SparkR (R on Spark)
- Overview
- SparkDataFrame
- Machine Learning
- Data type mapping between R and Spark
- Structured Streaming
- Apache Arrow in SparkR
- R Function Name Conflicts
- Migration Guide
Overview
SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 3.4.1, 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.
SparkDataFrame
A SparkDataFrame 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. SparkDataFrames 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: SparkSession
The entry point into SparkR is the SparkSession
which connects your R program to a Spark cluster.
You can create a SparkSession
using sparkR.session
and pass in options such as the application name, any spark packages depended on, etc. Further, you can also work with SparkDataFrames via SparkSession
. If you are working from the sparkR
shell, the SparkSession
should already be created for you, and you would not need to call sparkR.session
.
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.session
as below. It will check for the Spark installation, and, if not found, it will be downloaded and cached automatically. Alternatively, you can also run install.spark
manually.
In addition to calling sparkR.session
,
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 sparkConfig
argument to
sparkR.session()
.
The following Spark driver properties can be set in sparkConfig
with sparkR.session
from RStudio:
Property Name | Property group | spark-submit equivalent |
---|---|---|
spark.master |
Application Properties | --master |
spark.kerberos.keytab |
Application Properties | --keytab |
spark.kerberos.principal |
Application Properties | --principal |
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 SparkDataFrames
With a SparkSession
, applications can create SparkDataFrame
s 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 SparkDataFrame. Specifically, we can use as.DataFrame
or createDataFrame
and pass in the local R data frame to create a SparkDataFrame. As an example, the following creates a SparkDataFrame
based using the faithful
dataset from R.
From Data Sources
SparkR supports operating on a variety of data sources through the SparkDataFrame
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 SparkDataFrames from data sources is read.df
. This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically.
SparkR supports reading JSON, CSV and Parquet files natively, and through packages available from sources like Third Party Projects, 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 initializing SparkSession with sparkPackages
parameter when in an interactive R shell or from RStudio.
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. For more information, please see JSON Lines text format, also called newline-delimited JSON. As a consequence, a regular multi-line JSON file will most often fail.
The data sources API natively supports CSV formatted input files. For more information please refer to SparkR read.df API documentation.
The data sources API can also be used to save out SparkDataFrames into multiple file formats. For example, we can save the SparkDataFrame from the previous example
to a Parquet file using write.df
.
From Hive tables
You can also create SparkDataFrames from Hive tables. To do this we will need to create a SparkSession with Hive support which can access tables in the Hive MetaStore. Note that Spark should have been built with Hive support and more details can be found in the SQL programming guide. In SparkR, by default it will attempt to create a SparkSession with Hive support enabled (enableHiveSupport = TRUE
).
SparkDataFrame Operations
SparkDataFrames 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
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
In addition to standard aggregations, SparkR supports OLAP cube operators cube
:
and rollup
:
Operating on Columns
SparkR also provides a number of functions that can be directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
Applying User-Defined Function
In SparkR, we support several kinds of User-Defined Functions:
Run a given function on a large dataset using dapply
or dapplyCollect
dapply
Apply a function to each partition of a SparkDataFrame
. The function to be applied to each partition of the SparkDataFrame
and should have only one parameter, to which a data.frame
corresponds to each partition will be passed. The output of function should be a data.frame
. Schema specifies the row format of the resulting a SparkDataFrame
. It must match to data types of returned value.
dapplyCollect
Like dapply
, apply a function to each partition of a SparkDataFrame
and collect the result back. The output of function
should be a data.frame
. But, Schema is not required to be passed. Note that dapplyCollect
can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
Run a given function on a large dataset grouping by input column(s) and using gapply
or gapplyCollect
gapply
Apply a function to each group of a SparkDataFrame
. The function is to be applied to each group of the SparkDataFrame
and should have only two parameters: grouping key and R data.frame
corresponding to
that key. The groups are chosen from SparkDataFrame
s column(s).
The output of function should be a data.frame
. Schema specifies the row format of the resulting
SparkDataFrame
. It must represent R function’s output schema on the basis of Spark data types. The column names of the returned data.frame
are set by user.
gapplyCollect
Like gapply
, applies a function to each partition of a SparkDataFrame
and collect the result back to R data.frame. The output of the function should be a data.frame
. But, the schema is not required to be passed. Note that gapplyCollect
can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
Run local R functions distributed using spark.lapply
spark.lapply
Similar to lapply
in native R, spark.lapply
runs a function over a list of elements and distributes the computations with Spark.
Applies a function in a manner that is similar to doParallel
or lapply
to elements of a list. The results of all the computations
should fit in a single machine. If that is not the case they can do something like df <- createDataFrame(list)
and then use
dapply
Eager execution
If eager execution is enabled, the data will be returned to R client immediately when the SparkDataFrame
is created. By default, eager execution is not enabled and can be enabled by setting the configuration property spark.sql.repl.eagerEval.enabled
to true
when the SparkSession
is started up.
Maximum number of rows and maximum number of characters per column of data to display can be controlled by spark.sql.repl.eagerEval.maxNumRows
and spark.sql.repl.eagerEval.truncate
configuration properties, respectively. These properties are only effective when eager execution is enabled. If these properties are not set explicitly, by default, data up to 20 rows and up to 20 characters per column will be showed.
Note that to enable eager execution in sparkR
shell, add spark.sql.repl.eagerEval.enabled=true
configuration property to the --conf
option.
Running SQL Queries from SparkR
A SparkDataFrame can also be registered as a temporary view in Spark SQL and that 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 SparkDataFrame
.
Machine Learning
Algorithms
SparkR supports the following machine learning algorithms currently:
Classification
spark.logit
:Logistic Regression
spark.mlp
:Multilayer Perceptron (MLP)
spark.naiveBayes
:Naive Bayes
spark.svmLinear
:Linear Support Vector Machine
spark.fmClassifier
:Factorization Machines classifier
Regression
spark.survreg
:Accelerated Failure Time (AFT) Survival Model
spark.glm
orglm
:Generalized Linear Model (GLM)
spark.isoreg
:Isotonic Regression
spark.lm
:Linear Regression
spark.fmRegressor
:Factorization Machines regressor
Tree
spark.decisionTree
:Decision Tree for
Regression
and
Classification
spark.gbt
:Gradient Boosted Trees for
Regression
and
Classification
spark.randomForest
:Random Forest for
Regression
and
Classification
Clustering
spark.bisectingKmeans
:Bisecting k-means
spark.gaussianMixture
:Gaussian Mixture Model (GMM)
spark.kmeans
:K-Means
spark.lda
:Latent Dirichlet Allocation (LDA)
spark.powerIterationClustering (PIC)
:Power Iteration Clustering (PIC)
Collaborative Filtering
Frequent Pattern Mining
Statistics
spark.kstest
:Kolmogorov-Smirnov Test
Under the hood, SparkR uses MLlib to train the model. Please refer to the corresponding section of MLlib user guide for example code.
Users can call summary
to print a summary of the fitted model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.
SparkR supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.
Model persistence
The following example shows how to save/load a MLlib model by SparkR.
training <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm")
# Fit a generalized linear model of family "gaussian" with spark.glm
df_list <- randomSplit(training, c(7,3), 2)
gaussianDF <- df_list[[1]]
gaussianTestDF <- df_list[[2]]
gaussianGLM <- spark.glm(gaussianDF, label ~ features, family = "gaussian")
# Save and then load a fitted MLlib model
modelPath <- tempfile(pattern = "ml", fileext = ".tmp")
write.ml(gaussianGLM, modelPath)
gaussianGLM2 <- read.ml(modelPath)
# Check model summary
summary(gaussianGLM2)
# Check model prediction
gaussianPredictions <- predict(gaussianGLM2, gaussianTestDF)
head(gaussianPredictions)
unlink(modelPath)
Data type mapping between R and Spark
R | Spark |
---|---|
byte | byte |
integer | integer |
float | float |
double | double |
numeric | double |
character | string |
string | string |
binary | binary |
raw | binary |
logical | boolean |
POSIXct | timestamp |
POSIXlt | timestamp |
Date | date |
array | array |
list | array |
env | map |
Structured Streaming
SparkR supports the Structured Streaming API. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. For more information see the R API on the Structured Streaming Programming Guide
Apache Arrow in SparkR
Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer data between JVM and R processes. See also PySpark optimization done, PySpark Usage Guide for Pandas with Apache Arrow. This guide targets to explain how to use Arrow optimization in SparkR with some key points.
Ensure Arrow Installed
Arrow R library is available on CRAN and it can be installed as below.
Rscript -e 'install.packages("arrow", repos="https://cloud.r-project.org/")'
Please refer the official documentation of Apache Arrow for more details.
Note that you must ensure that Arrow R package is installed and available on all cluster nodes. The current supported minimum version is 1.0.0; however, this might change between the minor releases since Arrow optimization in SparkR is experimental.
Enabling for Conversion to/from R DataFrame, dapply
and gapply
Arrow optimization is available when converting a Spark DataFrame to an R DataFrame using the call collect(spark_df)
,
when creating a Spark DataFrame from an R DataFrame with createDataFrame(r_df)
, when applying an R native function to each partition
via dapply(...)
and when applying an R native function to grouped data via gapply(...)
.
To use Arrow when executing these, users need to set the Spark configuration ‘spark.sql.execution.arrow.sparkr.enabled’
to ‘true’ first. This is disabled by default.
Whether the optimization is enabled or not, SparkR produces the same results. In addition, the conversion between Spark DataFrame and R DataFrame falls back automatically to non-Arrow optimization implementation when the optimization fails for any reasons before the actual computation.
Note that even with Arrow, collect(spark_df)
results in the collection of all records in the DataFrame to
the driver program and should be done on a small subset of the data. In addition, the specified output schema
in gapply(...)
and dapply(...)
should be matched to the R DataFrame’s returned by the given function.
Supported SQL Types
Currently, all Spark SQL data types are supported by Arrow-based conversion except FloatType
, BinaryType
, ArrayType
, StructType
and MapType
.
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 |
|
filter in package:stats |
|
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
The migration guide is now archived on this page.