Article Directory
1. Use Spark SQL to realize word frequency statistics
(1) Data source - words.txt
(2) Create a Maven project
(3) Add dependencies and build plugins
<?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>net.py.wc</groupId>
<artifactId>SparkSQLWordCount</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>2.11.8</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>2.1.1</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.3.0</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.3.2</version>
<executions>
<execution>
<id>scala-compile-first</id>
<phase>process-resources</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>scala-test-compile</id>
<phase>process-test-resources</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
(4) Modify the source directory name
Change the source directory name Java to Scala
in the pom.xml file, set the source directory
(5) Create a log property file
log4j.rootLogger=ERROR, stdout, logfile
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spark.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n
(6) Create a word frequency statistics singleton object
Create a net.py.wc package and create a SparkSQLWordCount singleton object in the package
package net.py.wc
import org.apache.spark.sql.{Dataset,SparkSession}
object SparkSQLWordCount {
def main(args: Array[String]): Unit = {
// 设置HADOOP用户名属性,否则本地运行访问会被拒绝
System.setProperty("HADOOP_USER_NAME", "root")
// 创建或得到SparkSession
val spark = SparkSession.builder()
.appName("SparkSQLWordCount")
.master("local[*]")
.getOrCreate()
// 读取HDFS上的单词文件
val lines: Dataset[String] = spark.read.textFile("hdfs://master:9000/input/words.txt")
// 显示数据集lines内容
lines.show()
// 导入Spark会话对象的隐式转换
import spark.implicits._
// 将数据集中的数据按空格切分并合并
val words: Dataset[String] = lines.flatMap(_.split(" "))
// 显示数据集words内容
words.show()
// 将数据集默认列名由value改为word,并转换成数据帧
val df = words.withColumnRenamed("value", "word").toDF()
// 显示数据帧内容
df.show()
// 基于数据帧创建临时视图
df.createTempView("v_words")
// 执行SQL分组查询,实现词频统计
val wc = spark.sql(
"""
| select word, count(*) as count
| from v_words group by word
| order by count desc
|""".stripMargin)
// 显示词频统计结果
wc.show()
// 关闭会话
spark.close()
}
}
(7) Start the program and view the results