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总结Kafka异步JSON消息代码生成

claude3.5 sonnet 20241022(Anthropic)

# 消息队列方案概述(Message Queue Overview) 本示例使用 Kafka 作为消息队列,采用 Python 编程语言。Kafka 是一个分布式流处理平台,能够处理高吞吐量的实时数据流。我们将实现一个异步生产者和消费者,消息格式为 JSON,主题名为 "user-events"。 ## 生产者代码(Producer Code) ```python from kafka import KafkaProducer import json # 配置生产者 producer = KafkaProducer( bootstrap_servers='localhost:9092', # Kafka 服务器地址 value_serializer=lambda v: json.dumps(v).encode('utf-8') # JSON 序列化 ) # 发送消息 def send_message(topic, message): producer.send(topic, message) producer.flush() # 确保消息发送 # 示例发送 if __name__ == "__main__": message = {"event": "user_signup", "user_id": 123, "timestamp": "2025-04-29T20:17:11+08:00"} send_message('user-events', message) print("Message sent:", message) ``` ## 消费者代码(Consumer Code) ```python from kafka import KafkaConsumer import json # 配置消费者 consumer = KafkaConsumer( 'user-events', # 订阅的主题 bootstrap_servers='localhost:9092', # Kafka 服务器地址 value_deserializer=lambda x: json.loads(x.decode('utf-8')) # JSON 反序列化 ) # 消费消息 if __name__ == "__main__": for message in consumer: print("Message received:", message.value) ``` ## 配置说明(Configuration Instructions) 1. **Kafka 服务器配置**: - 确保 Kafka 服务器正在运行,并且地址为 `localhost:9092`,可以根据实际情况修改。 2. **生产者**: - 使用 `KafkaProducer` 创建生产者实例,设置 `bootstrap_servers` 和 `value_serializer`。 - 使用 `send_message` 函数发送消息到指定主题。 3. **消费者**: - 使用 `KafkaConsumer` 创建消费者实例,订阅主题并设置 `value_deserializer`。 - 通过循环接收并处理消息。 4. **运行示例**: - 先运行消费者代码,然后运行生产者代码以发送消息。 以上代码提供了一个基本的 Kafka 消息队列生产者与消费者的实现示例,便于开发者快速搭建异步消息通信模块。

DeepSeek V3(Deepseek)

```markdown # Kafka Message Queue Implementation (Python) ## Message Queue Overview - **Type**: Apache Kafka (v2.8+) - **Communication Pattern**: Asynchronous Pub/Sub - **Message Format**: JSON - **Topic**: `user-events` - **Features**: - High-throughput distributed messaging - Persistent message storage with retention policies - Consumer group load balancing ## Producer Code (Async Mode) ```python from kafka import KafkaProducer import json import time class AsyncKafkaProducer: def __init__(self, bootstrap_servers, topic): self.producer = KafkaProducer( bootstrap_servers=bootstrap_servers, value_serializer=lambda v: json.dumps(v).encode('utf-8'), acks='all', retries=3 ) self.topic = topic def send_message(self, message): """Asynchronously send JSON message with callback handling""" future = self.producer.send( topic=self.topic, value=message ) def callback(future_metadata): try: record_metadata = future_metadata.get(timeout=10) print(f"Successfully sent to {record_metadata.topic}[{record_metadata.partition}]") except Exception as e: print(f"Failed to send: {str(e)}") future.add_callback(callback) def close(self): self.producer.flush() self.producer.close() # Usage Example if __name__ == "__main__": producer = AsyncKafkaProducer( bootstrap_servers=['localhost:9092'], topic='user-events' ) try: sample_event = { "user_id": "12345", "event_type": "login", "timestamp": int(time.time()), "device_info": {"os": "Android", "version": "12"} } producer.send_message(sample_event) finally: producer.close() ``` ## Consumer Code ```python from kafka import KafkaConsumer import json class KafkaJsonConsumer: def __init__(self, bootstrap_servers, topic, group_id): self.consumer = KafkaConsumer( topic, bootstrap_servers=bootstrap_servers, group_id=group_id, auto_offset_reset='earliest', enable_auto_commit=True, value_deserializer=lambda x: json.loads(x.decode('utf-8')) def consume_messages(self): try: for message in self.consumer: print(f"Received: {message.value}") # Add your message processing logic here except KeyboardInterrupt: print("Stopping consumer...") finally: self.consumer.close() # Usage Example if __name__ == "__main__": consumer = KafkaJsonConsumer( bootstrap_servers=['localhost:9092'], topic='user-events', group_id='user-events-processor' ) consumer.consume_messages() ``` ## Configuration Instructions ### Required Dependencies ```bash pip install kafka-python ``` ### Producer Configuration | Parameter | Recommended Value | Description | |----------------------|-------------------------|--------------------------------------| | bootstrap_servers | ['host1:9092', ...] | Kafka broker addresses | | acks | 'all' | Wait for all replicas to acknowledge | | retries | 3 | Number of send retries | | compression_type | 'gzip' (optional) | Message compression | | batch_size | 16384 (optional) | Bytes per batch | ### Consumer Configuration | Parameter | Recommended Value | Description | |----------------------|-------------------------|--------------------------------------| | group_id | Your application name | Consumer group identifier | | auto_offset_reset | 'earliest'/'latest' | Offset policy when no offset exists | | session_timeout_ms | 10000 | Group session timeout | | max_poll_records | 500 | Max records per poll | ### Deployment Notes 1. For production, always configure at least 3 Kafka brokers 2. Monitor consumer lag using `kafka-consumer-groups.sh` 3. Consider implementing dead-letter queues for failed messages 4. Adjust `fetch.max.bytes` if dealing with large JSON payloads ```

gpt-4o(OpenAI)

```markdown ## Message Queue Overview Apache Kafka 是一种分布式流处理平台,适用于实时数据管道和流处理应用程序。Kafka 允许在生产者与消费者之间通过主题进行异步消息传递。在此示例中我们将创建一个 Kafka 生产者和消费者,使用 Python 编程语言,并以 JSON 格式进行异步消息传递。 ## Producer Code 下面的代码展示了如何创建一个 Kafka 异步生产者,用于将 JSON 格式的消息发布到指定的主题 `user-events`。 ```python from confluent_kafka import Producer import json # Kafka配置 kafka_config = { 'bootstrap.servers': 'localhost:9092', # Kafka服务器地址 } # 创建生产者实例 producer = Producer(**kafka_config) def delivery_report(err, msg): """回调函数,用于确认消息成功或失败发送""" if err is not None: print(f"Message delivery failed: {err}") else: print(f"Message delivered to {msg.topic()} [{msg.partition()}] at offset {msg.offset()}") def produce_message(): # 示例消息数据 data = { "id": 123, "event": "user_signup", "details": { "username": "john_doe", "email": "john@example.com" } } # 发送消息到Kafka producer.produce('user-events', key=str(data['id']), value=json.dumps(data), callback=delivery_report) # 刷新,以确保所有消息被发送 producer.flush() if __name__ == '__main__': produce_message() ``` ## Consumer Code 下面的代码展示了如何创建一个 Kafka 消费者,用于从指定的主题 `user-events` 中消费 JSON 格式的消息。 ```python from confluent_kafka import Consumer, KafkaError import json # Kafka消费者配置 consumer_config = { 'bootstrap.servers': 'localhost:9092', 'group.id': 'user-events-group', # 消费者组ID 'auto.offset.reset': 'earliest' # 从最早的消息开始消费 } # 创建消费者实例 consumer = Consumer(**consumer_config) consumer.subscribe(['user-events']) def consume_messages(): try: while True: msg = consumer.poll(1.0) # 从Kafka中拉取消息 if msg is None: continue if msg.error(): if msg.error().code() == KafkaError._PARTITION_EOF: print('End of partition reached {0}/{1}'.format(msg.topic(), msg.partition())) elif msg.error(): print('Error occurred: {0}'.format(msg.error().str())) else: # 成功接收消息 data = json.loads(msg.value().decode('utf-8')) print(f"Received message: {data}") finally: consumer.close() if __name__ == '__main__': consume_messages() ``` ## Configuration Instructions 1. 安装 `confluent_kafka` 库: ```bash pip install confluent-kafka ``` 2. 初始化 Kafka 服务,并确保服务运行在正确的服务器地址和端口 (`localhost:9092` 是默认配置,需根据你自己环境进行调整)。 3. 根据需要调整 `bootstrap.servers` 为你的 Kafka 服务器的主机名和端口。 4. 可以通过修改 `group.id` 来定义消费者组,以支持水平扩展和分区负载均衡。 5. 示例代码默认设置了消费者从最早的偏移量开始消费消息,可以通过修改 `auto.offset.reset` 配置项来调整。 该代码框架可扩展以支持更多的功能,如性能监控、断点续传、动态分区调整等。 ```

通义千问2.5-Coder-32B(阿里云百炼)

# Message Queue Overview Kafka is a distributed streaming platform capable of handling trillions of events a day. It provides a high-throughput, low-latency platform for handling real-time data feeds. Kafka is used for building real-time data pipelines and streaming applications. ## Producer Code Below is the Python code for a Kafka producer that sends messages asynchronously in JSON format using the `confluent_kafka` library. ```python from confluent_kafka import Producer import json import time 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(f'Message delivery failed: {err}') else: print(f'Message delivered to {msg.topic()} [{msg.partition()}] at offset {msg.offset()}') def create_producer(config): return Producer(config) def produce_message(producer, topic, message): producer.produce(topic, value=json.dumps(message), callback=delivery_report) producer.poll(0) # Serve delivery callback queue. if __name__ == '__main__': conf = { 'bootstrap.servers': 'localhost:9092', # Replace with your Kafka broker address 'client.id': 'python-producer' } producer = create_producer(conf) try: while True: message = {'event_type': 'login', 'user_id': 'user123', 'timestamp': time.time()} produce_message(producer, 'user-events', message) time.sleep(1) # Adjust the sleep time as needed except KeyboardInterrupt: pass finally: producer.flush() ``` ## Consumer Code Below is the Python code for a Kafka consumer that receives messages in JSON format using the `confluent_kafka` library. ```python from confluent_kafka import Consumer, KafkaException import json def create_consumer(config): return Consumer(config) def consume_messages(consumer, topics): try: consumer.subscribe(topics) while True: msg = consumer.poll(timeout=1.0) if msg is None: continue if msg.error(): if msg.error().code() == KafkaError._PARTITION_EOF: # End of partition event print(f'{msg.topic()} [{msg.partition()}] reached end at offset {msg.offset()}') elif msg.error(): raise KafkaException(msg.error()) else: # Process the message value = json.loads(msg.value().decode('utf-8')) print(f'Received message: {value}') except KeyboardInterrupt: pass finally: # Close down consumer to commit final offsets. consumer.close() if __name__ == '__main__': conf = { 'bootstrap.servers': 'localhost:9092', # Replace with your Kafka broker address 'group.id': 'python-consumer-group', 'auto.offset.reset': 'earliest' } topics = ['user-events'] consumer = create_consumer(conf) consume_messages(consumer, topics) ``` ## Configuration Instructions 1. **Bootstrap Servers**: Update the `bootstrap.servers` configuration with your Kafka broker addresses. 2. **Topic Name**: Ensure the `topicOrQueueName` matches the topic you want to send/receive messages from. 3. **Group ID**: For consumers, specify a unique `group.id` to identify the consumer group. 4. **Auto Offset Reset**: The `auto.offset.reset` setting determines the starting point for consuming messages when there is no initial offset in Kafka or if the current offset does not exist any more on the server (e.g., because that data has been deleted). This setup provides a basic framework for producing and consuming messages asynchronously in JSON format using Kafka and Python. Adjust the configurations and logic as needed for your specific use case.

模型名称 模型提供商 响应总时长(s) 生成 token 数 首 token 延迟(s) 生成速率(tokens/s)
15.09
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1.72
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46.17
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4.26
17.03
53.4
808
0.52
延迟最小
15.13
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