Use __slots__ for metrics (#2583)

Author: dpkp

kafka/metrics/compound_stat.py

@@ -21,6 +21,8 @@ def stats(self):
 
 
 class NamedMeasurable(object):
+    __slots__ = ('_name', '_stat')
+
     def __init__(self, metric_name, measurable_stat):
         self._name = metric_name
         self._stat = measurable_stat

kafka/metrics/kafka_metric.py

@@ -4,6 +4,8 @@
 
 
 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:
@@ -33,4 +35,4 @@ def config(self, config):
     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)

kafka/metrics/metric_config.py

@@ -5,6 +5,8 @@
 
 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):
         """

kafka/metrics/metric_name.py

@@ -38,6 +38,7 @@ 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):
         """

kafka/metrics/quota.py

@@ -3,6 +3,8 @@
 
 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

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)
 

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)
 

kafka/metrics/stats/histogram.py

@@ -4,6 +4,8 @@
 
 
 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 @@ def __str__(self):
         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 @@ def to_bin(self, x):
                 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

kafka/metrics/stats/max_stat.py

@@ -5,6 +5,8 @@
 
 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'))
 

kafka/metrics/stats/min_stat.py

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