PyFlink-Scala UDF-如何在Table API中转换Scala Map？

问题描述

我正在尝试将我的Scala UDF（Map[String,String]）的scala.collection.immutable.map对象输出映射到Table API中的某些有效数据类型，即通过java类型（java.util.Map）映射为在这里推荐：Flink Table API & SQL and map types (Scala)。但是我得到下面的错误。

关于正确的前进方式的任何想法吗？如果是，是否有办法将转换概括为Map[String,Any]类型的（嵌套）Scala对象？

代码

Scala UDF

class dummyMap() extends ScalarFunction {
  def eval() = {
    val whatevermap = Map("key1" -> "val1","key2" -> "val2")
    whatevermap.asInstanceOf[java.util.Map[java.lang.String,java.lang.String]]
  }
}

水槽

my_sink_ddl = f"""
    create table mySink (
        output_of_dummyMap_udf MAP<STRING,STRING>
    ) with (
        ...
    )
"""

错误

Py4JJavaError: An error occurred while calling o430.execute.
: org.apache.flink.table.api.ValidationException: Field types of query result and registered TableSink `default_catalog`.`default_database`.`mySink` do not match.
Query result schema: [output_of_my_scala_udf: GenericType<java.util.Map>]
TableSink schema:    [output_of_my_scala_udf: Map<String,String>]

谢谢！

解决方法

Wei Zhong的原始答案。我只是记者谢谢魏！

此时（Flink 1.11），两种方法都可以使用：

当前：UDF定义中的DataTypeHint +用于UDF注册的SQL
已过时：在UDF定义中覆盖getResultType +用于UDF注册的t_env.register_java_function

代码

Scala UDF

package com.dummy

import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.table.annotation.DataTypeHint
import org.apache.flink.table.api.Types
import org.apache.flink.table.functions.ScalarFunction
import org.apache.flink.types.Row

class dummyMap extends ScalarFunction {

  // If the udf would be registered by the SQL statement,you need add this typehint
  @DataTypeHint("ROW<s STRING,t STRING>")
  def eval(): Row = {

    Row.of(java.lang.String.valueOf("foo"),java.lang.String.valueOf("bar"))

  }

  // If the udf would be registered by the method 'register_java_function',you need override this
  // method.
  override def getResultType(signature: Array[Class[_]]): TypeInformation[_] = {
    // The type of the return values should be TypeInformation
    Types.ROW(Array("s","t"),Array[TypeInformation[_]](Types.STRING(),Types.STRING()))
  }
}

Python代码

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

s_env = StreamExecutionEnvironment.get_execution_environment()
st_env = StreamTableEnvironment.create(s_env)

# load the scala udf jar file,the path should be modified to yours
# or your can also load the jar file via other approaches
st_env.get_config().get_configuration().set_string("pipeline.jars","file:///Users/zhongwei/the-dummy-udf.jar")

# register the udf via 
st_env.execute_sql("CREATE FUNCTION dummyMap AS 'com.dummy.dummyMap' LANGUAGE SCALA")
# or register via the method
# st_env.register_java_function("dummyMap","com.dummy.dummyMap")

# prepare source and sink
t = st_env.from_elements([(1,'hi','hello'),(2,'hello')],['a','b','c'])
st_env.execute_sql("""create table mySink (
        output_of_my_scala_udf ROW<s STRING,t STRING>
    ) with (
        'connector' = 'print'
    )""")

# execute query
t.select("dummyMap()").execute_insert("mySink").get_job_client().get_job_execution_result().result()

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