flink on yarn 带kerberos 远程提交 实现

1. flink kerberos 配置
   
2. 先使用ugi进行一次认证
3. 正常提交

import com.google.common.io.Files;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.io.FileUtils;
import org.apache.flink.client.cli.CliFrontend;
import org.apache.flink.client.cli.CustomCommandLine;
import org.apache.flink.client.cli.DefaultCLI;
import org.apache.flink.client.cli.GenericCLI;
import org.apache.flink.client.deployment.ClusterDeploymentException;
import org.apache.flink.client.deployment.ClusterSpecification;
import org.apache.flink.client.deployment.application.ApplicationConfiguration;
import org.apache.flink.client.program.ClusterClientProvider;
import org.apache.flink.configuration.*;
import org.apache.flink.runtime.security.SecurityConfiguration;
import org.apache.flink.runtime.security.SecurityUtils;
import org.apache.flink.util.ExceptionUtils;
import org.apache.flink.yarn.YarnClientYarnClusterInformationRetriever;
import org.apache.flink.yarn.YarnClusterDescriptor;
import org.apache.flink.yarn.YarnClusterInformationRetriever;
import org.apache.flink.yarn.configuration.YarnConfigOptions;
import org.apache.flink.yarn.configuration.YarnDeploymentTarget;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.security.UserGroupInformation;
import org.apache.hadoop.yarn.api.records.ApplicationId;
import org.apache.hadoop.yarn.client.api.YarnClient;
import org.apache.hadoop.yarn.conf.YarnConfiguration;
import org.junit.Test;

import java.io.File;
import java.io.IOException;
import java.lang.reflect.Constructor;
import java.lang.reflect.UndeclaredThrowableException;
import java.net.MalformedURLException;
import java.util.*;
import java.util.stream.Collectors;
import java.util.stream.Stream;

import static org.apache.flink.util.Preconditions.checkNotNull;


/**
* @author: jiayeli.cn
* @description
* @date: 2023/8/29 下午9:09
*/

@Slf4j
public class YarnClientTestCase {

   @Test
   public void submitJobWithYarnDesc() throws ClusterDeploymentException, IOException {
       // hadoop
       String hadoopConfDir = "/x/x/software/spark-3.3.2-bin-hadoop3/etc/hadoop";
       //flink的本地配置目录，为了得到flink的配置
       String flinkConfDir = "/opt/flink-1.14.3/conf";
       //存放flink集群相关的jar包目录
       String flinkLibs = "hdfs://node01:8020/lib/flink";
       //用户jar
       String userJarPath =  "hdfs://node01:8020/jobs/streaming/testCase/TopSpeedWindowing.jar";
       String flinkDistJar = "hdfs://node01:8020/lib/flink/flink-dist_2.12-1.14.3.jar";
       String[] args = "".split("\\s+");
       String appMainClass = "org.apache.flink.streaming.examples.windowing.TopSpeedWindowing";
       String principal = "dev@JIAYELI.COM";
       String keyTab = "/x/x/workspace/bigdata/sparkLauncherTestcase/src/test/resource/dev_uer.keytab";

       enableKrb5(principal, keyTab);
       YarnClient yarnClient = YarnClient.createYarnClient();
       YarnConfiguration yarnConfiguration = new YarnConfiguration();
       Optional.ofNullable(hadoopConfDir)
           .map(e -> new File(e))
           .filter(dir -> dir.exists())
           .map(File::listFiles)
           .ifPresent(files -> {
               Arrays.asList(files).stream()
                       .filter(file -> Files.getFileExtension(file.getName()).equals(".xml"))
                       .forEach(conf -> yarnConfiguration.addResource(conf.getPath()));
           });

       yarnClient.init(yarnConfiguration);
       yarnClient.start();

       Configuration flinkConf = GlobalConfiguration.loadConfiguration(flinkConfDir);
       //set run model
       flinkConf.setString(DeploymentOptions.TARGET, YarnDeploymentTarget.APPLICATION.getName());
       //set application name
       flinkConf.setString(YarnConfigOptions.APPLICATION_NAME, "onYarnApiSubmitCase");
       //flink on yarn dependency
       flinkConf.set(YarnConfigOptions.PROVIDED_LIB_DIRS, Collections.singletonList(new Path(flinkLibs).toString()));
       flinkConf.set(YarnConfigOptions.FLINK_DIST_JAR, flinkDistJar);
       flinkConf.set(PipelineOptions.JARS, Collections.singletonList(new Path(userJarPath).toString()));
       //设置：资源/并发度
       flinkConf.setInteger(CoreOptions.DEFAULT_PARALLELISM, 1);
       flinkConf.set(JobManagerOptions.TOTAL_PROCESS_MEMORY, MemorySize.parse("1G"));
       flinkConf.set(TaskManagerOptions.TOTAL_PROCESS_MEMORY, MemorySize.parse("1G"));
       flinkConf.setInteger(TaskManagerOptions.NUM_TASK_SLOTS, 1);


       ClusterSpecification clusterSpecification = new ClusterSpecification
               .ClusterSpecificationBuilder()
               .setMasterMemoryMB(1024)
               .setTaskManagerMemoryMB(1024)
               .setSlotsPerTaskManager(2)
               .createClusterSpecification();

       YarnClusterInformationRetriever ycir = YarnClientYarnClusterInformationRetriever.create(yarnClient);

       YarnConfiguration yarnConf = (YarnConfiguration) yarnClient.getConfig();

       ApplicationConfiguration appConfig = new ApplicationConfiguration(args, appMainClass);

       YarnClusterDescriptor yarnClusterDescriptor = new YarnClusterDescriptor(
               flinkConf,
               yarnConf,
               yarnClient,
               ycir,
               false);

       ClusterClientProvider<ApplicationId> applicationCluster =
               yarnClusterDescriptor.deployApplicationCluster( clusterSpecification, appConfig );

       yarnClient.stop();

   }

   private void enableKrb5(String principal, String keyTab) throws IOException {
     System.setProperty("java.security.krb5.conf", "/x/x/Documents/kerberos/krb5.conf");

       org.apache.hadoop.conf.Configuration krb5conf = new org.apache.hadoop.conf.Configuration();


       String krb5ConfPath = "/x/x/Documents/kerberos/krb5.conf";

       krb5conf.set("hadoop.security.authentication", "kerberos");

       //      UserGroupInformation.setConfiguration(conf)
       UserGroupInformation.setConfiguration(krb5conf);

       // 登录Kerberos并获取UserGroupInformation实例
       UserGroupInformation.loginUserFromKeytab(principal, keyTab);
       UserGroupInformation ugi = UserGroupInformation.getCurrentUser();

       log.debug(ugi.toString());
   }