KafkaRouter

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

    Current middleware will be used as a most inner of already existed ones.
    """
    self._middlewares = (*self._middlewares, middleware)

    for sub in self._subscribers.values():
        sub.add_middleware(middleware)

    for pub in self._publishers.values():
        pub.add_middleware(middleware)

@override
def subscriber(
    self,
    *topics: Annotated[
        str,
        Doc("Kafka topics to consume messages from."),
    ],
    batch: Annotated[
        bool,
        Doc("Whether to consume messages in batches or not."),
    ] = False,
    group_id: Annotated[
        Optional[str],
        Doc(
            """
        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.
        """
        ),
    ] = None,
    key_deserializer: Annotated[
        Optional[Callable[[bytes], Any]],
        Doc(
            "Any callable that takes a raw message `bytes` "
            "key and returns a deserialized one."
        ),
    ] = None,
    value_deserializer: Annotated[
        Optional[Callable[[bytes], Any]],
        Doc(
            "Any callable that takes a raw message `bytes` "
            "value and returns a deserialized value."
        ),
    ] = None,
    fetch_max_bytes: Annotated[
        int,
        Doc(
            """
        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.
        """
        ),
    ] = 50 * 1024 * 1024,
    fetch_min_bytes: Annotated[
        int,
        Doc(
            """
        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.
        """
        ),
    ] = 1,
    fetch_max_wait_ms: Annotated[
        int,
        Doc(
            """
        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`.
        """
        ),
    ] = 500,
    max_partition_fetch_bytes: Annotated[
        int,
        Doc(
            """
        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.
        """
        ),
    ] = 1 * 1024 * 1024,
    auto_offset_reset: Annotated[
        Literal["latest", "earliest", "none"],
        Doc(
            """
        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
        """
        ),
    ] = "latest",
    auto_commit: Annotated[
        bool,
        Doc(
            """
        If `True` the consumer's offset will be
        periodically committed in the background.
        """
        ),
    ] = True,
    auto_commit_interval_ms: Annotated[
        int,
        Doc(
            """
        Milliseconds between automatic
        offset commits, if `auto_commit` is `True`."""
        ),
    ] = 5 * 1000,
    check_crcs: Annotated[
        bool,
        Doc(
            """
        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.
        """
        ),
    ] = True,
    partition_assignment_strategy: Annotated[
        Sequence["AbstractPartitionAssignor"],
        Doc(
            """
        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.
        """
        ),
    ] = (RoundRobinPartitionAssignor,),
    max_poll_interval_ms: Annotated[
        int,
        Doc(
            """
        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.
        """
        ),
    ] = 5 * 60 * 1000,
    rebalance_timeout_ms: Annotated[
        Optional[int],
        Doc(
            """
        The maximum time server will wait for this
        consumer to rejoin the group in a case of rebalance. In Java client
        this behaviour is bound to `max.poll.interval.ms` configuration,
        but as ``aiokafka`` will rejoin the group in the background, we
        decouple this setting to allow finer tuning by users that use
        `ConsumerRebalanceListener` to delay rebalacing. Defaults
        to ``session_timeout_ms``
        """
        ),
    ] = None,
    session_timeout_ms: Annotated[
        int,
        Doc(
            """
        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`.
        """
        ),
    ] = 10 * 1000,
    heartbeat_interval_ms: Annotated[
        int,
        Doc(
            """
        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.
        """
        ),
    ] = 3 * 1000,
    consumer_timeout_ms: Annotated[
        int,
        Doc(
            """
        Maximum wait timeout for background fetching
        routine. Mostly defines how fast the system will see rebalance and
        request new data for new partitions.
        """
        ),
    ] = 200,
    max_poll_records: Annotated[
        Optional[int],
        Doc(
            """
        The maximum number of records returned in a
        single call by batch consumer. Has no limit by default.
        """
        ),
    ] = None,
    exclude_internal_topics: Annotated[
        bool,
        Doc(
            """
        Whether records from internal topics
        (such as offsets) should be exposed to the consumer. If set to True
        the only way to receive records from an internal topic is
        subscribing to it.
        """
        ),
    ] = True,
    isolation_level: Annotated[
        Literal["read_uncommitted", "read_committed"],
        Doc(
            """
        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 (LSO), 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 LSO. See method docs below.
        """
        ),
    ] = "read_uncommitted",
    batch_timeout_ms: Annotated[
        int,
        Doc(
            """
        Milliseconds spent waiting if
        data is not available in the buffer. If 0, returns immediately
        with any records that are available currently in the buffer,
        else returns empty.
        """
        ),
    ] = 200,
    max_records: Annotated[
        Optional[int],
        Doc("Number of messages to consume as one batch."),
    ] = None,
    listener: Annotated[
        Optional["ConsumerRebalanceListener"],
        Doc(
            """
        Optionally include listener
           callback, which will be called before and after each rebalance
           operation.
           As part of group management, the consumer will keep track of
           the list of consumers that belong to a particular group and
           will trigger a rebalance operation if one of the following
           events trigger:

           * Number of partitions change for any of the subscribed topics
           * Topic is created or deleted
           * An existing member of the consumer group dies
           * A new member is added to the consumer group

           When any of these events are triggered, the provided listener
           will be invoked first to indicate that the consumer's
           assignment has been revoked, and then again when the new
           assignment has been received. Note that this listener will
           immediately override any listener set in a previous call
           to subscribe. It is guaranteed, however, that the partitions
           revoked/assigned
           through this interface are from topics subscribed in this call.
        """
        ),
    ] = None,
    pattern: Annotated[
        Optional[str],
        Doc(
            """
        Pattern to match available topics. You must provide either topics or pattern, but not both.
        """
        ),
    ] = None,
    partitions: Annotated[
        Iterable["TopicPartition"],
        Doc(
            """
        An explicit partitions list to assign.
        You can't use 'topics' and 'partitions' in the same time.
        """
        ),
    ] = (),
    # broker args
    dependencies: Annotated[
        Iterable["Depends"],
        Doc("Dependencies list (`[Depends(),]`) to apply to the subscriber."),
    ] = (),
    parser: Annotated[
        Optional["CustomCallable"],
        Doc("Parser to map original **ConsumerRecord** object to FastStream one."),
    ] = None,
    decoder: Annotated[
        Optional["CustomCallable"],
        Doc("Function to decode FastStream msg bytes body to python objects."),
    ] = None,
    middlewares: Annotated[
        Iterable["SubscriberMiddleware[KafkaMessage]"],
        Doc("Subscriber middlewares to wrap incoming message processing."),
    ] = (),
    max_workers: Annotated[
        int,
        Doc("Number of workers to process messages concurrently."),
    ] = 1,
    filter: Annotated[
        "Filter[KafkaMessage]",
        Doc(
            "Overload subscriber to consume various messages from the same source."
        ),
        deprecated(
            "Deprecated in **FastStream 0.5.0**. "
            "Please, create `subscriber` object and use it explicitly instead. "
            "Argument will be removed in **FastStream 0.6.0**."
        ),
    ] = default_filter,
    retry: Annotated[
        bool,
        Doc("Whether to `nack` message at processing exception."),
    ] = False,
    no_ack: Annotated[
        bool,
        Doc("Whether to disable **FastStream** autoacknowledgement logic or not."),
    ] = False,
    no_reply: Annotated[
        bool,
        Doc(
            "Whether to disable **FastStream** RPC and Reply To auto responses or not."
        ),
    ] = False,
    # AsyncAPI args
    title: Annotated[
        Optional[str],
        Doc("AsyncAPI subscriber object title."),
    ] = None,
    description: Annotated[
        Optional[str],
        Doc(
            "AsyncAPI subscriber object description. "
            "Uses decorated docstring as default."
        ),
    ] = None,
    include_in_schema: Annotated[
        bool,
        Doc("Whetever to include operation in AsyncAPI schema or not."),
    ] = True,
) -> Union[
    "AsyncAPIDefaultSubscriber",
    "AsyncAPIBatchSubscriber",
    "AsyncAPIConcurrentDefaultSubscriber",
]:
    subscriber = super().subscriber(
        create_subscriber(
            *topics,
            batch=batch,
            max_workers=max_workers,
            batch_timeout_ms=batch_timeout_ms,
            max_records=max_records,
            group_id=group_id,
            listener=listener,
            pattern=pattern,
            connection_args={
                "key_deserializer": key_deserializer,
                "value_deserializer": value_deserializer,
                "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,
                "enable_auto_commit": auto_commit,
                "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,
                "rebalance_timeout_ms": rebalance_timeout_ms,
                "session_timeout_ms": session_timeout_ms,
                "heartbeat_interval_ms": heartbeat_interval_ms,
                "consumer_timeout_ms": consumer_timeout_ms,
                "max_poll_records": max_poll_records,
                "exclude_internal_topics": exclude_internal_topics,
                "isolation_level": isolation_level,
            },
            partitions=partitions,
            is_manual=not auto_commit,
            # subscriber args
            no_ack=no_ack,
            no_reply=no_reply,
            retry=retry,
            broker_middlewares=self._middlewares,
            broker_dependencies=self._dependencies,
            # AsyncAPI
            title_=title,
            description_=description,
            include_in_schema=self._solve_include_in_schema(include_in_schema),
        )
    )

    if batch:
        return cast("AsyncAPIBatchSubscriber", subscriber).add_call(
            filter_=filter,
            parser_=parser or self._parser,
            decoder_=decoder or self._decoder,
            dependencies_=dependencies,
            middlewares_=middlewares,
        )

    else:
        if max_workers > 1:
            return cast("AsyncAPIConcurrentDefaultSubscriber", subscriber).add_call(
                filter_=filter,
                parser_=parser or self._parser,
                decoder_=decoder or self._decoder,
                dependencies_=dependencies,
                middlewares_=middlewares,
            )
        else:
            return cast("AsyncAPIDefaultSubscriber", subscriber).add_call(
                filter_=filter,
                parser_=parser or self._parser,
                decoder_=decoder or self._decoder,
                dependencies_=dependencies,
                middlewares_=middlewares,
            )

@override
def publisher(
    self,
    topic: Annotated[
        str,
        Doc("Topic where the message will be published."),
    ],
    *,
    key: Annotated[
        Union[bytes, Any, None],
        Doc(
            """
        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`.
        """
        ),
    ] = None,
    partition: Annotated[
        Optional[int],
        Doc(
            """
        Specify a partition. If not set, the partition will be
        selected using the configured `partitioner`.
        """
        ),
    ] = None,
    headers: Annotated[
        Optional[Dict[str, str]],
        Doc(
            "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."
        ),
    ] = None,
    reply_to: Annotated[
        str,
        Doc("Topic name to send response."),
    ] = "",
    batch: Annotated[
        bool,
        Doc("Whether to send messages in batches or not."),
    ] = False,
    # basic args
    middlewares: Annotated[
        Iterable["PublisherMiddleware"],
        Doc("Publisher middlewares to wrap outgoing messages."),
    ] = (),
    # AsyncAPI args
    title: Annotated[
        Optional[str],
        Doc("AsyncAPI publisher object title."),
    ] = None,
    description: Annotated[
        Optional[str],
        Doc("AsyncAPI publisher object description."),
    ] = None,
    schema: Annotated[
        Optional[Any],
        Doc(
            "AsyncAPI publishing message type. "
            "Should be any python-native object annotation or `pydantic.BaseModel`."
        ),
    ] = None,
    include_in_schema: Annotated[
        bool,
        Doc("Whetever to include operation in AsyncAPI schema or not."),
    ] = True,
) -> Union[
    "AsyncAPIBatchPublisher",
    "AsyncAPIDefaultPublisher",
]:
    """Creates long-living and AsyncAPI-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(...)`.
    """
    publisher = AsyncAPIPublisher.create(
        # batch flag
        batch=batch,
        # default args
        key=key,
        # both args
        topic=topic,
        partition=partition,
        headers=headers,
        reply_to=reply_to,
        # publisher-specific
        broker_middlewares=self._middlewares,
        middlewares=middlewares,
        # AsyncAPI
        title_=title,
        description_=description,
        schema_=schema,
        include_in_schema=self._solve_include_in_schema(include_in_schema),
    )

    if batch:
        return cast("AsyncAPIBatchPublisher", super().publisher(publisher))
    else:
        return cast("AsyncAPIDefaultPublisher", super().publisher(publisher))

def include_router(
    self,
    router: "ABCBroker[Any]",
    *,
    prefix: str = "",
    dependencies: Iterable["Depends"] = (),
    middlewares: Iterable["BrokerMiddleware[MsgType]"] = (),
    include_in_schema: Optional[bool] = None,
) -> None:
    """Includes a router in the current object."""
    for h in router._subscribers.values():
        h.add_prefix("".join((self.prefix, prefix)))

        if (key := hash(h)) not in self._subscribers:
            if include_in_schema is None:
                h.include_in_schema = self._solve_include_in_schema(
                    h.include_in_schema
                )
            else:
                h.include_in_schema = include_in_schema

            h._broker_middlewares = (
                *self._middlewares,
                *middlewares,
                *h._broker_middlewares,
            )
            h._broker_dependencies = (
                *self._dependencies,
                *dependencies,
                *h._broker_dependencies,
            )
            self._subscribers = {**self._subscribers, key: h}

    for p in router._publishers.values():
        p.add_prefix(self.prefix)

        if (key := hash(p)) not in self._publishers:
            if include_in_schema is None:
                p.include_in_schema = self._solve_include_in_schema(
                    p.include_in_schema
                )
            else:
                p.include_in_schema = include_in_schema

            p._broker_middlewares = (
                *self._middlewares,
                *middlewares,
                *p._broker_middlewares,
            )
            self._publishers = {**self._publishers, key: p}

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