Source code for bspump.kafka.sink

import json
import logging
import typing
import asyncio

from ..abc.sink import Sink

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L = logging.getLogger(__name__)

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[docs]class KafkaSink(Sink): """ KafkaSink is a sink processor that forwards the event to a Apache Kafka specified by a KafkaConnection object. KafkaSink expects bytes as an input. If the input is string or dictionary, it is automatically transformed to bytes using encoding charset specified in the configuration. .. code:: python class KafkaPipeline(bspump.Pipeline): def __init__(self, app, pipeline_id): super().__init__(app, pipeline_id) self.build( bspump.kafka.KafkaSource(app, self, "KafkaConnection", config={'topic': 'messages'}), bspump.kafka.KafkaSink(app, self, "KafkaConnection", config={'topic': 'messages2'}), ) There are two ways to use KafkaSink: - Specify a single topic in KafkaSink config - topic, to be used for all the events in pipeline. - Specify topic separetly for each event in event context - context['kafka_topic']. Topic from configuration is than used as a default topic. To provide business logic for event distribution, you can create topic selector processor. Processor example: .. code:: python class KafkaTopicSelector(bspump.Processor): def process(self, context, event): if event.get("weight") > 10: context["kafka_topic"] = "heavy" else: context["kafka_topic"] = "light" return event Every kafka message can be a key:value pair. Key is read from event context - context['kafka_key']. If kafka_key is not provided, key defaults to None. """ ConfigDefaults = { "topic": "", "encoding": "utf-8", "output_queue_max_size": 100, "client_id":"", # defaults set in AIOKafka "metadata_max_age_ms":"", "request_timeout_ms":"", "api_version":"", "acks":"", "key_serializer":"", "value_serializer":"", "max_batch_size":"", "max_request_size":"", "linger_ms":"", "send_backoff_ms":"", "retry_backoff_ms":"", "connections_max_idle_ms":"", "enable_idempotency":"", "transactional_id":"", "transaction_timeout_ms":"", } def __init__(self, app, pipeline, connection, key_serializer=None, id=None, config=None): super().__init__(app, pipeline, id=id, config=config) self.Connection = pipeline.locate_connection(app, connection) self.Topic = self.Config['topic'] self._key_serializer = key_serializer self.Encoding = self.Config['encoding'] self._output_queue = asyncio.Queue(loop=app.Loop) self._output_queue_max_size = int(self.Config['output_queue_max_size']) assert (self._output_queue_max_size >= 1) self._conn_future = None producer_param_names = [ "client_id", "metadata_max_age_ms", "request_timeout_ms", "api_version", "acks", "max_batch_size", "max_request_size", "linger_ms", "send_backoff_ms", "retry_backoff_ms", "connections_max_idle_ms", "enable_idempotence", "transactional_id", "transaction_timeout_ms", ] self._producer_params = {x:y for x,y in self.Config.items() if x in producer_param_names and y != ""} # Subscription self._on_health_check('connection.open!') app.PubSub.subscribe("Application.stop!", self._on_application_stop) app.PubSub.subscribe("Application.tick!", self._on_health_check) def _on_health_check(self, message_type): if self._conn_future is not None: # Connection future exists if not self._conn_future.done(): # Connection future didn't result yet # No sanitization needed return try: self._conn_future.result() except: # Connection future threw an error L.exception("Unexpected connection future error") # Connection future already resulted (with or without exception) self._conn_future = None assert (self._conn_future is None) self._conn_future = asyncio.ensure_future( self._connection(), loop=self.Loop ) def _on_application_stop(self, message_type, counter): self._output_queue.put_nowait((None, None, None)) async def _connection(self): producer = await self.Connection.create_producer(**self._producer_params) try: await producer.start() while True: topic, message, kafka_key = await self._output_queue.get() if topic is None and message is None: break if self._output_queue.qsize() == self._output_queue_max_size - 1: self.Pipeline.throttle(self, False) await producer.send_and_wait(topic, message, key=kafka_key) finally: await producer.stop()
[docs] def process(self, context, event:typing.Union[dict, str, bytes]): if type(event) == dict: event = json.dumps(event) event = event.encode(self.Encoding) elif type(event) == str: event = event.encode(self.Encoding) kafka_topic = context.get("kafka_topic", self.Topic) kafka_key = context.get("kafka_key") if self._key_serializer is not None and kafka_key is not None: kafka_key = self._key_serializer(kafka_key) self._output_queue.put_nowait((kafka_topic, event, kafka_key)) if self._output_queue.qsize() == self._output_queue_max_size: self.Pipeline.throttle(self, True)