Github user MLnick commented on a diff in the pull request:

    https://github.com/apache/spark/pull/20446#discussion_r165362364
  
    --- Diff: 
examples/src/main/java/org/apache/spark/examples/ml/JavaSummarizerExample.java 
---
    @@ -0,0 +1,71 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.examples.ml;
    +
    +import org.apache.spark.sql.*;
    +
    +// $example on$
    +import java.util.Arrays;
    +import java.util.List;
    +
    +import org.apache.spark.ml.linalg.Vector;
    +import org.apache.spark.ml.linalg.Vectors;
    +import org.apache.spark.ml.linalg.VectorUDT;
    +import org.apache.spark.ml.stat.Summarizer;
    +import org.apache.spark.sql.types.DataTypes;
    +import org.apache.spark.sql.types.Metadata;
    +import org.apache.spark.sql.types.StructField;
    +import org.apache.spark.sql.types.StructType;
    +// $example off$
    +
    +public class JavaSummarizerExample {
    +  public static void main(String[] args) {
    +    SparkSession spark = SparkSession
    +      .builder()
    +      .appName("JavaSummarizerExample")
    +      .getOrCreate();
    +
    +    // $example on$
    +    List<Row> data = Arrays.asList(
    +      RowFactory.create(Vectors.dense(2.0, 3.0, 5.0), 1.0),
    +      RowFactory.create(Vectors.dense(4.0, 6.0, 7.0), 2.0)
    +    );
    +
    +    StructType schema = new StructType(new StructField[]{
    +      new StructField("features", new VectorUDT(), false, 
Metadata.empty()),
    +      new StructField("weight", DataTypes.DoubleType, false, 
Metadata.empty())
    +    });
    +
    +    Dataset<Row> df = spark.createDataFrame(data, schema);
    +
    +    Row result1 = df.select(Summarizer.metrics("mean", "variance")
    +        .summary(new Column("features"), new Column("weight")))
    +        .first().getStruct(0);
    +    System.out.println("with weight: mean = " + 
result1.<Vector>getAs(0).toString() +
    --- End diff --
    
    Why not just `df.select(...).show()`?


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