Tocken.java

package com.topsec.ti.patronus;
import java.util.Arrays;
import java.util.List;

import org.apache.spark.sql.SparkSession;
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;

/**
 * Created by hhy on 2017/09/06.
 */
public class Tocken {
    public static void main(String[] args){
        SparkSession spark= SparkSession.builder().master("local").appName("").getOrCreate();
        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.show();
//        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);

    }
}