blink sql-client 提交flink任务

前置任务：
改造原有的sql-client，使之能够读取文件，提交里面的任务到flink集群。
启动blink集群参考 https://www.jianshu.com/p/4f59e512b178
目标：使用sql-client提交任务，读取kafka消息，自定义udtf解析，存入csv文件。

1.sql文件

create table kafka_stream(
  messageKey varbinary, 
  `message` varbinary, 
  topic varchar,
  `partition` int,
  `offset` bigint
) with (
  type ='kafka011',
  topic = 't102',
`group.id`='t1',
bootstrap.servers = 'localhost:9092'
);

create table csv_sink(
id varchar,
name varchar,
age varchar
) with (
type ='csv',
path = '/Users/IdeaProjects/github/apache-flink/build-target/bin/test4.csv'
);
insert into csv_sink
SELECT
    T.id,
    T.user_name,
    T.age
from
    kafka_stream as S LEFT JOIN
    LATERAL TABLE (parseDataMessage(message)) as T (
        id,
        user_name,
        age
    ) on true;

2.udtf 解析kafka 消息

kafka里消息格式是

{"attributes":{"schemaName":"dbtest","tableName":"result1"},"fieldCount":3,"fields":[{"index":0,"name":"id","null":false,"primaryKey":true,"type":"INTEGER","value":"90995"},{"index":1,"name":"user_name","null":false,"primaryKey":false,"type":"VARCHAR","value":"a"},{"index":2,"name":"age","null":false,"primaryKey":false,"type":"INTEGER","value":"90995"}],"timestamp":1550733014456}

udtf

public class ParseDataMessageUDTF extends TableFunction<Row> {
   public static final String __TIMESTAMP = "__timestamp";
   public static final String __EVENT_TYPE = "__event_type";
   public static final String __ATTRIBUTES = "__attributes";

   private List<String> fieldName = Lists.newArrayList();

   public ParseDataMessageUDTF(String args) {
       fieldName = Arrays.asList(args.split(","));
   }


   public void eval(byte[] message) {
       String mess = new String(message, Charset.forName("UTF-8"));
       DataMessage dataMessage = JSON.parseObject(mess, DataMessage.class);
       Row row = new Row(fieldName.size());
       Map<String, List<Field>> map = dataMessage.getFields().stream().collect(Collectors.groupingBy(Field::getName));
       for (int i = 0; i < fieldName.size(); i++) {
           switch (fieldName.get(i)) {
               case __TIMESTAMP:
                   row.setField(i, dataMessage.getTimestamp());
                   break;
               case __EVENT_TYPE:
                   row.setField(i, dataMessage.getEventType().toString());
                   break;
               case __ATTRIBUTES:
                   row.setField(i, dataMessage.getAttributes());
                   break;
               default:
                   List<Field> flist = map.get(fieldName.get(i));
                   if (flist != null && !flist.isEmpty()) {
                       row.setField(i, flist.get(0).getValue());
                   } else {
                       row.setField(i, null);
                   }
           }

       }
       collect(row);
   }

   @Override
   // 如果返回值是Row，就必须重载实现这个方法，显式地告诉系统返回的字段类型
   public DataType getResultType(Object[] arguments, Class[] argTypes) {
       TypeInformation[] typeInformations = new TypeInformation[fieldName.size()];

       for (int i = 0; i < fieldName.size(); i++) {
           switch (fieldName.get(i)) {
               case __TIMESTAMP:
                   typeInformations[i] = BasicTypeInfo.of(Long.class);
                   break;
               case __EVENT_TYPE:
                   typeInformations[i] = TypeInformation.of(String.class);
                   break;
               case __ATTRIBUTES:
                   typeInformations[i] = new MapTypeInfo<>(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO);
                  break;

               default:
                   typeInformations[i] = BasicTypeInfo.of(String.class);
           }
       }

       RowTypeInfo rowType = new RowTypeInfo(typeInformations);
       return new TypeInfoWrappedDataType(rowType);
   }
}

3.打shade包 参考 https://www.jianshu.com/p/4f481fd8c0cb

4.注册udtf funtion

拷贝sql-client-defaults.yaml 为sql-client-kafka.yaml

functions: # empty list
- name: parseDataMessage
  from: class
  class: io.bigdata.blink.udf.ParseDataMessageUDTF
  constructor: 
    - "id,user_name,age"

4.提交任务

./sql-client.sh embedded --sqlPath /Users/IdeaProjects/github/apache-flink/build-target/bin/kafka.sql -e sql-client-kafka.yaml -j ../lib/udtf-1.0-SNAPSHOT.jar

写入数据到kafka

5.运行结果

image.png image.png