用于行类型Spark数据集的编码器 [英] Encoder for Row Type Spark Datasets

问题描述

我想为行类型。基本上，我不明白如何编写编码器。

I would like to write an encoder for a Row type in DataSet, for a map operation that I am doing. Essentially, I do not understand how to write encoders.

下面是一个地图操作示例：

Below is an example of a map operation:

在下面的示例中，我不想返回数据集< String>，而是返回数据集< Row>

Dataset<String> output = dataset1.flatMap(new FlatMapFunction<Row, String>() {
            @Override
            public Iterator<String> call(Row row) throws Exception {

                ArrayList<String> obj = //some map operation
                return obj.iterator();
            }
        },Encoders.STRING());

据我所知，编码器需要编写如下代码：

I understand that instead of a string Encoder needs to be written as follows:

    Encoder<Row> encoder = new Encoder<Row>() {
        @Override
        public StructType schema() {
            return join.schema();
            //return null;
        }

        @Override
        public ClassTag<Row> clsTag() {
            return null;
        }
    };

但是，我不理解编码器中的clsTag（），我试图找到一个运行示例，可以演示类似的东西（即行类型的编码器）

However, I do not understand the clsTag() in the encoder, and I am trying to find a running example which can demostrate something similar (i.e. an encoder for a row type)

编辑 - 这不是所提问题的副本：尝试将数据帧行映射到更新行时出现编码器错误回答有关在Spark 2.x中使用Spark 1.x的讨论（我没有这样做），我也在寻找Row类的编码器，而不是解决错误。最后，我一直在寻找Java解决方案，而不是Scala。

Edit - This is not a copy of the question mentioned : Encoder error while trying to map dataframe row to updated row as the answer talks about using Spark 1.x in Spark 2.x (I am not doing so), also I am looking for an encoder for a Row class rather than resolve an error. Finally, I was looking for a solution in Java, not in Scala.

推荐答案

答案是使用 RowEncoder 和使用 StructType 。

以下是使用数据集的flatmap操作的一个工作示例：

Below is a working example of a flatmap operation with Datasets:

    StructType structType = new StructType();
    structType = structType.add("id1", DataTypes.LongType, false);
    structType = structType.add("id2", DataTypes.LongType, false);

    ExpressionEncoder<Row> encoder = RowEncoder.apply(structType);

    Dataset<Row> output = join.flatMap(new FlatMapFunction<Row, Row>() {
        @Override
        public Iterator<Row> call(Row row) throws Exception {
            // a static map operation to demonstrate
            List<Object> data = new ArrayList<>();
            data.add(1l);
            data.add(2l);
            ArrayList<Row> list = new ArrayList<>();
            list.add(RowFactory.create(data.toArray()));
            return list.iterator();
        }
    }, encoder);

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