debug 代码
JobExecution result = launcher.run(job, jobParametersBuilder.toJobParameters());
这是启动job的方法，如下是方法的具体实现：
SimpleJobLauncher.java run方法的具体实现（删除了部分代码）
@Override
public JobExecution run(final Job job, final JobParameters jobParameters)
throws JobExecutionAlreadyRunningException, JobRestartException, JobInstanceAlreadyCompleteException,
JobParametersInvalidException {
final JobExecution jobExecution;
/*
* There is a very small probability that a non-restartable job can be
* restarted, but only if another process or thread manages to launch
* <i>and</i> fail a job execution for this instance between the last
* assertion and the next method returning successfully.
*/
jobExecution = jobRepository.createJobExecution(job.getName(), jobParameters);
try {
taskExecutor.execute(new Runnable() {
@Override
public void run() {
try {
logger.info("Job: [" + job + "] launched with the following parameters: [" + jobParameters
+ "]");
job.execute(jobExecution);
logger.info("Job: [" + job + "] completed with the following parameters: [" + jobParameters
+ "] and the following status: [" + jobExecution.getStatus() + "]");
}
catch (Throwable t) {
logger.info("Job: [" + job
+ "] failed unexpectedly and fatally with the following parameters: [" + jobParameters
+ "]", t);
rethrow(t);
}
}
private void rethrow(Throwable t) {
if (t instanceof RuntimeException) {
throw (RuntimeException) t;
}
else if (t instanceof Error) {
throw (Error) t;
}
throw new IllegalStateException(t);
}
});
}
return jobExecution;
}
看taskExecutor.execute 方法的主要逻辑，就是把 job execute 封装进 一个抽象的任务内，通过taskExecutor 执行，那么这个taskExecutor 是一个什么类型——[email protected]，就是SyncTaskExecutor类型的对象，一个同步的任务执行类。这样就可以明确的知道每个chunk的处理都是在一个单线程内，循环往复的处理每个commit-interval。
taskExecutor.execute(new Runnable() {
@Override
public void run() {
job.execute(jobExecution);
}
});
继续走代码，那接下来执行的方法是job.execute(jobExecution);那先来看job 是什么类型的对象，
那么就是他了，FlowJob: [name=addPeopleDescJob]。进入job.execute ，
他就直接跳进了 org.springframework.batch.core.job.AbstractJob.execute(JobExecution execution)方法，这是FlowJob的父类，FlowJob 显然没有覆写 该方法。
AbstractJob.execute(JobExecution execution)
@Override
public final void execute(JobExecution execution) {
doExecute(execution);
}
这是方法的主要执行逻辑，那么这个doExecute 方法就是FlowJob覆写的 父类的方法，完成 job 的执行的任务。。。
继续debug
job 是任务的任务的抽象表示，完成的具体任务还要在step中，那么接下来就是step 的执行了，step 是如何执行的？？
我们的代码最终调到这org.springframework.batch.core.step.AbstractStep.execute(StepExecution stepExecution) ，step的抽象类。
这个方法的主要逻辑如下：
@Override
public final void execute(StepExecution stepExecution)
throws JobInterruptedException, UnexpectedJobExecutionException {
doExecute(stepExecution);
}
这个step 又是什么类型的对象——TaskletStep: [name=addDescStep]，TaskletStep 的对象，其父类为AbstractStep。
继续看 TaskletStep.doExecute(stepExecution);
@Override
protected void doExecute(StepExecution stepExecution) throws Exception {
stepExecution.getExecutionContext().put(TASKLET_TYPE_KEY, tasklet.getClass().getName());
stepExecution.getExecutionContext().put(STEP_TYPE_KEY, this.getClass().getName());
stream.update(stepExecution.getExecutionContext());
getJobRepository().updateExecutionContext(stepExecution);
// Shared semaphore per step execution, so other step executions can run
// in parallel without needing the lock
final Semaphore semaphore = createSemaphore();
stepOperations.iterate(new StepContextRepeatCallback(stepExecution) {
@Override
public RepeatStatus doInChunkContext(RepeatContext repeatContext, ChunkContext chunkContext)
throws Exception {
StepExecution stepExecution = chunkContext.getStepContext().getStepExecution();
// Before starting a new transaction, check for
// interruption.
interruptionPolicy.checkInterrupted(stepExecution);
RepeatStatus result;
try {
result = new TransactionTemplate(transactionManager, transactionAttribute)
.execute(new ChunkTransactionCallback(chunkContext, semaphore));
}
catch (UncheckedTransactionException e) {
// Allow checked exceptions to be thrown inside callback
throw (Exception) e.getCause();
}
chunkListener.afterChunk(chunkContext);
// Check for interruption after transaction as well, so that
// the interrupted exception is correctly propagated up to
// caller
interruptionPolicy.checkInterrupted(stepExecution);
return result;
}
});
}
上面是 TaskletStep.doExecute的完整代码，主要的逻辑是：
stepOperations.iterate(new StepContextRepeatCallback(stepExecution) {
@Override
public RepeatStatus doInChunkContext(RepeatContext repeatContext, ChunkContext chunkContext)
throws Exception {
StepExecution stepExecution = chunkContext.getStepContext().getStepExecution();
// Before starting a new transaction, check for
// interruption.
interruptionPolicy.checkInterrupted(stepExecution);
RepeatStatus result;
try {
result = new TransactionTemplate(transactionManager, transactionAttribute)
.execute(new ChunkTransactionCallback(chunkContext, semaphore));
}
catch (UncheckedTransactionException e) {
// Allow checked exceptions to be thrown inside callback
throw (Exception) e.getCause();
}
chunkListener.afterChunk(chunkContext);
// Check for interruption after transaction as well, so that
// the interrupted exception is correctly propagated up to
// caller
interruptionPolicy.checkInterrupted(stepExecution);
return result;
}
});
stepOperations.iterate() 方法传入StepContextRepeatCallback的一个匿名对象。
继续debug，转了一圈又回到 这个回调方法上StepContextRepeatCallback.doInChunkContext(),看这个方法 ：
result = new TransactionTemplate(transactionManager, transactionAttribute) .execute(new ChunkTransactionCallback(chunkContext, semaphore));
new TransactionTemplate().execute()方法忽略过去，在这个方法里面最终还要 调用ChunkTransactionCallback.doInTransaction()的回调方法，看下面：
ChunkTransactionCallback是 TaskletStep 的内部类：
ChunkTransactionCallback.doInTransaction()：
@Override
public RepeatStatus doInTransaction(TransactionStatus status) {
RepeatStatus result = RepeatStatus.CONTINUABLE;
result = tasklet.execute(contribution, chunkContext);
return result;
}
这个tasklet 的类型是 ：org.springframework.batch.core.step.item.ChunkOrientedTasklet@3e521715，ChunkOrientedTasklet 其父类 Tasklet，定义step的执行策略。
ChunkOrientedTasklet.execute() 方法：
@Override
public RepeatStatus execute(StepContribution contribution, ChunkContext chunkContext) throws Exception {
@SuppressWarnings("unchecked")
Chunk<I> inputs = (Chunk<I>) chunkContext.getAttribute(INPUTS_KEY);
if (inputs == null) {
inputs = chunkProvider.provide(contribution);
if (buffering) {
chunkContext.setAttribute(INPUTS_KEY, inputs);
}
}
chunkProcessor.process(contribution, inputs);
chunkProvider.postProcess(contribution, inputs);
// Allow a message coming back from the processor to say that we
// are not done yet
if (inputs.isBusy()) {
logger.debug("Inputs still busy");
return RepeatStatus.CONTINUABLE;
}
chunkContext.removeAttribute(INPUTS_KEY);
chunkContext.setComplete();
logger.debug("Inputs not busy, ended: " + inputs.isEnd());
return RepeatStatus.continueIf(!inputs.isEnd());
}
好了，Chunk<I> inputs，就是要读入的数据，他是怎么来的 ：
inputs = chunkProvider.provide(contribution);
chunkProvider 是 [email protected]529dc 的类型，provide方法：
@Override
public Chunk<I> provide(final StepContribution contribution) throws Exception {
final Chunk<I> inputs = new Chunk<I>();
repeatOperations.iterate(new RepeatCallback() {
@Override
public RepeatStatus doInIteration(final RepeatContext context) throws Exception {
I item = null;
try {
item = read(contribution, inputs);
}
catch (SkipOverflowException e) {
// read() tells us about an excess of skips by throwing an
// exception
return RepeatStatus.FINISHED;
}
if (item == null) {
inputs.setEnd();
return RepeatStatus.FINISHED;
}
inputs.add(item);
contribution.incrementReadCount();
return RepeatStatus.CONTINUABLE;
}
});
return inputs;
}
在provide 方法内，逐条读取 一条数据，然后放入inputs 结合，而不是一次读取多条。
数据读完了（此commit内）接下来就要处理inputs 数据了，到chunkProcessor.process(contribution, inputs);方法
@Override
public final void process(StepContribution contribution, Chunk<I> inputs) throws Exception {
// Allow temporary state to be stored in the user data field
initializeUserData(inputs);
// If there is no input we don‘t have to do anything more
if (isComplete(inputs)) {
return;
}
// Make the transformation, calling remove() on the inputs iterator if
// any items are filtered. Might throw exception and cause rollback.
Chunk<O> outputs = transform(contribution, inputs);
// Adjust the filter count based on available data
contribution.incrementFilterCount(getFilterCount(inputs, outputs));
// Adjust the outputs if necessary for housekeeping purposes, and then
// write them out...
write(contribution, inputs, getAdjustedOutputs(inputs, outputs));
}
在chunkProcessor.process 方法内定义了 两个步骤，一个是transform，也就是 process 读入的数据集合，一个是write。
代码跳到transform这：
protected Chunk<O> transform(StepContribution contribution, Chunk<I> inputs) throws Exception {
Chunk<O> outputs = new Chunk<O>();
for (Chunk<I>.ChunkIterator iterator = inputs.iterator(); iterator.hasNext();) {
final I item = iterator.next();
O output;
try {
output = doProcess(item);
}
catch (Exception e) {
/*
* For a simple chunk processor (no fault tolerance) we are done
* here, so prevent any more processing of these inputs.
*/
inputs.clear();
throw e;
}
if (output != null) {
outputs.add(output);
}
else {
iterator.remove();
}
}
return outputs;
}
对于 inputs 集合 ，遍历，处理后得到 outputs 集合。
output = doProcess(item);
上面这行代码就是要调用自定义的process 方法进行处理。
然后看write 方法是如何写入数据的，
protected void write(StepContribution contribution, Chunk<I> inputs, Chunk<O> outputs) throws Exception {
try {
doWrite(outputs.getItems());
}
catch (Exception e) {
/*
* For a simple chunk processor (no fault tolerance) we are done
* here, so prevent any more processing of these inputs.
*/
inputs.clear();
throw e;
}
contribution.incrementWriteCount(outputs.size());
}
在write 方法内调用了 doWrite() 方法 ,然后调用 自定义的 write 方法把 数据写入。
好的 ，上面就是 在一次commit-interval 内的主要过程，和主要的逻辑代码。那么 spring batch 是如何 重复commit-interval 的呢 （RepeatTemplate）？以后再详细说来。
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