sparkredshipt is an extension for sparklyr to read data from Amazon Redshift into Spark DataFrames. It uses the Spark package spark-redshift to load redshift data into Spark DataFrames.
sparkredshift requires the sparklyr package to run
I recommend the latest stable version of sparklyr available on CRAN
install.packages("sparklyr")
Install the development version of sparkredshift from this Github repo using devtools
library(devtools)
devtools::install_github("emaasit/sparkredshift")
If Spark is not already installed, use the following sparklyr command to install your preferred version of Spark:
library(sparklyr)
spark_install(version = "2.0.0")
The call to library(sparkredshift)
will make the sparkredshift
functions available on the R search path and will also ensure that the
dependencies required by the package are included when we connect to
Spark.
library(sparkredshift)
We can create a Spark connection as follows:
sc <- spark_connect(master = "local")
sparkredshift provides the function spark_read_redshift
to read
redshift data files into Spark DataFrames. It uses a Spark package
called spark-redshift. Here's an example.
mtcars_file <- system.file("extdata", "mtcars.redshift", package = "sparkredshift")
mtcars_df <- spark_read_redshift(sc, path = mtcars_file, table = "redshift_table")
mtcars_df
The resulting pointer to a Spark table can be further used in dplyr statements.
library(dplyr)
mtcars_df %>% group_by(cyl) %>%
summarise(count = n(), avg.mpg = mean(mpg), avg.displacment = mean(disp), avg.horsepower = mean(hp))
Look at the Spark log from R:
spark_log(sc, n = 100)
Now we disconnect from Spark:
spark_disconnect(sc)
Thanks to RStudio for the sparklyr package that provides functionality to create Extensions.
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