配置pom文件

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">

4.0.0

org.example

learning

1.0-SNAPSHOT

UTF-8

2.2.0

2.11.8

2.6.5

1.2.1

org.apache.spark

spark-core_2.11

${spark.version}

org.apache.spark

spark-sql_2.11

${spark.version}

org.apache.spark

spark-hive_2.11

${spark.version}

org.apache.spark

spark-streaming_2.11

${spark.version}

org.apache.hadoop

hadoop-common

${hadoop.version}

org.apache.hive

hive-exec

${hive.version}

mysql

mysql-connector-java

5.1.38

新建JavaWordCount

package spark;

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;

public class JavaWordCount {

private static final Pattern SPACE = Pattern.compile(" ");

public static void main(String[] args) {

SparkSession spark = SparkSession

.builder()

.master("local[*]")

.appName("WordCount")

.getOrCreate();

String paths = "D:\\workspace\\IdeaProjects\\learning\\src\\main\\resources\\word_count.txt";

JavaRDD lines = spark.read().textFile(paths).javaRDD();

JavaRDD words = lines.flatMap(s -> Arrays.asList(SPACE.split(s)).iterator());

JavaPairRDD ones = words.mapToPair(s -> new Tuple2<>(s, 1));

JavaPairRDD counts = ones.reduceByKey((i1, i2) -> (i1 + i2));

List> output = counts.collect();

for (Tuple2, ?> tuple : output) {

System.out.println(tuple._1() + ": " + tuple._2());

}

spark.stop();

}

}

在resources目录下新建log4j.properties和word_count.txt

log4j.properties文件

#

# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

#

# Set everything to be logged to the console

log4j.rootCategory=WARN, console

log4j.appender.console=org.apache.log4j.ConsoleAppender

log4j.appender.console.target=System.err

log4j.appender.console.layout=org.apache.log4j.PatternLayout

log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the

# log level for this class is used to overwrite the root logger's log level, so that

# the user can have different defaults for the shell and regular Spark apps.

log4j.logger.org.apache.spark.repl.Main=WARN

# Settings to quiet third party logs that are too verbose

log4j.logger.org.spark_project.jetty=WARN

log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR

log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO

log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO

log4j.logger.org.apache.parquet=ERROR

log4j.logger.parquet=ERROR

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support

log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL

log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

word_count.txt文件

Give me the strength lightly to bear my joys and sorrows.

Give me the strength to make my love fruitful in service.

Give me the strength never to disown the poor or bend my knees before insolent might.

Give me the strength to raise my mind high above daily trifles.

And give me the strength to surrender my strength to thy will with love.

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