def add(self, metric_name, stat, config=None):
"""
Register a metric with this sensor
Arguments:
metric_name (MetricName): The name of the metric
stat (AbstractMeasurableStat): The statistic to keep
config (MetricConfig): A special configuration for this metric.
If None use the sensor default configuration.
"""
with self._lock:
metric = KafkaMetric(metric_name, stat, config or self._config)
self._registry.register_metric(metric)
self._metrics.append(metric)
self._stats.append(stat)
def add_metric(self, metric_name, measurable, config=None):
"""
Add a metric to monitor an object that implements measurable.
This metric won't be associated with any sensor.
This is a way to expose existing values as metrics.
Arguments:
metricName (MetricName): The name of the metric
measurable (AbstractMeasurable): The measurable that will be
measured by this metric
config (MetricConfig, optional): The configuration to use when
measuring this measurable
"""
# NOTE there was a lock here, but i don't think it's needed
metric = KafkaMetric(metric_name, measurable, config or self.config)
self.register_metric(metric)
def add_compound(self, compound_stat, config=None):
"""
Register a compound statistic with this sensor which
yields multiple measurable quantities (like a histogram)
Arguments:
stat (AbstractCompoundStat): The stat to register
config (MetricConfig): The configuration for this stat.
If None then the stat will use the default configuration
for this sensor.
"""
if not compound_stat:
raise ValueError('compound stat must be non-empty')
self._stats.append(compound_stat)
for named_measurable in compound_stat.stats():
metric = KafkaMetric(named_measurable.name, named_measurable.stat,
config or self._config)
self._registry.register_metric(metric)
self._metrics.append(metric)
