SparkR (R on Spark)

• Overview
• SparkR DataFrames
  • Starting Up: SparkContext, SQLContext
  • Starting Up from RStudio
  • Creating DataFrames
    • From local data frames
    • From Data Sources
    • From Hive tables
  • DataFrame Operations
    • Selecting rows, columns
    • Grouping, Aggregation
    • Operating on Columns
  • Running SQL Queries from SparkR
• Machine Learning
  • Gaussian GLM model
  • Binomial GLM model
• R Function Name Conflicts
• Migration Guide
  • Upgrading From SparkR 1.6 to 1.7

Overview

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 1.6.0, 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

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

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"))

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
head(df)
##  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")
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: 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 (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)

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
head(results)
##  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
df
## 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
head(df)
##  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")
head(teenagers)
##    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 summary() function gives the summary of a model produced by glm().

• For gaussian GLM model, it returns a list with ‘devianceResiduals’ and ‘coefficients’ components. The ‘devianceResiduals’ gives the min/max deviance residuals of the estimation; the ‘coefficients’ gives the estimated coefficients and their estimated standard errors, t values and p-values. (It only available when model fitted by normal solver.)
• For binomial GLM model, it returns a list with ‘coefficients’ component which gives the estimated coefficients.

The examples below show the use of building gaussian GLM model and binomial GLM model using SparkR.

Gaussian GLM model

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

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

# Model summary are returned in a similar format to R's native glm().
summary(model)
##$devianceResiduals
## Min       Max     
## -1.307112 1.412532
##
##$coefficients
##                   Estimate  Std. Error t value  Pr(>|t|)    
##(Intercept)        2.251393  0.3697543  6.08889  9.568102e-09
##Sepal_Width        0.8035609 0.106339   7.556598 4.187317e-12
##Species_versicolor 1.458743  0.1121079  13.01195 0           
##Species_virginica  1.946817  0.100015   19.46525 0           

# 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

Binomial GLM model

# Create the DataFrame
df <- createDataFrame(sqlContext, iris)
training <- filter(df, df$Species != "setosa")

# Fit a binomial GLM model over the dataset.
model <- glm(Species ~ Sepal_Length + Sepal_Width, data = training, family = "binomial")

# Model coefficients are returned in a similar format to R's native glm().
summary(model)
##$coefficients
##               Estimate
##(Intercept)  -13.046005
##Sepal_Length   1.902373
##Sepal_Width    0.404655

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:

stats::cov(x, y = NULL, use = "everything",
           method = c("pearson", "kendall", "spearman"))

stats::filter(x, filter, method = c("convolution", "recursive"),
              sides = 2, circular = FALSE, init)

base::table(...,
            exclude = if (useNA == "no") c(NA, NaN),
            useNA = c("no", "ifany", "always"),
            dnn = list.names(...), deparse.level = 1)

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 1.6 to 1.7

• 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.