content: /*
   * 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.SparkSession;
  
  // $example on$
  import java.util.Arrays;
  import java.util.List;
  
  import scala.collection.mutable.WrappedArray;
  
  import org.apache.spark.ml.feature.RegexTokenizer;
  import org.apache.spark.ml.feature.Tokenizer;
  import org.apache.spark.sql.api.java.UDF1;
  import org.apache.spark.sql.Dataset;
  import org.apache.spark.sql.Row;
  import org.apache.spark.sql.RowFactory;
  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;
  // $example off$
  
  // $example on:untyped_ops$
  // col("...") is preferable to df.col("...")
  import static org.apache.spark.sql.functions.callUDF;
  import static org.apache.spark.sql.functions.col;
  // $example off:untyped_ops$
  
  public class JavaTokenizerExample {
    public static void main(String[] args) {
      SparkSession spark = SparkSession
        .builder()
        .appName("JavaTokenizerExample")
        .getOrCreate();
  
      // $example on$
      List<Row> data = Arrays.asList(
        RowFactory.create(0, "Hi I heard about Spark"),
        RowFactory.create(1, "I wish Java could use case classes"),
        RowFactory.create(2, "Logistic,regression,models,are,neat")
      );
  
      StructType schema = new StructType(new StructField[]{
        new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
        new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
      });
  
      Dataset<Row> sentenceDataFrame = spark.createDataFrame(data, schema);
  
      Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
  
      RegexTokenizer regexTokenizer = new RegexTokenizer()
          .setInputCol("sentence")
          .setOutputCol("words")
          .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);
  
      spark.udf().register("countTokens", new UDF1<WrappedArray, Integer>() {
        @Override
        public Integer call(WrappedArray words) {
          return words.size();
        }
      }, DataTypes.IntegerType);
  
      Dataset<Row> tokenized = tokenizer.transform(sentenceDataFrame);
      tokenized.select("sentence", "words")
          .withColumn("tokens", callUDF("countTokens", col("words"))).show(false);
  
      Dataset<Row> regexTokenized = regexTokenizer.transform(sentenceDataFrame);
      regexTokenized.select("sentence", "words")
          .withColumn("tokens", callUDF("countTokens", col("words"))).show(false);
      // $example off$
  
      spark.stop();
    }
  }
  