前面介绍的python文件和Engine都是用于做初始化准备，真正的执行都是在这里完成，start代码如下：
/**
* jobContainer主要负责的工作全部在start()里面，包括init、prepare、split、scheduler、
* post以及destroy和statistics
*/
@Override
public void start() {
LOG.info("DataX jobContainer starts job.");
boolean hasException = false;
boolean isDryRun = false;
try {
this.startTimeStamp = System.currentTimeMillis();
isDryRun = configuration.getBool(CoreConstant.DATAX_JOB_SETTING_DRYRUN, false);
if(isDryRun) {
LOG.info("jobContainer starts to do preCheck ...");
this.preCheck();
} else {
userConf = configuration.clone();
LOG.debug("jobContainer starts to do preHandle ...");
this.preHandle();
LOG.debug("jobContainer starts to do init ...");
this.init();
LOG.info("jobContainer starts to do prepare ...");
this.prepare();
LOG.info("jobContainer starts to do split ...");
this.totalStage = this.split();
LOG.info("jobContainer starts to do schedule ...");
this.schedule();
LOG.debug("jobContainer starts to do post ...");
this.post();
LOG.debug("jobContainer starts to do postHandle ...");
this.postHandle();
LOG.info("DataX jobId [{}] completed successfully.", this.jobId);
this.invokeHooks();
}
} catch (Throwable e) {
LOG.error("Exception when job run", e);
hasException = true;
if (e instanceof OutOfMemoryError) {
this.destroy();
System.gc();
}
if (super.getContainerCommunicator() == null) {
// 由于 containerCollector 是在 scheduler() 中初始化的，所以当在 scheduler() 之前出现异常时，需要在此处对 containerCollector 进行初始化
AbstractContainerCommunicator tempContainerCollector;
// standalone
tempContainerCollector = new StandAloneJobContainerCommunicator(configuration);
super.setContainerCommunicator(tempContainerCollector);
}
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);
} finally {
if(!isDryRun) {
this.destroy();
this.endTimeStamp = System.currentTimeMillis();
if (!hasException) {
//最后打印cpu的平均消耗，GC的统计
VMInfo vmInfo = VMInfo.getVmInfo();
if (vmInfo != null) {
vmInfo.getDelta(false);
LOG.info(vmInfo.totalString());
LogUtil.logVmInfo(vmInfo);
}
LOG.info(PerfTrace.getInstance().summarizeNoException());
this.logStatistics();
}
}
}
}
主要执行流程为：
1、preHandle()：job前置操作
2、init()：初始化reader和writer
3、prepare()：执行插件的prepare操作
4、split()：切分任务
5、schedule()：执行任务
6、post()：执行插件的post操作
7、postHandle()：job后置操作
8、invokeHooks()：调用hook
9、输出统计结果上述任务流是顺序执行的，第5步会将切分的task分配到多个taskGroup中并发执行，其中preHandle、postHandle、invokeHooks不影响整体执行，可以先忽略，下面主要介绍关键步骤
1）init()关键代码主要是初始化reader和writer插件
/**
* reader和writer的初始化
*/
private void init() {
this.jobReader = this.initJobReader(jobPluginCollector);
this.jobWriter = this.initJobWriter(jobPluginCollector);
}
/**
* reader job的初始化，返回Reader.Job
*
* @return
*/
private Reader.Job initJobReader(
JobPluginCollector jobPluginCollector) {
this.readerPluginName = this.configuration.getString(
CoreConstant.DATAX_JOB_CONTENT_READER_NAME);
classLoaderSwapper.setCurrentThreadClassLoader(LoadUtil.getJarLoader(
PluginType.READER, this.readerPluginName));
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;
}reader插件初始化的时候使用了自定义classLoader，这样可以做到插件级别的隔离，插件初始化完成之后调用了job的init函数，用于初始化插件的Job，writer插件初始化同理
2）prepare()：prepare操作比较简单，分别执行reader和writer插件Job中的prepare函数即可，同样，每次执行前都会先加载对应的classLoader用于隔离
private void prepare() {
this.prepareJobReader();
this.prepareJobWriter();
}3）split()：切分任务task
/**
* 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();
}上面函数主要分为3步：
1、计算限速和并发，即实际的channel数和每个channel的限速，主要在adjustChannelNumber()中，这里不做过多说明
2、根据实际的channel数，切分reader端，具体的切分逻辑reader插件可以自行实现
3、根据reader端切分的数目切分writer端，达到reader：writer=1：1，这样每个task中都包含一个reader和一个writer4）schedule()：执行切分出来的task
/**
* 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);
PerfTrace.getInstance().setChannelNumber(needChannelNumber);
/**
* 通过获取配置信息得到每个taskGroup需要运行哪些tasks任务
*/
List<Configuration> taskGroupConfigs = JobAssignUtil.assignFairly(this.configuration,
this.needChannelNumber, channelsPerTaskGroup);
LOG.info("Scheduler starts [{}] taskGroups.", taskGroupConfigs.size());
ExecuteMode executeMode = null;
AbstractScheduler scheduler;
try {
executeMode = ExecuteMode.STANDALONE;
scheduler = initStandaloneScheduler(this.configuration);
//设置 executeMode
for (Configuration taskGroupConfig : taskGroupConfigs) {
taskGroupConfig.set(CoreConstant.DATAX_CORE_CONTAINER_JOB_MODE, executeMode.getValue());
}
if (executeMode == ExecuteMode.LOCAL || executeMode == ExecuteMode.DISTRIBUTE) {
if (this.jobId <= 0) {
throw DataXException.asDataXException(FrameworkErrorCode.RUNTIME_ERROR,
"在[ local | distribute ]模式下必须设置jobId，并且其值 > 0 .");
}
}
LOG.info("Running by {} Mode.", executeMode);
this.startTransferTimeStamp = System.currentTimeMillis();
scheduler.schedule(taskGroupConfigs);
this.endTransferTimeStamp = System.currentTimeMillis();
} catch (Exception e) {
LOG.error("运行scheduler 模式[{}]出错.", executeMode);
this.endTransferTimeStamp = System.currentTimeMillis();
throw DataXException.asDataXException(
FrameworkErrorCode.RUNTIME_ERROR, e);
}
/**
* 检查任务执行情况
*/
this.checkLimit();
}schedule执行过程主要分为以下几步：
1、计算taskGroup个数
2、将切分的task分配到taskGroup中
3、启动线程池执行taskGroup，具体代码流程为scheduler.schedule(taskGroupConfigs) -> AbstractScheduler.schedule -> startAllTaskGroup -> ProcessInnerScheduler.startAllTaskGroup -> this.taskGroupContainerExecutorService.execute(taskGroupContainerRunner) -> TaskGroupContainerRunner.run() -> this.taskGroupContainer.start()
4、收集taskGroup汇报的信息5）post()：执行插件的post操作
private void post() {
this.postJobWriter();
this.postJobReader();
}