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.

Connect sparklyr to Databricks clusters

To establish a sparklyr connection, you can use "databricks" as the connection method in spark_connect(). 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")

Progress bars and Spark UI with sparklyr

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 progress

Use sparklyr

After you install sparklyr and establish the connection, all other sparklyr API work as they normally do. See the example notebook for some examples.

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.

Use sparklyr and SparkR together

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 frompackage: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, dplyr::arrange(). 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.


Use sparklyr in spark-submit jobs

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, see Create and run a spark-submit job for R scripts.

Unsupported features

Databricks does not support sparklyr methods such as spark_web() and 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.

Sparklyr notebook

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