说白了
Spark on YARN模式的安装，它是非常的简单，只需要下载编译好Spark安装包，在一台带有Hadoop YARN客户端的的机器上运行即可。
Spark on YARN简介与运行wordcount（master、slave1和slave2）（博主推荐）
Spark on YARN分为两种： YARN cluster（YARN standalone，0.9版本以前）和 YARN client。
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。（我一般是用这个）
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）
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
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
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
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）
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..0_60
core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
</property>
<property>
<name>io.file.buffer.size</name>
<value></value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/hadoop-2.6./tmp</value>
</property>
<property>
<name>hadoop.proxyuser.hadoop.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.hadoop.groups</name>
<value>*</value>
</property>
</configuration>
hdfs-site.xml
<configuration>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>master:</value>
</property>
<property>
<name>dfs.replication</name>
<value></value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>/usr/local/hadoop/hadoop-2.6./dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/usr/local/hadoop/hadoop-2.6./dfs/data</value>
</property>
<property>
<name>dfs.webhdfs.enabled</name>
<value>true</value>
</property>
</configuration>
mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>master:</value>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>master:</value>
</property>
</configuration>
yarn-site.xml
<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>
slaves
slave1
slave2
masters
master
然后，新建目录
mkdir -p /usr/local/hadoop/hadoop-2.6./dfs/name
mkdir -p /usr/local/hadoop/hadoop-2.6./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
$HADOOP_HOME/sbin/start-all.sh
输入
http://master:50070 http://master:8088
Spark on YARN配置与部署(这里，作为补充）
编译时包含YARN
mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7. -Phive -Phive-thriftserver -Psparkr -DskipTests clean package /make-distribution.sh --name hadoop2.7.1 --tgz -Psparkr -Phadoop-2.6 -Dhadoop.version=2.7. -Phive -Phive-thriftserver –Pyarn
注意：
hadoop的版本跟你使用的hadoop要对应，建议使用CDH或者HDP的hadoop发行版，对应关系已经处理好了。
export MAVEN_OPTS="-Xmx2g -XX:MaxPermSize=512M -XX:ReservedCodeCacheSize=512m"
Spark on YARN的配置(这里，本博文的重点）
Spark On YARN安装非常简单，只需要下载编译好的Spark安装包，在一台带有Hadoop Yarn客户端的机器上解压即可。
Spark on YARN分为两种： YARN cluster（YARN standalone，0.9版本以前）和 YARN client。
YARN cluster是...我是用这种。
YARN client是将Client和Driver运行在一起（运行在本地)，AM只用来管理资源。
如果需要返回数据到client就用YARN client模式。
如果数据存储到hdfs就用YARN cluster模式。
注意：3台都是一样的配置，master、slave1和slave2，我这里不多赘述。
Spark on YARN基本配置
配置HADOOP_CONF_DIR或者YARN_CONF_DIR环境变量。让Spark知道YARN的配置信息。
这句话是从哪里来的，其实，你若没有在spark-env.sh配置任何东西的话，直接去执行$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。
#
# 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
● spark-defaults.conf---spark提交job时的默认配置
#
# 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"
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）
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
● spark-env.sh—spark的环境变量
#!/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: )
# - SPARK_EXECUTOR_CORES, Number of cores for the executors (Default: ).
# - 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: $USER)
# - SPARK_NICENESS The scheduling priority for daemons. (Default: ) export JAVA_HOME=/usr/local/jdk/jdk1..0_60 （必须写）
export SCALA_HOME=/usr/local/scala/scala-2.10.5 （必须写）
export HADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0 （必须写）
export HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-2.6.0/etc/hadoop （必须写）
export SPARK_MASTER_IP=192.168.80.10
export SPARK_WORKER_MERMORY=1G （官网上说，至少1g）
spark-shell运行在YARN上（这是Spark on YARN模式）
(包含YARN client和YARN cluster）（作为补充）
登陆安装Spark那台机器
bin/spark-shell --master yarn-client
或者
bin/spark-shell --master yarn-cluster
包括可以加上其他的，比如控制内存啊等。这很简单，不多赘述。
我这里就以YARN Client演示了。
[spark@master spark-1.6.-bin-hadoop2.]$ bin/spark-shell --master yarn-client
// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
// :: INFO spark.SecurityManager: Changing view acls to: spark
// :: INFO spark.SecurityManager: Changing modify acls to: spark
// :: INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(spark); users with modify permissions: Set(spark)
// :: INFO spark.HttpServer: Starting HTTP Server
// :: INFO server.Server: jetty-.y.z-SNAPSHOT
// :: INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:
// :: INFO util.Utils: Successfully started service 'HTTP class server' on port .
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.6.
/_/ Using Scala version 2.10. (Java HotSpot(TM) -Bit Server VM, Java 1.8.0_60)
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$SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.JavaSparkPi \
--master yarn-client \
--num-executors \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores \
$SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar \ driver-memory不指定也可以，默认使用512M
executor-memory不指定的化， 默认是1G
chmod spark_pi.sh
./spark_pi.sh
或者
[spark@master ~]$ $SPARK_HOME/bin/spark-submit \
> --class org.apache.spark.examples.JavaSparkPi \
> --master yarn-cluster \
> --num-executors \
> --driver-memory 1g \
> --executor-memory 1g \
> --executor-cores \
> $SPARK_HOME/lib/spark-examples-1.6.-hadoop2.6.0.jar driver-memory不指定也可以，默认使用512M
executor-memory不指定的化， 默认是1G
2、用yarn-cluster模式提交spark作业
在/usr/local/spark目录下创建文件夹
vi spark_pi.sh$SPARK_HOME/bin/spark-submit \
--class org.apache.spark.examples.JavaSparkPi \
--master yarn-cluster \
--num-executors \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores \
$SPARK_HOME/lib/spark-examples-1.6.1-hadoop2.6.0.jar \ driver-memory不指定也可以，默认使用512M
executor-memory不指定的化， 默认是1G
chmod spark_pi.sh
./spark_pi.sh
或者
[spark@master ~]$ $SPARK_HOME/bin/spark-submit \
> --class org.apache.spark.examples.JavaSparkPi \
> --master yarn-cluster \
> --num-executors \
> --driver-memory 1g \
> --executor-memory 1g \
> --executor-cores \
> $SPARK_HOME/lib/spark-examples-1.6.-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
注意，这里的--master是固定参数