need DataFrameChannel?

need DataFrameChannel?

https://github.com/apache/incubator-wayang/blob/897797899866f373f93e5672b36d5e34611faece/wayang-platforms/wayang-spark/code/main/java/org/apache/wayang/spark/operators/ml/SparkKMeansOperator.java#L55

```java

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package org.apache.wayang.spark.operators.ml;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.ml.clustering.KMeans;
import org.apache.spark.ml.clustering.KMeansModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.wayang.basic.data.Tuple2;
import org.apache.wayang.basic.operators.KMeansOperator;
import org.apache.wayang.core.optimizer.OptimizationContext;
import org.apache.wayang.core.plan.wayangplan.ExecutionOperator;
import org.apache.wayang.core.platform.ChannelDescriptor;
import org.apache.wayang.core.platform.ChannelInstance;
import org.apache.wayang.core.platform.lineage.ExecutionLineageNode;
import org.apache.wayang.core.util.Tuple;
import org.apache.wayang.spark.channels.RddChannel;
import org.apache.wayang.spark.execution.SparkExecutor;
import org.apache.wayang.spark.operators.SparkExecutionOperator;

import java.util.*;

public class SparkKMeansOperator extends KMeansOperator implements SparkExecutionOperator {

public SparkKMeansOperator(int k) {
super(k);
}

public SparkKMeansOperator(KMeansOperator that) {
super(that);
}

@Override
public List getSupportedInputChannels(int index) {
// TODO need DataFrameChannel?
return Arrays.asList(RddChannel.UNCACHED_DESCRIPTOR, RddChannel.CACHED_DESCRIPTOR);
}

@Override
public List getSupportedOutputChannels(int index) {
// TODO need DataFrameChannel?
return Collections.singletonList(RddChannel.UNCACHED_DESCRIPTOR);
}

@Override
public Tuple, Collection> evaluate(
ChannelInstance[] inputs,
ChannelInstance[] outputs,
SparkExecutor sparkExecutor,
OptimizationContext.OperatorContext operatorContext) {
assert inputs.length == this.getNumInputs();
assert outputs.length == this.getNumInputs();

final RddChannel.Instance input = (RddChannel.Instance) inputs[0];
final RddChannel.Instance output = (RddChannel.Instance) outputs[0];

final JavaRDD inputRdd = input.provideRdd();
final JavaRDD dataRdd = inputRdd.map(Data::new);
final Dataset df = SparkSession.builder().getOrCreate().createDataFrame(dataRdd, Data.class);
final KMeansModel model = new KMeans()
.setK(this.k)
.fit(df);

final Dataset transform = model.transform(df);
final JavaRDD> outputRdd = transform.toJavaRDD()
.map(row -> new Tuple2<>(((Vector) row.get(0)).toArray(), (Integer) row.get(1)));

this.name(outputRdd);
output.accept(outputRdd, sparkExecutor);

return ExecutionOperator.modelLazyExecution(inputs, outputs, operatorContext);
}

// TODO support fit and transform

@Override
public boolean containsAction() {
return false;
}

public static class Data {
private final Vector features;

public Data(Vector features) {
this.features = features;
}

public Data(double[] features) {
this.features = Vectors.dense(features);
}

public Vector getFeatures() {
return features;
}

@Override
public String toString() {
return "Data{" +
"features=" + features +
'}';
}

@Override
public boolean equals(Object o) {
if (this == o) return true;
if (!(o instanceof Data)) return false;
Data data = (Data) o;
return Objects.equals(features, data.features);
}

@Override
public int hashCode() {
return Objects.hash(features);
}
}
}

```

bf0ae8055e229f8ccd3f6550b68ee2be4bde3acc

Start with wayang-platforms/wayang-spark/code/main/java/org/apache/wayang/spark/operators/ml/SparkKMeansOperator.java, especially getSupportedInputChannels and getSupportedOutputChannels around the DataFrameChannel TODOs. Read the existing RddChannel usage and determine whether DataFrameChannel support is required for this operator. Done means the channel choice and operator behavior are consistent, with the open question resolved.

Written by the indexing model from the issue text.