采用seatunnel提交Flink和Spark任务

1、seatunnel简单介绍

seatunnel 是一个非常易用，高性能、支持实时流式和离线批处理的海量数据处理产品，架构于Apache Spark 和 Apache Flink之上。

seatunnel 让Spark和Flink的使用更简单，更高效。

注：当前版本用的是2.1.3版本如果在github下载自己编译有问题可在此地址下载编译好的文件seatunnel-2.1.3-bin包

特性

简单易用，灵活配置，无需开发
模块化和插件化，易于扩展
支持利用SQL做数据处理和聚合

集成Spark和Flink官方教程

集成Spark教程

集成Flink教程

2、提交Spark任务

参考官方文档：https://interestinglab.github.io/seatunnel-docs/#/zh-cn/v2/spark/quick-start

案例：实现Hive表导出到ClickHouse

创建测试文件

vim  /usr/local/apache-seatunnel-incubating-2.1.3/config/hive-console.conf

ClickHouse建表

#配置Spark参数
spark {  spark.sql.catalogImplementation = "hive"
  spark.app.name = "hive2clickhouse"
  spark.executor.instances = 30
  spark.executor.cores = 1 
  spark.executor.memory = "2g"
  spark.ui.port = 13000
}


input {
    hive {
        pre_sql = "select id,name,create_time from table"
        table_name = "table_tmp"
    }
}


filter {
    convert {
        source_field = "data_source"
        new_type = "UInt8"
    }

    org.interestinglab.waterdrop.filter.Slice {
        source_table_name = "table_tmp"
        source_field = "id"
        slice_num = 2
        slice_code = 0
        result_table_name = "table_8123"
    }
    org.interestinglab.waterdrop.filter.Slice {
        source_table_name = "table_tmp"
        source_field = "id"
        slice_num = 2
        slice_code = 1
        result_table_name = "table_8124"
    }
}


output {
    clickhouse {
        source_table_name="table_8123"
        host = "ip1:8123"
        database = "db_name"
        username="username"
        password="xxxxx"
        table = "model_score_local"
        fields = ["id","name","create_time"]
            clickhouse.socket_timeout = 50000
            retry_codes = [209, 210]
            retry = 3
            bulk_size = 500000
    }
    clickhouse {
        source_table_name="table_8124"
        host = "ip2:8123"
        database = "db_name"
        username="username"
        password="xxxxx"
        table = "model_score_local"
        fields = ["id","name","create_time"]
            clickhouse.socket_timeout = 50000
            retry_codes = [209, 210]
            retry = 3
            bulk_size = 500000
    }
}

运行seatunnel将数据写入ClickHouse

/bin/start-seatunnel-spark.sh --master local --deploy-mode client --config/hive-console.conf

3、提交Flink任务

参考文档：Document

案例1：Kafka到Kafka的数据同步

业务场景：对test_csv主题中的数据进行过滤，仅保留年龄在18岁以上的记录

env {
  # You can set flink configuration here
  execution.parallelism = 1
  #execution.checkpoint.interval = 10000
  #execution.checkpoint.data-uri = "hdfs://hadoop102:9092/checkpoint"
}

# 在source所属的块中配置数据源
source {
    KafkaTableStream {
        consumer.bootstrap.servers = "node1:9092"
        consumer.group.id = "seatunnel-learn"
        topics = test_csv
        result_table_name = test
        format.type = csv
        schema = "[{\"field\":\"name\",\"type\":\"string\"},{\"field\":\"age\", \"type\": \"int\"}]"
        format.field-delimiter = ";"
        format.allow-comments = "true"
        format.ignore-parse-errors = "true"
    }
}
# 在transform的块中声明转换插件
transform {

  sql {
    sql = "select name,age from test  where age > '"${age}"'"
  }
}
# 在sink块中声明要输出到哪
sink {
   kafkaTable {
    topics = "test_sink"
    producer.bootstrap.servers = "node1:9092"
        }
}

启动Flink任务

bin/start-seatunnel-flink.sh --config config/kafka_kafka.conf -i age=18

案例2：Kafka 输出到Doris进行指标统计

业务场景：使用回话日志统计用户的总观看视频数，用户最常会话市场，用户最小会话时长，用户最后一次会话时间

Doris初始化操作

create database test_db;

CREATE TABLE `example_user_video` (
  `user_id` largeint(40) NOT NULL COMMENT "用户id",
  `city` varchar(20) NOT NULL COMMENT "用户所在城市",
  `age` smallint(6) NULL COMMENT "用户年龄",
  `video_sum` bigint(20) SUM NULL DEFAULT "0" COMMENT "总观看视频数",
  `max_duration_time` int(11) MAX NULL DEFAULT "0" COMMENT "用户最长会话时长",
  `min_duration_time` int(11) MIN NULL DEFAULT "999999999" COMMENT "用户最小会话时长",
  `last_session_date` datetime REPLACE NULL DEFAULT "1970-01-01 00:00:00" COMMENT "用户最后一次会话时间"
) ENGINE=OLAP
AGGREGATE KEY(`user_id`, `city`, `age`)
COMMENT "OLAP"
DISTRIBUTED BY HASH(`user_id`) BUCKETS 16
;

seatunnel任务配置：

env {
	execution.parallelism = 1
}

source {
    KafkaTableStream {
	    consumer.bootstrap.servers = "node1:9092"
	    consumer.group.id = "seatunnel5"
	    topics = test
	    result_table_name = test
	    format.type = json
	    schema = "{\"session_id\":\"string\",\"video_count\":\"int\",\"duration_time\":\"long\",\"user_id\":\"string\",\"user_age\":\"int\",\"city\":\"string\",\"session_start_time\":\"datetime\",\"session_end_time\":\"datetime\"}"
	    format.ignore-parse-errors = "true"
	}
}

transform{
	sql {
		sql = "select user_id,city,user_age as age,video_count as video_sum,duration_time as max_duration_time,duration_time as min_duration_time,session_end_time as last_session_date from test"
		result_table_name = test2
	}
}

sink{
	DorisSink {
		source_table_name = test2
    	fenodes = "node1:8030"
    	database = test_db
    	table = example_user_video
    	user = atguigu
    	password = 123321
    	batch_size = 50
    	doris.column_separator="\t"
    	doris.columns="user_id,city,age,video_sum,max_duration_time,min_duration_time,last_session_date"
	}
}

启动flink任务

bin/start-seatunnel-flink.sh --config config/kafka_doris.conf

采用seatunnel提交Flink和Spark任务

1、seatunnel简单介绍

2、提交Spark任务

3、提交Flink任务

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