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.
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);
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":