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train | SubscriptionState.need_offset_reset | Mark partition for offset reset using specified or default strategy.
Arguments:
partition (TopicPartition): partition to mark
offset_reset_strategy (OffsetResetStrategy, optional) | kafka/consumer/subscription_state.py | def need_offset_reset(self, partition, offset_reset_strategy=None):
"""Mark partition for offset reset using specified or default strategy.
Arguments:
partition (TopicPartition): partition to mark
offset_reset_strategy (OffsetResetStrategy, optional)
"""
if offse... | def need_offset_reset(self, partition, offset_reset_strategy=None):
"""Mark partition for offset reset using specified or default strategy.
Arguments:
partition (TopicPartition): partition to mark
offset_reset_strategy (OffsetResetStrategy, optional)
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train | KafkaProducer._cleanup_factory | Build a cleanup clojure that doesn't increase our ref count | kafka/producer/kafka.py | def _cleanup_factory(self):
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train | KafkaProducer.close | Close this producer.
Arguments:
timeout (float, optional): timeout in seconds to wait for completion. | kafka/producer/kafka.py | def close(self, timeout=None):
"""Close this producer.
Arguments:
timeout (float, optional): timeout in seconds to wait for completion.
"""
# drop our atexit handler now to avoid leaks
self._unregister_cleanup()
if not hasattr(self, '_closed') or self._clos... | def close(self, timeout=None):
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Arguments:
timeout (float, optional): timeout in seconds to wait for completion.
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train | KafkaProducer.partitions_for | Returns set of all known partitions for the topic. | kafka/producer/kafka.py | def partitions_for(self, topic):
"""Returns set of all known partitions for the topic."""
max_wait = self.config['max_block_ms'] / 1000.0
return self._wait_on_metadata(topic, max_wait) | def partitions_for(self, topic):
"""Returns set of all known partitions for the topic."""
max_wait = self.config['max_block_ms'] / 1000.0
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train | KafkaProducer.send | Publish a message to a topic.
Arguments:
topic (str): topic where the message will be published
value (optional): message value. Must be type bytes, or be
serializable to bytes via configured value_serializer. If value
is None, key is required and message... | kafka/producer/kafka.py | def send(self, topic, value=None, key=None, headers=None, partition=None, timestamp_ms=None):
"""Publish a message to a topic.
Arguments:
topic (str): topic where the message will be published
value (optional): message value. Must be type bytes, or be
serializabl... | def send(self, topic, value=None, key=None, headers=None, partition=None, timestamp_ms=None):
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Arguments:
topic (str): topic where the message will be published
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train | KafkaProducer.flush | Invoking this method makes all buffered records immediately available
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completion of the requests associated with these records. The
post-condition of :meth:`~kafka.KafkaProducer.flush` is that any
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Invoking this method makes all buffered records immediately available
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Invoking this method makes all buffered records immediately available
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train | KafkaProducer._ensure_valid_record_size | Validate that the record size isn't too large. | kafka/producer/kafka.py | def _ensure_valid_record_size(self, size):
"""Validate that the record size isn't too large."""
if size > self.config['max_request_size']:
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train | KafkaProducer._wait_on_metadata | Wait for cluster metadata including partitions for the given topic to
be available.
Arguments:
topic (str): topic we want metadata for
max_wait (float): maximum time in secs for waiting on the metadata
Returns:
set: partition ids for the topic
Raise... | kafka/producer/kafka.py | def _wait_on_metadata(self, topic, max_wait):
"""
Wait for cluster metadata including partitions for the given topic to
be available.
Arguments:
topic (str): topic we want metadata for
max_wait (float): maximum time in secs for waiting on the metadata
Re... | def _wait_on_metadata(self, topic, max_wait):
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Wait for cluster metadata including partitions for the given topic to
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Arguments:
topic (str): topic we want metadata for
max_wait (float): maximum time in secs for waiting on the metadata
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train | KafkaProducer.metrics | Get metrics on producer performance.
This is ported from the Java Producer, for details see:
https://kafka.apache.org/documentation/#producer_monitoring
Warning:
This is an unstable interface. It may change in future
releases without warning. | kafka/producer/kafka.py | def metrics(self, raw=False):
"""Get metrics on producer performance.
This is ported from the Java Producer, for details see:
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Warning:
This is an unstable interface. It may change in future
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This is ported from the Java Producer, for details see:
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train | SimpleConsumer.reset_partition_offset | Update offsets using auto_offset_reset policy (smallest|largest)
Arguments:
partition (int): the partition for which offsets should be updated
Returns: Updated offset on success, None on failure | kafka/consumer/simple.py | def reset_partition_offset(self, partition):
"""Update offsets using auto_offset_reset policy (smallest|largest)
Arguments:
partition (int): the partition for which offsets should be updated
Returns: Updated offset on success, None on failure
"""
LATEST = -1
... | def reset_partition_offset(self, partition):
"""Update offsets using auto_offset_reset policy (smallest|largest)
Arguments:
partition (int): the partition for which offsets should be updated
Returns: Updated offset on success, None on failure
"""
LATEST = -1
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train | SimpleConsumer.seek | Alter the current offset in the consumer, similar to fseek
Arguments:
offset: how much to modify the offset
whence: where to modify it from, default is None
* None is an absolute offset
* 0 is relative to the earliest available offset (head)
... | kafka/consumer/simple.py | def seek(self, offset, whence=None, partition=None):
"""
Alter the current offset in the consumer, similar to fseek
Arguments:
offset: how much to modify the offset
whence: where to modify it from, default is None
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... | def seek(self, offset, whence=None, partition=None):
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Alter the current offset in the consumer, similar to fseek
Arguments:
offset: how much to modify the offset
whence: where to modify it from, default is None
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train | SimpleConsumer.get_messages | Fetch the specified number of messages
Keyword Arguments:
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till all messages are fetched.
If block is a positive integer the API will block until that
many mes... | kafka/consumer/simple.py | def get_messages(self, count=1, block=True, timeout=0.1):
"""
Fetch the specified number of messages
Keyword Arguments:
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till all messages are fetched.
If bloc... | def get_messages(self, count=1, block=True, timeout=0.1):
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Fetch the specified number of messages
Keyword Arguments:
count: Indicates the maximum number of messages to be fetched
block: If True, the API will block till all messages are fetched.
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train | SimpleConsumer._get_message | If no messages can be fetched, returns None.
If get_partition_info is None, it defaults to self.partition_info
If get_partition_info is True, returns (partition, message)
If get_partition_info is False, returns message | kafka/consumer/simple.py | def _get_message(self, block=True, timeout=0.1, get_partition_info=None,
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"""
If no messages can be fetched, returns None.
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train | encode_varint_1 | Encode an integer to a varint presentation. See
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train | size_of_varint_1 | Number of bytes needed to encode an integer in variable-length format. | benchmarks/varint_speed.py | def size_of_varint_1(value):
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train | size_of_varint_2 | Number of bytes needed to encode an integer in variable-length format. | benchmarks/varint_speed.py | def size_of_varint_2(value):
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train | AbstractSampledStat.purge_obsolete_samples | Timeout any windows that have expired in the absence of any events | kafka/metrics/stats/sampled_stat.py | def purge_obsolete_samples(self, config, now):
"""
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expire_age = config.samples * config.time_window_ms
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train | KafkaAdminClient.close | Close the KafkaAdminClient connection to the Kafka broker. | kafka/admin/client.py | def close(self):
"""Close the KafkaAdminClient connection to the Kafka broker."""
if not hasattr(self, '_closed') or self._closed:
log.info("KafkaAdminClient already closed.")
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self._metrics.close()
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train | KafkaAdminClient._matching_api_version | Find the latest version of the protocol operation supported by both
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train | KafkaAdminClient._refresh_controller_id | Determine the Kafka cluster controller. | kafka/admin/client.py | def _refresh_controller_id(self):
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train | KafkaAdminClient._find_group_coordinator_id | Find the broker node_id of the coordinator of the given group.
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:param group_id: The consumer group ID. This is typically the group
... | kafka/admin/client.py | def _find_group_coordinator_id(self, group_id):
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the FindCoordinatorResponse is received. Any errors are immediately
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train | KafkaAdminClient._send_request_to_node | Send a Kafka protocol message to a specific broker.
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:param node_id: The broker id to which to send the message.
:param request: The message to send.
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train | KafkaAdminClient._send_request_to_controller | Send a Kafka protocol message to the cluster controller.
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train | KafkaAdminClient.create_topics | Create new topics in the cluster.
:param new_topics: A list of NewTopic objects.
:param timeout_ms: Milliseconds to wait for new topics to be created
before the broker returns.
:param validate_only: If True, don't actually create new topics.
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:param timeout_ms: Milliseconds to wait for new topics to be created
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:param validate_only... | def create_topics(self, new_topics, timeout_ms=None, validate_only=False):
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:param timeout_ms: Milliseconds to wait for new topics to be created
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train | KafkaAdminClient.delete_topics | Delete topics from the cluster.
:param topics: A list of topic name strings.
:param timeout_ms: Milliseconds to wait for topics to be deleted
before the broker returns.
:return: Appropriate version of DeleteTopicsResponse class. | kafka/admin/client.py | def delete_topics(self, topics, timeout_ms=None):
"""Delete topics from the cluster.
:param topics: A list of topic name strings.
:param timeout_ms: Milliseconds to wait for topics to be deleted
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:return: Appropriate version of DeleteTopicsResponse ... | def delete_topics(self, topics, timeout_ms=None):
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train | KafkaAdminClient.describe_configs | Fetch configuration parameters for one or more Kafka resources.
:param config_resources: An list of ConfigResource objects.
Any keys in ConfigResource.configs dict will be used to filter the
result. Setting the configs dict to None will get all values. An
empty dict will get... | kafka/admin/client.py | def describe_configs(self, config_resources, include_synonyms=False):
"""Fetch configuration parameters for one or more Kafka resources.
:param config_resources: An list of ConfigResource objects.
Any keys in ConfigResource.configs dict will be used to filter the
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train | KafkaAdminClient.alter_configs | Alter configuration parameters of one or more Kafka resources.
Warning:
This is currently broken for BROKER resources because those must be
sent to that specific broker, versus this always picks the
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... | kafka/admin/client.py | def alter_configs(self, config_resources):
"""Alter configuration parameters of one or more Kafka resources.
Warning:
This is currently broken for BROKER resources because those must be
sent to that specific broker, versus this always picks the
least-loaded node. See... | def alter_configs(self, config_resources):
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train | KafkaAdminClient.create_partitions | Create additional partitions for an existing topic.
:param topic_partitions: A map of topic name strings to NewPartition objects.
:param timeout_ms: Milliseconds to wait for new partitions to be
created before the broker returns.
:param validate_only: If True, don't actually create ... | kafka/admin/client.py | def create_partitions(self, topic_partitions, timeout_ms=None, validate_only=False):
"""Create additional partitions for an existing topic.
:param topic_partitions: A map of topic name strings to NewPartition objects.
:param timeout_ms: Milliseconds to wait for new partitions to be
... | def create_partitions(self, topic_partitions, timeout_ms=None, validate_only=False):
"""Create additional partitions for an existing topic.
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train | KafkaAdminClient.describe_consumer_groups | Describe a set of consumer groups.
Any errors are immediately raised.
:param group_ids: A list of consumer group IDs. These are typically the
group names as strings.
:param group_coordinator_id: The node_id of the groups' coordinator
broker. If set to None, it will quer... | kafka/admin/client.py | def describe_consumer_groups(self, group_ids, group_coordinator_id=None):
"""Describe a set of consumer groups.
Any errors are immediately raised.
:param group_ids: A list of consumer group IDs. These are typically the
group names as strings.
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train | KafkaAdminClient.list_consumer_groups | List all consumer groups known to the cluster.
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composed of the consumer group name and the consumer group protocol
type.
Only consumer groups that store their offsets in Kafka are returned.
The protocol type will be a... | kafka/admin/client.py | def list_consumer_groups(self, broker_ids=None):
"""List all consumer groups known to the cluster.
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train | KafkaAdminClient.list_consumer_group_offsets | Fetch Consumer Group Offsets.
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As soon as any error is encountered, it is immediately raised.
:param group_id: The consumer group id name for which to fetch offsets.
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train | crc_update | Update CRC-32C checksum with data.
Args:
crc: 32-bit checksum to update as long.
data: byte array, string or iterable over bytes.
Returns:
32-bit updated CRC-32C as long. | kafka/record/_crc32c.py | def crc_update(crc, data):
"""Update CRC-32C checksum with data.
Args:
crc: 32-bit checksum to update as long.
data: byte array, string or iterable over bytes.
Returns:
32-bit updated CRC-32C as long.
"""
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"""Update CRC-32C checksum with data.
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crc: 32-bit checksum to update as long.
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train | ProducerBatch.maybe_expire | Expire batches if metadata is not available
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* the batch is not in retry AND request timeout has elapsed after
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"""Expire batches if metadata is not available
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train | RecordAccumulator.append | Add a record to the accumulator, return the append result.
The append result will contain the future metadata, and flag for
whether the appended batch is full or a new batch is created
Arguments:
tp (TopicPartition): The topic/partition to which this record is
being... | kafka/producer/record_accumulator.py | def append(self, tp, timestamp_ms, key, value, headers, max_time_to_block_ms,
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The append result will contain the future metadata, and flag for
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train | RecordAccumulator.abort_expired_batches | Abort the batches that have been sitting in RecordAccumulator for
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Arguments:
request_timeout_ms (int): milliseconds to timeout
cluster (ClusterMetadata): current metadata for kafka cluster
... | kafka/producer/record_accumulator.py | def abort_expired_batches(self, request_timeout_ms, cluster):
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Arguments:
request_timeout_ms (int): milliseconds to timeout
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request_timeout_ms (int): milliseconds to timeout
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train | RecordAccumulator.reenqueue | Re-enqueue the given record batch in the accumulator to retry. | kafka/producer/record_accumulator.py | def reenqueue(self, batch):
"""Re-enqueue the given record batch in the accumulator to retry."""
now = time.time()
batch.attempts += 1
batch.last_attempt = now
batch.last_append = now
batch.set_retry()
assert batch.topic_partition in self._tp_locks, 'TopicPartitio... | def reenqueue(self, batch):
"""Re-enqueue the given record batch in the accumulator to retry."""
now = time.time()
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train | RecordAccumulator.ready | Get a list of nodes whose partitions are ready to be sent, and the
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Also return the flag for whether there are any unknown leaders for the
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A destination node is ready to send if:
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"""
Get a list of nodes whose partitions are ready to be sent, and the
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Also return the flag for whether there are any unknown leaders for the
accumulated partition batches.
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train | RecordAccumulator.has_unsent | Return whether there is any unsent record in the accumulator. | kafka/producer/record_accumulator.py | def has_unsent(self):
"""Return whether there is any unsent record in the accumulator."""
for tp in list(self._batches.keys()):
with self._tp_locks[tp]:
dq = self._batches[tp]
if len(dq):
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return False | def has_unsent(self):
"""Return whether there is any unsent record in the accumulator."""
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train | RecordAccumulator.drain | Drain all the data for the given nodes and collate them into a list of
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This method attempts to avoid choosing the same topic-node repeatedly.
Arguments:
cluster (ClusterMetadata): The current cluster metadata
... | kafka/producer/record_accumulator.py | def drain(self, cluster, nodes, max_size):
"""
Drain all the data for the given nodes and collate them into a list of
batches that will fit within the specified size on a per-node basis.
This method attempts to avoid choosing the same topic-node repeatedly.
Arguments:
... | def drain(self, cluster, nodes, max_size):
"""
Drain all the data for the given nodes and collate them into a list of
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train | RecordAccumulator.deallocate | Deallocate the record batch. | kafka/producer/record_accumulator.py | def deallocate(self, batch):
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self._incomplete.remove(batch)
self._free.deallocate(batch.buffer()) | def deallocate(self, batch):
"""Deallocate the record batch."""
self._incomplete.remove(batch)
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train | RecordAccumulator.await_flush_completion | Mark all partitions as ready to send and block until the send is complete | kafka/producer/record_accumulator.py | def await_flush_completion(self, timeout=None):
"""
Mark all partitions as ready to send and block until the send is complete
"""
try:
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log.debug('Waiting on produce to %s',
batch.produce_future.top... | def await_flush_completion(self, timeout=None):
"""
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train | RecordAccumulator.abort_incomplete_batches | This function is only called when sender is closed forcefully. It will fail all the
incomplete batches and return. | kafka/producer/record_accumulator.py | def abort_incomplete_batches(self):
"""
This function is only called when sender is closed forcefully. It will fail all the
incomplete batches and return.
"""
# We need to keep aborting the incomplete batch until no thread is trying to append to
# 1. Avoid losing batches.... | def abort_incomplete_batches(self):
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train | RecordAccumulator._abort_batches | Go through incomplete batches and abort them. | kafka/producer/record_accumulator.py | def _abort_batches(self):
"""Go through incomplete batches and abort them."""
error = Errors.IllegalStateError("Producer is closed forcefully.")
for batch in self._incomplete.all():
tp = batch.topic_partition
# Close the batch before aborting
with self._tp_loc... | def _abort_batches(self):
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train | MemoryRecordsBuilder.append | Append a message to the buffer.
Returns: RecordMetadata or None if unable to append | kafka/record/memory_records.py | def append(self, timestamp, key, value, headers=[]):
""" Append a message to the buffer.
Returns: RecordMetadata or None if unable to append
"""
if self._closed:
return None
offset = self._next_offset
metadata = self._builder.append(offset, timestamp, key, v... | def append(self, timestamp, key, value, headers=[]):
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train | DefaultRecordBatchBuilder.append | Write message to messageset buffer with MsgVersion 2 | kafka/record/default_records.py | def append(self, offset, timestamp, key, value, headers,
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train | DefaultRecordBatchBuilder.estimate_size_in_bytes | Get the upper bound estimate on the size of record | kafka/record/default_records.py | def estimate_size_in_bytes(cls, key, value, headers):
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train | Heartbeat.time_to_next_heartbeat | Returns seconds (float) remaining before next heartbeat should be sent | kafka/coordinator/heartbeat.py | def time_to_next_heartbeat(self):
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train | _address_family | Attempt to determine the family of an address (or hostname)
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train | collect_hosts | Collects a comma-separated set of hosts (host:port) and optionally
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train | dns_lookup | Returns a list of getaddrinfo structs, optionally filtered to an afi (ipv4 / ipv6) | kafka/conn.py | def dns_lookup(host, port, afi=socket.AF_UNSPEC):
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train | BrokerConnection.connect | Attempt to connect and return ConnectionState | kafka/conn.py | def connect(self):
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train | BrokerConnection._token_extensions | Return a string representation of the OPTIONAL key-value pairs that can be sent with an OAUTHBEARER
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"""
Return a string representation of the OPTIONAL key-value pairs that can be sent with an OAUTHBEARER
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train | BrokerConnection.blacked_out | Return true if we are disconnected from the given node and can't
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"""
Return true if we are disconnected from the given node and can't
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"""
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non-blocking connect to finish. When connected, returns a very large
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Default: kafka.errors.KafkaConnectionError. | kafka/conn.py | def close(self, error=None):
"""Close socket and fail all in-flight-requests.
Arguments:
error (Exception, optional): pending in-flight-requests
will be failed with this exception.
Default: kafka.errors.KafkaConnectionError.
"""
if self.state ... | def close(self, error=None):
"""Close socket and fail all in-flight-requests.
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train | BrokerConnection.send | Queue request for async network send, return Future() | kafka/conn.py | def send(self, request, blocking=True):
"""Queue request for async network send, return Future()"""
future = Future()
if self.connecting():
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train | BrokerConnection.send_pending_requests | Can block on network if request is larger than send_buffer_bytes | kafka/conn.py | def send_pending_requests(self):
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try:
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Return list of (response, future) tuples
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train | BrokerConnection._recv | Take all available bytes from socket, return list of any responses from parser | kafka/conn.py | def _recv(self):
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train | Sender.run | The main run loop for the sender thread. | kafka/producer/sender.py | def run(self):
"""The main run loop for the sender thread."""
log.debug("Starting Kafka producer I/O thread.")
# main loop, runs until close is called
while self._running:
try:
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train | Sender.run_once | Run a single iteration of sending. | kafka/producer/sender.py | def run_once(self):
"""Run a single iteration of sending."""
while self._topics_to_add:
self._client.add_topic(self._topics_to_add.pop())
# get the list of partitions with data ready to send
result = self._accumulator.ready(self._metadata)
ready_nodes, next_ready_che... | def run_once(self):
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train | Sender.initiate_close | Start closing the sender (won't complete until all data is sent). | kafka/producer/sender.py | def initiate_close(self):
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self._running = False
self._accumulator.close()
self.wakeup() | def initiate_close(self):
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train | Sender._handle_produce_response | Handle a produce response. | kafka/producer/sender.py | def _handle_produce_response(self, node_id, send_time, batches, response):
"""Handle a produce response."""
# if we have a response, parse it
log.debug('Parsing produce response: %r', response)
if response:
batches_by_partition = dict([(batch.topic_partition, batch)
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train | Sender._complete_batch | Complete or retry the given batch of records.
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error (Exception): The error (or None if none)
base_offset (int): The base offset assigned to the records if successful
timestamp_ms (int, optional): The timestamp retu... | kafka/producer/sender.py | def _complete_batch(self, batch, error, base_offset, timestamp_ms=None):
"""Complete or retry the given batch of records.
Arguments:
batch (RecordBatch): The record batch
error (Exception): The error (or None if none)
base_offset (int): The base offset assigned to th... | def _complete_batch(self, batch, error, base_offset, timestamp_ms=None):
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train | Sender._can_retry | We can retry a send if the error is transient and the number of
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"""
We can retry a send if the error is transient and the number of
attempts taken is fewer than the maximum allowed
"""
return (batch.attempts < self.config['retries']
and getattr(error, 'retriable', False)) | def _can_retry(self, batch, error):
"""
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"""
return (batch.attempts < self.config['retries']
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collated: {node_id: [RecordBatch]}
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dict: {node_id: ProduceRequest} (version depends on api_version) | kafka/producer/sender.py | def _create_produce_requests(self, collated):
"""
Transfer the record batches into a list of produce requests on a
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Arguments:
collated: {node_id: [RecordBatch]}
Returns:
dict: {node_id: ProduceRequest} (version depends on api_version)
... | def _create_produce_requests(self, collated):
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Transfer the record batches into a list of produce requests on a
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train | Sender._produce_request | Create a produce request from the given record batches.
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"""Create a produce request from the given record batches.
Returns:
ProduceRequest (version depends on api_version)
"""
produce_records_by_partition = collections.defaultdict(dict)
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ProduceRequest (version depends on api_version)
"""
produce_records_by_partition = collections.defaultdict(dict)
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train | snappy_encode | Encodes the given data with snappy compression.
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train | lz4f_decode | Decode payload using interoperable LZ4 framing. Requires Kafka >= 0.10 | kafka/codec.py | def lz4f_decode(payload):
"""Decode payload using interoperable LZ4 framing. Requires Kafka >= 0.10"""
# pylint: disable-msg=no-member
ctx = lz4f.createDecompContext()
data = lz4f.decompressFrame(payload, ctx)
lz4f.freeDecompContext(ctx)
# lz4f python module does not expose how much of the payl... | def lz4f_decode(payload):
"""Decode payload using interoperable LZ4 framing. Requires Kafka >= 0.10"""
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ctx = lz4f.createDecompContext()
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train | lz4_encode_old_kafka | Encode payload for 0.8/0.9 brokers -- requires an incorrect header checksum. | kafka/codec.py | def lz4_encode_old_kafka(payload):
"""Encode payload for 0.8/0.9 brokers -- requires an incorrect header checksum."""
assert xxhash is not None
data = lz4_encode(payload)
header_size = 7
flg = data[4]
if not isinstance(flg, int):
flg = ord(flg)
content_size_bit = ((flg >> 3) & 1)
... | def lz4_encode_old_kafka(payload):
"""Encode payload for 0.8/0.9 brokers -- requires an incorrect header checksum."""
assert xxhash is not None
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header_size = 7
flg = data[4]
if not isinstance(flg, int):
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train | ClusterMetadata.brokers | Get all BrokerMetadata
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"""Get all BrokerMetadata
Returns:
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"""
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train | ClusterMetadata.broker_metadata | Get BrokerMetadata
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broker_id (int): node_id for a broker to check
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"""Get BrokerMetadata
Arguments:
broker_id (int): node_id for a broker to check
Returns:
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"""
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train | ClusterMetadata.partitions_for_topic | Return set of all partitions for topic (whether available or not)
Arguments:
topic (str): topic to check for partitions
Returns:
set: {partition (int), ...} | kafka/cluster.py | def partitions_for_topic(self, topic):
"""Return set of all partitions for topic (whether available or not)
Arguments:
topic (str): topic to check for partitions
Returns:
set: {partition (int), ...}
"""
if topic not in self._partitions:
retur... | def partitions_for_topic(self, topic):
"""Return set of all partitions for topic (whether available or not)
Arguments:
topic (str): topic to check for partitions
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set: {partition (int), ...}
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train | ClusterMetadata.available_partitions_for_topic | Return set of partitions with known leaders
Arguments:
topic (str): topic to check for partitions
Returns:
set: {partition (int), ...}
None if topic not found. | kafka/cluster.py | def available_partitions_for_topic(self, topic):
"""Return set of partitions with known leaders
Arguments:
topic (str): topic to check for partitions
Returns:
set: {partition (int), ...}
None if topic not found.
"""
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topic (str): topic to check for partitions
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train | ClusterMetadata.leader_for_partition | Return node_id of leader, -1 unavailable, None if unknown. | kafka/cluster.py | def leader_for_partition(self, partition):
"""Return node_id of leader, -1 unavailable, None if unknown."""
if partition.topic not in self._partitions:
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train | ClusterMetadata.ttl | Milliseconds until metadata should be refreshed | kafka/cluster.py | def ttl(self):
"""Milliseconds until metadata should be refreshed"""
now = time.time() * 1000
if self._need_update:
ttl = 0
else:
metadata_age = now - self._last_successful_refresh_ms
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metadata_age = now - self._last_successful_refresh_ms
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train | ClusterMetadata.request_update | Flags metadata for update, return Future()
Actual update must be handled separately. This method will only
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Returns:
kafka.future.Future (value will be the cluster object after update) | kafka/cluster.py | def request_update(self):
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train | ClusterMetadata.topics | Get set of known topics.
Arguments:
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(such as offsets) should be exposed to the consumer. If set to
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"""Get set of known topics.
Arguments:
exclude_internal_topics (bool): Whether records from internal topics
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True the only way to receive records fro... | def topics(self, exclude_internal_topics=True):
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train | ClusterMetadata.failed_update | Update cluster state given a failed MetadataRequest. | kafka/cluster.py | def failed_update(self, exception):
"""Update cluster state given a failed MetadataRequest."""
f = None
with self._lock:
if self._future:
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self._future = None
if f:
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self._future = None
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f.failure(exception)
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train | ClusterMetadata.update_metadata | Update cluster state given a MetadataResponse.
Arguments:
metadata (MetadataResponse): broker response to a metadata request
Returns: None | kafka/cluster.py | def update_metadata(self, metadata):
"""Update cluster state given a MetadataResponse.
Arguments:
metadata (MetadataResponse): broker response to a metadata request
Returns: None
"""
# In the common case where we ask for a single topic and get back an
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metadata (MetadataResponse): broker response to a metadata request
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train | ClusterMetadata.add_group_coordinator | Update with metadata for a group coordinator
Arguments:
group (str): name of group from GroupCoordinatorRequest
response (GroupCoordinatorResponse): broker response
Returns:
bool: True if metadata is updated, False on error | kafka/cluster.py | def add_group_coordinator(self, group, response):
"""Update with metadata for a group coordinator
Arguments:
group (str): name of group from GroupCoordinatorRequest
response (GroupCoordinatorResponse): broker response
Returns:
bool: True if metadata is updat... | def add_group_coordinator(self, group, response):
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group (str): name of group from GroupCoordinatorRequest
response (GroupCoordinatorResponse): broker response
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train | ClusterMetadata.with_partitions | Returns a copy of cluster metadata with partitions added | kafka/cluster.py | def with_partitions(self, partitions_to_add):
"""Returns a copy of cluster metadata with partitions added"""
new_metadata = ClusterMetadata(**self.config)
new_metadata._brokers = copy.deepcopy(self._brokers)
new_metadata._partitions = copy.deepcopy(self._partitions)
new_metadata.... | def with_partitions(self, partitions_to_add):
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train | KafkaConsumer.assign | 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
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Warning:
... | kafka/consumer/group.py | def assign(self, partitions):
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Arguments:
partitions (list of TopicPartition): Assignment for this instance.
Raises:
IllegalStateError: If consumer has already called
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train | KafkaConsumer.close | Close the consumer, waiting indefinitely for any needed cleanup.
Keyword Arguments:
autocommit (bool): If auto-commit is configured for this consumer,
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pending consumed offsets prior to close. Default: Tr... | kafka/consumer/group.py | 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... | def close(self, autocommit=True):
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train | KafkaConsumer.commit_async | Commit offsets to kafka asynchronously, optionally firing callback.
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will be used on the first fetch after every rebalance and also on
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"""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
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train | KafkaConsumer.commit | 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 shou... | kafka/consumer/group.py | 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 anyth... | 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 anyth... | [
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train | KafkaConsumer.committed | 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
initial... | kafka/consumer/group.py | 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... | def committed(self, partition):
"""Get the last committed offset for the given partition.
This offset will be used as the position for the consumer
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train | KafkaConsumer.topics | Get all topics the user is authorized to view.
Returns:
set: topics | kafka/consumer/group.py | 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.... | def topics(self):
"""Get all topics the user is authorized to view.
Returns:
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"""
cluster = self._client.cluster
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train | KafkaConsumer.poll | 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... | kafka/consumer/group.py | def poll(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 consum... | def poll(self, timeout_ms=0, max_records=None):
"""Fetch data from assigned topics / partitions.
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train | KafkaConsumer._poll_once | 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). | kafka/consumer/group.py | 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:
... | def _poll_once(self, timeout_ms, max_records):
"""Do one round of polling. In addition to checking for new data, this does
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Arguments:
timeout_ms (int): The maximum time in milliseconds to block.
Returns:
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train | KafkaConsumer.position | Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): Partition to check
Returns:
int: Offset | kafka/consumer/group.py | 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('partiti... | def position(self, partition):
"""Get the offset of the next record that will be fetched
Arguments:
partition (TopicPartition): Partition to check
Returns:
int: Offset
"""
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train | KafkaConsumer.highwater | 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.... | kafka/consumer/group.py | 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
... | 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
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] | dpkp/kafka-python | python | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/consumer/group.py#L687-L709 | [
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train | KafkaConsumer.pause | 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 parti... | kafka/consumer/group.py | 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 no... | 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
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Note: This method does no... | [
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] | dpkp/kafka-python | python | https://github.com/dpkp/kafka-python/blob/f6a8a38937688ea2cc5dc13d3d1039493be5c9b5/kafka/consumer/group.py#L711-L728 | [
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train | KafkaConsumer.seek | 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.KafkaConsu... | kafka/consumer/group.py | 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 use... | 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
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train | KafkaConsumer.seek_to_beginning | 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 pa... | kafka/consumer/group.py | 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 i... | 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 i... | [
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