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import re
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import random
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from collections import deque
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from datetime import timedelta
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from repoze.lru import lru_cache
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from .timer import Timer
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TAG_INVALID_CHARS_RE = re.compile(
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r"[^\w\d_\-:/\.]",
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#re.UNICODE
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)
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TAG_INVALID_CHARS_SUBS = "_"
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# we save and expose methods called by statsd for discovery
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buckets_dict = {
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}
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@lru_cache(maxsize=500)
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def _normalize_tags_with_cache(tag_list):
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return [TAG_INVALID_CHARS_RE.sub(TAG_INVALID_CHARS_SUBS, tag) for tag in tag_list]
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def normalize_tags(tag_list):
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# We have to turn our input tag list into a non-mutable tuple for it to
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# be hashable (and thus usable) by the @lru_cache decorator.
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return _normalize_tags_with_cache(tuple(tag_list))
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class StatsClientBase:
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"""A Base class for various statsd clients."""
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def close(self):
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"""Used to close and clean up any underlying resources."""
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raise NotImplementedError()
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def _send(self):
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raise NotImplementedError()
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def pipeline(self):
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raise NotImplementedError()
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def timer(self, stat, rate=1, tags=None, auto_send=True):
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"""
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statsd = StatsdClient.statsd
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with statsd.timer('bucket_name', auto_send=True) as tmr:
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# This block will be timed.
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for i in range(0, 100000):
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i ** 2
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# you can access time here...
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elapsed_ms = tmr.ms
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"""
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return Timer(self, stat, rate, tags, auto_send=auto_send)
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def timing(self, stat, delta, rate=1, tags=None, use_decimals=True):
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"""
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Send new timing information.
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`delta` can be either a number of milliseconds or a timedelta.
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"""
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if isinstance(delta, timedelta):
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# Convert timedelta to number of milliseconds.
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delta = delta.total_seconds() * 1000.
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if use_decimals:
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fmt = '%0.6f|ms'
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else:
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fmt = '%s|ms'
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self._send_stat(stat, fmt % delta, rate, tags)
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def incr(self, stat, count=1, rate=1, tags=None):
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"""Increment a stat by `count`."""
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self._send_stat(stat, f'{count}|c', rate, tags)
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def decr(self, stat, count=1, rate=1, tags=None):
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"""Decrement a stat by `count`."""
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self.incr(stat, -count, rate, tags)
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def gauge(self, stat, value, rate=1, delta=False, tags=None):
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"""Set a gauge value."""
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if value < 0 and not delta:
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if rate < 1:
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if random.random() > rate:
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return
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with self.pipeline() as pipe:
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pipe._send_stat(stat, '0|g', 1)
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pipe._send_stat(stat, f'{value}|g', 1)
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else:
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prefix = '+' if delta and value >= 0 else ''
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self._send_stat(stat, f'{prefix}{value}|g', rate, tags)
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def set(self, stat, value, rate=1):
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"""Set a set value."""
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self._send_stat(stat, f'{value}|s', rate)
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def histogram(self, stat, value, rate=1, tags=None):
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"""Set a histogram"""
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self._send_stat(stat, f'{value}|h', rate, tags)
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def _send_stat(self, stat, value, rate, tags=None):
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self._after(self._prepare(stat, value, rate, tags))
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def _prepare(self, stat, value, rate, tags=None):
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global buckets_dict
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buckets_dict[stat] = 1
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if rate < 1:
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if random.random() > rate:
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return
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value = f'{value}|@{rate}'
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if self._prefix:
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stat = f'{self._prefix}.{stat}'
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res = '%s:%s%s' % (
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stat,
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value,
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("|#" + ",".join(normalize_tags(tags))) if tags else "",
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)
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return res
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def _after(self, data):
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if data:
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self._send(data)
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class PipelineBase(StatsClientBase):
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def __init__(self, client):
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self._client = client
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self._prefix = client._prefix
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self._stats = deque()
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def _send(self):
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raise NotImplementedError()
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def _after(self, data):
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if data is not None:
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self._stats.append(data)
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def __enter__(self):
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return self
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def __exit__(self, typ, value, tb):
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self.send()
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def send(self):
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if not self._stats:
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return
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self._send()
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def pipeline(self):
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return self.__class__(self)
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