Azure Databricks supports sparklyr in notebooks and jobs.
Databricks supports sparklyr 0.5.5 and above with Apache Spark 2.2 and above and Scala 2.11. This guide is based on Apache Spark 2.2.
Install the latest version of sparklyr from CRAN.
Some sparklyr dependencies are installed as source packages and require the latest version of the Rcpp package. Update this package before installing sparklyr.
# Installing latest version of Rcpp install.packages("Rcpp") # Installing sparklyr takes a few minutes, becauses it installs +10 dependencies. install.packages("sparklyr") # Load sparklyr package. library(sparklyr)
Alternatively, you can install the latest development version of sparklyr from GitHub.
# Installing latest version of Rcpp install.packages("Rcpp") # Using 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.
After installing sparklyr and establishing the connection, all other sparklyr API would work as they normally do. See the example notebook below for some examples.
sparklyr is usually used along with other tidyverse packages such as dplyr. Most of these packages are pre-installed 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.
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 Logs.