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"""
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
* <p>
* http://www.apache.org/licenses/LICENSE-2.0
* <p>
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
"""
from __future__ import absolute_import
import collections
import heapq
import kafka.errors as Errors
from kafka.structs import TopicPartition
from .record_queue import RecordQueue
TaskId = collections.namedtuple('TaskId', 'topic_group_id partition_id')
class RecordInfo(object):
def __init__(self):
self.queue = None
def node(self):
return self.queue.source
def partition(self):
return self.queue.partition
def queue(self):
return self.queue
class PartitionGroup(object):
"""A PartitionGroup is composed from a set of partitions. It also maintains
the timestamp of this group, hence the associated task as the min timestamp
across all partitions in the group.
"""
def __init__(self, partition_queues, timestamp_extractor):
self._queues_by_time = [] # heapq
self._partition_queues = partition_queues
self._timestamp_extractor = timestamp_extractor
self._total_buffered = 0
def next_record(self, record_info):
"""Get the next record and queue
Returns: (timestamp, ConsumerRecord)
"""
timestamp = record = queue = None
if self._queues_by_time:
_, queue = heapq.heappop(self._queues_by_time)
# get the first record from this queue.
timestamp, record = queue.poll()
if queue:
heapq.heappush(self._queues_by_time, (queue.timestamp(), queue))
record_info.queue = queue
if record:
self._total_buffered -= 1
return timestamp, record
def add_raw_records(self, partition, raw_records):
"""Adds raw records to this partition group
Arguments:
partition (TopicPartition): the partition
raw_records (list of ConsumerRecord): the raw records
Returns: the queue size for the partition
"""
record_queue = self._partition_queues[partition]
old_size = record_queue.size()
new_size = record_queue.add_raw_records(raw_records, self._timestamp_extractor)
# add this record queue to be considered for processing in the future
# if it was empty before
if old_size == 0 and new_size > 0:
heapq.heappush(self._queues_by_time, (record_queue.timestamp(), record_queue))
self._total_buffered += new_size - old_size
return new_size
def partitions(self):
return set(self._partition_queues.keys())
def timestamp(self):
"""Return the timestamp of this partition group
as the smallest partition timestamp among all its partitions
"""
# we should always return the smallest timestamp of all partitions
# to avoid group partition time goes backward
timestamp = float('inf')
for queue in self._partition_queues.values():
if timestamp > queue.timestamp():
timestamp = queue.timestamp()
return timestamp
def num_buffered(self, partition=None):
if partition is None:
return self._total_buffered
record_queue = self._partition_queues.get(partition)
if not record_queue:
raise Errors.IllegalStateError('Record partition does not belong to this partition-group.')
return record_queue.size()
def top_queue_size(self):
if not self._queues_by_time:
return 0
return self._queues_by_time[0][1].size() # XXX RecordQueue.__len__
def close(self):
self._queues_by_time = []
self._partition_queues.clear()
def partition_grouper(topic_groups, metadata):
"""Assign partitions to task/topic groups
Arguments:
topic_groups ({topic_group_id: [topics]})
metadata (kafka.Cluster)
Returns: {TaskId: set([TopicPartition])}
"""
groups = {}
for topic_group_id, topic_group in topic_groups.items():
partitions = set()
for topic in topic_group:
partitions.update(metadata.partitions_for_topic(topic))
for partition_id in partitions:
group = set()
for topic in topic_group:
if partition_id in metadata.partitions_for_topic(topic):
group.add(TopicPartition(topic, partition_id))
groups[TaskId(topic_group_id, partition_id)] = group
return groups
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