5.Spark 快速起步
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Quick Start
- Security
- Interactive Analysis with the Spark Shell
- Self-Contained Applications
- Where to Go from Here
This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark’s interactive shell (in Python or Scala), then show how to write applications in Java, Scala, and Python.
To follow along with this guide, first, download a packaged release of Spark from the Spark website. Since we won’t be using HDFS, you can download a package for any version of Hadoop.
Note that, before Spark 2.0, the main programming interface of Spark was the Resilient Distributed Dataset (RDD). After Spark 2.0, RDDs are replaced by Dataset, which is strongly-typed like an RDD, but with richer optimizations under the hood. The RDD interface is still supported, and you can get a more detailed reference at the RDD programming guide. However, we highly recommend you to switch to use Dataset, which has better performance than RDD. See the SQL programming guide to get more information about Dataset.
Security
Security in Spark is OFF by default. This could mean you are vulnerable to attack by default. Please see Spark Security before running Spark.
Interactive Analysis with the Spark Shell
Basics
Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python. Start it by running the following in the Spark directory:
./bin/spark-shell
Spark’s primary abstraction is a distributed collection of items called a Dataset. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. Let’s make a new Dataset from the text of the README file in the Spark source directory:
scala> val textFile = spark.read.textFile("README.md")
textFile: org.apache.spark.sql.Dataset[String] = [value: string]
You can get values from Dataset directly, by calling some actions, or transform the Dataset to get a new one. For more details, please read the API doc.
scala> textFile.count() // Number of items in this Dataset
res0: Long = 126 // May be different from yours as README.md will change over time, similar to other outputs
scala> textFile.first() // First item in this Dataset
res1: String = # Apache Spark
Now let’s transform this Dataset into a new one. We call filter
to return a new Dataset with a subset of the items in the file.
scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.sql.Dataset[String] = [value: string]
We can chain together transformations and actions:
scala> textFile.filter(line => line.contains("Spark")).count() // How many lines contain "Spark"?
res3: Long = 15
More on Dataset Operations
Dataset actions and transformations can be used for more complex computations. Let’s say we want to find the line with the most words:
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res4: Long = 15
This first maps a line to an integer value, creating a new Dataset. reduce
is called on that Dataset to find the largest word count. The arguments to map
and reduce
are Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We’ll use Math.max()
function to make this code easier to understand:
scala> import java.lang.Math
import java.lang.Math
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
res5: Int = 15
One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:
scala> val wordCounts = textFile.flatMap(line => line.split(" ")).groupByKey(identity).count()
wordCounts: org.apache.spark.sql.Dataset[(String, Long)] = [value: string, count(1): bigint]
Here, we call flatMap
to transform a Dataset of lines to a Dataset of words, and then combine groupByKey
and count
to compute the per-word counts in the file as a Dataset of (String, Long) pairs. To collect the word counts in our shell, we can call collect
:
scala> wordCounts.collect()
res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...)
Caching
Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small “hot” dataset or when running an iterative algorithm like PageRank. As a simple example, let’s mark our linesWithSpark
dataset to be cached:
scala> linesWithSpark.cache()
res7: linesWithSpark.type = [value: string]
scala> linesWithSpark.count()
res8: Long = 15
scala> linesWithSpark.count()
res9: Long = 15
It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting bin/spark-shell
to a cluster, as described in the RDD programming guide.
Self-Contained Applications
Suppose we wish to write a self-contained application using the Spark API. We will walk through a simple application in Scala (with sbt), Java (with Maven), and Python (pip).
We’ll create a very simple Spark application in Scala–so simple, in fact, that it’s named SimpleApp.scala
:
/* SimpleApp.scala */
import org.apache.spark.sql.SparkSession
object SimpleApp {
def main(args: Array[String]) {
val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
val logData = spark.read.textFile(logFile).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println(s"Lines with a: $numAs, Lines with b: $numBs")
spark.stop()
}
}
Note that applications should define a main()
method instead of extending scala.App
. Subclasses of scala.App
may not work correctly.
This program just counts the number of lines containing ‘a’ and the number containing ‘b’ in the Spark README. Note that you’ll need to replace YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkSession, we initialize a SparkSession as part of the program.
We call SparkSession.builder
to construct a [[SparkSession]], then set the application name, and finally call getOrCreate
to get the [[SparkSession]] instance.
Our application depends on the Spark API, so we’ll also include an sbt configuration file, build.sbt
, which explains that Spark is a dependency. This file also adds a repository that Spark depends on:
name := "Simple Project"
version := "1.0"
scalaVersion := "2.12.10"
libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.4.5"
For sbt to work correctly, we’ll need to layout SimpleApp.scala
and build.sbt
according to the typical directory structure. Once that is in place, we can create a JAR package containing the application’s code, then use the spark-submit
script to run our program.
# Your directory layout should look like this
$ find .
.
./build.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleApp.scala
# Package a jar containing your application
$ sbt package
...
[info] Packaging {..}/{..}/target/scala-2.12/simple-project_2.12-1.0.jar
# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit --class "SimpleApp" --master local[4] target/scala-2.12/simple-project_2.12-1.0.jar
...
Lines with a: 46, Lines with b: 23
Where to Go from Here
Congratulations on running your first Spark application!
- For an in-depth overview of the API, start with the RDD programming guide and the SQL programming guide, or see “Programming Guides” menu for other components.
- For running applications on a cluster, head to the deployment overview.
- Finally, Spark includes several samples in the
examples
directory (Scala, Java, Python, R). You can run them as follows:
# For Scala and Java, use run-example: ./bin/run-example SparkPi # For Python examples, use spark-submit directly: ./bin/spark-submit examples/src/main/python/pi.py # For R examples, use spark-submit directly: ./bin/spark-submit examples/src/main/r/dataframe.R
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