sparklyr v0.2.32

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by Javier Luraschi

R Interface to Apache Spark

Provision, connect and interface to Apache Spark from within R. This package supports connecting to local and remote Apache Spark clusters, provides a dplyr-compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

Readme

sparklyr: R interface for Apache Spark

Build Status

  • 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.

Installation

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).

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.

For more information on connecting to remote Spark clusters see the Deployment section.of the sparklyr website.

Using dplyr

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)

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 [?? 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.

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 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

Machine 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:
## 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.

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_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"

Extensions

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.

dplyr 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)
## 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)

RStudio IDE

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.

Functions in sparklyr

Name Description
ensure Enforce Specific Structure for R Objects
ft_index_to_string Feature Transformation -- IndexToString
ft_one_hot_encoder Feature Transformation -- OneHotEncoder
ft_sql_transformer Feature Transformation -- SQLTransformer
copy_to Copy a local R data frame to Spark
ft_elementwise_product Feature Transformation -- ElementwiseProduct
ft_bucketizer Feature Transformation -- Bucketizer
ft_discrete_cosine_transform Feature Transformation -- Discrete Cosine Transform (DCT)
ft_quantile_discretizer Feature Transformation -- QuantileDiscretizer
ft_binarizer Feature Transformation -- Binarizer
ml_create_dummy_variables Create Dummy Variables
ml_lda Spark ML -- Latent Dirichlet Allocation
ml_logistic_regression Spark ML -- Logistic Regression
ft_string_indexer Feature Transformation -- StringIndexer
ml_generalized_linear_regression Spark ML -- Generalized Linear Regression
ft_vector_assembler Feature Transformation -- VectorAssembler
ml_linear_regression Spark ML -- Linear Regression
ml_kmeans Spark ML -- K-Means Clustering
ml_gradient_boosted_trees Spark ML -- Gradient-Boosted Tree
ml_decision_tree Spark ML -- Decision Trees
ml_multilayer_perceptron Spark ML -- Multilayer Perceptron
ml_model Create an ML Model Object
ml_random_forest Spark ML -- Random Forests
ml_pca Spark ML -- Principal Components Analysis
%>% Pipe operator
ml_prepare_response_features_intercept Pre-process the Inputs to a Spark ML Routine
ml_naive_bayes Spark ML -- Naive-Bayes
ml_survival_regression Spark ML -- Survival Regression
ml_prepare_dataframe Prepare a Spark DataFrame for Spark ML Routines
ml_one_vs_rest Spark ML -- One vs Rest
sdf_copy_to Copy an Object into Spark
spark_connect Connect to Spark
spark_connection_is_open Check if a Spark connection is open
sdf_register Register a Spark DataFrame
sdf_sample Randomly Sample Rows from a Spark DataFrame
sdf_mutate Mutate a Spark DataFrame
spark_config Read Spark Configuration
sdf_partition Partition a Spark Dataframe
sdf_predict Model Predictions with Spark DataFrames
sdf_sort Sort a Spark DataFrame
spark_read_csv Read a CSV file into a Spark DataFrame
spark_read_json Read a JSON file into a Spark DataFrame
spark_install Download and install various versions of Spark
spark_log Objects exported from other packages
spark_disconnect Disconnect from Spark
spark_read_parquet Read a Parquet file into a Spark DataFrame
spark_home_dir Find the SPARK_HOME directory for a version of Spark
spark_web Objects exported from other packages
spark_write_csv Write a Spark DataFrame to a CSV
spark_write_json Write a Spark DataFrame to a JSON file
tbl_cache Load a table into memory
spark_write_parquet Write a Spark DataFrame to a Parquet file
tbl_uncache Unload table from memory
na.replace Replace Missing Values in Objects
ml_load Save / Load a Spark ML Model Fit
ml_als_factorization Spark ML -- Alternating Least Squares (ALS) matrix factorization.
sdf_with_unique_id Add a Unique ID Column to a Spark DataFrame
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