【kettle】pdi/data-integration 集成kerberos认证连接hdfs、hive或spark thriftserver

一、背景

kerberos认证是比较底层的认证，掌握好了用起来比较简单。
kettle完成kerberos认证后会存储认证信息在jvm中，之后直接连接hive就可以了无需提供额外的用户信息。

spark thriftserver本质就是通过hive jdbc协议连接并运行spark sql任务。

二、思路

kettle中可以使用js调用java类的方法。编写一个jar放到kettle的lib目录下并。在启动kettle后会自动加载此jar中的类。编写一个javascript转换完成kerbero即可。

二、kerberos认证模块开发

准备使用scala语言完成此项目。
hadoop 集群版本: cdh-6.2.0
kettle 版本: 8.2.0.0-342

2.1 生成kerberos工具jar包

2.1.1 创建maven项目并编写pom

创建maven项目，这里依赖比较多觉得没用的删掉即可：
注意：这里为了便于管理，很多包都是compile，最后通过maven-assembly-plugin复制到zip文件中！！！

  <properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <scala.version>2.11.12</scala.version>
    <scala.major.version>2.11</scala.major.version>
    <target.java.version>1.8</target.java.version>
    <hadoop.version>3.0.0-cdh6.2.0</hadoop.version>
    <spark.version>2.4.0-cdh6.2.0</spark.version>
    <hive.version>2.1.1-cdh6.2.0</hive.version>
    <zookeeper.version>3.4.5-cdh6.2.0</zookeeper.version>
    <jackson.version>2.14.2</jackson.version>
    <httpclient5.version>5.2.1</httpclient5.version>
  </properties>
  
  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-reflect</artifactId>
      <version>${scala.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-compiler</artifactId>
      <version>${scala.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-api</artifactId>
      <version>1.7.28</version>
      <scope>provided</scope>
    </dependency>
    
    <dependency>
      <groupId>org.apache.logging.log4j</groupId>
      <artifactId>log4j-slf4j-impl</artifactId>
      <version>2.9.1</version>
      <scope>provided</scope>
    </dependency>
    
    <dependency>
      <groupId>org.apache.logging.log4j</groupId>
      <artifactId>log4j-api</artifactId>
      <version>2.11.1</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.logging.log4j</groupId>
      <artifactId>log4j-core</artifactId>
      <version>2.11.1</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>${hadoop.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_${scala.major.version}</artifactId>
      <version>${spark.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_${scala.major.version}</artifactId>
      <version>${spark.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_${scala.major.version}</artifactId>
      <version>${spark.version}</version>
      <scope>provided</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.hive</groupId>
      <artifactId>hive-jdbc</artifactId>
      <version>${hive.version}</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive-thriftserver_${scala.major.version}</artifactId>
      <version>${spark.version}</version>
      <scope>compile</scope>
    </dependency>
    
    <dependency>
      <groupId>org.apache.zookeeper</groupId>
      <artifactId>zookeeper</artifactId>
      <version>${zookeeper.version}</version>
      <scope>compile</scope>
    </dependency>

    <!-- jackon -->
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-core</artifactId>
      <version>${jackson.version}</version>
      <scope>compile</scope>
    </dependency>
    
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-databind</artifactId>
      <version>${jackson.version}</version>
      <scope>compile</scope>
    </dependency>
    
    <dependency>
      <groupId>com.fasterxml.jackson.dataformat</groupId>
      <artifactId>jackson-dataformat-xml</artifactId>
      <version>${jackson.version}</version>
      <scope>compile</scope>
    </dependency>
    
    <dependency>
      <groupId>com.fasterxml.jackson.module</groupId>
      <artifactId>jackson-module-scala_2.11</artifactId>
      <version>${jackson.version}</version>
      <scope>compile</scope>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.junit.jupiter/junit-jupiter-api -->
    <dependency>
      <groupId>org.junit.jupiter</groupId>
      <artifactId>junit-jupiter-api</artifactId>
      <version>5.6.2</version>
      <scope>test</scope>
    </dependency>

    <dependency>
      <groupId>org.scalatest</groupId>
      <artifactId>scalatest_2.11</artifactId>
      <version>3.2.8</version>
      <scope>test</scope>
    </dependency>
    
    <dependency>
      <groupId>org.scalactic</groupId>
      <artifactId>scalactic_2.12</artifactId>
      <version>3.2.8</version>
      <scope>test</scope>
    </dependency>
    
    <dependency>
      <groupId>org.projectlombok</groupId>
      <artifactId>lombok</artifactId>
      <version>1.18.14</version>
      <scope>provided</scope>
    </dependency>

  </dependencies>

  <build>
    <plugins>
      <plugin>
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>4.5.6</version>
        <configuration>
        </configuration>
        <executions>
          <execution>
            <id>scala-compiler</id>
            <phase>process-resources</phase>
            <goals>
              <goal>add-source</goal>
              <goal>compile</goal>
            </goals>
          </execution>
          <execution>
            <id>scala-test-compiler</id>
            <phase>process-test-resources</phase>
            <goals>
              <goal>add-source</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
      </plugin>

      <!-- disable surefire -->
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>2.7</version>
        <configuration>
          <skipTests>true</skipTests>
        </configuration>
      </plugin>
      <!-- enable scalatest -->
      <plugin>
        <groupId>org.scalatest</groupId>
        <artifactId>scalatest-maven-plugin</artifactId>
        <version>2.2.0</version>
        <configuration>
          <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
          <junitxml>.</junitxml>
          <filereports>WDF TestSuite.txt</filereports>
        </configuration>
        <executions>
          <execution>
          </execution>
        </executions>
      </plugin>

      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.0.0</version>
        <configuration>
          <appendAssemblyId>false</appendAssemblyId>
<!--          <descriptorRefs>-->
<!--            <descriptorRef>jar-with-dependencies</descriptorRef>-->
<!--          </descriptorRefs>-->
          <descriptors>
            <descriptor>src/assembly/assembly.xml</descriptor>
          </descriptors>
          <archive>
            <!--          <manifest>-->
            <!--            &lt;!&ndash; 你的mainclass入口，我就是test.scala 在scala文件夹下， 目录就是scr/main/scala &ndash;&gt;-->
            <!--            <mainClass>com.chenxii.myspark.sparkcore.Test</mainClass>-->
            <!--          </manifest>-->
          </archive>
        </configuration>

        <executions>
          <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>

  <repositories>
    <repository>
      <id>cloudera</id>
      <name>cloudera</name>
      <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
    </repository>
  </repositories>
</project>

新建一个空白的xml文件，如下图：

maven-assembly-plugin插件会使用到assembly.xml，粘贴如下内容，至assembly.xml文件中，再修改下 自己的项目groupid。

<assembly xmlns="http://maven.apache.org/plugins/maven-assembly-plugin/assembly/1.1.3"
          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
          xsi:schemaLocation="http://maven.apache.org/plugins/maven-assembly-plugin/assembly/1.1.3 http://maven.apache.org/xsd/assembly-1.1.3.xsd">
  <id>appserverB</id>
  <formats>
    <format>zip</format>
  </formats>
  <dependencySets>
    <dependencySet>
      <outputDirectory>/ext-lib</outputDirectory>
      <includes>
        <include>org.apache.hadoop:*</include>
        <include>org.apache.hive:*</include>
        <include>org.apache.hive.shims:*</include>
        <include>org.apache.spark:spark-hive-thriftserver_*</include>
        <include>org.apache.zookeeper:*</include>
        <include>org.apache.curator:*</include>
        <include>org.apache.commons:commons-lang3</include>
        <include>org.apache.commons:commons-configuration2</include>
        <include>org.apache.commons:commons-math3</include>
        <include>com.fasterxml.jackson.core:*</include>
        <include>com.fasterxml.jackson.dataformat:*</include>
        <include>com.fasterxml.jackson.module:*</include>
        <include>org.scala-lang:*</include>
        <include>org.apache.thrift:libthrift</include>
        <include>com.thoughtworks.paranamer:paranamer</include>
        <include>com.google.re2j:re2j</include>
        <include>com.fasterxml.woodstox:woodstox-core</include>
        <include>org.codehaus.woodstox:stax2-api</include>
        <include>org.apache.httpcomponents.core5:*</include>
        <include>org.apache.httpcomponents.client5:*</include>
        <include>org.apache.htrace:*</include>
        <include>com.github.rholder:guava-retrying</include>
        <include>org.eclipse.jetty:jetty-util</include>
        <include>org.mortbay.jetty:*</include>
        <include>自己的项目groupid:*</include>
      </includes>
    </dependencySet>
  </dependencySets>
</assembly>

2.1.2 编写类

KerberosConf 暂时没啥用。

case class KerberosConf(principal: String, keyTabPath: String, conf: String="/etc/krb5.conf")

ConfigUtils 类用于生成hadoop 的Configuration，kerberos认证的时候会用到。

import org.apache.commons.lang3.StringUtils
import org.apache.hadoop.conf.Configuration
import java.io.FileInputStream
import java.nio.file.{Files, Paths}

object ConfigUtils {

  val LOGGER = org.slf4j.LoggerFactory.getLogger(KerberosUtils.getClass)

  var hadoopConfiguration: Configuration = null
  var hiveConfiguration: Configuration = null
  private var hadoopConfDir: String = null
  private var hiveConfDir: String = null

  def setHadoopConfDir(dir: String): Configuration = {
    hadoopConfDir = dir
    refreshHadoopConfig
  }

  def getHadoopConfDir: String = {
    if (StringUtils.isEmpty(hadoopConfDir)) {
      val tmpConfDir = System.getenv("HADOOP_CONF_DIR")
      if (StringUtils.isNotEmpty(tmpConfDir) && fileOrDirExists(tmpConfDir)) {
        hadoopConfDir = tmpConfDir
      } else {
        val tmpHomeDir = System.getenv("HADOOP_HOME")
        if (StringUtils.isNotEmpty(tmpHomeDir) && fileOrDirExists(tmpHomeDir)) {
          val tmpConfDirLong = s"${tmpHomeDir}/etc/hadoop"
          val tmpConfDirShort = s"${tmpHomeDir}/conf"
          if (fileOrDirExists(tmpConfDirLong)) {
            hadoopConfDir = tmpConfDirLong
          } else if (fileOrDirExists(tmpConfDirShort)) {
            hadoopConfDir = tmpConfDirShort
          }
        }
      }
    }
    LOGGER.info(s"discover hadoop conf from : ${hadoopConfDir}")
    hadoopConfDir
  }

  def getHadoopConfig: Configuration = {
    if (hadoopConfiguration == null) {
      hadoopConfiguration = new Configuration()
      configHadoop()
    }
    hadoopConfiguration
  }

  def refreshHadoopConfig: Configuration = {
    hadoopConfiguration = new Configuration()
    configHadoop()
  }

  def configHadoop(): Configuration = {
    var coreXml = ""
    var hdfsXml = ""
    val hadoopConfDir = getHadoopConfDir
    if (StringUtils.isNotEmpty(hadoopConfDir)) {
      val coreXmlTmp = s"${hadoopConfDir}/core-site.xml"
      val hdfsXmlTmp = s"${hadoopConfDir}/hdfs-site.xml"
      val coreExists = fileOrDirExists(coreXmlTmp)
      val hdfsExists = fileOrDirExists(hdfsXmlTmp)
      if (coreExists && hdfsExists) {
        LOGGER.info(s"discover hadoop conf from hadoop conf dir: ${hadoopConfDir}")
        coreXml = coreXmlTmp
        hdfsXml = hdfsXmlTmp
        hadoopAddSource(coreXml, hadoopConfiguration)
        hadoopAddSource(hdfsXml, hadoopConfiguration)
      }
    }
    LOGGER.info(s"core-site path : ${coreXml}, hdfs-site path : ${hdfsXml}")
    hadoopConfiguration
  }

  def getHiveConfDir: String = {
    if (StringUtils.isEmpty(hiveConfDir)) {
      val tmpConfDir = System.getenv("HIVE_CONF_DIR")
      if (StringUtils.isNotEmpty(tmpConfDir) && fileOrDirExists(tmpConfDir)){
        hiveConfDir = tmpConfDir
      } else {
        val tmpHomeDir = System.getenv("HIVE_HOME")
        if (StringUtils.isNotEmpty(tmpHomeDir) && fileOrDirExists(tmpHomeDir)) {
          val tmpConfDirShort = s"${tmpHomeDir}/conf}"
          if (fileOrDirExists(tmpConfDir)) {
            hiveConfDir = tmpConfDirShort
          }
        }
      }
    }
    LOGGER.info(s"discover hive conf from : ${hiveConfDir}")
    hiveConfDir
  }

  def configHive(): Configuration = {
    if (hiveConfiguration != null) {
      return hiveConfiguration
    } else {
      hiveConfiguration = new Configuration()
    }
    var hiveXml = ""
    val hiveConfDir = getHiveConfDir
    if (StringUtils.isEmpty(hiveConfDir)) {
      val hiveXmlTmp = s"${hiveConfDir}/hive-site.xml"
      val hiveExist = fileOrDirExists(hiveXml)
      if (hiveExist) {
        LOGGER.info(s"discover hive conf from : ${hiveConfDir}")
        hiveXml = hiveXmlTmp
        hadoopAddSource(hiveXml, hiveConfiguration)
      }
    }
    LOGGER.info(s"hive-site path : ${hiveXml}")
    hiveConfiguration
  }

  def getHiveConfig: Configuration = {
    if (hiveConfiguration == null) {
      hiveConfiguration = new Configuration()
      configHive()
    }
    hiveConfiguration
  }

  def refreshHiveConfig: Configuration = {
    hiveConfiguration = new Configuration()
    configHive()
  }

  def hadoopAddSource(confPath: String, conf: Configuration): Unit = {
    val exists = fileOrDirExists(confPath)
    if (exists) {
      LOGGER.warn(s"add [${confPath} to hadoop conf]")
      var fi: FileInputStream = null
      try {
        fi = new FileInputStream(confPath)
        conf.addResource(fi)
        conf.get("$$")
      } finally {
        if (fi != null) fi.close()
      }
    } else {
      LOGGER.error(s"[${confPath}] file does not exists!")
    }
  }

  def toUnixStyleSeparator(path: String): String = {
    path.replaceAll("\\\\", "/")
  }

  def fileOrDirExists(path: String): Boolean = {
    Files.exists(Paths.get(path))
  }
}

KerberosUtils 就是用于认证的类。

import org.apache.commons.lang3.StringUtils
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.security.UserGroupInformation
import org.apache.kerby.kerberos.kerb.keytab.Keytab
import org.slf4j.Logger

import java.io.File
import java.net.URL
import java.nio.file.{Files, Paths}
import scala.collection.JavaConversions._
import scala.collection.JavaConverters._

object KerberosUtils {
  val LOGGER: Logger = org.slf4j.LoggerFactory.getLogger(KerberosUtils.getClass)

  def loginKerberos(krb5Principal: String, krb5KeytabPath: String, krb5ConfPath: String, hadoopConf: Configuration): Boolean = {
    val authType = hadoopConf.get("hadoop.security.authentication")
    if (!"kerberos".equalsIgnoreCase(authType)) {
      LOGGER.error(s"kerberos utils get hadoop authentication type [${authType}] ,not kerberos!")
    } else {
      LOGGER.info(s"kerberos utils get hadoop authentication type [${authType}]!")
    }
    UserGroupInformation.setConfiguration(hadoopConf)
    System.setProperty("java.security.krb5.conf", krb5ConfPath)
    System.setProperty("javax.security.auth.useSubjectCredsOnly", "false")
    UserGroupInformation.loginUserFromKeytab(krb5Principal, krb5KeytabPath)
    val user = UserGroupInformation.getLoginUser
    if (user.getAuthenticationMethod == UserGroupInformation.AuthenticationMethod.KERBEROS) {
      val usnm: String = user.getShortUserName
      LOGGER.info(s"kerberos utils login success, curr user: ${usnm}")
      true
    } else {
      LOGGER.info("kerberos utils login failed")
      false
    }

  }

  def loginKerberos(krb5Principal: String, krb5KeytabPath: String, krb5ConfPath: String): Boolean = {
    val hadoopConf = ConfigUtils.getHadoopConfig
    loginKerberos(krb5Principal, krb5KeytabPath, krb5ConfPath, hadoopConf)
  }

  def loginKerberos(kerberosConf: KerberosConf): Boolean = {
    loginKerberos(kerberosConf.principal, kerberosConf.keyTabPath, kerberosConf.conf)
  }

  def loginKerberos(krb5Principal: String, krb5KeytabPath: String, krb5ConfPath: String,hadoopConfDir:String):Boolean={
    ConfigUtils.setHadoopConfDir(hadoopConfDir)
    loginKerberos(krb5Principal,krb5KeytabPath,krb5ConfPath)
  }

  def loginKerberos(): Boolean = {
    var principal: String = null
    var keytabPath: String = null
    var krb5ConfPath: String = null
    val classPath: URL = this.getClass.getResource("/")
    val classPathObj = Paths.get(classPath.toURI)
    var keytabPathList = Files.list(classPathObj).iterator().asScala.toList
    keytabPathList = keytabPathList.filter(p => p.toString.toLowerCase().endsWith(".keytab")).toList
    val krb5ConfPathList = keytabPathList.filter(p => p.toString.toLowerCase().endsWith("krb5.conf")).toList
    if (keytabPathList.nonEmpty) {
      val ktPath = keytabPathList.get(0)
      val absPath = ktPath.toAbsolutePath
      val keytab = Keytab.loadKeytab(new File(absPath.toString))
      val pri = keytab.getPrincipals.get(0).getName
      if (StringUtils.isNotEmpty(pri)) {
        principal = pri
        keytabPath = ktPath.toString
      }
    }
    if (krb5ConfPathList.nonEmpty) {
      val confPath = krb5ConfPathList.get(0)
      krb5ConfPath = confPath.toAbsolutePath.toString
    }
    if (StringUtils.isNotEmpty(principal) && StringUtils.isNotEmpty(keytabPath) && StringUtils.isNotEmpty(krb5ConfPath)) {
      ConfigUtils.configHadoop()
      // ConfigUtils.configHive()
      val hadoopConf = ConfigUtils.hadoopConfiguration
      loginKerberos(principal, keytabPath, krb5ConfPath, hadoopConf)
    } else {
      false
    }
  }
}

2.1.3 编译打包

mvn package 后maven-assembly-plugin会在target/生成出一个zip包。zip包最里层的各种jar就是需要的jar包了，将这些jar包都放到 kettle 的lib目录或自定义的目录（自定义方法请看下文）就好。

注意：
（1）本例中的kettle8.2中的KETTLE_HOME/plugins\pentaho-big-data-plugin\hadoop-configurations 目录下有几个hadoop plugin，在kettle9之前的版本全局只能有一个hadoop，选择使用哪个hadoop需要在文件KETTLE_HOME/plugins\pentaho-big-data-plugin\plugin.properties的active.hadoop.configuration=...配置，就是文件夹的名字，比如此次配置hdp30作为hadoop plugin 基础的hadoop版本，注kettle9以后的版本不是这么配置的，这个hadoop版本越接近实际集群的版本越好，kettle每次此启动都尝试加载此目录下的类，不一样也可以！！！

（2）由于集群版本可能和任何上边的hadoop plugin都不一致，此时需要把集群版本的依赖jar包提前加载，所以需要把相关依赖放在KETTLE_HOME/lib下，如果想放在另设目录比如KETTLE_HOME/ext-lib。win下可以把Spoon.bat中的两处set LIBSPATH=后都追加;..\ext-lib，注意是分号分隔。当然linux下，修改spoon.sh在LIBPATH=$CURRENTDIR最后都追加:..\ext-lib，注意是冒号分隔，之所有要有..是因为项目启动类是KETTLE_HOME/launcher\launcher.jar中。

类和包报错说明：
报错一：
kettle8.2报错很难理解，9之后的版本好得多：如下内容实际Watcher类找不到，但实际是有的。

2024/01/03 17:34:01 - spark-read-ha-sample.0 - Error connecting to database: (using class org.apache.hive.jdbc.HiveDriver)
2024/01/03 17:34:01 - spark-read-ha-sample.0 - org/apache/zookeeper/Watcher

报错二：
出现这种问题的是也是java类加载错误，应该是不同的classloader加载类导致不识别。

loader constraint violation: loader (instance of java/net/URLClassLoader) previously initiated loading for a different type with name "org/apache/curator/RetryPolicy"

报错三：
出现此问题，暂时无法解释。

java.lang.IllegalArgumentException: port out of range:-1
        at java.net.InetSocketAddress.checkPort(InetSocketAddress.java:143)
        at java.net.InetSocketAddress.createUnresolved(InetSocketAddress.java:254)
        at org.apache.zookeeper.client.ConnectStringParser.<init>(ConnectStringParser.java:76)
        at org.apache.zookeeper.ZooKeeper.<init>(ZooKeeper.java:447)
        at org.apache.curator.utils.DefaultZookeeperFactory.newZooKeeper(DefaultZookeeperFactory.java:29)
        at org.apache.curator.framework.imps.CuratorFrameworkImpl$2.newZooKeeper(CuratorFrameworkImpl.java:150)
        at org.apache.curator.HandleHolder$1.getZooKeeper(HandleHolder.java:94)
        at org.apache.curator.HandleHolder.getZooKeeper(HandleHolder.java:55)
        at org.apache.curator.ConnectionState.reset(ConnectionState.java:262)
        at org.apache.curator.ConnectionState.start(ConnectionState.java:109)
        at org.apache.curator.CuratorZookeeperClient.start(CuratorZookeeperClient.java:191)
        at org.apache.curator.framework.imps.CuratorFrameworkImpl.start(CuratorFrameworkImpl.java:259)
        at org.apache.hive.jdbc.ZooKeeperHiveClientHelper.configureConnParams(ZooKeeperHiveClientHelper.java:63)
        at org.apache.hive.jdbc.Utils.configureConnParams(Utils.java:520)
        at org.apache.hive.jdbc.Utils.parseURL(Utils.java:440)
        at org.apache.hive.jdbc.HiveConnection.<init>(HiveConnection.java:134)
        at org.apache.hive.jdbc.HiveDriver.connect(HiveDriver.java:107)

以上三个问题的解决方法是：
将hadoop，hive，zookeeper 和 curator包都放在lib目录或自定义加载目录！！！虽然KETTLE_HOME\plugins\pentaho-big-data-plugin\hadoop-configurations 目录下选定的hadoop plugin （本例中是hdp30）也有相关依赖，但实际上lib目录和hadoop plugin类不能混用，相关的包必须放在一起，切记，血泪教训！！！不用管lib目录和hadoop plugin中存在不同版本的jar包！！！
由于kettle设计的比较负责支持很多插件，此问题应该是由于不同进程不同类加载器所加载的类不能通用所致。

2.2 启动kettle和类加载说明

debug模式启动：SpoonDebug.bat
如果还想看类加载路径可以在Spoon.bat中的set OPT= 行尾添加jvm选项 "-verbose:class" 。
如果cmd黑窗口中文乱码可以把SpoonDebug.bat中的 "-Dfile.encoding=UTF-8" 删除即可。
kettle会把所有jar包都缓存，都存储在kettle-home\system\karaf\caches目录下。
日志里打印的所有 bundle数字目录下得jar包都是在缓存目录下。
如果kettle在运行过程中卡掉了，不反应了，八成是因为操作过程中点击了cmd黑窗口，此时在cmd黑窗口内敲击回车，cmd日志就会继续打印，窗口也会恢复响应。

2.3 编写js通过kerberos认证

配置信息就是填写kerberos的配置。
javascript代码完成kerberos认证。
配置信息内填写如下：
javascript代码内容如下：
kerberos认证中是需要HADOOP_CONF_DIR的，如果调用没有hadoop_conf_dir参数据方法就是去环境变量中取了。

// 给类起个别名，java没有这种写法，python有。
var utils = Packages.全类路径.KerberosUtils;
// 使用 HADOOP_CONF_DIR 或 HADOOP_HOME 环境变量,配置登录 Kerberos
var loginRes = utils.loginKerberos(krb5_principal,krb5_keytab,krb5_conf);

// 使用用户提供的 hadoop_conf_dir 登录kerberos
// hadoop_conf_dir 参数可以从上一步获取，也可以直接写死。
// var loginRes = utils.loginKerberos(krb5_principal,krb5_keytab,krb5_conf,hadoop_conf_dir);

添加一个写结果的模块！

好了，执行启动！

如果报如下错误，说明kettle没有找到java类，检查类路径和包是否错误！

TypeError: Cannot call property loginKerberos in object [JavaPackage utils]. It is not a function, it is "object". (script#6)

如果打印如下内容，说明执行认证成功了。

2024/01/02 18:18:04 - 写日志.0 -
2024/01/02 18:18:04 - 写日志.0 - ------------> 行号 1------------------------------
2024/01/02 18:18:04 - 写日志.0 - loginRes = Y

三、包装模块开发

keberos认证会在jvm存储信息，这些信息如果想使用必须前于hive或hadoop任务一个job
结构如下：
kerberos-login 就是刚刚写的转换。

必须如上包装，层数少了，认证不过去！！！

四、连接hdfs

如果项目中使用也必须使用前面的包装模块把hadoop任务包在里边！

五、连接hive或者spark thriftserver

连接hive和spark thriftserver是一样的。以下以spark举例说明。

注意连接hive或者spark之前一定先手动运行下刚刚的kerberos-login认证模块！！！，否则测试连接和特征列表都将就失败或报错！！！

4.1 zookeeper的ha方式连接

# 主机名称：
# 注意这里主机名会后少写一个:2181
zk-01.com:2181,zk-02.com:2181,zk-03.com

# 数据库名称：
# 后边把kerberos连接参数也加上。zooKeeperNamespace 参数从HIVE_HOME/conf/hive-site.xml或SPARK_HOME/conf/hive-site.xml文件获取即可。而serviceDiscoveryMode=zooKeeper是固定写法。
default;serviceDiscoveryMode=zooKeeper;zooKeeperNamespace=spark2_server

# 端口号：
# 主机名故意少写一个，就在这里补上了。
2181

先手动运行下kerberos认证模块，再测试连接下：
填写完毕后，可以点击特征列表按钮，找到URL项查看格式，应为
jdbc:hive2://zk-01.com:2181,zk-01.com:2181,zk-01.com:2181/default;serviceDiscoveryMode=zooKeeper;zooKeeperNamespace=...
测试连接：

4.2 单点连接方式

# 主机名称
# 就是hive server2 的主机 host，不要写IP

# 数据库名称：
# HIVE_HOME/conf/hive-site.xml或SPARK_HOME/conf/hive-site.xml中找到配置 hive.server2.authentication.kerberos.principal 
# 比如spark/_HOST@XXXXX.COM
# 本质也是在default数据库后边拼接连接字符串
default;principal=spark/_HOST@XXXXX.COM

# 端口号也在SPARK_HOME/conf/hive-site.xml中找到配置hive.server2.thrift.port有
10016

填写完毕后，可以点击特征列表按钮，找到URL项查看格式，应为：jdbc:hive2://host:port/default;principal=... 格式。

测试连接：

五、其他

kettle读取Hive表不支持bigint和timstamp类型解决

六 kettle使用技巧

6.1 kettle 任务嵌套使用相对目录

以上内容殊荣输入也是可以的：${Internal.Entry.Current.Directory} 。

参考文章：

hive 高可用详解： Hive MetaStore HA、hive server HA原理详解；hive高可用实现
kettle开发篇-JavaScript脚本-Day31
kettle组件javaScript脚本案例1
kettle配置javascript环境 kettle javascript
Javascript脚本组件
Kettle之【执行SQL脚本】控件用法 文章介绍了环境变量和 占位符 ? 的使用方法。