linux环境不使用hadoop安装单机版spark的方法

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大数据持续升温，不熟悉几个大数据组件，连装逼的口头禅都没有。最起码，你要会说个hadoop, hdfs, mapreduce, yarn, kafka, spark, zookeeper, neo4j吧，这些都是装逼的必备技能。

关于spark的详细介绍, 网上一大堆，搜搜便是，下面，我们来说单机版的spark的安装和简要使用。

0. 安装jdk, 由于我的机器上之前已经有了jdk, 所以这一步我可以省掉。 jdk已经是很俗气的老生常谈了, 不多说, 用java/scala的时候可少不了。

 ubuntu@VM-0-15-ubuntu:~$ java -version openjdk version "1.8.0_151" OpenJDK Runtime Environment (build 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12) OpenJDK 64-Bit Server VM (build 25.151-b12, mixed mode) ubuntu@VM-0-15-ubuntu:~$

1. 你并不一定需要安装hadoop, 只需要选择特定的spark版本即可。你并不需要下载scala, 因为spark会默认带上scala shell. 去spark官网下载，在没有hadoop的环境下，可以选择：spark-2.2.1-bin-hadoop2.7，然后解压，如下：

 ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc$ ll total 196436 drwxrwxr-x 3 ubuntu ubuntu   4096 Feb 2 19:57 ./ drwxrwxr-x 9 ubuntu ubuntu   4096 Feb 2 19:54 ../ drwxrwxr-x 13 ubuntu ubuntu   4096 Feb 2 19:58 spark-2.2.1-bin-hadoop2.7/ -rw-r--r-- 1 ubuntu ubuntu 200934340 Feb 2 19:53 spark-2.2.1-bin-hadoop2.7.tgz

2. spark中有python和scala版本的, 下面，我来用scala版本的shell, 如下：

 ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ bin/spark-shell Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). 18/02/02 20:12:16 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 18/02/02 20:12:16 WARN Utils: Your hostname, localhost resolves to a loopback address: 127.0.0.1; using 172.17.0.15 instead (on interface eth0) 18/02/02 20:12:16 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address Spark context Web UI available at http://172.17.0.15:4040 Spark context available as 'sc' (master = local[*], app id = local-1517573538209). Spark session available as 'spark'. Welcome to ____       __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\  version 2.2.1 /_/ Using Scala version 2.11.8 (OpenJDK 64-Bit Server VM, Java 1.8.0_151) Type in expressions to have them evaluated. Type :help for more information. scala>

来进行简单操作：

 scala> val lines = sc.textFile("README.md") lines: org.apache.spark.rdd.RDD[String] = README.md MapPartitionsRDD[1] at textFile at :24 scala> lines.count() res0: Long = 103 scala> lines.first() res1: String = # Apache Spark scala> :quit ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ wc -l README.md 103 README.md ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$ head -n 1 README.md # Apache Spark ubuntu@VM-0-15-ubuntu:~/taoge/spark_calc/spark-2.2.1-bin-hadoop2.7$

来看看可视化的web页面，在Windows上输入： http://ip:4040