Python Kafka流连接：Faust现状、替代方案与手动实现策略

# 示例：利用状态和窗口实现手动连接的伪代码
from quixstreams import Application, StreamConsumer, StreamProducer
from quixstreams.models.timestamps import auto_assign_timestamps
from datetime import timedelta
import time

# 初始化Quix Streams应用
app = Application(
    broker_address="localhost:9092",
    consumer_group="manual-join-group",
    auto_offset_reset="earliest"
)

# 定义输入和输出主题
input_topic_a = app.topic("topic-a") # 例如：订单流
input_topic_b = app.topic("topic-b") # 例如：用户详情流
output_topic = app.topic("joined-output") # 连接后的输出流

# 定义一个全局或由框架管理的状态存储
# 在实际的Quix Streams应用中，这会通过dataframe的stateful操作或更高级的API实现
# 这里为了演示概念，使用一个简单的字典作为共享状态
# 实际生产中应使用持久化或分布式状态存储
shared_join_state = {}

# 处理来自topic-a的流（例如，订单信息）
# 将订单信息按用户ID（key）存储在状态中
@app.dataframe(input_topic_a)
def process_topic_a(stream: StreamConsumer):
    stream = stream.update(auto_assign_timestamps) # 自动分配时间戳
    stream = stream.apply(lambda row: {"key": row["user_id"], "order_details": row["details"]})

    def store_order_in_state(row):
        user_id = row["key"]
        order_details = row["order_details"]
        # 假设我们只保留最近的几条订单，或者在一个窗口内
        # 这里简化为直接添加到列表，实际应考虑窗口和过期策略
        shared_join_state.setdefault(user_id, {"orders": [], "user_info": None})["orders"].append(order_details)
        print(f"Stored order for user {user_id}: {order_details}")
        return None # 不直接向下游发送

    stream = stream.apply(store_order_in_state)
    return stream # 返回stream，但这个dataframe不直接向output_topic发送

# 处理来自topic-b的流（例如，用户详情）并尝试与topic-a的状态进行连接
@app.dataframe(input_topic_b)
def process_topic_b_and_join(stream: StreamConsumer):
    stream = stream.update(auto_assign_timestamps) # 自动分配时间戳
    stream = stream.apply(lambda row: {"key": row["user_id"], "user_info": row["details"]})

    def join_with_state(row):
        user_id = row["key"]
        user_info = row["user_info"]

        # 更新用户详情到共享状态
        shared_join_state.setdefault(user_id, {"orders": [], "user_info": None})["user_info"] = user_info
        print(f"Stored user info for user {user_id}: {user_info}")

        # 尝试进行连接
        if user_id in shared_join_state and shared_join_state[user_id]["orders"] and shared_join_state[user_id]["user_info"]:
            # 找到匹配项，执行连接逻辑
            joined_data = {
                "user_id": user_id,
                "user_info": shared_join_state[user_id]["user_info"],
                "orders": shared_join_state[user_id]["orders"],
                "joined_timestamp": time.time()
            }
            print(f"Joined data for user {user_id}: {joined_data}")
            # 清理状态中已连接的订单，或者根据窗口策略自动过期
            # shared_join_state[user_id]["orders"] = [] # 简单清理
            return joined_data
        else:
            # 尚未完全匹配，或者等待更多数据
            print(f"Partial data for user {user_id}. Waiting for full join.")
            return None # 不发送不完整的连接结果

    # 应用连接逻辑，并将结果发送到输出主题
    stream = stream.apply(join_with_state).filter(lambda row: row is not None)
    return stream.to_topic(output_topic)

# 运行应用程序
# if __name__ == "__main__":
#     print("Starting Quix Streams application for manual join...")
#     app.run()