Spark算子（六）

Point 1：FlatMapOperator

package com.spark.operator;

import java.util.Arrays;
import java.util.List;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.VoidFunction;

public class FlatMapOperator {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("LineCount").setMaster(
                "local");
        JavaSparkContext sc = new JavaSparkContext(conf);

        List<String> lineList = Arrays.asList("hello xuruyun" , "hello xuruyun", "hello wangfei");
        JavaRDD<String> lines = sc.parallelize(lineList);

        // flatMap = flat + map
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

            private static final long serialVersionUID = 1L;

            @Override
            public Iterable<String> call(String line) throws Exception {
                return Arrays.asList(line.split(" "));
            }
        });

        words.foreach(new VoidFunction<String>() {

            private static final long serialVersionUID = 1L;

            @Override
            public void call(String result) throws Exception {
                System.out.println(result);
            }
        });

        sc.close();
    }
}

Point 2：FilterOperator

package com.spark.operator;

import java.util.Arrays;
import java.util.List;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.VoidFunction;

public class FilterOperator {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("LineCount").setMaster(
                "local");
        JavaSparkContext sc = new JavaSparkContext(conf);

        // filter算子是过滤，里面的逻辑如果返回的是true就保留下来，false就过滤掉
        List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5);
        JavaRDD<Integer> numberRDD = sc.parallelize(numbers);

        JavaRDD<Integer> results = numberRDD
                .filter(new Function<Integer, Boolean>() {

                    private static final long serialVersionUID = 1L;

                    @Override
                    public Boolean call(Integer number) throws Exception {
                        return number % 2 == 0;
                    }
                });

        results.foreach(new VoidFunction<Integer>() {

            private static final long serialVersionUID = 1L;

            @Override
            public void call(Integer result) throws Exception {
                System.out.println(result);
            }
        });

        sc.close();
    }
}

Point 3：DinstinctOperator

package com.spark.operator;

import java.util.Arrays;
import java.util.List;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.VoidFunction;

public class DinstinctOperator {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("SampleOperator")
                .setMaster("local");
        JavaSparkContext sc = new JavaSparkContext(conf);

        List<String> names = Arrays
                .asList("xuruyun", "liangyongqi", "wangfei","xuruyun");
        JavaRDD<String> nameRDD = sc.parallelize(names,2);

        nameRDD.distinct().foreach(new VoidFunction<String>() {

            private static final long serialVersionUID = 1L;

            @Override
            public void call(String name) throws Exception {
                System.out.println(name);
            }
        });

        sc.close();
    }
}