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#
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#
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from __future__ import annotations
from typing import Any, Callable, Sequence
from airflow.models import BaseOperator
from airflow.providers.apache.kafka.triggers.await_message import AwaitMessageTrigger
[docs]VALID_COMMIT_CADENCE = {"never", "end_of_batch", "end_of_operator"}
[docs]class AwaitMessageSensor(BaseOperator):
"""
An Airflow sensor that defers until a specific message is published to Kafka.
The sensor creates a consumer that reads the Kafka log until it encounters a positive event.
The behavior of the consumer for this trigger is as follows:
- poll the Kafka topics for a message
- if no message returned, sleep
- process the message with provided callable and commit the message offset
- if callable returns any data, raise a TriggerEvent with the return data
- else continue to next message
- return event (as default xcom or specific xcom key)
:param kafka_config_id: The connection object to use, defaults to "kafka_default"
:param topics: Topics (or topic regex) to use for reading from
:param apply_function: The function to apply to messages to determine if an event occurred. As a dot
notation string.
:param apply_function_args: Arguments to be applied to the processing function,
defaults to None
:param apply_function_kwargs: Key word arguments to be applied to the processing function,
defaults to None
:param poll_timeout: How long the kafka consumer should wait for a message to arrive from the kafka
cluster,defaults to 1
:param poll_interval: How long the kafka consumer should sleep after reaching the end of the Kafka log,
defaults to 5
:param xcom_push_key: the name of a key to push the returned message to, defaults to None
"""
[docs] template_fields = (
"topics",
"apply_function",
"apply_function_args",
"apply_function_kwargs",
"kafka_config_id",
)
def __init__(
self,
topics: Sequence[str],
apply_function: str,
kafka_config_id: str = "kafka_default",
apply_function_args: Sequence[Any] | None = None,
apply_function_kwargs: dict[Any, Any] | None = None,
poll_timeout: float = 1,
poll_interval: float = 5,
xcom_push_key=None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.topics = topics
self.apply_function = apply_function
self.apply_function_args = apply_function_args
self.apply_function_kwargs = apply_function_kwargs
self.kafka_config_id = kafka_config_id
self.poll_timeout = poll_timeout
self.poll_interval = poll_interval
self.xcom_push_key = xcom_push_key
[docs] def execute(self, context) -> Any:
self.defer(
trigger=AwaitMessageTrigger(
topics=self.topics,
apply_function=self.apply_function,
apply_function_args=self.apply_function_args,
apply_function_kwargs=self.apply_function_kwargs,
kafka_config_id=self.kafka_config_id,
poll_timeout=self.poll_timeout,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context, event=None):
if self.xcom_push_key:
self.xcom_push(context, key=self.xcom_push_key, value=event)
return event
[docs]class AwaitMessageTriggerFunctionSensor(BaseOperator):
"""
Defer until a specific message is published to Kafka, trigger a registered function, then resume waiting.
The behavior of the consumer for this trigger is as follows:
- poll the Kafka topics for a message
- if no message returned, sleep
- process the message with provided callable and commit the message offset
- if callable returns any data, raise a TriggerEvent with the return data
- else continue to next message
- return event (as default xcom or specific xcom key)
:param kafka_config_id: The connection object to use, defaults to "kafka_default"
:param topics: Topics (or topic regex) to use for reading from
:param apply_function: The function to apply to messages to determine if an event occurred. As a dot
notation string.
:param event_triggered_function: The callable to trigger once the apply_function encounters a
positive event.
:param apply_function_args: Arguments to be applied to the processing function, defaults to None
:param apply_function_kwargs: Key word arguments to be applied to the processing function,
defaults to None
:param poll_timeout: How long the kafka consumer should wait for a message to arrive from the kafka
cluster, defaults to 1
:param poll_interval: How long the kafka consumer should sleep after reaching the end of the Kafka log,
defaults to 5
"""
[docs] template_fields = (
"topics",
"apply_function",
"apply_function_args",
"apply_function_kwargs",
"kafka_config_id",
)
def __init__(
self,
topics: Sequence[str],
apply_function: str,
event_triggered_function: Callable,
kafka_config_id: str = "kafka_default",
apply_function_args: Sequence[Any] | None = None,
apply_function_kwargs: dict[Any, Any] | None = None,
poll_timeout: float = 1,
poll_interval: float = 5,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.topics = topics
self.apply_function = apply_function
self.apply_function_args = apply_function_args
self.apply_function_kwargs = apply_function_kwargs
self.kafka_config_id = kafka_config_id
self.poll_timeout = poll_timeout
self.poll_interval = poll_interval
self.event_triggered_function = event_triggered_function
if not callable(self.event_triggered_function):
raise TypeError(
"parameter event_triggered_function is expected to be of type callable,"
f"got {type(event_triggered_function)}"
)
[docs] def execute(self, context, event=None) -> Any:
self.defer(
trigger=AwaitMessageTrigger(
topics=self.topics,
apply_function=self.apply_function,
apply_function_args=self.apply_function_args,
apply_function_kwargs=self.apply_function_kwargs,
kafka_config_id=self.kafka_config_id,
poll_timeout=self.poll_timeout,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)
return event
[docs] def execute_complete(self, context, event=None):
self.event_triggered_function(event, **context)
self.defer(
trigger=AwaitMessageTrigger(
topics=self.topics,
apply_function=self.apply_function,
apply_function_args=self.apply_function_args,
apply_function_kwargs=self.apply_function_kwargs,
kafka_config_id=self.kafka_config_id,
poll_timeout=self.poll_timeout,
poll_interval=self.poll_interval,
),
method_name="execute_complete",
)