《智创未来：AI驱动MySQL数据全生命周期管理的革新之路》

    from kafka import KafkaProducer
    import json
    
    # 初始化Kafka生产者实例
    producer = KafkaProducer(bootstrap_servers=['localhost:9092'], value_serializer=lambda v: json.dumps(v).encode('utf-8'))
    
    def send_data_to_kafka(topic, data):
    """
    向指定主题发送JSON格式的消息
    
    参数:
        topic (str): 目标Kafka Topic名称
        data (dict): 要发送的数据字典对象
    """
    try:
        producer.send(topic, value=data)
        print(f"Sent message to topic {topic}: {data}")
    except Exception as e:
        print(f"Failed to send message: {e}")
    
    # 示例用法
    sample_data = {"user_id": "u123456", "event_type": "click", "timestamp": "2023-01-01T12:00:00Z"}
    send_data_to_kafka('mysql_events', sample_data)

    from pyspark.sql import SparkSession
    from pyspark.sql.functions import col
    
    # 创建Spark会话
    spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
    
    def process_raw_data(input_path, output_path):
    """
    对原始CSV文件进行预处理，并保存结果至新位置
    
    参数:
        input_path (str): 输入文件路径
        output_path (str): 输出文件路径
    """
    df = spark.read.csv(input_path, header=True, inferSchema=True)
    
    # 进行一些基本的数据清洗工作
    cleaned_df = df.dropna().filter(col("value") > 0)
    
    # 写回HDFS或其他存储系统
    cleaned_df.write.parquet(output_path)
    
    # 示例用法
    process_raw_data("/path/to/raw_data.csv", "/path/to/processed_data.parquet")

    import tensorflow as tf
    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Dense
    
    def build_and_train_model(training_data, validation_data, epochs=10):
    """
    构建并训练一个简单的神经网络分类器
    
    参数:
        training_data (tuple): 训练集特征矩阵和标签向量
        validation_data (tuple): 验证集特征矩阵和标签向量
        epochs (int): 迭代次数，默认值为10
    返回:
        trained_model (tf.keras.Model): 已经训练好的模型对象
    """
    model = Sequential([
        Dense(64, activation='relu', input_shape=(training_data[0].shape[1],)),
        Dense(32, activation='relu'),
        Dense(1, activation='sigmoid')
    ])
    
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    
    history = model.fit(training_data[0], training_data[1],
                        epochs=epochs,
                        validation_data=validation_data,
                        verbose=1)
    
    return model
    
    # 假设我们已经有了准备好的训练和验证数据
    X_train, y_train = ...  # 训练集特征和标签
    X_val, y_val = ...      # 验证集特征和标签
    trained_model = build_and_train_model((X_train, y_train), (X_val, y_val))

    from flask import Flask, request, jsonify
    
    app = Flask(__name__)
    
    @app.route('/predict', methods=['POST'])
    def predict():
    """
    接收客户端提交的数据，调用内部模型进行预测，并返回结果。
    
    请求体应包含JSON格式的输入特征数组。
    """
    content = request.get_json()
    features = content['features']
    
    # 使用之前训练好的模型进行预测
    prediction = trained_model.predict([features])
    
    response = {
        'prediction': float(prediction[0][0]),
        'status': 'success'
    }
    
    return jsonify(response)
    
    if __name__ == '__main__':
    app.run(debug=True)