Using Spark from R: SparkR

SparkR is an R package that provides a lightweight front end for using Apache Spark from R, supporting large-scale analytics on Hortonworks Data Platform (HDP) from the R language and environment.

SparkR provides a distributed data frame implementation that supports operations like selection, filtering, and aggregation on large datasets. In addition, SparkR supports distributed machine learning through MLlib.

This section lists prerequisites, followed by a SparkR example. Here are several links to additional information:

Before you run SparkR, ensure that your cluster meets the following prerequisites:

R must be installed on all nodes. Commands for installing R are specific to the operating system. For example, for CentOS you would log on as root and run the following command:
```
yum  install R
```
JAVA_HOME must be set on all nodes.

SparkR Example

The following example launches SparkR, and then uses R to create a people DataFrame in Spark. The example then lists part of the DataFrame, and reads the DataFrame. (For more information about Spark DataFrames, see "Using the Spark DataFrame API").

Launch SparkR:

su spark
cd /usr/hdp/2.6.0.0-598/spark/bin
./sparkR

Output similar to the following displays:

Welcome to
    ____              __ 
   / __/__  ___ _____/ /__ 
  _\ \/ _ \/ _ `/ __/  '_/ 
 /___/ .__/\_,_/_/ /_/\_\   version  2.0.0
    /_/ 

Spark context is available as sc, SQL context is available as sqlContext
>

From your R prompt (not the Spark shell), initialize SQLContext, create a DataFrame, and list the first few rows:
```
sqlContext <- sparkRSQL.init(sc)
df <- createDataFrame(sqlContext, faithful)
head(df)
```
You should see results similar to the following:
```
...
 eruptions waiting
1     3.600      79
2     1.800      54
3     3.333      74
4     2.283      62
5     4.533      85
6     2.883      55
```

Read the people DataFrame:

people <- read.df(sqlContext, "people.json", "json")
head(people)

You should see results similar to the following:

 age    name
1  NA Michael
2  30    Andy
3  19  Justin