This file is indexed.

/usr/lib/python3/dist-packages/kafka/client.py is in python3-kafka 0.9.5-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
import collections
import copy
import functools
import logging
import select
import time

import kafka.common
from kafka.common import (TopicAndPartition, BrokerMetadata,
                          ConnectionError, FailedPayloadsError,
                          KafkaTimeoutError, KafkaUnavailableError,
                          LeaderNotAvailableError, UnknownTopicOrPartitionError,
                          NotLeaderForPartitionError, ReplicaNotAvailableError)

from kafka.conn import collect_hosts, KafkaConnection, DEFAULT_SOCKET_TIMEOUT_SECONDS
from kafka.protocol import KafkaProtocol
from kafka.util import kafka_bytestring


log = logging.getLogger(__name__)


class KafkaClient(object):

    CLIENT_ID = b'kafka-python'

    # NOTE: The timeout given to the client should always be greater than the
    # one passed to SimpleConsumer.get_message(), otherwise you can get a
    # socket timeout.
    def __init__(self, hosts, client_id=CLIENT_ID,
                 timeout=DEFAULT_SOCKET_TIMEOUT_SECONDS,
                 correlation_id=0):
        # We need one connection to bootstrap
        self.client_id = kafka_bytestring(client_id)
        self.timeout = timeout
        self.hosts = collect_hosts(hosts)
        self.correlation_id = correlation_id

        # create connections only when we need them
        self.conns = {}
        self.brokers = {}            # broker_id -> BrokerMetadata
        self.topics_to_brokers = {}  # TopicAndPartition -> BrokerMetadata
        self.topic_partitions = {}   # topic -> partition -> PartitionMetadata

        self.load_metadata_for_topics()  # bootstrap with all metadata


    ##################
    #   Private API  #
    ##################

    def _get_conn(self, host, port):
        """Get or create a connection to a broker using host and port"""
        host_key = (host, port)
        if host_key not in self.conns:
            self.conns[host_key] = KafkaConnection(
                host,
                port,
                timeout=self.timeout
            )

        return self.conns[host_key]

    def _get_leader_for_partition(self, topic, partition):
        """
        Returns the leader for a partition or None if the partition exists
        but has no leader.

        UnknownTopicOrPartitionError will be raised if the topic or partition
        is not part of the metadata.

        LeaderNotAvailableError is raised if server has metadata, but there is
        no current leader
        """

        key = TopicAndPartition(topic, partition)

        # Use cached metadata if it is there
        if self.topics_to_brokers.get(key) is not None:
            return self.topics_to_brokers[key]

        # Otherwise refresh metadata

        # If topic does not already exist, this will raise
        # UnknownTopicOrPartitionError if not auto-creating
        # LeaderNotAvailableError otherwise until partitions are created
        self.load_metadata_for_topics(topic)

        # If the partition doesn't actually exist, raise
        if partition not in self.topic_partitions.get(topic, []):
            raise UnknownTopicOrPartitionError(key)

        # If there's no leader for the partition, raise
        meta = self.topic_partitions[topic][partition]
        if meta.leader == -1:
            raise LeaderNotAvailableError(meta)

        # Otherwise return the BrokerMetadata
        return self.brokers[meta.leader]

    def _get_coordinator_for_group(self, group):
        """
        Returns the coordinator broker for a consumer group.

        ConsumerCoordinatorNotAvailableCode will be raised if the coordinator
        does not currently exist for the group.

        OffsetsLoadInProgressCode is raised if the coordinator is available
        but is still loading offsets from the internal topic
        """

        resp = self.send_consumer_metadata_request(group)

        # If there's a problem with finding the coordinator, raise the
        # provided error
        kafka.common.check_error(resp)

        # Otherwise return the BrokerMetadata
        return BrokerMetadata(resp.nodeId, resp.host, resp.port)

    def _next_id(self):
        """Generate a new correlation id"""
        # modulo to keep w/i int32
        self.correlation_id = (self.correlation_id + 1) % 2**31
        return self.correlation_id

    def _send_broker_unaware_request(self, payloads, encoder_fn, decoder_fn):
        """
        Attempt to send a broker-agnostic request to one of the available
        brokers. Keep trying until you succeed.
        """
        for (host, port) in self.hosts:
            requestId = self._next_id()
            log.debug('Request %s: %s', requestId, payloads)
            try:
                conn = self._get_conn(host, port)
                request = encoder_fn(client_id=self.client_id,
                                     correlation_id=requestId,
                                     payloads=payloads)

                conn.send(requestId, request)
                response = conn.recv(requestId)
                decoded = decoder_fn(response)
                log.debug('Response %s: %s', requestId, decoded)
                return decoded

            except Exception:
                log.exception('Error sending request [%s] to server %s:%s, '
                              'trying next server', requestId, host, port)

        raise KafkaUnavailableError('All servers failed to process request')

    def _send_broker_aware_request(self, payloads, encoder_fn, decoder_fn):
        """
        Group a list of request payloads by topic+partition and send them to
        the leader broker for that partition using the supplied encode/decode
        functions

        Arguments:

        payloads: list of object-like entities with a topic (str) and
            partition (int) attribute; payloads with duplicate topic-partitions
            are not supported.

        encode_fn: a method to encode the list of payloads to a request body,
            must accept client_id, correlation_id, and payloads as
            keyword arguments

        decode_fn: a method to decode a response body into response objects.
            The response objects must be object-like and have topic
            and partition attributes

        Returns:

        List of response objects in the same order as the supplied payloads
        """
        # encoders / decoders do not maintain ordering currently
        # so we need to keep this so we can rebuild order before returning
        original_ordering = [(p.topic, p.partition) for p in payloads]

        # Group the requests by topic+partition
        brokers_for_payloads = []
        payloads_by_broker = collections.defaultdict(list)

        responses = {}
        for payload in payloads:
            try:
                leader = self._get_leader_for_partition(payload.topic,
                                                        payload.partition)
                payloads_by_broker[leader].append(payload)
                brokers_for_payloads.append(leader)
            except KafkaUnavailableError as e:
                log.warning('KafkaUnavailableError attempting to send request '
                            'on topic %s partition %d', payload.topic, payload.partition)
                topic_partition = (payload.topic, payload.partition)
                responses[topic_partition] = FailedPayloadsError(payload)

        # For each broker, send the list of request payloads
        # and collect the responses and errors
        broker_failures = []

        # For each KafkaConnection keep the real socket so that we can use
        # a select to perform unblocking I/O
        connections_by_socket = {}
        for broker, payloads in payloads_by_broker.items():
            requestId = self._next_id()
            log.debug('Request %s to %s: %s', requestId, broker, payloads)
            request = encoder_fn(client_id=self.client_id,
                                 correlation_id=requestId, payloads=payloads)

            # Send the request, recv the response
            try:
                conn = self._get_conn(broker.host.decode('utf-8'), broker.port)
                conn.send(requestId, request)

            except ConnectionError as e:
                broker_failures.append(broker)
                log.warning('ConnectionError attempting to send request %s '
                            'to server %s: %s', requestId, broker, e)

                for payload in payloads:
                    topic_partition = (payload.topic, payload.partition)
                    responses[topic_partition] = FailedPayloadsError(payload)

            # No exception, try to get response
            else:

                # decoder_fn=None signal that the server is expected to not
                # send a response.  This probably only applies to
                # ProduceRequest w/ acks = 0
                if decoder_fn is None:
                    log.debug('Request %s does not expect a response '
                              '(skipping conn.recv)', requestId)
                    for payload in payloads:
                        topic_partition = (payload.topic, payload.partition)
                        responses[topic_partition] = None
                    continue
                else:
                    connections_by_socket[conn.get_connected_socket()] = (conn, broker, requestId)

        conn = None
        while connections_by_socket:
            sockets = connections_by_socket.keys()
            rlist, _, _ = select.select(sockets, [], [], None)
            conn, broker, requestId = connections_by_socket.pop(rlist[0])
            try:
                response = conn.recv(requestId)
            except ConnectionError as e:
                broker_failures.append(broker)
                log.warning('ConnectionError attempting to receive a '
                            'response to request %s from server %s: %s',
                            requestId, broker, e)

                for payload in payloads_by_broker[broker]:
                    topic_partition = (payload.topic, payload.partition)
                    responses[topic_partition] = FailedPayloadsError(payload)

            else:
                _resps = []
                for payload_response in decoder_fn(response):
                    topic_partition = (payload_response.topic,
                                       payload_response.partition)
                    responses[topic_partition] = payload_response
                    _resps.append(payload_response)
                log.debug('Response %s: %s', requestId, _resps)

        # Connection errors generally mean stale metadata
        # although sometimes it means incorrect api request
        # Unfortunately there is no good way to tell the difference
        # so we'll just reset metadata on all errors to be safe
        if broker_failures:
            self.reset_all_metadata()

        # Return responses in the same order as provided
        return [responses[tp] for tp in original_ordering]

    def _send_consumer_aware_request(self, group, payloads, encoder_fn, decoder_fn):
        """
        Send a list of requests to the consumer coordinator for the group
        specified using the supplied encode/decode functions. As the payloads
        that use consumer-aware requests do not contain the group (e.g.
        OffsetFetchRequest), all payloads must be for a single group.

        Arguments:

        group: the name of the consumer group (str) the payloads are for
        payloads: list of object-like entities with topic (str) and
            partition (int) attributes; payloads with duplicate
            topic+partition are not supported.

        encode_fn: a method to encode the list of payloads to a request body,
            must accept client_id, correlation_id, and payloads as
            keyword arguments

        decode_fn: a method to decode a response body into response objects.
            The response objects must be object-like and have topic
            and partition attributes

        Returns:

        List of response objects in the same order as the supplied payloads
        """
        # encoders / decoders do not maintain ordering currently
        # so we need to keep this so we can rebuild order before returning
        original_ordering = [(p.topic, p.partition) for p in payloads]

        broker = self._get_coordinator_for_group(group)

        # Send the list of request payloads and collect the responses and
        # errors
        responses = {}
        requestId = self._next_id()
        log.debug('Request %s to %s: %s', requestId, broker, payloads)
        request = encoder_fn(client_id=self.client_id,
                             correlation_id=requestId, payloads=payloads)

        # Send the request, recv the response
        try:
            conn = self._get_conn(broker.host.decode('utf-8'), broker.port)
            conn.send(requestId, request)

        except ConnectionError as e:
            log.warning('ConnectionError attempting to send request %s '
                        'to server %s: %s', requestId, broker, e)

            for payload in payloads:
                topic_partition = (payload.topic, payload.partition)
                responses[topic_partition] = FailedPayloadsError(payload)

        # No exception, try to get response
        else:

            # decoder_fn=None signal that the server is expected to not
            # send a response.  This probably only applies to
            # ProduceRequest w/ acks = 0
            if decoder_fn is None:
                log.debug('Request %s does not expect a response '
                          '(skipping conn.recv)', requestId)
                for payload in payloads:
                    topic_partition = (payload.topic, payload.partition)
                    responses[topic_partition] = None
                return []

            try:
                response = conn.recv(requestId)
            except ConnectionError as e:
                log.warning('ConnectionError attempting to receive a '
                            'response to request %s from server %s: %s',
                            requestId, broker, e)

                for payload in payloads:
                    topic_partition = (payload.topic, payload.partition)
                    responses[topic_partition] = FailedPayloadsError(payload)

            else:
                _resps = []
                for payload_response in decoder_fn(response):
                    topic_partition = (payload_response.topic,
                                       payload_response.partition)
                    responses[topic_partition] = payload_response
                    _resps.append(payload_response)
                log.debug('Response %s: %s', requestId, _resps)

        # Return responses in the same order as provided
        return [responses[tp] for tp in original_ordering]

    def __repr__(self):
        return '<KafkaClient client_id=%s>' % (self.client_id)

    def _raise_on_response_error(self, resp):

        # Response can be an unraised exception object (FailedPayloadsError)
        if isinstance(resp, Exception):
            raise resp

        # Or a server api error response
        try:
            kafka.common.check_error(resp)
        except (UnknownTopicOrPartitionError, NotLeaderForPartitionError):
            self.reset_topic_metadata(resp.topic)
            raise

        # Return False if no error to enable list comprehensions
        return False

    #################
    #   Public API  #
    #################
    def close(self):
        for conn in self.conns.values():
            conn.close()

    def copy(self):
        """
        Create an inactive copy of the client object, suitable for passing
        to a separate thread.

        Note that the copied connections are not initialized, so reinit() must
        be called on the returned copy.
        """
        c = copy.deepcopy(self)
        for key in c.conns:
            c.conns[key] = self.conns[key].copy()
        return c

    def reinit(self):
        for conn in self.conns.values():
            conn.reinit()

    def reset_topic_metadata(self, *topics):
        for topic in topics:
            for topic_partition in list(self.topics_to_brokers.keys()):
                if topic_partition.topic == topic:
                    del self.topics_to_brokers[topic_partition]
            if topic in self.topic_partitions:
                del self.topic_partitions[topic]

    def reset_all_metadata(self):
        self.topics_to_brokers.clear()
        self.topic_partitions.clear()

    def has_metadata_for_topic(self, topic):
        topic = kafka_bytestring(topic)
        return (
          topic in self.topic_partitions
          and len(self.topic_partitions[topic]) > 0
        )

    def get_partition_ids_for_topic(self, topic):
        topic = kafka_bytestring(topic)
        if topic not in self.topic_partitions:
            return []

        return sorted(list(self.topic_partitions[topic]))

    @property
    def topics(self):
        return list(self.topic_partitions.keys())

    def ensure_topic_exists(self, topic, timeout = 30):
        start_time = time.time()

        while not self.has_metadata_for_topic(topic):
            if time.time() > start_time + timeout:
                raise KafkaTimeoutError('Unable to create topic {0}'.format(topic))
            try:
                self.load_metadata_for_topics(topic)
            except LeaderNotAvailableError:
                pass
            except UnknownTopicOrPartitionError:
                # Server is not configured to auto-create
                # retrying in this case will not help
                raise
            time.sleep(.5)

    def load_metadata_for_topics(self, *topics):
        """
        Fetch broker and topic-partition metadata from the server,
        and update internal data:
        broker list, topic/partition list, and topic/parition -> broker map

        This method should be called after receiving any error

        Arguments:
            *topics (optional): If a list of topics is provided,
                the metadata refresh will be limited to the specified topics only.

        Exceptions:
        ----------
        If the broker is configured to not auto-create topics,
        expect UnknownTopicOrPartitionError for topics that don't exist

        If the broker is configured to auto-create topics,
        expect LeaderNotAvailableError for new topics
        until partitions have been initialized.

        Exceptions *will not* be raised in a full refresh (i.e. no topic list)
        In this case, error codes will be logged as errors

        Partition-level errors will also not be raised here
        (a single partition w/o a leader, for example)
        """
        topics = [kafka_bytestring(t) for t in topics]

        if topics:
            for topic in topics:
                self.reset_topic_metadata(topic)
        else:
            self.reset_all_metadata()

        resp = self.send_metadata_request(topics)

        log.debug('Updating broker metadata: %s', resp.brokers)
        log.debug('Updating topic metadata: %s', resp.topics)

        self.brokers = dict([(broker.nodeId, broker)
                             for broker in resp.brokers])

        for topic_metadata in resp.topics:
            topic = topic_metadata.topic
            partitions = topic_metadata.partitions

            # Errors expected for new topics
            try:
                kafka.common.check_error(topic_metadata)
            except (UnknownTopicOrPartitionError, LeaderNotAvailableError) as e:

                # Raise if the topic was passed in explicitly
                if topic in topics:
                    raise

                # Otherwise, just log a warning
                log.error('Error loading topic metadata for %s: %s', topic, type(e))
                continue

            self.topic_partitions[topic] = {}
            for partition_metadata in partitions:
                partition = partition_metadata.partition
                leader = partition_metadata.leader

                self.topic_partitions[topic][partition] = partition_metadata

                # Populate topics_to_brokers dict
                topic_part = TopicAndPartition(topic, partition)

                # Check for partition errors
                try:
                    kafka.common.check_error(partition_metadata)

                # If No Leader, topics_to_brokers topic_partition -> None
                except LeaderNotAvailableError:
                    log.error('No leader for topic %s partition %d', topic, partition)
                    self.topics_to_brokers[topic_part] = None
                    continue
                # If one of the replicas is unavailable -- ignore
                # this error code is provided for admin purposes only
                # we never talk to replicas, only the leader
                except ReplicaNotAvailableError:
                    log.debug('Some (non-leader) replicas not available for topic %s partition %d', topic, partition)

                # If Known Broker, topic_partition -> BrokerMetadata
                if leader in self.brokers:
                    self.topics_to_brokers[topic_part] = self.brokers[leader]

                # If Unknown Broker, fake BrokerMetadata so we dont lose the id
                # (not sure how this could happen. server could be in bad state)
                else:
                    self.topics_to_brokers[topic_part] = BrokerMetadata(
                        leader, None, None
                    )

    def send_metadata_request(self, payloads=[], fail_on_error=True,
                              callback=None):
        encoder = KafkaProtocol.encode_metadata_request
        decoder = KafkaProtocol.decode_metadata_response

        return self._send_broker_unaware_request(payloads, encoder, decoder)

    def send_consumer_metadata_request(self, payloads=[], fail_on_error=True,
                                       callback=None):
        encoder = KafkaProtocol.encode_consumer_metadata_request
        decoder = KafkaProtocol.decode_consumer_metadata_response

        return self._send_broker_unaware_request(payloads, encoder, decoder)

    def send_produce_request(self, payloads=[], acks=1, timeout=1000,
                             fail_on_error=True, callback=None):
        """
        Encode and send some ProduceRequests

        ProduceRequests will be grouped by (topic, partition) and then
        sent to a specific broker. Output is a list of responses in the
        same order as the list of payloads specified

        Arguments:
            payloads (list of ProduceRequest): produce requests to send to kafka
                ProduceRequest payloads must not contain duplicates for any
                topic-partition.
            acks (int, optional): how many acks the servers should receive from replica
                brokers before responding to the request. If it is 0, the server
                will not send any response. If it is 1, the server will wait
                until the data is written to the local log before sending a
                response.  If it is -1, the server will wait until the message
                is committed by all in-sync replicas before sending a response.
                For any value > 1, the server will wait for this number of acks to
                occur (but the server will never wait for more acknowledgements than
                there are in-sync replicas). defaults to 1.
            timeout (int, optional): maximum time in milliseconds the server can
                await the receipt of the number of acks, defaults to 1000.
            fail_on_error (bool, optional): raise exceptions on connection and
                server response errors, defaults to True.
            callback (function, optional): instead of returning the ProduceResponse,
                first pass it through this function, defaults to None.

        Returns:
            list of ProduceResponses, or callback results if supplied, in the
            order of input payloads
        """

        encoder = functools.partial(
            KafkaProtocol.encode_produce_request,
            acks=acks,
            timeout=timeout)

        if acks == 0:
            decoder = None
        else:
            decoder = KafkaProtocol.decode_produce_response

        resps = self._send_broker_aware_request(payloads, encoder, decoder)

        return [resp if not callback else callback(resp) for resp in resps
                if resp is not None and
                (not fail_on_error or not self._raise_on_response_error(resp))]

    def send_fetch_request(self, payloads=[], fail_on_error=True,
                           callback=None, max_wait_time=100, min_bytes=4096):
        """
        Encode and send a FetchRequest

        Payloads are grouped by topic and partition so they can be pipelined
        to the same brokers.
        """

        encoder = functools.partial(KafkaProtocol.encode_fetch_request,
                          max_wait_time=max_wait_time,
                          min_bytes=min_bytes)

        resps = self._send_broker_aware_request(
            payloads, encoder,
            KafkaProtocol.decode_fetch_response)

        return [resp if not callback else callback(resp) for resp in resps
                if not fail_on_error or not self._raise_on_response_error(resp)]

    def send_offset_request(self, payloads=[], fail_on_error=True,
                            callback=None):
        resps = self._send_broker_aware_request(
            payloads,
            KafkaProtocol.encode_offset_request,
            KafkaProtocol.decode_offset_response)

        return [resp if not callback else callback(resp) for resp in resps
                if not fail_on_error or not self._raise_on_response_error(resp)]

    def send_offset_commit_request(self, group, payloads=[],
                                   fail_on_error=True, callback=None):
        encoder = functools.partial(KafkaProtocol.encode_offset_commit_request,
                          group=group)
        decoder = KafkaProtocol.decode_offset_commit_response
        resps = self._send_broker_aware_request(payloads, encoder, decoder)

        return [resp if not callback else callback(resp) for resp in resps
                if not fail_on_error or not self._raise_on_response_error(resp)]

    def send_offset_fetch_request(self, group, payloads=[],
                                  fail_on_error=True, callback=None):

        encoder = functools.partial(KafkaProtocol.encode_offset_fetch_request,
                          group=group)
        decoder = KafkaProtocol.decode_offset_fetch_response
        resps = self._send_broker_aware_request(payloads, encoder, decoder)

        return [resp if not callback else callback(resp) for resp in resps
                if not fail_on_error or not self._raise_on_response_error(resp)]

    def send_offset_fetch_request_kafka(self, group, payloads=[],
                                  fail_on_error=True, callback=None):

        encoder = functools.partial(KafkaProtocol.encode_offset_fetch_request,
                          group=group, from_kafka=True)
        decoder = KafkaProtocol.decode_offset_fetch_response
        resps = self._send_consumer_aware_request(group, payloads, encoder, decoder)

        return [resp if not callback else callback(resp) for resp in resps
                if not fail_on_error or not self._raise_on_response_error(resp)]