No Current Assignment For Partition Magic

[docs]classKafkaConsumer(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). The consumer is not thread safe and should not be shared across threads. Arguments: *topics (str): optional list of topics to subscribe to. If not set, call :meth:`~kafka.KafkaConsumer.subscribe` or :meth:`~kafka.KafkaConsumer.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): The 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: None 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. fetch_max_bytes (int): The maximum amount of data the server should return for a fetch request. This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that the consumer can make progress. NOTE: consumer performs fetches to multiple brokers in parallel so memory usage will depend on the number of brokers containing partitions for the topic. Supported Kafka version >= 0.10.1.0. Default: 52428800 (50 MB). 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. reconnect_backoff_max_ms (int): The maximum amount of time in milliseconds to wait when reconnecting to a broker that has repeatedly failed to connect. If provided, the backoff per host will increase exponentially for each consecutive connection failure, up to this maximum. To avoid connection storms, a randomization factor of 0.2 will be applied to the backoff resulting in a random range between 20% below and 20% above the computed value. Default: 1000. 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 other 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): Number of 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 an 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] max_poll_records (int): The maximum number of records returned in a single call to :meth:`~kafka.KafkaConsumer.poll`. Default: 500 max_poll_interval_ms (int): The maximum delay between invocations of :meth:`~kafka.KafkaConsumer.poll` when using consumer group management. This places an upper bound on the amount of time that the consumer can be idle before fetching more records. If :meth:`~kafka.KafkaConsumer.poll` is not called before expiration of this timeout, then the consumer is considered failed and the group will rebalance in order to reassign the partitions to another member. Default 300000 session_timeout_ms (int): The timeout used to detect failures when using Kafka's group management facilities. The consumer sends periodic heartbeats to indicate its liveness to the broker. If no heartbeats are received by the broker before the expiration of this session timeout, then the broker will remove this consumer from the group and initiate a rebalance. Note that the value must be in the allowable range as configured in the broker configuration by group.min.session.timeout.ms and group.max.session.timeout.ms. Default: 10000 heartbeat_interval_ms (int): The expected time in milliseconds between heartbeats to the consumer coordinator when using Kafka's group management facilities. 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 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. 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. socket_options (list): List of tuple-arguments to socket.setsockopt to apply to broker connection sockets. Default: [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)] consumer_timeout_ms (int): number of milliseconds to block during message iteration before raising StopIteration (i.e., ending the iterator). Default block forever [float('inf')]. 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 verification. 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_password (str): Optional password to be used when loading the certificate chain. 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. Different versions enable different functionality. 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. Default: None 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 selector (selectors.BaseSelector): Provide a specific selector implementation to use for I/O multiplexing. Default: selectors.DefaultSelector exclude_internal_topics (bool): Whether records from internal topics (such as offsets) should be exposed to the consumer. If set to True the only way to receive records from an internal topic is subscribing to it. Requires 0.10+ Default: True sasl_mechanism (str): String picking sasl mechanism when security_protocol is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported. Default: None sasl_plain_username (str): Username for sasl PLAIN authentication. Default: None sasl_plain_password (str): Password for sasl PLAIN authentication. Default: None sasl_kerberos_service_name (str): Service name to include in GSSAPI sasl mechanism handshake. Default: 'kafka' Note: Configuration parameters are described in more detail at https://kafka.apache.org/documentation/#newconsumerconfigs """DEFAULT_CONFIG={'bootstrap_servers':'localhost','client_id':'kafka-python-'+__version__,'group_id':None,'key_deserializer':None,'value_deserializer':None,'fetch_max_wait_ms':500,'fetch_min_bytes':1,'fetch_max_bytes':52428800,'max_partition_fetch_bytes':1*1024*1024,'request_timeout_ms':305000,# chosen to be higher than the default of max_poll_interval_ms'retry_backoff_ms':100,'reconnect_backoff_ms':50,'reconnect_backoff_max_ms':1000,'max_in_flight_requests_per_connection':5,'auto_offset_reset':'latest','enable_auto_commit':True,'auto_commit_interval_ms':5000,'default_offset_commit_callback':lambdaoffsets,response:True,'check_crcs':True,'metadata_max_age_ms':5*60*1000,'partition_assignment_strategy':(RangePartitionAssignor,RoundRobinPartitionAssignor),'max_poll_records':500,'max_poll_interval_ms':300000,'session_timeout_ms':10000,'heartbeat_interval_ms':3000,'receive_buffer_bytes':None,'send_buffer_bytes':None,'socket_options':[(socket.IPPROTO_TCP,socket.TCP_NODELAY,1)],'sock_chunk_bytes':4096,# undocumented experimental option'sock_chunk_buffer_count':1000,# undocumented experimental option'consumer_timeout_ms':float('inf'),'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,'metric_reporters':[],'metrics_num_samples':2,'metrics_sample_window_ms':30000,'metric_group_prefix':'consumer','selector':selectors.DefaultSelector,'exclude_internal_topics':True,'sasl_mechanism':None,'sasl_plain_username':None,'sasl_plain_password':None,'sasl_kerberos_service_name':'kafka'}DEFAULT_SESSION_TIMEOUT_MS_0_9=30000def__init__(self,*topics,**configs):# Only check for extra config keys in top-level classextra_configs=set(configs).difference(self.DEFAULT_CONFIG)ifextra_configs:raiseKafkaConfigurationError("Unrecognized configs: %s"%extra_configs)self.config=copy.copy(self.DEFAULT_CONFIG)self.config.update(configs)deprecated={'smallest':'earliest','largest':'latest'}ifself.config['auto_offset_reset']indeprecated: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_configrequest_timeout_ms=self.config['request_timeout_ms']fetch_max_wait_ms=self.config['fetch_max_wait_ms']ifrequest_timeout_ms<=fetch_max_wait_ms:raiseKafkaConfigurationError("Request timeout (%s) must be larger than fetch-max-wait-ms (%s)"%(request_timeout_ms,fetch_max_wait_ms))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()forreporterinself.config['metric_reporters']]self._metrics=Metrics(metric_config,reporters)# TODO _metrics likely needs to be passed to KafkaClient, etc.# api_version was previously a str. Accept old format for nowifisinstance(self.config['api_version'],str):str_version=self.config['api_version']ifstr_version=='auto':self.config['api_version']=Noneelse:self.config['api_version']=tuple(map(int,str_version.split('.')))log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',str(self.config['api_version']),str_version)self._client=KafkaClient(metrics=self._metrics,**self.config)# Get auto-discovered version from client if necessaryifself.config['api_version']isNone:self.config['api_version']=self._client.config['api_version']# Coordinator configurations are different for older brokers# max_poll_interval_ms is not supported directly -- it must the be# the same as session_timeout_ms. If the user provides one of them,# use it for both. Otherwise use the old default of 30secsifself.config['api_version']<(0,10,1):if'session_timeout_ms'notinconfigs:if'max_poll_interval_ms'inconfigs:self.config['session_timeout_ms']=configs['max_poll_interval_ms']else:self.config['session_timeout_ms']=self.DEFAULT_SESSION_TIMEOUT_MS_0_9if'max_poll_interval_ms'notinconfigs:self.config['max_poll_interval_ms']=self.config['session_timeout_ms']ifself.config['group_id']isnotNone:ifself.config['request_timeout_ms']<=self.config['session_timeout_ms']:raiseKafkaConfigurationError("Request timeout (%s) must be larger than session timeout (%s)"%(self.config['request_timeout_ms'],self.config['session_timeout_ms']))self._subscription=SubscriptionState(self.config['auto_offset_reset'])self._fetcher=Fetcher(self._client,self._subscription,self._metrics,**self.config)self._coordinator=ConsumerCoordinator(self._client,self._subscription,self._metrics,assignors=self.config['partition_assignment_strategy'],**self.config)self._closed=Falseself._iterator=Noneself._consumer_timeout=float('inf')iftopics:self._subscription.subscribe(topics=topics)self._client.set_topics(topics)

[docs]defassign(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 :meth:`~kafka.KafkaConsumer.subscribe`. Warning: It is not possible to use both manual partition assignment with :meth:`~kafka.KafkaConsumer.assign` and group assignment with :meth:`~kafka.KafkaConsumer.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.topicfortpinpartitions])

[docs]defassignment(self):"""Get the TopicPartitions currently assigned to this consumer. If partitions were directly assigned using :meth:`~kafka.KafkaConsumer.assign`, then this will simply return the same partitions that were previously assigned. If topics were subscribed using :meth:`~kafka.KafkaConsumer.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, ...} """returnself._subscription.assigned_partitions()

[docs]defclose(self,autocommit=True):"""Close the consumer, waiting indefinitely for any needed cleanup. Keyword Arguments: autocommit (bool): If auto-commit is configured for this consumer, this optional flag causes the consumer to attempt to commit any pending consumed offsets prior to close. Default: True """ifself._closed:returnlog.debug("Closing the KafkaConsumer.")self._closed=Trueself._coordinator.close(autocommit=autocommit)self._metrics.close()self._client.close()try:self.config['key_deserializer'].close()exceptAttributeError:passtry:self.config['value_deserializer'].close()exceptAttributeError:passlog.debug("The KafkaConsumer has closed.")

[docs]defcommit_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 currently consumed offsets for all subscribed partitions. callback (callable, optional): Called as callback(offsets, response) with response as either an Exception or an OffsetCommitResponse struct. This callback can be used to trigger custom actions when a commit request completes. Returns: kafka.future.Future """assertself.config['api_version']>=(0,8,1),'Requires >= Kafka 0.8.1'assertself.config['group_id']isnotNone,'Requires group_id'ifoffsetsisNone:offsets=self._subscription.all_consumed_offsets()log.debug("Committing offsets: %s",offsets)future=self._coordinator.commit_offsets_async(offsets,callback=callback)returnfuture

[docs]defcommit(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 currently consumed offsets for all subscribed partitions. """assertself.config['api_version']>=(0,8,1),'Requires >= Kafka 0.8.1'assertself.config['group_id']isnotNone,'Requires group_id'ifoffsetsisNone:offsets=self._subscription.all_consumed_offsets()self._coordinator.commit_offsets_sync(offsets)

[docs]defcommitted(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. """assertself.config['api_version']>=(0,8,1),'Requires >= Kafka 0.8.1'assertself.config['group_id']isnotNone,'Requires group_id'ifnotisinstance(partition,TopicPartition):raiseTypeError('partition must be a TopicPartition namedtuple')ifself._subscription.is_assigned(partition):committed=self._subscription.assignment[partition].committedifcommittedisNone:self._coordinator.refresh_committed_offsets_if_needed()committed=self._subscription.assignment[partition].committedelse:commit_map=self._coordinator.fetch_committed_offsets([partition])ifpartitionincommit_map:committed=commit_map[partition].offsetelse:committed=Nonereturncommitted

[docs]deftopics(self):"""Get all topics the user is authorized to view. Returns: set: topics """cluster=self._client.clusterifself._client._metadata_refresh_in_progressandself._client._topics:future=cluster.request_update()self._client.poll(future=future)stash=cluster.need_all_topic_metadatacluster.need_all_topic_metadata=Truefuture=cluster.request_update()self._client.poll(future=future)cluster.need_all_topic_metadata=stashreturncluster.topics()

[docs]defpartitions_for_topic(self,topic):"""Get metadata about the partitions for a given topic. Arguments: topic (str): Topic to check. Returns: set: Partition ids """returnself._client.cluster.partitions_for_topic(topic)

[docs]defpoll(self,timeout_ms=0,max_records=None):"""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 :meth:`~kafka.KafkaConsumer.seek` 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 max_records (int, optional): The maximum number of records returned in a single call to :meth:`~kafka.KafkaConsumer.poll`. Default: Inherit value from max_poll_records. Returns: dict: Topic to list of records since the last fetch for the subscribed list of topics and partitions. """asserttimeout_ms>=0,'Timeout must not be negative'ifmax_recordsisNone:max_records=self.config['max_poll_records']assertisinstance(max_records,int),'max_records must be an integer'assertmax_records>0,'max_records must be positive'# Poll for new data until the timeout expiresstart=time.time()remaining=timeout_mswhileTrue:records=self._poll_once(remaining,max_records)ifrecords:returnrecordselapsed_ms=(time.time()-start)*1000remaining=timeout_ms-elapsed_msifremaining<=0:return{}

def_poll_once(self,timeout_ms,max_records):"""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). """self._coordinator.poll()# Fetch positions if we have partitions we're subscribed to that we# don't know the offset forifnotself._subscription.has_all_fetch_positions():self._update_fetch_positions(self._subscription.missing_fetch_positions())# If data is available already, e.g. from a previous network client# poll() call to commit, then just return it immediatelyrecords,partial=self._fetcher.fetched_records(max_records)ifrecords:# 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.ifnotpartial:self._fetcher.send_fetches()returnrecords# Send any new fetches (won't resend pending fetches)self._fetcher.send_fetches()timeout_ms=min(timeout_ms,self._coordinator.time_to_next_poll()*1000)self._client.poll(timeout_ms=timeout_ms)# after the long poll, we should check whether the group needs to rebalance# prior to returning data so that the group can stabilize fasterifself._coordinator.need_rejoin():return{}records,_=self._fetcher.fetched_records(max_records)returnrecords

[docs]defposition(self,partition):"""Get the offset of the next record that will be fetched Arguments: partition (TopicPartition): Partition to check Returns: int: Offset """ifnotisinstance(partition,TopicPartition):raiseTypeError('partition must be a TopicPartition namedtuple')assertself._subscription.is_assigned(partition),'Partition is not assigned'offset=self._subscription.assignment[partition].positionifoffsetisNone:self._update_fetch_positions([partition])offset=self._subscription.assignment[partition].positionreturnoffset

[docs]defhighwater(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 no FetchRequests have been sent for this partition yet. Arguments: partition (TopicPartition): Partition to check Returns: int or None: Offset if available """ifnotisinstance(partition,TopicPartition):raiseTypeError('partition must be a TopicPartition namedtuple')assertself._subscription.is_assigned(partition),'Partition is not assigned'returnself._subscription.assignment[partition].highwater

[docs]defpause(self,*partitions):"""Suspend fetching from the requested partitions. Future calls to :meth:`~kafka.KafkaConsumer.poll` will not return any records from these partitions until they have been resumed using :meth:`~kafka.KafkaConsumer.resume`. Note: 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. """ifnotall([isinstance(p,TopicPartition)forpinpartitions]):raiseTypeError('partitions must be TopicPartition namedtuples')forpartitioninpartitions:log.debug("Pausing partition %s",partition)self._subscription.pause(partition)

[docs]defpaused(self):"""Get the partitions that were previously paused using :meth:`~kafka.KafkaConsumer.pause`. Returns: set: {partition (TopicPartition), ...} """returnself._subscription.paused_partitions()

[docs]defresume(self,*partitions):"""Resume fetching from the specified (paused) partitions. Arguments: *partitions (TopicPartition): Partitions to resume. """ifnotall([isinstance(p,TopicPartition)forpinpartitions]):raiseTypeError('partitions must be TopicPartition namedtuples')forpartitioninpartitions:log.debug("Resuming partition %s",partition)self._subscription.resume(partition)

[docs]defseek(self,partition,offset):"""Manually specify the fetch offset for a TopicPartition. Overrides the fetch offsets that the consumer will use on the next :meth:`~kafka.KafkaConsumer.poll`. If this API is invoked for the same partition more than once, the latest offset will be used on the next :meth:`~kafka.KafkaConsumer.poll`. Note: 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. """ifnotisinstance(partition,TopicPartition):raiseTypeError('partition must be a TopicPartition namedtuple')assertisinstance(offset,int)andoffset>=0,'Offset must be >= 0'assertpartitioninself._subscription.assigned_partitions(),'Unassigned partition'log.debug("Seeking to offset %s for partition %s",offset,partition)self._subscription.assignment[partition].seek(offset)

[docs]defseek_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. """ifnotall([isinstance(p,TopicPartition)forpinpartitions]):raiseTypeError('partitions must be TopicPartition namedtuples')ifnotpartitions:partitions=self._subscription.assigned_partitions()assertpartitions,'No partitions are currently assigned'else:forpinpartitions:assertpinself._subscription.assigned_partitions(),'Unassigned partition'fortpinpartitions:log.debug("Seeking to beginning of partition %s",tp)self._subscription.need_offset_reset(tp,OffsetResetStrategy.EARLIEST)

consumer指定位置消费offset的用法

consumer.seek(topicPartition, offset);有人用过吗?

*****************java.lang.IllegalStateException: No current assignment for partition HighAvailabilityTest-1 at org.apache.kafka.clients.consumer.internals.SubscriptionState.assignedState(SubscriptionState.java:231) at org.apache.kafka.clients.consumer.internals.SubscriptionState.seek(SubscriptionState.java:256) at org.apache.kafka.clients.consumer.KafkaConsumer.seek(KafkaConsumer.java:1134) at com.masai.kafka.ConsumerOnce.run(ConsumerOnce.java:54) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)

分区首先肯定是存在的,这个assignment是什么,需要怎么配置还是?求解








发表于: 1年前   最后更新时间: 1年前   游览量:6362

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