KafkaRouter

faststream.confluent.broker.KafkaRouter

KafkaRouter(
    prefix="",
    handlers=(),
    *,
    dependencies=(),
    middlewares=(),
    routers=(),
    parser=None,
    decoder=None,
    include_in_schema=None,
)

Bases: KafkaRegistrator, BrokerRouter[Union['Message', tuple['Message', ...]]]

Includable to KafkaBroker router.

Initialize KafkaRouter.

PARAMETER	DESCRIPTION
prefix	String prefix to add to all subscribers queues. TYPE: str DEFAULT: ''
handlers	Route object to include. TYPE: Iterable[KafkaRoute] DEFAULT: ()
dependencies	Dependencies list ([Dependant(),]) to apply to all routers' publishers/subscribers. TYPE: Iterable[Dependant] DEFAULT: ()
middlewares	Router middlewares to apply to all routers' publishers/subscribers. TYPE: Sequence[BrokerMiddleware[Any, Any]] DEFAULT: ()
routers	Routers to apply to broker. TYPE: Iterable[KafkaRegistrator] DEFAULT: ()
parser	Parser to map original Message object to FastStream one. TYPE: Optional[CustomCallable] DEFAULT: None
decoder	Function to decode FastStream msg bytes body to python objects. TYPE: Optional[CustomCallable] DEFAULT: None
include_in_schema	Whetever to include operation in AsyncAPI schema or not. TYPE: bool | None DEFAULT: None

Source code in faststream/confluent/broker/router.py

def __init__(
    self,
    prefix: str = "",
    handlers: Iterable[KafkaRoute] = (),
    *,
    dependencies: Iterable["Dependant"] = (),
    middlewares: Sequence["BrokerMiddleware[Any, Any]"] = (),
    routers: Iterable[KafkaRegistrator] = (),
    parser: Optional["CustomCallable"] = None,
    decoder: Optional["CustomCallable"] = None,
    include_in_schema: bool | None = None,
) -> None:
    """Initialize KafkaRouter.

    Args:
        prefix: String prefix to add to all subscribers queues.
        handlers: Route object to include.
        dependencies: Dependencies list (`[Dependant(),]`) to apply to all routers' publishers/subscribers.
        middlewares: Router middlewares to apply to all routers' publishers/subscribers.
        routers: Routers to apply to broker.
        parser: Parser to map original **Message** object to FastStream one.
        decoder: Function to decode FastStream msg bytes body to python objects.
        include_in_schema: Whetever to include operation in AsyncAPI schema or not.
    """
    super().__init__(
        handlers=handlers,
        config=KafkaBrokerConfig(
            broker_middlewares=middlewares,
            broker_dependencies=dependencies,
            broker_parser=parser,
            broker_decoder=decoder,
            include_in_schema=include_in_schema,
            prefix=prefix,
        ),
        routers=routers,
    )

config instance-attribute

config = ConfigComposition(config)

routers instance-attribute

routers = []

subscribers property

subscribers

publishers property

publishers

add_middleware

add_middleware(middleware)

Append BrokerMiddleware to the end of middlewares list.

Current middleware will be used as a most inner of the stack.

Source code in faststream/_internal/broker/registrator.py

def add_middleware(self, middleware: "BrokerMiddleware[Any, Any]") -> None:
    """Append BrokerMiddleware to the end of middlewares list.

    Current middleware will be used as a most inner of the stack.
    """
    self.config.add_middleware(middleware)

insert_middleware

insert_middleware(middleware)

Insert BrokerMiddleware to the start of middlewares list.

Current middleware will be used as a most outer of the stack.

Source code in faststream/_internal/broker/registrator.py

def insert_middleware(self, middleware: "BrokerMiddleware[Any, Any]") -> None:
    """Insert BrokerMiddleware to the start of middlewares list.

    Current middleware will be used as a most outer of the stack.
    """
    self.config.insert_middleware(middleware)

subscriber

subscriber(
    *topics: str,
    partitions: Sequence[TopicPartition] = (),
    polling_interval: float = 0.1,
    group_id: str | None = None,
    group_instance_id: str | None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal[
        "latest", "earliest", "none"
    ] = "latest",
    auto_commit: bool = EMPTY,
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = (
        "roundrobin",
    ),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted", "read_committed"
    ] = "read_uncommitted",
    batch: Literal[False] = False,
    max_records: int | None = None,
    persistent: bool = True,
    dependencies: Iterable[Dependant] = (),
    parser: Optional[CustomCallable] = None,
    decoder: Optional[CustomCallable] = None,
    middlewares: Sequence[
        SubscriberMiddleware[KafkaMessage]
    ] = (),
    no_ack: bool = EMPTY,
    max_workers: None = None,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
) -> DefaultSubscriber

subscriber(
    *topics: str,
    partitions: Sequence[TopicPartition] = (),
    polling_interval: float = 0.1,
    group_id: None = None,
    group_instance_id: None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal[
        "latest", "earliest", "none"
    ] = "latest",
    auto_commit: bool = EMPTY,
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = (
        "roundrobin",
    ),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted", "read_committed"
    ] = "read_uncommitted",
    batch: Literal[True] = ...,
    max_records: int | None = None,
    persistent: bool = True,
    dependencies: Iterable[Dependant] = (),
    parser: Optional[CustomCallable] = None,
    decoder: Optional[CustomCallable] = None,
    middlewares: Sequence[
        SubscriberMiddleware[KafkaMessage]
    ] = (),
    no_ack: bool = EMPTY,
    max_workers: None = None,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
) -> BatchSubscriber

subscriber(
    *topics: str,
    partitions: Sequence[TopicPartition] = (),
    polling_interval: float = 0.1,
    group_id: str | None = None,
    group_instance_id: str | None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal[
        "latest", "earliest", "none"
    ] = "latest",
    auto_commit: bool = EMPTY,
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = (
        "roundrobin",
    ),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted", "read_committed"
    ] = "read_uncommitted",
    batch: Literal[False] = False,
    max_records: int | None = None,
    persistent: bool = True,
    dependencies: Iterable[Dependant] = (),
    parser: Optional[CustomCallable] = None,
    decoder: Optional[CustomCallable] = None,
    middlewares: Sequence[
        SubscriberMiddleware[KafkaMessage]
    ] = (),
    no_ack: bool = EMPTY,
    max_workers: int = ...,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
) -> ConcurrentDefaultSubscriber

subscriber(
    *topics: str,
    partitions: Sequence[TopicPartition] = (),
    polling_interval: float = 0.1,
    group_id: str | None = None,
    group_instance_id: str | None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal[
        "latest", "earliest", "none"
    ] = "latest",
    auto_commit: bool = EMPTY,
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = (
        "roundrobin",
    ),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted", "read_committed"
    ] = "read_uncommitted",
    batch: bool = False,
    max_records: int | None = None,
    persistent: bool = True,
    dependencies: Iterable[Dependant] = (),
    parser: Optional[CustomCallable] = None,
    decoder: Optional[CustomCallable] = None,
    middlewares: Sequence[
        SubscriberMiddleware[KafkaMessage]
    ] = (),
    no_ack: bool = EMPTY,
    max_workers: int | None = None,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
) -> Union[
    DefaultSubscriber,
    BatchSubscriber,
    ConcurrentDefaultSubscriber,
]

subscriber(
    *topics,
    partitions=(),
    polling_interval=0.1,
    group_id=None,
    group_instance_id=None,
    fetch_max_wait_ms=500,
    fetch_max_bytes=50 * 1024 * 1024,
    fetch_min_bytes=1,
    max_partition_fetch_bytes=1 * 1024 * 1024,
    auto_offset_reset="latest",
    auto_commit=EMPTY,
    auto_commit_interval_ms=5 * 1000,
    check_crcs=True,
    partition_assignment_strategy=("roundrobin",),
    max_poll_interval_ms=5 * 60 * 1000,
    session_timeout_ms=10 * 1000,
    heartbeat_interval_ms=3 * 1000,
    isolation_level="read_uncommitted",
    batch=False,
    max_records=None,
    persistent=True,
    dependencies=(),
    parser=None,
    decoder=None,
    middlewares=(),
    no_ack=EMPTY,
    ack_policy=EMPTY,
    no_reply=False,
    title=None,
    description=None,
    include_in_schema=True,
    max_workers=None,
)

Create a subscriber for Kafka topics.

PARAMETER	DESCRIPTION
*topics	Kafka topics to consume messages from. TYPE: str DEFAULT: ()
partitions	Sequence of topic partitions. TYPE: Sequence[TopicPartition] DEFAULT: ()
polling_interval	Polling interval in seconds. TYPE: float DEFAULT: 0.1
group_id	Name of the consumer group to join for dynamic partition assignment (if enabled), and to use for fetching and committing offsets. If None, auto-partition assignment (via group coordinator) and offset commits are disabled. TYPE: str | None DEFAULT: None
group_instance_id	A unique string that identifies the consumer instance. If set, the consumer is treated as a static member of the group and does not participate in consumer group management (e.g. partition assignment, rebalances). This can be used to assign partitions to specific consumers, rather than letting the group assign partitions based on consumer metadata. TYPE: str | None DEFAULT: None
fetch_max_wait_ms	The maximum amount of time in milliseconds the server will block before answering the fetch request if there isn't sufficient data to immediately satisfy the requirement given by fetch_min_bytes. TYPE: int DEFAULT: 500
fetch_max_bytes	The maximum amount of data the server should return for a fetch request. This is not an absolute maximum, if the first message in the first non-empty partition of the fetch is larger than this value, the message will still be returned to ensure that the consumer can make progress. NOTE: consumer performs fetches to multiple brokers in parallel so memory usage will depend on the number of brokers containing partitions for the topic. TYPE: int DEFAULT: 50 * 1024 * 1024
fetch_min_bytes	Minimum amount of data the server should return for a fetch request, otherwise wait up to fetch_max_wait_ms for more data to accumulate. TYPE: int DEFAULT: 1
max_partition_fetch_bytes	The maximum amount of data per-partition the server will return. The maximum total memory used for a request = #partitions * max_partition_fetch_bytes. This size must be at least as large as the maximum message size the server allows or else it is possible for the producer to send messages larger than the consumer can fetch. If that happens, the consumer can get stuck trying to fetch a large message on a certain partition. TYPE: int DEFAULT: 1 * 1024 * 1024
auto_offset_reset	A policy for resetting offsets on OffsetOutOfRangeError errors: earliest will move to the oldest available message latest will move to the most recent none will raise an exception so you can handle this case TYPE: Literal['latest', 'earliest', 'none'] DEFAULT: 'latest'
auto_commit	If True the consumer's offset will be periodically committed in the background. TYPE: bool DEFAULT: EMPTY
auto_commit_interval_ms	Milliseconds between automatic offset commits, if auto_commit is True. TYPE: int DEFAULT: 5 * 1000
check_crcs	Automatically check the CRC32 of the records consumed. This ensures no on-the-wire or on-disk corruption to the messages occurred. This check adds some overhead, so it may be disabled in cases seeking extreme performance. TYPE: bool DEFAULT: True
partition_assignment_strategy	List of objects to use to distribute partition ownership amongst consumer instances when group management is used. This preference is implicit in the order of the strategies in the list. When assignment strategy changes: to support a change to the assignment strategy, new versions must enable support both for the old assignment strategy and the new one. The coordinator will choose the old assignment strategy until all members have been updated. Then it will choose the new strategy. TYPE: Sequence[str] DEFAULT: ('roundrobin',)
max_poll_interval_ms	Maximum allowed time between calls to consume messages in batches. If this interval is exceeded the consumer is considered failed and the group will rebalance in order to reassign the partitions to another consumer group member. If API methods block waiting for messages, that time does not count against this timeout. TYPE: int DEFAULT: 5 * 60 * 1000
session_timeout_ms	Client group session and failure detection timeout. The consumer sends periodic heartbeats (heartbeat.interval.ms) to indicate its liveness to the broker. If no hearts are received by the broker for a group member within the session timeout, the broker will remove the consumer from the group and trigger a rebalance. The allowed range is configured with the broker configuration properties group.min.session.timeout.ms and group.max.session.timeout.ms. TYPE: int DEFAULT: 10 * 1000
heartbeat_interval_ms	The expected time in milliseconds between heartbeats to the consumer coordinator when using Kafka's group management feature. Heartbeats are used to ensure that the consumer's session stays active and to facilitate rebalancing when new consumers join or leave the group. The value must be set lower than session_timeout_ms, but typically should be set no higher than 1/3 of that value. It can be adjusted even lower to control the expected time for normal rebalances. TYPE: int DEFAULT: 3 * 1000
isolation_level	Controls how to read messages written transactionally. read_committed, batch consumer will only return transactional messages which have been committed. read_uncommitted (the default), batch consumer will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. Messages will always be returned in offset order. Hence, in read_committed mode, batch consumer will only return messages up to the last stable offset (ALSO), which is the one less than the offset of the first open transaction. In particular any messages appearing after messages belonging to ongoing transactions will be withheld until the relevant transaction has been completed. As a result, read_committed consumers will not be able to read up to the high watermark when there are in flight transactions. Further, when in read_committed the seek_to_end method will return the ALSO. See method docs below. TYPE: Literal['read_uncommitted', 'read_committed'] DEFAULT: 'read_uncommitted'
batch	Whether to consume messages in batches or not. TYPE: bool DEFAULT: False
max_records	Number of messages to consume as one batch. TYPE: int | None DEFAULT: None
dependencies	Dependencies list ([Dependant(),]) to apply to the subscriber. TYPE: Iterable[Dependant] DEFAULT: ()
parser	Parser to map original Message object to FastStream one. TYPE: Optional[CustomCallable] DEFAULT: None
decoder	Function to decode FastStream msg bytes body to python objects. TYPE: Optional[CustomCallable] DEFAULT: None
middlewares	Subscriber middlewares to wrap incoming message processing. TYPE: Sequence[SubscriberMiddleware[KafkaMessage]] DEFAULT: ()
no_ack	Whether to disable FastStream auto acknowledgement logic or not. TYPE: bool DEFAULT: EMPTY
ack_policy	Acknowledgement policy for the subscriber. TYPE: AckPolicy DEFAULT: EMPTY
no_reply	Whether to disable FastStream RPC and Reply To auto responses or not. TYPE: bool DEFAULT: False
title	Specification subscriber object title. TYPE: str | None DEFAULT: None
description	Specification subscriber object description. Uses decorated docstring as default. TYPE: str | None DEFAULT: None
include_in_schema	Whether to include operation in Specification schema or not. TYPE: bool DEFAULT: True
max_workers	Number of workers to process messages concurrently. TYPE: int | None DEFAULT: None
persistent	Whether to make the subscriber persistent or not. TYPE: bool DEFAULT: True

RETURNS	DESCRIPTION
Union[DefaultSubscriber, BatchSubscriber, ConcurrentDefaultSubscriber]	Union of DefaultSubscriber, BatchSubscriber, or ConcurrentDefaultSubscriber depending on the configuration.

Source code in faststream/confluent/broker/registrator.py

@override
def subscriber(
    self,
    *topics: str,
    partitions: Sequence["TopicPartition"] = (),
    polling_interval: float = 0.1,
    group_id: str | None = None,
    group_instance_id: str | None = None,
    fetch_max_wait_ms: int = 500,
    fetch_max_bytes: int = 50 * 1024 * 1024,
    fetch_min_bytes: int = 1,
    max_partition_fetch_bytes: int = 1 * 1024 * 1024,
    auto_offset_reset: Literal["latest", "earliest", "none"] = "latest",
    auto_commit: Annotated[
        bool,
        deprecated(
            "This option is deprecated and will be removed in 0.7.0 release. "
            "Please, use `ack_policy=AckPolicy.ACK_FIRST` instead."
        ),
    ] = EMPTY,
    auto_commit_interval_ms: int = 5 * 1000,
    check_crcs: bool = True,
    partition_assignment_strategy: Sequence[str] = ("roundrobin",),
    max_poll_interval_ms: int = 5 * 60 * 1000,
    session_timeout_ms: int = 10 * 1000,
    heartbeat_interval_ms: int = 3 * 1000,
    isolation_level: Literal[
        "read_uncommitted",
        "read_committed",
    ] = "read_uncommitted",
    batch: bool = False,
    max_records: int | None = None,
    # broker args
    persistent: bool = True,
    dependencies: Iterable["Dependant"] = (),
    parser: Optional["CustomCallable"] = None,
    decoder: Optional["CustomCallable"] = None,
    middlewares: Annotated[
        Sequence["SubscriberMiddleware[KafkaMessage]"],
        deprecated(
            "This option was deprecated in 0.6.0. Use router-level middlewares instead."
            "Scheduled to remove in 0.7.0",
        ),
    ] = (),
    no_ack: Annotated[
        bool,
        deprecated(
            "This option was deprecated in 0.6.0 to prior to **ack_policy=AckPolicy.MANUAL**. "
            "Scheduled to remove in 0.7.0",
        ),
    ] = EMPTY,
    ack_policy: AckPolicy = EMPTY,
    no_reply: bool = False,
    # Specification args
    title: str | None = None,
    description: str | None = None,
    include_in_schema: bool = True,
    max_workers: int | None = None,
) -> Union[
    "DefaultSubscriber",
    "BatchSubscriber",
    "ConcurrentDefaultSubscriber",
]:
    """Create a subscriber for Kafka topics.

    Args:
        *topics: Kafka topics to consume messages from.
        partitions: Sequence of topic partitions.
        polling_interval: Polling interval in seconds.
        group_id: Name of the consumer group to join for dynamic
            partition assignment (if enabled), and to use for fetching and
            committing offsets. If `None`, auto-partition assignment (via
            group coordinator) and offset commits are disabled.
        group_instance_id: A unique string that identifies the consumer instance.
            If set, the consumer is treated as a static member of the group
            and does not participate in consumer group management (e.g.
            partition assignment, rebalances). This can be used to assign
            partitions to specific consumers, rather than letting the group
            assign partitions based on consumer metadata.
        fetch_max_wait_ms: The maximum amount of time in milliseconds
            the server will block before answering the fetch request if
            there isn't sufficient data to immediately satisfy the
            requirement given by `fetch_min_bytes`.
        fetch_max_bytes: The maximum amount of data the server should
            return for a fetch request. This is not an absolute maximum, if
            the first message in the first non-empty partition of the fetch
            is larger than this value, the message will still be returned
            to ensure that the consumer can make progress. NOTE: consumer
            performs fetches to multiple brokers in parallel so memory
            usage will depend on the number of brokers containing
            partitions for the topic.
        fetch_min_bytes: Minimum amount of data the server should
            return for a fetch request, otherwise wait up to
            `fetch_max_wait_ms` for more data to accumulate.
        max_partition_fetch_bytes: The maximum amount of data
            per-partition the server will return. The maximum total memory
            used for a request ``= #partitions * max_partition_fetch_bytes``.
            This size must be at least as large as the maximum message size
            the server allows or else it is possible for the producer to
            send messages larger than the consumer can fetch. If that
            happens, the consumer can get stuck trying to fetch a large
            message on a certain partition.
        auto_offset_reset: A policy for resetting offsets on `OffsetOutOfRangeError` errors:

            * `earliest` will move to the oldest available message
            * `latest` will move to the most recent
            * `none` will raise an exception so you can handle this case
        auto_commit: If `True` the consumer's offset will be
            periodically committed in the background.
        auto_commit_interval_ms: Milliseconds between automatic
            offset commits, if `auto_commit` is `True`.
        check_crcs: Automatically check the CRC32 of the records
            consumed. This ensures no on-the-wire or on-disk corruption to
            the messages occurred. This check adds some overhead, so it may
            be disabled in cases seeking extreme performance.
        partition_assignment_strategy: List of objects to use to
            distribute partition ownership amongst consumer instances when
            group management is used. This preference is implicit in the order
            of the strategies in the list. When assignment strategy changes:
            to support a change to the assignment strategy, new versions must
            enable support both for the old assignment strategy and the new
            one. The coordinator will choose the old assignment strategy until
            all members have been updated. Then it will choose the new
            strategy.
        max_poll_interval_ms: Maximum allowed time between calls to
            consume messages in batches. If this interval
            is exceeded the consumer is considered failed and the group will
            rebalance in order to reassign the partitions to another consumer
            group member. If API methods block waiting for messages, that time
            does not count against this timeout.
        session_timeout_ms: Client group session and failure detection
            timeout. The consumer sends periodic heartbeats
            (`heartbeat.interval.ms`) to indicate its liveness to the broker.
            If no hearts are received by the broker for a group member within
            the session timeout, the broker will remove the consumer from the
            group and trigger a rebalance. The allowed range is configured with
            the **broker** configuration properties
            `group.min.session.timeout.ms` and `group.max.session.timeout.ms`.
        heartbeat_interval_ms: The expected time in milliseconds
            between heartbeats to the consumer coordinator when using
            Kafka's group management feature. Heartbeats are used to ensure
            that the consumer's session stays active and to facilitate
            rebalancing when new consumers join or leave the group. The
            value must be set lower than `session_timeout_ms`, but typically
            should be set no higher than 1/3 of that value. It can be
            adjusted even lower to control the expected time for normal
            rebalances.
        isolation_level: Controls how to read messages written
            transactionally.

            * `read_committed`, batch consumer will only return
            transactional messages which have been committed.

            * `read_uncommitted` (the default), batch consumer will
            return all messages, even transactional messages which have been
            aborted.

            Non-transactional messages will be returned unconditionally in
            either mode.

            Messages will always be returned in offset order. Hence, in
            `read_committed` mode, batch consumer will only return
            messages up to the last stable offset (ALSO), which is the one less
            than the offset of the first open transaction. In particular any
            messages appearing after messages belonging to ongoing transactions
            will be withheld until the relevant transaction has been completed.
            As a result, `read_committed` consumers will not be able to read up
            to the high watermark when there are in flight transactions.
            Further, when in `read_committed` the seek_to_end method will
            return the ALSO. See method docs below.
        batch: Whether to consume messages in batches or not.
        max_records: Number of messages to consume as one batch.
        dependencies: Dependencies list (`[Dependant(),]`) to apply to the subscriber.
        parser: Parser to map original **Message** object to FastStream one.
        decoder: Function to decode FastStream msg bytes body to python objects.
        middlewares: Subscriber middlewares to wrap incoming message processing.
        no_ack: Whether to disable **FastStream** auto acknowledgement logic or not.
        ack_policy: Acknowledgement policy for the subscriber.
        no_reply: Whether to disable **FastStream** RPC and Reply To auto responses or not.
        title: Specification subscriber object title.
        description: Specification subscriber object description.
            Uses decorated docstring as default.
        include_in_schema: Whether to include operation in Specification schema or not.
        max_workers: Number of workers to process messages concurrently.
        persistent: Whether to make the subscriber persistent or not.

    Returns:
        Union of DefaultSubscriber, BatchSubscriber, or ConcurrentDefaultSubscriber
            depending on the configuration.
    """
    workers = max_workers or 1

    subscriber = create_subscriber(
        *topics,
        max_workers=workers,
        polling_interval=polling_interval,
        partitions=partitions,
        batch=batch,
        max_records=max_records,
        group_id=group_id,
        connection_data={
            "group_instance_id": group_instance_id,
            "fetch_max_wait_ms": fetch_max_wait_ms,
            "fetch_max_bytes": fetch_max_bytes,
            "fetch_min_bytes": fetch_min_bytes,
            "max_partition_fetch_bytes": max_partition_fetch_bytes,
            "auto_offset_reset": auto_offset_reset,
            "auto_commit_interval_ms": auto_commit_interval_ms,
            "check_crcs": check_crcs,
            "partition_assignment_strategy": partition_assignment_strategy,
            "max_poll_interval_ms": max_poll_interval_ms,
            "session_timeout_ms": session_timeout_ms,
            "heartbeat_interval_ms": heartbeat_interval_ms,
            "isolation_level": isolation_level,
        },
        auto_commit=auto_commit,
        # subscriber args
        ack_policy=ack_policy,
        no_ack=no_ack,
        no_reply=no_reply,
        config=cast("KafkaBrokerConfig", self.config),
        # Specification
        title_=title,
        description_=description,
        include_in_schema=include_in_schema,
    )

    if batch:
        subscriber = cast("BatchSubscriber", subscriber)
    elif workers > 1:
        subscriber = cast("ConcurrentDefaultSubscriber", subscriber)
    else:
        subscriber = cast("DefaultSubscriber", subscriber)

    subscriber = super().subscriber(subscriber, persistent=persistent)  # type: ignore[assignment]

    return subscriber.add_call(
        parser_=parser or self._parser,
        decoder_=decoder or self._decoder,
        dependencies_=dependencies,
        middlewares_=middlewares,
    )

publisher

publisher(
    topic: str,
    *,
    key: bytes | str | None = None,
    partition: int | None = None,
    headers: dict[str, str] | None = None,
    reply_to: str = "",
    batch: Literal[False] = False,
    persistent: bool = True,
    middlewares: Sequence[PublisherMiddleware] = (),
    title: str | None = None,
    description: str | None = None,
    schema: Any | None = None,
    include_in_schema: bool = True,
    autoflush: bool = False,
) -> DefaultPublisher

publisher(
    topic: str,
    *,
    key: bytes | str | None = None,
    partition: int | None = None,
    headers: dict[str, str] | None = None,
    reply_to: str = "",
    batch: Literal[True] = ...,
    persistent: bool = True,
    middlewares: Sequence[PublisherMiddleware] = (),
    title: str | None = None,
    description: str | None = None,
    schema: Any | None = None,
    include_in_schema: bool = True,
    autoflush: bool = False,
) -> BatchPublisher

publisher(
    topic: str,
    *,
    key: bytes | str | None = None,
    partition: int | None = None,
    headers: dict[str, str] | None = None,
    reply_to: str = "",
    batch: bool = False,
    persistent: bool = True,
    middlewares: Sequence[PublisherMiddleware] = (),
    title: str | None = None,
    description: str | None = None,
    schema: Any | None = None,
    include_in_schema: bool = True,
    autoflush: bool = False,
) -> Union[BatchPublisher, DefaultPublisher]

publisher(
    topic,
    *,
    key=None,
    partition=None,
    headers=None,
    reply_to="",
    batch=False,
    persistent=True,
    middlewares=(),
    title=None,
    description=None,
    schema=None,
    include_in_schema=True,
    autoflush=False,
)

Creates long-living and Specification-documented publisher object.

You can use it as a handler decorator (handler should be decorated by @broker.subscriber(...) too) - @broker.publisher(...). In such case publisher will publish your handler return value.

Or you can create a publisher object to call it lately - broker.publisher(...).publish(...).

PARAMETER	DESCRIPTION
topic	Topic where the message will be published. TYPE: str
key	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. TYPE: bytes | str | None DEFAULT: None
partition	Specify a partition. If not set, the partition will be selected using the configured partitioner. TYPE: int | None DEFAULT: None
headers	Message headers to store metainformation. content-type and correlation_id will be set automatically by framework anyway. Can be overridden by publish.headers if specified. TYPE: dict[str, str] | None DEFAULT: None
reply_to	Topic name to send response. TYPE: str DEFAULT: ''
batch	Whether to send messages in batches or not. TYPE: bool DEFAULT: False
middlewares	Publisher middlewares to wrap outgoing messages. TYPE: Sequence[PublisherMiddleware] DEFAULT: ()
title	Specification publisher object title. TYPE: str | None DEFAULT: None
description	Specification publisher object description. TYPE: str | None DEFAULT: None
schema	Specification publishing message type. Should be any python-native object annotation or pydantic.BaseModel. TYPE: Any | None DEFAULT: None
include_in_schema	Whetever to include operation in Specification schema or not. TYPE: bool DEFAULT: True
autoflush	Whether to flush the producer or not on every publish call. TYPE: bool DEFAULT: False
persistent	Whether to make the publisher persistent or not. TYPE: bool DEFAULT: True

Source code in faststream/confluent/broker/registrator.py

@override
def publisher(
    self,
    topic: str,
    *,
    key: bytes | str | None = None,
    partition: int | None = None,
    headers: dict[str, str] | None = None,
    reply_to: str = "",
    batch: bool = False,
    # basic args
    persistent: bool = True,
    middlewares: Annotated[
        Sequence["PublisherMiddleware"],
        deprecated(
            "This option was deprecated in 0.6.0. Use router-level middlewares instead."
            "Scheduled to remove in 0.7.0",
        ),
    ] = (),
    # Specification args
    title: str | None = None,
    description: str | None = None,
    schema: Any | None = None,
    include_in_schema: bool = True,
    autoflush: bool = False,
) -> Union[
    "BatchPublisher",
    "DefaultPublisher",
]:
    """Creates long-living and Specification-documented publisher object.

    You can use it as a handler decorator (handler should be decorated by `@broker.subscriber(...)` too) - `@broker.publisher(...)`.
    In such case publisher will publish your handler return value.

    Or you can create a publisher object to call it lately - `broker.publisher(...).publish(...)`.

    Args:
        topic: Topic where the message will be published.
        key: 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`.
        partition: Specify a partition. If not set, the partition will be
            selected using the configured `partitioner`.
        headers: Message headers to store metainformation.
            **content-type** and **correlation_id** will be set automatically by framework anyway.
            Can be overridden by `publish.headers` if specified.
        reply_to: Topic name to send response.
        batch: Whether to send messages in batches or not.
        middlewares: Publisher middlewares to wrap outgoing messages.
        title: Specification publisher object title.
        description: Specification publisher object description.
        schema: Specification publishing message type.
            Should be any python-native object annotation or `pydantic.BaseModel`.
        include_in_schema: Whetever to include operation in Specification schema or not.
        autoflush: Whether to flush the producer or not on every publish call.
        persistent: Whether to make the publisher persistent or not.
    """
    publisher = create_publisher(
        # batch flag
        batch=batch,
        # default args
        key=key,
        # both args
        topic=topic,
        partition=partition,
        headers=headers,
        reply_to=reply_to,
        # publisher-specific
        config=cast("KafkaBrokerConfig", self.config),
        middlewares=middlewares,
        # Specification
        title_=title,
        description_=description,
        schema_=schema,
        include_in_schema=include_in_schema,
        autoflush=autoflush,
    )

    super().publisher(publisher, persistent=persistent)

    if batch:
        return cast("BatchPublisher", publisher)
    return cast("DefaultPublisher", publisher)

include_router

include_router(
    router,
    *,
    prefix="",
    dependencies=(),
    middlewares=(),
    include_in_schema=None,
)

Source code in faststream/confluent/broker/registrator.py

@override
def include_router(
    self,
    router: "KafkaRegistrator",  # type: ignore[override]
    *,
    prefix: str = "",
    dependencies: Iterable["Dependant"] = (),
    middlewares: Sequence["BrokerMiddleware[Any, Any]"] = (),
    include_in_schema: bool | None = None,
) -> None:
    if not isinstance(router, KafkaRegistrator):
        msg = (
            f"Router must be an instance of KafkaRegistrator, "
            f"got {type(router).__name__} instead"
        )
        raise SetupError(msg)

    super().include_router(
        router,
        prefix=prefix,
        dependencies=dependencies,
        middlewares=middlewares,
        include_in_schema=include_in_schema,
    )

include_routers

include_routers(*routers)

Includes routers in the object.

Source code in faststream/_internal/broker/registrator.py

def include_routers(
    self,
    *routers: "Registrator[MsgType, Any]",
) -> None:
    """Includes routers in the object."""
    for r in routers:
        self.include_router(r)