main commit

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

@@ -3,16 +3,16 @@ from __future__ import absolute_import
 import abc
 
 from kafka.metrics.stat import AbstractStat
-from kafka.vendor.six import add_metaclass
 
 
-@add_metaclass(abc.ABCMeta)
 class AbstractCompoundStat(AbstractStat):
     """
     A compound stat is a stat where a single measurement and associated
     data structure feeds many metrics. This is the example for a
     histogram which has many associated percentiles.
     """
+    __metaclass__ = abc.ABCMeta
+
     def stats(self):
         """
         Return list of NamedMeasurable
@@ -21,8 +21,6 @@ class AbstractCompoundStat(AbstractStat):
 
 
 class NamedMeasurable(object):
-    __slots__ = ('_name', '_stat')
-
     def __init__(self, metric_name, measurable_stat):
         self._name = metric_name
         self._stat = measurable_stat

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

@@ -4,8 +4,6 @@ import time
 
 
 class KafkaMetric(object):
-    __slots__ = ('_metric_name', '_measurable', '_config')
-
     # NOTE java constructor takes a lock instance
     def __init__(self, metric_name, measurable, config):
         if not metric_name:
@@ -35,4 +33,4 @@ class KafkaMetric(object):
     def value(self, time_ms=None):
         if time_ms is None:
             time_ms = time.time() * 1000
-        return self._measurable.measure(self._config, time_ms)
+        return self.measurable.measure(self.config, time_ms)

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

@@ -4,10 +4,8 @@ import abc
 
 from kafka.metrics.measurable import AbstractMeasurable
 from kafka.metrics.stat import AbstractStat
-from kafka.vendor.six import add_metaclass
 
 
-@add_metaclass(abc.ABCMeta)
 class AbstractMeasurableStat(AbstractStat, AbstractMeasurable):
     """
     An AbstractMeasurableStat is an AbstractStat that is also
@@ -15,3 +13,4 @@ class AbstractMeasurableStat(AbstractStat, AbstractMeasurable):
     This is the interface used for most of the simple statistics such
     as Avg, Max, Count, etc.
     """
+    __metaclass__ = abc.ABCMeta

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

@@ -5,8 +5,6 @@ import sys
 
 class MetricConfig(object):
     """Configuration values for metrics"""
-    __slots__ = ('quota', '_samples', 'event_window', 'time_window_ms', 'tags')
-
     def __init__(self, quota=None, samples=2, event_window=sys.maxsize,
                  time_window_ms=30 * 1000, tags=None):
         """

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

@@ -38,7 +38,6 @@ class MetricName(object):
         # as messages are sent we record the sizes
         sensor.record(message_size)
     """
-    __slots__ = ('_name', '_group', '_description', '_tags', '_hash')
 
     def __init__(self, name, group, description=None, tags=None):
         """
@@ -94,7 +93,7 @@ class MetricName(object):
             return True
         if other is None:
             return False
-        return (isinstance(self, type(other)) and
+        return (type(self) == type(other) and
                 self.group == other.group and
                 self.name == other.name and
                 self.tags == other.tags)

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

@@ -55,11 +55,10 @@ class Metrics(object):
         self._reporters = reporters or []
         for reporter in self._reporters:
             reporter.init([])
-        self._closed = False
 
         if enable_expiration:
             def expire_loop():
-                while not self._closed:
+                while True:
                     # delay 30 seconds
                     time.sleep(30)
                     self.ExpireSensorTask.run(self)
@@ -260,4 +259,3 @@ class Metrics(object):
             reporter.close()
 
         self._metrics.clear()
-        self._closed = True

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

@@ -2,15 +2,14 @@ from __future__ import absolute_import
 
 import abc
 
-from kafka.vendor.six import add_metaclass
 
-
-@add_metaclass(abc.ABCMeta)
 class AbstractMetricsReporter(object):
     """
     An abstract class to allow things to listen as new metrics
     are created so they can be reported.
     """
+    __metaclass__ = abc.ABCMeta
+
     @abc.abstractmethod
     def init(self, metrics):
         """

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

@@ -3,8 +3,6 @@ from __future__ import absolute_import
 
 class Quota(object):
     """An upper or lower bound for metrics"""
-    __slots__ = ('_bound', '_upper')
-
     def __init__(self, bound, is_upper):
         self._bound = bound
         self._upper = is_upper
@@ -36,7 +34,7 @@ class Quota(object):
     def __eq__(self, other):
         if self is other:
             return True
-        return (isinstance(self, type(other)) and
+        return (type(self) == type(other) and
                 self.bound == other.bound and
                 self.is_upper_bound() == other.is_upper_bound())
 

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

@@ -2,15 +2,14 @@ from __future__ import absolute_import
 
 import abc
 
-from kafka.vendor.six import add_metaclass
 
-
-@add_metaclass(abc.ABCMeta)
 class AbstractStat(object):
     """
     An AbstractStat is a quantity such as average, max, etc that is computed
     off the stream of updates to a sensor
     """
+    __metaclass__ = abc.ABCMeta
+
     @abc.abstractmethod
     def record(self, config, value, time_ms):
         """

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

@@ -7,8 +7,6 @@ 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

@@ -7,8 +7,6 @@ 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

@@ -4,8 +4,6 @@ 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
@@ -42,8 +40,6 @@ 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.')
@@ -73,8 +69,6 @@ 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

@@ -5,8 +5,6 @@ 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

@@ -7,8 +7,6 @@ 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

@@ -2,8 +2,6 @@ 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

@@ -13,9 +13,6 @@ 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)
@@ -30,7 +27,7 @@ class Percentiles(AbstractSampledStat, AbstractCompoundStat):
                                  ' to be 0.0.')
             self.bin_scheme = Histogram.LinearBinScheme(self._buckets, max_val)
         else:
-            raise ValueError('Unknown bucket type: %s' % (bucketing,))
+            ValueError('Unknown bucket type: %s' % (bucketing,))
 
     def stats(self):
         measurables = []

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

@@ -37,8 +37,6 @@ 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
@@ -107,7 +105,6 @@ 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

@@ -3,10 +3,8 @@ 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
@@ -22,7 +20,7 @@ class AbstractSampledStat(AbstractMeasurableStat):
     Subclasses of this class define different statistics measured
     using this basic pattern.
     """
-    __slots__ = ('_initial_value', '_samples', '_current')
+    __metaclass__ = abc.ABCMeta
 
     def __init__(self, initial_value):
         self._initial_value = initial_value

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

@@ -15,10 +15,6 @@ 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

@@ -5,8 +5,6 @@ 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