRIC_TYPES = {
"c": CounterMetric,
"g": GaugeMetric,
"d": DistributionMetric,
"s": SetMetric,
} # type: dict[MetricType, type[Metric]]
# some of these are dumb
TIMING_FUNCTIONS = {
"nanosecond": nanosecond_time,
"microsecond": lambda: nanosecond_time() / 1000.0,
"millisecond": lambda: nanosecond_time() / 1000000.0,
"second": now,
"minute": lambda: now() / 60.0,
"hour": lambda: now() / 3600.0,
"day": lambda: now() / 3600.0 / 24.0,
"week": lambda: now() / 3600.0 / 24.0 / 7.0,
}
class LocalAggregator:
__slots__ = ("_measurements",)
def __init__(self):
# type: (...) -> None
self._measurements = (
{}
) # type: Dict[Tuple[str, MetricTagsInternal], Tuple[float, float, int, float]]
def add(
self,
ty, # type: MetricType
key, # type: str
value, # type: float
unit, # type: MeasurementUnit
tags, # type: MetricTagsInternal
):
# type: (...) -> None
export_key = "%s:%s@%s" % (ty, key, unit)
bucket_key = (export_key, tags)
old = self._measurements.get(bucket_key)
if old is not None:
v_min, v_max, v_count, v_sum = old
v_min = min(v_min, value)
v_max = max(v_max, value)
v_count += 1
v_sum += value
else:
v_min = v_max = v_sum = value
v_count = 1
self._measurements[bucket_key] = (v_min, v_max, v_count, v_sum)
def to_json(self):
# type: (...) -> Dict[str, Any]
rv = {} # type: Any
for (export_key, tags), (
v_min,
v_max,
v_count,
v_sum,
) in self._measurements.items():
rv.setdefault(export_key, []).append(
{
"tags": _tags_to_dict(tags),
"min": v_min,
"max": v_max,
"count": v_count,
"sum": v_sum,
}
)
return rv
class MetricsAggregator:
ROLLUP_IN_SECONDS = 10.0
MAX_WEIGHT = 100000
FLUSHER_SLEEP_TIME = 5.0
def __init__(
self,
capture_func, # type: Callable[[Envelope], None]
enable_code_locations=False, # type: bool
):
# type: (...) -> None
self.buckets = {} # type: Dict[int, Any]
self._enable_code_locations = enable_code_locations
self._seen_locations = _set() # type: Set[Tuple[int, MetricMetaKey]]
self._pending_locations = {} # type: Dict[int, List[Tuple[MetricMetaKey, Any]]]
self._buckets_total_weight = 0
self._capture_func = capture_func
self._running = True
self._lock = threading.Lock()
self._flush_event = threading.Event() # type: threading.Event
self._force_flush = False
# The aggregator shifts its flushing by up to an entire rollup window to
# avoid multiple clients trampling on end of a 10 second window as all the
# buckets are anchored to multiples of ROLLUP seconds. We randomize this
# number once per aggregator boot to achieve some level of offsetting
# across a fleet of deployed SDKs. Relay itself will also apply independent
# jittering.
self._flush_shift = random.random() * self.ROLLUP_IN_SECONDS
self._flusher = None # type: Optional[threading.Thread]
self._flusher_pid = None # type: Optional[int]
def _ensure_thread(self):
# type: (...) -> bool
"""For forking processes we might need to restart this thread.
This ensures that our process actually has that thread running.
"""
if not self._running:
return False
pid = os.getpid()
if self._flusher_pid == pid:
return True
with self._lock:
# Recheck to make sure another thread didn't get here and start the
# the flusher in the meantime
if self._flusher_pid == pid:
return True
self._flusher_pid = pid
self._flusher = threading.Thread(target=self._flush_loop)
self._flusher.daemon = True
try:
self._flusher.start()
except RuntimeError:
# Unfortunately at this point the interpreter is in a state that no
# longer allows us to spawn a thread and we have to bail.
self._running = False
return False
return True
def _flush_loop(self):
# type: (...) -> None
_in_metrics.set(True)
while self._running or self._force_flush:
if self._running:
self._flush_event.wait(self.FLUSHER_SLEEP_TIME)
self._flush()
def _flush(self):
# type: (...) -> None
self._emit(self._flushable_buckets(), self._flushable_locations())
def _flushable_buckets(self):
# type: (...) -> (Iterable[Tuple[int, Dict[BucketKey, Metric]]])
with self._lock:
force_flush = self._force_flush
cutoff = time.time() - self.ROLLUP_IN_SECONDS - self._flush_shift
flushable_buckets = () # type: Iterable[Tuple[int, Dict[BucketKey, Metric]]]
weight_to_remove = 0
if force_flush:
flushable_buckets = self.buckets.items()
self.buckets = {}
self._buckets_total_weight = 0
self._force_flush = False
else:
flushable_buckets = []
for buckets_timestamp, buckets in self.buckets.items():
# If the timestamp of the bucket is newer that the rollup we want to skip it.
if buckets_timestamp <= cutoff:
flushable_buckets.append((buckets_timestamp, buckets))
# We will clear the elements while holding the lock, in order to avoid requesting it downstream again.
for buckets_timestamp, buckets in flushable_buckets:
for metric in buckets.values():
weight_to_remove += metric.weight
del self.buckets[buckets_timestamp]
self._buckets_total_weight -= weight_to_remove
return flushable_buckets
def _flushable_locations(self):
# type: (...) -> Dict[int, List[Tuple[MetricMetaKey, Dict[str, Any]]]]
with self._lock:
locations = self._pending_locations
self._pending_locations = {}
return locations
@metrics_noop
def add(
self,
ty, # type: MetricType
key, # type: str
value, # type: MetricValue
unit, # type: MeasurementUnit
tags, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
local_aggregator=None, # type: Optional[LocalAggregator]
stacklevel=0, # type: Optional[int]
):
# type: (...) -> None
if not self._ensure_thread() or self._flusher is None:
return None
if timestamp is None:
timestamp = time.time()
elif isinstance(timestamp, datetime):
timestamp = to_timestamp(timestamp)
bucket_timestamp = int(
(timestamp // self.ROLLUP_IN_SECONDS) * self.ROLLUP_IN_SECONDS
)
serialized_tags = _serialize_tags(tags)
bucket_key = (
ty,
key,
unit,
serialized_tags,
)
with self._lock:
local_buckets = self.buckets.setdefault(bucket_timestamp, {})
metric = local_buckets.get(bucket_key)
if metric is not None:
previous_weight = metric.weight
metric.add(value)
else:
metric = local_buckets[bucket_key] = METRIC_TYPES[ty](value)
previous_weight = 0
added = metric.weight - previous_weight
if stacklevel is not None:
self.record_code_location(ty, key, unit, stacklevel + 2, timestamp)
# Given the new weight we consider whether we want to force flush.
self._consider_force_flush()
# For sets, we only record that a value has been added to the set but not which one.
# See develop docs: https://develop.sentry.dev/sdk/metrics/#sets
if local_aggregator is not None:
local_value = float(added if ty == "s" else value)
local_aggregator.add(ty, key, local_value, unit, serialized_tags)
def record_code_location(
self,
ty, # type: MetricType
key, # type: str
unit, # type: MeasurementUnit
stacklevel, # type: int
timestamp=None, # type: Optional[float]
):
# type: (...) -> None
if not self._enable_code_locations:
return
if timestamp is None:
timestamp = time.time()
meta_key = (ty, key, unit)
start_of_day = datetime.fromtimestamp(timestamp, timezone.utc).replace(
hour=0, minute=0, second=0, microsecond=0, tzinfo=None
)
start_of_day = int(to_timestamp(start_of_day))
if (start_of_day, meta_key) not in self._seen_locations:
self._seen_locations.add((start_of_day, meta_key))
loc = get_code_location(stacklevel + 3)
if loc is not None:
# Group metadata by day to make flushing more efficient.
# There needs to be one envelope item per timestamp.
self._pending_locations.setdefault(start_of_day, []).append(
(meta_key, loc)
)
@metrics_noop
def need_code_location(
self,
ty, # type: MetricType
key, # type: str
unit, # type: MeasurementUnit
timestamp, # type: float
):
# type: (...) -> bool
if self._enable_code_locations:
return False
meta_key = (ty, key, unit)
start_of_day = datetime.fromtimestamp(timestamp, timezone.utc).replace(
hour=0, minute=0, second=0, microsecond=0, tzinfo=None
)
start_of_day = int(to_timestamp(start_of_day))
return (start_of_day, meta_key) not in self._seen_locations
def kill(self):
# type: (...) -> None
if self._flusher is None:
return
self._running = False
self._flush_event.set()
self._flusher = None
@metrics_noop
def flush(self):
# type: (...) -> None
self._force_flush = True
self._flush()
def _consider_force_flush(self):
# type: (...) -> None
# It's important to acquire a lock around this method, since it will touch shared data structures.
total_weight = len(self.buckets) + self._buckets_total_weight
if total_weight >= self.MAX_WEIGHT:
self._force_flush = True
self._flush_event.set()
def _emit(
self,
flushable_buckets, # type: (Iterable[Tuple[int, Dict[BucketKey, Metric]]])
code_locations, # type: Dict[int, List[Tuple[MetricMetaKey, Dict[str, Any]]]]
):
# type: (...) -> Optional[Envelope]
envelope = Envelope()
if flushable_buckets:
encoded_metrics = _encode_metrics(flushable_buckets)
envelope.add_item(Item(payload=encoded_metrics, type="statsd"))
for timestamp, locations in code_locations.items():
encoded_locations = _encode_locations(timestamp, locations)
envelope.add_item(Item(payload=encoded_locations, type="metric_meta"))
if envelope.items:
self._capture_func(envelope)
return envelope
return None
def _serialize_tags(
tags, # type: Optional[MetricTags]
):
# type: (...) -> MetricTagsInternal
if not tags:
return ()
rv = []
for key, value in tags.items():
# If the value is a collection, we want to flatten it.
if isinstance(value, (list, tuple)):
for inner_value in value:
if inner_value is not None:
rv.append((key, str(inner_value)))
elif value is not None:
rv.append((key, str(value)))
# It's very important to sort the tags in order to obtain the
# same bucket key.
return tuple(sorted(rv))
def _tags_to_dict(tags):
# type: (MetricTagsInternal) -> Dict[str, Any]
rv = {} # type: Dict[str, Any]
for tag_name, tag_value in tags:
old_value = rv.get(tag_name)
if old_value is not None:
if isinstance(old_value, list):
old_value.append(tag_value)
else:
rv[tag_name] = [old_value, tag_value]
else:
rv[tag_name] = tag_value
return rv
def _get_aggregator():
# type: () -> Optional[MetricsAggregator]
client = sentry_sdk.get_client()
return (
client.metrics_aggregator
if client.is_active() and client.metrics_aggregator is not None
else None
)
def _get_aggregator_and_update_tags(key, value, unit, tags):
# type: (str, Optional[MetricValue], MeasurementUnit, Optional[MetricTags]) -> Tuple[Optional[MetricsAggregator], Optional[LocalAggregator], Optional[MetricTags]]
client = sentry_sdk.get_client()
if not client.is_active() or client.metrics_aggregator is None:
return None, None, tags
updated_tags = dict(tags or ()) # type: Dict[str, MetricTagValue]
updated_tags.setdefault("release", client.options["release"])
updated_tags.setdefault("environment", client.options["environment"])
scope = sentry_sdk.get_current_scope()
local_aggregator = None
# We go with the low-level API here to access transaction information as
# this one is the same between just errors and errors + performance
transaction_source = scope._transaction_info.get("source")
if transaction_source in GOOD_TRANSACTION_SOURCES:
transaction_name = scope._transaction
if transaction_name:
updated_tags.setdefault("transaction", transaction_name)
if scope._span is not None:
local_aggregator = scope._span._get_local_aggregator()
experiments = client.options.get("_experiments", {})
before_emit_callback = experiments.get("before_emit_metric")
if before_emit_callback is not None:
with recursion_protection() as in_metrics:
if not in_metrics:
if not before_emit_callback(key, value, unit, updated_tags):
return None, None, updated_tags
return client.metrics_aggregator, local_aggregator, updated_tags
def increment(
key, # type: str
value=1.0, # type: float
unit="none", # type: MeasurementUnit
tags=None, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
stacklevel=0, # type: int
):
# type: (...) -> None
"""Increments a counter."""
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
key, value, unit, tags
)
if aggregator is not None:
aggregator.add(
"c", key, value, unit, tags, timestamp, local_aggregator, stacklevel
)
# alias as incr is relatively common in python
incr = increment
class _Timing:
def __init__(
self,
key, # type: str
tags, # type: Optional[MetricTags]
timestamp, # type: Optional[Union[float, datetime]]
value, # type: Optional[float]
unit, # type: DurationUnit
stacklevel, # type: int
):
# type: (...) -> None
self.key = key
self.tags = tags
self.timestamp = timestamp
self.value = value
self.unit = unit
self.entered = None # type: Optional[float]
self._span = None # type: Optional[sentry_sdk.tracing.Span]
self.stacklevel = stacklevel
def _validate_invocation(self, context):
# type: (str) -> None
if self.value is not None:
raise TypeError(
"cannot use timing as %s when a value is provided" % context
)
def __enter__(self):
# type: (...) -> _Timing
self.entered = TIMING_FUNCTIONS[self.unit]()
self._validate_invocation("context-manager")
self._span = sentry_sdk.start_span(op="metric.timing", name=self.key)
if self.tags:
for key, value in self.tags.items():
if isinstance(value, (tuple, list)):
value = ",".join(sorted(map(str, value)))
self._span.set_tag(key, value)
self._span.__enter__()
# report code locations here for better accuracy
aggregator = _get_aggregator()
if aggregator is not None:
aggregator.record_code_location("d", self.key, self.unit, self.stacklevel)
return self
def __exit__(self, exc_type, exc_value, tb):
# type: (Any, Any, Any) -> None
assert self._span, "did not enter"
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
self.key,
self.value,
self.unit,
self.tags,
)
if aggregator is not None:
elapsed = TIMING_FUNCTIONS[self.unit]() - self.entered # type: ignore
aggregator.add(
"d",
self.key,
elapsed,
self.unit,
tags,
self.timestamp,
local_aggregator,
None, # code locations are reported in __enter__
)
self._span.__exit__(exc_type, exc_value, tb)
self._span = None
def __call__(self, f):
# type: (Any) -> Any
self._validate_invocation("decorator")
@wraps(f)
def timed_func(*args, **kwargs):
# type: (*Any, **Any) -> Any
with timing(
key=self.key,
tags=self.tags,
timestamp=self.timestamp,
unit=self.unit,
stacklevel=self.stacklevel + 1,
):
return f(*args, **kwargs)
return timed_func
def timing(
key, # type: str
value=None, # type: Optional[float]
unit="second", # type: DurationUnit
tags=None, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
stacklevel=0, # type: int
):
# type: (...) -> _Timing
"""Emits a distribution with the time it takes to run the given code block.
This method supports three forms of invocation:
- when a `value` is provided, it functions similar to `distribution` but with
- it can be used as a context manager
- it can be used as a decorator
"""
if value is not None:
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
key, value, unit, tags
)
if aggregator is not None:
aggregator.add(
"d", key, value, unit, tags, timestamp, local_aggregator, stacklevel
)
return _Timing(key, tags, timestamp, value, unit, stacklevel)
def distribution(
key, # type: str
value, # type: float
unit="none", # type: MeasurementUnit
tags=None, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
stacklevel=0, # type: int
):
# type: (...) -> None
"""Emits a distribution."""
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
key, value, unit, tags
)
if aggregator is not None:
aggregator.add(
"d", key, value, unit, tags, timestamp, local_aggregator, stacklevel
)
def set(
key, # type: str
value, # type: Union[int, str]
unit="none", # type: MeasurementUnit
tags=None, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
stacklevel=0, # type: int
):
# type: (...) -> None
"""Emits a set."""
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
key, value, unit, tags
)
if aggregator is not None:
aggregator.add(
"s", key, value, unit, tags, timestamp, local_aggregator, stacklevel
)
def gauge(
key, # type: str
value, # type: float
unit="none", # type: MeasurementUnit
tags=None, # type: Optional[MetricTags]
timestamp=None, # type: Optional[Union[float, datetime]]
stacklevel=0, # type: int
):
# type: (...) -> None
"""Emits a gauge."""
aggregator, local_aggregator, tags = _get_aggregator_and_update_tags(
key, value, unit, tags
)
if aggregator is not None:
aggregator.add(
"g", key, value, unit, tags, timestamp, local_aggregator, stacklevel
)