Databricks supports sparklyr in notebooks, jobs, and RStudio Desktop.
Databricks distributes the latest stable version of sparklyr with every runtime release. You can use sparklyr in Databricks R notebooks or inside RStudio Server hosted on Databricks by importing the installed version of sparklyr.
In RStudio Desktop, Databricks Connect allows you to connect sparklyr from your local machine to Databricks clusters and run Apache Spark code. See Use sparklyr and RStudio Desktop with Databricks Connect.
- Databricks Runtime 5.3 and above installs the latest stable version of sparklyr. For these runtimes you can skip the installation step.
- Some sparklyr dependencies are installed as source packages and require the latest version of the Rcpp package. Update this package before installing sparklyr.
You can install sparklyr from CRAN or GitHub.
Install the latest version of sparklyr from CRAN.
# Install latest version of Rcpp install.packages("Rcpp") # Install sparklyr. It can take a few minutes, because it installs +10 dependencies. install.packages("sparklyr") # Load sparklyr package. library(sparklyr)
Install the latest development version of sparklyr from GitHub.
# Install latest version of Rcpp install.packages("Rcpp") # Use devtools to install sparklyr from GitHub devtools::install_github("rstudio/sparklyr") # Load sparklyr package. library(sparklyr)
To establish a sparklyr connection, you can use
"databricks" as the connection method in
No additional parameters to
spark_connect() are needed, nor is calling
spark_install() needed because Spark is already installed on a Databricks cluster.
# create a sparklyr connection sc <- spark_connect(method = "databricks")
If you assign the sparklyr connection object to a variable named
sc as in the above example,
you will see Spark progress bars in the notebook after each command that triggers Spark jobs.
In addition, you can click the link next to the progress bar to view the Spark UI associated with
the given Spark job.
sparklyr is usually used along with other tidyverse packages such as dplyr. Most of these packages are preinstalled on Databricks for your convenience. You can simply import them and start using the API.
SparkR and sparklyr can be used together in a single notebook or job. You can import SparkR along with sparklyr and use its functionality. In Databricks notebooks, the SparkR connection is pre-configured.
Some of the functions in SparkR mask a number of functions in dplyr:
> library(SparkR) The following objects are masked from ‘package:dplyr’: arrange, between, coalesce, collect, contains, count, cume_dist, dense_rank, desc, distinct, explain, filter, first, group_by, intersect, lag, last, lead, mutate, n, n_distinct, ntile, percent_rank, rename, row_number, sample_frac, select, sql, summarize, union
If you import SparkR after you imported dplyr, you can reference the functions in dplyr by using
the fully qualified names, for example,
Similarly if you import dplyr after SparkR, the functions in SparkR are masked by dplyr.
Alternatively, you can selectively detach one of the two packages while you do not need it.
You can run scripts that use sparklyr on Databricks as spark-submit jobs, with minor code modifications. Some of the instructions above do not apply to using sparklyr in spark-submit jobs on Databricks. In particular, you must provide the Spark master URL to
spark_connect. For an example, refer to Create and run a spark-submit job for R scripts.
Databricks does not support sparklyr methods such as
spark_log() that require a
local browser. However, since the Spark UI is built-in on Databricks, you can inspect Spark jobs and logs easily.
See Cluster driver and worker logs.