| from __future__ import absolute_import |
|
|
| from kafka.metrics import AnonMeasurable, NamedMeasurable |
| from kafka.metrics.compound_stat import AbstractCompoundStat |
| from kafka.metrics.stats import Histogram |
| from kafka.metrics.stats.sampled_stat import AbstractSampledStat |
|
|
|
|
| class BucketSizing(object): |
| CONSTANT = 0 |
| LINEAR = 1 |
|
|
|
|
| class Percentiles(AbstractSampledStat, AbstractCompoundStat): |
| """A compound stat that reports one or more percentiles""" |
| def __init__(self, size_in_bytes, bucketing, max_val, min_val=0.0, |
| percentiles=None): |
| super(Percentiles, self).__init__(0.0) |
| self._percentiles = percentiles or [] |
| self._buckets = int(size_in_bytes / 4) |
| if bucketing == BucketSizing.CONSTANT: |
| self._bin_scheme = Histogram.ConstantBinScheme(self._buckets, |
| min_val, max_val) |
| elif bucketing == BucketSizing.LINEAR: |
| if min_val != 0.0: |
| raise ValueError('Linear bucket sizing requires min_val' |
| ' to be 0.0.') |
| self.bin_scheme = Histogram.LinearBinScheme(self._buckets, max_val) |
| else: |
| ValueError('Unknown bucket type: %s' % (bucketing,)) |
|
|
| def stats(self): |
| measurables = [] |
|
|
| def make_measure_fn(pct): |
| return lambda config, now: self.value(config, now, |
| pct / 100.0) |
|
|
| for percentile in self._percentiles: |
| measure_fn = make_measure_fn(percentile.percentile) |
| stat = NamedMeasurable(percentile.name, AnonMeasurable(measure_fn)) |
| measurables.append(stat) |
| return measurables |
|
|
| def value(self, config, now, quantile): |
| self.purge_obsolete_samples(config, now) |
| count = sum(sample.event_count for sample in self._samples) |
| if count == 0.0: |
| return float('NaN') |
| sum_val = 0.0 |
| quant = float(quantile) |
| for b in range(self._buckets): |
| for sample in self._samples: |
| assert type(sample) is self.HistogramSample |
| hist = sample.histogram.counts |
| sum_val += hist[b] |
| if sum_val / count > quant: |
| return self._bin_scheme.from_bin(b) |
| return float('inf') |
|
|
| def combine(self, samples, config, now): |
| return self.value(config, now, 0.5) |
|
|
| def new_sample(self, time_ms): |
| return Percentiles.HistogramSample(self._bin_scheme, time_ms) |
|
|
| def update(self, sample, config, value, time_ms): |
| assert type(sample) is self.HistogramSample |
| sample.histogram.record(value) |
|
|
| class HistogramSample(AbstractSampledStat.Sample): |
| def __init__(self, scheme, now): |
| super(Percentiles.HistogramSample, self).__init__(0.0, now) |
| self.histogram = Histogram(scheme) |
|
|
