"""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

group_instance_id (str): the unique identifier to distinguish

each client instance. If set and leave_group_on_close is

False consumer group rebalancing won't be triggered until

sessiont_timeout_ms is met. Requires 2.3.0+.

leave_group_on_close (bool or None): whether to leave a consumer

group or not on consumer shutdown.

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: 305000.

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 backoff/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. Once the maximum is reached,

reconnection attempts will continue periodically with this fixed

rate. 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')].

security_protocol (str): Protocol used to communicate with brokers.

Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_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.

ssl_ciphers (str): optionally set the available ciphers for ssl

connections. It should be a string in the OpenSSL cipher list

format. If no cipher can be selected (because compile-time options

or other configuration forbids use of all the specified ciphers),

an ssl.SSLError will be raised. See ssl.SSLContext.set_ciphers

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 None.

connections_max_idle_ms: Close idle connections after the number of

milliseconds specified by this config. The broker closes idle

connections after connections.max.idle.ms, so this avoids hitting

unexpected socket disconnected errors on the client.

Default: 540000

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): Authentication mechanism when security_protocol

is configured for SASL_PLAINTEXT or SASL_SSL. Valid values are:

PLAIN, GSSAPI, OAUTHBEARER, SCRAM-SHA-256, SCRAM-SHA-512.

sasl_plain_username (str): username for sasl PLAIN and SCRAM authentication.

Required if sasl_mechanism is PLAIN or one of the SCRAM mechanisms.

sasl_plain_password (str): password for sasl PLAIN and SCRAM authentication.

Required if sasl_mechanism is PLAIN or one of the SCRAM mechanisms.

sasl_kerberos_service_name (str): Service name to include in GSSAPI

sasl mechanism handshake. Default: 'kafka'

sasl_kerberos_domain_name (str): kerberos domain name to use in GSSAPI

sasl mechanism handshake. Default: one of bootstrap servers

sasl_oauth_token_provider (AbstractTokenProvider): OAuthBearer token provider

instance. (See kafka.oauth.abstract). Default: None

kafka_client (callable): Custom class / callable for creating KafkaClient instances

coordinator (callable): Custom class / callable for creating ConsumerCoordinator instances

Note:

Configuration parameters are described in more detail at

https://kafka.apache.org/documentation/#consumerconfigs

"""

DEFAULT_CONFIG = {

'bootstrap_servers': 'localhost',

'client_id': 'kafka-python-' + __version__,

'group_id': None,

'group_instance_id': '',

'leave_group_on_close': 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': lambda offsets, 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'),

'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,

'ssl_ciphers': 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',

'sasl_kerberos_domain_name': None,

'sasl_oauth_token_provider': None,

'legacy_iterator': False, # enable to revert to < 1.4.7 iterator

'kafka_client': KafkaClient,

'coordinator': ConsumerCoordinator,

}

DEFAULT_SESSION_TIMEOUT_MS_0_9 = 30000

def __init__(self, *topics, **configs):

# Only check for extra config keys in top-level class

extra_configs = set(configs).difference(self.DEFAULT_CONFIG)

if extra_configs:

raise KafkaConfigurationError(f"Unrecognized configs: {extra_configs}")

self.config = copy.copy(self.DEFAULT_CONFIG)

self.config.update(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

connections_max_idle_ms = self.config['connections_max_idle_ms']

request_timeout_ms = self.config['request_timeout_ms']

fetch_max_wait_ms = self.config['fetch_max_wait_ms']

if not (fetch_max_wait_ms < request_timeout_ms < connections_max_idle_ms):

raise KafkaConfigurationError(

"connections_max_idle_ms ({}) must be larger than "

"request_timeout_ms ({}) which must be larger than "

"fetch_max_wait_ms ({})."

.format(connections_max_idle_ms, 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() for reporter in self.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 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 [tuple] -- "%s" as str is deprecated',

str(self.config['api_version']), str_version)

self._client = self.config['kafka_client'](metrics=self._metrics, **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']

# 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 30secs

if self.config['api_version'] < (0, 10, 1):

if 'session_timeout_ms' not in configs:

if 'max_poll_interval_ms' in configs:

self.config['session_timeout_ms'] = configs['max_poll_interval_ms']

else:

self.config['session_timeout_ms'] = self.DEFAULT_SESSION_TIMEOUT_MS_0_9

if 'max_poll_interval_ms' not in configs:

self.config['max_poll_interval_ms'] = self.config['session_timeout_ms']

if self.config['group_id'] is not None:

if self.config['request_timeout_ms'] <= self.config['session_timeout_ms']:

raise KafkaConfigurationError(

"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 = self.config['coordinator'](

self._client, self._subscription, self._metrics,

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 bootstrap_connected(self):

"""Return True if the bootstrap is connected."""

return self._client.bootstrap_connected()

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

: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.topic for tp in partitions])

def assignment(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, ...}

"""

return self._subscription.assigned_partitions()

def close(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

"""

if self._closed:

return

log.debug("Closing the KafkaConsumer.")

self._closed = True

self._coordinator.close(autocommit=autocommit)

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 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

"""

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 currently

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, metadata=False):

"""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.

metadata (bool, optional): If True, return OffsetAndMetadata struct

instead of offset int. Default: False.

Returns:

The last committed offset (int or OffsetAndMetadata), 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]

else:

committed = None

if committed is not None:

if metadata:

return committed

else:

return committed.offset

def _fetch_all_topic_metadata(self):

"""A blocking call that fetches topic metadata for all topics in the

cluster that the user is authorized to view.

"""

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

def topics(self):

"""Get all topics the user is authorized to view.

This will always issue a remote call to the cluster to fetch the latest

information.

Returns:

set: topics

"""

self._fetch_all_topic_metadata()

return self._client.cluster.topics()

def partitions_for_topic(self, topic):

"""This method first checks the local metadata cache for information

about the topic. If the topic is not found (either because the topic

does not exist, the user is not authorized to view the topic, or the

metadata cache is not populated), then it will issue a metadata update

call to the cluster.

Arguments:

topic (str): Topic to check.

Returns:

set: Partition ids

"""

cluster = self._client.cluster

partitions = cluster.partitions_for_topic(topic)

if partitions is None:

self._fetch_all_topic_metadata()

partitions = cluster.partitions_for_topic(topic)

return partitions

def poll(self, timeout_ms=0, max_records=None, update_offsets=True):

"""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.

"""

# Note: update_offsets is an internal-use only argument. It is used to

# support the python iterator interface, and which wraps consumer.poll()

# and requires that the partition offsets tracked by the fetcher are not

# updated until the iterator returns each record to the user. As such,

# the argument is not documented and should not be relied on by library

# users to not break in the future.

assert timeout_ms >= 0, 'Timeout must not be negative'

if max_records is None:

max_records = self.config['max_poll_records']

assert isinstance(max_records, int), 'max_records must be an integer'

assert max_records > 0, 'max_records must be positive'

assert not self._closed, 'KafkaConsumer is closed'

# Poll for new data until the timeout expires

start = time.time()

remaining = timeout_ms

while not self._closed:

records = self._poll_once(remaining, max_records, update_offsets=update_offsets)

if records:

return records

elapsed_ms = (time.time() - start) * 1000

remaining = timeout_ms - elapsed_ms

if remaining <= 0:

break

return {}

def _poll_once(self, timeout_ms, max_records, update_offsets=True):

"""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 for

if not self._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 immediately

records, partial = self._fetcher.fetched_records(max_records, update_offsets=update_offsets)

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.

if not partial:

futures = self._fetcher.send_fetches()

if len(futures):

self._client.poll(timeout_ms=0)

return records

# Send any new fetches (won't resend pending fetches)

futures = self._fetcher.send_fetches()

if len(futures):

self._client.poll(timeout_ms=0)

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 faster

if self._coordinator.need_rejoin():

return {}

records, _ = self._fetcher.fetched_records(max_records, update_offsets=update_offsets)

return 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 no 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 :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.

"""

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)

# Because the iterator checks is_fetchable() on each iteration

# we expect pauses to get handled automatically and therefore

# we do not need to reset the full iterator (forcing a full refetch)

def paused(self):

"""Get the partitions that were previously paused using

:meth:`~kafka.KafkaConsumer.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

: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.

"""

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)

if not self.config['legacy_iterator']:

self._iterator = None

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)

if not self.config['legacy_iterator']:

self._iterator = None

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)

if not self.config['legacy_iterator']:

self._iterator = None

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 :meth:`~kafka.KafkaConsumer.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

:meth:`~kafka.KafkaConsumer.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([])

self._client.cluster.request_update()

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, ...}

"""

if self._subscription.subscription is None:

return None

return self._subscription.subscription.copy()

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")

if not self.config['legacy_iterator']:

self._iterator = None

def metrics(self, raw=False):

"""Get metrics on consumer performance.

This is ported from the Java Consumer, for details see:

https://kafka.apache.org/documentation/#consumer_monitoring

Warning:

This is an unstable interface. It may change in future

releases without warning.

"""

if raw:

return self._metrics.metrics.copy()

metrics = {}

for k, v in self._metrics.metrics.copy().items():

if k.group not in metrics:

metrics[k.group] = {}

if k.name not in metrics[k.group]:

metrics[k.group][k.name] = {}

metrics[k.group][k.name] = v.value()

return metrics

def offsets_for_times(self, timestamps):

"""Look up the offsets for the given partitions by timestamp. The

returned offset for each partition is the earliest offset whose

timestamp is greater than or equal to the given timestamp in the

corresponding partition.

This is a blocking call. The consumer does not have to be assigned the

partitions.

If the message format version in a partition is before 0.10.0, i.e.

the messages do not have timestamps, ``None`` will be returned for that

partition. ``None`` will also be returned for the partition if there

are no messages in it.

Note:

This method may block indefinitely if the partition does not exist.