這篇文章主要為大家展示了“Spark2.3中HA集群的分布式安裝示例”,內容簡而易懂,條理清晰,希望能夠幫助大家解決疑惑,下面讓小編帶領大家一起研究并學習一下“Spark2.3中HA集群的分布式安裝示例”這篇文章吧。
成都創新互聯公司專注于站前企業網站建設,響應式網站建設,商城建設。站前網站建設公司,為站前等地區提供建站服務。全流程按需網站建設,專業設計,全程項目跟蹤,成都創新互聯公司專業和態度為您提供的服務
http://spark.apache.org/downloads.html
http://mirrors.hust.edu.cn/apache/
https://mirrors.tuna.tsinghua.edu.cn/apache/
1、Java8安裝成功
2、zookeeper安裝成功
3、hadoop2.7.5 HA安裝成功
4、Scala安裝成功(不安裝進程也可以啟動)
[hadoop@hadoop1 ~]$ lsapps data exam inithive.conf movie spark-2.3.0-bin-hadoop2.7.tgz udf.jar cookies data.txt executions json.txt projects student zookeeper.out course emp hive.sql log sougou temp [hadoop@hadoop1 ~]$ tar -zxvf spark-2.3.0-bin-hadoop2.7.tgz -C apps/
[hadoop@hadoop1 ~]$ cd apps/[hadoop@hadoop1 apps]$ lshadoop-2.7.5 hbase-1.2.6 spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.10 zookeeper.out [hadoop@hadoop1 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark[hadoop@hadoop1 apps]$ ll總用量 36 drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5 drwxrwxr-x. 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6 lrwxrwxrwx. 1 hadoop hadoop 26 4月 20 13:48 spark -> spark-2.3.0-bin-hadoop2.7/drwxr-xr-x. 13 hadoop hadoop 4096 2月 23 03:42 spark-2.3.0-bin-hadoop2.7 drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 2017 zookeeper-3.4.10 -rw-rw-r--. 1 hadoop hadoop 17559 3月 29 13:37 zookeeper.out [hadoop@hadoop1 apps]$
(1)進入配置文件所在目錄
[hadoop@hadoop1 ~]$ cd apps/spark/conf/[hadoop@hadoop1 conf]$ ll總用量 36 -rw-r--r--. 1 hadoop hadoop 996 2月 23 03:42 docker.properties.template -rw-r--r--. 1 hadoop hadoop 1105 2月 23 03:42 fairscheduler.xml.template -rw-r--r--. 1 hadoop hadoop 2025 2月 23 03:42 log4j.properties.template -rw-r--r--. 1 hadoop hadoop 7801 2月 23 03:42 metrics.properties.template -rw-r--r--. 1 hadoop hadoop 865 2月 23 03:42 slaves.template -rw-r--r--. 1 hadoop hadoop 1292 2月 23 03:42 spark-defaults.conf.template -rwxr-xr-x. 1 hadoop hadoop 4221 2月 23 03:42 spark-env.sh.template [hadoop@hadoop1 conf]$
(2)復制spark-env.sh.template并重命名為spark-env.sh,并在文件最后添加配置內容
[hadoop@hadoop1 conf]$ cp spark-env.sh.template spark-env.sh[hadoop@hadoop1 conf]$ vi spark-env.sh
export JAVA_HOME=/usr/local/jdk1.8.0_73 #export SCALA_HOME=/usr/share/scala export HADOOP_HOME=/home/hadoop/apps/hadoop-2.7.5 export HADOOP_CONF_DIR=/home/hadoop/apps/hadoop-2.7.5/etc/hadoop export SPARK_WORKER_MEMORY=500m export SPARK_WORKER_CORES=1 export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181 -Dspark.deploy.zookeeper.dir=/spark"
注:
#export SPARK_MASTER_IP=hadoop1 這個配置要注釋掉。
集群搭建時配置的spark參數可能和現在的不一樣,主要是考慮個人電腦配置問題,如果memory配置太大,機器運行很慢。
說明:
-Dspark.deploy.recoveryMode=ZOOKEEPER #說明整個集群狀態是通過zookeeper來維護的,整個集群狀態的恢復也是通過zookeeper來維護的。就是說用zookeeper做了spark的HA配置,Master(Active)掛掉的話,Master(standby)要想變成Master(Active)的話,Master(Standby)就要像zookeeper讀取整個集群狀態信息,然后進行恢復所有Worker和Driver的狀態信息,和所有的Application狀態信息;
-Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181#將所有配置了zookeeper,并且在這臺機器上有可能做master(Active)的機器都配置進來;(我用了4臺,就配置了4臺)-Dspark.deploy.zookeeper.dir=/spark
這里的dir和zookeeper配置文件zoo.cfg中的dataDir的區別???
-Dspark.deploy.zookeeper.dir是保存spark的元數據,保存了spark的作業運行狀態;
zookeeper會保存spark集群的所有的狀態信息,包括所有的Workers信息,所有的Applactions信息,所有的Driver信息,如果集群
(3)復制slaves.template成slaves
[hadoop@hadoop1 conf]$ cp slaves.template slaves[hadoop@hadoop1 conf]$ vi slaves
添加如下內容
hadoop1 hadoop2 hadoop3 hadoop4
(4)將安裝包分發給其他節點
[hadoop@hadoop1 ~]$ cd apps/[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop2:$PWD[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop3:$PWD[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop4:$PWD
創建軟連接
[hadoop@hadoop2 ~]$ cd apps/[hadoop@hadoop2 apps]$ lshadoop-2.7.5 hbase-1.2.6 spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.10 [hadoop@hadoop2 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark[hadoop@hadoop2 apps]$ ll總用量 16 drwxr-xr-x 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5 drwxrwxr-x 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6 lrwxrwxrwx 1 hadoop hadoop 26 4月 20 19:26 spark -> spark-2.3.0-bin-hadoop2.7/drwxr-xr-x 13 hadoop hadoop 4096 4月 20 19:24 spark-2.3.0-bin-hadoop2.7drwxr-xr-x 10 hadoop hadoop 4096 3月 21 19:31 zookeeper-3.4.10 [hadoop@hadoop2 apps]$
所有節點均要配置
[hadoop@hadoop1 spark]$ vi ~/.bashrc
#Spark export SPARK_HOME=/home/hadoop/apps/spark export PATH=$PATH:$SPARK_HOME/bin
保存并使其立即生效
[hadoop@hadoop1 spark]$ source ~/.bashrc
所有節點均要執行
[hadoop@hadoop1 ~]$ zkServer.sh startZooKeeper JMX enabled by default Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg Starting zookeeper ... STARTED [hadoop@hadoop1 ~]$ zkServer.sh statusZooKeeper JMX enabled by default Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg Mode: follower[hadoop@hadoop1 ~]$
任意一個節點執行即可
[hadoop@hadoop1 ~]$ start-dfs.sh
在一個節點上執行
[hadoop@hadoop1 ~]$ cd apps/spark/sbin/[hadoop@hadoop1 sbin]$ start-all.sh
查看進程發現spark集群只有hadoop1成功啟動了Master進程,其他3個節點均沒有啟動成功,需要手動啟動,進入到/home/hadoop/apps/spark/sbin目錄下執行以下命令,3個節點都要執行
[hadoop@hadoop2 ~]$ cd ~/apps/spark/sbin/ [hadoop@hadoop2 sbin]$ start-master.sh
Master進程和Worker進程都以啟動成功
hadoop1是ALIVE狀態,hadoop2、hadoop3和hadoop4均是STANDBY狀態
hadoop1節點
hadoop2節點
hadoop3
hadoop4
手動干掉hadoop1上面的Master進程,觀察是否會自動進行切換
干掉hadoop1上的Master進程之后,再次查看web界面
hadoo1節點,由于Master進程被干掉,所以界面無法訪問
hadoop2節點,Master被干掉之后,hadoop2節點上的Master成功篡位成功,成為ALIVE狀態
hadoop3節點
hadoop4節點
[hadoop@hadoop3 ~]$ /home/hadoop/apps/spark/bin/spark-submit \ > --class org.apache.spark.examples.SparkPi \ > --master spark://hadoop1:7077 \ > --executor-memory 500m \ > --total-executor-cores 1 \ > /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \ > 100
其中的spark://hadoop1:7077是下圖中的地址
運行結果
[hadoop@hadoop1 ~]$ /home/hadoop/apps/spark/bin/spark-shell \> --master spark://hadoop1:7077 \> --executor-memory 500m \> --total-executor-cores 1
參數說明:
--master spark://hadoop1:7077 指定Master的地址
--executor-memory 500m:指定每個worker可用內存為500m
--total-executor-cores 1:指定整個集群使用的cup核數為1個
注意:
如果啟動spark shell時沒有指定master地址,但是也可以正常啟動spark shell和執行spark shell中的程序,其實是啟動了spark的local模式,該模式僅在本機啟動一個進程,沒有與集群建立聯系。
Spark Shell中已經默認將SparkContext類初始化為對象sc。用戶代碼如果需要用到,則直接應用sc即可
Spark Shell中已經默認將SparkSQl類初始化為對象spark。用戶代碼如果需要用到,則直接應用spark即可
(1)編寫一個hello.txt文件并上傳到HDFS上的spark目錄下
[hadoop@hadoop1 ~]$ vi hello.txt [hadoop@hadoop1 ~]$ hadoop fs -mkdir -p /spark [hadoop@hadoop1 ~]$ hadoop fs -put hello.txt /spark
hello.txt的內容如下
you,jump i,jump you,jump i,jump jump
(2)在spark shell中用scala語言編寫spark程序
scala> sc.textFile("/spark/hello.txt").flatMap(_.split(",")).map((_,1)).reduceByKey(_+_).saveAsTextFile("/spark/out")
說明:
sc是SparkContext對象,該對象是提交spark程序的入口
textFile("/spark/hello.txt")是hdfs中讀取數據
flatMap(_.split(" "))先map再壓平
map((_,1))將單詞和1構成元組
reduceByKey(_+_)按照key進行reduce,并將value累加
saveAsTextFile("/spark/out")將結果寫入到hdfs中
(3)使用hdfs命令查看結果
[hadoop@hadoop2 ~]$ hadoop fs -cat /spark/out/p* (jump,5) (you,2) (i,2) [hadoop@hadoop2 ~]$
成功啟動zookeeper集群、HDFS集群、YARN集群
[hadoop@hadoop1 bin]$ spark-shell --master yarn --deploy-mode client
報錯如下:
報錯原因:內存資源給的過小,yarn直接kill掉進程,則報rpc連接失敗、ClosedChannelException等錯誤。
解決方法:
先停止YARN服務,然后修改yarn-site.xml,增加如下內容
<property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> <description>Whether virtual memory limits will be enforced for containers</description> </property> <property> <name>yarn.nodemanager.vmem-pmem-ratio</name> <value>4</value> <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description> </property>
將新的yarn-site.xml文件分發到其他Hadoop節點對應的目錄下,最后在重新啟動YARN。
重新執行以下命令啟動spark on yarn
[hadoop@hadoop1 hadoop]$ spark-shell --master yarn --deploy-mode client
啟動成功
打開YARN WEB頁面:http://hadoop4:8088
可以看到Spark shell應用程序正在運行
單擊ID號鏈接,可以看到該應用程序的詳細信息
單擊“ApplicationMaster”鏈接
scala> val array = Array(1,2,3,4,5) array: Array[Int] = Array(1, 2, 3, 4, 5) scala> val rdd = sc.makeRDD(array) rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at <console>:26 scala> rdd.count res0: Long = 5 scala>
再次查看YARN的web界面
查看executors
[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \ > --master yarn \ > --deploy-mode cluster \ > --driver-memory 500m \ > --executor-memory 500m \ > --executor-cores 1 \ > /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \ > 10
執行過程
[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \ > --master yarn \ > --deploy-mode cluster \ > --driver-memory 500m \ > --executor-memory 500m \ > --executor-cores 1 \ > /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \ > 10 2018-04-21 17:57:32 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2018-04-21 17:57:34 INFO ConfiguredRMFailoverProxyProvider:100 - Failing over to rm2 2018-04-21 17:57:34 INFO Client:54 - Requesting a new application from cluster with 4 NodeManagers 2018-04-21 17:57:34 INFO Client:54 - Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container) 2018-04-21 17:57:34 INFO Client:54 - Will allocate AM container, with 884 MB memory including 384 MB overhead 2018-04-21 17:57:34 INFO Client:54 - Setting up container launch context for our AM 2018-04-21 17:57:34 INFO Client:54 - Setting up the launch environment for our AM container 2018-04-21 17:57:34 INFO Client:54 - Preparing resources for our AM container 2018-04-21 17:57:36 WARN Client:66 - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME. 2018-04-21 17:57:39 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_libs__8262081479435245591.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_libs__8262081479435245591.zip 2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/spark-examples_2.11-2.3.0.jar 2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_conf__2498510663663992254.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_conf__.zip 2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls to: hadoop 2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls to: hadoop 2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls groups to: 2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls groups to: 2018-04-21 17:57:44 INFO SecurityManager:54 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); groups with view permissions: Set(); users with modify permissions: Set(hadoop); groups with modify permissions: Set() 2018-04-21 17:57:44 INFO Client:54 - Submitting application application_1524303370510_0005 to ResourceManager 2018-04-21 17:57:44 INFO YarnClientImpl:273 - Submitted application application_1524303370510_0005 2018-04-21 17:57:45 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:45 INFO Client:54 - client token: N/A diagnostics: N/A ApplicationMaster host: N/A ApplicationMaster RPC port: -1 queue: default start time: 1524304664749 final status: UNDEFINED tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/ user: hadoop 2018-04-21 17:57:46 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:47 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:48 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:49 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:50 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:51 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:52 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:53 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED) 2018-04-21 17:57:54 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:57:54 INFO Client:54 - client token: N/A diagnostics: N/A ApplicationMaster host: 192.168.123.104 ApplicationMaster RPC port: 0 queue: default start time: 1524304664749 final status: UNDEFINED tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/ user: hadoop 2018-04-21 17:57:55 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:57:56 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:57:57 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:57:58 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:57:59 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:00 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:01 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:02 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:03 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:04 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:05 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:06 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:07 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:08 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING) 2018-04-21 17:58:09 INFO Client:54 - Application report for application_1524303370510_0005 (state: FINISHED) 2018-04-21 17:58:09 INFO Client:54 - client token: N/A diagnostics: N/A ApplicationMaster host: 192.168.123.104 ApplicationMaster RPC port: 0 queue: default start time: 1524304664749 final status: SUCCEEDED tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/ user: hadoop 2018-04-21 17:58:09 INFO Client:54 - Deleted staging directory hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005 2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Shutdown hook called 2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720 2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-06de6905-8067-4f1e-a0a0-bc8a51daf535 [hadoop@hadoop1 ~]$
以上是“Spark2.3中HA集群的分布式安裝示例”這篇文章的所有內容,感謝各位的閱讀!相信大家都有了一定的了解,希望分享的內容對大家有所幫助,如果還想學習更多知識,歡迎關注創新互聯行業資訊頻道!
網站標題:Spark2.3中HA集群的分布式安裝示例
地址分享:http://m.newbst.com/article36/gciipg.html
成都網站建設公司_創新互聯,為您提供品牌網站制作、域名注冊、App開發、商城網站、網站策劃、自適應網站
聲明:本網站發布的內容(圖片、視頻和文字)以用戶投稿、用戶轉載內容為主,如果涉及侵權請盡快告知,我們將會在第一時間刪除。文章觀點不代表本網站立場,如需處理請聯系客服。電話:028-86922220;郵箱:631063699@qq.com。內容未經允許不得轉載,或轉載時需注明來源: 創新互聯