- Install and connect to Spark using YARN, Mesos, Livy or Kubernetes.
- Use dplyr to filter and aggregate Spark datasets and streams then bring them into R for analysis and visualization.
- Use MLlib, H2O, XGBoost and GraphFrames to train models at scale in Spark.
- Create interoperable machine learning pipelines and productionize them with MLeap.
- Create extensions that call the full Spark API or run distributed R code to support new functionality.
- Installation
- Connecting to Spark
- Using dplyr
- Using SQL
- Machine Learning
- Reading and Writing Data
- Distributed R
- Extensions
- Table Utilities
- Connection Utilities
- RStudio IDE
- Using H2O
- Connecting through Livy
- Connecting through Databricks Connect
You can install the sparklyr package from CRAN as follows:
install.packages("sparklyr")
You should also install a local version of Spark for development purposes:
library(sparklyr)
spark_install()
To upgrade to the latest version of sparklyr, run the following command and restart your r session:
install.packages("devtools")
devtools::install_github("sparklyr/sparklyr")
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 now 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, overwrite = TRUE)
flights_tbl <- copy_to(sc, nycflights13::flights, "flights", overwrite = TRUE)
batting_tbl <- copy_to(sc, Lahman::Batting, "batting", overwrite = TRUE)
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: spark<?> [?? x 19]
#> year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#> <int> <int> <int> <int> <int> <dbl> <int> <int>
#> 1 2013 1 1 517 515 2 830 819
#> 2 2013 1 1 542 540 2 923 850
#> 3 2013 1 1 702 700 2 1058 1014
#> 4 2013 1 1 715 713 2 911 850
#> 5 2013 1 1 752 750 2 1025 1029
#> 6 2013 1 1 917 915 2 1206 1211
#> 7 2013 1 1 932 930 2 1219 1225
#> 8 2013 1 1 1028 1026 2 1350 1339
#> 9 2013 1 1 1042 1040 2 1325 1326
#> 10 2013 1 1 1231 1229 2 1523 1529
#> # … with more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
#> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> # distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>
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)
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
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: spark<?> [?? x 7]
#> # Groups: playerID
#> # Ordered by: playerID, yearID, teamID
#> playerID yearID teamID G AB R H
#> <chr> <int> <chr> <int> <int> <int> <int>
#> 1 aaronha01 1959 ML1 154 629 116 223
#> 2 aaronha01 1963 ML1 161 631 121 201
#> 3 abbotji01 1999 MIL 20 21 0 2
#> 4 abnersh01 1992 CHA 97 208 21 58
#> 5 abnersh01 1990 SDN 91 184 17 45
#> 6 acklefr01 1963 CHA 2 5 0 1
#> 7 acklefr01 1964 CHA 3 1 0 1
#> 8 acunaro01 2019 ATL 156 626 127 175
#> 9 acunaro01 2018 ATL 111 433 78 127
#> 10 adamecr01 2016 COL 121 225 25 49
#> # … 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, overwrite = TRUE)
# 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
#> Formula: mpg ~ wt + cyl
#>
#> Coefficients:
#> (Intercept) wt cyl
#> 37.1464554 -4.3408005 -0.5830515
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)
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.5134 -0.9158 -0.1683 1.1503 2.1534
#>
#> Coefficients:
#> (Intercept) wt cyl
#> 37.1464554 -4.3408005 -0.5830515
#>
#> R-Squared: 0.9428
#> Root Mean Squared Error: 1.409
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 local 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_json(iris_tbl, temp_json)
iris_json_tbl <- spark_read_json(sc, "iris_json", temp_json)
src_tbls(sc)
#> [1] "batting" "flights" "iris" "iris_csv" "iris_json"
#> [6] "iris_parquet" "mtcars"
You can execute arbitrary r code across your cluster using
spark_apply(). For example, we can apply rgamma over iris as
follows:
spark_apply(iris_tbl, function(data) {
data[1:4] + rgamma(1,2)
})
#> # Source: spark<?> [?? x 4]
#> Sepal_Length Sepal_Width Petal_Length Petal_Width
#> <dbl> <dbl> <dbl> <dbl>
#> 1 6.45 4.85 2.75 1.55
#> 2 6.25 4.35 2.75 1.55
#> 3 6.05 4.55 2.65 1.55
#> 4 5.95 4.45 2.85 1.55
#> 5 6.35 4.95 2.75 1.55
#> 6 6.75 5.25 3.05 1.75
#> 7 5.95 4.75 2.75 1.65
#> 8 6.35 4.75 2.85 1.55
#> 9 5.75 4.25 2.75 1.55
#> 10 6.25 4.45 2.85 1.45
#> # … with more rows
You can also group by columns to perform an operation over each group of rows and make use of any package within the closure:
spark_apply(
iris_tbl,
function(e) broom::tidy(lm(Petal_Width ~ Petal_Length, e)),
columns = c("term", "estimate", "std.error", "statistic", "p.value"),
group_by = "Species"
)
#> # Source: spark<?> [?? x 6]
#> Species term estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 versicolor (Intercept) -0.0843 0.161 -0.525 6.02e- 1
#> 2 versicolor Petal_Length 0.331 0.0375 8.83 1.27e-11
#> 3 virginica (Intercept) 1.14 0.379 2.99 4.34e- 3
#> 4 virginica Petal_Length 0.160 0.0680 2.36 2.25e- 2
#> 5 setosa (Intercept) -0.0482 0.122 -0.396 6.94e- 1
#> 6 setosa Petal_Length 0.201 0.0826 2.44 1.86e- 2
The facilities used internally by sparklyr for its dplyr and machine
learning interfaces are available to extension packages. 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:
# 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)
#> 22/05/25 15:05:25 INFO BlockManagerInfo: Removed broadcast_84_piece0 on localhost:58163 in memory (size: 9.2 KiB, free: 912.1 MiB)
#> 22/05/25 15:05:25 INFO BlockManagerInfo: Removed broadcast_87_piece0 on localhost:58163 in memory (size: 18.4 KiB, free: 912.1 MiB)
#> 22/05/25 15:05:25 INFO BlockManagerInfo: Removed broadcast_77_piece0 on localhost:58163 in memory (size: 16.7 KiB, free: 912.1 MiB)
#> 22/05/25 15:05:25 INFO Executor: Finished task 0.0 in stage 67.0 (TID 83). 1004 bytes result sent to driver
#> 22/05/25 15:05:25 INFO TaskSetManager: Finished task 0.0 in stage 67.0 (TID 83) in 244 ms on localhost (executor driver) (1/1)
#> 22/05/25 15:05:25 INFO TaskSchedulerImpl: Removed TaskSet 67.0, whose tasks have all completed, from pool
#> 22/05/25 15:05:25 INFO DAGScheduler: ResultStage 67 (count at NativeMethodAccessorImpl.java:0) finished in 0.259 s
#> 22/05/25 15:05:25 INFO DAGScheduler: Job 49 is finished. Cancelling potential speculative or zombie tasks for this job
#> 22/05/25 15:05:25 INFO TaskSchedulerImpl: Killing all running tasks in stage 67: Stage finished
#> 22/05/25 15:05:25 INFO DAGScheduler: Job 49 finished: count at NativeMethodAccessorImpl.java:0, took 0.268655 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 and preview Spark DataFrames using the standard RStudio data viewer:
You can also connect to Spark through Livy through a new connection dialog:
The RStudio IDE features for sparklyr are available now as part of the RStudio Preview Release.
rsparkling is a CRAN package from H2O that extends sparklyr to provide an interface into Sparkling Water. For instance, the following example installs, configures and runs h2o.glm:
library(rsparkling)
library(sparklyr)
library(dplyr)
library(h2o)
sc <- spark_connect(master = "local", version = "2.3.2")
mtcars_tbl <- copy_to(sc, mtcars, "mtcars", overwrite = TRUE)
mtcars_h2o <- as_h2o_frame(sc, mtcars_tbl, strict_version_check = FALSE)
mtcars_glm <- h2o.glm(x = c("wt", "cyl"),
y = "mpg",
training_frame = mtcars_h2o,
lambda_search = TRUE)
mtcars_glm
#> Model Details:
#> ==============
#>
#> H2ORegressionModel: glm
#> Model ID: GLM_model_R_1527265202599_1
#> GLM Model: summary
#> family link regularization
#> 1 gaussian identity Elastic Net (alpha = 0.5, lambda = 0.1013 )
#> lambda_search
#> 1 nlambda = 100, lambda.max = 10.132, lambda.min = 0.1013, lambda.1se = -1.0
#> number_of_predictors_total number_of_active_predictors
#> 1 2 2
#> number_of_iterations training_frame
#> 1 100 frame_rdd_31_ad5c4e88ec97eb8ccedae9475ad34e02
#>
#> Coefficients: glm coefficients
#> names coefficients standardized_coefficients
#> 1 Intercept 38.941654 20.090625
#> 2 cyl -1.468783 -2.623132
#> 3 wt -3.034558 -2.969186
#>
#> H2ORegressionMetrics: glm
#> ** Reported on training data. **
#>
#> MSE: 6.017684
#> RMSE: 2.453097
#> MAE: 1.940985
#> RMSLE: 0.1114801
#> Mean Residual Deviance : 6.017684
#> R^2 : 0.8289895
#> Null Deviance :1126.047
#> Null D.o.F. :31
#> Residual Deviance :192.5659
#> Residual D.o.F. :29
#> AIC :156.2425
spark_disconnect(sc)
Livy enables remote connections to Apache Spark clusters. However, please notice that connecting to Spark clusters through Livy is much slower than any other connection method.
Before connecting to Livy, you will need the connection information to
an existing service running Livy. Otherwise, to test livy in your
local environment, you can install it and run it locally as follows:
livy_install()
livy_service_start()
To connect, use the Livy service address as master and
method = "livy" in spark_connect(). Once connection completes, use
sparklyr as usual, for instance:
sc <- spark_connect(master = "http://localhost:8998", method = "livy", version = "3.0.0")
copy_to(sc, iris, overwrite = TRUE)
spark_disconnect(sc)
Once you are done using livy locally, you should stop this service
with:
livy_service_stop()
To connect to remote livy clusters that support basic authentication
connect as:
config <- livy_config(username="<username>", password="<password>")
sc <- spark_connect(master = "<address>", method = "livy", config = config)
spark_disconnect(sc)
Databricks Connect allows you to connect sparklyr to a remote Databricks Cluster. You can install Databricks Connect python package and use it to submit Spark jobs written in sparklyr APIs and have them execute remotely on a Databricks cluster instead of in the local Spark session.
To use sparklyr with Databricks Connect first launch a Cluster on Databricks. Then follow these instructions to setup the client:
- Make sure pyspark is not installed
- Install the Databricks Connect python package. The latest supported version is 6.4.1.
- Run
databricks-connect configureand provide the configuration information- Databricks account URL of the form
https://<account>.cloud.databricks.com. - User token
- Cluster ID
- Port (default port number is
15001)
- Databricks account URL of the form
To configure sparklyr with Databricks Connect, set the following
environment variables:
export SPARK_VERSION=2.4.4
Now simply create a spark connection as follows
spark_home <- system("databricks-connect get-spark-home")
sc <- spark_connect(method = "databricks",
spark_home = spark_home)
copy_to(sc, iris, overwrite = TRUE)
spark_disconnect(sc)