rm -rf job.py
cat>job.py<<EOF
import os
from pyflink.datastream import StreamExecutionEnvironment, TimeCharacteristic
from pyflink.table import StreamTableEnvironment, DataTypes, EnvironmentSettings
from pyflink.table.udf import udf


provinces = ("beijing", "shanghai", "hangzhou", "shenzhen", "jiangxi", "chongqing", "xizang")


@udf(input_types=[DataTypes.INT()], result_type=DataTypes.STRING())
def province_id_to_name(id):
    return provinces[id]

#请根据创建的Kafka集群，输入以下信息。
def log_processing():
    kafka_servers = "xx.xx.xx.xx:9092,xx.xx.xx.xx:9092,xx.xx.xx.xx:9092"
    kafka_zookeeper_servers = "xx.xx.xx.xx:2181,xx.xx.xx.xx:2181,xx.xx.xx.xx:2181"
    source_topic = "payment_msg"
    sink_topic = "results"
    kafka_consumer_group_id = "test_3"

    env = StreamExecutionEnvironment.get_execution_environment()
    env.set_stream_time_characteristic(TimeCharacteristic.EventTime)
    env_settings = EnvironmentSettings.Builder().use_blink_planner().build()
    t_env = StreamTableEnvironment.create(stream_execution_environment=env, environment_settings=env_settings)
    t_env.get_config().get_configuration().set_boolean("python.fn-execution.memory.managed", True)

    source_ddl = f"""
            CREATE TABLE payment_msg(
                createTime VARCHAR,
                rt as TO_TIMESTAMP(createTime),
                orderId BIGINT,
                payAmount DOUBLE,
                payPlatform INT,
                provinceId INT,
                WATERMARK FOR rt as rt - INTERVAL '2' SECOND
            ) WITH (
              'connector.type' = 'kafka',
              'connector.version' = 'universal',
              'connector.topic' = '{source_topic}',
              'connector.properties.bootstrap.servers' = '{kafka_servers}',
              'connector.properties.zookeeper.connect' = '{kafka_zookeeper_servers}',
              'connector.properties.group.id' = '{kafka_consumer_group_id}',
              'connector.startup-mode' = 'latest-offset',
              'format.type' = 'json'
            )
            """

    es_sink_ddl = f"""
            CREATE TABLE es_sink (
            province VARCHAR,
            pay_amount DOUBLE,
            rowtime TIMESTAMP(3)
            ) with (
              'connector.type' = 'kafka',
              'connector.version' = 'universal',
              'connector.topic' = '{sink_topic}',
              'connector.properties.bootstrap.servers' = '{kafka_servers}',
              'connector.properties.zookeeper.connect' = '{kafka_zookeeper_servers}',
              'connector.properties.group.id' = '{kafka_consumer_group_id}',
              'connector.startup-mode' = 'latest-offset',
              'format.type' = 'json'
            )
    """

    t_env.sql_update(source_ddl)
    t_env.sql_update(es_sink_ddl)

    t_env.register_function('province_id_to_name', province_id_to_name)

    query = """
    select province_id_to_name(provinceId) as province, sum(payAmount) as pay_amount, tumble_start(rt, interval '5' second) as rowtime
    from payment_msg
    group by tumble(rt, interval '5' second), provinceId
    """

    t_env.sql_query(query).insert_into("es_sink")

    t_env.execute("payment_demo")


if __name__ == '__main__':
    log_processing()
EOF

rm -rf lib
mkdir lib
cd lib
wget https://maven.aliyun.com/nexus/content/groups/public/org/apache/flink/flink-sql-connector-kafka_2.11/1.10.1/flink-sql-connector-kafka_2.11-1.10.1.jar
wget https://maven.aliyun.com/nexus/content/groups/public/org/apache/flink/flink-json/1.10.1/flink-json-1.10.1-sql-jar.jar
cd ../
zip -r lib.jar lib/*

参考文档：
https://help.aliyun.com/document_detail/181568.html
https://blog.csdn.net/chenshijie2011/article/details/117399883
https://blog.csdn.net/chenshijie2011/article/details/117401621
https://www.cnblogs.com/maoxiangyi/p/13509782.html
https://www.cnblogs.com/Springmoon-venn/p/13726089.html
https://www.jianshu.com/p/295066a24092
https://blog.csdn.net/m0_37592814/article/details/108044830