Intro to Data Analysis using R & Apache Spark
Buu Thong Tran
January 13, 2017
The original article of this tutorial can be found here
Installation
You can install the sparklyr package from CRAN as follows:
install.packages("sparklyr", repos="http://cran.rstudio.com/")## package 'sparklyr' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Baoco\AppData\Local\Temp\RtmpOgwhNO\downloaded_packagesYou 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:
library(dplyr)
devtools::install_github("rstudio/sparklyr")Connecting to Spark
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.
Using dplyr
We can 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"), repos="http://cran.rstudio.com/")## package 'nycflights13' successfully unpacked and MD5 sums checked
## package 'Lahman' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Baoco\AppData\Local\Temp\RtmpOgwhNO\downloaded_packageslibrary(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 [6,233 x 19]
## Database: spark connection master=local[4] 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 6,223 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)
Window Functions
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 [2.562e+04 x 7]
## Database: spark connection master=local[4] app=sparklyr local=TRUE
## Groups: playerID
##
## playerID yearID teamID G AB R H
## <chr> <int> <chr> <int> <int> <int> <int>
## 1 abadan01 2003 BOS 9 17 1 2
## 2 abbated01 1905 BSN 153 610 70 170
## 3 abbated01 1904 BSN 154 579 76 148
## 4 abbotda01 1890 TL2 3 7 0 1
## 5 abbotod01 1910 SLN 22 70 2 13
## 6 abbotpa01 2000 SEA 35 5 1 2
## 7 abbotpa01 2004 PHI 10 11 1 2
## 8 aberal01 1954 DET 32 39 3 5
## 9 aberal01 1953 DET 17 23 2 3
## 10 aberal01 1956 DET 42 10 0 3
## # ... with 2.561e+04 more rowsFor additional documentation on using dplyr with Spark see the dplyr section of the sparklyr website.
Using SQL
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 WHERE Species = 'virginica' ORDER BY Sepal_Length LIMIT 10")
iris_preview## Sepal_Length Sepal_Width Petal_Length Petal_Width Species
## 1 4.9 2.5 4.5 1.7 virginica
## 2 5.6 2.8 4.9 2.0 virginica
## 3 5.7 2.5 5.0 2.0 virginica
## 4 5.8 2.7 5.1 1.9 virginica
## 5 5.8 2.7 5.1 1.9 virginica
## 6 5.8 2.8 5.1 2.4 virginica
## 7 5.9 3.0 5.1 1.8 virginica
## 8 6.0 2.2 5.0 1.5 virginica
## 9 6.0 3.0 4.8 1.8 virginica
## 10 6.1 3.0 4.9 1.8 virginicaMachine Learning
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: ml_linear_regression(., response = "mpg", features = c("wt", "cyl"))
##
## Coefficients:
## (Intercept) wt cyl
## 37.066699 -2.309504 -1.639546For 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: ml_linear_regression(., response = "mpg", features = c("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: 1.799Spark 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.
Reading and Writing Data
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_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"
## [5] "iris_json" "iris_parquet" "mtcars"Extensions
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] 336777To learn more about creating extensions see the Extensions section of the sparklyr website.
Table Utilities
You can cache a table into memory with:
tbl_cache(sc, "batting")and unload from memory using:
tbl_uncache(sc, "batting")Connection Utilities
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)## 17/01/13 23:03:50 INFO Executor: Running task 0.0 in stage 95.0 (TID 156)
## 17/01/13 23:03:50 INFO ShuffleBlockFetcherIterator: Getting 2 non-empty blocks out of 2 blocks
## 17/01/13 23:03:50 INFO ShuffleBlockFetcherIterator: Started 0 remote fetches in 0 ms
## 17/01/13 23:03:50 INFO Executor: Finished task 0.0 in stage 95.0 (TID 156). 1830 bytes result sent to driver
## 17/01/13 23:03:50 INFO DAGScheduler: ResultStage 95 (collect at utils.scala:195) finished in 0.009 s
## 17/01/13 23:03:50 INFO DAGScheduler: Job 63 finished: collect at utils.scala:195, took 0.036041 s
## 17/01/13 23:03:50 INFO TaskSetManager: Finished task 0.0 in stage 95.0 (TID 156) in 8 ms on localhost (1/1)
## 17/01/13 23:03:50 INFO TaskSchedulerImpl: Removed TaskSet 95.0, whose tasks have all completed, from pool
## 17/01/13 23:03:51 INFO MapPartitionsRDD: Removing RDD 70 from persistence list
## 17/01/13 23:03:51 INFO BlockManager: Removing RDD 70Finally, we disconnect from Spark:
spark_disconnect(sc)