python confluent_kafka

Python Confluent Kafka 是一个 Python 库，用于与 Apache Kafka 通信。它使用 librdkafka C 库提供高性能的消息传递。该库提供了生产者和消费者 API，可以在 Python 应用程序中使用。使用 Python Confluent Kafka，您可以轻松地连接到 Kafka 集群，编写生产者和消费者代码，并进行高效的消息传递。此外，Python Confluent Kafka 还提供了一些高级功能，例如事务支持和分区分配器。

相关问题

python confluent_kafka

Confluent Kafka is a Python client library for Apache Kafka, developed by Confluent. It provides an easy-to-use interface for interacting with Kafka clusters, allowing you to produce and consume messages from Kafka.

To use the confluent_kafka library in, you first need to install it. You can do this by running the following command:

pip install confluent-kafka

Once installed, you can import the library in your Python code as follows:

from confluent_kafka import Producer, Consumer

To produce messages to a Kafka topic, you can create a `Producer` instance and use its `produce()` method. Here's an example:

producer = Producer({'bootstrap.servers': 'localhost:9092'})

topic = 'my_topic'
message = 'Hello, Kafka!'

producer.produce(topic, message.encode('utf-8'))
producer.flush()

To consume messages from a Kafka topic, you can create a `Consumer` instance and use its `subscribe()` and `poll()` methods. Here's an example:

consumer = Consumer({
    'bootstrap.servers': 'localhost:9092',
    'group.id': 'my_consumer_group',
    'auto.offset.reset': 'earliest'
})

topic = 'my_topic'

consumer.subscribe([topic])

while True:
    msg = consumer.poll(1.0)

    if msg is None:
        continue

    if msg.error():
        print(f"Consumer error: {msg.error()}")
        continue

    print(f"Received message: {msg.value().decode('utf-8')}")

consumer.close()

These are just basic examples to get you started with the confluent_kafka library. You can refer to the official documentation for more advanced usage and configuration options.

Please note that you need a running Kafka cluster to use the confluent_kafka library.

python confluent_kafka producer

As an AI language model, I don't have the capability to execute Python code. However, I can provide you with an example code snippet for creating a producer using the Confluent Kafka Python library:

from confluent_kafka import Producer

def delivery_report(err, msg):
    """
    Called once for each message produced to indicate delivery result.
    Triggered by poll() or flush().
    """
    if err is not None:
        print('Message delivery failed: {}'.format(err))
    else:
        print('Message delivered to {} [{}]'.format(msg.topic(), msg.partition()))

# Set up configuration properties
conf = {
    'bootstrap.servers': '<your-bootstrap-server>',
    'client.id': 'python-producer'
}

# Create a Kafka producer instance
producer = Producer(conf)

# Produce a message to a topic
topic = '<your-topic>'
message = 'Hello, Kafka!'
producer.produce(topic, message.encode('utf-8'), callback=delivery_report)

# Wait for any outstanding messages to be delivered and delivery reports to be received
producer.flush()

In this example, a producer is created using the `Producer()` class of the Confluent Kafka Python library. The `delivery_report()` function is defined to handle delivery reports for produced messages. The producer instance is used to produce a message to a topic using the `produce()` method. The `flush()` method is called to wait for any outstanding messages to be delivered and delivery reports to be received.

Note: Make sure to replace `<your-bootstrap-server>` and `<your-topic>` with your actual bootstrap server and topic names, respectively.