idea中打jar包並放在Linux cdh-spark環境下運行


(1)添加pom.xml中的依賴包

注意依賴包必須跟cdh中的組件版本一致。附上cdh3.2.1版的pom.xml內容:

<?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>spark_lenovo</groupId>
    <artifactId>spark</artifactId>
    <version>1.0-SNAPSHOT</version>


    <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>

        <spark.scala.version>2.11</spark.scala.version>
        <spark.version>2.4.0</spark.version>
        <hadoop.version>3.0.0-cdh6.3.2</hadoop.version>
        <hbase.version>2.1.0-cdh6.3.2</hbase.version>

        <jar.scope>compile</jar.scope>
    </properties>

    <dependencies>
        <!--spark-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${spark.scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${spark.scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${spark.scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${spark.scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${spark.scala.version}</artifactId>
            <version>${spark.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
            <scope>${jar.scope}</scope>
        </dependency>

        <!--mysql jdbc驅動 -->
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>6.0.5</version>
        </dependency>
<!--        <dependency>-->
<!--            <groupId>mysql</groupId>-->
<!--            <artifactId>mysql-connector-java</artifactId>-->
<!--            <version>5.1.39</version>-->
<!--        </dependency>-->
<!--        <dependency>-->
<!--            <groupId>junit</groupId>-->
<!--            <artifactId>junit</artifactId>-->
<!--            <version>4.12</version>-->
<!--        </dependency>-->

        <!--hbase-->
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase</artifactId>
            <version>${hbase.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>${hbase.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>${hbase.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>${hbase.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-mapreduce</artifactId>
            <version>${hbase.version}</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <!-- 編譯scala的插件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <!-- 編譯java的插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.5.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.1</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF</exclude>
                                        <exclude>META-INF/*.DSA</exclude>
                                        <exclude>META-INF/*.RSA</exclude>
                                    </excludes>
                                </filter>
                            </filters>
                            <transformers>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                                    <resource>META-INF/spring.handlers</resource>
                                </transformer>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                                    <resource>META-INF/spring.schemas</resource>
                                </transformer>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>${groupId}.com.bigdata.CellPhoneToHbase</mainClass>
                                </transformer>
                            </transformers>
                            <createDependencyReducedPom>false</createDependencyReducedPom>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
    <repositories>
        <!--    由於hadoop版本是cdh的,所以需要添加cdh倉庫-->
        <repository>
            <id>cloudera</id>
            <name>cloudera</name>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos</url>
        </repository>
    </repositories>

</project>

(2)打包

A.   編譯

這里選擇extract to the target JAR就是將所有的依賴包也都一並打包了;如果選擇copy to the output…就只打包自己寫的文件。

如果選擇extract to the target JAR就會出現以下內容:

否則會出現以下內容:

B.   構建

 

在彈出的選擇框中點擊build

 

C.   查看

打包前是這樣:

 

打包后是這樣:

如果選擇extract to the target JAR就會出現以下內容:

否則會出現以下內容:

 

 

 

 

使用解壓軟件打開jar包,可以看到里面的內容:

 

 

 

 

 

 

(3)執行jar包

上傳jar包至Linux的其中一台spark節點服務器上

執行命令:

spark-submit --class lenovo.didi202009demo --master local /data/lrxtest/spark.jar

 

 

 

 

(4)Q&A

A.   org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: hdfs://Master11:9000/user

在寫spark 讀取本地文件命令的時候報hdfs上文件不存在的錯…

讀取文件是分兩種情況:

(首先要確保文件路徑寫對了!!!!!)

1. 如果讀取hdfs上的文件時報這個錯,那么去看hdfs上是否有這個文件!!

hdfs dfs -ls /   (  / 后面寫要讀取的文件的路徑)

1

如果沒有那么就創建文件,或者把本地文件上傳到hdfs上:

上傳本地文件:

hdfs dfs -put /usr/local/spark/test.txt /user/

 

創建文件:

hdfs dfs -mkdir -p /user/test/

 

hdfs上傳文件的詳細步驟點擊此處

2. 如果讀取的是本地文件,那么就好好看看命令,讀取本地文件的時候文件路徑前面要加 file:

我出錯就是因為沒加file: 這個單詞

錯誤的命令:

scala> sc.textFile("/usr/local/spark/test.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect

 

准確的命令:

sc.textFile("file:/usr/local/spark/test.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect


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