- Connect to Spark from R. The sparklyr package provides a
complete dplyr backend. - Filter and aggregate Spark datasets then bring them into R for analysis and visualization.
- Use Spark's distributed machine learning library from R.
- Create extensions that call the full Spark API and provide
interfaces to Spark packages.
You can install the development version of the sparklyr package using devtools as follows:
install.packages("devtools")
devtools::install_github("rstudio/sparklyr")You should also install a local version of Spark for development purposes:
library(sparklyr)
spark_install(version = "1.6.2")To upgrade to the latest version of sparklyr, run the following command and restart your r session:
devtools::install_github("rstudio/sparklyr")If you use the RStudio IDE, you should also download the latest preview release of the IDE which includes several enhancements for interacting with Spark (see the RStudio IDE section below for more details).
You can connect to both local instances of Spark as well as remote Spark clusters. Here we'll connect to a local instance of Spark via the spark_connect function:
library(sparklyr)
sc <- spark_connect(master = "local")The returned Spark connection (sc) provides a remote dplyr data source to the Spark cluster.
For more information on connecting to remote Spark clusters see the Deployment section.of the sparklyr website.
We can new use all of the available dplyr verbs against the tables within the cluster.
We'll start by copying some datasets from R into the Spark cluster (note that you may need to install the nycflights13 and Lahman packages in order to execute this code):
install.packages(c("nycflights13", "Lahman"))library(dplyr)
iris_tbl <- copy_to(sc, iris)
flights_tbl <- copy_to(sc, nycflights13::flights, "flights")
batting_tbl <- copy_to(sc, Lahman::Batting, "batting")
src_tbls(sc)## [1] "batting" "flights" "iris"
To start with here's a simple filtering example:
# filter by departure delay and print the first few records
flights_tbl %>% filter(dep_delay == 2)## Source: query [?? x 19]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
##
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 542 540 2 923
## 3 2013 1 1 702 700 2 1058
## 4 2013 1 1 715 713 2 911
## 5 2013 1 1 752 750 2 1025
## 6 2013 1 1 917 915 2 1206
## 7 2013 1 1 932 930 2 1219
## 8 2013 1 1 1028 1026 2 1350
## 9 2013 1 1 1042 1040 2 1325
## 10 2013 1 1 1231 1229 2 1523
## # ... with more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dbl>
Introduction to dplyr provides additional dplyr examples you can try. For example, consider the last example from the tutorial which plots data on flight delays:
delay <- flights_tbl %>%
group_by(tailnum) %>%
summarise(count = n(), dist = mean(distance), delay = mean(arr_delay)) %>%
filter(count > 20, dist < 2000, !is.na(delay)) %>%
collect
# plot delays
library(ggplot2)
ggplot(delay, aes(dist, delay)) +
geom_point(aes(size = count), alpha = 1/2) +
geom_smooth() +
scale_size_area(max_size = 2)
dplyr window functions are also supported, for example:
batting_tbl %>%
select(playerID, yearID, teamID, G, AB:H) %>%
arrange(playerID, yearID, teamID) %>%
group_by(playerID) %>%
filter(min_rank(desc(H)) <= 2 & H > 0)## Source: query [?? x 7]
## Database: spark connection master=local[8] app=sparklyr local=TRUE
## Groups: playerID
##
## playerID yearID teamID G AB R H
## <chr> <int> <chr> <int> <int> <int> <int>
## 1 anderal01 1941 PIT 70 223 32 48
## 2 anderal01 1942 PIT 54 166 24 45
## 3 balesco01 2008 WAS 15 15 1 3
## 4 balesco01 2009 WAS 7 8 0 1
## 5 bandoch01 1986 CLE 92 254 28 68
## 6 bandoch01 1984 CLE 75 220 38 64
## 7 bedelho01 1962 ML1 58 138 15 27
## 8 bedelho01 1968 PHI 9 7 0 1
## 9 biittla01 1977 CHN 138 493 74 147
## 10 biittla01 1975 MON 121 346 34 109
## # ... with more rows
For additional documentation on using dplyr with Spark see the dplyr section of the sparklyr website.
It's also possible to execute SQL queries directly against tables within a Spark cluster. The spark_connection object implements a DBI interface for Spark, so you can use dbGetQuery to execute SQL and return the result as an R data frame:
library(DBI)
iris_preview <- dbGetQuery(sc, "SELECT * FROM iris LIMIT 10")
iris_preview## 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
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
You can orchestrate machine learning algorithms in a Spark cluster via the machine learning functions within sparklyr. These functions connect to a set of high-level APIs built on top of DataFrames that help you create and tune machine learning workflows.
Here's an example where we use ml_linear_regression to fit a linear regression model. We'll use the built-in mtcars dataset, and see if we can predict a car's fuel consumption (mpg) based on its weight (wt), and the number of cylinders the engine contains (cyl). We'll assume in each case that the relationship between mpg and each of our features is linear.
# copy mtcars into spark
mtcars_tbl <- copy_to(sc, mtcars)
# transform our data set, and then partition into 'training', 'test'
partitions <- mtcars_tbl %>%
filter(hp >= 100) %>%
mutate(cyl8 = cyl == 8) %>%
sdf_partition(training = 0.5, test = 0.5, seed = 1099)
# fit a linear model to the training dataset
fit <- partitions$training %>%
ml_linear_regression(response = "mpg", features = c("wt", "cyl"))
fit## Call:
## mpg ~ wt + cyl
##
## Coefficients:
## (Intercept) wt cyl
## 37.066699 -2.309504 -1.639546
For linear regression models produced by Spark, we can use summary() to learn a bit more about the quality of our fit, and the statistical significance of each of our predictors.
summary(fit)## Call:
## mpg ~ wt + cyl
##
## Deviance Residuals::
## Min 1Q Median 3Q Max
## -2.6881 -1.0507 -0.4420 0.4757 3.3858
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.06670 2.76494 13.4059 2.981e-07 ***
## wt -2.30950 0.84748 -2.7252 0.02341 *
## cyl -1.63955 0.58635 -2.7962 0.02084 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-Squared: 0.8665
## Root Mean Squared Error: 1.799
Spark machine learning supports a wide array of algorithms and feature transformations and as illustrated above it's easy to chain these functions together with dplyr pipelines. To learn more see the machine learning section.
You can read and write data in CSV, JSON, and Parquet formats. Data can be stored in HDFS, S3, or on the lcoal filesystem of cluster nodes.
temp_csv <- tempfile(fileext = ".csv")
temp_parquet <- tempfile(fileext = ".parquet")
temp_json <- tempfile(fileext = ".json")
spark_write_csv(iris_tbl, temp_csv)
iris_csv_tbl <- spark_read_csv(sc, "iris_csv", temp_csv)
spark_write_parquet(iris_tbl, temp_parquet)
iris_parquet_tbl <- spark_read_parquet(sc, "iris_parquet", temp_parquet)
spark_write_csv(iris_tbl, temp_json)
iris_json_tbl <- spark_read_csv(sc, "iris_json", temp_json)
src_tbls(sc)## [1] "batting" "flights" "iris" "iris_csv"
## [5] "iris_json" "iris_parquet" "mtcars"
The facilities used internally by sparklyr for its dplyr and machine learning interfaces are available to extension packages via the sparkapi package. Since Spark is a general purpose cluster computing system there are many potential applications for extensions (e.g. interfaces to custom machine learning pipelines, interfaces to 3rd party Spark packages, etc.).
Here's a simple example that wraps a Spark text file line counting function with an R function:
library(sparkapi)##
## Attaching package: 'sparkapi'
## The following object is masked from 'package:sparklyr':
##
## spark_web
# write a CSV
tempfile <- tempfile(fileext = ".csv")
write.csv(nycflights13::flights, tempfile, row.names = FALSE, na = "")
# define an R interface to Spark line counting
count_lines <- function(sc, path) {
spark_context(sc) %>%
invoke("textFile", path, 1L) %>%
invoke("count")
}
# call spark to count the lines of the CSV
count_lines(sc, tempfile)## [1] 336777
To learn more about creating extensions see the Extensions section of the sparklyr website.
You can cache a table into memory with:
tbl_cache(sc, "batting")and unload from memory using:
tbl_uncache(sc, "batting")You can view the Spark web console using the spark_web function:
spark_web(sc)You can show the log using the spark_log function:
spark_log(sc, n = 10)## 16/07/11 08:02:53 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 67 (/var/folders/st/b1kz7ydn54nfzfsrl7_hggyc0000gn/T//RtmpxqBOpz/file74f16edc3460.csv MapPartitionsRDD[300] at textFile at NativeMethodAccessorImpl.java:-2)
## 16/07/11 08:02:53 INFO TaskSchedulerImpl: Adding task set 67.0 with 1 tasks
## 16/07/11 08:02:53 INFO TaskSetManager: Starting task 0.0 in stage 67.0 (TID 501, localhost, partition 0,PROCESS_LOCAL, 2473 bytes)
## 16/07/11 08:02:53 INFO Executor: Running task 0.0 in stage 67.0 (TID 501)
## 16/07/11 08:02:53 INFO HadoopRDD: Input split: file:/var/folders/st/b1kz7ydn54nfzfsrl7_hggyc0000gn/T/RtmpxqBOpz/file74f16edc3460.csv:0+33313106
## 16/07/11 08:02:53 INFO Executor: Finished task 0.0 in stage 67.0 (TID 501). 2082 bytes result sent to driver
## 16/07/11 08:02:53 INFO TaskSetManager: Finished task 0.0 in stage 67.0 (TID 501) in 103 ms on localhost (1/1)
## 16/07/11 08:02:53 INFO TaskSchedulerImpl: Removed TaskSet 67.0, whose tasks have all completed, from pool
## 16/07/11 08:02:53 INFO DAGScheduler: ResultStage 67 (count at NativeMethodAccessorImpl.java:-2) finished in 0.103 s
## 16/07/11 08:02:53 INFO DAGScheduler: Job 47 finished: count at NativeMethodAccessorImpl.java:-2, took 0.107400 s
Finally, we disconnect from Spark:
spark_disconnect(sc)The latest RStudio Preview Release of the RStudio IDE includes integrated support for Spark and the sparklyr package, including tools for:
- Creating and managing Spark connections
- Browsing the tables and columns of Spark DataFrames
- Previewing the first 1,000 rows of Spark DataFrames
Once you've installed the sparklyr package, you should find a new Spark pane within the IDE. This pane includes a New Connection dialog which can be used to make connections to local or remote Spark instances:
Once you've connected to Spark you'll be able to browse the tables contained within the Spark cluster:
The Spark DataFrame preview uses the standard RStudio data viewer:
The RStudio IDE features for sparklyr are available now as part of the RStudio Preview Release.