We’re happy to announce that version 0.5 of the sparklyr package is now available on CRAN. The new version comes with many improvements over the first release, including:
Extended dplyr support by implementing:
New functions including
Improved compatibility, sparklyr now respects the value of the ‘na.action’ R option and
Improved connections by simplifying initialization and providing error diagnostics.
Additional changes and improvements can be found in the sparklyr NEWS file.
sparklyr 0.5 adds supports for
n_distinct() as a faster and more concise equivalent of
length(unique(x)) and also adds support for
do() as a convenient way to perform multiple serial computations over a
library(sparklyr) sc <- spark_connect(master = "local") mtcars_tbl <- copy_to(sc, mtcars, overwrite = TRUE) by_cyl <- group_by(mtcars_tbl, cyl) fit_sparklyr <- by_cyl %>% do(mod = ml_linear_regression(mpg ~ disp, data = .)) # display results fit_sparklyr$mod
In this case,
. represents a Spark DataFrame, which allows us to perform operations at scale (like this linear regression) for a small set of groups. However, since each group operation is performed sequentially, it is not recommended to use
do() with a large number of groups. The code above performs multiple linear regressions with the following output:
[] Call: ml_linear_regression(mpg ~ disp, data = .) Coefficients: (Intercept) disp 19.081987419 0.003605119 [] Call: ml_linear_regression(mpg ~ disp, data = .) Coefficients: (Intercept) disp 40.8719553 -0.1351418 [] Call: ml_linear_regression(mpg ~ disp, data = .) Coefficients: (Intercept) disp 22.03279891 -0.01963409
It’s worth mentioning that while
sparklyr provides comprehensive support for
dplyr is not strictly required while using
sparklyr. For instance, one can make use of
dplyr as follows:
library(sparklyr) library(DBI) sc <- spark_connect(master = "local") sdf_copy_to(sc, iris) dbGetQuery(sc, "SELECT * FROM iris LIMIT 4")
Sepal_Length Sepal_Width Petal_Length Petal_Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa
sdf_quantile() function computes approximate quantiles (to some relative error), while the new
ft_regex_tokenizer() functions split a string by white spaces or regex patterns.
ft_tokenizer() can be used as follows:
library(sparklyr) library(janeaustenr) library(dplyr) sc %>% spark_dataframe() %>% na.omit() %>% ft_tokenizer(input.col = "text", output.col = "tokens") %>% head(4)
Which produces the following output:
text book tokens <chr> <chr> <list> 1 SENSE AND SENSIBILITY Sense & Sensibility <list > 2 Sense & Sensibility <list > 3 by Jane Austen Sense & Sensibility <list > 4 Sense & Sensibility <list >
Tokens can be further processed through, for instance, HashingTF.
‘na.action’ is a parameter accepted as part of the ‘ml.options’ argument, which defaults to
getOption("na.action", "na.omit"). This allows
sparklyr to match the behavior of R while processing NA records, for instance, the following linear model drops NA record appropriately:
library(sparklyr) library(dplyr) library(nycflights13) sc <- spark_connect(master = "local") flights_clean <- na.omit(copy_to(sc, flights)) ml_linear_regression( flights_tbl response = "dep_delay", features = c("arr_delay", "arr_time"))
* Dropped 9430 rows with 'na.omit' (336776 => 327346) Call: ml_linear_regression(flights_tbl, response = "dep_delay", features = c("arr_delay", "arr_time")) Coefficients: (Intercept) arr_delay arr_time 6.1001212994 0.8210307947 0.0005284729
ncol() are now supported against Spark DataFrames.
Livy, “An Open Source REST Service for Apache Spark (Apache License)", is now available in
sparklyr 0.5 as an experimental feature. Among many scenarios, this enables connections from the RStudio desktop to Apache Spark when Livy is available and correctly configured in the remote cluster.
To work with Livy locally,
livy_install() which installs Livy in your local environment, this is similar to
spark_install(). Since Livy is a service to enable remote connections into Apache Spark, the service needs to be started with
livy_service_start(). Once the service is running,
spark_connect() needs to reference the running service and use
method = "Livy", then
sparklyr can be used as usual. A short example follows:
livy_install() livy_service_start() sc <- spark_connect(master = "http://localhost:8998", method = "livy") copy_to(sc, iris) spark_disconnect(sc) livy_service_stop()
Microsoft Azure supports Apache Spark clusters configured with Livy and protected with basic authentication in HDInsight clusters. To use
sparklyr with HDInsight clusters through Livy, first create the HDInsight cluster with Spark support:
Creating Spark Cluster in Microsoft Azure HDInsight
Once the cluster is created, you can connect with
sparklyr as follows:
library(sparklyr) library(dplyr) config <- livy_config(user = "admin", password = "password") sc <- spark_connect(master = "https://dm.azurehdinsight.net/livy/", method = "livy", config = config) copy_to(sc, iris)
From a desktop running RStudio, the remote connection looks like this:
sparklyr 0.5 no longer requires internet connectivity to download additional Apache Spark packages. This enables connections in secure clusters that do not have internet access or while on the go.
Some community members reported a generic “Ports file does not exists” error while connecting with
sparklyr 0.4. In
0.5, we’ve deprecated the ports file and improved error reporting. For instance, the following invalid connection example throws: a descriptive error, the
spark-submit parameters and logging information that helps us troubleshoot connection issues.
> library(sparklyr) > sc <- spark_connect(master = "local", config = list("sparklyr.gateway.port" = "0")) Error in force(code) : Failed while connecting to sparklyr to port (0) for sessionid (5305): Gateway in port (0) did not respond. Path: /spark-1.6.2-bin-hadoop2.6/bin/spark-submit Parameters: --class, sparklyr.Backend, 'sparklyr-1.6-2.10.jar', 0, 5305 ---- Output Log ---- 16/12/12 12:42:35 INFO sparklyr: Session (5305) starting ---- Error Log ----
Additional technical details can be found in the sparklyr gateway socket pull request.
sparklyr 0.4, sparklyr 0.5, RStudio Server Pro 1.0 and ShinyServer Pro 1.5 went through Cloudera’s certification and are now certified with Cloudera. Among various benefits, authentication features like Kerberos, have been tested and validated against secured clusters.
For more information see Cloudera’s partner listings.
gt 0.6.0 includes new features that will make your display/summary tables look and work better.
We are thrilled to announce the release of vetiver, a framework for MLOps tasks in R and Python. Use vetiver to version, share, deploy, and monitor a trained model.