2、有订单数据,格式为: (订单ID，用户ID，时间戳/事件时间，订单金额)，请完成如下功能： （1）模拟每1秒钟生成一个订单数据，并模拟随机参生一个延时。 （2）要求每隔10s,计算10秒内，每个用户的订单总金额 （3）并添加Watermaker来解决一定程度上的数据延迟和数据乱序问题（最大允许延迟为5秒）。 （4）并使用OutputTag+allowedLateness解决数据丢失问题 Flink代码怎么打

好的，下面是针对你提出的问题的Flink代码实现：

from pyflink.common.serialization import SimpleStringSchema
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.connectors import FlinkKafkaProducer
from pyflink.datastream.functions import KeyedProcessFunction
from pyflink.datastream.state import ValueStateDescriptor
from pyflink.datastream.util import OutputTag
from pyflink.streaming.api.environment import CheckpointConfig, StreamExecutionEnvironment
from pyflink.streaming.api.functions import AssignerWithPeriodicWatermarks, ProcessFunction
from pyflink.streaming.api.watermark import Watermark
from pyflink.table.descriptors import Kafka, Schema
from pyflink.table.types import DataTypes
from pyflink.table.udf import udf

import json
import random
import time

# 定义订单数据的生成函数
def generate_order():
    order_id = random.randint(1, 1000)
    user_id = random.randint(1, 100)
    timestamp = int(time.time() * 1000)
    order_amount = random.randint(1, 100)
    delay_time = random.randint(1, 5)
    time.sleep(delay_time)
    return (order_id, user_id, timestamp, order_amount)

# 定义Watermark生成器
class OrderTimestampExtractor(AssignerWithPeriodicWatermarks):
    def __init__(self):
        self.current_timestamp = 0
        self.max_delay = 5000 # 最大延迟时间为5秒

    def extract_timestamp(self, element):
        self.current_timestamp = element[2]
        return self.current_timestamp

    def get_watermark(self):
        return Watermark(self.current_timestamp - self.max_delay)

# 定义ProcessFunction计算订单总金额
class OrderAmountProcessFunction(ProcessFunction):
    def process_element(self, element, context, collector):
        user_id = element[1]
        order_amount = element[3]
        state_name = "order_amount"
        state_descriptor = ValueStateDescriptor(state_name, Types.LONG())
        order_amount_state = context.get_state(state_descriptor)
        if order_amount_state.value() is None:
            order_amount_state.update(0)
        order_amount_state.update(order_amount_state.value() + order_amount)
        context.timer_service().register_event_time_timer(context.timestamp() + 10000) # 注册10秒后的定时器

    def on_timer(self, timestamp, context, collector):
        user_id = context.key[0]
        state_name = "order_amount"
        state_descriptor = ValueStateDescriptor(state_name, Types.LONG())
        order_amount_state = context.get_state(state_descriptor)
        order_amount = order_amount_state.value()
        collector.collect((user_id, order_amount))
        order_amount_state.clear()

# 定义OutputTag
side_output_tag = OutputTag('late-data', Types.TUPLE([Types.INT(), Types.INT(), Types.LONG(), Types.INT()]))

# 定义主函数
def main():
    # 创建StreamExecutionEnvironment对象
    env = StreamExecutionEnvironment.get_execution_environment()
    env.set_parallelism(1)
    env.set_stream_time_characteristic(TimeCharacteristic.EventTime)

    # 定义Kafka连接参数
    kafka_properties = {
        "bootstrap.servers": "localhost:9092",
        "group.id": "order-group"
    }

    # 创建Kafka数据源
    kafka_source = Kafka()
    kafka_source.version('universal')
    kafka_source.topic('order')
    kafka_source.properties(kafka_properties)
    kafka_source.start_from_earliest()

    # 定义Schema
    schema = Schema()
    schema.field("order_id", DataTypes.INT())
    schema.field("user_id", DataTypes.INT())
    schema.field("timestamp", DataTypes.BIGINT())
    schema.field("order_amount", DataTypes.INT())

    # 创建TableEnvironment对象
    table_env = StreamTableEnvironment.create(env)
    table_env.connect(kafka_source).with_schema(schema).register_table_source("orders")
    table_env.connect(FlinkKafkaProducer(topic='order-result', 
                                         producer_config=kafka_properties, 
                                         serialization_schema=SimpleStringSchema())).register_table_sink("results")

    # 定义UDF函数
    @udf(input_types=[Types.INT(), Types.LONG(), Types.INT()], result_type=Types.TUPLE([Types.INT(), Types.LONG(), Types.INT()]))
    def add_watermark(order_id, timestamp, order_amount):
        return (order_id, timestamp, order_amount)

    # 使用Watermark生成器生成Watermark
    orders = table_env.scan("orders")
    orders = orders.assign_timestamps_and_watermarks(OrderTimestampExtractor())
    orders = orders.select("user_id, order_amount, timestamp")
    orders = orders.process(
        KeyedProcessFunction()
            .key_by(lambda x: x[0])
            .process(OrderAmountProcessFunction())
            .side_output_with_timestamp(side_output_tag)
            .forward()
    )

    # 处理延迟数据
    late_data = orders.get_side_output(side_output_tag)
    late_data = late_data.assign_timestamps_and_watermarks(OrderTimestampExtractor())
    late_data = late_data.select("user_id, order_amount, timestamp")
    late_data = late_data.process(
        KeyedProcessFunction()
            .key_by(lambda x: x[0])
            .process(OrderAmountProcessFunction())
            .forward()
    )

    # 合并延迟数据
    orders = orders.union(late_data)

    # 定义输出Schema
    result_schema = Schema()
    result_schema.field("user_id", DataTypes.INT())
    result_schema.field("order_amount", DataTypes.BIGINT())

    # 输出结果到Kafka
    results = orders.group_by("user_id").select("user_id, order_amount.sum as order_amount")
    results = results.select("user_id, order_amount").insert_into("results")

    # 执行任务
    env.execute()

if __name__ == '__main__':
    main()

上面的代码实现包含了订单数据的生成函数、Watermark生成器、ProcessFunction计算订单总金额、OutputTag、以及主函数的实现。你可以根据自己的需求进行修改和优化。需要注意的是，上面的代码仅供参考，可能需要根据具体情况进行调整和改进。