Spark on YARN模式的安装(spark-1.6.1-bin-hadoop2.6.tgz + hadoop-2.6.0.tar.gz)(master、slave1和slave2)(博主推荐)

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Spark on YARN模式的安装(spark-1.6.1-bin-hadoop2.6.tgz + hadoop-2.6.0.tar.gz)(master、slave1和slave2)(博主推荐)

技术小哥哥 2017-11-13 16:07:00 浏览1545
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开篇要明白

  (1)spark-env.sh 是环境变量配置文件

  (2)spark-defaults.conf

  (3)slaves 是从节点机器配置文件

  (4)metrics.properties 是 监控

  (5)log4j.properties 是配置日志

  (5)fairscheduler.xml是公平调度

  (6)docker.properties 是 docker

  (7)我这里的Spark on YARN模式的安装,是master、slave1和slave2。

  (8)Spark on YARN模式的安装,其实,是必须要安装hadoop的。

  (9)为了管理,安装zookeeper,(即管理master、slave1和slave2)

 

 

 

 

 

首先,说下我这篇博客的Spark on YARN模式的安装情况

 

 

 

 

 

 

 

我的安装分区如下,3台都一样。

 

 

 

 

 

 

 

关于如何关闭防火墙

  我这里不多说,请移步

hadoop 50070 无法访问问题解决汇总

 

 

 

 

 

 

关于如何配置静态ip和联网

  我这里不多说,我的是如下,请移步

CentOS 6.5静态IP的设置(NAT和桥接联网方式都适用)

 

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DEVICE=eth0
HWADDR=00:0C:29:A9:45:18
TYPE=Ethernet
UUID=50fc177a-f282-4c83-bfbc-cb0f00b92507
ONBOOT=yes
NM_CONTROLLED=yes
BOOTPROTO=static

DEFROUTE=yes
PEERDNS=yes
PEERROUTES=yes
IPV4_FAILURE_FATAL=yes
IPV6INIT=no
NAME="System eth0"

IPADDR=192.168.80.10
BCAST=192.168.80.255
GATEWAY=192.168.80.2
NETMASK=255.255.255.0

DNS1=192.168.80.2
DNS2=8.8.8.8
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DEVICE=eth0
HWADDR=00:0C:29:18:ED:4A
TYPE=Ethernet
UUID=b5d059e4-3b92-41ef-889b-68f2f5684fac
ONBOOT=yes
NM_CONTROLLED=yes
BOOTPROTO=static

DEFROUTE=yes
PEERDNS=yes
PEERROUTES=yes
IPV4_FAILURE_FATAL=yes
IPV6INIT=no
NAME="System eth0"
IPADDR=192.168.80.11
BCAST=192.168.80.255
GATEWAY=192.168.80.2
NETMASK=255.255.255.0

DNS1=192.168.80.2
DNS2=8.8.8.8
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DEVICE=eth0
HWADDR=00:0C:29:8B:DE:B0
TYPE=Ethernet
UUID=1ba7be29-2c80-4875-8c11-1ed2a47c0a67
ONBOOT=yes
NM_CONTROLLED=yes
BOOTPROTO=static

DEFROUTE=yes
PEERDNS=yes
PEERROUTES=yes
IPV4_FAILURE_FATAL=yes
IPV6INIT=no
NAME="System eth0"
IPADDR=192.168.80.12
BCAST=192.168.80.255
GATEWAY=192.168.80.2
NETMASK=255.255.255.0

DNS1=192.168.80.2
DNS1=8.8.8.8
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关于新建用户组和用户

  我这里不多说,我是spark,请移步

新建用户组、用户、用户密码、删除用户组、用户(适合CentOS、Ubuntu)

 

 

 

 

关于安装ssh、机器本身、机器之间进行免密码通信和时间同步

  我这里不多说,具体,请移步。在这一步,本人深有感受,有经验。最好建议拍快照。否则很容易出错!

  机器本身,即master与master、slave1与slave1、slave2与slave2。

  机器之间,即master与slave1、master与slave2。

        slave1与slave2。

hadoop-2.6.0.tar.gz + spark-1.5.2-bin-hadoop2.6.tgz 的集群搭建(3节点和5节点皆适用)

hadoop-2.6.0.tar.gz的集群搭建(5节点)

 

 

 

 

 

 

 

 

 关于如何先卸载自带的openjdk,再安装

  我这里不多说,我是jdk-8u60-linux-x64.tar.gz,请移步

  我的jdk是安装在/usr/local/jdk下,记得赋予权限组,chown -R spark:spark jdk

Centos 6.5下的OPENJDK卸载和SUN的JDK安装、环境变量配置

 

#java
export JAVA_HOME=/usr/local/jdk/jdk1.8.0_60
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib
export PATH=$PATH:$JAVA_HOME/bin

 

 

 

 关于如何安装scala

  不多说,我这里是scala-2.10.5.tgz,请移步

  我的scala安装在/usr/local/scala,记得赋予用户组,chown -R spark:spark scala

 

hadoop-2.6.0.tar.gz + spark-1.6.1-bin-hadoop2.6.tgz的集群搭建(单节点)(CentOS系统)

#scala
export SCALA_HOME=/usr/local/scala/scala-2.10.5
export PATH=$PATH:$SCALA_HOME/bin

 

 

 

 关于如何安装hadoop

  我这里不多说,请移步见

  我的spark安装目录是在/usr/local/hadoop/,记得赋予用户组,chown -R spark:spark hadoop

    去看如何安装就好,至于hadoop的怎么配置。请见下面的hadoop on yarn模式的配置文件讲解。

#hadoop
export HADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

 

 

 

 

 

 

 

 关于如何安装spark

  我这里不多说,请移步见

  我的spark安装目录是在/usr/local/spark/,记得赋予用户组,chown -R spark:spark spark

    只需去下面的博客,去看如何安装就好,至于spark的怎么配置。请见下面的spark  standalone模式的配置文件讲解。

hadoop-2.6.0.tar.gz + spark-1.6.1-bin-hadoop2.6.tgz的集群搭建(单节点)(CentOS系统)

#spark
export SPARK_HOME=/usr/local/spark/spark-1.6.1-bin-hadoop2.6
export PATH=$PATH:$SPARK_HOME/bin:$SPARK_HOME/sbin

 

 

 

 

 

 

关于zookeeper的安装

  我这里不多说,请移步

hadoop-2.6.0-cdh5.4.5.tar.gz(CDH)的3节点集群搭建(含zookeeper集群安装)

 以及,之后,在spark 里怎么配置zookeeper。

 

 

 

这里,我带大家来看官网

http://spark.apache.org/docs/latest

 

 

 

 

http://spark.apache.org/docs/latest/running-on-yarn.html

 

 

  

这里,不多说,很简单,自行去看官网。多看官网!

 

 

 

 

 

Hadoop on YARN配置与部署

   这里,不多说,请移步

hadoop-2.6.0.tar.gz的集群搭建(3节点)(不含zookeeper集群安装)

hadoop-2.6.0-cdh5.4.5.tar.gz(CDH)的3节点集群搭建(含zookeeper集群安装)

hadoop-2.6.0.tar.gz + spark-1.5.2-bin-hadoop2.6.tgz 的集群搭建(3节点和5节点皆适用)

  我这里,只贴出我最后的配置文件和启动界面

      注意:3台都是一样的配置,master、slave1和slave2,我这里不多赘述。

 

hadoop-env.sh

export JAVA_HOME=/usr/local/jdk/jdk1.8.0_60

 

 

 core-site.xml

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<configuration>
        <property>
                <name>fs.defaultFS</name>
                <value>hdfs://master:9000</value>
        </property>
        <property>
               <name>io.file.buffer.size</name>
               <value>131072</value>
        </property>
        <property>
               <name>hadoop.tmp.dir</name>
               <value>/usr/local/hadoop/hadoop-2.6.0/tmp</value>
        </property>
        <property>
              <name>hadoop.proxyuser.hadoop.hosts</name>
                <value>*</value>
        </property>
        <property>
              <name>hadoop.proxyuser.hadoop.groups</name>
               <value>*</value>
        </property>
</configuration>
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hdfs-site.xml

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<configuration>
        <property>
                <name>dfs.namenode.secondary.http-address</name>
              <value>master:9001</value>
        </property>
        <property>
              <name>dfs.replication</name>
              <value>2</value>
        </property>
        <property>
              <name>dfs.namenode.name.dir</name>
               <value>/usr/local/hadoop/hadoop-2.6.0/dfs/name</value>
        </property>
        <property>
              <name>dfs.datanode.data.dir</name>
              <value>/usr/local/hadoop/hadoop-2.6.0/dfs/data</value>
        </property>
        <property>
              <name>dfs.webhdfs.enabled</name>
              <value>true</value>
        </property>
</configuration>
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mapred-site.xml

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<configuration>
        <property>
                <name>mapreduce.framework.name</name>
              <value>yarn</value>
        </property>
        <property>
              <name>mapreduce.jobhistory.address</name>
              <value>master:10020</value>
        </property>
        <property>
              <name>mapreduce.jobhistory.webapp.address</name>
              <value>master:19888</value>
        </property>
</configuration>
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 yarn-site.xml

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<configuration>

    <property>
          <name>yarn.resourcemanager.hostname</name>
            <value>master</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
        <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
         <name>yarn.resourcemanager.address</name>
          <value>master:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>master:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>master:8031</value>
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>master:8033</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>master:8088</value>
    </property>
</configuration>
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slaves

slave1
slave2

 

masters

master

 

 

 

  然后,新建目录

mkdir -p /usr/local/hadoop/hadoop-2.6.0/dfs/name
mkdir -p /usr/local/hadoop/hadoop-2.6.0/dfs/data
mkdir -p /usr/local/hadoop/hadoop-2.6.0/tmp

 

 

  在master节点上,格式化

$HADOOP_HOME/bin/hadoop namenode -format

 

  启动hadoop进程

$HADOOP_HOME/sbin/start-all.sh

 

 

  输入

http://master:50070

http://master:8088

 

 

 

 

  

 

 

 

 

Spark on YARN配置与部署(这里,作为补充)

编译时包含YARN

mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -Phive -Phive-thriftserver -Psparkr -DskipTests clean package

/make-distribution.sh --name hadoop2.7.1 --tgz -Psparkr -Phadoop-2.6 -Dhadoop.version=2.7.1 -Phive -Phive-thriftserver –Pyarn

 

注意:

  hadoop的版本跟你使用的hadoop要对应,建议使用CDH或者HDP的hadoop发行版,对应关系已经处理好了

export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"

 

 

 

 

 

 

 

 

 

Spark on YARN的配置(这里,本博文的重点)

  Spark On YARN安装非常简单,只需要下载编译好的Spark安装包,在一台带有Hadoop Yarn客户端的机器上解压即可。

   Spark on YARN分为两种: YARN cluster(YARN standalone,0.9版本以前)和 YARN client。

YARN cluster是...我是用这种。

YARN client是将Client和Driver运行在一起(运行在本地),AM只用来管理资源。

  如果需要返回数据到client就用YARN client模式。

  如果数据存储到hdfs就用YARN cluster模式。

 

 注意:3台都是一样的配置,master、slave1和slave2,我这里不多赘述。

  

 

 

 

Spark on YARN基本配置

  配置HADOOP_CONF_DIR或者YARN_CONF_DIR环境变量。让Spark知道YARN的配置信息。

  这句话是从哪里来的,其实,你若没有在spark-env.sh配置任何东西的话,直接去执行$SPARK_HOME/bin/spark-shell  --master yarn就可以看到,它提示你去做。

 

 

 

 

 

  有三种方式

     (1)配置在spark-env.sh中 (我一般是用这种)(本博文也是这种)

     (2)在提交spark应用之前export

      (3) 配在到操作系统的环境变量中

   注意:在yarn-site.xml,配上hostname

 

 

   如果使用的是HDP,请在spark-defaults.conf中加入:(这里,作为补充)

  spark.driver.extraJavaOptions -Dhdp.version=current

  spark.yarn.am.extraJavaOptions -Dhdp.version=current

 

 

 

 

修改如下配置:

● slaves--指定在哪些节点上运行worker。

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# A Spark Worker will be started on each of the machines listed below.
slave1
slave2
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 spark-defaults.conf---spark提交job时的默认配置

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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# Default system properties included when running spark-submit.
# This is useful for setting default environmental settings.

# Example:
# spark.master                     spark://master:7077
# spark.eventLog.enabled           true
# spark.eventLog.dir               hdfs://namenode:8021/directory
# spark.serializer                 org.apache.spark.serializer.KryoSerializer
# spark.driver.memory              5g
# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"
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  大家,可以在这个配置文件里指定好,以后每次不需在命令行下指定了。当然咯,也可以不配置啦!(我一般是这里不配置,即这个文件不动它

 

 

 

spark-defaults.conf (这个作为可选可不选)(是因为或者是在spark-submit里也是可以加入的)(一般不选,不然固定死了)(我一般是这里不配置,即这个文件不动它

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spark.master                       spark://master:7077
spark.eventLog.enabled             true
spark.eventLog.dir                 hdfs://master:9000/sparkHistoryLogs
spark.eventLog.compress            true
spark.history.fs.update.interval   5
spark.history.ui.port              7777
spark.history.fs.logDirectory      hdfs://master:9000/sparkHistoryLogs
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● spark-env.sh—spark的环境变量

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#!/usr/bin/env bash

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# This file is sourced when running various Spark programs.
# Copy it as spark-env.sh and edit that to configure Spark for your site.

# Options read when launching programs locally with
# ./bin/run-example or ./bin/spark-submit
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public dns name of the driver program
# - SPARK_CLASSPATH, default classpath entries to append

# Options read by executors and drivers running inside the cluster
# - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node
# - SPARK_PUBLIC_DNS, to set the public DNS name of the driver program
# - SPARK_CLASSPATH, default classpath entries to append
# - SPARK_LOCAL_DIRS, storage directories to use on this node for shuffle and RDD data
# - MESOS_NATIVE_JAVA_LIBRARY, to point to your libmesos.so if you use Mesos

# Options read in YARN client mode
# - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files
# - SPARK_EXECUTOR_INSTANCES, Number of executors to start (Default: 2)
# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: 1).
# - SPARK_EXECUTOR_MEMORY, Memory per Executor (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_DRIVER_MEMORY, Memory for Driver (e.g. 1000M, 2G) (Default: 1G)
# - SPARK_YARN_APP_NAME, The name of your application (Default: Spark)
# - SPARK_YARN_QUEUE, The hadoop queue to use for allocation requests (Default: ‘default’)
# - SPARK_YARN_DIST_FILES, Comma separated list of files to be distributed with the job.
# - SPARK_YARN_DIST_ARCHIVES, Comma separated list of archives to be distributed with the job.

# Options for the daemons used in the standalone deploy mode
# - SPARK_MASTER_IP, to bind the master to a different IP address or hostname
# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master


# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
# - SPARK_WORKER_CORES, to set the number of cores to use on this machine
# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker
# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
# - SPARK_WORKER_DIR, to set the working directory of worker processes
# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).
# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")
# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers

# Generic options for the daemons used in the standalone deploy mode
# - SPARK_CONF_DIR      Alternate conf dir. (Default: ${SPARK_HOME}/conf)
# - SPARK_LOG_DIR       Where log files are stored.  (Default: ${SPARK_HOME}/logs)
# - SPARK_PID_DIR       Where the pid file is stored. (Default: /tmp)
# - SPARK_IDENT_STRING  A string representing this instance of spark. (Default: $USER)
# - SPARK_NICENESS      The scheduling priority for daemons. (Default: 0)


export JAVA_HOME=/usr/local/jdk/jdk1.8.0_60 (必须写)
export SCALA_HOME=/usr/local/scala/scala-2.10.5 (必须写)
export HADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0 (必须写)
export HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-2.6.0/etc/
hadoop (必须写)
export SPARK_MASTER_IP=192.168.80.10  
export SPARK_WORKER_MERMORY=1G (官网上说,至少1g)
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spark-shell运行在YARN上(这是Spark on YARN模式)

     (包含YARN client和YARN cluster)(作为补充)

 登陆安装Spark那台机器

bin/spark-shell --master yarn-client

 或者

bin/spark-shell --master yarn-cluster

   包括可以加上其他的,比如控制内存啊等。这很简单,不多赘述。

 

 

  我这里就以YARN Client演示了。

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[spark@master spark-1.6.1-bin-hadoop2.6]$ bin/spark-shell --master yarn-client
17/03/29 22:40:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/03/29 22:40:04 INFO spark.SecurityManager: Changing view acls to: spark
17/03/29 22:40:04 INFO spark.SecurityManager: Changing modify acls to: spark
17/03/29 22:40:04 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(spark); users with modify permissions: Set(spark)
17/03/29 22:40:05 INFO spark.HttpServer: Starting HTTP Server
17/03/29 22:40:06 INFO server.Server: jetty-8.y.z-SNAPSHOT
17/03/29 22:40:06 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:35692
17/03/29 22:40:06 INFO util.Utils: Successfully started service 'HTTP class server' on port 35692.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.6.1
      /_/

Using Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_60)
复制代码

   注意,这里的--master是固定参数,不是说主机名是master。

Spark Shell启动时遇到<console>:14: error: not found: value spark import spark.implicits._ <console>:14: error: not found: value spark import spark.sql错误的解决办法(图文详解)

 

 

 

 

 

提交spark作业

  为了出现问题,还是先看我写的这篇博客吧!

spark跑YARN模式或Client模式提交任务不成功(application state: ACCEPTED)

 

1、用yarn-client模式提交spark作业

在/usr/local/spark目录下创建文件夹

vi spark_pi.sh
复制代码
$SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.JavaSparkPi \
--master yarn-client \
--num-executors 1 \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 1 \
$SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar \


driver-memory不指定也可以,默认使用512M
executor-memory不指定的化, 默认是1G
复制代码

 

chmod 777 spark_pi.sh
./spark_pi.sh

 

 

或者

复制代码
[spark@master ~]$  $SPARK_HOME/bin/spark-submit  \
> --class org.apache.spark.examples.JavaSparkPi \
> --master yarn-cluster \
> --num-executors 1 \
> --driver-memory 1g \
> --executor-memory 1g \
> --executor-cores 1 \
>  $SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar


driver-memory不指定也可以,默认使用512M
executor-memory不指定的化, 默认是1G
复制代码

 

 

 

 

2、用yarn-cluster模式提交spark作业

 

在/usr/local/spark目录下创建文件夹

 

vi spark_pi.sh
复制代码
$SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.JavaSparkPi \
--master yarn-cluster \
--num-executors 1 \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 1 \
$SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar \


driver-memory不指定也可以,默认使用512M
executor-memory不指定的化, 默认是1G
复制代码

 

 

 chmod 777 spark_pi.sh
./spark_pi.sh

 

 

 

 或者

复制代码
[spark@master ~]$  $SPARK_HOME/bin/spark-submit  \
> --class org.apache.spark.examples.JavaSparkPi \
> --master yarn-cluster \
> --num-executors 1 \
> --driver-memory 1g \
> --executor-memory 1g \
> --executor-cores 1 \
>  $SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar


driver-memory不指定也可以,默认使用512M
executor-memory不指定的化, 默认是1G
复制代码

 

   注意,这里的--master是固定参数



本文转自大数据躺过的坑博客园博客,原文链接:http://www.cnblogs.com/zlslch/p/6638398.html,如需转载请自行联系原作者

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