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SparkR-Install

时间:2017-03-30 23:05:18      阅读:478      评论:0      收藏:0      [点我收藏+]

1.下载R

https://cran.r-project.org/src/base/R-3/

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1.2 环境变量配置:

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1.3 测试安装:

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2.下载Rtools33

https://cran.r-project.org/bin/windows/Rtools/

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2.1 配置环境变量

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2.2 测试:

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3.安装RStudio

    https://www.rstudio.com/products/rstudio/download/ 直接下一步即可安装

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4.安装JDK并设置环境变量

4.1环境变量配置:

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4.2测试:

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5.下载Spark安装程序

  5.1 URL: http://spark.apache.org/downloads.html

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     5.2解压到本地磁盘的对应目录

 

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6.安装Spark并设置环境变量

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7.测试SparkR

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  注意:如果发现了提示 WARN NativeCodeLader:Unable to load native-hadoop library for your platform.....using

builtin-java classes where applicable  需要安装本地的hadoop库

8.下载hadoop库并安装

  http://hadoop.apache.org/releases.html

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9.设置hadoop环境变量

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10.重新测试SparkR

   10.1 如果测试时候出现以下提示,需要修改log4j文件INFO为WARN,位于\spark\conf下

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    10.2 修改conf中的log4j文件:

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     10.3 重新运行SparkR

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11.运行SprkR代码

    在Spark2.0中增加了RSparkSql进行Sql查询

    dataframe为数据框操作

    data-manipulation为数据转化

    ml为机器学习

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   11.1 使用crtl+ALT+鼠標左鍵 打开控制台在此文件夹下

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  11.2 执行spark-submit xxx.R文件即可

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12.安装SparkR包

    12.1 将spark安装目录下的R/lib中的SparkR文件拷贝到..\R-3.3.2\library中,注意是将整个Spark文件夹,而非里面每一个文件。

    源文件夹:

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     目的文件夹:

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     12.2  在RStudio中打开SparkR文件并运行代码dataframe.R文件,采用Ctrl+Enter一行行执行即可

SparkR语言的dataframe.R源代码如下

#
# 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.
#

library(SparkR)

# Initialize SparkContext and SQLContext
sc <- sparkR.init(appName="SparkR-DataFrame-example")
sqlContext <- sparkRSQL.init(sc)

# Create a simple local data.frame
localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18))

# Convert local data frame to a SparkR DataFrame
df <- createDataFrame(sqlContext, localDF)

# Print its schema
printSchema(df)
# root
#  |-- name: string (nullable = true)
#  |-- age: double (nullable = true)

# Create a DataFrame from a JSON file
path <- file.path(Sys.getenv("SPARK_HOME"), "examples/src/main/resources/people.json")
peopleDF <- read.json(sqlContext, path)
printSchema(peopleDF)

# Register this DataFrame as a table.
registerTempTable(peopleDF, "people")

# SQL statements can be run by using the sql methods provided by sqlContext
teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19")

# Call collect to get a local data.frame
teenagersLocalDF <- collect(teenagers)

# Print the teenagers in our dataset 
print(teenagersLocalDF)

# Stop the SparkContext now
sparkR.stop()

13.Rsudio 运行结果

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END~

SparkR-Install

原文:http://www.cnblogs.com/jackchen-Net/p/6648755.html

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