Prefer ABC subclass to ABCmeta metaclass (#3055)

Author: dpkp

kafka/consumer/subscription_state.py

@@ -1,4 +1,4 @@
-import abc
+from abc import ABC, abstractmethod
 from collections import OrderedDict
 from collections.abc import Sequence
 from enum import IntEnum
@@ -731,7 +731,7 @@ def complete_validation(self, validated_position=None):
             self._position = validated_position
 
 
-class ConsumerRebalanceListener(metaclass=abc.ABCMeta):
+class ConsumerRebalanceListener(ABC):
     """
     A callback interface that the user can implement to trigger custom actions
     when the set of partitions assigned to the consumer changes.
@@ -779,7 +779,7 @@ class ConsumerRebalanceListener(metaclass=abc.ABCMeta):
     taking over that partition has their on_partitions_assigned() callback
     called to load the state.
     """
-    @abc.abstractmethod
+    @abstractmethod
     def on_partitions_revoked(self, revoked):
         """
         A callback method the user can implement to provide handling of offset
@@ -801,7 +801,7 @@ def on_partitions_revoked(self, revoked):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def on_partitions_assigned(self, assigned):
         """
         A callback method the user can implement to provide handling of
@@ -840,7 +840,7 @@ def on_partitions_lost(self, lost):
         return self.on_partitions_revoked(lost)
 
 
-class AsyncConsumerRebalanceListener(metaclass=abc.ABCMeta):
+class AsyncConsumerRebalanceListener(ABC):
     """
     Async variant of :class:`ConsumerRebalanceListener`.
 
@@ -856,7 +856,7 @@ class AsyncConsumerRebalanceListener(metaclass=abc.ABCMeta):
     invokes ``on_partitions_assigned``. Both methods must be defined as
     ``async def``; otherwise use :class:`ConsumerRebalanceListener`.
     """
-    @abc.abstractmethod
+    @abstractmethod
     async def on_partitions_revoked(self, revoked):
         """Async-callback for the start of a rebalance operation.
 
@@ -870,7 +870,7 @@ async def on_partitions_revoked(self, revoked):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     async def on_partitions_assigned(self, assigned):
         """Async-callback for the completion of a partition re-assignment.
 

kafka/coordinator/assignors/abstract.py

@@ -1,4 +1,4 @@
-import abc
+from abc import ABC, abstractmethod, abstractproperty
 from enum import IntEnum
 
 from kafka.protocol.consumer.metadata import (
@@ -25,13 +25,13 @@ class RebalanceProtocol(IntEnum):
     COOPERATIVE = 1
 
 
-class AbstractPartitionAssignor(metaclass=abc.ABCMeta):
+class AbstractPartitionAssignor(ABC):
     """
     Abstract assignor implementation which does some common grunt work (in particular collecting
     partition counts which are always needed in assignors).
     """
 
-    @abc.abstractproperty
+    @abstractproperty
     def name(self):
         """.name should be a string identifying the assignor"""
         pass
@@ -48,7 +48,7 @@ def supported_protocols(self):
         """
         return [RebalanceProtocol.EAGER]
 
-    @abc.abstractmethod
+    @abstractmethod
     def assign(self, cluster, members):
         """Perform group assignment given cluster metadata and member subscriptions
 
@@ -64,7 +64,7 @@ def assign(self, cluster, members):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def metadata(self, topics):
         """Generate ProtocolMetadata to be submitted via JoinGroupRequest.
 
@@ -76,7 +76,7 @@ def metadata(self, topics):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def on_assignment(self, assignment, generation):
         """Callback that runs on each assignment.
 

kafka/coordinator/base.py

@@ -1,4 +1,4 @@
-import abc
+from abc import ABC, abstractmethod
 import copy
 import logging
 import threading
@@ -70,7 +70,7 @@ class UnjoinedGroupException(Errors.RetriableError):
     pass
 
 
-class BaseCoordinator(metaclass=abc.ABCMeta):
+class BaseCoordinator(ABC):
     """
     BaseCoordinator implements group management for a single group member
     by interacting with a designated Kafka broker (the coordinator). Group
@@ -225,7 +225,7 @@ def group_id(self):
     def group_instance_id(self):
         return self.config['group_instance_id']
 
-    @abc.abstractmethod
+    @abstractmethod
     def protocol_type(self):
         """
         Unique identifier for the class of supported protocols
@@ -236,7 +236,7 @@ def protocol_type(self):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def group_protocols(self):
         """Return the list of supported group protocols and metadata.
 
@@ -270,7 +270,7 @@ async def _on_join_prepare_async(self, generation, member_id, timeout_ms=None):
         """
         pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def _perform_assignment(self, leader_id, protocol, members):
         """Perform assignment for the group.
 

kafka/metrics/compound_stat.py

@@ -1,19 +1,20 @@
-import abc
+from abc import ABC, abstractmethod
 
 from kafka.metrics.stat import AbstractStat
 
 
-class AbstractCompoundStat(AbstractStat, metaclass=abc.ABCMeta):
+class AbstractCompoundStat(AbstractStat, ABC):
     """
     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.
     """
+    @abstractmethod
     def stats(self):
         """
         Return list of NamedMeasurable
         """
-        raise NotImplementedError
+        pass
 
 
 class NamedMeasurable:

kafka/metrics/measurable.py

@@ -1,9 +1,9 @@
-import abc
+from abc import ABC, abstractmethod
 
 
-class AbstractMeasurable(metaclass=abc.ABCMeta):
+class AbstractMeasurable(ABC):
     """A measurable quantity that can be registered as a metric"""
-    @abc.abstractmethod
+    @abstractmethod
     def measure(self, config, now):
         """
         Measure this quantity and return the result
@@ -16,7 +16,7 @@ def measure(self, config, now):
         Returns:
             The measured value
         """
-        raise NotImplementedError
+        pass
 
 
 class AnonMeasurable(AbstractMeasurable):

kafka/metrics/measurable_stat.py

@@ -1,10 +1,10 @@
-import abc
+from abc import ABC
 
 from kafka.metrics.measurable import AbstractMeasurable
 from kafka.metrics.stat import AbstractStat
 
 
-class AbstractMeasurableStat(AbstractStat, AbstractMeasurable, metaclass=abc.ABCMeta):
+class AbstractMeasurableStat(AbstractStat, AbstractMeasurable, ABC):
     """
     An AbstractMeasurableStat is an AbstractStat that is also
     an AbstractMeasurable (i.e. can produce a single floating point value).

kafka/metrics/metrics_reporter.py

@@ -1,12 +1,12 @@
-import abc
+from abc import ABC, abstractmethod
 
 
-class AbstractMetricsReporter(metaclass=abc.ABCMeta):
+class AbstractMetricsReporter(ABC):
     """
     An abstract class to allow things to listen as new metrics
     are created so they can be reported.
     """
-    @abc.abstractmethod
+    @abstractmethod
     def init(self, metrics):
         """
         This is called when the reporter is first registered
@@ -15,39 +15,39 @@ def init(self, metrics):
         Arguments:
             metrics (list of KafkaMetric): All currently existing metrics
         """
-        raise NotImplementedError
+        pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def metric_change(self, metric):
         """
         This is called whenever a metric is updated or added
 
         Arguments:
             metric (KafkaMetric)
         """
-        raise NotImplementedError
+        pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def metric_removal(self, metric):
         """
         This is called whenever a metric is removed
 
         Arguments:
             metric (KafkaMetric)
         """
-        raise NotImplementedError
+        pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def configure(self, configs):
         """
         Configure this class with the given key-value pairs
 
         Arguments:
             configs (dict of {str, ?})
         """
-        raise NotImplementedError
+        pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def close(self):
         """Called when the metrics repository is closed."""
-        raise NotImplementedError
+        pass

kafka/metrics/stat.py

@@ -1,12 +1,12 @@
-import abc
+from abc import ABC, abstractmethod
 
 
-class AbstractStat(metaclass=abc.ABCMeta):
+class AbstractStat(ABC):
     """
     An AbstractStat is a quantity such as average, max, etc that is computed
     off the stream of updates to a sensor
     """
-    @abc.abstractmethod
+    @abstractmethod
     def record(self, config, value, time_ms):
         """
         Record the given value
@@ -16,4 +16,4 @@ def record(self, config, value, time_ms):
             value (float): The value to record
             timeMs (int): The POSIX time in milliseconds this value occurred
         """
-        raise NotImplementedError
+        pass

kafka/metrics/stats/sampled_stat.py

@@ -1,9 +1,9 @@
-import abc
+from abc import ABC, abstractmethod
 
 from kafka.metrics.measurable_stat import AbstractMeasurableStat
 
 
-class AbstractSampledStat(AbstractMeasurableStat, metaclass=abc.ABCMeta):
+class AbstractSampledStat(AbstractMeasurableStat, ABC):
     """
     An AbstractSampledStat records a single scalar value measured over
     one or more samples. Each sample is recorded over a configurable
@@ -25,13 +25,13 @@ def __init__(self, initial_value):
         self._samples = []
         self._current = 0
 
-    @abc.abstractmethod
+    @abstractmethod
     def update(self, sample, config, value, time_ms):
-        raise NotImplementedError
+        pass
 
-    @abc.abstractmethod
+    @abstractmethod
     def combine(self, samples, config, now):
-        raise NotImplementedError
+        pass
 
     def record(self, config, value, time_ms):
         sample = self.current(time_ms)

kafka/partitioner/abc.py

@@ -1,8 +1,8 @@
-import abc
+from abc import ABC, abstractmethod
 
 
-class Partitioner(metaclass=abc.ABCMeta):
-    @abc.abstractmethod
+class Partitioner(ABC):
+    @abstractmethod
     def partition(self, topic, key, serialized_key, value, serialized_value, cluster):
         pass