說白了

　　Spark on YARN模式的安裝，它是非常的簡單，只需要下載編譯好Spark安裝包，在一臺帶有Hadoop YARN客戶端的的機器上執行即可。

 　　Spark on YARN分為兩種： YARN cluster（YARN standalone，0.9版本以前）和 YARN client。

 　   如果需要返回資料到client就用YARN client模式。

　 　如果資料儲存到hdfs就用YARN cluster模式。（我一般是用這個）

開篇要明白

　　（1）spark-env.sh 是環境變數配置檔案

　　（2）spark-defaults.conf

　　（3）slaves 是從節點機器配置檔案

　　（4）metrics.properties 是 監控

　　（5）log4j.properties 是配置日誌

　　（5）fairscheduler.xml是公平排程

　　（6）docker.properties 是 docker

　　（7）我這裡的Spark on YARN模式的安裝，是master、slave1和slave2。

　　（8）Spark on YARN模式的安裝，其實，是必須要安裝hadoop的。

　　（9）為了管理，安裝zookeeper，（即管理master、slave1和slave2）

首先，說下我這篇部落格的Spark on YARN模式的安裝情況

我的安裝分割槽如下，3臺都一樣。

 

關於如何關閉防火牆

　　我這裡不多說，請移步

關於如何配置靜態ip和聯網

　　我這裡不多說，我的是如下，請移步

 

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

關於新建使用者組和使用者

　　我這裡不多說，我是spark，請移步

關於安裝ssh、機器本身、機器之間進行免密碼通訊和時間同步

　　我這裡不多說，具體，請移步。在這一步，本人深有感受，有經驗。最好建議拍快照。否則很容易出錯！

　　機器本身，即master與master、slave1與slave1、slave2與slave2。

　　機器之間，即master與slave1、master與slave2。

　　　　　　　　slave1與slave2。

 

 關於如何先解除安裝自帶的openjdk，再安裝

　　我這裡不多說，我是jdk-8u60-linux-x64.tar.gz，請移步

　　我的jdk是安裝在/usr/local/jdk下，記得賦予許可權組，chown -R spark:spark jdk

 

#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

 關於如何安裝scala

　　不多說，我這裡是scala-2.10.5.tgz，請移步

　　我的scala安裝在/usr/local/scala，記得賦予使用者組，chown -R spark:spark scala

 

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

 關於如何安裝hadoop

　　我這裡不多說，請移步見

　　我的spark安裝目錄是在/usr/local/hadoop/，記得賦予使用者組，chown -R spark:spark hadoop

 　  去看如何安裝就好，至於hadoop的怎麼配置。請見下面的hadoop on yarn模式的配置檔案講解。

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

 關於如何安裝spark

　　我這裡不多說，請移步見

　　我的spark安裝目錄是在/usr/local/spark/，記得賦予使用者組，chown -R spark:spark spark

 　  只需去下面的部落格，去看如何安裝就好，至於spark的怎麼配置。請見下面的spark  standalone模式的配置檔案講解。

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

關於zookeeper的安裝

　　我這裡不多說，請移步

 以及，之後，在spark 裡怎麼配置zookeeper。

這裡，我帶大家來看官網

 

這裡，不多說，很簡單，自行去看官網。多看官網！

Hadoop on YARN配置與部署

 　　這裡，不多說，請移步

　　我這裡，只貼出我最後的配置檔案和啟動介面

      注意：3臺都是一樣的配置，master、slave1和slave2，我這裡不多贅述。

hadoop-env.sh

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

 core-site.xml

<configuration>
        <property>
                <name>fs.defaultFS</name>
                <value>hdfs://master:9000</value>
        </property>
        <property>
        　      <name>io.file.buffer.size</name>
        　      <value>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>

hdfs-site.xml

<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>

mapred-site.xml

<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>

 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

　　然後，新建目錄

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節點上，格式化

$HADOOP_HOME/bin/hadoop namenode -format

　　啟動hadoop程序

$HADOOP_HOME/sbin/start-all.sh

　　輸入

http://master:50070

http://master:8088

Spark on YARN配置與部署(這裡，作為補充）

編譯時包含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要對應，建議使用CDH或者HDP的hadoop發行版，對應關係已經處理好了。

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

Spark on YARN的配置(這裡，本博文的重點）

　　Spark On YARN安裝非常簡單，只需要下載編譯好的Spark安裝包，在一臺帶有Hadoop Yarn客戶端的機器上解壓即可。

 　　Spark on YARN分為兩種： YARN cluster（YARN standalone，0.9版本以前）和 YARN client。

YARN cluster是...我是用這種。

YARN client是將Client和Driver執行在一起（執行在本地)，AM只用來管理資源。

　　如果需要返回資料到client就用YARN client模式。

　　如果資料儲存到hdfs就用YARN cluster模式。

 注意：3臺都是一樣的配置，master、slave1和slave2，我這裡不多贅述。

Spark on YARN基本配置

　　配置HADOOP_CONF_DIR或者YARN_CONF_DIR環境變數。讓Spark知道YARN的配置資訊。

　　這句話是從哪裡來的，其實，你若沒有在spark-env.sh配置任何東西的話，直接去執行$SPARK_HOME/bin/spark-shell  --master yarn就可以看到，它提示你去做。

　　有三種方式

　　　　　（1）配置在spark-env.sh中 （我一般是用這種）（本博文也是這種）

　　　　　（2）在提交spark應用之前export

　　　　　 (3) 配在到作業系統的環境變數中

　　　注意：在yarn-site.xml，配上hostname

　　　如果使用的是HDP，請在spark-defaults.conf中加入：（這裡，作為補充）

　　spark.driver.extraJavaOptions -Dhdp.version=current

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

修改如下配置：

● slaves--指定在哪些節點上執行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時的預設配置

#
# 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，可以在這個配置檔案裡指定好，以後每次不需在命令列下指定了。當然咯，也可以不配置啦！（我一般是這裡不配置，即這個檔案不動它）

spark-defaults.conf （這個作為可選可不選）（是因為或者是在spark-submit裡也是可以加入的）（一般不選，不然固定死了）（我一般是這裡不配置，即這個檔案不動它）

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的環境變數

#!/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         （必須寫）
export SCALA_HOME=/usr/local/scala/scala-2.10.5    （必須寫）
export HADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0    （必須寫）
export HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-2.6.0/etc/hadoop   （必須寫）
export SPARK_MASTER_IP=192.168.80.10　　
export SPARK_WORKER_MERMORY=1G     （官網上說，至少1g）

spark-shell執行在YARN上（這是Spark on YARN模式）

     (包含YARN client和YARN cluster）（作為補充）

 登陸安裝Spark那臺機器

bin/spark-shell --master yarn-client

 或者

bin/spark-shell --master yarn-cluster

 　　包括可以加上其他的，比如控制記憶體啊等。這很簡單，不多贅述。

　　我這裡就以YARN Client演示了。

[[email protected] 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 [email protected]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)

 　　注意，這裡的--master是固定引數，不是說主機名是master。

提交spark作業

　　為了出現問題，還是先看我寫的這篇部落格吧！

1、用yarn-client模式提交spark作業

在/usr/local/spark目錄下建立資料夾

vi spark_pi.sh

$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，預設使用512M
executor-memory不指定的化， 預設是1G

chmod 777 spark_pi.sh
./spark_pi.sh

或者

[[email protected] ~]$  $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，預設使用512M
executor-memory不指定的化， 預設是1G

2、用yarn-cluster模式提交spark作業

在/usr/local/spark目錄下建立資料夾

vi spark_pi.sh

$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，預設使用512M
executor-memory不指定的化， 預設是1G

 chmod 777 spark_pi.sh
 ./spark_pi.sh

 或者

[[email protected] ~]$  $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，預設使用512M
executor-memory不指定的化， 預設是1G

 　　注意，這裡的--master是固定引數