Major fixes and new features

venv/lib/python3.12/site-packages/kafka/metrics/stats/percentiles.py

@@ -0,0 +1,74 @@
+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)