655 lines
32 KiB
Python
655 lines
32 KiB
Python
from __future__ import absolute_import
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import atexit
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import copy
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import logging
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import socket
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import threading
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import time
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import weakref
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from .. import errors as Errors
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from ..client_async import KafkaClient, selectors
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from ..metrics import MetricConfig, Metrics
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from ..partitioner.default import DefaultPartitioner
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from ..protocol.message import Message, MessageSet
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from ..serializer import Serializer
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from ..structs import TopicPartition
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from .future import FutureRecordMetadata, FutureProduceResult
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from .record_accumulator import AtomicInteger, RecordAccumulator
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from .sender import Sender
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log = logging.getLogger(__name__)
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PRODUCER_CLIENT_ID_SEQUENCE = AtomicInteger()
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class KafkaProducer(object):
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"""A Kafka client that publishes records to the Kafka cluster.
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The producer is thread safe and sharing a single producer instance across
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threads will generally be faster than having multiple instances.
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The producer consists of a pool of buffer space that holds records that
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haven't yet been transmitted to the server as well as a background I/O
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thread that is responsible for turning these records into requests and
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transmitting them to the cluster.
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:meth:`~kafka.KafkaProducer.send` is asynchronous. When called it adds the
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record to a buffer of pending record sends and immediately returns. This
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allows the producer to batch together individual records for efficiency.
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The 'acks' config controls the criteria under which requests are considered
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complete. The "all" setting will result in blocking on the full commit of
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the record, the slowest but most durable setting.
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If the request fails, the producer can automatically retry, unless
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'retries' is configured to 0. Enabling retries also opens up the
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possibility of duplicates (see the documentation on message
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delivery semantics for details:
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http://kafka.apache.org/documentation.html#semantics
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).
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The producer maintains buffers of unsent records for each partition. These
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buffers are of a size specified by the 'batch_size' config. Making this
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larger can result in more batching, but requires more memory (since we will
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generally have one of these buffers for each active partition).
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By default a buffer is available to send immediately even if there is
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additional unused space in the buffer. However if you want to reduce the
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number of requests you can set 'linger_ms' to something greater than 0.
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This will instruct the producer to wait up to that number of milliseconds
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before sending a request in hope that more records will arrive to fill up
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the same batch. This is analogous to Nagle's algorithm in TCP. Note that
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records that arrive close together in time will generally batch together
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even with linger_ms=0 so under heavy load batching will occur regardless of
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the linger configuration; however setting this to something larger than 0
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can lead to fewer, more efficient requests when not under maximal load at
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the cost of a small amount of latency.
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The buffer_memory controls the total amount of memory available to the
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producer for buffering. If records are sent faster than they can be
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transmitted to the server then this buffer space will be exhausted. When
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the buffer space is exhausted additional send calls will block.
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The key_serializer and value_serializer instruct how to turn the key and
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value objects the user provides into bytes.
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Keyword Arguments:
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bootstrap_servers: 'host[:port]' string (or list of 'host[:port]'
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strings) that the producer should contact to bootstrap initial
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cluster metadata. This does not have to be the full node list.
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It just needs to have at least one broker that will respond to a
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Metadata API Request. Default port is 9092. If no servers are
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specified, will default to localhost:9092.
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client_id (str): a name for this client. This string is passed in
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each request to servers and can be used to identify specific
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server-side log entries that correspond to this client.
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Default: 'kafka-python-producer-#' (appended with a unique number
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per instance)
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key_serializer (callable): used to convert user-supplied keys to bytes
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If not None, called as f(key), should return bytes. Default: None.
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value_serializer (callable): used to convert user-supplied message
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values to bytes. If not None, called as f(value), should return
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bytes. Default: None.
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acks (0, 1, 'all'): The number of acknowledgments the producer requires
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the leader to have received before considering a request complete.
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This controls the durability of records that are sent. The
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following settings are common:
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0: Producer will not wait for any acknowledgment from the server.
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The message will immediately be added to the socket
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buffer and considered sent. No guarantee can be made that the
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server has received the record in this case, and the retries
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configuration will not take effect (as the client won't
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generally know of any failures). The offset given back for each
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record will always be set to -1.
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1: Wait for leader to write the record to its local log only.
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Broker will respond without awaiting full acknowledgement from
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all followers. In this case should the leader fail immediately
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after acknowledging the record but before the followers have
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replicated it then the record will be lost.
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all: Wait for the full set of in-sync replicas to write the record.
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This guarantees that the record will not be lost as long as at
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least one in-sync replica remains alive. This is the strongest
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available guarantee.
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If unset, defaults to acks=1.
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compression_type (str): The compression type for all data generated by
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the producer. Valid values are 'gzip', 'snappy', 'lz4', or None.
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Compression is of full batches of data, so the efficacy of batching
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will also impact the compression ratio (more batching means better
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compression). Default: None.
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retries (int): Setting a value greater than zero will cause the client
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to resend any record whose send fails with a potentially transient
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error. Note that this retry is no different than if the client
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resent the record upon receiving the error. Allowing retries
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without setting max_in_flight_requests_per_connection to 1 will
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potentially change the ordering of records because if two batches
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are sent to a single partition, and the first fails and is retried
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but the second succeeds, then the records in the second batch may
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appear first.
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Default: 0.
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batch_size (int): Requests sent to brokers will contain multiple
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batches, one for each partition with data available to be sent.
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A small batch size will make batching less common and may reduce
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throughput (a batch size of zero will disable batching entirely).
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Default: 16384
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linger_ms (int): The producer groups together any records that arrive
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in between request transmissions into a single batched request.
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Normally this occurs only under load when records arrive faster
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than they can be sent out. However in some circumstances the client
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may want to reduce the number of requests even under moderate load.
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This setting accomplishes this by adding a small amount of
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artificial delay; that is, rather than immediately sending out a
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record the producer will wait for up to the given delay to allow
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other records to be sent so that the sends can be batched together.
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This can be thought of as analogous to Nagle's algorithm in TCP.
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This setting gives the upper bound on the delay for batching: once
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we get batch_size worth of records for a partition it will be sent
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immediately regardless of this setting, however if we have fewer
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than this many bytes accumulated for this partition we will
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'linger' for the specified time waiting for more records to show
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up. This setting defaults to 0 (i.e. no delay). Setting linger_ms=5
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would have the effect of reducing the number of requests sent but
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would add up to 5ms of latency to records sent in the absense of
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load. Default: 0.
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partitioner (callable): Callable used to determine which partition
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each message is assigned to. Called (after key serialization):
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partitioner(key_bytes, all_partitions, available_partitions).
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The default partitioner implementation hashes each non-None key
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using the same murmur2 algorithm as the java client so that
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messages with the same key are assigned to the same partition.
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When a key is None, the message is delivered to a random partition
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(filtered to partitions with available leaders only, if possible).
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buffer_memory (int): The total bytes of memory the producer should use
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to buffer records waiting to be sent to the server. If records are
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sent faster than they can be delivered to the server the producer
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will block up to max_block_ms, raising an exception on timeout.
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In the current implementation, this setting is an approximation.
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Default: 33554432 (32MB)
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max_block_ms (int): Number of milliseconds to block during
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:meth:`~kafka.KafkaProducer.send` and
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:meth:`~kafka.KafkaProducer.partitions_for`. These methods can be
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blocked either because the buffer is full or metadata unavailable.
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Blocking in the user-supplied serializers or partitioner will not be
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counted against this timeout. Default: 60000.
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max_request_size (int): The maximum size of a request. This is also
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effectively a cap on the maximum record size. Note that the server
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has its own cap on record size which may be different from this.
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This setting will limit the number of record batches the producer
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will send in a single request to avoid sending huge requests.
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Default: 1048576.
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metadata_max_age_ms (int): The period of time in milliseconds after
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which we force a refresh of metadata even if we haven't seen any
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partition leadership changes to proactively discover any new
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brokers or partitions. Default: 300000
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retry_backoff_ms (int): Milliseconds to backoff when retrying on
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errors. Default: 100.
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request_timeout_ms (int): Client request timeout in milliseconds.
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Default: 30000.
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receive_buffer_bytes (int): The size of the TCP receive buffer
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(SO_RCVBUF) to use when reading data. Default: None (relies on
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system defaults). Java client defaults to 32768.
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send_buffer_bytes (int): The size of the TCP send buffer
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(SO_SNDBUF) to use when sending data. Default: None (relies on
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system defaults). Java client defaults to 131072.
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socket_options (list): List of tuple-arguments to socket.setsockopt
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to apply to broker connection sockets. Default:
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[(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)]
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reconnect_backoff_ms (int): The amount of time in milliseconds to
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wait before attempting to reconnect to a given host.
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Default: 50.
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max_in_flight_requests_per_connection (int): Requests are pipelined
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to kafka brokers up to this number of maximum requests per
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broker connection. Default: 5.
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security_protocol (str): Protocol used to communicate with brokers.
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Valid values are: PLAINTEXT, SSL, SASL_PLAINTEXT, SASL_SSL.
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Default: PLAINTEXT.
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ssl_context (ssl.SSLContext): pre-configured SSLContext for wrapping
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socket connections. If provided, all other ssl_* configurations
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will be ignored. Default: None.
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ssl_check_hostname (bool): flag to configure whether ssl handshake
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should verify that the certificate matches the brokers hostname.
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default: true.
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ssl_cafile (str): optional filename of ca file to use in certificate
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veriication. default: none.
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ssl_certfile (str): optional filename of file in pem format containing
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the client certificate, as well as any ca certificates needed to
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establish the certificate's authenticity. default: none.
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ssl_keyfile (str): optional filename containing the client private key.
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default: none.
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ssl_password (str): optional password to be used when loading the
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certificate chain. default: none.
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ssl_crlfile (str): optional filename containing the CRL to check for
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certificate expiration. By default, no CRL check is done. When
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providing a file, only the leaf certificate will be checked against
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this CRL. The CRL can only be checked with Python 3.4+ or 2.7.9+.
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default: none.
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api_version (tuple): Specify which Kafka API version to use. If set to
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None, the client will attempt to infer the broker version by probing
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various APIs. For a full list of supported versions, see
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KafkaClient.API_VERSIONS. Default: None
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api_version_auto_timeout_ms (int): number of milliseconds to throw a
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timeout exception from the constructor when checking the broker
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api version. Only applies if api_version set to 'auto'
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metric_reporters (list): A list of classes to use as metrics reporters.
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Implementing the AbstractMetricsReporter interface allows plugging
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in classes that will be notified of new metric creation. Default: []
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metrics_num_samples (int): The number of samples maintained to compute
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metrics. Default: 2
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metrics_sample_window_ms (int): The maximum age in milliseconds of
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samples used to compute metrics. Default: 30000
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selector (selectors.BaseSelector): Provide a specific selector
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implementation to use for I/O multiplexing.
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Default: selectors.DefaultSelector
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sasl_mechanism (str): string picking sasl mechanism when security_protocol
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is SASL_PLAINTEXT or SASL_SSL. Currently only PLAIN is supported.
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Default: None
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sasl_plain_username (str): username for sasl PLAIN authentication.
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Default: None
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sasl_plain_password (str): password for sasl PLAIN authentication.
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Default: None
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Note:
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Configuration parameters are described in more detail at
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https://kafka.apache.org/0100/configuration.html#producerconfigs
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"""
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DEFAULT_CONFIG = {
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'bootstrap_servers': 'localhost',
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'client_id': None,
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'key_serializer': None,
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'value_serializer': None,
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'acks': 1,
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'compression_type': None,
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'retries': 0,
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'batch_size': 16384,
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'linger_ms': 0,
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'partitioner': DefaultPartitioner(),
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'buffer_memory': 33554432,
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'connections_max_idle_ms': 9 * 60 * 1000,
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'max_block_ms': 60000,
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'max_request_size': 1048576,
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'metadata_max_age_ms': 300000,
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'retry_backoff_ms': 100,
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'request_timeout_ms': 30000,
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'receive_buffer_bytes': None,
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'send_buffer_bytes': None,
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'socket_options': [(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)],
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'reconnect_backoff_ms': 50,
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'max_in_flight_requests_per_connection': 5,
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'security_protocol': 'PLAINTEXT',
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'ssl_context': None,
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'ssl_check_hostname': True,
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'ssl_cafile': None,
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'ssl_certfile': None,
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'ssl_keyfile': None,
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'ssl_crlfile': None,
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'ssl_password': None,
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'api_version': None,
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'api_version_auto_timeout_ms': 2000,
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'metric_reporters': [],
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'metrics_num_samples': 2,
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'metrics_sample_window_ms': 30000,
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'selector': selectors.DefaultSelector,
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'sasl_mechanism': None,
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'sasl_plain_username': None,
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'sasl_plain_password': None,
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}
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def __init__(self, **configs):
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log.debug("Starting the Kafka producer") # trace
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self.config = copy.copy(self.DEFAULT_CONFIG)
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for key in self.config:
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if key in configs:
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self.config[key] = configs.pop(key)
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# Only check for extra config keys in top-level class
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assert not configs, 'Unrecognized configs: %s' % configs
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if self.config['client_id'] is None:
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self.config['client_id'] = 'kafka-python-producer-%s' % \
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PRODUCER_CLIENT_ID_SEQUENCE.increment()
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if self.config['acks'] == 'all':
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self.config['acks'] = -1
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# api_version was previously a str. accept old format for now
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if isinstance(self.config['api_version'], str):
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deprecated = self.config['api_version']
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if deprecated == 'auto':
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self.config['api_version'] = None
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else:
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self.config['api_version'] = tuple(map(int, deprecated.split('.')))
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log.warning('use api_version=%s [tuple] -- "%s" as str is deprecated',
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str(self.config['api_version']), deprecated)
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# Configure metrics
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metrics_tags = {'client-id': self.config['client_id']}
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metric_config = MetricConfig(samples=self.config['metrics_num_samples'],
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time_window_ms=self.config['metrics_sample_window_ms'],
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tags=metrics_tags)
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reporters = [reporter() for reporter in self.config['metric_reporters']]
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self._metrics = Metrics(metric_config, reporters)
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client = KafkaClient(metrics=self._metrics, metric_group_prefix='producer',
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**self.config)
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# Get auto-discovered version from client if necessary
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if self.config['api_version'] is None:
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self.config['api_version'] = client.config['api_version']
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if self.config['compression_type'] == 'lz4':
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assert self.config['api_version'] >= (0, 8, 2), 'LZ4 Requires >= Kafka 0.8.2 Brokers'
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message_version = 1 if self.config['api_version'] >= (0, 10) else 0
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self._accumulator = RecordAccumulator(message_version=message_version, metrics=self._metrics, **self.config)
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self._metadata = client.cluster
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guarantee_message_order = bool(self.config['max_in_flight_requests_per_connection'] == 1)
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self._sender = Sender(client, self._metadata,
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self._accumulator, self._metrics,
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guarantee_message_order=guarantee_message_order,
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**self.config)
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self._sender.daemon = True
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self._sender.start()
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self._closed = False
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self._cleanup = self._cleanup_factory()
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atexit.register(self._cleanup)
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log.debug("Kafka producer started")
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def _cleanup_factory(self):
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"""Build a cleanup clojure that doesn't increase our ref count"""
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_self = weakref.proxy(self)
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def wrapper():
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try:
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_self.close()
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except (ReferenceError, AttributeError):
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pass
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return wrapper
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def _unregister_cleanup(self):
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if getattr(self, '_cleanup', None):
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if hasattr(atexit, 'unregister'):
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atexit.unregister(self._cleanup) # pylint: disable=no-member
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# py2 requires removing from private attribute...
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else:
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# ValueError on list.remove() if the exithandler no longer exists
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# but that is fine here
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try:
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atexit._exithandlers.remove( # pylint: disable=no-member
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(self._cleanup, (), {}))
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except ValueError:
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pass
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self._cleanup = None
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def __del__(self):
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self.close(timeout=0)
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def close(self, timeout=None):
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"""Close this producer.
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Arguments:
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timeout (float, optional): timeout in seconds to wait for completion.
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"""
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# drop our atexit handler now to avoid leaks
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self._unregister_cleanup()
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if not hasattr(self, '_closed') or self._closed:
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log.info('Kafka producer closed')
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return
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if timeout is None:
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# threading.TIMEOUT_MAX is available in Python3.3+
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timeout = getattr(threading, 'TIMEOUT_MAX', 999999999)
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if getattr(threading, 'TIMEOUT_MAX', False):
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assert 0 <= timeout <= getattr(threading, 'TIMEOUT_MAX')
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else:
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assert timeout >= 0
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log.info("Closing the Kafka producer with %s secs timeout.", timeout)
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#first_exception = AtomicReference() # this will keep track of the first encountered exception
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invoked_from_callback = bool(threading.current_thread() is self._sender)
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if timeout > 0:
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if invoked_from_callback:
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log.warning("Overriding close timeout %s secs to 0 in order to"
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" prevent useless blocking due to self-join. This"
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" means you have incorrectly invoked close with a"
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" non-zero timeout from the producer call-back.",
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timeout)
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else:
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# Try to close gracefully.
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if self._sender is not None:
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self._sender.initiate_close()
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self._sender.join(timeout)
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if self._sender is not None and self._sender.is_alive():
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log.info("Proceeding to force close the producer since pending"
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" requests could not be completed within timeout %s.",
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timeout)
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self._sender.force_close()
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# Only join the sender thread when not calling from callback.
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if not invoked_from_callback:
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self._sender.join()
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self._metrics.close()
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try:
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self.config['key_serializer'].close()
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except AttributeError:
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pass
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try:
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self.config['value_serializer'].close()
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except AttributeError:
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pass
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self._closed = True
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log.debug("The Kafka producer has closed.")
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def partitions_for(self, topic):
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"""Returns set of all known partitions for the topic."""
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max_wait = self.config['max_block_ms'] / 1000.0
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return self._wait_on_metadata(topic, max_wait)
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def send(self, topic, value=None, key=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
|
|
serializable to bytes via configured value_serializer. If value
|
|
is None, key is required and message acts as a 'delete'.
|
|
See kafka compaction documentation for more details:
|
|
http://kafka.apache.org/documentation.html#compaction
|
|
(compaction requires kafka >= 0.8.1)
|
|
partition (int, optional): optionally specify a partition. If not
|
|
set, the partition will be selected using the configured
|
|
'partitioner'.
|
|
key (optional): a key to associate with the message. Can be used to
|
|
determine which partition to send the message to. If partition
|
|
is None (and producer's partitioner config is left as default),
|
|
then messages with the same key will be delivered to the same
|
|
partition (but if key is None, partition is chosen randomly).
|
|
Must be type bytes, or be serializable to bytes via configured
|
|
key_serializer.
|
|
timestamp_ms (int, optional): epoch milliseconds (from Jan 1 1970 UTC)
|
|
to use as the message timestamp. Defaults to current time.
|
|
|
|
Returns:
|
|
FutureRecordMetadata: resolves to RecordMetadata
|
|
|
|
Raises:
|
|
KafkaTimeoutError: if unable to fetch topic metadata, or unable
|
|
to obtain memory buffer prior to configured max_block_ms
|
|
"""
|
|
assert value is not None or self.config['api_version'] >= (0, 8, 1), (
|
|
'Null messages require kafka >= 0.8.1')
|
|
assert not (value is None and key is None), 'Need at least one: key or value'
|
|
key_bytes = value_bytes = None
|
|
try:
|
|
# first make sure the metadata for the topic is
|
|
# available
|
|
self._wait_on_metadata(topic, self.config['max_block_ms'] / 1000.0)
|
|
|
|
key_bytes = self._serialize(
|
|
self.config['key_serializer'],
|
|
topic, key)
|
|
value_bytes = self._serialize(
|
|
self.config['value_serializer'],
|
|
topic, value)
|
|
partition = self._partition(topic, partition, key, value,
|
|
key_bytes, value_bytes)
|
|
|
|
message_size = MessageSet.HEADER_SIZE + Message.HEADER_SIZE
|
|
if key_bytes is not None:
|
|
message_size += len(key_bytes)
|
|
if value_bytes is not None:
|
|
message_size += len(value_bytes)
|
|
self._ensure_valid_record_size(message_size)
|
|
|
|
tp = TopicPartition(topic, partition)
|
|
if timestamp_ms is None:
|
|
timestamp_ms = int(time.time() * 1000)
|
|
log.debug("Sending (key=%r value=%r) to %s", key, value, tp)
|
|
result = self._accumulator.append(tp, timestamp_ms,
|
|
key_bytes, value_bytes,
|
|
self.config['max_block_ms'])
|
|
future, batch_is_full, new_batch_created = result
|
|
if batch_is_full or new_batch_created:
|
|
log.debug("Waking up the sender since %s is either full or"
|
|
" getting a new batch", tp)
|
|
self._sender.wakeup()
|
|
|
|
return future
|
|
# handling exceptions and record the errors;
|
|
# for API exceptions return them in the future,
|
|
# for other exceptions raise directly
|
|
except Errors.KafkaTimeoutError:
|
|
raise
|
|
except AssertionError:
|
|
raise
|
|
except Exception as e:
|
|
log.debug("Exception occurred during message send: %s", e)
|
|
return FutureRecordMetadata(
|
|
FutureProduceResult(TopicPartition(topic, partition)),
|
|
-1, None, None,
|
|
len(key_bytes) if key_bytes is not None else -1,
|
|
len(value_bytes) if value_bytes is not None else -1
|
|
).failure(e)
|
|
|
|
def flush(self, timeout=None):
|
|
"""
|
|
Invoking this method makes all buffered records immediately available
|
|
to send (even if linger_ms is greater than 0) and blocks on the
|
|
completion of the requests associated with these records. The
|
|
post-condition of :meth:`~kafka.KafkaProducer.flush` is that any
|
|
previously sent record will have completed
|
|
(e.g. Future.is_done() == True). A request is considered completed when
|
|
either it is successfully acknowledged according to the 'acks'
|
|
configuration for the producer, or it results in an error.
|
|
|
|
Other threads can continue sending messages while one thread is blocked
|
|
waiting for a flush call to complete; however, no guarantee is made
|
|
about the completion of messages sent after the flush call begins.
|
|
|
|
Arguments:
|
|
timeout (float, optional): timeout in seconds to wait for completion.
|
|
"""
|
|
log.debug("Flushing accumulated records in producer.") # trace
|
|
self._accumulator.begin_flush()
|
|
self._sender.wakeup()
|
|
self._accumulator.await_flush_completion(timeout=timeout)
|
|
|
|
def _ensure_valid_record_size(self, size):
|
|
"""Validate that the record size isn't too large."""
|
|
if size > self.config['max_request_size']:
|
|
raise Errors.MessageSizeTooLargeError(
|
|
"The message is %d bytes when serialized which is larger than"
|
|
" the maximum request size you have configured with the"
|
|
" max_request_size configuration" % size)
|
|
if size > self.config['buffer_memory']:
|
|
raise Errors.MessageSizeTooLargeError(
|
|
"The message is %d bytes when serialized which is larger than"
|
|
" the total memory buffer you have configured with the"
|
|
" buffer_memory configuration." % size)
|
|
|
|
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
|
|
|
|
Returns:
|
|
set: partition ids for the topic
|
|
|
|
Raises:
|
|
TimeoutException: if partitions for topic were not obtained before
|
|
specified max_wait timeout
|
|
"""
|
|
# add topic to metadata topic list if it is not there already.
|
|
self._sender.add_topic(topic)
|
|
begin = time.time()
|
|
elapsed = 0.0
|
|
metadata_event = None
|
|
while True:
|
|
partitions = self._metadata.partitions_for_topic(topic)
|
|
if partitions is not None:
|
|
return partitions
|
|
|
|
if not metadata_event:
|
|
metadata_event = threading.Event()
|
|
|
|
log.debug("Requesting metadata update for topic %s", topic)
|
|
|
|
metadata_event.clear()
|
|
future = self._metadata.request_update()
|
|
future.add_both(lambda e, *args: e.set(), metadata_event)
|
|
self._sender.wakeup()
|
|
metadata_event.wait(max_wait - elapsed)
|
|
elapsed = time.time() - begin
|
|
if not metadata_event.is_set():
|
|
raise Errors.KafkaTimeoutError(
|
|
"Failed to update metadata after %s secs.", max_wait)
|
|
elif topic in self._metadata.unauthorized_topics:
|
|
raise Errors.TopicAuthorizationFailedError(topic)
|
|
else:
|
|
log.debug("_wait_on_metadata woke after %s secs.", elapsed)
|
|
|
|
def _serialize(self, f, topic, data):
|
|
if not f:
|
|
return data
|
|
if isinstance(f, Serializer):
|
|
return f.serialize(topic, data)
|
|
return f(data)
|
|
|
|
def _partition(self, topic, partition, key, value,
|
|
serialized_key, serialized_value):
|
|
if partition is not None:
|
|
assert partition >= 0
|
|
assert partition in self._metadata.partitions_for_topic(topic), 'Unrecognized partition'
|
|
return partition
|
|
|
|
all_partitions = sorted(self._metadata.partitions_for_topic(topic))
|
|
available = list(self._metadata.available_partitions_for_topic(topic))
|
|
return self.config['partitioner'](serialized_key,
|
|
all_partitions,
|
|
available)
|
|
|
|
def metrics(self, raw=False):
|
|
"""Warning: this is an unstable interface.
|
|
It may change in future releases without warning"""
|
|
if raw:
|
|
return self._metrics.metrics
|
|
|
|
metrics = {}
|
|
for k, v in self._metrics.metrics.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
|