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.
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.
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. 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:
With a SparkSession, applications can create SparkDataFrames from a local R data frame, from a Hive table, or from other data sources.
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.
head(df)
##1 3.600 79 ##2 1.800 54 ##3 3.333 74
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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 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 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. As a consequence, a regular multi-line JSON file will most often fail.
printSchema(people)
people <- read.json(c(“./examples/src/main/resources/people.json”, “./examples/src/main/resources/people2.json”))
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The data sources API natively supports CSV formatted input files. For more information please refer to SparkR read.df API documentation.
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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.
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).
sql(“CREATE TABLE IF NOT EXISTS src (key INT, value STRING)”) sql(“LOAD DATA LOCAL INPATH ‘examples/src/main/resources/kv1.txt’ INTO TABLE src”)
results <- sql(“FROM src SELECT key, value”)
head(results)
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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:
df
head(select(df, df$eruptions))
##1 3.600 ##2 1.800 ##3 3.333
head(select(df, “eruptions”))
head(filter(df, df$waiting < 50))
##1 1.750 47 ##2 1.750 47 ##3 1.867 48
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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
n operator to count the number of times each waiting time appearshead(summarize(groupBy(df, df$waiting), count = n(df$waiting)))
##1 70 4 ##2 67 1 ##3 69 2
waiting_counts <- summarize(groupBy(df, df$waiting), count = n(df$waiting)) head(arrange(waiting_counts, desc(waiting_counts$count)))
##1 78 15 ##2 83 14 ##3 81 13
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SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
df$waiting_secs <- df$waiting * 60 head(df)
##1 3.600 79 4740 ##2 1.800 54 3240 ##3 3.333 74 4440
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In SparkR, we support several kinds of User-Defined Functions:
dapply or dapplyCollectApply a function to each partition of a SparkDataFrame. The function to be applied to each partition of the SparkDataFrame and should have only one parameter, to which a data.frame corresponds to each partition will be passed. The output of function should be a data.frame. Schema specifies the row format of the resulting a SparkDataFrame. It must match to data types of returned value.
schema <- structType(structField(“eruptions”, “double”), structField(“waiting”, “double”), structField(“waiting_secs”, “double”)) df1 <- dapply(df, function(x) { x <- cbind(x, x$waiting * 60) }, schema) head(collect(df1))
##1 3.600 79 4740 ##2 1.800 54 3240 ##3 3.333 74 4440 ##4 2.283 62 3720 ##5 4.533 85 5100 ##6 2.883 55 3300 {% endhighlight %}
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.
ldf <- dapplyCollect( df, function(x) { x <- cbind(x, “waiting_secs” = x$waiting * 60) }) head(ldf, 3)
##1 3.600 79 4740 ##2 1.800 54 3240 ##3 3.333 74 4440
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gapply or gapplyCollectApply a function to each group of a SparkDataFrame. The function is to be applied to each group of the SparkDataFrame and should have only two parameters: grouping key and R data.frame corresponding to that key. The groups are chosen from SparkDataFrames column(s). The output of function should be a data.frame. Schema specifies the row format of the resulting SparkDataFrame. It must represent R function's output schema on the basis of Spark data types. The column names of the returned data.frame are set by user. Below is the data type mapping between R and Spark.
schema <- structType(structField(“waiting”, “double”), structField(“max_eruption”, “double”)) result <- gapply( df, “waiting”, function(key, x) { y <- data.frame(key, max(x$eruptions)) }, schema) head(collect(arrange(result, “max_eruption”, decreasing = TRUE)))
##1 64 5.100 ##2 69 5.067 ##3 71 5.033 ##4 87 5.000 ##5 63 4.933 ##6 89 4.900 {% endhighlight %}
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.
result <- gapplyCollect( df, “waiting”, function(key, x) { y <- data.frame(key, max(x$eruptions)) colnames(y) <- c(“waiting”, “max_eruption”) y }) head(result[order(result$max_eruption, decreasing = TRUE), ])
##1 64 5.100 ##2 69 5.067 ##3 71 5.033 ##4 87 5.000 ##5 63 4.933 ##6 89 4.900
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spark.lapplySimilar 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
print(model.summaries)
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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.
createOrReplaceTempView(people, “people”)
teenagers <- sql(“SELECT name FROM people WHERE age >= 13 AND age <= 19”) head(teenagers)
##1 Justin
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SparkR supports the following machine learning algorithms currently: Generalized Linear Model, Accelerated Failure Time (AFT) Survival Regression Model, Naive Bayes Model and KMeans Model. Under the hood, SparkR uses MLlib to train the model. 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 ‘-‘.
spark.glm() or glm() fits generalized linear model against a Spark DataFrame. Currently “gaussian”, “binomial”, “poisson” and “gamma” families are supported. {% include_example glm r/ml.R %}
spark.survreg() fits an accelerated failure time (AFT) survival regression model on a SparkDataFrame. Note that the formula of spark.survreg() does not support operator ‘.’ currently. {% include_example survreg r/ml.R %}
spark.naiveBayes() fits a Bernoulli naive Bayes model against a SparkDataFrame. Only categorical data is supported. {% include_example naiveBayes r/ml.R %}
spark.kmeans() fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans(). {% include_example kmeans r/ml.R %}
The following example shows how to save/load a MLlib model by SparkR. {% include_example read_write r/ml.R %}
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:
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()
append. It was changed in Spark 1.6.0 to error to match the Scala API.NA in R to null and vice-versa.table has been removed and replaced by tableToDF.DataFrame has been renamed to SparkDataFrame to avoid name conflicts.SQLContext and HiveContext have been deprecated to be replaced by SparkSession. Instead of sparkR.init(), call sparkR.session() in its place to instantiate the SparkSession. Once that is done, that currently active SparkSession will be used for SparkDataFrame operations.sparkExecutorEnv is not supported by sparkR.session. To set environment for the executors, set Spark config properties with the prefix “spark.executorEnv.VAR_NAME”, for example, “spark.executorEnv.PATH”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.registerTempTable has been deprecated to be replaced by createOrReplaceTempView.dropTempTable has been deprecated to be replaced by dropTempView.sc SparkContext parameter is no longer required for these functions: setJobGroup, clearJobGroup, cancelJobGroup