def _start_flush_thread(self):
""" Start a thread to flush metrics. """
from dogapi.stats.periodic_timer import PeriodicTimer
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
# A small helper for logging and flushing.
def flush():
try:
log.debug("Flushing metrics in thread")
self.flush()
except:
try:
log.exception("Error flushing in thread")
except:
pass
log.info("Starting flush thread with interval %s." % self.flush_interval)
self._flush_thread = PeriodicTimer(self.flush_interval, flush)
self._flush_thread.start()
def _start_flush_thread(self):
""" Start a thread to flush metrics. """
from dogapi.stats.periodic_timer import PeriodicTimer
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
# A small helper for logging and flushing.
def flush():
try:
log.debug("Flushing metrics in thread")
self.flush()
except:
try:
log.exception("Error flushing in thread")
except:
pass
log.info("Starting flush thread with interval %s." % self.flush_interval)
self._flush_thread = PeriodicTimer(self.flush_interval, flush)
self._flush_thread.start()
class DogStatsApi(object):
def __init__(self):
""" Initialize a dogstats object. """
# Don't collect until start is called.
self._disabled = True
def start(self, api_key=None,
flush_interval=10,
roll_up_interval=10,
host=None,
device=None,
api_host=None,
use_ec2_instance_ids=False,
flush_in_thread=True,
flush_in_greenlet=False,
disabled=False,
statsd=False,
statsd_host='localhost',
statsd_port=8125):
"""
Configure the DogStatsApi instance and optionally, begin auto-flusing metrics.
:param api_key: Your DataDog API key.
:param flush_interval: The number of seconds to wait between flushes.
:param flush_in_thread: True if you'd like to spawn a thread to flush metrics. It will run every `flush_interval` seconds.
:param flush_in_greenlet: Set to true if you'd like to flush in a gevent greenlet.
"""
self.flush_interval = flush_interval
self.roll_up_interval = roll_up_interval
self.device = device
self._disabled = disabled
self.host = host or socket.gethostname()
if use_ec2_instance_ids:
self.host = get_ec2_instance_id()
self._is_auto_flushing = False
if statsd:
# If we're configured to send to a statsd instance, use an aggregator
# which forwards packets over UDP.
log.info("Initializing dog api to use statsd: %s, %s" % (statsd_host, statsd_port))
self._needs_flush = False
self._aggregator = StatsdAggregator(statsd_host, statsd_port)
else:
# Otherwise create an aggreagtor that while aggregator metrics
# in process.
self._needs_flush = True
self._aggregator = MetricsAggregator(self.roll_up_interval)
# The reporter is responsible for sending metrics off to their final destination.
# It's abstracted to support easy unit testing and in the near future, forwarding
# to the datadog agent.
self.reporter = HttpReporter(api_key=api_key, api_host=api_host)
self._is_flush_in_progress = False
self.flush_count = 0
if self._disabled:
log.info("dogapi is disabled. No metrics will flush.")
else:
if flush_in_greenlet:
self._start_flush_greenlet()
elif flush_in_thread:
self._start_flush_thread()
def stop(self):
if not self._is_auto_flushing:
return True
if self._flush_thread:
self._flush_thread.end()
self._is_auto_flushing = False
return True
def gauge(self, metric_name, value, timestamp=None, tags=None, sample_rate=1):
"""
Record the instantaneous *value* of a metric. They most recent value in
a given flush interval will be recorded. Optionally, specify a set of
tags to associate with the metric.
>>> dog_stats_api.gauge('process.uptime', time.time() - process_start_time)
>>> dog_stats_api.gauge('cache.bytes.free', cache.get_free_bytes(), tags=['version:1.0'])
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Gauge, sample_rate)
def increment(self, metric_name, value=1, timestamp=None, tags=None, sample_rate=1):
"""
Increment the counter by the given *value*. Optionally, specify a list of
*tags* to associate with the metric.
>>> dog_stats_api.increment('home.page.hits')
>>> dog_stats_api.increment('bytes.processed', file.size())
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Counter, sample_rate)
def histogram(self, metric_name, value, timestamp=None, tags=None, sample_rate=1):
"""
Sample a histogram value. Histograms will produce metrics that
describe the distribution of the recorded values, namely the minimum,
maximum, average, count and the 75th, 85th, 95th and 99th percentiles.
Optionally, specify a list of *tags* to associate with the metric.
>>> dog_stats_api.histogram('uploaded_file.size', uploaded_file.size())
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Histogram, sample_rate)
def timed(self, metric_name, sample_rate=1, tags=None):
"""
A decorator that will track the distribution of a function's run time.
Optionally specify a list of tags to associate with the metric.
::
@dog_stats_api.timed('user.query.time')
def get_user(user_id):
# Do what you need to ...
pass
# Is equivalent to ...
start = time.time()
try:
get_user(user_id)
finally:
dog_stats_api.histogram('user.query.time', time.time() - start)
"""
def wrapper(func):
def wrapped(*args, **kwargs):
start = time()
result = func(*args, **kwargs)
end = time()
self.histogram(metric_name, end - start, end, tags=tags, sample_rate=sample_rate)
return result
wrapped.__name__ = func.__name__
wrapped.__doc__ = func.__doc__
wrapped.__dict__.update(func.__dict__)
return wrapped
return wrapper
def flush(self, timestamp=None):
"""
Flush and post all metrics to the server. Note that this is a blocking
call, so it is likely not suitable for user facing processes. In those
cases, it's probably best to flush in a thread or greenlet.
"""
try:
if not self._needs_flush:
return False
if self._is_flush_in_progress:
log.debug("A flush is already in progress. Skipping this one.")
return False
elif self._disabled:
log.info("Not flushing because we're disabled.")
return False
self._is_flush_in_progress = True
metrics = self._get_aggregate_metrics(timestamp or time())
count = len(metrics)
if count:
self.flush_count += 1
log.info("Flush #%s sending %s metrics" % (self.flush_count, count))
self.reporter.flush(metrics)
else:
log.debug("No metrics to flush. Continuing.")
except:
try:
log.exception("Error flushing metrics")
except:
pass
finally:
self._is_flush_in_progress = False
def _get_aggregate_metrics(self, flush_time=None):
# Get rolled up metrics
rolled_up_metrics = self._aggregator.flush(flush_time)
# FIXME: emit a dictionary from the aggregator
metrics = []
for timestamp, value, name, tags in rolled_up_metrics:
metric = {
'metric' : name,
'points' : [[timestamp, value]],
'type': MetricType.Gauge,
'host': self.host,
'device': self.device,
'tags' : tags
}
metrics.append(metric)
return metrics
def _start_flush_thread(self):
""" Start a thread to flush metrics. """
from dogapi.stats.periodic_timer import PeriodicTimer
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
# A small helper for logging and flushing.
def flush():
try:
log.debug("Flushing metrics in thread")
self.flush()
except:
try:
log.exception("Error flushing in thread")
except:
pass
log.info("Starting flush thread with interval %s." % self.flush_interval)
self._flush_thread = PeriodicTimer(self.flush_interval, flush)
self._flush_thread.start()
def _start_flush_greenlet(self):
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
import gevent
# A small helper for flushing.
def flush():
while True:
try:
log.debug("Flushing metrics in greenlet")
self.flush()
gevent.sleep(self.flush_interval)
except:
try:
log.exception("Error flushing in greenlet")
except:
pass
log.info("Starting flush greenlet with interval %s." % self.flush_interval)
gevent.spawn(flush)
class DogStatsApi(object):
def __init__(self):
""" Initialize a dogstats object. """
# Don't collect until start is called.
self._disabled = True
def start(self, api_key=None,
flush_interval=10,
roll_up_interval=10,
host=None,
device=None,
api_host=None,
use_ec2_instance_ids=False,
flush_in_thread=True,
flush_in_greenlet=False,
disabled=False,
statsd=False,
statsd_host='localhost',
statsd_port=8125):
"""
Configure the DogStatsApi instance and optionally, begin auto-flusing metrics.
:param api_key: Your DataDog API key.
:param flush_interval: The number of seconds to wait between flushes.
:param flush_in_thread: True if you'd like to spawn a thread to flush metrics. It will run every `flush_interval` seconds.
:param flush_in_greenlet: Set to true if you'd like to flush in a gevent greenlet.
"""
self.flush_interval = flush_interval
self.roll_up_interval = roll_up_interval
self.device = device
self._disabled = disabled
self.host = host or socket.gethostname()
if use_ec2_instance_ids:
self.host = get_ec2_instance_id()
self._is_auto_flushing = False
if statsd:
# If we're configured to send to a statsd instance, use an aggregator
# which forwards packets over UDP.
log.info("Initializing dog api to use statsd: %s, %s" % (statsd_host, statsd_port))
self._needs_flush = False
self._aggregator = StatsdAggregator(statsd_host, statsd_port)
else:
# Otherwise create an aggreagtor that while aggregator metrics
# in process.
self._needs_flush = True
self._aggregator = MetricsAggregator(self.roll_up_interval)
# The reporter is responsible for sending metrics off to their final destination.
# It's abstracted to support easy unit testing and in the near future, forwarding
# to the datadog agent.
self.reporter = HttpReporter(api_key=api_key, api_host=api_host)
self._is_flush_in_progress = False
self.flush_count = 0
if self._disabled:
log.info("dogapi is disabled. No metrics will flush.")
else:
if flush_in_greenlet:
self._start_flush_greenlet()
elif flush_in_thread:
self._start_flush_thread()
def stop(self):
if not self._is_auto_flushing:
return True
if self._flush_thread:
self._flush_thread.end()
self._is_auto_flushing = False
return True
def gauge(self, metric_name, value, timestamp=None, tags=None, sample_rate=1, host=None):
"""
Record the current *value* of a metric. They most recent value in
a given flush interval will be recorded. Optionally, specify a set of
tags to associate with the metric. This should be used for sum values
such as total hard disk space, process uptime, total number of active
users, or number of rows in a database table.
>>> dog_stats_api.gauge('process.uptime', time.time() - process_start_time)
>>> dog_stats_api.gauge('cache.bytes.free', cache.get_free_bytes(), tags=['version:1.0'])
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Gauge,
sample_rate=sample_rate, host=host)
def increment(self, metric_name, value=1, timestamp=None, tags=None, sample_rate=1, host=None):
"""
Increment the counter by the given *value*. Optionally, specify a list of
*tags* to associate with the metric. This is useful for counting things
such as incrementing a counter each time a page is requested.
>>> dog_stats_api.increment('home.page.hits')
>>> dog_stats_api.increment('bytes.processed', file.size())
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Counter,
sample_rate=sample_rate, host=host)
def histogram(self, metric_name, value, timestamp=None, tags=None, sample_rate=1, host=None):
"""
Sample a histogram value. Histograms will produce metrics that
describe the distribution of the recorded values, namely the minimum,
maximum, average, count and the 75th, 85th, 95th and 99th percentiles.
Optionally, specify a list of *tags* to associate with the metric.
>>> dog_stats_api.histogram('uploaded_file.size', uploaded_file.size())
"""
if not self._disabled:
self._aggregator.add_point(metric_name, tags, timestamp or time(), value, Histogram,
sample_rate=sample_rate, host=host)
@contextmanager
def timer(self, metric_name, sample_rate=1, tags=None, host=None):
"""
A context manager that will track the distribution of the contained code's run time.
Optionally specify a list of tags to associate with the metric.
::
def get_user(user_id):
with dog_stats_api.timer('user.query.time'):
# Do what you need to ...
pass
# Is equivalent to ...
def get_user(user_id):
start = time.time()
try:
# Do what you need to ...
pass
finally:
dog_stats_api.histogram('user.query.time', time.time() - start)
"""
start = time()
try:
yield
finally:
end = time()
self.histogram(metric_name, end - start, end, tags=tags,
sample_rate=sample_rate, host=host)
def timed(self, metric_name, sample_rate=1, tags=None, host=None):
"""
A decorator that will track the distribution of a function's run time.
Optionally specify a list of tags to associate with the metric.
::
@dog_stats_api.timed('user.query.time')
def get_user(user_id):
# Do what you need to ...
pass
# Is equivalent to ...
start = time.time()
try:
get_user(user_id)
finally:
dog_stats_api.histogram('user.query.time', time.time() - start)
"""
def wrapper(func):
@wraps(func)
def wrapped(*args, **kwargs):
with self.timer(metric_name, sample_rate, tags, host):
result = func(*args, **kwargs)
return result
return wrapped
return wrapper
def flush(self, timestamp=None):
"""
Flush and post all metrics to the server. Note that this is a blocking
call, so it is likely not suitable for user facing processes. In those
cases, it's probably best to flush in a thread or greenlet.
"""
try:
if not self._needs_flush:
return False
if self._is_flush_in_progress:
log.debug("A flush is already in progress. Skipping this one.")
return False
elif self._disabled:
log.info("Not flushing because we're disabled.")
return False
self._is_flush_in_progress = True
metrics = self._get_aggregate_metrics(timestamp or time())
count = len(metrics)
if count:
self.flush_count += 1
log.debug("Flush #%s sending %s metrics" % (self.flush_count, count))
self.reporter.flush(metrics)
else:
log.debug("No metrics to flush. Continuing.")
except:
try:
log.exception("Error flushing metrics")
except:
pass
finally:
self._is_flush_in_progress = False
def _get_aggregate_metrics(self, flush_time=None):
# Get rolled up metrics
rolled_up_metrics = self._aggregator.flush(flush_time)
# FIXME: emit a dictionary from the aggregator
metrics = []
for timestamp, value, name, tags, host in rolled_up_metrics:
if host is None:
host = self.host
metric = {
'metric' : name,
'points' : [[timestamp, value]],
'type': MetricType.Gauge,
'host': host,
'device': self.device,
'tags' : tags
}
metrics.append(metric)
return metrics
def _start_flush_thread(self):
""" Start a thread to flush metrics. """
from dogapi.stats.periodic_timer import PeriodicTimer
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
# A small helper for logging and flushing.
def flush():
try:
log.debug("Flushing metrics in thread")
self.flush()
except:
try:
log.exception("Error flushing in thread")
except:
pass
log.info("Starting flush thread with interval %s." % self.flush_interval)
self._flush_thread = PeriodicTimer(self.flush_interval, flush)
self._flush_thread.start()
def _start_flush_greenlet(self):
if self._is_auto_flushing:
log.info("Autoflushing already started.")
return
self._is_auto_flushing = True
import gevent
# A small helper for flushing.
def flush():
while True:
try:
log.debug("Flushing metrics in greenlet")
self.flush()
gevent.sleep(self.flush_interval)
except:
try:
log.exception("Error flushing in greenlet")
except:
pass
log.info("Starting flush greenlet with interval %s." % self.flush_interval)
gevent.spawn(flush)
