Yes, it's necessary to cast the lambda in Java as (MapFunction<X,Y>)
in many cases. This is because the Scala-specific and Java-specific
versions of .map() both end up accepting a function object that the
lambda can match, and an Encoder. What I'd have to go back and look up
is why that would be different in Spark 3; some of that has always
been the case with Java 8 in Spark 2. I think it might be related to
Scala 2.12; were you using Spark 2 with Scala 2.11 before?
On Sat, Dec 28, 2019 at 11:38 AM Jean-Georges Perrin <j...@jgp.net> wrote:
>
> Hey guys,
>
> This code:
>
> Dataset<Row> incrementalDf = spark
> .createDataset(l, Encoders.INT())
> .toDF();
> Dataset<Integer> dotsDs = incrementalDf
> .map(status -> {
> double x = Math.random() * 2 - 1;
> double y = Math.random() * 2 - 1;
> counter++;
> if (counter % 100000 == 0) {
> System.out.println("" + counter + " darts thrown so far");
> }
> return (x * x + y * y <= 1) ? 1 : 0;
> }, Encoders.INT());
>
> used to work with Spark 2.x, in the two previous, it says:
>
> The method map(Function1<Row,Integer>, Encoder<Integer>) is ambiguous for the
> type Dataset<Row>
>
> IfI define my mapping function as a class it works fine. Here is the class:
>
> private final class DartMapper
> implements MapFunction<Row, Integer> {
> private static final long serialVersionUID = 38446L;
>
> @Override
> public Integer call(Row r) throws Exception {
> double x = Math.random() * 2 - 1;
> double y = Math.random() * 2 - 1;
> counter++;
> if (counter % 1000 == 0) {
> System.out.println("" + counter + " operations done so far");
> }
> return (x * x + y * y <= 1) ? 1 : 0;
> }
> }
>
> Any hint on what/if I did wrong?
>
> jg
>
>
>
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