How to set up a Spark environment

1. The IDE supports Maven and creates the simplest Maven-quickstart type artifact.

 2. Edit pom.xml to add spark support.

<dependency>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-resources-plugin</artifactId>
    <version>2.4.3</version>
	</dependency>
	<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.10</artifactId>
    <version>1.1.0</version>
	</dependency>

3. Right-click project maven-clean, maven-install. 

4. Add a Spark word segmentation code

package MavenDemo.SparkDemoSrc;

/**
 * Hello world!
 *
 */

/**
4  * User: hadoop
5  * Date: 2014/10/10 0010
6  * Time: 19:26
7  */

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
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.Function2;
import org.apache.spark.api.java.function.PairFunction;
import scala.Tuple2;

import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;

public final class App {
	private static final Pattern SPACE = Pattern.compile(" ");

	public static void main(String[] args) throws Exception {

		if (args.length < 1) {
			System.err.println("Usage: JavaWordCount <file>");
			System.exit(1);
		}

		SparkConf sparkConf = new SparkConf (). SetAppName ("JavaWordCount");
		JavaSparkContext ctx = new JavaSparkContext(sparkConf);
		JavaRDD<String> lines = ctx.textFile(args[0], 1);

		JavaRDD<String> words = lines
				.flatMap(new FlatMapFunction<String, String>() {

					public Iterable<String> call(String s) {
						return Arrays.asList(SPACE.split(s));
					}
				});

		JavaPairRDD<String, Integer> ones = words
				.mapToPair(new PairFunction<String, String, Integer>() {

					public Tuple2<String, Integer> call(String s) {
						return new Tuple2<String, Integer>(s, 1);
					}
				});

		JavaPairRDD<String, Integer> counts = ones
				.reduceByKey(new Function2<Integer, Integer, Integer>() {

					public Integer call(Integer i1, Integer i2) {
						return i1 + i2;
					}
				});

		List<Tuple2<String, Integer>> output = counts.collect();
		for (Tuple2<?, ?> tuple : output) {
			System.out.println(tuple._1() + ": " + tuple._2());
		}
		ctx.stop();
	}
}

 4. Run main in local mode

 5.

Download spark-1.6.0-bin-hadoop2.6 and configure SPARK_HOME.

 

6. Note that this configuration is specifically for Windows.

Download the hadoop toolkit under windows (divided into 32-bit and 64-bit), and create a new hadoop directory locally. There must be a bin directory, for example: D:\spark\hadoop-2.6.0\bin

Then put winutil and other files in the bin directory

Address: https://github.com/sdravida/hadoop2.6_Win_x64/tree/master/bin

Placement HADOOP_HOME

 

7. Run main access, you can see the result of word segmentation