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这篇文章主要介绍了Spark1.0.0如何实现伪分布安装，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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软件准备：
spark-1.0.0-bin-hadoop1.tgz 下载地址：spark1.0.0
scala-2.10.4.tgz 下载下载：Scala 2.10.4
hadoop-1.2.1-bin.tar.gz 下载地址：hadoop-1.2.1-bin.tar.gz
jdk-7u60-linux-i586.tar.gz 下载地址：去官网下载就行，这个1.7.x都行
hadoop-1.2.1安装步骤，请看： http://my.oschina.net/dataRunner/blog/292584
1.解压：
tar -zxvf scala-2.10.4.tgz mv scala-2.10.4 scala tar -zxvf spark-1.0.0-bin-hadoop1.tgz mv spark-1.0.0-bin-hadoop1 spark
2. 配置环境变量：
vim /etc/profile (在最后一行加入以下内容就行)
export HADOOP_HOME_WARN_SUPPRESS=1
export JAVA_HOME=/home/big_data/jdk
export JRE_HOME=${JAVA_HOME}/jre
export CLASS_PATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export HADOOP_HOME=/home/big_data/hadoop
export HIVE_HOME=/home/big_data/hive
export SCALA_HOME=/home/big_data/scala
export SPARK_HOME=/home/big_data/spark
export PATH=.:$SPARK_HOME/bin:$SCALA_HOME/bin:$HIVE_HOME/bin:$HADOOP_HOME/bin:$JAVA_HOME/bin:$PATH3.修改spark的spark-env.sh文件
cd spark/conf cp spark-env.sh.template spark-env.sh vim spark-env.sh (在最后一行加入以下内容就行) export JAVA_HOME=/home/big_data/jdk export SCALA_HOME=/home/big_data/scala export SPARK_MASTER_IP=192.168.80.100 export SPARK_WORKER_MEMORY=200m export HADOOP_CONF_DIR=/home/big_data/hadoop/conf
然后就配置完毕勒！！！（就这么简单，艹，很多人都知道，但是共享的人太少勒）
hadoop-1.2.1测试步骤，请看： http://my.oschina.net/dataRunner/blog/292584
1.验证scala
[root@master ~]# scala -version Scala code runner version 2.10.4 -- Copyright 2002-2013, LAMP/EPFL [root@master ~]# [root@master big_data]# scala Welcome to Scala version 2.10.4 (Java HotSpot(TM) Client VM, Java 1.7.0_60). Type in expressions to have them evaluated. Type :help for more information. scala> 1+1 res0: Int = 2 scala> :q
2.验证spark (先启动hadoop-dfs.sh)
[root@master big_data]# cd spark
[root@master spark]# cd sbin/start-all.sh
（ 也可以分别启动
[root@master spark]$ sbin/start-master.sh
可以通过 http://master:8080/ 看到对应界面
[root@master spark]$ sbin/start-slaves.sh park://master:7077
可以通过 http://master:8081/ 看到对应界面
）
[root@master spark]# jps
[root@master ~]# jps
4629 NameNode （hadoop的）
5007 Master (spark的)
6150 Jps
4832 SecondaryNameNode （hadoop的）
5107 Worker （spark的）
4734 DataNode （hadoop的）
可以通过 http://192.168.80.100:8080/ 看到对应界面
[root@master big_data]# spark-shell
Spark assembly has been built with Hive, including Datanucleus jars on classpath
14/07/20 21:41:04 INFO spark.SecurityManager: Changing view acls to: root
14/07/20 21:41:04 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root)
14/07/20 21:41:04 INFO spark.HttpServer: Starting HTTP Server
14/07/20 21:41:05 INFO server.Server: jetty-8.y.z-SNAPSHOT
14/07/20 21:41:05 INFO server.AbstractConnector: Started SocketConnector@0.0.0.0:43343
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/___/ .__/\_,_/_/ /_/\_\ version 1.0.0
/_/
Using Scala version 2.10.4 (Java HotSpot(TM) Client VM, Java 1.7.0_60)
。。。
scala>
可以通过 http://192.168.80.100:4040/ 看到对应界面
（随便上传一个文件，里面随便一些英文单词，到hdfs上面）
scala> val file=sc.textFile("hdfs://master:9000/input")
14/07/20 21:51:05 INFO storage.MemoryStore: ensureFreeSpace(608) called with curMem=31527, maxMem=311387750
14/07/20 21:51:05 INFO storage.MemoryStore: Block broadcast_1 stored as values to memory (estimated size 608.0 B, free 296.9 MB)
file: org.apache.spark.rdd.RDD[String] = MappedRDD[5] at textFile at :12
scala> val count=file.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey(_+_)
14/07/20 21:51:14 INFO mapred.FileInputFormat: Total input paths to process : 1
count: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[10] at reduceByKey at :14
scala> count.collect()
14/07/20 21:51:48 INFO spark.SparkContext: Job finished: collect at :17, took 2.482381535 s
res0: Array[(String, Int)] = Array((previously-registered,1), (this,3), (Spark,1), (it,3), (original,1), (than,1), (its,1), (previously,1), (have,2), (upon,1), (order,2), (whenever,1), (it’s,1), (could,3), (Configuration,1), (Master's,1), (SPARK_DAEMON_JAVA_OPTS,1), (This,2), (which,2), (applications,2), (register,,1), (doing,1), (for,3), (just,2), (used,1), (any,1), (go,1), ((equivalent,1), (Master,4), (killing,1), (time,1), (availability,,1), (stop-master.sh,1), (process.,1), (Future,1), (node,1), (the,9), (Workers,1), (however,,1), (up,2), (Details,1), (not,3), (recovered,1), (process,1), (enable,3), (spark-env,1), (enough,1), (can,4), (if,3), (While,2), (provided,1), (be,5), (mode.,1), (minute,1), (When,1), (all,2), (written,1), (store,1), (enter,1), (then,1), (as,1), (officially,1)...
scala>
scala> count.saveAsTextFile("hdfs://master:9000/output") （结果保存到hdfs上的/output文件夹下）
scala> :q
Stopping spark context.
[root@master ~]# hadoop fs -ls /
Found 3 items
drwxr-xr-x - root supergroup 0 2014-07-18 21:10 /home
-rw-r--r-- 1 root supergroup 1722 2014-07-18 06:18 /input
drwxr-xr-x - root supergroup 0 2014-07-20 21:53 /output
[root@master ~]#
[root@master ~]# hadoop fs -cat /output/p*
。。。
(mount,1)
(production-level,1)
(recovery).,1)
(Workers/applications,1)
(perspective.,1)
(so,2)
(and,1)
(ZooKeeper,2)
(System,1)
(needs,1)
(property Meaning,1)
(solution,1)
(seems,1) 感谢你能够认真阅读完这篇文章，希望小编分享的“Spark1.0.0如何实现伪分布安装”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!