SparkR (R on Spark)


SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 1.5.1, 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.

sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)

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.

df <- createDataFrame(sqlContext, faithful) 

# Displays the content of the DataFrame to stdout
##  eruptions waiting
##1     3.600      79
##2     1.800      54
##3     3.333      74

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 and Parquet files natively and through Spark Packages you can find data source connectors for popular file formats like CSV and 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.

sc <- sparkR.init(sparkPackages="com.databricks:spark-csv_2.11:1.0.3")
sqlContext <- sparkRSQL.init(sc)

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.

people <- read.df(sqlContext, "./examples/src/main/resources/people.json", "json")
##  age    name
##1  NA Michael
##2  30    Andy
##3  19  Justin

# SparkR automatically infers the schema from the JSON file
# root
#  |-- age: integer (nullable = true)
#  |-- name: string (nullable = true)

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

write.df(people, path="people.parquet", source="parquet", mode="overwrite")

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.

# 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
##  key   value
## 1 238 val_238
## 2  86  val_86
## 3 311 val_311

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

# Create the DataFrame
df <- createDataFrame(sqlContext, faithful) 

# Get basic information about the DataFrame
## DataFrame[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

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

# We use the `n` operator to count the number of times each waiting time appears
head(summarize(groupBy(df, df$waiting), count = n(df$waiting)))
##  waiting count
##1      81    13
##2      60     6
##3      68     1

# We can also sort the output from the aggregation to get the most common waiting times
waiting_counts <- summarize(groupBy(df, df$waiting), count = n(df$waiting))
head(arrange(waiting_counts, desc(waiting_counts$count)))

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

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.

# Convert waiting time from hours to seconds.
# Note that we can assign this to a new column in the same DataFrame
df$waiting_secs <- df$waiting * 60
##  eruptions waiting waiting_secs
##1     3.600      79         4740
##2     1.800      54         3240
##3     3.333      74         4440

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.

# 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")
##    name
##1 Justin

Machine Learning

SparkR allows the fitting of generalized linear models over DataFrames using the glm() function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘+’, and ‘-‘. The example below shows the use of building a gaussian GLM model using SparkR.

# Create the DataFrame
df <- createDataFrame(sqlContext, iris)

# Fit a linear model over the dataset.
model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")

# Model coefficients are returned in a similar format to R's native glm().
##                    Estimate
##(Intercept)        2.2513930
##Sepal_Width        0.8035609
##Species_versicolor 1.4587432
##Species_virginica  1.9468169

# Make predictions based on the model.
predictions <- predict(model, newData = df)
head(select(predictions, "Sepal_Length", "prediction"))
##  Sepal_Length prediction
##1          5.1   5.063856
##2          4.9   4.662076
##3          4.7   4.822788
##4          4.6   4.742432
##5          5.0   5.144212
##6          5.4   5.385281