Flatten k-object (FlattenKObjectStreamOp)

Java 类名：com.alibaba.alink.operator.stream.recommendation.FlattenKObjectStreamOp

Python 类名：FlattenKObjectStreamOp

功能介绍

将流式推荐结果从json序列化格式转为table格式。

参数说明

名称	中文名称	描述	类型	是否必须？	取值范围	默认值
outputCols	输出结果列列名数组	输出结果列列名数组，必选	String[]	✓
selectedCol	选中的列名	计算列对应的列名	String	✓	所选列类型为 [STRING]
outputColTypes	输出结果列列类型数组	输出结果列类型数组	String[]			null
reservedCols	算法保留列名	算法保留列	String[]			null

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
    [1,'{"rating":"[0.6]","object":"[1]"}'],
    [2,'{"rating":"[0.8,0.6]","object":"[2,3]"}'],
    [3,'{"rating":"[0.6,0.3,0.4]","object":"[1,2,3]"}']
])

data = BatchOperator.fromDataframe(df_data, schemaStr='user bigint, rec string')
sdata = StreamOperator.fromDataframe(df_data, schemaStr='user bigint, rec string')

recList = FlattenKObjectStreamOp()\
			.setSelectedCol("rec")\
			.setOutputCols(["object", "rating"])\
			.setOutputColTypes(["long", "double"])\
			.setReservedCols(["user"])\
			.linkFrom(sdata).print();
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.FlattenKObjectStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class FlattenKObjectStreamOpTest {
	@Test
	public void testFlattenKObjectStreamOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of(1, "{\"rating\":\"[0.6]\",\"object\":\"[1]\"}"),
			Row.of(2, "{\"rating\":\"[0.8,0.6]\",\"object\":\"[2,3]\"}"),
			Row.of(3, "{\"rating\":\"[0.6,0.3,0.4]\",\"object\":\"[1,2,3]\"}")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, rec string");
		StreamOperator <?> sdata = new MemSourceStreamOp(df_data, "user int, rec string");
		StreamOperator <?> recList = new FlattenKObjectStreamOp()
			.setSelectedCol("rec")
			.setOutputCols("object", "rating")
			.setOutputColTypes("long", "double")
			.setReservedCols("user")
			.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user	object	rating
1	1	0.6000
2	2	0.8000
2	3	0.6000
3	1	0.6000
3	2	0.3000
3	3	0.4000