``I always wanted to be a wizard.''
— Samwell Tarly
After reading the Introduction chapter, you should now be familiar with the kind of problems that Spark can help you solve; it should be clear that Spark solves problems by making use of multiple computers when data does not fit in a single machine or when computation is too slow. For those newer to R, it should also be clear that combining Spark with data science tools, like ggplot2 for visualization and dplyr to perform data transformations, brings a promising landscape for doing data science at scale. We also hope you are excited to become proficient in large-scale computing.
This chapter, Getting Started, will walk you through the tools you’ll need to become proficient in Spark. We encourage you to walk through the code in this chapter since it will force you to go through the motions of analyzing, modeling, reading and writing data. Read, you will have to do some wax-on, wax-off, repeat; before you get fully immersed in the world of Spark.
The next chapter, Analysis, will dive into this topic followed by Modeling; which will present examples using a single-cluster machine, your personal computer. Subsequent chapters will properly introduce cluster computing and the concepts and techniques you’ll need to successfully run code across multiple machines.
From R, getting started with Spark using sparklyr and a local cluster is as easy as installing and loading the sparklyr package followed by installing Spark using sparklyr; however, we will assume you are starting with a brand new computer running Windows, OS X or Linux and walk you through the prerequisites you’ll need before connecting to a local Spark cluster.
While this chapter is designed to help you get ready to use Spark in your personal computer, it’s also likely that some readers will already have a Spark cluster available or might prefer to get started with an online Spark cluster. For instance, Databricks hosts a free community editionfootnote:[] of Spark that can be easily accessed from your web browser. If you end up choosing this path, skip to the prerequisites section of this chapter but make sure you consult the proper resources for your existing or online Spark cluster.
Either way, once you are done with the prerequisites, you will first learn how to connect to Spark. We will then present the most important tools and operations that you’ll use through the rest of this book. Less emphasis is placed on teaching concepts or how to use them, we can’t possibly explain modeling or streaming in a single paragraph; however, going through this chapter should give you a brief glimpse of what to expect and confidence that you have the tools correctly configured to tackle harder problems later on.
The tools you’ll use are mostly divided into R code and the Spark web interface. All Spark operations are run from R; however, monitoring execution of distributed operations is performed from Sparks web interface; a web interface that you can load from any web browser. We will then disconnect from this local cluster, which is easy to forget to do but highly recommended while working with local clusters – and in shared Spark clusters as well!
We will close this chapter by walking you through some of the features that make using Spark with Rstudio easier; more specifically, we will present the RStudio extensions that sparklyr implements. However, if you are inclined to use Jupyter notebooks, or if your cluster is already equipped with a different R user interface, rest assured that you can use Spark with R through plain R code. Let’s move along and get your prerequisites properly configured.
R can run in many platforms and environments; therefore, whether you use Windows, Mac or Linux, the first step is to install R from the r-project.org, detailed instructions are provided in the Installing R appendix.
Most people use programming languages with tools to make them more productive; for R, RStudio would be such a tool. Strictly speaking, RStudio is an Integrated Development Environment (or IDE), which also happens to support many platforms and environments. We strongly recommend you get RStudio installed if you haven’t done so already, see details under the Installing RStudio appendix.
|
Tip
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When using Windows, we recommend using avoiding directories with spaces in their path. If running |
Additionally, since Spark is built in the Scala programming language which is run by the Java Virtual Machine, you also need to install Java 8 in your system. It is likely that your system already has Java installed, but you should still check the version and update or downgrade as described in the Installing Java appendix. You can use the following R command to check which version is installed installed:
system("java -version")java version "1.8.0_201" Java(TM) SE Runtime Environment (build 1.8.0_201-b09) Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)
You can also use the JAVA_HOME environment variable to point to a specific Java version by running Sys.setenv(JAVA_HOME = "<path-to-java-8>"); either way, before moving on to installing sparklyr, make sure that Java 8 is the version available for R.
As many other R packages, sparkylr is available to be installed from CRAN and can be easily installed as follows:
install.packages("sparklyr")The examples in this book assume you are using the latest version of sparklyr, you can verify your version is as new as the one we are using by running:
packageVersion("sparklyr")[1] ‘1.0.2’
Start by loading sparklyr,
library(sparklyr)This makes all sparklyr functions available in R, which is really helpful; otherwise, we would have to run each sparklyr command prefixed with sparklyr::.
Spark can be easily installed by running spark_install(); this will download, install and configure the latest version of Spark locally in your computer; however, since we’ve written this book with Spark 2.3, you should also install this version to make sure you can follow all the examples provided without any unexpected surprises.
spark_install("2.3")All the versions of Spark that are available for installation can be displayed by running:
spark_available_versions()## spark ## 1 1.6 ## 2 2.0 ## 3 2.1 ## 4 2.2 ## 5 2.3 ## 6 2.4
A specific version can be installed using the Spark version and, optionally, by also specifying the Hadoop version. For instance, to install Spark 1.6.3, we would run:
spark_install(version = "1.6")You can also check which versions are installed by running:
spark_installed_versions()spark hadoop dir 7 2.3.1 2.7 /spark/spark-2.3.1-bin-hadoop2.7
The path where Spark is installed is referenced as Spark’s home, which is defined in R code and system configuration settings with the SPARK_HOME identifier. When using a local Spark cluster installed with sparklyr, this path is already known and no additional configuration needs to take place.
Finally, in order to uninstall a specific version of Spark you can run spark_uninstall() by specifying the Spark and Hadoop versions, for instance:
spark_uninstall(version = "1.6.3", hadoop = "2.6")|
Note
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The default installation paths are |
It’s important to mention that, so far, we’ve only installed a local Spark cluster. A local cluster is really helpful to get started, test code and troubleshoot with ease. Further chapters will explain where to find, install and connect to real Spark clusters with many machines, but for the first few chapters, we will focus on using local clusters.
To connect to this local cluster we simply run:
library(sparklyr)
sc <- spark_connect(master = "local", version = "2.3")|
Note
|
If you are using your own or online Spark cluster, make sure to connect as specified by your cluster administrator or the online documentation. If you need some pointers, you can take a quick pick at the Connections chapter which will explain in detail how to connect to any Spark cluster. |
The master parameter identifies which is the `main'' machine from the Spark cluster; this machine is often called the driver node. While working with real clusters using many machines, most machines will be worker machines and one will be the master. Since we only have a local cluster with only one machine, we will default to use `"local" for now.
After a connection is established, spark_connect() retrieves an active Spark connection which most code usually names sc; you will then make use of sc to execute Spark commands.
If connection fails, the Connections chapter contains a troubleshooting section which can help you resolve your connection issue.
Now that you are connected, we can run a few simple commands. For instance, let’s start by copying the mtcars dataset into Apache Spark using copy_to().
cars <- copy_to(sc, mtcars)The data was copied into Spark but we can access it from R using the cars reference. To print it’s contents we can simply type cars.
cars# Source: spark<mtcars> [?? x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
# … with more rows
Congrats! You have successfully connected and loaded your first dataset into Spark.
Let’s explain what’s going on in copy_to(). The first parameter, sc, gives the function a reference to the active Spark Connection that was earlier created with spark_connect(). The second parameter specifies a dataset to load into Spark. Now, copy_to() returns a reference to the dataset in Spark which R automatically prints. Whenever a Spark dataset is printed, Spark will collect some of the records and display them for you. In this particular case, that dataset contains only a few rows describing automobile models and some of their specifications like Horsepower and expected Miles per Gallon.
Most of the Spark commands are executed from the R console; however, monitoring and analyzing execution is done through Spark’s web interface, see Apache Spark Web Interface. This interface is a web application provided by Spark which can be accessed by running:
spark_web(sc)
Printing the cars dataset collected a few records to be displayed in the R console. You can see in the Spark web interface that a job was started to collect this information back from Spark. You can also select the storage tab to see the ``mtcars'' dataset cached in-memory in Spark, Apache Spark Web Interface - Storage Tab.
Notice that this dataset is fully loaded into memory since the fraction cached is 100%, you can know exactly how much memory this dataset is using through the size in memory column.
The executors tab, Apache Spark Web Interface - Executors Tab, provides a view of your cluster resources. For local connections, you will find only one executor active with only 2GB of memory allocated to Spark and 384MB available for computation. The Tuning chapter you will learn how request more compute instances, resources and learn how memory is allocated.
The last tab to explore is the environment tab, Apache Spark Web Interface - Environment Tab, this tab lists all the settings for this Spark application which the tuning will also introduce them in detail. As you will learn, most settings don’t need to be configured explicitly, but in order to properly run at scale, you will have to become familiar with some of them, eventually.
Next, you will make use of a small subset of the practices that the Analysis chapter will cover.
When using Spark from R to analyze data, you can use SQL (Structured Query Language) or dplyr (a grammar of data manipulation). SQL can be used through the DBI package; for instance, to count how many records are available in our cars dataset we can run:
library(DBI)
dbGetQuery(sc, "SELECT count(*) FROM mtcars")count(1) 1 32
When using dplyr, you write less code and it’s often much easier to write than SQL; which is why we won’t make use SQL in this book; however, if you are proficient in SQL, this is a viable option to you. For instance, counting records in dplyr is more compact and easier to understand.
library(dplyr)
count(cars)# Source: spark<?> [?? x 1]
n
<dbl>
1 32
In general, we usually start by analysing data in Spark with dplyr, followed by sampling rows and selecting a subset of the available columns, the last step is to collect data from Spark to perform further data processing in R, like data visualization. Let’s perform a very simple data analysis example by selecting, sampling and plotting the cars dataset in Spark:
select(cars, hp, mpg) %>%
sample_n(100) %>%
collect() %>%
plot()
The plot in Horsepower vs Miles per Gallon, shows that as we increase the horsepower in a vehicle, their fuel efficiency measured in miles per gallon gets reduced. While this is insightful, it’s hard to predict numerically how increased horsepower would affect fuel efficiency, modeling can help us overcome this.
While data analysis can take you quite far when understanding data, building a mathematical model that describes and generalizes the dataset is quite powerful. In the Introduction chapter you learned that the fields of machine learning and data science make use of mathematical models to perform predictions and find additional insights.
For instance, we can use a linear model to approximate the relationship between fuel efficiency and horsepower:
model <- ml_linear_regression(cars, mpg ~ hp)
modelFormula: mpg ~ hp Coefficients: (Intercept) hp 30.09886054 -0.06822828
This model can now be used to predict values that are not in the original dataset. For instance, we can add entries for cars with horsepower beyond 250 and also visualize the predicted values as shown in Horsepower vs miles per gallon with predictions.
model %>%
ml_predict(copy_to(sc, data.frame(hp = 250 + 10 * 1:10))) %>%
transmute(hp = hp, mpg = prediction) %>%
full_join(select(cars, hp, mpg)) %>%
collect() %>%
plot()
While the previous example lacks many of the appropriate techniques you should use while modeling, it’s also a simple example to briefly introduce the modeling capabilities of Spark. All the Spark models, techniques and best practices will be properly introduced in the Modeling chapter.
For simplicity, we copied the mtcars dataset into Spark; however, data is usually not copied into Spark. Instead, data is read from existing data sources in a variety of formats like plain text, CSV, JSON, JDBC and many more which, the Data chapter will introduce in detail. For instance, we can export our cars dataset as a CSV file:
spark_write_csv(cars, "cars.csv")In practice, we would read an existing dataset from a distributed storage system like HDFS, but we can also read back from the local file system:
cars <- spark_read_csv(sc, "cars.csv")In the same way that R is known for its vibrant community of package authors, at a smaller scale, many extensions for Spark and R have been written and are available to you. The Extensions chapter will introduce many interesting ones to perform advanced modeling, graph analysis, preprocess datasets for deep learning, etc.
For instance, the sparkly.nested extension is an R package that extends sparklyr to help you manage values that contain nested information. A common use case arises while dealing with JSON files which contain nested lists that require preprocessing before doing meaningful data analysis. To use this extension, we have to first install it as follows:
install.packages("sparklyr.nested")Then we can use this extension to group all the horsepower data points over the number of cylinders:
sparklyr.nested::sdf_nest(cars, hp) %>%
group_by(cyl) %>%
summarise(data = collect_list(data))# Source: spark<?> [?? x 2]
cyl data
<int> <list>
1 6 <list [7]>
2 4 <list [11]>
3 8 <list [14]>
While nesting data makes it harder to read, it is a requirement while dealing with nested data formats like JSON using the spark_read_json() and spark_write_json() functions.
For those few cases where a particular functionality is not available in Spark and no extension has been developed, you can consider distributing your own R code across the Spark cluster. This is a powerful tool but comes with additional complexity that you should only use as a last resort option.
Suppose that we need to round all the values across all the columns in our dataset, one approach would be running custom R code making use of R’s round() function:
cars %>% spark_apply(~round(.x))# Source: spark<?> [?? x 11]
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 21 6 160 110 4 3 16 0 1 4 4
2 21 6 160 110 4 3 17 0 1 4 4
3 23 4 108 93 4 2 19 1 1 4 1
4 21 6 258 110 3 3 19 1 0 3 1
5 19 8 360 175 3 3 17 0 0 3 2
6 18 6 225 105 3 3 20 1 0 3 1
7 14 8 360 245 3 4 16 0 0 3 4
8 24 4 147 62 4 3 20 1 0 4 2
9 23 4 141 95 4 3 23 1 0 4 2
10 19 6 168 123 4 3 18 1 0 4 4
# … with more rows
If you are a proficient R user, it can be quite tempting to use spark_apply() for everything, but please, don’t! spark_apply() was designed for advanced use cases where Spark falls short; instead, you will learn how to do proper data analysis and modeling without having to distribute custom R code across your cluster.
While processing large static datasets is the most typical use case for Spark, processing dynamic datasets in real-time is also possible and for some applications, a requirement. You can think of a streaming dataset as a static data source with new data arriving continuously, like stock market quotes. Streaming data is usually read from Kafka (an open-source stream-processing software platform) or from distributed storage that receives new data continuously.
To try out streaming, lets first create an input/ folder with some data that we will use as the input for this stream:
dir.create("input")
write.csv(mtcars, "input/cars_1.csv", row.names = F)Then we will define a stream that processes incoming data from the input/ folder, performs a custom transformation in R and, pushes the output into an output/ folder
stream <- stream_read_csv(sc, "input/") %>%
select(mpg, cyl, disp) %>%
stream_write_csv("output/")As soon as the stream of real-time data starts, the input/ folder is processed and turned into a set of new files under the output/ folder containing the new transformed files. Since the input contained only one file, the output folder will also contain a single file resulting from applying the custom spark_apply() transformation.
dir("output", pattern = ".csv")[1] "part-00000-eece04d8-7cfa-4231-b61e-f1aef8edeb97-c000.csv"
Up to this point, this resembles static data processing; however, we can keep adding files to the input/ location and Spark will parallelize and process data automatically. Let’s add one more file and validate that it’s automatically processed.
# Write more data into the stream source
write.csv(mtcars, "input/cars_2.csv", row.names = F)Wait a few seconds and validate that the data gets processed by the Spark stream,
# Check the contents of the stream destination
dir("output", pattern = ".csv")[1] "part-00000-2d8e5c07-a2eb-449d-a535-8a19c671477d-c000.csv" [2] "part-00000-eece04d8-7cfa-4231-b61e-f1aef8edeb97-c000.csv"
You should then stop the stream,
stream_stop(stream)You can use dplyr, SQL, Spark models or distributed R to analyze streams in real-time, we will properly introduce you to all the interesting transformations you can perform to analyze real-time data during the Streaming chapter.
Logging is definitely less interesting that real-time data processing; however, it’s a tool you should be familiar with. A log is just a text file where Spark will append information relevant to the execution of tasks in the cluster. For local clusters, we can retrieve all the recent log by running:
spark_log(sc)18/10/09 19:41:46 INFO Executor: Finished task 0.0 in stage 5.0 (TID 5)... 18/10/09 19:41:46 INFO TaskSetManager: Finished task 0.0 in stage 5.0... 18/10/09 19:41:46 INFO TaskSchedulerImpl: Removed TaskSet 5.0, whose... 18/10/09 19:41:46 INFO DAGScheduler: ResultStage 5 (collect at utils... 18/10/09 19:41:46 INFO DAGScheduler: Job 3 finished: collect at utils...
Or we can retrieve specific log entries containing, say sparklyr, by using the filter parameter as follows:
spark_log(sc, filter = "sparklyr")## 18/10/09 18:53:23 INFO SparkContext: Submitted application: sparklyr ## 18/10/09 18:53:23 INFO SparkContext: Added JAR... ## 18/10/09 18:53:27 INFO Executor: Fetching spark://localhost:52930/... ## 18/10/09 18:53:27 INFO Utils: Fetching spark://localhost:52930/... ## 18/10/09 18:53:27 INFO Executor: Adding file:/private/var/folders/...
Most of the time, you won’t need to worry about Spark logs, except in cases where you need to troubleshoot a failed computation; in those cases, logs are an invaluable resource to be aware of, now you know.
For local clusters (really, any cluster) once you are done processing data you should disconnect by running:
spark_disconnect(sc)This will terminate the connection to the cluster as well as the cluster tasks . If multiple Spark connections are active, or if the connection instance sc is no longer available, you can also disconnect all your Spark connections by running:
spark_disconnect_all()Notice that exiting R, RStudio or restarting your R session will also cause the Spark connection to terminate, which in turn terminates the Spark cluster and cached data that is not explicitly persisted.
Since it’s very common to use RStudio with R, sparklyr provides RStudio extensions to help simplify your workflows and increase your productivity while using Spark in RStudio. If you are not familiar with RStudio, take a quick look at the Using RStudio appendix section. Otherwise, there are a couple extensions worth highlighting.
First, instead of starting a new connections using spark_connect() from RStudio’s R console, you can use the new connection action from the connections pane and then, select the Spark connection which will open the dialog shown in RStudio New Spark Connection. You can then customize the versions and connect to Spark which will simply generate the right spark_connect() command and execute this in the R console for you.
Second, once connected to Spark, either by using the R console or through RStudio’s connections pane, RStudio will display your datasets available in the connections pane, see RStudio Connections Pane. This is a useful way to track your existing datasets and provides an easy way to explore each of them.
Additionally, an active connection provides the following custom actions:
- Spark
-
Opens the Spark web interface, a shortcut to
spark_ui(sc). - Log
-
Opens the Spark web logs, a shortcut to
spark_log(sc). - SQL
-
Opens a new SQL query, see
DBIand SQL support in the data Analysis chapter. - Help
-
Opens the reference documentation in a new web browser window.
- Disconnect
-
Disconnects from Spark, a shortcut to
spark_disconnect(sc).
The rest of this book will use plain R code, it is up to you to execute this code in the R console, RStudio, Jupyter Notebooks or any other tool that support executing R code since the code provided in this book executes in any R environment.
While we’ve put significant effort into simplifying the onboarding process, there are many additional resources that can help you troubleshoot particular issues while getting started and, in general, introduce you to the broader Spark and R communities to help you get specific answers, discuss topics and get connected with many users actively using Spark with R.
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Documentation: The documentation site hosted in the RStudio’s Spark website should be your first stop to learn more about Spark when using R. The documentation is kept up to date with examples, reference functions and many more relevant resources.
-
Blog: To keep up to date with major
sparklyrannouncements, you can follow the RStudio blog. -
Community: For general
sparklyrquestions, you can post then in the RStudio Community tagged assparklyr. -
Stack Overflow: For general Spark questions, Stack Overflow is a great resource; there are also many topics specifically about
sparklyr. -
Github: If you believe something needs to be fixed, open a GitHub issue or send us a pull request.
-
Gitter: For urgent issues, or to keep in touch, you can chat with us in Gitter.
In this chapter you learned about the prerequisites required to work with Spark, how to connect to Spark using spark_connect(), install a local cluster using spark_install(), load a simple dataset, launch the web interface and display logs using spark_web(sc) and spark_log(sc) respectively, disconnect from RStudio using spark_disconnect() and we closed this chapter presenting the RStudio extensions sparklyr provides.
It is our hope that this chapter will help anyone interested in learning cluster computing using Spark and R getting started, ready to experiment on your own and ready to tackle actual data analysis and modeling problems which, the next two chapters will introduce you. The next chapter, Analysis, will present data analysis as the process of inspecting, cleaning, and transforming data with the goal of discovering useful information. Modeling can be considered part of data analysis; however, it deserves it’s own chapter to truly understand and take advantage of the modeling functionality available in Spark.