如何将两个 spark 数据集连接到一个具有 java 个对象的数据集?

How to join two spark dataset to one with java objects?

我在 spark 中连接两个数据集时遇到一点问题,我有这个:

SparkConf conf = new SparkConf()
    .setAppName("MyFunnyApp")
    .setMaster("local[*]");

SparkSession spark = SparkSession
    .builder()
    .config(conf)
    .config("spark.debug.maxToStringFields", 150)
    .getOrCreate();
//...
//Do stuff
//...
Encoder<MyOwnObject1> encoderObject1 = Encoders.bean(MyOwnObject1.class);
Encoder<MyOwnObject2> encoderObject2 = Encoders.bean(MyOwnObject2.class);

Dataset<MyOwnObject1> object1DS = spark.read()
    .option("header","true")
    .option("delimiter",";")
    .option("inferSchema","true")
    .csv(pathToFile1)
    .as(encoderObject1);

Dataset<MyOwnObject2> object2DS = spark.read()
    .option("header","true")
    .option("delimiter",";")
    .option("inferSchema","true")
    .csv(pathToFile2)
    .as(encoderObject2);

我可以打印架构并正确显示它。

//Here start the problem
Dataset<Tuple2<MyOwnObject1, MyOwnObject2>> joinObjectDS = 
    object1DS.join(object2DS, object1DS.col("column01")
    .equalTo(object2DS.col("column01")))
    .as(Encoders.tuple(MyOwnObject1,MyOwnObject2));

最后一行无法加入并给我这个错误:

Exception in thread "main" org.apache.spark.sql.AnalysisException: Try to map struct<"LIST WITH ALL VARS FROM TWO OBJECT"> to Tuple2, but failed as the number of fields does not line up.;

没错,因为 Tuple2 (object2) 没有所有变量...

然后我尝试了这个:

 Dataset<Tuple2<MyOwnObject1, MyOwnObject2>> joinObjectDS = object1DS
    .joinWith(object2DS, object1DS
        .col("column01")
        .equalTo(object2DS.col("column01")));

并且工作正常!但是,我需要一个没有元组的新数据集,我有一个 object3,它有一些来自 object1 和 object2 的变量,然后我遇到了这个问题:

Encoder<MyOwnObject3> encoderObject3 = Encoders.bean(MyOwnObject3.class);
Dataset<MyOwnObject3> object3DS = joinObjectDS.map(tupleObject1Object2 -> {
    MyOwnObject1 myOwnObject1 = tupleObject1Object2._1();
    MyOwnObject2 myOwnObject2 = tupleObject1Object2._2();
    MyOwnObject3 myOwnObject3 = new MyOwnObject3(); //Sets all vars with start values
    //...
    //Sets data from object 1 and 2 to 3.
    //...
    return myOwnObject3;
}, encoderObject3);

失败!...这是错误:

17/05/10 12:17:43 ERROR CodeGenerator: failed to compile: org.codehaus.commons.compiler.CompileException: File 'generated.java', Line 593, Column 72: A method named "toString" is not declared in any enclosing class nor any supertype, nor through a static import

超过数千行错误...

我能做什么?我试过:

我想使用数据集,使用数据帧的速度和 JavaRDD 的对象语法...

帮忙?

谢谢

终于找到解决办法了,

当我的代码创建数据集时,选项 inferSchema 出现问题。我有一个字符串列,选项 inferSchema return 我是一个整数列,因为所有值都是 "numeric",但我需要将它们用作字符串(如“0001”、“0002”...)我需要做一个模式,但我有很多变量,然后我用我所有的 类:

写这个
List<StructField> fieldsObject1 = new ArrayList<>();
for (Field field : MyOwnObject1.class.getDeclaredFields()) {
    fieldsObject1.add(DataTypes.createStructField(
        field.getName(),
        CatalystSqlParser.parseDataType(field.getType().getSimpleName()),
        true)
    );
}
StructType schemaObject1 = DataTypes.createStructType(fieldsObject1);

Dataset<MyOwnObject1> object1DS = spark.read()
    .option("header","true")
    .option("delimiter",";")
    .schema(schemaObject1)
    .csv(pathToFile1)
    .as(encoderObject1);

工作正常。

"best" 解决方案是这样的:

  Dataset<MyOwnObject1> object1DS = spark.read()
    .option("header","true")
    .option("delimiter",";")
    .schema(encoderObject1.schema())
    .csv(pathToFile1)
    .as(encoderObject1);

但是 encoderObject1.schema() return 给我一个 Schema,其中的变量按字母顺序排列,而不是按原始顺序排列,然后当我读取 csv 时此选项失败。也许编码器应该 return 具有原始顺序而不是字母顺序的 vars 的模式