一种可能的解决方法是在 Apache Beam 中使用 KafkaPython 库，并配置正确的安全协议和认证机制。下面是一个代码示例：

import apache_beam as beam
from apache_beam.transforms import window
from kafka import KafkaProducer
from kafka import KafkaConsumer
from kafka.errors import KafkaError

class KafkaCustomSink(beam.PTransform):
    """A custom Apache Beam sink for producing messages to Apache Kafka"""

    def __init__(self, bootstrap_servers, topic, security_protocol='SASL_SSL', sasl_mechanism='OAUTHBEARER', sasl_oauth_token_provider=pks_utils.oauth2_provider_from_environment()):
        self.bootstrap_servers = bootstrap_servers
        self.topic = topic
        self.security_protocol = security_protocol
        self.sasl_mechanism = sasl_mechanism
        self.sasl_oauth_token_provider = sasl_oauth_token_provider

    # In this transform, the elements of the PCollection are simply
    # sent as messages to the Kafka topic.
    def expand(self, pcoll):
        producer = KafkaProducer(
            bootstrap_servers=self.bootstrap_servers,
            security_protocol=self.security_protocol,
            sasl_mechanism=self.sasl_mechanism,
            sasl_oauth_token_provider=self.sasl_oauth_token_provider)

        def send_to_kafka(element):
            future = producer.send(self.topic, element)
            try:
                record_metadata = future.get(timeout=10)
            except KafkaError as ex:
                # Decide what to do if produce request failed...
                print('Error producing to topic {0}: {1}'.format(self.topic, str(ex)))
            except:
                # Decide what to do if produce request failed...
                print('Error producing to topic {0}: unexpected error'.format(self.topic))
            else:
                # Print metadata of the produced message for reference...
                print('Message produced with metadata: {0}'.format(str(record_metadata)))

        pcoll | 'Kafka send' >> beam.Map(send_to_kafka)

        return pcoll

# Example usage of the KafkaCustomSink custom sink...
with beam.Pipeline() as pipeline:
    data = ['{"value": "ABCD"}', '{"value": "EFGH"}', '{"value": "IJKL"}']
    (pipeline
     | "Create data" >> beam.Create(data)
     | "Write to Kafka" >> KafkaCustomSink(bootstrap_servers='kafka-brokers', topic='test-topic')
     )

在代码示例中，我们使用 KafkaCustomSink 类作为 Apache Beam 的自定义 sink，将 PCollections 中的消息发送到 Apache Kafka。在初始化 KafkaProducer 时，我们设置