SparkR (R on Spark)
- SparkR DataFrames
- Machine Learning
- R Function Name Conflicts
- Migration Guide
SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 2.0.0-preview, 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.
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
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
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
which can be created from the SparkContext. If you are working from the
sparkR shell, the
SparkContext should already be created for you, and you would not need to call
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
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
The following options can be set in
sparkR.init from RStudio:
|Property Name||Property group|
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.
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
sparkR commands, or if creating context through
you can specify the packages with the
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.
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)
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.
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
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
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.
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.
sql function enables applications to run SQL queries programmatically and returns the result as a
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 ‘-‘.
- 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
Binomial GLM model
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|
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,
You can inspect the search path in R with
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
errorto match the Scala API.
Upgrading From SparkR 1.6.x to 2.0
- The method
tablehas been removed and replaced by
- The class
DataFramehas been renamed to
SparkDataFrameto avoid name conflicts.