1）java(App.java)

package com.ejiajie.bi.hello;

import org.apache.spark.api.java.JavaSparkContext;

import org.apache.spark.api.java.JavaRDD;

import org.apache.spark.SparkConf;

/**

 * Hello world!

 *

 */

public class App

{

    public static void main( String[] args )

    {

	SparkConf conf = new SparkConf();

	JavaSparkContext sc = new JavaSparkContext(conf);

        System.out.println( "Hello World!" );

    }

}

2）python(PyHelloWorld.py)

from pyspark import SparkContext, SparkConf  

conf = SparkConf()

sc = SparkContext(conf=conf)

import numpy as np

import scipy.sparse as sps

from pyspark.mllib.linalg import Vectors

# Use a NumPy array as a dense vector.

dv1 = np.array([1.0, 0.0, 3.0])

# Use a Python list as a dense vector.

dv2 = [1.0, 0.0, 3.0]

# Create a SparseVector.

sv1 = Vectors.sparse(3, [0, 2], [1.0, 3.0])

# Use a single-column SciPy csc_matrix as a sparse vector.

sv2 = sps.csc_matrix((np.array([1.0, 3.0]), np.array([0, 2]), np.array([0, 2])), shape = (3, 1))

from pyspark.mllib.linalg import SparseVector

from pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.

pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.

neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))

3）执行命令

java

# spark-submit --class com.ejiajie.bi.hello.App --master yarn --deploy-mode client ./hello-1.0-SNAPSHOT-jar-with-dependencies.jar

# spark-submit --class com.ejiajie.bi.hello.App --master yarn --deploy-mode cluster ./hello-1.0-SNAPSHOT-jar-with-dependencies.jar

python

# spark-submit  --master yarn --deploy-mode client /home/lihanhui/work/spark-example/PyHelloWorld.py

# spark-submit  --master yarn --deploy-mode cluster /home/lihanhui/work/spark-example/PyHelloWorld.py

4）hadoop查看任务状态