API refactor

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

@@ -7,6 +7,8 @@ class Avg(AbstractSampledStat):
     """
     An AbstractSampledStat that maintains a simple average over its samples.
     """
+    __slots__ = ('_initial_value', '_samples', '_current')
+
     def __init__(self):
         super(Avg, self).__init__(0.0)
 

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

@@ -7,6 +7,8 @@ class Count(AbstractSampledStat):
     """
     An AbstractSampledStat that maintains a simple count of what it has seen.
     """
+    __slots__ = ('_initial_value', '_samples', '_current')
+
     def __init__(self):
         super(Count, self).__init__(0.0)
 

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

@@ -4,6 +4,8 @@ import math
 
 
 class Histogram(object):
+    __slots__ = ('_hist', '_count', '_bin_scheme')
+
     def __init__(self, bin_scheme):
         self._hist = [0.0] * bin_scheme.bins
         self._count = 0.0
@@ -40,6 +42,8 @@ class Histogram(object):
         return '{%s}' % ','.join(values)
 
     class ConstantBinScheme(object):
+        __slots__ = ('_min', '_max', '_bins', '_bucket_width')
+
         def __init__(self, bins, min_val, max_val):
             if bins < 2:
                 raise ValueError('Must have at least 2 bins.')
@@ -69,6 +73,8 @@ class Histogram(object):
                 return int(((x - self._min) / self._bucket_width) + 1)
 
     class LinearBinScheme(object):
+        __slots__ = ('_bins', '_max', '_scale')
+
         def __init__(self, num_bins, max_val):
             self._bins = num_bins
             self._max = max_val

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

@@ -5,6 +5,8 @@ from kafka.metrics.stats.sampled_stat import AbstractSampledStat
 
 class Max(AbstractSampledStat):
     """An AbstractSampledStat that gives the max over its samples."""
+    __slots__ = ('_initial_value', '_samples', '_current')
+
     def __init__(self):
         super(Max, self).__init__(float('-inf'))
 

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

@@ -7,6 +7,8 @@ from kafka.metrics.stats.sampled_stat import AbstractSampledStat
 
 class Min(AbstractSampledStat):
     """An AbstractSampledStat that gives the min over its samples."""
+    __slots__ = ('_initial_value', '_samples', '_current')
+
     def __init__(self):
         super(Min, self).__init__(float(sys.maxsize))
 

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

@@ -2,6 +2,8 @@ from __future__ import absolute_import
 
 
 class Percentile(object):
+    __slots__ = ('_metric_name', '_percentile')
+
     def __init__(self, metric_name, percentile):
         self._metric_name = metric_name
         self._percentile = float(percentile)

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

@@ -13,6 +13,9 @@ class BucketSizing(object):
 
 class Percentiles(AbstractSampledStat, AbstractCompoundStat):
     """A compound stat that reports one or more percentiles"""
+    __slots__ = ('_initial_value', '_samples', '_current',
+                 '_percentiles', '_buckets', '_bin_scheme')
+
     def __init__(self, size_in_bytes, bucketing, max_val, min_val=0.0,
                  percentiles=None):
         super(Percentiles, self).__init__(0.0)
@@ -27,7 +30,7 @@ class Percentiles(AbstractSampledStat, AbstractCompoundStat):
                                  ' to be 0.0.')
             self.bin_scheme = Histogram.LinearBinScheme(self._buckets, max_val)
         else:
-            ValueError('Unknown bucket type: %s' % (bucketing,))
+            raise ValueError('Unknown bucket type: %s' % (bucketing,))
 
     def stats(self):
         measurables = []

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

@@ -37,6 +37,8 @@ class Rate(AbstractMeasurableStat):
     occurrences (e.g. the count of values measured over the time interval)
     or other such values.
     """
+    __slots__ = ('_stat', '_unit')
+
     def __init__(self, time_unit=TimeUnit.SECONDS, sampled_stat=None):
         self._stat = sampled_stat or SampledTotal()
         self._unit = time_unit
@@ -105,6 +107,7 @@ class Rate(AbstractMeasurableStat):
 
 
 class SampledTotal(AbstractSampledStat):
+    __slots__ = ('_initial_value', '_samples', '_current')
     def __init__(self, initial_value=None):
         if initial_value is not None:
             raise ValueError('initial_value cannot be set on SampledTotal')

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

@@ -3,8 +3,10 @@ from __future__ import absolute_import
 import abc
 
 from kafka.metrics.measurable_stat import AbstractMeasurableStat
+from kafka.vendor.six import add_metaclass
 
 
+@add_metaclass(abc.ABCMeta)
 class AbstractSampledStat(AbstractMeasurableStat):
     """
     An AbstractSampledStat records a single scalar value measured over
@@ -20,7 +22,7 @@ class AbstractSampledStat(AbstractMeasurableStat):
     Subclasses of this class define different statistics measured
     using this basic pattern.
     """
-    __metaclass__ = abc.ABCMeta
+    __slots__ = ('_initial_value', '_samples', '_current')
 
     def __init__(self, initial_value):
         self._initial_value = initial_value

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

@@ -15,6 +15,10 @@ class Sensor(object):
     the `record(double)` api and would maintain a set
     of metrics about request sizes such as the average or max.
     """
+    __slots__ = ('_lock', '_registry', '_name', '_parents', '_metrics',
+                 '_stats', '_config', '_inactive_sensor_expiration_time_ms',
+                 '_last_record_time')
+
     def __init__(self, registry, name, parents, config,
                  inactive_sensor_expiration_time_seconds):
         if not name:

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

@@ -5,6 +5,8 @@ from kafka.metrics.measurable_stat import AbstractMeasurableStat
 
 class Total(AbstractMeasurableStat):
     """An un-windowed cumulative total maintained over all time."""
+    __slots__ = ('_total')
+
     def __init__(self, value=0.0):
         self._total = value