local单机模式：

结果xshell可见：

./bin/spark-submit –class org.apache.spark.examples.SparkPi –master local[1] ./lib/spark-examples-1.3.1-hadoop2.4.0.jar 100

Standalone集群模式：

需要的配置项

(1)slaves文件

(2)spark-env.sh

export JAVA_HOME=/usr/soft/jdk1.7.0_71

export SPARK_MASTER_IP=spark001

export SPARK_MASTER_PORT=7077

export SPARK_WORKER_CORES=1

export SPARK_WORKER_INSTANCES=1

export SPARK_WORKER_MEMORY=1g

standalone集群模式：

之client模式：

结果xshell可见：

./bin/spark-submit –class org.apache.spark.examples.SparkPi –master spark://spark001:7077 –executor-memory 1G –total-executor-cores 1 ./lib/spark-examples-1.3.1-hadoop2.4.0.jar 100

standalone集群模式：

之cluster模式：

结果spark001:8080里面可见！

./bin/spark-submit –class org.apache.spark.examples.SparkPi –master spark://spark001:7077 –deploy-mode cluster –supervise –executor-memory 1G –total-executor-cores 1 ./lib/spark-examples-1.3.1-hadoop2.4.0.jar 100

Yarn集群模式：

需要的配置项

(1)spark-env.sh

export HADOOP_CONF_DIR=$HADOOP_INSTALL/etc/hadoop

export YARN_CONF_DIR=$HADOOP_INSTALL/etc/hadoop

export SPARK_HOME=/usr/hadoopsoft/spark-1.3.1-bin-hadoop2.4

export SPARK_JAR=/usr/hadoopsoft/spark-1.3.1-bin-hadoop2.4/lib/spark-assembly-1.3.1-hadoop2.4.0.jar

export PATH=$SPARK_HOME/bin:$PATH

(2)~/.bash_profile

配置好hadoop环境变量

Yarn集群模式：

之client模式：

结果xshell可见：

./bin/spark-submit –class org.apache.spark.examples.SparkPi –master yarn-client –executor-memory 1G –num-executors 1 ./lib/spark-examples-1.3.1-hadoop2.4.0.jar 100

Yarn集群模式：

之cluster模式：

结果spark001:8088里面可见！

./bin/spark-submit –class org.apache.spark.examples.SparkPi –master yarn-cluster –executor-memory 1G –num-executors 1 ./lib/spark-examples-1.3.1-hadoop2.4.0.jar 100