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Spark: 单词计数(Word Count)的MapReduce实现(Java/Python)
小开开
2022-06-21 21:16:15 2022-06-21
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1 导引

我们在博客《Hadoop: 单词计数(Word Count)的MapReduce实现 》中学习了如何用Hadoop-MapReduce实现单词计数,现在我们来看如何用Spark来实现同样的功能。

2. Spark的MapReudce原理

Spark框架也是MapReduce-like模型,采用“分治-聚合”策略来对数据分布进行分布并行处理。不过该框架相比Hadoop-MapReduce,具有以下两个特点:

  • 对大数据处理框架的输入/输出,中间数据进行建模,将这些数据抽象为统一的数据结构命名为弹性分布式数据集(Resilient Distributed Dataset),并在此数据结构上构建了一系列通用的数据操作,使得用户可以简单地实现复杂的数据处理流程。
  • 采用了基于内存的数据聚合、数据缓存等机制来加速应用执行尤其适用于迭代和交互式应用。

Spark社区推荐用户使用Dataset、DataFrame等面向结构化数据的高层API(Structured API)来替代底层的RDD API,因为这些高层API含有更多的数据类型信息(Schema),支持SQL操作,并且可以利用经过高度优化的Spark SQL引擎来执行。不过,由于RDD API更基础,更适合用来展示基本概念和原理,后面我们的代码都使用RDD API。

Spark的RDD/dataset分为多个分区。RDD/Dataset的每一个分区都映射一个或多个数据文件, Spark通过该映射读取数据输入到RDD/dataset中。

Spark的分区数和以下参数都有关系:

  • spark.default.parallelism (默认为CPU的核数)
  • spark.sql.files.maxPartitionBytes (默认为128 MB)读取文件时打包到单个分区中的最大字节数)
  • spark.sql.files.openCostInBytes (默认为4 MB) 该参数默认4M,表示小于4M的小文件会合并到一个分区中,用于减小小文件,防止太多单个小文件占一个分区情况。这个参数就是合并小文件的阈值,小于这个阈值的文件将会合并。

我们下面的流程描述中,假设每个文件对应一个分区(实际上因为文件很小,导致三个文件都在同一个分区中,大家可以通过调用RDD对象的getNumPartitions()查看)。

Spark的Map示意图如下:

Spark的Reduce示意图如下:

3. Word Count的Java实现

项目架构如下图:

Word-Count-Spark
├─ input
│ ├─ file1.txt
│ ├─ file2.txt
│ └─ file3.txt
├─ output
│ └─ result.txt
├─ pom.xml
├─ src
│ ├─ main
│ │ └─ java
│ │ └─ WordCount.java
│ └─ test
└─ target

WordCount.java文件如下:

import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.sql.SparkSession;
import scala.Tuple2;
import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;
import java.io.*;
import java.nio.file.*;
public class WordCount {
private static Pattern SPACE = Pattern.compile(" ");
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("Usage: WordCount <intput directory> <output directory>");
System.exit(1);
}
String input_path = args[0];
String output_path = args[1];
SparkSession spark = SparkSession.builder()
.appName("WordCount")
.master("local")
.getOrCreate();
JavaRDD<String> lines = spark.read().textFile(input_path).javaRDD();
JavaRDD<String> words = lines.flatMap(s -> Arrays.asList(SPACE.split(s)).iterator());
JavaPairRDD<String, Integer> ones = words.mapToPair(s -> new Tuple2<>(s, 1));
JavaPairRDD<String, Integer> counts = ones.reduceByKey((i1, i2) -> i1 + i2);
List<Tuple2<String, Integer>> output = counts.collect();
String filePath = Paths.get(output_path, "result.txt").toString();
BufferedWriter out = new BufferedWriter(new FileWriter(filePath));
for (Tuple2<?, ?> tuple : output) {
out.write(tuple._1() + ": " + tuple._2() + "\n");
}
out.close();
spark.stop();
}
}

pom.xml文件配置如下:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.WordCount</groupId>
<artifactId>WordCount</artifactId>
<version>1.0-SNAPSHOT</version>
<name>WordCount</name>
<!-- FIXME change it to the project's website -->
<url>http://www.example.com</url>
<!-- 集中定义版本号 -->
<properties>
<scala.version>2.12.10</scala.version>
<scala.compat.version>2.12</scala.compat.version>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<project.timezone>UTC</project.timezone>
<java.version>11</java.version>
<scoverage.plugin.version>1.4.0</scoverage.plugin.version>
<site.plugin.version>3.7.1</site.plugin.version>
<scalatest.version>3.1.2</scalatest.version>
<scalatest-maven-plugin>2.0.0</scalatest-maven-plugin>
<scala.maven.plugin.version>4.4.0</scala.maven.plugin.version>
<maven.compiler.plugin.version>3.8.0</maven.compiler.plugin.version>
<maven.javadoc.plugin.version>3.2.0</maven.javadoc.plugin.version>
<maven.source.plugin.version>3.2.1</maven.source.plugin.version>
<maven.deploy.plugin.version>2.8.2</maven.deploy.plugin.version>
<nexus.staging.maven.plugin.version>1.6.8</nexus.staging.maven.plugin.version>
<maven.help.plugin.version>3.2.0</maven.help.plugin.version>
<maven.gpg.plugin.version>1.6</maven.gpg.plugin.version>
<maven.surefire.plugin.version>2.22.2</maven.surefire.plugin.version>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<spark.version>3.2.1</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.11</version>
<scope>test</scope>
</dependency>
<!--======SCALA======-->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
<scope>provided</scope>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
<dependency> <!-- Spark dependency -->
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.12</artifactId>
<version>${spark.version}</version>
<scope>provided</scope>
</dependency>
</dependencies>
<build>
<pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to parent pom) -->
<plugins>
<!-- clean lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->
<plugin>
<artifactId>maven-clean-plugin</artifactId>
<version>3.1.0</version>
</plugin>
<!-- default lifecycle, jar packaging: see https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->
<plugin>
<artifactId>maven-resources-plugin</artifactId>
<version>3.0.2</version>
</plugin>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.0</version>
</plugin>
<plugin>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.22.1</version>
</plugin>
<plugin>
<artifactId>maven-jar-plugin</artifactId>
<version>3.0.2</version>
</plugin>
<plugin>
<artifactId>maven-install-plugin</artifactId>
<version>2.5.2</version>
</plugin>
<plugin>
<artifactId>maven-deploy-plugin</artifactId>
<version>2.8.2</version>
</plugin>
<!-- site lifecycle, see https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle -->
<plugin>
<artifactId>maven-site-plugin</artifactId>
<version>3.7.1</version>
</plugin>
<plugin>
<artifactId>maven-project-info-reports-plugin</artifactId>
<version>3.0.0</version>
</plugin>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.0</version>
<configuration>
<source>11</source>
<target>11</target>
<fork>true</fork>
<executable>/Library/Java/JavaVirtualMachines/jdk-11.0.15.jdk/Contents/Home/bin/javac</executable>
</configuration>
</plugin>
</plugins>
</pluginManagement>
</build>
</project>

记得配置输入参数input和output代表输入目录和输出目录(在VSCode中在launch.json文件中配置)。编译运行后可在output目录下查看result.txt:

Tom: 1
Hello: 3
Goodbye: 1
World: 2
David: 1

可见成功完成了单词计数功能。

4. Word Count的Python实现

先使用pip按照pyspark==3.8.2:

pip install pyspark==3.8.2

注意PySpark只支持Java 8/11,请勿使用更高级的版本。这里我使用的是Java 11。运行java -version可查看本机Java版本。

(base) orion-orion@MacBook-Pro ~ % java -version
java version "11.0.15" 2022-04-19 LTS
Java(TM) SE Runtime Environment 18.9 (build 11.0.15+8-LTS-149)
Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.15+8-LTS-149, mixed mode)

项目架构如下:

Word-Count-Spark
├─ input
│ ├─ file1.txt
│ ├─ file2.txt
│ └─ file3.txt
├─ output
│ └─ result.txt
├─ src
│ └─ word_count.py

word_count.py编写如下:

from pyspark.sql import SparkSession
import sys
import os
from operator import add
if len(sys.argv) != 3:
print("Usage: WordCount <intput directory> <output directory>", file=sys.stderr)
exit(1)
input_path, output_path = sys.argv[1], sys.argv[2]
spark = SparkSession.builder.appName("WordCount").master("local").getOrCreate()
lines = spark.read.text(input_path).rdd.map(lambda r: r[0])
counts = lines.flatMap(lambda s: s.split(" "))\
.map(lambda word: (word, 1))\
.reduceByKey(add)
output = counts.collect()
with open(os.path.join(output_path, "result.txt"), "wt") as f:
for (word, count) in output:
f.write(str(word) +": " + str(count) + "\n")
spark.stop()

使用python word_count.py input output运行后,可在output中查看对应的输出文件result.txt:

Hello: 3
World: 2
Goodbye: 1
David: 1
Tom: 1

可见成功完成了单词计数功能。

参考

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