Flink 1.12.2 源码浅析 : yarn-per-job模式解析 [二]

忘是亡心i 2022-11-08 14:13 283阅读 0赞

### . ###

*  一 .前言
 *  二 .启动解析
 *   *  2.1. StreamExecutionEnvironment\#execute
     *  2.2. StreamExecutionEnvironment\#executeAsync
     *  2.3. YarnClusterDescriptor\#deployJobCluster
     *  2.4. YarnClusterDescriptor\#deployInternal \[核心\]
     *  2.5. StreamExecutionEnvironment\#startAppMaster
 *  三. 提交的文件清单
 *   *  3.1.提交的安装包清单
     *  3.2.HDFS上的程序清单

# 一 .前言 #

接上文[Flink 1.12.2 源码分析 : yarn-per-job模式浅析 \[一\]][Flink 1.12.2 _ _ yarn-per-job_] .  
CliFrontend类最终会调用我们自己写的代码,入口类是main方法.

*  整体流程图  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70]
 *  细节图  
    ![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70 1]

整体代码如下:

package org.apache.flink.streaming.examples.socket;
    
    import org.apache.flink.api.common.functions.FlatMapFunction;
    import org.apache.flink.api.common.functions.ReduceFunction;
    import org.apache.flink.api.java.utils.ParameterTool;
    import org.apache.flink.streaming.api.datastream.DataStream;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
    import org.apache.flink.streaming.api.windowing.time.Time;
    import org.apache.flink.util.Collector;
    
    /** * Implements a streaming windowed version of the "WordCount" program. * * <p>This program connects to a server socket and reads strings from the socket. The easiest way to * try this out is to open a text server (at port 12345) using the <i>netcat</i> tool via * * <pre> * nc -l 12345 on Linux or nc -l -p 12345 on Windows * </pre> * * <p>and run this example with the hostname and the port as arguments. */
    @SuppressWarnings("serial")
    public class SocketWindowWordCount { 
    
        public static void main(String[] args) throws Exception { 
    
            // the host and the port to connect to
            final String hostname;
            final int port;
            try { 
                final ParameterTool params = ParameterTool.fromArgs(args);
                hostname = params.has("hostname") ? params.get("hostname") : "localhost";
                port = params.getInt("port");
            } catch (Exception e) { 
                System.err.println(
                        "No port specified. Please run 'SocketWindowWordCount "
                                + "--hostname <hostname> --port <port>', where hostname (localhost by default) "
                                + "and port is the address of the text server");
                System.err.println(
                        "To start a simple text server, run 'netcat -l <port>' and "
                                + "type the input text into the command line");
                return;
            }
    
            // get the execution environment
            final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            // get input data by connecting to the socket
            DataStream<String> text = env.socketTextStream(hostname, port, "\n");
    
            // parse the data, group it, window it, and aggregate the counts
            DataStream<WordWithCount> windowCounts =
                    text.flatMap(
                                    new FlatMapFunction<String, WordWithCount>() { 
                                        @Override
                                        public void flatMap(
                                                String value, Collector<WordWithCount> out) { 
                                            for (String word : value.split("\\s")) { 
                                                out.collect(new WordWithCount(word, 1L));
                                            }
                                        }
                                    })
                            .keyBy(value -> value.word)
                            .window(TumblingProcessingTimeWindows.of(Time.seconds(5)))
                            .reduce(
                                    new ReduceFunction<WordWithCount>() { 
                                        @Override
                                        public WordWithCount reduce(WordWithCount a, WordWithCount b) { 
                                            return new WordWithCount(a.word, a.count + b.count);
                                        }
                                    });
    
            // print the results with a single thread, rather than in parallel
            windowCounts.print().setParallelism(1);
    
            env.execute("Socket Window WordCount");
        }
    
        // ------------------------------------------------------------------------
    
        /** Data type for words with count. */
        public static class WordWithCount { 
    
            public String word;
            public long count;
    
            public WordWithCount() { }
    
            public WordWithCount(String word, long count) { 
                this.word = word;
                this.count = count;
            }
    
            @Override
            public String toString() { 
                return word + " : " + count;
            }
        }
    }

# 二 .启动解析 #

## 2.1. StreamExecutionEnvironment\#execute ##

在查看SocketWindowWordCount类的时候, 可以知道,这就是一个每5秒统计一次单词数量的类.  
我们都知道, Flink代码最终执行的入口是env.execute, 所以我们从这里开始看.

*  StreamExecutionEnvironment\#execute  
    在这里我们看到首先通过getStreamGraph(jobName) 生成StreamGraph , 然后交由execute(StreamGraph streamGraph) 开始执行

/** * Triggers the program execution. The environment will execute all parts of the program that * have resulted in a "sink" operation. Sink operations are for example printing results or * forwarding them to a message queue. * * <p>The program execution will be logged and displayed with the provided name * * @param jobName Desired name of the job * @return The result of the job execution, containing elapsed time and accumulators. * @throws Exception which occurs during job execution. */
        public JobExecutionResult execute(String jobName) throws Exception { 
            Preconditions.checkNotNull(jobName, "Streaming Job name should not be null.");
    
            // 获取 getStreamGraph 继续执行...
            return execute(getStreamGraph(jobName));
        }
    
    
        /** * Triggers the program execution. The environment will execute all parts of the program that * have resulted in a "sink" operation. Sink operations are for example printing results or * forwarding them to a message queue. * * @param streamGraph the stream graph representing the transformations * @return The result of the job execution, containing elapsed time and accumulators. * @throws Exception which occurs during job execution. */
        @Internal
        public JobExecutionResult execute(StreamGraph streamGraph) throws Exception { 
            // ----------------------
            // 提交代码的streamGraph
            // ----------------------
            final JobClient jobClient = executeAsync(streamGraph);
    
            try { 
                final JobExecutionResult jobExecutionResult;
    
                if (configuration.getBoolean(DeploymentOptions.ATTACHED)) { 
                    jobExecutionResult = jobClient.getJobExecutionResult().get();
                } else { 
                    jobExecutionResult = new DetachedJobExecutionResult(jobClient.getJobID());
                }
    
                jobListeners.forEach(
                        jobListener -> jobListener.onJobExecuted(jobExecutionResult, null));
    
                return jobExecutionResult;
            } catch (Throwable t) { 
                // get() on the JobExecutionResult Future will throw an ExecutionException. This
                // behaviour was largely not there in Flink versions before the PipelineExecutor
                // refactoring so we should strip that exception.
                Throwable strippedException = ExceptionUtils.stripExecutionException(t);
    
                jobListeners.forEach(
                        jobListener -> { 
                            jobListener.onJobExecuted(null, strippedException);
                        });
                ExceptionUtils.rethrowException(strippedException);
    
                // never reached, only make javac happy
                return null;
            }
        }

在接下来我们就看到 executeAsync(streamGraph);

## 2.2. StreamExecutionEnvironment\#executeAsync ##

这个方法是通过PipelineExecutorFactory获取对应的YarnJobClusterExecutorFactory .  
然后再由YarnJobClusterExecutorFactory获取执行的容器 YarnJobClusterExecutor

@Override
        public CompletableFuture<JobClient> execute(
                @Nonnull final Pipeline pipeline,
                @Nonnull final Configuration configuration,
                @Nonnull final ClassLoader userCodeClassloader)
                throws Exception { 
            // 流图 抓换为 作业图
            // JobGraph(jobId: 536af83b56ddfc2ef4ffda8b43a21e15)
            final JobGraph jobGraph = PipelineExecutorUtils.getJobGraph(pipeline, configuration);
    
    
            // 获取yarn集群的描述符
            try (final ClusterDescriptor<ClusterID> clusterDescriptor =
                    clusterClientFactory.createClusterDescriptor(configuration)) { 
    
                // { 
                // taskmanager.memory.process.size=1728m,
                // jobmanager.execution.failover-strategy=region,
                // jobmanager.rpc.address=localhost,
                // execution.target=yarn-per-job,
                // jobmanager.memory.process.size=1600m,
                // jobmanager.rpc.port=6123,
                // execution.savepoint.ignore-unclaimed-state=false,
                // execution.attached=true,
                // execution.shutdown-on-attached-exit=false,
                // pipeline.jars=[file:/opt/tools/flink-1.12.2/examples/streaming/SocketWindowWordCount.jar],
                // parallelism.default=1,
                // taskmanager.numberOfTaskSlots=1,
                // pipeline.classpaths=[],
                // $internal.deployment.config-dir=/opt/tools/flink-1.12.2/conf,
                // $internal.yarn.log-config-file=/opt/tools/flink-1.12.2/conf/log4j.properties
                // }
                final ExecutionConfigAccessor configAccessor =
                        ExecutionConfigAccessor.fromConfiguration(configuration);
                // 获取指定资源的描述
                // masterMemoryMB = 1600
                // taskManagerMemoryMB = 1728
                // slotsPerTaskManager = 1
                final ClusterSpecification clusterSpecification =
                        clusterClientFactory.getClusterSpecification(configuration);
    
                // 部署集群....
                final ClusterClientProvider<ClusterID> clusterClientProvider =
                        clusterDescriptor.deployJobCluster(
                                clusterSpecification, jobGraph, configAccessor.getDetachedMode());
                LOG.info("Job has been submitted with JobID " + jobGraph.getJobID());
    
                return CompletableFuture.completedFuture(
                        new ClusterClientJobClientAdapter<>(
                                clusterClientProvider, jobGraph.getJobID(), userCodeClassloader));
            }
        }

## 2.3. YarnClusterDescriptor\#deployJobCluster ##

直接调用deployInternal 方法开始 : 开始部署 yarn per-job cluster

@Override
        public ClusterClientProvider<ApplicationId> deployJobCluster(
                ClusterSpecification clusterSpecification, JobGraph jobGraph, boolean detached)
                throws ClusterDeploymentException { 
            try { 
    
    
                // 开始部署 yarn per-job cluster
                return deployInternal(
                        clusterSpecification,
                        "Flink per-job cluster",
    
                        // 这个数yarn集群入口的名字 !!!!!!
                        getYarnJobClusterEntrypoint(),
                        jobGraph,
                        detached);
    
    
            } catch (Exception e) { 
                throw new ClusterDeploymentException("Could not deploy Yarn job cluster.", e);
            }
        }

## 2.4. YarnClusterDescriptor\#deployInternal \[核心\] ##

/** * This method will block until the ApplicationMaster/JobManager have been deployed on YARN. * * @param clusterSpecification Initial cluster specification for the Flink cluster to be * deployed * @param applicationName name of the Yarn application to start * @param yarnClusterEntrypoint Class name of the Yarn cluster entry point. * @param jobGraph A job graph which is deployed with the Flink cluster, {@code null} if none * @param detached True if the cluster should be started in detached mode */
        private ClusterClientProvider<ApplicationId> deployInternal(
                ClusterSpecification clusterSpecification,
                String applicationName,
                String yarnClusterEntrypoint,
                @Nullable JobGraph jobGraph,
                boolean detached)
                throws Exception { 
    
            // 安心配置信息相关.....
            // currentUser: sysadmin (auth:SIMPLE)
            final UserGroupInformation currentUser = UserGroupInformation.getCurrentUser();
            if (HadoopUtils.isKerberosSecurityEnabled(currentUser)) { 
                boolean useTicketCache =
                        flinkConfiguration.getBoolean(SecurityOptions.KERBEROS_LOGIN_USETICKETCACHE);
    
                if (!HadoopUtils.areKerberosCredentialsValid(currentUser, useTicketCache)) { 
                    throw new RuntimeException(
                            "Hadoop security with Kerberos is enabled but the login user "
                                    + "does not have Kerberos credentials or delegation tokens!");
                }
            }
    
            // 开启一系列检测, 是否有足够的资源启动集群 (jar , conf , yarn core)
            isReadyForDeployment(clusterSpecification);
    
            // ------------------ Check if the specified queue exists --------------------
    
            // 检测队列是否存在...
            checkYarnQueues(yarnClient);
    
            // ------------------ Check if the YARN ClusterClient has the requested resources
            // --------------
    
            // 构建 application 的 客户端
            // Create application via yarnClient
            final YarnClientApplication yarnApplication = yarnClient.createApplication();
    
            // 获取响应信息.
            // application_id { 
            // id: 10
            // cluster_timestamp: 1615446205104
            // }
            // maximumCapability { 
            // memory: 8192
            // virtual_cores: 4
            // resource_value_map { 
            // key: "memory-mb"
            // value: 8192
            // units: "Mi"
            // type: COUNTABLE
            // }
            // resource_value_map { 
            // key: "vcores"
            // value: 4
            // units: ""
            // type: COUNTABLE
            // }
            // }
            final GetNewApplicationResponse appResponse = yarnApplication.getNewApplicationResponse();
    
            // 获取yarn集群资源的最大值...
            // <memory:8192, vCores:4>
            Resource maxRes = appResponse.getMaximumResourceCapability();
    
            final ClusterResourceDescription freeClusterMem;
    
            try { 
                // 获取空间内存大小....
    
                // freeClusterMem :
                // totalFreeMemory = 104857600
                // containerLimit = 104857600
                // nodeManagersFree = {int[1]@4603}
                freeClusterMem = getCurrentFreeClusterResources(yarnClient);
            } catch (YarnException | IOException e) { 
                failSessionDuringDeployment(yarnClient, yarnApplication);
                throw new YarnDeploymentException(
                        "Could not retrieve information about free cluster resources.", e);
            }
    
            // 获取yarn最小分配的内存大小, 默认 1024MB
            final int yarnMinAllocationMB =
                    yarnConfiguration.getInt(
                            YarnConfiguration.RM_SCHEDULER_MINIMUM_ALLOCATION_MB,
                            YarnConfiguration.DEFAULT_RM_SCHEDULER_MINIMUM_ALLOCATION_MB);
    
            // 如果最小分配的内存资源 < 0 , 抛出异常....
            if (yarnMinAllocationMB <= 0) { 
                throw new YarnDeploymentException(
                        "The minimum allocation memory "
                                + "("
                                + yarnMinAllocationMB
                                + " MB) configured via '"
                                + YarnConfiguration.RM_SCHEDULER_MINIMUM_ALLOCATION_MB
                                + "' should be greater than 0.");
            }
    
    
            final ClusterSpecification validClusterSpecification;
            try { 
                // 开启验证, 验证资源是否满足需求...
                validClusterSpecification =
                        validateClusterResources(
                                clusterSpecification, yarnMinAllocationMB, maxRes, freeClusterMem);
            } catch (YarnDeploymentException yde) { 
                failSessionDuringDeployment(yarnClient, yarnApplication);
                throw yde;
            }
    
            LOG.info("Cluster specification: {}", validClusterSpecification);
    
            // 开始获取设定的启动模式... : NORMAL
            final ClusterEntrypoint.ExecutionMode executionMode =
                    detached
                            ? ClusterEntrypoint.ExecutionMode.DETACHED
                            : ClusterEntrypoint.ExecutionMode.NORMAL;
    
            // 设置启动模式 : internal.cluster.execution-mode =>
            flinkConfiguration.setString(ClusterEntrypoint.EXECUTION_MODE, executionMode.toString());
    
            // 开始启动AppMaster, 获取响应信息
    
            ApplicationReport report =
                    startAppMaster(
                            flinkConfiguration,
                            applicationName,
                            yarnClusterEntrypoint,
                            jobGraph,
                            yarnClient,
                            yarnApplication,
                            validClusterSpecification);
    
    
            // 输出application id, 主要用去取消任务...
            // print the application id for user to cancel themselves.
            if (detached) { 
                final ApplicationId yarnApplicationId = report.getApplicationId();
                logDetachedClusterInformation(yarnApplicationId, LOG);
            }
    
            // 设置集群配置...
            setClusterEntrypointInfoToConfig(report);
    
            return () -> { 
                try { 
                    // 构建反馈的集群客户端...
                    return new RestClusterClient<>(flinkConfiguration, report.getApplicationId());
                } catch (Exception e) { 
                    throw new RuntimeException("Error while creating RestClusterClient.", e);
                }
            };
        }

## 2.5. StreamExecutionEnvironment\#startAppMaster ##

提交任务, 启动AppMaster .

private ApplicationReport startAppMaster(
                Configuration configuration,
                String applicationName,
                String yarnClusterEntrypoint,
                JobGraph jobGraph,
                YarnClient yarnClient,
                YarnClientApplication yarnApplication,
                ClusterSpecification clusterSpecification)
                throws Exception { 
    
            // ------------------ Initialize the file systems -------------------------
    
    
    
    
            // 初始化文件系统
            org.apache.flink.core.fs.FileSystem.initialize(
                    configuration, PluginUtils.createPluginManagerFromRootFolder(configuration));
    
            // 获取文件系统: LocalFileSystem
            final FileSystem fs = FileSystem.get(yarnConfiguration);
    
            // 硬编码检查GoogleHadoopFileSystem, 因为他没有复写getScheme 方法
            // hard coded check for the GoogleHDFS client because its not overriding the getScheme() method.
            if (!fs.getClass().getSimpleName().equals("GoogleHadoopFileSystem")
                    && fs.getScheme().startsWith("file")) { 
                LOG.warn(
                        "The file system scheme is '"
                                + fs.getScheme()
                                + "'. This indicates that the "
                                + "specified Hadoop configuration path is wrong and the system is using the default Hadoop configuration values."
                                + "The Flink YARN client needs to store its files in a distributed file system");
            }
    
            ApplicationSubmissionContext appContext = yarnApplication.getApplicationSubmissionContext();
    
    
    
            /// jar
            final List<Path> providedLibDirs =
                    Utils.getQualifiedRemoteSharedPaths(configuration, yarnConfiguration);
    
    
    
            // 文件上传工具
            // fileSystem = {LocalFileSystem@5001}
            // applicationId = {ApplicationIdPBImpl@5007} "application_1615446205104_0010"
            // homeDir = {Path@5067} "file:/Users/sysadmin"
            // applicationDir = {Path@5068} "file:/Users/sysadmin/.flink/application_1615446205104_0010"
            // providedSharedLibs = {Collections$UnmodifiableMap@5069} size = 0
            // localResources = {HashMap@5070} size = 2
            // "log4j.properties" -> {LocalResourcePBImpl@5080} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0010/log4j.properties" } size: 2620 timestamp: 1615717946000 type: FILE visibility: APPLICATION"
            // "SocketWindowWordCount.jar" -> {LocalResourcePBImpl@5082} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0010/SocketWindowWordCount.jar" } size: 14708 timestamp: 1615717946000 type: FILE visibility: APPLICATION"
            // fileReplication = 3
            // remotePaths = {ArrayList@5071} size = 2
            // 0 = {Path@5086} "file:/opt/tools/flink-1.12.2/conf/log4j.properties"
            // 1 = {Path@5087} "file:/opt/tools/flink-1.12.2/examples/streaming/SocketWindowWordCount.jar"
            // envShipResourceList = {ArrayList@5072} size = 2
            // 0 = {YarnLocalResourceDescriptor@5091} "YarnLocalResourceDescriptor{key=log4j.properties, path=file:/Users/sysadmin/.flink/application_1615446205104_0010/log4j.properties, size=2620, modificationTime=1615717946000, visibility=APPLICATION, type=FILE}"
            // 1 = {YarnLocalResourceDescriptor@5092} "YarnLocalResourceDescriptor{key=SocketWindowWordCount.jar, path=file:/Users/sysadmin/.flink/application_1615446205104_0010/SocketWindowWordCount.jar, size=14708, modificationTime=1615717946000, visibility=APPLICATION, type=FILE}"
            // flinkDist = null
            final YarnApplicationFileUploader fileUploader =
                    YarnApplicationFileUploader.from(
                            fs,
                            getStagingDir(fs),
                            providedLibDirs,
                            appContext.getApplicationId(),
                            getFileReplication());
    
    
            // The files need to be shipped and added to classpath.
            // /opt/tools/flink-1.12.2/conf/log4j.properties
            Set<File> systemShipFiles = new HashSet<>(shipFiles.size());
    
            for (File file : shipFiles) { 
                systemShipFiles.add(file.getAbsoluteFile());
            }
    
    
            // 获取日志配置目录
            final String logConfigFilePath =  configuration.getString(YarnConfigOptionsInternal.APPLICATION_LOG_CONFIG_FILE);
            if (logConfigFilePath != null) { 
                systemShipFiles.add(new File(logConfigFilePath));
            }
    
            // Set-up ApplicationSubmissionContext for the application
            // 获取 ApplicationId : application_1615446205104_0010
            final ApplicationId appId = appContext.getApplicationId();
    
            // ------------------ Add Zookeeper namespace to local flinkConfiguraton ------
            setHAClusterIdIfNotSet(configuration, appId);
    
            if (HighAvailabilityMode.isHighAvailabilityModeActivated(configuration)) { 
                // 是否高可用 : 做大重试次数 : yarn.application-attempts => 2
                // activate re-execution of failed applications
                appContext.setMaxAppAttempts(
                        configuration.getInteger(
                                YarnConfigOptions.APPLICATION_ATTEMPTS.key(),
                                YarnConfiguration.DEFAULT_RM_AM_MAX_ATTEMPTS));
    
                activateHighAvailabilitySupport(appContext);
            } else { 
                // 设置重试次数为 1
                // set number of application retries to 1 in the default case
                appContext.setMaxAppAttempts(
                        configuration.getInteger(YarnConfigOptions.APPLICATION_ATTEMPTS.key(), 1));
            }
    
            // 处理用户jar
            // 0 : file:/opt/tools/flink-1.12.2/examples/streaming/SocketWindowWordCount.jar
            final Set<Path> userJarFiles = new HashSet<>();
    
    
    
            if (jobGraph != null) { 
    
    
    
    
                userJarFiles.addAll(
                        jobGraph.getUserJars().stream()
                                .map(f -> f.toUri())
                                .map(Path::new)
                                .collect(Collectors.toSet()));
            }
    
    
            // 0 : file:/opt/tools/flink-1.12.2/examples/streaming/SocketWindowWordCount.jar
            final List<URI> jarUrls =  ConfigUtils.decodeListFromConfig(configuration, PipelineOptions.JARS, URI::create);
    
    
    
            if (jarUrls != null
                    && YarnApplicationClusterEntryPoint.class.getName().equals(yarnClusterEntrypoint)) { 
    
    
    
    
                userJarFiles.addAll(jarUrls.stream().map(Path::new).collect(Collectors.toSet()));
            }
    
            // only for per job mode
            if (jobGraph != null) { 
    
    
    
                for (Map.Entry<String, DistributedCache.DistributedCacheEntry> entry :
                        jobGraph.getUserArtifacts().entrySet()) { 
    
    
                    // 仅仅更新本地文件 ???
                    // only upload local files
                    if (!Utils.isRemotePath(entry.getValue().filePath)) { 
    
    
    
                        Path localPath = new Path(entry.getValue().filePath);
    
    
    
    
    
                        Tuple2<Path, Long> remoteFileInfo = fileUploader.uploadLocalFileToRemote(localPath, entry.getKey());
    
    
    
                        jobGraph.setUserArtifactRemotePath( entry.getKey(), remoteFileInfo.f0.toString());
    
                    }
                }
    
                jobGraph.writeUserArtifactEntriesToConfiguration();
            }
    
            if (providedLibDirs == null || providedLibDirs.isEmpty()) { 
                addLibFoldersToShipFiles(systemShipFiles);
            }
    
            // Register all files in provided lib dirs as local resources with public visibility
            // and upload the remaining dependencies as local resources with APPLICATION visibility.
    
            // systemClassPaths:
            // 0 = ""
            // 1 = "SocketWindowWordCount.jar"
            final List<String> systemClassPaths = fileUploader.registerProvidedLocalResources();
    
    
    
            final List<String> uploadedDependencies =
                    fileUploader.registerMultipleLocalResources(
                            systemShipFiles.stream()
                                    .map(e -> new Path(e.toURI()))
                                    .collect(Collectors.toSet()),
                            Path.CUR_DIR,
                            LocalResourceType.FILE);
    
    
    
            systemClassPaths.addAll(uploadedDependencies);
    
            // upload and register ship-only files
            // Plugin files only need to be shipped and should not be added to classpath.
            if (providedLibDirs == null || providedLibDirs.isEmpty()) { 
                Set<File> shipOnlyFiles = new HashSet<>();
                addPluginsFoldersToShipFiles(shipOnlyFiles);
                fileUploader.registerMultipleLocalResources(
                        shipOnlyFiles.stream()
                                .map(e -> new Path(e.toURI()))
                                .collect(Collectors.toSet()),
                        Path.CUR_DIR,
                        LocalResourceType.FILE);
            }
    
            if (!shipArchives.isEmpty()) { 
                fileUploader.registerMultipleLocalResources(
                        shipArchives.stream().map(e -> new Path(e.toURI())).collect(Collectors.toSet()),
                        Path.CUR_DIR,
                        LocalResourceType.ARCHIVE);
            }
    
    
            // 上传&注册用户jar : SocketWindowWordCount.jar
            // Upload and register user jars
            final List<String> userClassPaths =
                    fileUploader.registerMultipleLocalResources(
                            userJarFiles,
                            userJarInclusion == YarnConfigOptions.UserJarInclusion.DISABLED
                                    ? ConfigConstants.DEFAULT_FLINK_USR_LIB_DIR
                                    : Path.CUR_DIR,
                            LocalResourceType.FILE);
    
            if (userJarInclusion == YarnConfigOptions.UserJarInclusion.ORDER) { 
                systemClassPaths.addAll(userClassPaths);
            }
    
    
    
    
            // normalize classpath by sorting
            Collections.sort(systemClassPaths);
            Collections.sort(userClassPaths);
    
    
    
    
            // classpath assembler
            StringBuilder classPathBuilder = new StringBuilder();
            if (userJarInclusion == YarnConfigOptions.UserJarInclusion.FIRST) { 
                for (String userClassPath : userClassPaths) { 
                    classPathBuilder.append(userClassPath).append(File.pathSeparator);
                }
            }
    
    
    
    
    
            for (String classPath : systemClassPaths) { 
                classPathBuilder.append(classPath).append(File.pathSeparator);
            }
    
    
    
    
    
            // Setup jar for ApplicationMaster
            final YarnLocalResourceDescriptor localResourceDescFlinkJar = fileUploader.uploadFlinkDist(flinkJarPath);
    
    
            classPathBuilder
                    .append(localResourceDescFlinkJar.getResourceKey())
                    .append(File.pathSeparator);
    
    
    
    
            // write job graph to tmp file and add it to local resource
            // TODO: server use user main method to generate job graph
            if (jobGraph != null) { 
                File tmpJobGraphFile = null;
                try { 
                    tmpJobGraphFile = File.createTempFile(appId.toString(), null);
                    try (FileOutputStream output = new FileOutputStream(tmpJobGraphFile);
                            ObjectOutputStream obOutput = new ObjectOutputStream(output)) { 
                        obOutput.writeObject(jobGraph);
                    }
    
                    final String jobGraphFilename = "job.graph";
                    configuration.setString(JOB_GRAPH_FILE_PATH, jobGraphFilename);
    
                    fileUploader.registerSingleLocalResource(
                            jobGraphFilename,
                            new Path(tmpJobGraphFile.toURI()),
                            "",
                            LocalResourceType.FILE,
                            true,
                            false);
                    classPathBuilder.append(jobGraphFilename).append(File.pathSeparator);
                } catch (Exception e) { 
                    LOG.warn("Add job graph to local resource fail.");
                    throw e;
                } finally { 
                    if (tmpJobGraphFile != null && !tmpJobGraphFile.delete()) { 
                        LOG.warn("Fail to delete temporary file {}.", tmpJobGraphFile.toPath());
                    }
                }
            }
    
            // 上传flink的配置文件....
            // Upload the flink configuration
            // write out configuration file
            File tmpConfigurationFile = null;
            try { 
                tmpConfigurationFile = File.createTempFile(appId + "-flink-conf.yaml", null);
                BootstrapTools.writeConfiguration(configuration, tmpConfigurationFile);
    
                String flinkConfigKey = "flink-conf.yaml";
                fileUploader.registerSingleLocalResource(
                        flinkConfigKey,
                        new Path(tmpConfigurationFile.getAbsolutePath()),
                        "",
                        LocalResourceType.FILE,
                        true,
                        true);
                classPathBuilder.append("flink-conf.yaml").append(File.pathSeparator);
            } finally { 
                if (tmpConfigurationFile != null && !tmpConfigurationFile.delete()) { 
                    LOG.warn("Fail to delete temporary file {}.", tmpConfigurationFile.toPath());
                }
            }
    
            if (userJarInclusion == YarnConfigOptions.UserJarInclusion.LAST) { 
                for (String userClassPath : userClassPaths) { 
                    classPathBuilder.append(userClassPath).append(File.pathSeparator);
                }
            }
    
            // 权限相关的验证......
            // To support Yarn Secure Integration Test Scenario
            // In Integration test setup, the Yarn containers created by YarnMiniCluster does not have
            // the Yarn site XML
            // and KRB5 configuration files. We are adding these files as container local resources for
            // the container
            // applications (JM/TMs) to have proper secure cluster setup
            Path remoteYarnSiteXmlPath = null;
            if (System.getenv("IN_TESTS") != null) { 
                File f = new File(System.getenv("YARN_CONF_DIR"), Utils.YARN_SITE_FILE_NAME);
                LOG.info(
                        "Adding Yarn configuration {} to the AM container local resource bucket",
                        f.getAbsolutePath());
                Path yarnSitePath = new Path(f.getAbsolutePath());
                remoteYarnSiteXmlPath =
                        fileUploader
                                .registerSingleLocalResource(
                                        Utils.YARN_SITE_FILE_NAME,
                                        yarnSitePath,
                                        "",
                                        LocalResourceType.FILE,
                                        false,
                                        false)
                                .getPath();
                if (System.getProperty("java.security.krb5.conf") != null) { 
                    configuration.set(
                            SecurityOptions.KERBEROS_KRB5_PATH,
                            System.getProperty("java.security.krb5.conf"));
                }
            }
    
            // kerberos 验证......
            Path remoteKrb5Path = null;
            boolean hasKrb5 = false;
            String krb5Config = configuration.get(SecurityOptions.KERBEROS_KRB5_PATH);
            if (!StringUtils.isNullOrWhitespaceOnly(krb5Config)) { 
                final File krb5 = new File(krb5Config);
                LOG.info(
                        "Adding KRB5 configuration {} to the AM container local resource bucket",
                        krb5.getAbsolutePath());
                final Path krb5ConfPath = new Path(krb5.getAbsolutePath());
                remoteKrb5Path =
                        fileUploader
                                .registerSingleLocalResource(
                                        Utils.KRB5_FILE_NAME,
                                        krb5ConfPath,
                                        "",
                                        LocalResourceType.FILE,
                                        false,
                                        false)
                                .getPath();
                hasKrb5 = true;
            }
    
            Path remotePathKeytab = null;
            String localizedKeytabPath = null;
    
    
    
    
            String keytab = configuration.getString(SecurityOptions.KERBEROS_LOGIN_KEYTAB);
    
    
    
            if (keytab != null) { 
                boolean localizeKeytab =
                        flinkConfiguration.getBoolean(YarnConfigOptions.SHIP_LOCAL_KEYTAB);
                localizedKeytabPath =
                        flinkConfiguration.getString(YarnConfigOptions.LOCALIZED_KEYTAB_PATH);
                if (localizeKeytab) { 
                    // Localize the keytab to YARN containers via local resource.
                    LOG.info("Adding keytab {} to the AM container local resource bucket", keytab);
                    remotePathKeytab =
                            fileUploader
                                    .registerSingleLocalResource(
                                            localizedKeytabPath,
                                            new Path(keytab),
                                            "",
                                            LocalResourceType.FILE,
                                            false,
                                            false)
                                    .getPath();
                } else { 
                    // // Assume Keytab is pre-installed in the container.
                    localizedKeytabPath =
                            flinkConfiguration.getString(YarnConfigOptions.LOCALIZED_KEYTAB_PATH);
                }
            }
    
    
    
    
            final JobManagerProcessSpec processSpec =
                    JobManagerProcessUtils.processSpecFromConfigWithNewOptionToInterpretLegacyHeap(
                            flinkConfiguration, JobManagerOptions.TOTAL_PROCESS_MEMORY);
    
    
    
    
            final ContainerLaunchContext amContainer =
                    setupApplicationMasterContainer(yarnClusterEntrypoint, hasKrb5, processSpec);
    
    
    
    
    
    
            // setup security tokens
            if (UserGroupInformation.isSecurityEnabled()) { 
                // set HDFS delegation tokens when security is enabled
                LOG.info("Adding delegation token to the AM container.");
                List<Path> yarnAccessList =
                        ConfigUtils.decodeListFromConfig(
                                configuration, YarnConfigOptions.YARN_ACCESS, Path::new);
                Utils.setTokensFor(
                        amContainer,
                        ListUtils.union(yarnAccessList, fileUploader.getRemotePaths()),
                        yarnConfiguration);
            }
    
            amContainer.setLocalResources(fileUploader.getRegisteredLocalResources());
    
            // 上传完成,关闭连接...
            fileUploader.close();
    
            // Setup CLASSPATH and environment variables for ApplicationMaster
            // 创建AM的环境信息.
            final Map<String, String> appMasterEnv = new HashMap<>();
    
    
            // 设置用户指定的环境变量
            // set user specified app master environment variables
            appMasterEnv.putAll(
                    ConfigurationUtils.getPrefixedKeyValuePairs(
                            ResourceManagerOptions.CONTAINERIZED_MASTER_ENV_PREFIX, configuration));
    
    
            // 设置Flink的路径
            // set Flink app class path : _FLINK_CLASSPATH -> :
            // SocketWindowWordCount.jar:flink-dist_2.11-1.12.2.jar:job.graph:flink-conf.yaml:
            appMasterEnv.put(YarnConfigKeys.ENV_FLINK_CLASSPATH, classPathBuilder.toString());
    
            // set Flink on YARN internal configuration values
    
            // _FLINK_DIST_JAR -> YarnLocalResourceDescriptor{ 
            // key=flink-dist_2.11-1.12.2.jar,
            // path=file:/Users/sysadmin/.flink/application_1615446205104_0011/flink-dist_2.11-1.12.2.jar,
            // size=114224188,
            // modificationTime=1615718665000,
            // visibility=APPLICATION,
            // type=FILE
            // }
            appMasterEnv.put(YarnConfigKeys.FLINK_DIST_JAR, localResourceDescFlinkJar.toString());
    
    
            // _APP_ID -> application_1615446205104_0011
            appMasterEnv.put(YarnConfigKeys.ENV_APP_ID, appId.toString());
    
            // _CLIENT_HOME_DIR -> file:/Users/sysadmin
            appMasterEnv.put(YarnConfigKeys.ENV_CLIENT_HOME_DIR, fileUploader.getHomeDir().toString());
    
            // _CLIENT_SHIP_FILES -> YarnLocalResourceDescriptor{ 
            // key=log4j.properties,
            // path=file:/Users/sysadmin/.flink/application_1615446205104_0011/log4j.properties, size=2620,
            // modificationTime=1615718651000,
            // visibility=APPLICATION,
            // type=FILE
            // };
            //
            // YarnLocalResourceDescriptor{ 
            // key=SocketWindowWordCount.jar,
            // path=file:/Users/sysadmin/.flink/application_1615446205104_0011/SocketWindowWordCount.jar,
            // size=14708,
            // modificationTime=1615718656000,
            // visibility=APPLICATION,
            // type=FILE
            // };
            // YarnLocalResourceDescriptor{ 
            // key=flink-conf.yaml,
            // path=file:/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_0011-flink-conf.yaml3338435768180930238.tmp,
            // size=775,
            // modificationTime=1615718773000,
            // visibility=APPLICATION, type=FILE
            // }
    
            appMasterEnv.put(
                    YarnConfigKeys.ENV_CLIENT_SHIP_FILES,
                    encodeYarnLocalResourceDescriptorListToString(
                            fileUploader.getEnvShipResourceList()));
    
    
            // _FLINK_YARN_FILES -> file:/Users/sysadmin/.flink/application_1615446205104_0011
            appMasterEnv.put(
                    YarnConfigKeys.FLINK_YARN_FILES,
                    fileUploader.getApplicationDir().toUri().toString());
    
    
    
            // HADOOP_USER_NAME -> sysadmin
            // https://github.com/apache/hadoop/blob/trunk/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-site/src/site/markdown/YarnApplicationSecurity.md#identity-on-an-insecure-cluster-hadoop_user_name
            appMasterEnv.put(
                    YarnConfigKeys.ENV_HADOOP_USER_NAME,
                    UserGroupInformation.getCurrentUser().getUserName());
    
    
    
    
            if (localizedKeytabPath != null) { 
                appMasterEnv.put(YarnConfigKeys.LOCAL_KEYTAB_PATH, localizedKeytabPath);
                String principal = configuration.getString(SecurityOptions.KERBEROS_LOGIN_PRINCIPAL);
                appMasterEnv.put(YarnConfigKeys.KEYTAB_PRINCIPAL, principal);
                if (remotePathKeytab != null) { 
                    appMasterEnv.put(YarnConfigKeys.REMOTE_KEYTAB_PATH, remotePathKeytab.toString());
                }
            }
    
    
    
    
            // To support Yarn Secure Integration Test Scenario
            if (remoteYarnSiteXmlPath != null) { 
                appMasterEnv.put(
                        YarnConfigKeys.ENV_YARN_SITE_XML_PATH, remoteYarnSiteXmlPath.toString());
            }
    
    
    
            if (remoteKrb5Path != null) { 
                appMasterEnv.put(YarnConfigKeys.ENV_KRB5_PATH, remoteKrb5Path.toString());
            }
    
    
    
    
            // set classpath from YARN configuration
            //
            // "_APP_ID" -> "application_1615446205104_0011"
            // "_FLINK_YARN_FILES" -> "file:/Users/sysadmin/.flink/application_1615446205104_0011"
            // "HADOOP_USER_NAME" -> "sysadmin"
            // "_FLINK_CLASSPATH" -> ":SocketWindowWordCount.jar:flink-dist_2.11-1.12.2.jar:job.graph:flink-conf.yaml:"
            // "_CLIENT_SHIP_FILES" -> "YarnLocalResourceDescriptor{key=log4j.properties, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/log4j.properties, size=2620, modificationTime=1615718651000, visibility=APPLICATION, type=FILE};YarnLocalResourceDescriptor{key=SocketWindowWordCount.jar, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/SocketWindowWordCount.jar, size=14708, modificationTime=1615718656000, visibility=APPLICATION, type=FILE};YarnLocalResourceDescriptor{key=flink-conf.yaml, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_0011-flink-conf.yaml3338435768180930238.tmp, size=775, modificationTime=1615718773000, visibility=APPLICATION, type=FILE}"
            // "_FLINK_DIST_JAR" -> "YarnLocalResourceDescriptor{key=flink-dist_2.11-1.12.2.jar, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/flink-dist_2.11-1.12.2.jar, size=114224188, modificationTime=1615718665000, visibility=APPLICATION, type=FILE}"
            // "_CLIENT_HOME_DIR" -> "file:/Users/sysadmin"
            // "CLASSPATH" -> ":SocketWindowWordCount.jar:flink-dist_2.11-1.12.2.jar:job.graph:flink-conf.yaml::$HADOOP_CONF_DIR:$HADOOP_COMMON_HOME/share/hadoop/common/*:$HADOOP_COMMON_HOME/share/hadoop/common/lib/*:$HADOOP_HDFS_HOME/share/hadoop/hdfs/*:$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*:$HADOOP_YARN_HOME/share/hadoop/yarn/*:$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*"
            //
    
            Utils.setupYarnClassPath(yarnConfiguration, appMasterEnv);
    
    
    
            // 设置环境信息到amContainer
            amContainer.setEnvironment(appMasterEnv);
    
    
            // Set up resource type requirements for ApplicationMaster
    
    
            // Resource capability:
    
            // proto = {YarnProtos$ResourceProto@5995} "memory: 1600\nvirtual_cores: 1\nresource_value_map {\n key: "memory-mb"\n value: 1600\n units: "Mi"\n type: COUNTABLE\n}\nresource_value_map {\n key: "vcores"\n value: 1\n units: ""\n type: COUNTABLE\n}\n"
            // builder = {YarnProtos$ResourceProto$Builder@5996}
            // viaProto = true
            // resources = {ResourceInformation[2]@5997}
            // 0 = {ResourceInformation@6009} "name: memory-mb, units: Mi, type: COUNTABLE, value: 1600, minimum allocation: 0, maximum allocation: 9223372036854775807"
            // 1 = {ResourceInformation@6010} "name: vcores, units: , type: COUNTABLE, value: 1, minimum allocation: 0, maximum allocation: 9223372036854775807"
    
            Resource capability = Records.newRecord(Resource.class);
            capability.setMemory(clusterSpecification.getMasterMemoryMB());
            capability.setVirtualCores(
                    flinkConfiguration.getInteger(YarnConfigOptions.APP_MASTER_VCORES));
    
    
            // Flink per-job cluster
            final String customApplicationName = customName != null ? customName : applicationName;
            appContext.setApplicationName(customApplicationName);
    
    
            appContext.setApplicationType(applicationType != null ? applicationType : "Apache Flink");
    
    
    
            appContext.setAMContainerSpec(amContainer);
    
            appContext.setResource(capability);
    
            // Set priority for application : -1
            int priorityNum = flinkConfiguration.getInteger(YarnConfigOptions.APPLICATION_PRIORITY);
    
    
            if (priorityNum >= 0) { 
    
    
                Priority priority = Priority.newInstance(priorityNum);
    
    
                appContext.setPriority(priority);
            }
    
    
            if (yarnQueue != null) { 
                //
                //设置队列...
                appContext.setQueue(yarnQueue);
            }
    
            setApplicationNodeLabel(appContext);
    
    
            setApplicationTags(appContext);
    
            // add a hook to clean up in case deployment fails
            Thread deploymentFailureHook =
                    new DeploymentFailureHook(yarnApplication, fileUploader.getApplicationDir());
    
    
    
            Runtime.getRuntime().addShutdownHook(deploymentFailureHook);
    
    
            LOG.info("Submitting application master " + appId);
    
            // amContainer :
            //
            // proto = {YarnProtos$ContainerLaunchContextProto@6013} "localResources {\n key: "flink-conf.yaml"\n value {\n resource {\n scheme: "file"\n port: -1\n file: "/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_0011-flink-conf.yaml3338435768180930238.tmp"\n }\n size: 775\n timestamp: 1615718773000\n type: FILE\n visibility: APPLICATION\n }\n}\nlocalResources {\n key: "flink-dist_2.11-1.12.2.jar"\n value {\n resource {\n scheme: "file"\n port: -1\n file: "/Users/sysadmin/.flink/application_1615446205104_0011/flink-dist_2.11-1.12.2.jar"\n }\n size: 114224188\n timestamp: 1615718665000\n type: FILE\n visibility: APPLICATION\n }\n}\nlocalResources {\n key: "log4j.properties"\n value {\n resource {\n scheme: "file"\n port: -1\n file: "/Users/sysadmin/.flink/application_1615446205104_0011/log4j.properties"\n }\n size: 2620\n timestamp: 1615718651000\n type: FILE\n visibility: APPLICATION\n }\n}\nlocalResources {\n key: "job.graph"\n value {\n resource {"
            // builder = {YarnProtos$ContainerLaunchContextProto$Builder@6014}
            // viaProto = true
            // localResources = {HashMap@6015} size = 5
            // "flink-conf.yaml" -> {LocalResourcePBImpl@6039} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_0011-flink-conf.yaml3338435768180930238.tmp" } size: 775 timestamp: 1615718773000 type: FILE visibility: APPLICATION"
            // "flink-dist_2.11-1.12.2.jar" -> {LocalResourcePBImpl@6041} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0011/flink-dist_2.11-1.12.2.jar" } size: 114224188 timestamp: 1615718665000 type: FILE visibility: APPLICATION"
            // "log4j.properties" -> {LocalResourcePBImpl@6043} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0011/log4j.properties" } size: 2620 timestamp: 1615718651000 type: FILE visibility: APPLICATION"
            // "job.graph" -> {LocalResourcePBImpl@6044} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_00114338987264188416150.tmp" } size: 47295 timestamp: 1615718765000 type: FILE visibility: APPLICATION"
            // "SocketWindowWordCount.jar" -> {LocalResourcePBImpl@6046} "resource { scheme: "file" port: -1 file: "/Users/sysadmin/.flink/application_1615446205104_0011/SocketWindowWordCount.jar" } size: 14708 timestamp: 1615718656000 type: FILE visibility: APPLICATION"
            // tokens = null
            // tokensConf = null
            // serviceData = null
            // environment = {HashMap@6016} size = 8
            // "_APP_ID" -> "application_1615446205104_0011"
            // "_FLINK_YARN_FILES" -> "file:/Users/sysadmin/.flink/application_1615446205104_0011"
            // "HADOOP_USER_NAME" -> "sysadmin"
            // "_FLINK_CLASSPATH" -> ":SocketWindowWordCount.jar:flink-dist_2.11-1.12.2.jar:job.graph:flink-conf.yaml:"
            // "_CLIENT_SHIP_FILES" -> "YarnLocalResourceDescriptor{key=log4j.properties, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/log4j.properties, size=2620, modificationTime=1615718651000, visibility=APPLICATION, type=FILE};YarnLocalResourceDescriptor{key=SocketWindowWordCount.jar, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/SocketWindowWordCount.jar, size=14708, modificationTime=1615718656000, visibility=APPLICATION, type=FILE};YarnLocalResourceDescriptor{key=flink-conf.yaml, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/application_1615446205104_0011-flink-conf.yaml3338435768180930238.tmp, size=775, modificationTime=1615718773000, visibility=APPLICATION, type=FILE}"
            // "_FLINK_DIST_JAR" -> "YarnLocalResourceDescriptor{key=flink-dist_2.11-1.12.2.jar, path=file:/Users/sysadmin/.flink/application_1615446205104_0011/flink-dist_2.11-1.12.2.jar, size=114224188, modificationTime=1615718665000, visibility=APPLICATION, type=FILE}"
            // "_CLIENT_HOME_DIR" -> "file:/Users/sysadmin"
            // "CLASSPATH" -> ":SocketWindowWordCount.jar:flink-dist_2.11-1.12.2.jar:job.graph:flink-conf.yaml::$HADOOP_CONF_DIR:$HADOOP_COMMON_HOME/share/hadoop/common/*:$HADOOP_COMMON_HOME/share/hadoop/common/lib/*:$HADOOP_HDFS_HOME/share/hadoop/hdfs/*:$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*:$HADOOP_YARN_HOME/share/hadoop/yarn/*:$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*"
            // commands = {ArrayList@6017} size = 1
            // 0 = "$JAVA_HOME/bin/java -Xmx1073741824 -Xms1073741824 -XX:MaxMetaspaceSize=268435456 -Dlog.file="<LOG_DIR>/jobmanager.log" -Dlog4j.configuration=file:log4j.properties -Dlog4j.configurationFile=file:log4j.properties org.apache.flink.yarn.entrypoint.YarnJobClusterEntrypoint -D jobmanager.memory.off-heap.size=134217728b -D jobmanager.memory.jvm-overhead.min=201326592b -D jobmanager.memory.jvm-metaspace.size=268435456b -D jobmanager.memory.heap.size=1073741824b -D jobmanager.memory.jvm-overhead.max=201326592b 1> <LOG_DIR>/jobmanager.out 2> <LOG_DIR>/jobmanager.err"
            // applicationACLS = null
            // containerRetryContext = null
    
    
            //提交应用...
            yarnClient.submitApplication(appContext);
    
            LOG.info("Waiting for the cluster to be allocated");
            final long startTime = System.currentTimeMillis();
    
    
    
            ApplicationReport report;
    
    
    
            YarnApplicationState lastAppState = YarnApplicationState.NEW;
    
    
            loop:
            while (true) { 
    
    
    
                try { 
                    report = yarnClient.getApplicationReport(appId);
                } catch (IOException e) { 
                    throw new YarnDeploymentException("Failed to deploy the cluster.", e);
                }
                YarnApplicationState appState = report.getYarnApplicationState();
    
    
    
                LOG.debug("Application State: {}", appState);
    
    
    
                switch (appState) { 
                    case FAILED:
                    case KILLED:
                        throw new YarnDeploymentException(
                                "The YARN application unexpectedly switched to state "
                                        + appState
                                        + " during deployment. \n"
                                        + "Diagnostics from YARN: "
                                        + report.getDiagnostics()
                                        + "\n"
                                        + "If log aggregation is enabled on your cluster, use this command to further investigate the issue:\n"
                                        + "yarn logs -applicationId "
                                        + appId);
                        // break ..
                    case RUNNING:
                        LOG.info("YARN application has been deployed successfully.");
                        break loop;
                    case FINISHED:
                        LOG.info("YARN application has been finished successfully.");
                        break loop;
                    default:
                        if (appState != lastAppState) { 
                            LOG.info("Deploying cluster, current state " + appState);
                        }
                        if (System.currentTimeMillis() - startTime > 60000) { 
                            LOG.info(
                                    "Deployment took more than 60 seconds. Please check if the requested resources are available in the YARN cluster");
                        }
                }
    
    
                lastAppState = appState;
    
    
                Thread.sleep(250);
            }
    
    
    
            // since deployment was successful, remove the hook
            ShutdownHookUtil.removeShutdownHook(deploymentFailureHook, getClass().getSimpleName(), LOG);
    
            return report;
        }

# 三. 提交的文件清单 #

## 3.1.提交的安装包清单 ##

<table> 
 <thead> 
  <tr> 
   <th>包</th> 
   <th>作用</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>SocketWindowWordCount.jar</td> 
   <td>主程序包</td> 
  </tr> 
  <tr> 
   <td>log4j.properties</td> 
   <td>日志配置</td> 
  </tr> 
  <tr> 
   <td>flink-dist_2.11-1.12.2.jar</td> 
   <td>flink依赖包</td> 
  </tr> 
  <tr> 
   <td>application_1615446205104_00132401475399357854657.tmp</td> 
   <td>二进制文件,作用未知</td> 
  </tr> 
  <tr> 
   <td>application_1615446205104_0013-flink-conf.yaml7045906712053828949.tmp</td> 
   <td>配置信息</td> 
  </tr> 
 </tbody> 
</table>

application\_1615446205104\_0013-flink-conf.yaml7045906712053828949.tmp 信息如下 :

taskmanager.memory.process.size: 1728m
    internal.jobgraph-path: job.graph
    jobmanager.execution.failover-strategy: region
    high-availability.cluster-id: application_1615446205104_0013
    jobmanager.rpc.address: localhost
    execution.target: yarn-per-job
    jobmanager.memory.process.size: 1600m
    jobmanager.rpc.port: 6123
    execution.savepoint.ignore-unclaimed-state: false
    execution.attached: true
    internal.cluster.execution-mode: NORMAL
    execution.shutdown-on-attached-exit: false
    pipeline.jars: file:/opt/tools/flink-1.12.2/examples/streaming/SocketWindowWordCount.jar
    parallelism.default: 1
    taskmanager.numberOfTaskSlots: 1
    pipeline.classpaths: 
    $internal.deployment.config-dir: /opt/tools/flink-1.12.2/conf
    $inclassloader.check-leaked-classloadeternal.yarn.log-config-file: /opt/tools/flink-1.12.2/conf/log4j.properties

## 3.2.HDFS上的程序清单 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70 2]

*  application\_1615446205104\_0004

BoYi-Pro:.flink sysadmin$ hadoop fs -ls /user/sysadmin/.flink/application_1615446205104_0004
    Found 7 items
    -rw-r--r--   1 sysadmin supergroup      14708 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/SocketWindowWordCount.jar
    -rw-r--r--   1 sysadmin supergroup        775 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/application_1615446205104_0004-flink-conf.yaml6535310756280875271.tmp
    -rw-r--r--   1 sysadmin supergroup      47295 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/application_1615446205104_00041541924063981626988.tmp
    -rw-r--r--   1 sysadmin supergroup  114224188 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/flink-dist_2.11-1.12.2.jar
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib
    -rw-r--r--   1 sysadmin supergroup       2620 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/log4j.properties
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins

*  application\_1615446205104\_0004/lib

BoYi-Pro:.flink sysadmin$ hadoop fs -ls /user/sysadmin/.flink/application_1615446205104_0004/lib
    Found 10 items
    -rw-r--r--   1 sysadmin supergroup      91745 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-csv-1.12.2.jar
    -rw-r--r--   1 sysadmin supergroup     137004 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-json-1.12.2.jar
    -rw-r--r--   1 sysadmin supergroup   59381853 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-shaded-hadoop-3-uber-3.1.1.7.1.1.0-565-9.0.jar
    -rw-r--r--   1 sysadmin supergroup    7709741 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-shaded-zookeeper-3.4.14.jar
    -rw-r--r--   1 sysadmin supergroup   40316352 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-table-blink_2.11-1.12.2.jar
    -rw-r--r--   1 sysadmin supergroup   36149872 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/flink-table_2.11-1.12.2.jar
    -rw-r--r--   1 sysadmin supergroup      67114 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/log4j-1.2-api-2.12.1.jar
    -rw-r--r--   1 sysadmin supergroup     276771 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/log4j-api-2.12.1.jar
    -rw-r--r--   1 sysadmin supergroup    1674433 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/log4j-core-2.12.1.jar
    -rw-r--r--   1 sysadmin supergroup      23518 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/lib/log4j-slf4j-impl-2.12.1.jar

*  application\_1615446205104\_0004/plugins

BoYi-Pro:.flink sysadmin$ hadoop fs -ls /user/sysadmin/.flink/application_1615446205104_0004/plugins
    Found 10 items
    -rw-r--r--   1 sysadmin supergroup       8196 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/.DS_Store
    -rw-r--r--   1 sysadmin supergroup        654 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/README.txt
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/external-resource-gpu
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-datadog
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-graphite
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-influx
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-jmx
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-prometheus
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-slf4j
    drwx------   - sysadmin supergroup          0 2021-03-11 18:20 /user/sysadmin/.flink/application_1615446205104_0004/plugins/metrics-statsd

[Flink 1.12.2 _ _ yarn-per-job_]: https://zhangboyi.blog.csdn.net/article/details/114675925
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70]: /images/20221023/36586c93b8464238b93bab3423926c88.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70 1]: /images/20221023/a5b4e88968924b7d865d8ef4b52d7733.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5nbG9uZ180NDQ0_size_16_color_FFFFFF_t_70 2]: /images/20221023/d428bba4781c4c3698d6815414437e70.png