from __future__ import absolute_import import copy import logging import time import six from kafka.client_async import KafkaClient from kafka.consumer.fetcher import Fetcher from kafka.consumer.subscription_state import SubscriptionState from kafka.coordinator.consumer import ConsumerCoordinator from kafka.coordinator.assignors.range import RangePartitionAssignor from kafka.coordinator.assignors.roundrobin import RoundRobinPartitionAssignor from kafka.metrics import DictReporter, MetricConfig, Metrics from kafka.protocol.offset import OffsetResetStrategy from kafka.structs import TopicPartition from kafka.version import __version__ log = logging.getLogger(__name__) class KafkaConsumer(six.Iterator): """Consume records from a Kafka cluster. The consumer will transparently handle the failure of servers in the Kafka cluster, and adapt as topic-partitions are created or migrate between brokers. It also interacts with the assigned kafka Group Coordinator node to allow multiple consumers to load balance consumption of topics (requires kafka >= 0.9.0.0). Arguments: *topics (str): optional list of topics to subscribe to. If not set, call subscribe() or assign() before consuming records. Keyword Arguments: bootstrap_servers: 'host[:port]' string (or list of 'host[:port]' strings) that the consumer should contact to bootstrap initial cluster metadata. This does not have to be the full node list. It just needs to have at least one broker that will respond to a Metadata API Request. Default port is 9092. If no servers are specified, will default to localhost:9092. client_id (str): a name for this client. This string is passed in each request to servers and can be used to identify specific server-side log entries that correspond to this client. Also submitted to GroupCoordinator for logging with respect to consumer group administration. Default: 'kafka-python-{version}' group_id (str or None): name of the consumer group to join for dynamic partition assignment (if enabled), and to use for fetching and committing offsets. If None, auto-partition assignment (via group coordinator) and offset commits are disabled. Default: 'kafka-python-default-group' key_deserializer (callable): Any callable that takes a raw message key and returns a deserialized key. value_deserializer (callable): Any callable that takes a raw message value and returns a deserialized value. fetch_min_bytes (int): Minimum amount of data the server should return for a fetch request, otherwise wait up to fetch_max_wait_ms for more data to accumulate. Default: 1. fetch_max_wait_ms (int): The maximum amount of time in milliseconds the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy the requirement given by fetch_min_bytes. Default: 500. max_partition_fetch_bytes (int): The maximum amount of data per-partition the server will return. The maximum total memory used for a request = #partitions * max_partition_fetch_bytes. This size must be at least as large as the maximum message size the server allows or else it is possible for the producer to send messages larger than the consumer can fetch. If that happens, the consumer can get stuck trying to fetch a large message on a certain partition. Default: 1048576. request_timeout_ms (int): Client request timeout in milliseconds. Default: 40000. retry_backoff_ms (int): Milliseconds to backoff when retrying on errors. Default: 100. reconnect_backoff_ms (int): The amount of time in milliseconds to wait before attempting to reconnect to a given host. Default: 50. max_in_flight_requests_per_connection (int): Requests are pipelined to kafka brokers up to this number of maximum requests per broker connection. Default: 5. auto_offset_reset (str): A policy for resetting offsets on OffsetOutOfRange errors: 'earliest' will move to the oldest available message, 'latest' will move to the most recent. Any ofther value will raise the exception. Default: 'latest'. enable_auto_commit (bool): If true the consumer's offset will be periodically committed in the background. Default: True. auto_commit_interval_ms (int): milliseconds between automatic offset commits, if enable_auto_commit is True. Default: 5000. default_offset_commit_callback (callable): called as callback(offsets, response) response will be either an Exception or a OffsetCommitResponse struct. This callback can be used to trigger custom actions when a commit request completes. check_crcs (bool): Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance. Default: True metadata_max_age_ms (int): The period of time in milliseconds after which we force a refresh of metadata even if we haven't seen any partition leadership changes to proactively discover any new brokers or partitions. Default: 300000 partition_assignment_strategy (list): List of objects to use to distribute partition ownership amongst consumer instances when group management is used. Default: [RangePartitionAssignor, RoundRobinPartitionAssignor] heartbeat_interval_ms (int): The expected time in milliseconds between heartbeats to the consumer coordinator when using Kafka's group management feature. Heartbeats are used to ensure that the consumer's session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than session_timeout_ms, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances. Default: 3000 session_timeout_ms (int): The timeout used to detect failures when using Kafka's group managementment facilities. Default: 30000 send_buffer_bytes (int): The size of the TCP send buffer (SO_SNDBUF) to use when sending data. Default: None (relies on system defaults). The java client defaults to 131072. receive_buffer_bytes (int): The size of the TCP receive buffer (SO_RCVBUF) to use when reading data. Default: None (relies on system defaults). The java client defaults to 32768. consumer_timeout_ms (int): number of milliseconds to block during message iteration before raising StopIteration (i.e., ending the iterator). Default -1 (block forever). skip_double_compressed_messages (bool): A bug in KafkaProducer <= 1.2.4 caused some messages to be corrupted via double-compression. By default, the fetcher will return these messages as a compressed blob of bytes with a single offset, i.e. how the message was actually published to the cluster. If you prefer to have the fetcher automatically detect corrupt messages and skip them, set this option to True. Default: False. security_protocol (str): Protocol used to communicate with brokers. Valid values are: PLAINTEXT, SSL. Default: PLAINTEXT. ssl_context (ssl.SSLContext): pre-configured SSLContext for wrapping socket connections. If provided, all other ssl_* configurations will be ignored. Default: None. ssl_check_hostname (bool): flag to configure whether ssl handshake should verify that the certificate matches the brokers hostname. default: true. ssl_cafile (str): optional filename of ca file to use in certificate veriication. default: none. ssl_certfile (str): optional filename of file in pem format containing the client certificate, as well as any ca certificates needed to establish the certificate's authenticity. default: none. ssl_keyfile (str): optional filename containing the client private key. default: none. ssl_crlfile (str): optional filename containing the CRL to check for certificate expiration. By default, no CRL check is done. When providing a file, only the leaf certificate will be checked against this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+. default: none. api_version (tuple): specify which kafka API version to use. If set to None, the client will attempt to infer the broker version by probing various APIs. Default: None Examples: (0, 9) enables full group coordination features with automatic partition assignment and rebalancing, (0, 8, 2) enables kafka-storage offset commits with manual partition assignment only, (0, 8, 1) enables zookeeper-storage offset commits with manual partition assignment only, (0, 8, 0) enables basic functionality but requires manual partition assignment and offset management. For a full list of supported versions, see KafkaClient.API_VERSIONS api_version_auto_timeout_ms (int): number of milliseconds to throw a timeout exception from the constructor when checking the broker api version. Only applies if api_version set to 'auto' metric_reporters (list): A list of classes to use as metrics reporters. Implementing the AbstractMetricsReporter interface allows plugging in classes that will be notified of new metric creation. Default: [] metrics_num_samples (int): The number of samples maintained to compute metrics. Default: 2 metrics_sample_window_ms (int): The maximum age in milliseconds of samples used to compute metrics. Default: 30000 Note: Configuration parameters are described in more detail at https://kafka.apache.org/090/configuration.html#newconsumerconfigs """ DEFAULT_CONFIG = { 'bootstrap_servers': 'localhost', 'client_id': 'kafka-python-' + __version__, 'group_id': 'kafka-python-default-group', 'key_deserializer': None, 'value_deserializer': None, 'fetch_max_wait_ms': 500, 'fetch_min_bytes': 1, 'max_partition_fetch_bytes': 1 * 1024 * 1024, 'request_timeout_ms': 40 * 1000, 'retry_backoff_ms': 100, 'reconnect_backoff_ms': 50, 'max_in_flight_requests_per_connection': 5, 'auto_offset_reset': 'latest', 'enable_auto_commit': True, 'auto_commit_interval_ms': 5000, 'default_offset_commit_callback': lambda offsets, response: True, 'check_crcs': True, 'metadata_max_age_ms': 5 * 60 * 1000, 'partition_assignment_strategy': (RangePartitionAssignor, RoundRobinPartitionAssignor), 'heartbeat_interval_ms': 3000, 'session_timeout_ms': 30000, 'send_buffer_bytes': None, 'receive_buffer_bytes': None, 'consumer_timeout_ms': -1, 'skip_double_compressed_messages': False, 'security_protocol': 'PLAINTEXT', 'ssl_context': None, 'ssl_check_hostname': True, 'ssl_cafile': None, 'ssl_certfile': None, 'ssl_keyfile': None, 'ssl_crlfile': None, 'ssl_password': None, 'api_version': None, 'api_version_auto_timeout_ms': 2000, 'connections_max_idle_ms': 9 * 60 * 1000, # not implemented yet 'metric_reporters': [], 'metrics_num_samples': 2, 'metrics_sample_window_ms': 30000, } def __init__(self, *topics, **configs): self.config = copy.copy(self.DEFAULT_CONFIG) for key in self.config: if key in configs: self.config[key] = configs.pop(key) # Only check for extra config keys in top-level class assert not configs, 'Unrecognized configs: %s' % configs deprecated = {'smallest': 'earliest', 'largest': 'latest'} if self.config['auto_offset_reset'] in deprecated: new_config = deprecated[self.config['auto_offset_reset']] log.warning('use auto_offset_reset=%s (%s is deprecated)', new_config, self.config['auto_offset_reset']) self.config['auto_offset_reset'] = new_config metrics_tags = {'client-id': self.config['client_id']} metric_config = MetricConfig(samples=self.config['metrics_num_samples'], time_window_ms=self.config['metrics_sample_window_ms'], tags=metrics_tags) reporters = [reporter() for reporter in self.config['metric_reporters']] reporters.append(DictReporter('kafka.consumer')) self._metrics = Metrics(metric_config, reporters) metric_group_prefix = 'consumer' # TODO _metrics likely needs to be passed to KafkaClient, etc. # api_version was previously a str. accept old format for now if isinstance(self.config['api_version'], str): str_version = self.config['api_version'] if str_version == 'auto': self.config['api_version'] = None else: self.config['api_version'] = tuple(map(int, str_version.split('.'))) log.warning('use api_version=%s (%s is deprecated)', str(self.config['api_version']), str_version) self._client = KafkaClient(**self.config) # Get auto-discovered version from client if necessary if self.config['api_version'] is None: self.config['api_version'] = self._client.config['api_version'] self._subscription = SubscriptionState(self.config['auto_offset_reset']) self._fetcher = Fetcher( self._client, self._subscription, self._metrics, metric_group_prefix, **self.config) self._coordinator = ConsumerCoordinator( self._client, self._subscription, self._metrics, metric_group_prefix, assignors=self.config['partition_assignment_strategy'], **self.config) self._closed = False self._iterator = None self._consumer_timeout = float('inf') if topics: self._subscription.subscribe(topics=topics) self._client.set_topics(topics) def assign(self, partitions): """Manually assign a list of TopicPartitions to this consumer. Arguments: partitions (list of TopicPartition): assignment for this instance. Raises: IllegalStateError: if consumer has already called subscribe() Warning: It is not possible to use both manual partition assignment with assign() and group assignment with subscribe(). Note: This interface does not support incremental assignment and will replace the previous assignment (if there was one). Note: Manual topic assignment through this method does not use the consumer's group management functionality. As such, there will be no rebalance operation triggered when group membership or cluster and topic metadata change. """ self._subscription.assign_from_user(partitions) self._client.set_topics([tp.topic for tp in partitions]) def assignment(self): """Get the TopicPartitions currently assigned to this consumer. If partitions were directly assigned using assign(), then this will simply return the same partitions that were previously assigned. If topics were subscribed using subscribe(), then this will give the set of topic partitions currently assigned to the consumer (which may be none if the assignment hasn't happened yet, or if the partitions are in the process of being reassigned). Returns: set: {TopicPartition, ...} """ return self._subscription.assigned_partitions() def close(self): """Close the consumer, waiting indefinitely for any needed cleanup.""" if self._closed: return log.debug("Closing the KafkaConsumer.") self._closed = True self._coordinator.close() self._metrics.close() self._client.close() try: self.config['key_deserializer'].close() except AttributeError: pass try: self.config['value_deserializer'].close() except AttributeError: pass log.debug("The KafkaConsumer has closed.") def commit_async(self, offsets=None, callback=None): """Commit offsets to kafka asynchronously, optionally firing callback This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API should not be used. To avoid re-processing the last message read if a consumer is restarted, the committed offset should be the next message your application should consume, i.e.: last_offset + 1. This is an asynchronous call and will not block. Any errors encountered are either passed to the callback (if provided) or discarded. Arguments: offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict to commit with the configured group_id. Defaults to current consumed offsets for all subscribed partitions. callback (callable, optional): called as callback(offsets, response) with response as either an Exception or a OffsetCommitResponse struct. This callback can be used to trigger custom actions when a commit request completes. Returns: kafka.future.Future """ assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1' assert self.config['group_id'] is not None, 'Requires group_id' if offsets is None: offsets = self._subscription.all_consumed_offsets() log.debug("Committing offsets: %s", offsets) future = self._coordinator.commit_offsets_async( offsets, callback=callback) return future def commit(self, offsets=None): """Commit offsets to kafka, blocking until success or error This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API should not be used. To avoid re-processing the last message read if a consumer is restarted, the committed offset should be the next message your application should consume, i.e.: last_offset + 1. Blocks until either the commit succeeds or an unrecoverable error is encountered (in which case it is thrown to the caller). Currently only supports kafka-topic offset storage (not zookeeper) Arguments: offsets (dict, optional): {TopicPartition: OffsetAndMetadata} dict to commit with the configured group_id. Defaults to current consumed offsets for all subscribed partitions. """ assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1' assert self.config['group_id'] is not None, 'Requires group_id' if offsets is None: offsets = self._subscription.all_consumed_offsets() self._coordinator.commit_offsets_sync(offsets) def committed(self, partition): """Get the last committed offset for the given partition This offset will be used as the position for the consumer in the event of a failure. This call may block to do a remote call if the partition in question isn't assigned to this consumer or if the consumer hasn't yet initialized its cache of committed offsets. Arguments: partition (TopicPartition): the partition to check Returns: The last committed offset, or None if there was no prior commit. """ assert self.config['api_version'] >= (0, 8, 1), 'Requires >= Kafka 0.8.1' assert self.config['group_id'] is not None, 'Requires group_id' if not isinstance(partition, TopicPartition): raise TypeError('partition must be a TopicPartition namedtuple') if self._subscription.is_assigned(partition): committed = self._subscription.assignment[partition].committed if committed is None: self._coordinator.refresh_committed_offsets_if_needed() committed = self._subscription.assignment[partition].committed else: commit_map = self._coordinator.fetch_committed_offsets([partition]) if partition in commit_map: committed = commit_map[partition].offset else: committed = None return committed def topics(self): """Get all topics the user is authorized to view. Returns: set: topics """ cluster = self._client.cluster if self._client._metadata_refresh_in_progress and self._client._topics: future = cluster.request_update() self._client.poll(future=future) stash = cluster.need_all_topic_metadata cluster.need_all_topic_metadata = True future = cluster.request_update() self._client.poll(future=future) cluster.need_all_topic_metadata = stash return cluster.topics() def partitions_for_topic(self, topic): """Get metadata about the partitions for a given topic. Arguments: topic (str): topic to check Returns: set: partition ids """ return self._client.cluster.partitions_for_topic(topic) def poll(self, timeout_ms=0): """Fetch data from assigned topics / partitions. Records are fetched and returned in batches by topic-partition. On each poll, consumer will try to use the last consumed offset as the starting offset and fetch sequentially. The last consumed offset can be manually set through seek(partition, offset) or automatically set as the last committed offset for the subscribed list of partitions. Incompatible with iterator interface -- use one or the other, not both. Arguments: timeout_ms (int, optional): milliseconds spent waiting in poll if data is not available in the buffer. If 0, returns immediately with any records that are available currently in the buffer, else returns empty. Must not be negative. Default: 0 Returns: dict: topic to list of records since the last fetch for the subscribed list of topics and partitions """ assert timeout_ms >= 0, 'Timeout must not be negative' assert self._iterator is None, 'Incompatible with iterator interface' # poll for new data until the timeout expires start = time.time() remaining = timeout_ms while True: records = self._poll_once(remaining) if records: # before returning the fetched records, we can send off the # next round of fetches and avoid block waiting for their # responses to enable pipelining while the user is handling the # fetched records. self._fetcher.init_fetches() return records elapsed_ms = (time.time() - start) * 1000 remaining = timeout_ms - elapsed_ms if remaining <= 0: return {} def _poll_once(self, timeout_ms): """ Do one round of polling. In addition to checking for new data, this does any needed heart-beating, auto-commits, and offset updates. Arguments: timeout_ms (int): The maximum time in milliseconds to block Returns: dict: map of topic to list of records (may be empty) """ if self._use_consumer_group(): self._coordinator.ensure_coordinator_known() self._coordinator.ensure_active_group() # 0.8.2 brokers support kafka-backed offset storage via group coordinator elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2): self._coordinator.ensure_coordinator_known() # fetch positions if we have partitions we're subscribed to that we # don't know the offset for if not self._subscription.has_all_fetch_positions(): self._update_fetch_positions(self._subscription.missing_fetch_positions()) # init any new fetches (won't resend pending fetches) records = self._fetcher.fetched_records() # if data is available already, e.g. from a previous network client # poll() call to commit, then just return it immediately if records: return records self._fetcher.init_fetches() self._client.poll(timeout_ms=timeout_ms, sleep=True) return self._fetcher.fetched_records() def position(self, partition): """Get the offset of the next record that will be fetched Arguments: partition (TopicPartition): partition to check Returns: int: offset """ if not isinstance(partition, TopicPartition): raise TypeError('partition must be a TopicPartition namedtuple') assert self._subscription.is_assigned(partition), 'Partition is not assigned' offset = self._subscription.assignment[partition].position if offset is None: self._update_fetch_positions([partition]) offset = self._subscription.assignment[partition].position return offset def highwater(self, partition): """Last known highwater offset for a partition A highwater offset is the offset that will be assigned to the next message that is produced. It may be useful for calculating lag, by comparing with the reported position. Note that both position and highwater refer to the *next* offset -- i.e., highwater offset is one greater than the newest available message. Highwater offsets are returned in FetchResponse messages, so will not be available if not FetchRequests have been sent for this partition yet. Arguments: partition (TopicPartition): partition to check Returns: int or None: offset if available """ if not isinstance(partition, TopicPartition): raise TypeError('partition must be a TopicPartition namedtuple') assert self._subscription.is_assigned(partition), 'Partition is not assigned' return self._subscription.assignment[partition].highwater def pause(self, *partitions): """Suspend fetching from the requested partitions. Future calls to poll() will not return any records from these partitions until they have been resumed using resume(). Note that this method does not affect partition subscription. In particular, it does not cause a group rebalance when automatic assignment is used. Arguments: *partitions (TopicPartition): partitions to pause """ if not all([isinstance(p, TopicPartition) for p in partitions]): raise TypeError('partitions must be TopicPartition namedtuples') for partition in partitions: log.debug("Pausing partition %s", partition) self._subscription.pause(partition) def paused(self): """Get the partitions that were previously paused by a call to pause(). Returns: set: {partition (TopicPartition), ...} """ return self._subscription.paused_partitions() def resume(self, *partitions): """Resume fetching from the specified (paused) partitions. Arguments: *partitions (TopicPartition): partitions to resume """ if not all([isinstance(p, TopicPartition) for p in partitions]): raise TypeError('partitions must be TopicPartition namedtuples') for partition in partitions: log.debug("Resuming partition %s", partition) self._subscription.resume(partition) def seek(self, partition, offset): """Manually specify the fetch offset for a TopicPartition. Overrides the fetch offsets that the consumer will use on the next poll(). If this API is invoked for the same partition more than once, the latest offset will be used on the next poll(). Note that you may lose data if this API is arbitrarily used in the middle of consumption, to reset the fetch offsets. Arguments: partition (TopicPartition): partition for seek operation offset (int): message offset in partition Raises: AssertionError: if offset is not an int >= 0; or if partition is not currently assigned. """ if not isinstance(partition, TopicPartition): raise TypeError('partition must be a TopicPartition namedtuple') assert isinstance(offset, int) and offset >= 0, 'Offset must be >= 0' assert partition in self._subscription.assigned_partitions(), 'Unassigned partition' log.debug("Seeking to offset %s for partition %s", offset, partition) self._subscription.assignment[partition].seek(offset) def seek_to_beginning(self, *partitions): """Seek to the oldest available offset for partitions. Arguments: *partitions: optionally provide specific TopicPartitions, otherwise default to all assigned partitions Raises: AssertionError: if any partition is not currently assigned, or if no partitions are assigned """ if not all([isinstance(p, TopicPartition) for p in partitions]): raise TypeError('partitions must be TopicPartition namedtuples') if not partitions: partitions = self._subscription.assigned_partitions() assert partitions, 'No partitions are currently assigned' else: for p in partitions: assert p in self._subscription.assigned_partitions(), 'Unassigned partition' for tp in partitions: log.debug("Seeking to beginning of partition %s", tp) self._subscription.need_offset_reset(tp, OffsetResetStrategy.EARLIEST) def seek_to_end(self, *partitions): """Seek to the most recent available offset for partitions. Arguments: *partitions: optionally provide specific TopicPartitions, otherwise default to all assigned partitions Raises: AssertionError: if any partition is not currently assigned, or if no partitions are assigned """ if not all([isinstance(p, TopicPartition) for p in partitions]): raise TypeError('partitions must be TopicPartition namedtuples') if not partitions: partitions = self._subscription.assigned_partitions() assert partitions, 'No partitions are currently assigned' else: for p in partitions: assert p in self._subscription.assigned_partitions(), 'Unassigned partition' for tp in partitions: log.debug("Seeking to end of partition %s", tp) self._subscription.need_offset_reset(tp, OffsetResetStrategy.LATEST) def subscribe(self, topics=(), pattern=None, listener=None): """Subscribe to a list of topics, or a topic regex pattern Partitions will be dynamically assigned via a group coordinator. Topic subscriptions are not incremental: this list will replace the current assignment (if there is one). This method is incompatible with assign() Arguments: topics (list): List of topics for subscription. pattern (str): Pattern to match available topics. You must provide either topics or pattern, but not both. listener (ConsumerRebalanceListener): Optionally include listener callback, which will be called before and after each rebalance operation. As part of group management, the consumer will keep track of the list of consumers that belong to a particular group and will trigger a rebalance operation if one of the following events trigger: * Number of partitions change for any of the subscribed topics * Topic is created or deleted * An existing member of the consumer group dies * A new member is added to the consumer group When any of these events are triggered, the provided listener will be invoked first to indicate that the consumer's assignment has been revoked, and then again when the new assignment has been received. Note that this listener will immediately override any listener set in a previous call to subscribe. It is guaranteed, however, that the partitions revoked/assigned through this interface are from topics subscribed in this call. Raises: IllegalStateError: if called after previously calling assign() AssertionError: if neither topics or pattern is provided TypeError: if listener is not a ConsumerRebalanceListener """ # SubscriptionState handles error checking self._subscription.subscribe(topics=topics, pattern=pattern, listener=listener) # regex will need all topic metadata if pattern is not None: self._client.cluster.need_all_topic_metadata = True self._client.set_topics([]) log.debug("Subscribed to topic pattern: %s", pattern) else: self._client.cluster.need_all_topic_metadata = False self._client.set_topics(self._subscription.group_subscription()) log.debug("Subscribed to topic(s): %s", topics) def subscription(self): """Get the current topic subscription. Returns: set: {topic, ...} """ return self._subscription.subscription def unsubscribe(self): """Unsubscribe from all topics and clear all assigned partitions.""" self._subscription.unsubscribe() self._coordinator.close() self._client.cluster.need_all_topic_metadata = False self._client.set_topics([]) log.debug("Unsubscribed all topics or patterns and assigned partitions") def _use_consumer_group(self): """Return True iff this consumer can/should join a broker-coordinated group.""" if self.config['api_version'] < (0, 9): return False elif self.config['group_id'] is None: return False elif not self._subscription.partitions_auto_assigned(): return False return True def _update_fetch_positions(self, partitions): """ Set the fetch position to the committed position (if there is one) or reset it using the offset reset policy the user has configured. Arguments: partitions (List[TopicPartition]): The partitions that need updating fetch positions Raises: NoOffsetForPartitionError: If no offset is stored for a given partition and no offset reset policy is defined """ if (self.config['api_version'] >= (0, 8, 1) and self.config['group_id'] is not None): # refresh commits for all assigned partitions self._coordinator.refresh_committed_offsets_if_needed() # then do any offset lookups in case some positions are not known self._fetcher.update_fetch_positions(partitions) def _message_generator(self): assert self.assignment() or self.subscription() is not None, 'No topic subscription or manual partition assignment' while time.time() < self._consumer_timeout: if self._use_consumer_group(): self._coordinator.ensure_coordinator_known() self._coordinator.ensure_active_group() # 0.8.2 brokers support kafka-backed offset storage via group coordinator elif self.config['group_id'] is not None and self.config['api_version'] >= (0, 8, 2): self._coordinator.ensure_coordinator_known() # fetch offsets for any subscribed partitions that we arent tracking yet if not self._subscription.has_all_fetch_positions(): partitions = self._subscription.missing_fetch_positions() self._update_fetch_positions(partitions) poll_ms = 1000 * (self._consumer_timeout - time.time()) if not self._fetcher.in_flight_fetches(): poll_ms = 0 self._client.poll(timeout_ms=poll_ms, sleep=True) # We need to make sure we at least keep up with scheduled tasks, # like heartbeats, auto-commits, and metadata refreshes timeout_at = self._next_timeout() # Because the consumer client poll does not sleep unless blocking on # network IO, we need to explicitly sleep when we know we are idle # because we haven't been assigned any partitions to fetch / consume if self._use_consumer_group() and not self.assignment(): sleep_time = max(timeout_at - time.time(), 0) if sleep_time > 0 and not self._client.in_flight_request_count(): log.debug('No partitions assigned; sleeping for %s', sleep_time) time.sleep(sleep_time) continue # Short-circuit the fetch iterator if we are already timed out # to avoid any unintentional interaction with fetcher setup if time.time() > timeout_at: continue for msg in self._fetcher: yield msg if time.time() > timeout_at: log.debug("internal iterator timeout - breaking for poll") break # an else block on a for loop only executes if there was no break # so this should only be called on a StopIteration from the fetcher # and we assume that it is safe to init_fetches when fetcher is done # i.e., there are no more records stored internally else: self._fetcher.init_fetches() def _next_timeout(self): timeout = min(self._consumer_timeout, self._client._delayed_tasks.next_at() + time.time(), self._client.cluster.ttl() / 1000.0 + time.time()) # Although the delayed_tasks timeout above should cover processing # HeartbeatRequests, it is still possible that HeartbeatResponses # are left unprocessed during a long _fetcher iteration without # an intermediate poll(). And because tasks are responsible for # rescheduling themselves, an unprocessed response will prevent # the next heartbeat from being sent. This check should help # avoid that. if self._use_consumer_group(): heartbeat = time.time() + self._coordinator.heartbeat.ttl() timeout = min(timeout, heartbeat) return timeout def __iter__(self): # pylint: disable=non-iterator-returned return self def __next__(self): if not self._iterator: self._iterator = self._message_generator() self._set_consumer_timeout() try: return next(self._iterator) except StopIteration: self._iterator = None raise def _set_consumer_timeout(self): # consumer_timeout_ms can be used to stop iteration early if self.config['consumer_timeout_ms'] >= 0: self._consumer_timeout = time.time() + ( self.config['consumer_timeout_ms'] / 1000.0) # old KafkaConsumer methods are deprecated def configure(self, **configs): raise NotImplementedError( 'deprecated -- initialize a new consumer') def set_topic_partitions(self, *topics): raise NotImplementedError( 'deprecated -- use subscribe() or assign()') def fetch_messages(self): raise NotImplementedError( 'deprecated -- use poll() or iterator interface') def get_partition_offsets(self, topic, partition, request_time_ms, max_num_offsets): raise NotImplementedError( 'deprecated -- send an OffsetRequest with KafkaClient') def offsets(self, group=None): raise NotImplementedError('deprecated -- use committed(partition)') def task_done(self, message): raise NotImplementedError( 'deprecated -- commit offsets manually if needed')