文件列表:

AuraSparkTraining.iml (19925, 2018-10-15)
bin (0, 2018-10-15)
bin\run-pi-local.sh (372, 2018-10-15)
bin\run-pi-yarn.sh (613, 2018-10-15)
bin\run.sh (147, 2018-10-15)
conf (0, 2018-10-15)
conf\hadoop (0, 2018-10-15)
conf\hadoop\core-site.xml (335, 2018-10-15)
conf\hadoop\hadoop-env.sh (4230, 2018-10-15)
conf\hadoop\hdfs-site.xml (545, 2018-10-15)
conf\hadoop\mapred-site.xml (433, 2018-10-15)
conf\hadoop\slaves (42, 2018-10-15)
conf\hadoop\yarn-site.xml (1009, 2018-10-15)
conf\spark (0, 2018-10-15)
conf\spark\spark-defaults.conf (301, 2018-10-15)
conf\spark\spark-env.sh (74, 2018-10-15)
data (0, 2018-10-15)
data\access.log (13888, 2018-10-15)
data\basketball (0, 2018-10-15)
data\basketball\leagues_NBA_1970_per_game_per_game.csv (18863, 2018-10-15)
data\basketball\leagues_NBA_1971_per_game_per_game.csv (23859, 2018-10-15)
data\basketball\leagues_NBA_1972_per_game_per_game.csv (23809, 2018-10-15)
data\basketball\leagues_NBA_1973_per_game_per_game.csv (23655, 2018-10-15)
data\basketball\leagues_NBA_1974_per_game_per_game.csv (27138, 2018-10-15)
data\basketball\leagues_NBA_1975_per_game_per_game.csv (28657, 2018-10-15)
data\basketball\leagues_NBA_1976_per_game_per_game.csv (29040, 2018-10-15)
data\basketball\leagues_NBA_1977_per_game_per_game.csv (36003, 2018-10-15)
data\basketball\leagues_NBA_1978_per_game_per_game.csv (35722, 2018-10-15)
data\basketball\leagues_NBA_1979_per_game_per_game.csv (35055, 2018-10-15)
data\basketball\leagues_NBA_1980_per_game_per_game.csv (38681, 2018-10-15)
data\basketball\leagues_NBA_1981_per_game_per_game.csv (40957, 2018-10-15)
data\basketball\leagues_NBA_1982_per_game_per_game.csv (42993, 2018-10-15)
data\basketball\leagues_NBA_1983_per_game_per_game.csv (43051, 2018-10-15)
data\basketball\leagues_NBA_1984_per_game_per_game.csv (42232, 2018-10-15)
data\basketball\leagues_NBA_1985_per_game_per_game.csv (43594, 2018-10-15)
data\basketball\leagues_NBA_1986_per_game_per_game.csv (44332, 2018-10-15)
data\basketball\leagues_NBA_1987_per_game_per_game.csv (45693, 2018-10-15)
... ...

## sparktraining Examples for Spark Training in aura.cn ## 本地运行Spark方法 - 下载spark安装包 - 解压spark安装包 - 进入spark解压目录下,运行: ```bash $ bin/spark-shell ``` - 在命令行提示符下拷贝以下代码并查看执行结果 ```scala import scala.math.random val tasks = 10 val n = tasks * 100000 val count = sc.parallelize(1 until n, tasks).map { i => val x = random * 2 - 1 val y = random * 2 - 1 if (x*x + y*y <= 1) 1 else 0 }.reduce(_ + _) println("Pi is roughly " + 4.0 * count / n ) ``` ## 分布式运行Spark方法 ### 搭建hadoop集群 Hadoop YARN/HDFS配置文件参考:conf/hadoop目录 ### 配置Spark客户端,并启动spark history server - Spark客户端配置文件参考:conf/spark目录 - 启动spark history server: sbin/start-history-server.sh ### 将spark-shell运行在yarn client或cluster模式 - yarn client模式:bin/spark-shell --master yarn --deploy-mode client - yarn cluster:bin/spark-shell --master yarn --deploy-mode cluster

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