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
- [1] Spark官方文档: Quick Start
- [2] 许利杰,方亚芬. 大数据处理框架Apache Spark设计与实现[M]. 电子工业出版社, 2021.
- [3] GiHub: Spark官方Java样例
- [4] similarface: Spark数据分区数量的原理