win10下将spark的程序提交给远程集群中运行

时间：2023-03-03 22:58:00

一，开发环境：

　操作系统：win19 64位

IDE：IntelliJ IDEA

JDK：1.8

scala:scala-2.10.6

集群：linux上cdh集群，其中spark为1.5.2,hadoop:2.6.0（其实我也想用spark最新版和hadoop的最新版，但1.6以前有spark-assembly-1.x.x-hadoop2.x.x.jar)

二，实现步骤：

　　1，设置maven的pom.xml

<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/maven-v4_0_0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <groupId>spark</groupId>

  <artifactId>test</artifactId>

  <version>1.0-SNAPSHOT</version>

  <inceptionYear>2008</inceptionYear>

  <properties>

    <scala.version>2.10.6</scala.version>

  </properties>

  <repositories>

    <repository>

      <id>scala-tools.org</id>

      <name>Scala-Tools Maven2 Repository</name>

      <url>http://scala-tools.org/repo-releases</url>

    </repository>

  </repositories>

  <pluginRepositories>

    <pluginRepository>

      <id>scala-tools.org</id>

      <name>Scala-Tools Maven2 Repository</name>

      <url>http://scala-tools.org/repo-releases</url>

    </pluginRepository>

  </pluginRepositories>

  <dependencies>

    <dependency>

      <groupId>junit</groupId>

      <artifactId>junit</artifactId>

      <version>4.12</version>

    </dependency>

    <dependency>

      <groupId>org.specs</groupId>

      <artifactId>specs</artifactId>

      <version>1.2.5</version>

      <scope>test</scope>

    </dependency>

    <dependency>

      <groupId>commons-logging</groupId>

      <artifactId>commons-logging</artifactId>

      <version>1.1.1</version>

      <type>jar</type>

    </dependency>

    <dependency>

      <groupId>org.apache.commons</groupId>

      <artifactId>commons-lang3</artifactId>

      <version>3.1</version>

    </dependency>

    <dependency>

      <groupId>log4j</groupId>

      <artifactId>log4j</artifactId>

      <version>1.2.9</version>

    </dependency>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-core_2.10</artifactId>

      <version>1.5.2</version>

    </dependency>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-sql_2.10</artifactId>

      <version>1.5.2</version>

    </dependency>

      <dependency>

          <groupId>org.apache.hadoop</groupId>

          <artifactId>hadoop-client</artifactId>

          <version>2.6.0</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-mllib_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-hive_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-streaming_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

      <dependency>

      <groupId>org.scala-lang</groupId>

      <artifactId>scala-library</artifactId>

      <version>2.10.6</version>

    </dependency>

  </dependencies>

  <build>

    <sourceDirectory>src/main/scala</sourceDirectory>

    <testSourceDirectory>src/test/scala</testSourceDirectory>

    <plugins>

      <plugin>

        <groupId>org.scala-tools</groupId>

        <artifactId>maven-scala-plugin</artifactId>

        <executions>

          <execution>

            <goals>

              <goal>compile</goal>

              <goal>testCompile</goal>

            </goals>

          </execution>

        </executions>

        <configuration>

          <scalaVersion>${scala.version}</scalaVersion>

          <args>

            <arg>-target:jvm-1.5</arg>

          </args>

        </configuration>

      </plugin>

      <plugin>

        <groupId>org.apache.maven.plugins</groupId>

        <artifactId>maven-eclipse-plugin</artifactId>

        <configuration>

          <downloadSources>true</downloadSources>

          <buildcommands>

            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>

          </buildcommands>

          <additionalProjectnatures>

            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>

          </additionalProjectnatures>

          <classpathContainers>

            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>

            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>

          </classpathContainers>

        </configuration>

      </plugin>

    </plugins>

  </build>

  <reporting>

    <plugins>

      <plugin>

        <groupId>org.scala-tools</groupId>

        <artifactId>maven-scala-plugin</artifactId>

        <configuration>

          <scalaVersion>${scala.version}</scalaVersion>

        </configuration>

      </plugin>

    </plugins>

  </reporting>

</project>

2，编写简单程序：

object test {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setMaster("spark://xxxxx:7077").setAppName("test")

    val sc = new SparkContext(conf)

    sc.addJar("E:\\sparkTest\\out\\artifacts\\sparkTest_jar\\sparkTest.jar")

    val count = sc.parallelize(1 to 4).filter { _ =>

      val x = math.random

      val y = math.random

      x*x + y*y < 1

    }.count()

    println(s"Pi is roughly ${4.0 * count / 4}")

    sc.stop()

  }

}

3,打jar包，即：file->projectStruct->Artifacts->Build->Build Artifacts，点击run运行即可（刚刚试试了下，发现不要jar也能运行，只是控制台还没结果输出？）

4，pom.xml的spark版本号要和集群中spark的版本号一致(不一致出现：exception1:java.lang.IllegalArgumentException: requirement failed: Can only call getServletHandlers on a running MetricsSystem）

5，异常： Could not locate executable null\bin\winutils.exe in the Hadoop binaries

解决方法：

1，下载hadoop的包，我下了hadoop-2.7.3，解压，并配置HADOOP_HOME即可

2，下载https://github.com/srccodes/hadoop-common-2.2.0-bin下载winutils.exe,放到hadoop目录下的bin中

3，重启idea异常消失

6， Exception while deleting Spark temp dir: C:\U
sers\tend\AppData\Local\Temp\spark-70484fc4-167d-48fa-a8f6-54db9752402e\userFiles-27a65cc7
-817f-4476-a2a2-58967d7b6cc1    解决方法：目前spark在windows系统下存在这个问题。不想看的话，就把log4j.properties中log的level设置为FATAL吧（呵呵呵）

7，com.google.protobuf.InvalidProtocolBufferException: Protocol message end-gro：hdfs的ip地址或端口号输入有问题，

hdfs://xxxx:8020//usr/xxx (新版本端口多为9000）

8，oracle读写操作：

package spark

import org.apache.log4j.{Level, Logger}

import org.apache.spark.sql.hive.HiveContext

object readFromOracle {
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.FATAL)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
    val conf=new SparkConf().setMaster("spark://xxxxxx:7077").setAppName("read")
      .setJars(List("E:\\softs\\softDownload\\ojdbc14.jar"))//添加ojdbc14的jar包，会出现
    val sc=new SparkContext(conf)
    val oracleDriverUrl="jdbc:oracle:thin:@xxxxxxxx:1521:testdb11g"
    val jdbcMap=Map("url" -> oracleDriverUrl,"user"->"xxxxx","password"->"xxxxx","dbtable"->"MYTABLE","driver"->"oracle.jdbc.driver.OracleDriver")
    val sqlContext = new HiveContext(sc)
    val jdbcDF = sqlContext.read.options(jdbcMap).format("jdbc").load
    jdbcDF.show(3)
  }
}
package spark
import java.sql.{Connection, DriverManager, PreparedStatement}
import java.util.Properties

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.jdbc.{JdbcDialect, JdbcDialects, JdbcType}
import org.apache.spark.sql.types._

/**
  * Created by Administrator on 2017/7/17.
  */
object writeToOracle {
  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.FATAL)
    Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)
    /*
    记得设置jar包，虽然build时添加了ojdbc.jar,但仍然出现jdbc:oracle:thin:@xxxxxxxx:testdb11g
 at java.sql.DriverManager.getConnection(DriverManager.java:689)，看来build时不行
   最好将依赖的jar包上传到hdfs上不要在本地
     */
    val conf=new SparkConf().setMaster("spark://xxxxxxx:7077").setAppName("write")
      .setJars(List("E:\\sparkTest\\out\\artifacts\\writeToOracle_jar\\sparkTest.jar","E:\\softs\\softDownload\\ojdbc14.jar"))
    val sc=new SparkContext(conf)
    val sqlContext = new HiveContext(sc)
    val oracleDriverUrl="jdbc:oracle:thin:@xxxxxxx:testdb11g"
    val jdbcMap=Map("url" -> oracleDriverUrl,"user"->"xxxx","password"->"xxxxxx","dbtable"->"MYTABLE","driver"->"oracle.jdbc.driver.OracleDriver")
    val jdbcDF = sqlContext.read.options(jdbcMap).format("jdbc").load
    jdbcDF.foreachPartition(rows => {
      Class.forName("oracle.jdbc.driver.OracleDriver")
      val connection: Connection = DriverManager.getConnection(oracleDriverUrl, "xxxx","xxxxxxx")
      val prepareStatement: PreparedStatement = connection.prepareStatement("insert into MYTABLE2 values(?,?,?,?,?,?,?,?,?)")
      rows.foreach(row => {
        prepareStatement.setString(1, row.getString(0))
        prepareStatement.setString(2, row.getString(0))
        prepareStatement.setString(3, row.getString(0))
        prepareStatement.setString(4, row.getString(0))
        prepareStatement.setString(5, row.getString(0))
        prepareStatement.setString(6, row.getString(0))
        prepareStatement.setString(7, row.getString(0))
        prepareStatement.setString(8, row.getString(0))
        prepareStatement.setString(9,row.getString(0))
        prepareStatement.addBatch()
      })
      prepareStatement.executeBatch()
      prepareStatement.close()
      connection.close()
    })
  }
}

复制数据库，操作：

package spark.sql

import java.util.Properties

import org.apache.log4j.{Level, Logger}

import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils

import org.apache.spark.{SparkConf, SparkContext}

import org.apache.spark.sql.hive.HiveContext

import org.apache.spark.sql.jdbc.{JdbcDialect, JdbcDialects, JdbcType}

import org.apache.spark.sql.types._

/**

  * Created by Administrator on 2017/7/21.

  */

object OperateOracle {

  Logger.getLogger("org.apache.spark").setLevel(Level.WARN)

  Logger.getLogger("org.eclipse.jetty.server").setLevel(Level.OFF)

  val oracleDriverUrl="jdbc:oracle:thin:@xxxxxxx:1521:testdb11g"

  val jdbcMap=Map("url" -> oracleDriverUrl,

    "user"->"xxxxxx","password"->"xxxxxxx",

    "dbtable"->"MYTABLE",

    "driver"->"oracle.jdbc.driver.OracleDriver")

  def main(args: Array[String]) {

    //创建SparkContext

    val sc = createSparkContext

    //创建sqlContext用来连接oracle、Hive等

    val sqlContext = new HiveContext(sc)

    //加载oracle表数据，为lazy方式

    val jdbcDF = sqlContext.read.options(jdbcMap).format("jdbc").load

    jdbcDF.registerTempTable("MYTABLEDF")

    val df2Oracle = sqlContext.sql("select * from MYTABLEDF")

    //Registering the OracleDialect

    JdbcDialects.registerDialect(OracleDialect)

    val connectProperties = new Properties()

    connectProperties.put("user", "xxxxxx")

    connectProperties.put("password", "xxxxxxx")

    Class.forName("oracle.jdbc.driver.OracleDriver").newInstance()

    //write back Oracle

    //Note: When writing the results back orale, be sure that the target table existing

    JdbcUtils.saveTable(df2Oracle, oracleDriverUrl, "MYTABLE2", connectProperties)

    sc.stop

  }

  def createSparkContext: SparkContext = {

    val conf = new SparkConf().setAppName("Operate")

      .setMaster("spark://xxxxxx:7077")

      .setJars(List("hdfs://xxxxx:8020//user//ojdbc14.jar"))

    //SparkConf parameters setting

    //conf.set("spark.sql.autoBroadcastJoinThreshold", "50M")

    /*spark.sql.codegen 是否预编译sql成java字节码，长时间或频繁的sql有优化效果*/

    //conf.set("spark.sql.codegen", "true")

    /*spark.sql.inMemoryColumnarStorage.batchSize 一次处理的row数量，小心oom*/

    //conf.set("spark.sql.inMemoryColumnarStorage.batchSize", "1000")

    /*spark.sql.inMemoryColumnarStorage.compressed 设置内存中的列存储是否需要压缩*/

    //conf.set("spark.sql.inMemoryColumnarStorage.compressed", "true")

    val sc = new SparkContext(conf)

    sc

  }

  //overwrite JdbcDialect fitting for Oracle

  val OracleDialect = new JdbcDialect {

    override def canHandle(url: String): Boolean = url.startsWith("jdbc:oracle") || url.contains("oracle")

    //getJDBCType is used when writing to a JDBC table

    override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {

      case StringType => Some(JdbcType("VARCHAR2(255)", java.sql.Types.VARCHAR))

      case BooleanType => Some(JdbcType("NUMBER(1)", java.sql.Types.NUMERIC))

      case IntegerType => Some(JdbcType("NUMBER(16)", java.sql.Types.NUMERIC))

      case LongType => Some(JdbcType("NUMBER(16)", java.sql.Types.NUMERIC))

      case DoubleType => Some(JdbcType("NUMBER(16,4)", java.sql.Types.NUMERIC))

      case FloatType => Some(JdbcType("NUMBER(16,4)", java.sql.Types.NUMERIC))

      case ShortType => Some(JdbcType("NUMBER(5)", java.sql.Types.NUMERIC))

      case ByteType => Some(JdbcType("NUMBER(3)", java.sql.Types.NUMERIC))

      case BinaryType => Some(JdbcType("BLOB", java.sql.Types.BLOB))

      case TimestampType => Some(JdbcType("DATE", java.sql.Types.DATE))

      case DateType => Some(JdbcType("DATE", java.sql.Types.DATE))

      //        case DecimalType.Fixed(precision, scale) => Some(JdbcType("NUMBER(" + precision + "," + scale + ")", java.sql.Types.NUMERIC))

      case DecimalType.Unlimited => Some(JdbcType("NUMBER(38,4)", java.sql.Types.NUMERIC))

      case _ => None

    }

  }

}

此时的pom.xml:

<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/maven-v4_0_0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <groupId>spark</groupId>

  <artifactId>test</artifactId>

  <version>1.0-SNAPSHOT</version>

  <inceptionYear>2008</inceptionYear>

  <properties>

    <scala.version>2.10.6</scala.version>

  </properties>

  <repositories>

    <repository>

      <id>scala-tools.org</id>

      <name>Scala-Tools Maven2 Repository</name>

      <url>http://scala-tools.org/repo-releases</url>

    </repository>

  </repositories>

  <pluginRepositories>

    <pluginRepository>

      <id>scala-tools.org</id>

      <name>Scala-Tools Maven2 Repository</name>

      <url>http://scala-tools.org/repo-releases</url>

    </pluginRepository>

  </pluginRepositories>

  <dependencies>

    <dependency>

      <groupId>junit</groupId>

      <artifactId>junit</artifactId>

      <version>4.12</version>

    </dependency>

    <dependency>

      <groupId>org.specs</groupId>

      <artifactId>specs</artifactId>

      <version>1.2.5</version>

      <scope>test</scope>

    </dependency>

    <dependency>

      <groupId>commons-logging</groupId>

      <artifactId>commons-logging</artifactId>

      <version>1.1.1</version>

      <type>jar</type>

    </dependency>

    <dependency>

      <groupId>org.apache.commons</groupId>

      <artifactId>commons-lang3</artifactId>

      <version>3.1</version>

    </dependency>

    <dependency>

      <groupId>log4j</groupId>

      <artifactId>log4j</artifactId>

      <version>1.2.9</version>

    </dependency>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-core_2.10</artifactId>

      <version>1.5.2</version>

    </dependency>

    <dependency>

      <groupId>org.apache.spark</groupId>

      <artifactId>spark-sql_2.10</artifactId>

      <version>1.5.2</version>

    </dependency>

      <dependency>

          <groupId>org.apache.hadoop</groupId>

          <artifactId>hadoop-client</artifactId>

          <version>2.6.0</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-mllib_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-hive_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

      <dependency>

          <groupId>org.apache.spark</groupId>

          <artifactId>spark-streaming_2.10</artifactId>

          <version>1.5.2</version>

      </dependency>

    <dependency>

      <groupId>com.databricks</groupId>

      <artifactId>spark-csv_2.10</artifactId>

      <version>1.5.0</version>

    </dependency>

    <dependency>

      <groupId>org.scala-lang</groupId>

      <artifactId>scala-library</artifactId>

      <version>2.10.6</version>

    </dependency>

  </dependencies>

  <build>

    <sourceDirectory>src/main/scala</sourceDirectory>

    <testSourceDirectory>src/test/scala</testSourceDirectory>

    <plugins>

      <plugin>

        <groupId>org.scala-tools</groupId>

        <artifactId>maven-scala-plugin</artifactId>

        <executions>

          <execution>

            <goals>

              <goal>compile</goal>

              <goal>testCompile</goal>

            </goals>

          </execution>

        </executions>

        <configuration>

          <scalaVersion>${scala.version}</scalaVersion>

          <args>

            <arg>-target:jvm-1.5</arg>

          </args>

        </configuration>

      </plugin>

      <plugin>

        <groupId>org.apache.maven.plugins</groupId>

        <artifactId>maven-eclipse-plugin</artifactId>

        <configuration>

          <downloadSources>true</downloadSources>

          <buildcommands>

            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>

          </buildcommands>

          <additionalProjectnatures>

            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>

          </additionalProjectnatures>

          <classpathContainers>

            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>

            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>

          </classpathContainers>

        </configuration>

      </plugin>

    </plugins>

  </build>

  <reporting>

    <plugins>

      <plugin>

        <groupId>org.scala-tools</groupId>

        <artifactId>maven-scala-plugin</artifactId>

        <configuration>

          <scalaVersion>${scala.version}</scalaVersion>

        </configuration>

      </plugin>

    </plugins>

  </reporting>

</project>
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