R interface for Apache Spark
sparklyr: R interface for Apache Spark
================

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- Install and connect to [Spark](https://spark.apache.org/) using YARN,
  Mesos, Livy or Kubernetes.
- Use [dplyr](#using-dplyr) to filter and aggregate Spark datasets and
  [streams](https://spark.rstudio.com/guides/streaming/) then bring them
  into R for analysis and visualization.
- Use [MLlib](#machine-learning), [H2O](#using-h2o),
  [XGBoost](https://spark.rstudio.com/packages/sparkxgb/latest/) and
  [GraphFrames](https://spark.rstudio.com/packages/graphframes/latest/)
  to train models at scale in Spark.
- Create interoperable machine learning
  [pipelines](https://spark.rstudio.com/guides/pipelines.html) and
  productionize them with
  [MLeap](https://spark.rstudio.com/packages/mleap/latest/).
- Create [extensions](#extensions) that call the full Spark API or run
  [distributed R](#distributed-r) code to support new functionality.

## Table of Contents

- [Installation](#installation)
- [Connecting to Spark](#connecting-to-spark)
- [Using dplyr](#using-dplyr)
  - [Window Functions](#window-functions)
- [Using SQL](#using-sql)
- [Machine Learning](#machine-learning)
- [Reading and Writing Data](#reading-and-writing-data)
- [Distributed R](#distributed-r)
- [Extensions](#extensions)
- [Table Utilities](#table-utilities)
- [Connection Utilities](#connection-utilities)
- [RStudio IDE](#rstudio-ide)
- [Using H2O](#using-h2o)
- [Connecting through Livy](#connecting-through-livy)
- [Connecting through Databricks
  Connect](#connecting-through-databricks-connect)

## Installation

You can install the **sparklyr** package from
[CRAN](https://CRAN.r-project.org) as follows:

``` r
install.packages("sparklyr")
```

You should also install a local version of Spark for development
purposes:

``` r
library(sparklyr)
spark_install()
```

To upgrade to the latest version of sparklyr, run the following command
and restart your r session:

``` r
install.packages("devtools")
devtools::install_github("sparklyr/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](https://spark.rstudio.com/packages/sparklyr/latest/reference/spark-connections.html)
function:

``` r
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](https://spark.rstudio.com/deployment.html) section of the
sparklyr website.

## Using dplyr

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):

``` r
install.packages(c("nycflights13", "Lahman"))
```

``` r
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:

``` r
# filter by departure delay and print the first few records
flights_tbl %>% filter(dep_delay == 2)
#> # Source: spark<?> [?? x 19]
#>     year month   day dep_t…¹ sched…² dep_d…³ arr_t…⁴ sched…⁵
#>    <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, 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>, and abbreviated variable names
#> #   ¹​dep_time, ²​sched_dep_time, ³​dep_delay, ⁴​arr_time,
#> #   ⁵​sched_arr_time
```

[Introduction to dplyr](https://spark.rstudio.com/guides/dplyr.html)
provides additional `dplyr` examples you can try. For example, consider
the last example from the tutorial which plots data on flight delays:

``` r
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")'
```

<img src="tools/readme/dplyr-ggplot2-1.png" width="100%" />

### Window Functions

dplyr [window
functions](https://spark.rstudio.com/guides/dplyr.html#grouping) are
also supported, for example:

``` r
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](https://spark.rstudio.com/dplyr.html) 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](https://github.com/r-dbi/DBI) interface for Spark, so you can use
`dbGetQuery()` to execute SQL and return the result as an R data frame:

``` r
library(DBI)
iris_preview <- dbGetQuery(sc, "SELECT * FROM iris LIMIT 10")
iris_preview
#>    Sepal_Length Sepal_Width Petal_Length Petal_Width
#> 1           5.1         3.5          1.4         0.2
#> 2           4.9         3.0          1.4         0.2
#> 3           4.7         3.2          1.3         0.2
#> 4           4.6         3.1          1.5         0.2
#> 5           5.0         3.6          1.4         0.2
#> 6           5.4         3.9          1.7         0.4
#> 7           4.6         3.4          1.4         0.3
#> 8           5.0         3.4          1.5         0.2
#> 9           4.4         2.9          1.4         0.2
#> 10          4.9         3.1          1.5         0.1
#>    Species
#> 1   setosa
#> 2   setosa
#> 3   setosa
#> 4   setosa
#> 5   setosa
#> 6   setosa
#> 7   setosa
#> 8   setosa
#> 9   setosa
#> 10  setosa
```

## Machine Learning

You can orchestrate machine learning algorithms in a Spark cluster via
the [machine
learning](https://spark.apache.org/docs/latest/mllib-guide.html)
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](https://spark.rstudio.com/packages/sparklyr/latest/reference/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.

``` r
# 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.

``` r
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](https://spark.rstudio.com/mlib/) 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 local filesystem of cluster nodes.

``` r
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"        
#> [4] "iris_csv"     "iris_json"    "iris_parquet"
#> [7] "mtcars"
```

## Distributed R

You can execute arbitrary r code across your cluster using
`spark_apply()`. For example, we can apply `rgamma` over `iris` as
follows:

``` r
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         5.51        3.91         1.81       0.610
#>  2         5.31        3.41         1.81       0.610
#>  3         5.11        3.61         1.71       0.610
#>  4         5.01        3.51         1.91       0.610
#>  5         5.41        4.01         1.81       0.610
#>  6         5.81        4.31         2.11       0.810
#>  7         5.01        3.81         1.81       0.710
#>  8         5.41        3.81         1.91       0.610
#>  9         4.81        3.31         1.81       0.610
#> 10         5.31        3.51         1.91       0.510
#> # … 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:

``` r
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.er…¹ stati…²  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
#> # … with abbreviated variable names ¹​std.error, ²​statistic
```

## 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:

``` r
# 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](https://spark.rstudio.com/guides/extensions.html) section
of the sparklyr website.

## Table Utilities

You can cache a table into memory with:

``` r
tbl_cache(sc, "batting")
```

and unload from memory using:

``` r
tbl_uncache(sc, "batting")
```

## Connection Utilities

You can view the Spark web console using the `spark_web()` function:

``` r
spark_web(sc)
```

You can show the log using the `spark_log()` function:

``` r
spark_log(sc, n = 10)
#> 22/12/08 10:13:49 INFO BlockManagerInfo: Removed broadcast_84_piece0 on localhost:54296 in memory (size: 9.2 KiB, free: 912.1 MiB)
#> 22/12/08 10:13:49 INFO BlockManagerInfo: Removed broadcast_86_piece0 on localhost:54296 in memory (size: 5.0 KiB, free: 912.1 MiB)
#> 22/12/08 10:13:49 INFO BlockManagerInfo: Removed broadcast_76_piece0 on localhost:54296 in memory (size: 8.7 KiB, free: 912.1 MiB)
#> 22/12/08 10:13:49 INFO Executor: Finished task 0.0 in stage 67.0 (TID 83). 1004 bytes result sent to driver
#> 22/12/08 10:13:49 INFO TaskSetManager: Finished task 0.0 in stage 67.0 (TID 83) in 187 ms on localhost (executor driver) (1/1)
#> 22/12/08 10:13:49 INFO TaskSchedulerImpl: Removed TaskSet 67.0, whose tasks have all completed, from pool 
#> 22/12/08 10:13:49 INFO DAGScheduler: ResultStage 67 (count at NativeMethodAccessorImpl.java:0) finished in 0.199 s
#> 22/12/08 10:13:49 INFO DAGScheduler: Job 49 is finished. Cancelling potential speculative or zombie tasks for this job
#> 22/12/08 10:13:49 INFO TaskSchedulerImpl: Killing all running tasks in stage 67: Stage finished
#> 22/12/08 10:13:49 INFO DAGScheduler: Job 49 finished: count at NativeMethodAccessorImpl.java:0, took 0.204972 s
```

Finally, we disconnect from Spark:

``` r
  spark_disconnect(sc)
```

## RStudio IDE

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:

<img src="tools/readme/spark-connect.png" class="screenshot" width=389 />

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:

<img src="tools/readme/spark-dataview.png" class="screenshot" width=639 />

You can also connect to Spark through [Livy](https://livy.apache.org/)
through a new connection dialog:

<img src="tools/readme/spark-connect-livy.png" class="screenshot" width=389 />

<div style="margin-bottom: 15px;">

</div>

## Using H2O

[rsparkling](https://cran.r-project.org/package=rsparkling) is a CRAN
package from [H2O](https://h2o.ai/) that extends
[sparklyr](https://spark.rstudio.com/) to provide an interface into
[Sparkling Water](https://github.com/h2oai/sparkling-water). For
instance, the following example installs, configures and runs
[h2o.glm](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/glm.html):

``` r
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)
```

``` r
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

``` r
spark_disconnect(sc)
```

## Connecting through Livy

[Livy](https://github.com/cloudera/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:

``` r
livy_install()
```

``` r
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:

``` r
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:

``` r
livy_service_stop()
```

To connect to remote `livy` clusters that support basic authentication
connect as:

``` r
config <- livy_config(username="<username>", password="<password>")
sc <- spark_connect(master = "<address>", method = "livy", config = config)
spark_disconnect(sc)
```

## Connecting through Databricks Connect

[Databricks
Connect](https://docs.databricks.com/dev-tools/databricks-connect.html#databricks-connect)
allows you to connect sparklyr to a remote Databricks Cluster. You can
install [Databricks Connect python
package](https://pypi.org/project/databricks-connect/) 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](https://docs.databricks.com/dev-tools/databricks-connect.html#client-setup)
to setup the client:

1.  Make sure pyspark is not installed
2.  Install the Databricks Connect python package. The latest supported
    version is 6.4.1.
3.  Run `databricks-connect configure` and provide the configuration
    information
    - Databricks account URL of the form
      `https://<account>.cloud.databricks.com`.
    - [User
      token](https://docs.databricks.com/dev-tools/api/latest/authentication.html#token-management)
    - Cluster ID
    - Port (default port number is `15001`)

To configure `sparklyr` with Databricks Connect, set the following
environment variables:

``` bash
export SPARK_VERSION=2.4.4
```

Now simply create a spark connection as follows

``` r
spark_home <- system("databricks-connect get-spark-home")
sc <- spark_connect(method = "databricks",
                    spark_home = spark_home)
copy_to(sc, iris, overwrite = TRUE)
```

<img src="tools/readme/databricks-connect.png" class="screenshot" width=750 />

``` r
spark_disconnect(sc)
```
