Read in SAS data in parallel into Spark"

This R package allows R users to easily import large SAS datasets into Spark tables in parallel.

The package uses the spark-sas7bdat Spark package in order to read a SAS dataset in Spark. That Spark package imports the data in parallel on the Spark cluster using the Parso library and this process is launched from R using the sparklyr functionality.

More information about the spark-sas7bdat Spark package and sparklyr can be found at:


The following example reads in a file called iris.sas7bdat in parallel in a table called sas_example in Spark. Do try this with bigger data on your cluster and look at the help of the sparklyr package to connect to your Spark cluster.

mysasfile <- system.file("extdata", "iris.sas7bdat", package = "spark.sas7bdat")

sc <- spark_connect(master = "local")
x <- spark_read_sas(sc, path = mysasfile, table = "sas_example")

The resulting pointer to a Spark table can be further used in dplyr statements. These will be executed in parallel using the Spark functionalities of the spark-sas7bdat package.

x %>% group_by(Species) %>%
  summarise(count = n(), length = mean(Sepal_Length), width = mean(Sepal_Width))

Support in big data and Spark analysis

Need support in big data and Spark analysis? Contact BNOSAC:

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spark.sas7bdat documentation built on May 2, 2019, 2:43 a.m.