Spark官方文档翻译:Quick Start

Quick Start

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 complete 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.


Interactive Analysis with the Spark Shell


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:

Spark shell提供了一个简单的方法来学习API,以及一个强大的工具来分析数据交互。你可以选择scala或者python来进行shell交互。在Spark目录中运行以下命令启动它:


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 ="")
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 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 to 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> => 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> => 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), ...)


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:

spark还支持将数据缓存的集群内存中。当重复访问数据时,如查询一个小的热点数据集或运行一个类似PageRank的迭代算法时,这是非常有用的。看一个简单的例子,我们将linesWithSpark Dataset缓存起来:

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.

如果我们想使用spark API来编写一个独立的应用程序,我们可以使用scala(SBT构建,也可以使用Maven),Java(Maven构建),Python,一个简单的例子如下:

/* SimpleApp.scala */
import org.apache.spark.sql.SparkSession

object SimpleApp {
  def main(args: Array[String]) {
    val logFile = "YOUR_SPARK_HOME/" // Should be some file on your system
    val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
    val logData =
    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")

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.

调用 SparkSession.builder 方法创建一个SparkSession,并且可以设置应用的名称,最后调用getOrCreate方法获取到一个SparkSession的实例

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:

应用程序依赖于Spark API,所有我们需要配置SBT配置文件,build.sbt,其中需要配置spark所需依赖,如下:

name := "Simple Project"

version := "1.0"

scalaVersion := "2.11.8"

libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.2.0"

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.

SBT的想要正确工作,我们需要布置SimpleApp.scala和build.sbt成如下的目录结构。完成之后,我们就可以创建包含应用程序代码的jar包,然后使用spark-submit 脚本运行我们的程序。

# Your directory layout should look like this
$ find .

# Package a jar containing your application
$ sbt package
[info] Packaging {..}/{..}/target/scala-2.11/simple-project_2.11-1.0.jar

# Use spark-submit to run your application
$ YOUR_SPARK_HOME/bin/spark-submit 
  --class "SimpleApp" 
  --master local[4] 
Lines with a: 46, Lines with b: 23
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