聊聊flink LocalEnvironment的execute方法

序

本文主要研究一下flink LocalEnvironment的execute方法

實例

final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        DataSet<RecordDto> csvInput = env.readCsvFile(csvFilePath)
                .pojoType(RecordDto.class, "playerName", "country", "year", "game", "gold", "silver", "bronze", "total");

        DataSet<Tuple2<String, Integer>> groupedByCountry = csvInput
                .flatMap(new FlatMapFunction<RecordDto, Tuple2<String, Integer>>() {

                    private static final long serialVersionUID = 1L;

                    @Override
                    public void flatMap(RecordDto record, Collector<Tuple2<String, Integer>> out) throws Exception {

                        out.collect(new Tuple2<String, Integer>(record.getCountry(), 1));
                    }
                }).groupBy(0).sum(1);
        System.out.println("===groupedByCountry===");
        groupedByCountry.print();

• 這裏使用DataSet從csv讀取數據，而後進行flatMap、groupBy、sum操做，最後調用print輸出

DataSet.print

flink-java-1.6.2-sources.jar!/org/apache/flink/api/java/DataSet.java

/**
     * Prints the elements in a DataSet to the standard output stream {@link System#out} of the JVM that calls
     * the print() method. For programs that are executed in a cluster, this method needs
     * to gather the contents of the DataSet back to the client, to print it there.
     *
     * <p>The string written for each element is defined by the {@link Object#toString()} method.
     *
     * <p>This method immediately triggers the program execution, similar to the
     * {@link #collect()} and {@link #count()} methods.
     *
     * @see #printToErr()
     * @see #printOnTaskManager(String)
     */
    public void print() throws Exception {
        List<T> elements = collect();
        for (T e: elements) {
            System.out.println(e);
        }
    }

• print方法這裏主要是調用collect方法，獲取結果，而後挨個打印

DataSet.collect

flink-java-1.6.2-sources.jar!/org/apache/flink/api/java/DataSet.java

/**
     * Convenience method to get the elements of a DataSet as a List.
     * As DataSet can contain a lot of data, this method should be used with caution.
     *
     * @return A List containing the elements of the DataSet
     */
    public List<T> collect() throws Exception {
        final String id = new AbstractID().toString();
        final TypeSerializer<T> serializer = getType().createSerializer(getExecutionEnvironment().getConfig());

        this.output(new Utils.CollectHelper<>(id, serializer)).name("collect()");
        JobExecutionResult res = getExecutionEnvironment().execute();

        ArrayList<byte[]> accResult = res.getAccumulatorResult(id);
        if (accResult != null) {
            try {
                return SerializedListAccumulator.deserializeList(accResult, serializer);
            } catch (ClassNotFoundException e) {
                throw new RuntimeException("Cannot find type class of collected data type.", e);
            } catch (IOException e) {
                throw new RuntimeException("Serialization error while deserializing collected data", e);
            }
        } else {
            throw new RuntimeException("The call to collect() could not retrieve the DataSet.");
        }
    }

• 這裏調用了getExecutionEnvironment().execute()來獲取JobExecutionResult；executionEnvironment這裏是LocalEnvironment

ExecutionEnvironment.execute

flink-java-1.6.2-sources.jar!/org/apache/flink/api/java/ExecutionEnvironment.java

/**
     * 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 ({@link DataSet#print()},
     * writing results (e.g. {@link DataSet#writeAsText(String)},
     * {@link DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other generic
     * data sinks created with {@link DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
     *
     * <p>The program execution will be logged and displayed with a generated default name.
     *
     * @return The result of the job execution, containing elapsed time and accumulators.
     * @throws Exception Thrown, if the program executions fails.
     */
    public JobExecutionResult execute() throws Exception {
        return execute(getDefaultName());
    }

    /**
     * Gets a default job name, based on the timestamp when this method is invoked.
     *
     * @return A default job name.
     */
    private static String getDefaultName() {
        return "Flink Java Job at " + Calendar.getInstance().getTime();
    }

    /**
     * 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 ({@link DataSet#print()},
     * writing results (e.g. {@link DataSet#writeAsText(String)},
     * {@link DataSet#write(org.apache.flink.api.common.io.FileOutputFormat, String)}, or other generic
     * data sinks created with {@link DataSet#output(org.apache.flink.api.common.io.OutputFormat)}.
     *
     * <p>The program execution will be logged and displayed with the given job name.
     *
     * @return The result of the job execution, containing elapsed time and accumulators.
     * @throws Exception Thrown, if the program executions fails.
     */
    public abstract JobExecutionResult execute(String jobName) throws Exception;

• 具體的execute抽象方法由子類去實現，這裏咱們主要看一下LocalEnvironment的execute方法

LocalEnvironment.execute

flink-java-1.6.2-sources.jar!/org/apache/flink/api/java/LocalEnvironment.java

@Override
    public JobExecutionResult execute(String jobName) throws Exception {
        if (executor == null) {
            startNewSession();
        }

        Plan p = createProgramPlan(jobName);

        // Session management is disabled, revert this commit to enable
        //p.setJobId(jobID);
        //p.setSessionTimeout(sessionTimeout);

        JobExecutionResult result = executor.executePlan(p);

        this.lastJobExecutionResult = result;
        return result;
    }

    @Override
    @PublicEvolving
    public void startNewSession() throws Exception {
        if (executor != null) {
            // we need to end the previous session
            executor.stop();
            // create also a new JobID
            jobID = JobID.generate();
        }

        // create a new local executor
        executor = PlanExecutor.createLocalExecutor(configuration);
        executor.setPrintStatusDuringExecution(getConfig().isSysoutLoggingEnabled());

        // if we have a session, start the mini cluster eagerly to have it available across sessions
        if (getSessionTimeout() > 0) {
            executor.start();

            // also install the reaper that will shut it down eventually
            executorReaper = new ExecutorReaper(executor);
        }
    }

• 這裏判斷executor爲null的話，會調用startNewSession，startNewSession經過PlanExecutor.createLocalExecutor(configuration)來建立executor；若是sessionTimeout大於0，則這裏會立馬調用executor.start()，默認該值爲0
• 以後經過createProgramPlan方法來建立plan
• 最後經過executor.executePlan(p)來獲取JobExecutionResult

PlanExecutor.createLocalExecutor

flink-core-1.6.2-sources.jar!/org/apache/flink/api/common/PlanExecutor.java

private static final String LOCAL_EXECUTOR_CLASS = "org.apache.flink.client.LocalExecutor";

    /**
     * Creates an executor that runs the plan locally in a multi-threaded environment.
     * 
     * @return A local executor.
     */
    public static PlanExecutor createLocalExecutor(Configuration configuration) {
        Class<? extends PlanExecutor> leClass = loadExecutorClass(LOCAL_EXECUTOR_CLASS);
        
        try {
            return leClass.getConstructor(Configuration.class).newInstance(configuration);
        }
        catch (Throwable t) {
            throw new RuntimeException("An error occurred while loading the local executor ("
                    + LOCAL_EXECUTOR_CLASS + ").", t);
        }
    }

    private static Class<? extends PlanExecutor> loadExecutorClass(String className) {
        try {
            Class<?> leClass = Class.forName(className);
            return leClass.asSubclass(PlanExecutor.class);
        }
        catch (ClassNotFoundException cnfe) {
            throw new RuntimeException("Could not load the executor class (" + className
                    + "). Do you have the 'flink-clients' project in your dependencies?");
        }
        catch (Throwable t) {
            throw new RuntimeException("An error occurred while loading the executor (" + className + ").", t);
        }
    }

• PlanExecutor.createLocalExecutor方法經過反射建立org.apache.flink.client.LocalExecutor

LocalExecutor.executePlan

flink-clients_2.11-1.6.2-sources.jar!/org/apache/flink/client/LocalExecutor.java

/**
     * Executes the given program on a local runtime and waits for the job to finish.
     *
     * <p>If the executor has not been started before, this starts the executor and shuts it down
     * after the job finished. If the job runs in session mode, the executor is kept alive until
     * no more references to the executor exist.</p>
     *
     * @param plan The plan of the program to execute.
     * @return The net runtime of the program, in milliseconds.
     *
     * @throws Exception Thrown, if either the startup of the local execution context, or the execution
     *                   caused an exception.
     */
    @Override
    public JobExecutionResult executePlan(Plan plan) throws Exception {
        if (plan == null) {
            throw new IllegalArgumentException("The plan may not be null.");
        }

        synchronized (this.lock) {

            // check if we start a session dedicated for this execution
            final boolean shutDownAtEnd;

            if (jobExecutorService == null) {
                shutDownAtEnd = true;

                // configure the number of local slots equal to the parallelism of the local plan
                if (this.taskManagerNumSlots == DEFAULT_TASK_MANAGER_NUM_SLOTS) {
                    int maxParallelism = plan.getMaximumParallelism();
                    if (maxParallelism > 0) {
                        this.taskManagerNumSlots = maxParallelism;
                    }
                }

                // start the cluster for us
                start();
            }
            else {
                // we use the existing session
                shutDownAtEnd = false;
            }

            try {
                // TODO: Set job's default parallelism to max number of slots
                final int slotsPerTaskManager = jobExecutorServiceConfiguration.getInteger(TaskManagerOptions.NUM_TASK_SLOTS, taskManagerNumSlots);
                final int numTaskManagers = jobExecutorServiceConfiguration.getInteger(ConfigConstants.LOCAL_NUMBER_TASK_MANAGER, 1);
                plan.setDefaultParallelism(slotsPerTaskManager * numTaskManagers);

                Optimizer pc = new Optimizer(new DataStatistics(), jobExecutorServiceConfiguration);
                OptimizedPlan op = pc.compile(plan);

                JobGraphGenerator jgg = new JobGraphGenerator(jobExecutorServiceConfiguration);
                JobGraph jobGraph = jgg.compileJobGraph(op, plan.getJobId());

                return jobExecutorService.executeJobBlocking(jobGraph);
            }
            finally {
                if (shutDownAtEnd) {
                    stop();
                }
            }
        }
    }

• 這裏當jobExecutorService爲null的時候，會調用start方法啓動cluster建立jobExecutorService
• 以後建立JobGraphGenerator，而後經過JobGraphGenerator.compileJobGraph方法，將plan構建爲JobGraph
• 最後調用jobExecutorService.executeJobBlocking(jobGraph)，執行這個jobGraph，而後返回JobExecutionResult

LocalExecutor.start

flink-clients_2.11-1.6.2-sources.jar!/org/apache/flink/client/LocalExecutor.java

@Override
    public void start() throws Exception {
        synchronized (lock) {
            if (jobExecutorService == null) {
                // create the embedded runtime
                jobExecutorServiceConfiguration = createConfiguration();

                // start it up
                jobExecutorService = createJobExecutorService(jobExecutorServiceConfiguration);
            } else {
                throw new IllegalStateException("The local executor was already started.");
            }
        }
    }

    private Configuration createConfiguration() {
        Configuration newConfiguration = new Configuration();
        newConfiguration.setInteger(TaskManagerOptions.NUM_TASK_SLOTS, getTaskManagerNumSlots());
        newConfiguration.setBoolean(CoreOptions.FILESYTEM_DEFAULT_OVERRIDE, isDefaultOverwriteFiles());

        newConfiguration.addAll(baseConfiguration);

        return newConfiguration;
    }

    private JobExecutorService createJobExecutorService(Configuration configuration) throws Exception {
        final JobExecutorService newJobExecutorService;
        if (CoreOptions.NEW_MODE.equals(configuration.getString(CoreOptions.MODE))) {

            if (!configuration.contains(RestOptions.PORT)) {
                configuration.setInteger(RestOptions.PORT, 0);
            }

            final MiniClusterConfiguration miniClusterConfiguration = new MiniClusterConfiguration.Builder()
                .setConfiguration(configuration)
                .setNumTaskManagers(
                    configuration.getInteger(
                        ConfigConstants.LOCAL_NUMBER_TASK_MANAGER,
                        ConfigConstants.DEFAULT_LOCAL_NUMBER_TASK_MANAGER))
                .setRpcServiceSharing(RpcServiceSharing.SHARED)
                .setNumSlotsPerTaskManager(
                    configuration.getInteger(
                        TaskManagerOptions.NUM_TASK_SLOTS, 1))
                .build();

            final MiniCluster miniCluster = new MiniCluster(miniClusterConfiguration);
            miniCluster.start();

            configuration.setInteger(RestOptions.PORT, miniCluster.getRestAddress().getPort());

            newJobExecutorService = miniCluster;
        } else {
            final LocalFlinkMiniCluster localFlinkMiniCluster = new LocalFlinkMiniCluster(configuration, true);
            localFlinkMiniCluster.start();

            newJobExecutorService = localFlinkMiniCluster;
        }

        return newJobExecutorService;
    }

• start方法這裏先經過createConfiguration建立配置文件，再經過createJobExecutorService建立JobExecutorService
• createConfiguration主要設置了TaskManagerOptions.NUM_TASK_SLOTS以及CoreOptions.FILESYTEM_DEFAULT_OVERRIDE
• createJobExecutorService方法這裏主要是根據configuration.getString(CoreOptions.MODE)的配置來建立不一樣的newJobExecutorService
• 默認是CoreOptions.NEW_MODE模式，它先建立MiniClusterConfiguration，而後建立MiniCluster(JobExecutorService)，而後調用MiniCluster.start方法啓動以後返回
• 非CoreOptions.NEW_MODE模式，則建立的是LocalFlinkMiniCluster(JobExecutorService)，而後調用LocalFlinkMiniCluster.start()啓動以後返回

MiniCluster.executeJobBlocking

flink-runtime_2.11-1.6.2-sources.jar!/org/apache/flink/runtime/minicluster/MiniCluster.java

/**
     * This method runs a job in blocking mode. The method returns only after the job
     * completed successfully, or after it failed terminally.
     *
     * @param job  The Flink job to execute
     * @return The result of the job execution
     *
     * @throws JobExecutionException Thrown if anything went amiss during initial job launch,
     *         or if the job terminally failed.
     */
    @Override
    public JobExecutionResult executeJobBlocking(JobGraph job) throws JobExecutionException, InterruptedException {
        checkNotNull(job, "job is null");

        final CompletableFuture<JobSubmissionResult> submissionFuture = submitJob(job);

        final CompletableFuture<JobResult> jobResultFuture = submissionFuture.thenCompose(
            (JobSubmissionResult ignored) -> requestJobResult(job.getJobID()));

        final JobResult jobResult;

        try {
            jobResult = jobResultFuture.get();
        } catch (ExecutionException e) {
            throw new JobExecutionException(job.getJobID(), "Could not retrieve JobResult.", ExceptionUtils.stripExecutionException(e));
        }

        try {
            return jobResult.toJobExecutionResult(Thread.currentThread().getContextClassLoader());
        } catch (IOException | ClassNotFoundException e) {
            throw new JobExecutionException(job.getJobID(), e);
        }
    }

• MiniCluster.executeJobBlocking方法，先調用submitJob(job)方法，提交這個JobGraph，它返回一個CompletableFuture(submissionFuture)
• 該CompletableFuture(submissionFuture)經過thenCompose鏈接了requestJobResult方法來根據jobId請求jobResult(jobResultFuture)
• 最後經過jobResultFuture.get()獲取JobExecutionResult

小結

• DataSet的print方法調用了collect方法，而collect方法則調用getExecutionEnvironment().execute()來獲取JobExecutionResult，executionEnvironment這裏是LocalEnvironment
• ExecutionEnvironment.execute方法內部調用了抽象方法execute(String jobName)，該抽象方法由子類實現，這裏是LocalEnvironment.execute，它先經過startNewSession，使用PlanExecutor.createLocalExecutor建立LocalExecutor，以後經過createProgramPlan建立plan，最後調用LocalExecutor.executePlan來獲取JobExecutionResult
• LocalExecutor.executePlan方法它先判斷jobExecutorService，若是爲null，則調用start方法建立jobExecutorService(這裏根據CoreOptions.MODE配置，若是是CoreOptions.NEW_MODE則建立的jobExecutorService是MiniCluster，不然建立的jobExecutorService是LocalFlinkMiniCluster)，這裏建立的jobExecutorService爲MiniCluster；以後經過JobGraphGenerator將plan轉換爲jobGraph；最後調用jobExecutorService.executeJobBlocking(jobGraph)，執行這個jobGraph，而後返回JobExecutionResult

doc

• LocalEnvironment
• LocalExecutor
• MiniCluster