venv

virtual-env/lib/python3.12/site-packages/kafka/coordinator/assignors/sticky/partition_movements.py

+import logging
+from collections import defaultdict, namedtuple
+from copy import deepcopy
+
+from kafka.vendor import six
+
+log = logging.getLogger(__name__)
+
+
+ConsumerPair = namedtuple("ConsumerPair", ["src_member_id", "dst_member_id"])
+"""
+Represents a pair of Kafka consumer ids involved in a partition reassignment.
+Each ConsumerPair corresponds to a particular partition or topic, indicates that the particular partition or some
+partition of the particular topic was moved from the source consumer to the destination consumer
+during the rebalance. This class helps in determining whether a partition reassignment results in cycles among
+the generated graph of consumer pairs.
+"""
+
+
+def is_sublist(source, target):
+    """Checks if one list is a sublist of another.
+
+    Arguments:
+      source: the list in which to search for the occurrence of target.
+      target: the list to search for as a sublist of source
+
+    Returns:
+      true if target is in source; false otherwise
+    """
+    for index in (i for i, e in enumerate(source) if e == target[0]):
+        if tuple(source[index: index + len(target)]) == target:
+            return True
+    return False
+
+
+class PartitionMovements:
+    """
+    This class maintains some data structures to simplify lookup of partition movements among consumers.
+    At each point of time during a partition rebalance it keeps track of partition movements
+    corresponding to each topic, and also possible movement (in form a ConsumerPair object) for each partition.
+    """
+
+    def __init__(self):
+        self.partition_movements_by_topic = defaultdict(
+            lambda: defaultdict(set)
+        )
+        self.partition_movements = {}
+
+    def move_partition(self, partition, old_consumer, new_consumer):
+        pair = ConsumerPair(src_member_id=old_consumer, dst_member_id=new_consumer)
+        if partition in self.partition_movements:
+            # this partition has previously moved
+            existing_pair = self._remove_movement_record_of_partition(partition)
+            assert existing_pair.dst_member_id == old_consumer
+            if existing_pair.src_member_id != new_consumer:
+                # the partition is not moving back to its previous consumer
+                self._add_partition_movement_record(
+                    partition, ConsumerPair(src_member_id=existing_pair.src_member_id, dst_member_id=new_consumer)
+                )
+        else:
+            self._add_partition_movement_record(partition, pair)
+
+    def get_partition_to_be_moved(self, partition, old_consumer, new_consumer):
+        if partition.topic not in self.partition_movements_by_topic:
+            return partition
+        if partition in self.partition_movements:
+            # this partition has previously moved
+            assert old_consumer == self.partition_movements[partition].dst_member_id
+            old_consumer = self.partition_movements[partition].src_member_id
+        reverse_pair = ConsumerPair(src_member_id=new_consumer, dst_member_id=old_consumer)
+        if reverse_pair not in self.partition_movements_by_topic[partition.topic]:
+            return partition
+
+        return next(iter(self.partition_movements_by_topic[partition.topic][reverse_pair]))
+
+    def are_sticky(self):
+        for topic, movements in six.iteritems(self.partition_movements_by_topic):
+            movement_pairs = set(movements.keys())
+            if self._has_cycles(movement_pairs):
+                log.error(
+                    "Stickiness is violated for topic {}\n"
+                    "Partition movements for this topic occurred among the following consumer pairs:\n"
+                    "{}".format(topic, movement_pairs)
+                )
+                return False
+        return True
+
+    def _remove_movement_record_of_partition(self, partition):
+        pair = self.partition_movements[partition]
+        del self.partition_movements[partition]
+
+        self.partition_movements_by_topic[partition.topic][pair].remove(partition)
+        if not self.partition_movements_by_topic[partition.topic][pair]:
+            del self.partition_movements_by_topic[partition.topic][pair]
+        if not self.partition_movements_by_topic[partition.topic]:
+            del self.partition_movements_by_topic[partition.topic]
+
+        return pair
+
+    def _add_partition_movement_record(self, partition, pair):
+        self.partition_movements[partition] = pair
+        self.partition_movements_by_topic[partition.topic][pair].add(partition)
+
+    def _has_cycles(self, consumer_pairs):
+        cycles = set()
+        for pair in consumer_pairs:
+            reduced_pairs = deepcopy(consumer_pairs)
+            reduced_pairs.remove(pair)
+            path = [pair.src_member_id]
+            if self._is_linked(pair.dst_member_id, pair.src_member_id, reduced_pairs, path) and not self._is_subcycle(
+                path, cycles
+            ):
+                cycles.add(tuple(path))
+                log.error("A cycle of length {} was found: {}".format(len(path) - 1, path))
+
+        # for now we want to make sure there is no partition movements of the same topic between a pair of consumers.
+        # the odds of finding a cycle among more than two consumers seem to be very low (according to various randomized
+        # tests with the given sticky algorithm) that it should not worth the added complexity of handling those cases.
+        for cycle in cycles:
+            if len(cycle) == 3:  # indicates a cycle of length 2
+                return True
+        return False
+
+    @staticmethod
+    def _is_subcycle(cycle, cycles):
+        super_cycle = deepcopy(cycle)
+        super_cycle = super_cycle[:-1]
+        super_cycle.extend(cycle)
+        for found_cycle in cycles:
+            if len(found_cycle) == len(cycle) and is_sublist(super_cycle, found_cycle):
+                return True
+        return False
+
+    def _is_linked(self, src, dst, pairs, current_path):
+        if src == dst:
+            return False
+        if not pairs:
+            return False
+        if ConsumerPair(src, dst) in pairs:
+            current_path.append(src)
+            current_path.append(dst)
+            return True
+        for pair in pairs:
+            if pair.src_member_id == src:
+                reduced_set = deepcopy(pairs)
+                reduced_set.remove(pair)
+                current_path.append(pair.src_member_id)
+                return self._is_linked(pair.dst_member_id, dst, reduced_set, current_path)
+        return False

virtual-env/lib/python3.12/site-packages/kafka/coordinator/assignors/sticky/sorted_set.py

+class SortedSet:
+    def __init__(self, iterable=None, key=None):
+        self._key = key if key is not None else lambda x: x
+        self._set = set(iterable) if iterable is not None else set()
+
+        self._cached_last = None
+        self._cached_first = None
+
+    def first(self):
+        if self._cached_first is not None:
+            return self._cached_first
+
+        first = None
+        for element in self._set:
+            if first is None or self._key(first) > self._key(element):
+                first = element
+        self._cached_first = first
+        return first
+
+    def last(self):
+        if self._cached_last is not None:
+            return self._cached_last
+
+        last = None
+        for element in self._set:
+            if last is None or self._key(last) < self._key(element):
+                last = element
+        self._cached_last = last
+        return last
+
+    def pop_last(self):
+        value = self.last()
+        self._set.remove(value)
+        self._cached_last = None
+        return value
+
+    def add(self, value):
+        if self._cached_last is not None and self._key(value) > self._key(self._cached_last):
+            self._cached_last = value
+        if self._cached_first is not None and self._key(value) < self._key(self._cached_first):
+            self._cached_first = value
+
+        return self._set.add(value)
+
+    def remove(self, value):
+        if self._cached_last is not None and self._cached_last == value:
+            self._cached_last = None
+        if self._cached_first is not None and self._cached_first == value:
+            self._cached_first = None
+
+        return self._set.remove(value)
+
+    def __contains__(self, value):
+        return value in self._set
+
+    def __iter__(self):
+        return iter(sorted(self._set, key=self._key))
+
+    def _bool(self):
+        return len(self._set) != 0
+
+    __nonzero__ = _bool
+    __bool__ = _bool

virtual-env/lib/python3.12/site-packages/kafka/coordinator/protocol.py

+from __future__ import absolute_import
+
+from kafka.protocol.struct import Struct
+from kafka.protocol.types import Array, Bytes, Int16, Int32, Schema, String
+from kafka.structs import TopicPartition
+
+
+class ConsumerProtocolMemberMetadata(Struct):
+    SCHEMA = Schema(
+        ('version', Int16),
+        ('subscription', Array(String('utf-8'))),
+        ('user_data', Bytes))
+
+
+class ConsumerProtocolMemberAssignment(Struct):
+    SCHEMA = Schema(
+        ('version', Int16),
+        ('assignment', Array(
+            ('topic', String('utf-8')),
+            ('partitions', Array(Int32)))),
+        ('user_data', Bytes))
+
+    def partitions(self):
+        return [TopicPartition(topic, partition)
+                for topic, partitions in self.assignment # pylint: disable-msg=no-member
+                for partition in partitions]
+
+
+class ConsumerProtocol(object):
+    PROTOCOL_TYPE = 'consumer'
+    ASSIGNMENT_STRATEGIES = ('range', 'roundrobin')
+    METADATA = ConsumerProtocolMemberMetadata
+    ASSIGNMENT = ConsumerProtocolMemberAssignment

virtual-env/lib/python3.12/site-packages/kafka/metrics/__init__.py

+from __future__ import absolute_import
+
+from kafka.metrics.compound_stat import NamedMeasurable
+from kafka.metrics.dict_reporter import DictReporter
+from kafka.metrics.kafka_metric import KafkaMetric
+from kafka.metrics.measurable import AnonMeasurable
+from kafka.metrics.metric_config import MetricConfig
+from kafka.metrics.metric_name import MetricName
+from kafka.metrics.metrics import Metrics
+from kafka.metrics.quota import Quota
+
+__all__ = [
+    'AnonMeasurable', 'DictReporter', 'KafkaMetric', 'MetricConfig',
+    'MetricName', 'Metrics', 'NamedMeasurable', 'Quota'
+]