Spark入门（十）之Distinct去重

一、Distinct

计算文本里面的每个单词去重，输出结果。

二、maven设置

<?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.mk</groupId><artifactId>spark-test</artifactId><version>1.0</version><name>spark-test</name><url>http://spark.mk.com</url><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><maven.compiler.source>1.8</maven.compiler.source><maven.compiler.target>1.8</maven.compiler.target><scala.version>2.11.1</scala.version><spark.version>2.4.4</spark.version><hadoop.version>2.6.0</hadoop.version></properties><dependencies><!-- scala依赖--><dependency><groupId>org.scala-lang</groupId><artifactId>scala-library</artifactId><version>${scala.version}</version></dependency><!-- spark依赖--><dependency><groupId>org.apache.spark</groupId><artifactId>spark-core_2.11</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.11</artifactId><version>${spark.version}</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.11</version><scope>test</scope></dependency></dependencies><build><pluginManagement><plugins><plugin><artifactId>maven-clean-plugin</artifactId><version>3.1.0</version></plugin><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></plugins></pluginManagement></build>
</project>

三、编程代码

public class DistinctApp implements SparkConfInfo{public static void main(String[]args){String filePath = "F:\\test\\log.txt";SparkSession sparkSession = new DistinctApp().getSparkConf("distinct");List<String> words = sparkSession.sparkContext().textFile(filePath, 4).toJavaRDD().flatMap(v -> Arrays.asList(v.split("[(\\s+)(\r?\n),.。'’]")).iterator()).filter(v -> v.matches("[a-zA-Z-]+")).map(String::toLowerCase).distinct().collect();words.forEach((v) -> System.out.println(v));sparkSession.stop();}
}public interface SparkConfInfo {default SparkSession getSparkConf(String appName){SparkConf sparkConf = new SparkConf();if(System.getProperty("os.name").toLowerCase().contains("win")) {sparkConf.setMaster("local[4]");System.out.println("使用本地模拟是spark");}else{sparkConf.setMaster("spark://hadoop01:7077,hadoop02:7077,hadoop03:7077");sparkConf.set("spark.driver.host","192.168.150.1");//本地ip，必须与spark集群能够相互访问，如：同一个局域网sparkConf.setJars(new String[] {".\\out\\artifacts\\spark_test\\spark-test.jar"});//项目构建生成的路径}SparkSession session = SparkSession.builder().appName(appName).config(sparkConf).config(sparkConf).getOrCreate();return session;}
}

文件内容

Spark Streaming is an extension of the core Spark API that enables scalable,high-throughput, fault-tolerant stream processing of live 。data streams. Data， can be ，ingested from many sources like Kafka, Flume, Kinesis, or TCP sockets, and can be processed using complex algorithms expressed with high-level functions like map, reduce, join and window. Finally, processed data can be pushed out to filesystems,Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data. DStreams can be created either from input data streams from sources such as Kafka, Flume, and Kinesis, or by applying high-level operations on other DStreams. Internally, a DStream is represented as a sequence of RDDs.This guide shows you how to start writing Spark Streaming programs with DStreams. You can write Spark Streaming programs in Scala, Java or Python (introduced in Spark 1.2), all of which are presented in this guide. You will find tabs throughout this guide that let you choose between code snippets of different languages. databases, and live dashboards. In fact, you can apply Spark’s machine learning and graph processing algorithms on data streams.

输出

discretized
stream
created
are
dstream
fault-tolerant
guide
sockets
can
writing
between
how
python
live
complex
operations
sources
window
using
map
databases
fact
kinesis
filesystems
other
enables
algorithms
applying
throughout
find
spark
different
either
a
high-level
all
pushed
tcp
high-throughput
in
code
scalable
which
introduced
sequence
an
called
rdds
the
scala
this
learning
provides
internally
is
processed
apply
tabs
start
snippets
with
will
data
abstraction
as
graph
shows
java
streaming
out
input
api
from
join
presented
reduce
kafka
s
you
represented
extension
that
let
or
on
many
be
such
flume
dstreams
processing
expressed
to
core
finally
languages
of
by
like
programs
functions
represents
and
choose
continuous
machine
dashboards
write
streams

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Spark入门（十）之Distinct去重

一、Distinct

二、maven设置

三、编程代码

Spark入门（十）之Distinct去重相关推荐

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