sparkr.md

layout: global
displayTitle: SparkR (R on Spark)
title: SparkR (R on Spark)

• This will become a table of contents (this text will be scraped). {:toc}

Overview

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark {{site.SPARK_VERSION}}, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames, dplyr) but on large datasets. SparkR also supports distributed machine learning using MLlib.

SparkR DataFrames

A DataFrame is a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external databases, or existing local R data frames.

All of the examples on this page use sample data included in R or the Spark distribution and can be run using the ./bin/sparkR shell.

Starting Up: SparkContext, SQLContext

The entry point into SparkR is the `SparkContext` which connects your R program to a Spark cluster. You can create a `SparkContext` using `sparkR.init` and pass in options such as the application name , any spark packages depended on, etc. Further, to work with DataFrames we will need a `SQLContext`, which can be created from the SparkContext. If you are working from the `sparkR` shell, the `SQLContext` and `SparkContext` should already be created for you, and you would not need to call `sparkR.init`.

{% highlight r %} sc <- sparkR.init() sqlContext <- sparkRSQL.init(sc) {% endhighlight %}

Starting Up from RStudio

You can also start SparkR from RStudio. You can connect your R program to a Spark cluster from RStudio, R shell, Rscript or other R IDEs. To start, make sure SPARK_HOME is set in environment (you can check Sys.getenv), load the SparkR package, and call sparkR.init as below. In addition to calling sparkR.init, you could also specify certain Spark driver properties. Normally these Application properties and Runtime Environment cannot be set programmatically, as the driver JVM process would have been started, in this case SparkR takes care of this for you. To set them, pass them as you would other configuration properties in the sparkEnvir argument to sparkR.init().

{% highlight r %} if (nchar(Sys.getenv("SPARK_HOME")) < 1) { Sys.setenv(SPARK_HOME = "/home/spark") } library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"))) sc <- sparkR.init(master = "local[*]", sparkEnvir = list(spark.driver.memory="2g")) {% endhighlight %}

The following options can be set in sparkEnvir with sparkR.init from RStudio:

Property Name	Property group	spark-submit equivalent
spark.driver.memory	Application Properties	--driver-memory
spark.driver.extraClassPath	Runtime Environment	--driver-class-path
spark.driver.extraJavaOptions	Runtime Environment	--driver-java-options
spark.driver.extraLibraryPath	Runtime Environment	--driver-library-path

Creating DataFrames

With a SQLContext, applications can create DataFrames from a local R data frame, from a Hive table, or from other data sources.

From local data frames

The simplest way to create a data frame is to convert a local R data frame into a SparkR DataFrame. Specifically we can use createDataFrame and pass in the local R data frame to create a SparkR DataFrame. As an example, the following creates a DataFrame based using the faithful dataset from R.

{% highlight r %} df <- createDataFrame(sqlContext, faithful)

Displays the content of the DataFrame to stdout

head(df)

eruptions waiting

##1 3.600 79 ##2 1.800 54 ##3 3.333 74

{% endhighlight %}

From Data Sources

SparkR supports operating on a variety of data sources through the DataFrame interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more specific options that are available for the built-in data sources.

The general method for creating DataFrames from data sources is read.df. This method takes in the SQLContext, the path for the file to load and the type of data source. SparkR supports reading JSON, CSV and Parquet files natively and through Spark Packages you can find data source connectors for popular file formats like Avro. These packages can either be added by specifying --packages with spark-submit or sparkR commands, or if creating context through init you can specify the packages with the packages argument.

{% highlight r %} sc <- sparkR.init(sparkPackages="com.databricks:spark-avro_2.11:2.0.1") sqlContext <- sparkRSQL.init(sc) {% endhighlight %}

We can see how to use data sources using an example JSON input file. Note that the file that is used here is not a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. As a consequence, a regular multi-line JSON file will most often fail.

{% highlight r %} people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json") head(people)

age name

##1 NA Michael ##2 30 Andy ##3 19 Justin

SparkR automatically infers the schema from the JSON file

printSchema(people)

root

|-- age: long (nullable = true)

|-- name: string (nullable = true)

{% endhighlight %}

The data sources API can also be used to save out DataFrames into multiple file formats. For example we can save the DataFrame from the previous example to a Parquet file using write.df (Until Spark 1.6, the default mode for writes was append. It was changed in Spark 1.7 to error to match the Scala API)

{% highlight r %} write.df(people, path="people.parquet", source="parquet", mode="overwrite") {% endhighlight %}

From Hive tables

You can also create SparkR DataFrames from Hive tables. To do this we will need to create a HiveContext which can access tables in the Hive MetaStore. Note that Spark should have been built with Hive support and more details on the difference between SQLContext and HiveContext can be found in the SQL programming guide.

{% highlight r %} # sc is an existing SparkContext. hiveContext <- sparkRHive.init(sc)

sql(hiveContext, "CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") sql(hiveContext, "LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")

Queries can be expressed in HiveQL.

results <- sql(hiveContext, "FROM src SELECT key, value")

results is now a DataFrame

head(results)

key value

1 238 val_238

2 86 val_86

3 311 val_311

{% endhighlight %}

DataFrame Operations

SparkR DataFrames support a number of functions to do structured data processing. Here we include some basic examples and a complete list can be found in the API docs:

Selecting rows, columns

{% highlight r %} # Create the DataFrame df <- createDataFrame(sqlContext, faithful)

Get basic information about the DataFrame

df

SparkDataFrame[eruptions:double, waiting:double]

Select only the "eruptions" column

head(select(df, df$eruptions))

eruptions

##1 3.600 ##2 1.800 ##3 3.333

You can also pass in column name as strings

head(select(df, "eruptions"))

Filter the DataFrame to only retain rows with wait times shorter than 50 mins

head(filter(df, df$waiting < 50))

eruptions waiting

##1 1.750 47 ##2 1.750 47 ##3 1.867 48

{% endhighlight %}

Grouping, Aggregation

SparkR data frames support a number of commonly used functions to aggregate data after grouping. For example we can compute a histogram of the waiting time in the faithful dataset as shown below

{% highlight r %}

We use the n operator to count the number of times each waiting time appears

head(summarize(groupBy(df, dfwaiting), count = n(dfwaiting)))

waiting count

##1 70 4 ##2 67 1 ##3 69 2

We can also sort the output from the aggregation to get the most common waiting times

waiting_counts <- summarize(groupBy(df, dfwaiting), count = n(dfwaiting)) head(arrange(waiting_counts, desc(waiting_counts$count)))

waiting count

##1 78 15 ##2 83 14 ##3 81 13

{% endhighlight %}

Operating on Columns

SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.

{% highlight r %}

Convert waiting time from hours to seconds.

Note that we can assign this to a new column in the same DataFrame

dfwaiting_secs <- dfwaiting * 60 head(df)

eruptions waiting waiting_secs

##1 3.600 79 4740 ##2 1.800 54 3240 ##3 3.333 74 4440

{% endhighlight %}

Running SQL Queries from SparkR

A SparkR DataFrame can also be registered as a temporary table in Spark SQL and registering a DataFrame as a table allows you to run SQL queries over its data. The sql function enables applications to run SQL queries programmatically and returns the result as a DataFrame.

{% highlight r %} # Load a JSON file people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json")

Register this DataFrame as a table.

registerTempTable(people, "people")

SQL statements can be run by using the sql method

teenagers <- sql(sqlContext, "SELECT name FROM people WHERE age >= 13 AND age <= 19") head(teenagers)

name

##1 Justin

{% endhighlight %}

Machine Learning

SparkR supports the following Machine Learning algorithms.

• Generalized Linear Regression Model spark.glm()
• Naive Bayes spark.naiveBayes()
• KMeans spark.kmeans()
• AFT Survival Regression spark.survreg()

Generalized Linear Regression can be used to train a model from a specified family. Currently the Gaussian, Binomial, Poisson and Gamma families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', ':', '+', and '-'.

The summary() function gives the summary of a model produced by different algorithms listed above. It produces the similar result compared with R summary function.

Model persistence

• write.ml allows users to save a fitted model in a given input path
• read.ml allows users to read/load the model which was saved using write.ml in a given path

Model persistence is supported for all Machine Learning algorithms for all families.

The examples below show how to build several models:

• GLM using the Gaussian and Binomial model families
• AFT survival regression model
• Naive Bayes model
• K-Means model

{% include_example r/ml.R %}

R Function Name Conflicts

When loading and attaching a new package in R, it is possible to have a name conflict, where a function is masking another function.

The following functions are masked by the SparkR package:

Masked function	How to Access
cov in package:stats	stats::cov(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))
filter in package:stats	stats::filter(x, filter, method = c("convolution", "recursive"), sides = 2, circular = FALSE, init)
sample in package:base	base::sample(x, size, replace = FALSE, prob = NULL)

Since part of SparkR is modeled on the dplyr package, certain functions in SparkR share the same names with those in dplyr. Depending on the load order of the two packages, some functions from the package loaded first are masked by those in the package loaded after. In such case, prefix such calls with the package name, for instance, SparkR::cume_dist(x) or dplyr::cume_dist(x).

You can inspect the search path in R with search()

Migration Guide

Upgrading From SparkR 1.5.x to 1.6.x

• Before Spark 1.6.0, the default mode for writes was append. It was changed in Spark 1.6.0 to error to match the Scala API.
• SparkSQL converts NA in R to null and vice-versa.

Upgrading From SparkR 1.6.x to 2.0

• The method table has been removed and replaced by tableToDF.
• The class DataFrame has been renamed to SparkDataFrame to avoid name conflicts.
• The sqlContext parameter is no longer required for these functions: createDataFrame, as.DataFrame, read.json, jsonFile, read.parquet, parquetFile, read.text, sql, tables, tableNames, cacheTable, uncacheTable, clearCache, dropTempTable, read.df, loadDF, createExternalTable