- 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.
- Leverage Spark's MLlib (machine learning library) for distributed machine learning 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 = "1.6.1")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)
library(dplyr)
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):
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 16]
## Database: spark connection master=local app=sparklyr local=TRUE
##
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 1 1 517 2 830 11 UA N14228
## 2 2013 1 1 542 2 923 33 AA N619AA
## 3 2013 1 1 702 2 1058 44 B6 N779JB
## 4 2013 1 1 715 2 911 21 UA N841UA
## 5 2013 1 1 752 2 1025 -4 UA N511UA
## 6 2013 1 1 917 2 1206 -5 B6 N568JB
## 7 2013 1 1 932 2 1219 -6 VX N641VA
## 8 2013 1 1 1028 2 1350 11 UA N76508
## 9 2013 1 1 1042 2 1325 -1 B6 N529JB
## 10 2013 1 1 1231 2 1523 -6 UA N402UA
## .. ... ... ... ... ... ... ... ... ...
## Variables not shown: flight <int>, origin <chr>, dest <chr>, air_time
## <dbl>, distance <dbl>, hour <dbl>, minute <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 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
## .. ... ... ... ... ... ... ...
For additional documentation on using dplyr with Spark see the dplyr section of the sparklyr website.
You can orchestrate machine learning algorithms in a Spark cluster via the MLlib functions in 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
## 33.499452 -2.818463 -0.923187
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
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.752 -1.134 -0.499 1.296 2.282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 33.49945 3.62256 9.2475 0.0002485 ***
## wt -2.81846 0.96619 -2.9171 0.0331257 *
## cyl -0.92319 0.54639 -1.6896 0.1518998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-Squared: 0.8274
## Root Mean Squared Error: 1.422
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 MLlib 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 MLlib 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)
# 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)## 16/06/27 16:01:03 INFO BlockManagerInfo: Removed broadcast_79_piece0 on localhost:49309 in memory (size: 4.6 KB, free: 487.2 MB)
## 16/06/27 16:01:03 INFO ContextCleaner: Cleaned accumulator 266
## 16/06/27 16:01:03 INFO BlockManagerInfo: Removed broadcast_78_piece0 on localhost:49309 in memory (size: 8.6 KB, free: 487.2 MB)
## 16/06/27 16:01:03 INFO ContextCleaner: Cleaned accumulator 265
## 16/06/27 16:01:03 INFO ContextCleaner: Cleaned shuffle 19
## 16/06/27 16:01:03 INFO Executor: Finished task 0.0 in stage 66.0 (TID 464). 2082 bytes result sent to driver
## 16/06/27 16:01:03 INFO TaskSetManager: Finished task 0.0 in stage 66.0 (TID 464) in 97 ms on localhost (1/1)
## 16/06/27 16:01:03 INFO TaskSchedulerImpl: Removed TaskSet 66.0, whose tasks have all completed, from pool
## 16/06/27 16:01:03 INFO DAGScheduler: ResultStage 66 (count at NativeMethodAccessorImpl.java:-2) finished in 0.097 s
## 16/06/27 16:01:03 INFO DAGScheduler: Job 46 finished: count at NativeMethodAccessorImpl.java:-2, took 0.100145 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.