DataX 原理解析和性能优化

Posted 2022-12-16 浪子尘晨

datax简介

datax是阿里开源的用于异构数据源之间的同步工具，由于其精巧的设计和抽象，数据同步效率极高，在很多公司数据部门都有广泛的使用。本司基于datax在阿里云普通版的rds服务器上实现了通过公网，从阿里云杭州到美国西部俄勒冈aws emr集群峰值30M以上带宽的传输效率。全量传输上亿条记录、大小30G的数据，最快不到30分钟。要知道如果拉跨洋专线的话，1M带宽每个月至少需要1千大洋呢。走公网照样能达到类似的稳定性，本文通过原理设计来阐述我们是如何基于datax做到的。

datax工作原理

在讲解datax原理之前，需要明确一些概念：

• Job: Job是DataX用以描述从一个源头到一个目的端的同步作业，是DataX数据同步的最小业务单元。比如：从一张mysql的表同步到hive的一个表的特定分区。

• Task: Task是为最大化而把Job拆分得到的最小执行单元。比如：读一张有1024个分表的mysql分库分表的Job，拆分成1024个读Task，若干个任务并发执行。或者将一个大表按照id拆分成1024个分片，若干个分片任务并发执行。

• TaskGroup: 描述的是一组Task集合。在同一个TaskGroupContainer执行下的Task集合称之为TaskGroup。

• JobContainer: Job执行器，负责Job全局拆分、调度、前置语句和后置语句等工作的工作单元。

• TaskGroupContainer: TaskGroup执行器，负责执行一组Task的工作单元。

job和task是datax两种维度的抽象，后面源码分析中还会涉及到。

datax的处理过程可描述为：

1. DataX完成单个数据同步的作业，我们称之为Job，DataX接受到一个Job之后，将启动一个进程来完成整个作业同步过程。DataX Job模块是单个作业的中枢管理节点，承担了数据清理、子任务切分(将单一作业计算转化为多个子Task)、TaskGroup管理等功能。

2. DataXJob启动后，会根据不同的源端切分策略，将Job切分成多个小的Task(子任务)，以便于并发执行。Task便是DataX作业的最小单元，每一个Task都会负责一部分数据的同步工作。

3. 切分多个Task之后，DataX Job会调用Scheduler模块，根据配置的并发数据量，将拆分成的Task重新组合，组装成TaskGroup(任务组)。每一个TaskGroup负责以一定的并发运行完毕分配好的所有Task，默认单个任务组的并发数量为5。

4. 每一个Task都由TaskGroup负责启动，Task启动后，会固定启动Reader—>Channel—>Writer的线程来完成任务同步工作。

5. DataX作业运行起来之后， Job监控并等待多个TaskGroup模块任务完成，等待所有TaskGroup任务完成后Job成功退出。否则，异常退出，进程退出值非0。

上述流程可图像化描述为：

其中Channel是连接Reader和Writer的数据交换通道，所有的数据都会经由Channel进行传输，一个channel代表一个并发传输通道，通过该通道实现传输速率控制。接下来我们通过源码的角度，在抽取其核心逻辑，以mysql到hdfs的传输为例分析其工作流程。通过分析源码将会有以下几点收获：

• datax 工作流程

• datax 插件机制

• datax 同步实现

datax源码分析

datax 工作流程

public class Engine 
    private static final Logger LOG = LoggerFactory.getLogger(Engine.class);

    private static String RUNTIME_MODE;

    public void start(Configuration allConf) 
        boolean isJob = !("taskGroup".equalsIgnoreCase(allConf.getString(CoreConstant.DATAX_CORE_CONTAINER_MODEL)));
        //JobContainer会在schedule后再行进行设置和调整值
        int channelNumber =0;
        AbstractContainer container;
        long instanceId;
        int taskGroupId = -1;
            if (isJob) 
            allConf.set(CoreConstant.DATAX_CORE_CONTAINER_JOB_MODE, RUNTIME_MODE);
            container = new JobContainer(allConf);
            instanceId = allConf.getLong(
                    CoreConstant.DATAX_CORE_CONTAINER_JOB_ID, 0);

         else 
            container = new TaskGroupContainer(allConf);
            instanceId = allConf.getLong(
                    CoreConstant.DATAX_CORE_CONTAINER_JOB_ID);
            taskGroupId = allConf.getInt(
                    CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_ID);
            channelNumber = allConf.getInt(
                    CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_CHANNEL);
        
        container.start();
    

job实例运行在jobContainer容器中，它是所有任务的master，负责初始化、拆分、调度、运行、回收、监控和汇报，但它并不做实际的数据同步操作

public class JobContainer extends AbstractContainer 
    private static final Logger LOG = LoggerFactory
            .getLogger(JobContainer.class);

    public JobContainer(Configuration configuration) 
        super(configuration);
    
    /**
     * jobContainer主要负责的工作全部在start()里面，包括init、prepare、split、scheduler以及destroy和statistics
     */
    @Override
    public void start() 
        LOG.info("DataX jobContainer starts job.");
        try
            userConf = configuration.clone();
            this.init();
            this.prepare();
            this.totalStage = this.split();
            this.schedule();
         catch (Throwable e) 
            Communication communication = super.getContainerCommunicator().collect();
            // 汇报前的状态，不需要手动进行设置
            // communication.setState(State.FAILED);
            communication.setThrowable(e);
            communication.setTimestamp(this.endTimeStamp);

            Communication tempComm = new Communication();
            tempComm.setTimestamp(this.startTransferTimeStamp);

            Communication reportCommunication = CommunicationTool.getReportCommunication(communication, tempComm, this.totalStage);
            super.getContainerCommunicator().report(reportCommunication);

            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR, e);
        
    

    /**
     * reader和writer的初始化
    */
    private void init() 
        Thread.currentThread().setName("job-" + this.jobId);

        JobPluginCollector jobPluginCollector = new DefaultJobPluginCollector(
                this.getContainerCommunicator());
        //必须先Reader ，后Writer
        this.jobReader = this.initJobReader(jobPluginCollector);
        this.jobWriter = this.initJobWriter(jobPluginCollector);
    

    /**
     *schedule首先完成的工作是把上一步reader和writer split的结果整合到具体taskGroupContainer中,
     * 同时不同的执行模式调用不同的调度策略，将所有任务调度起来
     */
    private void schedule() 
        /**
         * 这里的全局speed和每个channel的速度设置为B/s
         */
        int channelsPerTaskGroup = this.configuration.getInt(
                CoreConstant.DATAX_CORE_CONTAINER_TASKGROUP_CHANNEL, 5);
        int taskNumber = this.configuration.getList(
                CoreConstant.DATAX_JOB_CONTENT).size();

        this.needChannelNumber = Math.min(this.needChannelNumber, taskNumber);
        /**
         * 通过获取配置信息得到每个taskGroup需要运行哪些tasks任务。
         会考虑 task 中对资源负载作的 load 标识进行更均衡的作业分配操作。
         */
        List<Configuration> taskGroupConfigs = JobAssignUtil.assignFairly(this.configuration,
                this.needChannelNumber, channelsPerTaskGroup);
                        LOG.info("Scheduler starts [] taskGroups.", taskGroupConfigs.size());

        AbstractScheduler scheduler;
        try 
            scheduler = initStandaloneScheduler(this.configuration);

            this.startTransferTimeStamp = System.currentTimeMillis();

            scheduler.schedule(taskGroupConfigs);

            this.endTransferTimeStamp = System.currentTimeMillis();
         catch (Exception e) 
            LOG.error("运行scheduler出错.");
            this.endTransferTimeStamp = System.currentTimeMillis();
            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR, e);
        
    

    private AbstractScheduler initStandaloneScheduler(Configuration configuration) 
        AbstractContainerCommunicator containerCommunicator = new StandAloneJobContainerCommunicator(configuration);
        super.setContainerCommunicator(containerCommunicator);

        return new StandAloneScheduler(containerCommunicator);
    
    

public abstract class AbstractScheduler 
    private static final Logger LOG = LoggerFactory
            .getLogger(AbstractScheduler.class);
    public void schedule(List<Configuration> configurations) 
        /**
         * 给 taskGroupContainer 的 Communication 注册
         */
        this.containerCommunicator.registerCommunication(configurations);
        int totalTasks = calculateTaskCount(configurations);
        startAllTaskGroup(configurations);
        try 
            while (true) 
                Communication nowJobContainerCommunication = this.containerCommunicator.collect();
                //汇报周期
                long now = System.currentTimeMillis();
                if (now - lastReportTimeStamp > jobReportIntervalInMillSec) 
                    Communication reportCommunication = CommunicationTool
                            .getReportCommunication(nowJobContainerCommunication, lastJobContainerCommunication, totalTasks);

                    this.containerCommunicator.report(reportCommunication);
                     if (nowJobContainerCommunication.getState() == State.SUCCEEDED) 
                    LOG.info("Scheduler accomplished all tasks.");
                    break;
                
                if (nowJobContainerCommunication.getState() == State.FAILED) 
                    dealFailedStat(this.containerCommunicator, nowJobContainerCommunication.getThrowable());
                

                Thread.sleep(jobSleepIntervalInMillSec);
            
         catch (InterruptedException e) 
            // 以 failed 状态退出
            LOG.error("捕获到InterruptedException异常!", e);

            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR, e);
        
    

    @Override
    public void startAllTaskGroup(List<Configuration> configurations) 
        this.taskGroupContainerExecutorService = Executors
                .newFixedThreadPool(configurations.size());

        for (Configuration taskGroupConfiguration : configurations) 
            TaskGroupContainerRunner taskGroupContainerRunner = newTaskGroupContainerRunner(taskGroupConfiguration);
            this.taskGroupContainerExecutorService.execute(taskGroupContainerRunner);
        

        this.taskGroupContainerExecutorService.shutdown();
    


    @Override
    public void dealFailedStat(AbstractContainerCommunicator frameworkCollector, Throwable throwable) 
        this.taskGroupContainerExecutorService.shutdownNow();
    



public class TaskGroupContainer extends AbstractContainer 
    private static final Logger LOG = LoggerFactory
            .getLogger(TaskGroupContainer.class);
            @Override
    public void start() 
        try 
            while (true) 
                //1.判断task状态
                boolean failedOrKilled = false;
                Map<Integer, Communication> communicationMap = containerCommunicator.getCommunicationMap();
                for(Map.Entry<Integer, Communication> entry :         communicationMap.entrySet())
                    Integer taskId = entry.getKey();
                    Communication taskCommunication = entry.getValue();
                    if(!taskCommunication.isFinished())
                        continue;
                    
                    TaskExecutor taskExecutor = removeTask(runTasks, taskId);
                    if(taskCommunication.getState() == State.FAILED)
                        failedOrKilled = true;
                            break;
                    
                    else if(taskCommunication.getState() == State.SUCCEEDED)
                                               Long taskStartTime = taskStartTimeMap.get(taskId);
                        if(taskStartTime != null)
                            Long usedTime = System.currentTimeMillis() - taskStartTime;
                            LOG.info("taskGroup[] taskId[] is successed, used[]ms",
                                    this.taskGroupId, taskId, usedTime);
                            //usedTime*1000*1000 
                            taskStartTimeMap.remove(taskId);
                            taskConfigMap.remove(taskId);
                        
                    
                
                // 2.发现该taskGroup下taskExecutor的总状态失败则汇报错误
                if (failedOrKilled) 
                    lastTaskGroupContainerCommunication = reportTaskGroupCommunication(
                            lastTaskGroupContainerCommunication, taskCountInThisTaskGroup);

                    throw DataXException.asDataXException(
                            FrameworkErrorCode.PLUGIN_RUNTIME_ERROR, lastTaskGroupContainerCommunication.getThrowable());
                
                //3.有任务未执行，且正在运行的任务数小于最大通道限制
                Iterator<Configuration> iterator = taskQueue.iterator();
                while(iterator.hasNext() && runTasks.size() < channelNumber)
                    Configuration taskConfig = iterator.next();
                    Integer taskId = taskConfig.getInt(CoreConstant.TASK_ID);
                    Configuration taskConfigForRun =taskConfig.clone()
                    TaskExecutor taskExecutor = new TaskExecutor(taskConfigForRun);
                    taskStartTimeMap.put(taskId, System.currentTimeMillis());
                    taskExecutor.doStart();
                    terator.remove();
                    runTasks.add(taskExecutor);
                    LOG.info("taskGroup[] taskId[] is started",
                            this.taskGroupId, taskId);
            
            //4.任务列表为空，executor已结束, 搜集状态为success--->成功
            if (taskQueue.isEmpty() && isAllTaskDone(runTasks) && containerCommunicator.collectState() == State.SUCCEEDED) 
                // 成功的情况下，也需要汇报一次。否则在任务结束非常快的情况下，采集的信息将会不准确
                lastTaskGroupContainerCommunication = reportTaskGroupCommunication(
                        lastTaskGroupContainerCommunication, taskCountInThisTaskGroup);

                LOG.info("taskGroup[] completed it's tasks.", this.taskGroupId);
                break;
            
         catch (Throwable e) 
            Communication nowTaskGroupContainerCommunication = this.containerCommunicator.collect();

            if (nowTaskGroupContainerCommunication.getThrowable() == null) 
                nowTaskGroupContainerCommunication.setThrowable(e);
            
            nowTaskGroupContainerCommunication.setState(State.FAILED);
            this.containerCommunicator.report(nowTaskGroupContainerCommunication);

            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR, e);
        
    


/**
 * TaskExecutor是一个完整task的执行器
 * 其中包括1：1的reader和writer
 */
class TaskExecutor 

    private Thread readerThread;

    private Thread writerThread;

    private ReaderRunner readerRunner;

    private WriterRunner writerRunner;

    public TaskExecutor(Configuration taskConf, int attemptCount) 
        writerRunner = (WriterRunner) generateRunner(PluginType.WRITER);
        //生成writerThread
        this.writerThread = new Thread(writerRunner,
                    String.format("%d-%d-%d-writer",
                            jobId, taskGroupId, this.taskId));
        //生成readerThread
        readerRunner = (ReaderRunner) generateRunner(PluginType.READER,transformerInfoExecs);
        this.readerThread = new Thread(readerRunner,
                    String.format("%d-%d-%d-reader",
                            jobId, taskGroupId, this.taskId));
    

    public void doStart() 
        this.writerThread.start();
        // reader没有起来，writer不可能结束
        if (!this.writerThread.isAlive() || this.taskCommunication.getState() == State.FAILED) 
            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR,
                    this.taskCommunication.getThrowable());
        

        this.readerThread.start();

        // 这里reader可能很快结束
        if (!this.readerThread.isAlive() && this.taskCommunication.getState() == State.FAILED) 
            // 这里有可能出现Reader线上启动即挂情况 对于这类情况 需要立刻抛出异常
            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR,
                    this.taskCommunication.getThrowable());
        
    

从上面总体流程中可以看到JobContainer通过线程池调度起所有的TaskGroupContainer，然后轮训TaskGroupContainer的运行状态。每个TaskGroupContainer则是根据分配的chanel并发数量依次执行分配的Task实例。

插件机制

在工作流程中的init步骤，我们看到的jobReader和jobWriter的实现就是通过插件动态生成的。jobReader和jobWriter就对应datax中的Job概念模型。而在TaskExecutor中初始化的readerRunner和writerRunner对应的是Task模型。通过插件datax插件机制支持了数十种不同的数据源之间的读写同步，同时也很方便的支持新的数据源接入。

Job初始化过程

public class JobContainer extends AbstractContainer 

    //reader job的初始化，返回Reader.Job
    private Reader.Job initJobReader(
            JobPluginCollector jobPluginCollector) 
        this.readerPluginName = this.configuration.getString(
                CoreConstant.DATAX_JOB_CONTENT_READER_NAME);

        Reader.Job jobReader = (Reader.Job) LoadUtil.loadJobPlugin(
                PluginType.READER, this.readerPluginName);

        // 设置reader的jobConfig
        jobReader.setPluginJobConf(this.configuration.getConfiguration(
                CoreConstant.DATAX_JOB_CONTENT_READER_PARAMETER));

        // 设置reader的readerConfig
        jobReader.setPeerPluginJobConf(this.configuration.getConfiguration(
                CoreConstant.DATAX_JOB_CONTENT_WRITER_PARAMETER));

        jobReader.setJobPluginCollector(jobPluginCollector);
        jobReader.init();

        classLoaderSwapper.restoreCurrentThreadClassLoader();
        return jobReader;
    
  

插件加载器，大体上分reader、transformer（还未实现）和writer三中插件类型，
reader和writer在执行时又可能出现Job和Task两种运行时（加载的类不同）

public class LoadUtil 
    //加载JobPlugin，reader、writer都可能要加载
    public static AbstractJobPlugin loadJobPlugin(PluginType pluginType,
                                                  String pluginName) 
        Class<? extends AbstractPlugin> clazz = LoadUtil.loadPluginClass(
                pluginType, pluginName, ContainerType.Job);

        try 
            AbstractJobPlugin jobPlugin = (AbstractJobPlugin) clazz
                    .newInstance();
            jobPlugin.setPluginConf(getPluginConf(pluginType, pluginName));
            return jobPlugin;
         catch (Exception e) 
            throw DataXException.asDataXException(
                    FrameworkErrorCode.RUNTIME_ERROR,
                    String.format("DataX找到plugin[%s]的Job配置.",
                            pluginName), e);
        
    

    //反射出具体plugin实例
    private static synchronized Class<? extends AbstractPlugin> loadPluginClass(
            PluginType pluginType, String pluginName,
            ContainerType pluginRunType) 
        Configuration pluginConf = getPluginConf(pluginType, pluginName);
        JarLoader jarLoader = LoadUtil.getJarLoader(pluginType, pluginName);
        try 
            return (Class<? extends AbstractPlugin>) jarLoader
                    .loadClass(pluginConf.getString("class") + "$"
                            + pluginRunType.value());
         catch (Exception e) 
            throw DataXException.asDataXException(FrameworkErrorCode.RUNTIME_ERROR, e);
        
    

    public static synchronized JarLoader getJarLoader(PluginType pluginType,
                                                      String pluginName) 
        Configuration pluginConf = getPluginConf(pluginType, pluginName);

        JarLoader jarLoader = jarLoaderCenter.get(generatePluginKey(pluginType,
                pluginName));
        if (null == jarLoader) 
            String pluginPath = pluginConf.getString("path");
            if (StringUtils.isBlank(pluginPath)) 
                throw DataXException.asDataXException(
                        FrameworkErrorCode.RUNTIME_ERROR,
                        String.format(
                                "%s插件[%s]路径非法!",
                                pluginType, pluginName));
            
            jarLoader = new JarLoader(new String[]pluginPath);
            jarLoaderCenter.put(generatePluginKey(pluginType, pluginName),
                    jarLoader);
        

        return jarLoader;
    



//提供Jar隔离的加载机制，会把传入的路径、及其子路径、以及路径中的jar文件加入到class path。
public class JarLoader extends URLClassLoader 
    public JarLoader(String[] paths) 
        this(paths, JarLoader.class.getClassLoader());
    

    public JarLoader(String[] paths, ClassLoader parent) 
        super(getURLs(paths), parent);
    

    private static URL[] getURLs(String[] paths) 
        Validate.isTrue(null != paths && 0 != paths.length,
                "jar包路径不能为空.");

        List<String> dirs = new ArrayList<String>();
        for (String path : paths) 
            dirs.add(path);
            JarLoader.collectDirs(path, dirs);
        

        List<URL> urls = new ArrayList<URL>();
        for (String path : dirs) 
            urls.addAll(doGetURLs(path));
        

        return urls.toArray(new URL[0]);
    

    private static void collectDirs(String path, List<String> collector) 
        if (null == path || StringUtils.isBlank(path)) 
            return;
        

        File current = new File(path);
        if (!current.exists() || !current.isDirectory()) 
            return;
        

        for (File child : current.listFiles()) 
            if (!child.isDirectory()) 
                continue;
            

            collector.add(child.getAbsolutePath());
            collectDirs(child.getAbsolutePath(), collector);
        
    

Task 初始化过程

class TaskExecutor 
private AbstractRunner generateRunner(PluginType pluginType) 
            return generateRunner(pluginType, null);
        

        private AbstractRunner generateRunner(PluginType pluginType, List<TransformerExecution> transformerInfoExecs) 
            AbstractRunner newRunner = null;
            TaskPluginCollector pluginCollector;

            switch (pluginType) 
                case READER:
                    newRunner = LoadUtil.loadPluginRunner(pluginType,
                            this.taskConfig.getString(CoreConstant.JOB_READER_NAME));
                    newRunner.setJobConf(this.taskConfig.getConfiguration(
                            CoreConstant.JOB_READER_PARAMETER));

                    pluginCollector = ClassUtil.instantiate(
                            taskCollectorClass, AbstractTaskPluginCollector.class,
                            configuration, this.taskCommunication,
                            PluginType.READER);

                    RecordSender recordSender;
                    if (transformerInfoExecs != null && transformerInfoExecs.size() > 0) 
                        recordSender = new BufferedRecordTransformerExchanger(taskGroupId, this.taskId, this.channel,this.taskCommunication ,pluginCollector, transformerInfoExecs);
                     else 
                        recordSender = new BufferedRecordExchanger(this.channel, pluginCollector);
                    

                    ((ReaderRunner) newRunner).setRecordSender(recordSender);

                    /**
                     * 设置taskPlugin的collector，用来处理脏数据和job/task通信
                     */
                    newRunner.setTaskPluginCollector(pluginCollector);
                    break;
                case WRITER:
                    newRunner = LoadUtil.loadPluginRunner(pluginType,
                            this.taskConfig.getString(CoreConstant.JOB_WRITER_NAME));
                    newRunner.setJobConf(this.taskConfig
                            .getConfiguration(CoreConstant.JOB_WRITER_PARAMETER));

                    pluginCollector = ClassUtil.instantiate(
                            taskCollectorClass, AbstractTaskPluginCollector.class,
                            configuration, this.taskCommunication,
                            PluginType.WRITER);
                    ((WriterRunner) newRunner).setRecordReceiver(new BufferedRecordExchanger(
                            this.channel, pluginCollector));
                    /**
                     * 设置taskPlugin的collector，用来处理脏数据和job/task通信
                     */
                    newRunner.setTaskPluginCollector(pluginCollector);
                    break;
                default:
                    throw DataXException.asDataXException(FrameworkErrorCode.ARGUMENT_ERROR, "Cant generateRunner for:" + pluginType);
            

            newRunner.setTaskGroupId(taskGroupId);
            newRunner.setTaskId(this.taskId);
            newRunner.setRunnerCommunication(this.taskCommunication);

            return newRunner;
        


public class LoadUtil 
  /**
     * 根据插件类型、名字和执行时taskGroupId加载对应运行器
     *
     * @param pluginType
     * @param pluginName
     * @return
     */
    public static AbstractRunner loadPluginRunner(PluginType pluginType, String pluginName) 
        AbstractTaskPlugin taskPlugin = LoadUtil.loadTaskPlugin(pluginType,
                pluginName);

        switch (pluginType) 
            case READER:
                return new ReaderRunner(taskPlugin);
            case WRITER:
                return new WriterRunner(taskPlugin);
            default:
                throw DataXException.asDataXException(
                        FrameworkErrorCode.RUNTIME_ERROR,
                        String.format("插件[%s]的类型必须是[reader]或[writer]!",
                                pluginName));
        
    

同步实现

这部分就是经过split后的具体的Task的执行逻辑。Task的划分逻辑如下：

public class JobContainer extends AbstractContainer 
    private static final Logger LOG = LoggerFactory
            .getLogger(JobContainer.class);
    /**
     * 执行reader和writer最细粒度的切分，需要注意的是，writer的切分结果要参照reader的切分结果，
     * 达到切分后数目相等，才能满足1：1的通道模型，所以这里可以将reader和writer的配置整合到一起，
     * 然后，为避免顺序给读写端带来长尾影响，将整合的结果shuffler掉
     */
    private int split() 
        this.adjustChannelNumber();

        if (this.needChannelNumber <= 0) 
            this.needChannelNumber = 1;
        

        List<Configuration> readerTaskConfigs = this
                .doReaderSplit(this.needChannelNumber);
        int taskNumber = readerTaskConfigs.size();
        List<Configuration> writerTaskConfigs = this
                .doWriterSplit(taskNumber);

        List<Configuration> transformerList = this.configuration.getListConfiguration(CoreConstant.DATAX_JOB_CONTENT_TRANSFORMER);

        LOG.debug("transformer configuration: "+ JSON.toJSONString(transformerList));
        /**
         * 输入是reader和writer的parameter list，输出是content下面元素的list
         */
        List<Configuration> contentConfig = mergeReaderAndWriterTaskConfigs(
                readerTaskConfigs, writerTaskConfigs, transformerList);


        LOG.debug("contentConfig configuration: "+ JSON.toJSONString(contentConfig));

        this.configuration.set(CoreConstant.DATAX_JOB_CONTENT, contentConfig);

        return contentConfig.size();
    

 每个Task都执行相同的逻辑和流程，下面以读mysql和写hdfs为例，展示其读写过程。

 //单个slice的reader执行调用
public class ReaderRunner extends AbstractRunner implements Runnable 
   @Override
    public void run() 
        Reader.Task taskReader = (Reader.Task) this.getPlugin();
        taskReader.init();
        taskReader.prepare();
        taskReader.startRead(recordSender);
        recordSender.terminate();
    


public class MysqlReader extends Reader 
        @Override
        public void startRead(RecordSender recordSender) 
            int fetchSize = this.readerSliceConfig.getInt(Constant.FETCH_SIZE);

            this.commonRdbmsReaderTask.startRead(this.readerSliceConfig, recordSender,
                    super.getTaskPluginCollector(), fetchSize);
        


public class CommonRdbmsReader 
        public static class Task 
        private static final Logger LOG = LoggerFactory
                .getLogger(Task.class);
        public void startRead(Configuration readerSliceConfig,
                              RecordSender recordSender,
                              TaskPluginCollector taskPluginCollector, int fetchSize) 
            String querySql = readerSliceConfig.getString(Key.QUERY_SQL);
            String table = readerSliceConfig.getString(Key.TABLE);

            PerfTrace.getInstance().addTaskDetails(taskId, table + "," + basicMsg);

            LOG.info("Begin to read record by Sql: [\\n] .",
                    querySql, basicMsg);

            Connection conn = DBUtil.getConnection(this.dataBaseType, jdbcUrl,
                    username, password);
            int columnNumber = 0;
            ResultSet rs = null;
            try 
                rs = DBUtil.query(conn, querySql, fetchSize);
                while (rs.next()) 
                    //将数据记录放入channel通道，writer从中获取写数据
                    this.transportOneRecord(recordSender, rs,
                            metaData, columnNumber, mandatoryEncoding, taskPluginCollector);
                
            catch (Exception e) 
                throw RdbmsException.asQueryException(this.dataBaseType, e, querySql, table, username);
             finally 
                DBUtil.closeDBResources(null, conn);
            
        
    

 //单个slice的writer执行调用

public class WriterRunner extends AbstractRunner implements Runnable 
    @Override
    public void run() 
        Writer.Task taskWriter = (Writer.Task) this.getPlugin();
        taskWriter.init();
        taskWriter.prepare();
        taskWriter.startWrite(recordReceiver);
    


public class HdfsWriter extends Writer 
    public static class Task extends Writer.Task 
        private static final Logger LOG = LoggerFactory.getLogger(Task.class);

        @Override
        public void startWrite(RecordReceiver lineReceiver) 
            LOG.info("begin do write...");
            LOG.info(String.format("write to file : [%s]", this.fileName));
            if(fileType.equalsIgnoreCase("TEXT"))
                //写TEXT FILE
                hdfsHelper.textFileStartWrite(lineReceiver,this.writerSliceConfig, this.fileName,
                        this.getTaskPluginCollector());
            else if(fileType.equalsIgnoreCase("ORC"))
                //写ORC FILE
                hdfsHelper.orcFileStartWrite(lineReceiver,this.writerSliceConfig, this.fileName,
                        this.getTaskPluginCollector());
            
            LOG.info("end do write");
        
    


public  class HdfsHelper 
    public void textFileStartWrite(RecordReceiver lineReceiver, Configuration config, String fileName,TaskPluginCollector taskPluginCollector)
        try 
            RecordWriter writer = outFormat.getRecordWriter(fileSystem, conf, outputPath.toString(), Reporter.NULL);
            Record record = null;
            while ((record = lineReceiver.getFromReader()) != null) 
                MutablePair<Text, Boolean> transportResult = transportOneRecord(record, fieldDelimiter, columns, taskPluginCollector);
                if (!transportResult.getRight()) 
                    writer.write(NullWritable.get(),transportResult.getLeft());
                
            
            writer.close(Reporter.NULL);
         catch (Exception e) 
            String message = String.format("写文件文件[%s]时发生IO异常,请检查您的网络是否正常！", fileName);
            LOG.error(message);
            Path path = new Path(fileName);
            deleteDir(path.getParent());
            throw DataXException.asDataXException(HdfsWriterErrorCode.Write_FILE_IO_ERROR, e);
        
    

reader和writer通过BufferedRecordExchanger建立联系，在其内部实现了基于ArrayBlockingQueue的MemoryChannel。

public class BufferedRecordExchanger implements RecordSender, RecordReceiver 
    @Override
    public void sendToWriter(Record record) 
        if(shutdown)
            throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
        

        Validate.notNull(record, "record不能为空.");

        if (record.getMemorySize() > this.byteCapacity) 
            this.pluginCollector.collectDirtyRecord(record, new Exception(String.format("单条记录超过大小限制，当前限制为:%s", this.byteCapacity)));
            return;
        

        boolean isFull = (this.bufferIndex >= this.bufferSize || this.memoryBytes.get() + record.getMemorySize() > this.byteCapacity);
        if (isFull) 
            flush();
        

        this.buffer.add(record);
        this.bufferIndex++;
        memoryBytes.addAndGet(record.getMemorySize());
    

    @Override
    public void flush() 
        if(shutdown)
            throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
        
        this.channel.pushAll(this.buffer);
        this.buffer.clear();
        this.bufferIndex = 0;
        this.memoryBytes.set(0);
    

    @Override
    public Record getFromReader() 
        if(shutdown)
            throw DataXException.asDataXException(CommonErrorCode.SHUT_DOWN_TASK, "");
        
        boolean isEmpty = (this.bufferIndex >= this.buffer.size());
        if (isEmpty) 
            receive();
        

        Record record = this.buffer.get(this.bufferIndex++);
        if (record instanceof TerminateRecord) 
            record = null;
        
        return record;
    

datax性能优化

通过datax原理和实现的理解，自然可以知道如何提升datax的同步效率。以mysql同步hdfs为例，自然最直接的方式就是提高mysql和hdfs的硬件性能如cpu、内存、IOPS、网络带宽等。当硬件资源受限的情况下，可以有如下几种办法：

1. 将不同的集群划分到同一个网络或者区域内，减少跨网络的不稳定性，如将阿里云集群迁移到amazon集群，或者同一个amazon集群中不同区域划分到同一个子网络内。

2. 对数据库按照主键划分。datax对单个表默认一个通道，如果指定拆分主键，将会大大提升同步并发数和吞吐量。

3. 在cpu、内存以及mysql负载满足的情况下，提升通道并发数。通道并发数意味着更多的内存开销，jvm调优是重中之重。

4. 当无法提升通道数量时，而且每个拆分依然很大的时候，可以考虑对每个拆分再次拆分。

5. 设定合适的参数，如mysql超时等。

总结

本文通过原理介绍和源码分析，逐步理清datax的工作流程和实现原理，并结合实际经验给出几点优化建议。然而在实际中涉及到更多的分库分表和特大量级的表，数据库的承载压力也是一大考虑因素，否则遭到dba的吊打肯定会在所难免。尤其是我们涉及到跨大洋数据同步，网络的稳定性也是一大挑战，此时基于增量同步方案或许是更好的选择。