Encoder for Row Type Spark Datasets

Question

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.

Below is an example of a map operation:

In the example below, instead of returning Dataset<String>, I would like to return Dataset<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());

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;
        }
    };

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)

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.

Answer 1

The answer is to use a RowEncoder and the schema of the dataset using StructType.

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);

Answer 2

I had the same problem... Encoders.kryo(Row.class)) worked for me.

As a bonus, the Apache Spark tuning docs refer to Kryo it since it’s faster at serialization "often as much as 10x":

https://spark.apache.org/docs/latest/tuning.html