Merge pull request #637 from zackdever/metrics

Metrics java port

1 files changed, 93 insertions, 0 deletions

diff --git a/kafka/metrics/stats/histogram.py b/kafka/metrics/stats/histogram.py
new file mode 100644
index 0000000..42aacdb
--- /dev/null
+++ b/kafka/metrics/stats/histogram.py
@@ -0,0 +1,93 @@
+import math
+
+
+class Histogram(object):
+    def __init__(self, bin_scheme):
+        self._hist = [0.0] * bin_scheme.bins
+        self._count = 0.0
+        self._bin_scheme = bin_scheme
+
+    def record(self, value):
+        self._hist[self._bin_scheme.to_bin(value)] += 1.0
+        self._count += 1.0
+
+    def value(self, quantile):
+        if self._count == 0.0:
+            return float('NaN')
+        _sum = 0.0
+        quant = float(quantile)
+        for i, value in enumerate(self._hist[:-1]):
+            _sum += value
+            if _sum / self._count > quant:
+                return self._bin_scheme.from_bin(i)
+        return float('inf')
+
+    @property
+    def counts(self):
+        return self._hist
+
+    def clear(self):
+        for i in range(self._hist):
+            self._hist[i] = 0.0
+        self._count = 0
+
+    def __str__(self):
+        values = ['%.10f:%.0f' % (self._bin_scheme.from_bin(i), value) for
+                  i, value in enumerate(self._hist[:-1])]
+        values.append('%s:%s' % (float('inf'), self._hist[-1]))
+        return '{%s}' % ','.join(values)
+
+    class ConstantBinScheme(object):
+        def __init__(self, bins, min_val, max_val):
+            if bins < 2:
+                raise ValueError('Must have at least 2 bins.')
+            self._min = float(min_val)
+            self._max = float(max_val)
+            self._bins = int(bins)
+            self._bucket_width = (max_val - min_val) / (bins - 2)
+
+        @property
+        def bins(self):
+            return self._bins
+
+        def from_bin(self, b):
+            if b == 0:
+                return float('-inf')
+            elif b == self._bins - 1:
+                return float('inf')
+            else:
+                return self._min + (b - 1) * self._bucket_width
+
+        def to_bin(self, x):
+            if x < self._min:
+                return 0
+            elif x > self._max:
+                return self._bins - 1
+            else:
+                return int(((x - self._min) / self._bucket_width) + 1)
+
+    class LinearBinScheme(object):
+        def __init__(self, num_bins, max_val):
+            self._bins = num_bins
+            self._max = max_val
+            self._scale = max_val / (num_bins * (num_bins - 1) / 2)
+
+        @property
+        def bins(self):
+            return self._bins
+
+        def from_bin(self, b):
+            if b == self._bins - 1:
+                return float('inf')
+            else:
+                unscaled = (b * (b + 1.0)) / 2.0
+                return unscaled * self._scale
+
+        def to_bin(self, x):
+            if x < 0.0:
+                raise ValueError('Values less than 0.0 not accepted.')
+            elif x > self._max:
+                return self._bins - 1
+            else:
+                scaled = x / self._scale
+                return int(-0.5 + math.sqrt(2.0 * scaled + 0.25))