引言
quratz是眼下最为成熟,使用最广泛的java任务调度框架,功能强大配置灵活.在企业应用中占重要地位.quratz在集群环境中的使用方式是每一个企业级系统都要考虑的问题.早在2006年,在ITeye上就有一篇关于quratz集群方案的讨论:http://www.iteye.com/topic/40970 ITeye创始人@Robbin在8楼给出了自己对quartz集群应用方案的意见.
后来有人总结了三种quratz集群方案:http://www.iteye.com/topic/114965
1.单独启动一个Job Server来跑job，不部署在web容器中.其它web节点当须要启动异步任务的时候，能够通过种种方式(DB, JMS, Web Service, etc)通知Job Server，而Job Server收到这个通知之后。把异步任务载入到自己的任务队列中去。
2.独立出一个job server，这个server上跑一个spring+quartz的应用。这个应用专门用来启动任务。在jobserver上加上hessain，得到业务接口，这样jobserver就能够调用web container中的业务操作，也就是正真执行任务的还是在cluster中的tomcat。在jobserver启动定时任务之后，轮流调用各地址上的业务操作（类似apache分发tomcat一样）。这样能够让不同的定时任务在不同的节点上执行，减低了一台某个node的压力
3.quartz本身其实也是支持集群的。在这样的方案下。cluster上的每个node都在跑quartz。然后也是通过数据中记录的状态来推断这个操作是否正在运行，这就要求cluster上全部的node的时间应该是一样的。并且每个node都跑应用就意味着每个node都须要有自己的线程池来跑quartz.
总的来说,第一种方法,在单独的server上运行任务,对任务的适用范围有非常大的限制,要訪问在web环境中的各种资源非常麻烦.可是集中式的管理easy从架构上规避了分布式环境的种种同步问题.另外一种方法在在第一种方法的基础上减轻了jobserver的重量,仅仅发送调用请求,不直接运行任务,这样攻克了独立server无法訪问web环境的问题,并且能够做到节点的轮询.能够有效地均衡负载.第三种方案是quartz自身支持的集群方案,在架构上全然是分布式的,没有集中的管理,quratz通过数据库锁以及标识字段保证多个节点对任务不反复获取,并且有负载平衡机制和容错机制,用少量的冗余,换取了高可用性(high
avilable HA)和高可靠性.(个人觉得和git的机制有异曲同工之处,分布式的冗余设计,换取可靠性和速度).
本文旨在研究quratz为解决分布式任务调度中存在的防止反复运行和负载均衡等问题而建立的机制.以调度流程作为顺序,配合源代码理解当中原理.
quratz的配置,及详细应用请參考CRM项目组的还有一篇文章:CRM使用Quartz集群总结分享
quartz集群架构
quartz的分布式架构如上图,能够看到数据库是各节点上调度器的枢纽.各个节点并不感知其它节点的存在,仅仅是通过数据库来进行间接的沟通.
实际上,quartz的分布式策略就是一种以数据库作为边界资源的并发策略.每一个节点都遵守同样的操作规范,使得对数据库的操作能够串行执行.而不同名称的调度器又能够互不影响的并行执行.
组件间的通讯图例如以下：（*注：基本的sql语句附在文章最后）
quartz执行时由QuartzSchedulerThread类作为主体，循环执行调度流程。
JobStore作为中间层，依照quartz的并发策略执行数据库操作，完毕基本的调度逻辑。JobRunShellFactory负责实例化JobDetail对象。将其放入线程池执行。LockHandler负责获取LOCKS表中的数据库锁。
整个quartz对任务调度的时序大致例如以下:
梳理一下当中的流程,能够表示为:
0.调度器线程run()
1.获取待触发trigger
1.1数据库LOCKS表TRIGGER_ACCESS行加锁
1.2读取JobDetail信息
1.3读取trigger表中触发器信息并标记为"已获取"
1.4commit事务,释放锁
2.触发trigger
2.1数据库LOCKS表STATE_ACCESS行加锁
2.2确认trigger的状态
2.3读取trigger的JobDetail信息
2.4读取trigger的Calendar信息
2.3更新trigger信息
2.3commit事务,释放锁
3实例化并运行Job
3.1从线程池获取线程运行JobRunShell的run方法
能够看到,这个过程中有两个相似的过程:相同是对数据表的更新操作,相同是在运行操作前获取锁 操作完毕后释放锁.这一规则能够看做是quartz解决集群问题的核心思想.
规则流程图:
进一步解释这条规则就是:一个调度器实例在运行涉及到分布式问题的数据库操作前,首先要获取QUARTZ2_LOCKS表中相应当前调度器的行级锁,获取锁后就可以运行其它表中的数据库操作,随着操作事务的提交,行级锁被释放,供其它调度器实例获取.
集群中的每个调度器实例都遵循这样一种严格的操作规程,那么对于同一类调度器来说,每个实例对数据库的操作仅仅能是串行的.而不同名的调度器之间却能够并行运行.
以下我们深入源代码,从微观上观察quartz集群调度的细节
调度器实例化
一个最简单的quartz helloworld应用例如以下:
public class HelloWorldMain
{
Log
log = LogFactory.getLog(HelloWorldMain.class);
public void run()
{
try {
SchedulerFactory
sf = new StdSchedulerFactory();
Scheduler
sch = sf.getScheduler();
JobDetail
jd = new JobDetail("HelloWorldJobDetail",Scheduler.DEFAULT_GROUP,HelloWorldJob.class);
Trigger
tg = TriggerUtils.makeMinutelyTrigger(1);
tg.setName("HelloWorldTrigger");
sch.scheduleJob(jd,
tg);
sch.start();
} catch (
Exception e ) {
e.printStackTrace();
}
}
public static void main(String[]
args) {
HelloWorldMain
hw = new HelloWorldMain();
hw.run();
}
}
|
我们看到初始化一个调度器须要用工厂类获取实例:
SchedulerFactory
sf = new StdSchedulerFactory();
Scheduler
sch = sf.getScheduler();
|
然后启动:
以下跟进StdSchedulerFactory的getScheduler()方法:
public Scheduler
getScheduler() throws SchedulerException
{
if (cfg
== null)
{
initialize();
}
SchedulerRepository
schedRep = SchedulerRepository.getInstance();
Scheduler
sched = schedRep.lookup(getSchedulerName());
if (sched
!= null)
{
if (sched.isShutdown())
{
schedRep.remove(getSchedulerName());
} else {
return sched;
}
}
sched
= instantiate();
return sched;
}
|
跟进初始化调度器方法sched = instantiate();发现是一个700多行的初始化方法,涉及到
- 读取配置资源,
- 生成QuartzScheduler对象,
- 创建该对象的执行线程,并启动线程;
- 初始化JobStore,QuartzScheduler,DBConnectionManager等重要组件,
至此,调度器的初始化工作已完毕,初始化工作中quratz读取了数据库中存放的相应当前调度器的锁信息,相应CRM中的表QRTZ2_LOCKS,中的STATE_ACCESS,TRIGGER_ACCESS两个LOCK_NAME.
public void initialize(ClassLoadHelper
loadHelper,
SchedulerSignaler
signaler) throws SchedulerConfigException
{
if (dsName
== null)
{
throw new SchedulerConfigException("DataSource
name not set.");
}
classLoadHelper
= loadHelper;
if(isThreadsInheritInitializersClassLoadContext())
{
log.info("JDBCJobStore
threads will inherit ContextClassLoader of thread: " +
Thread.currentThread().getName());
initializersLoader
= Thread.currentThread().getContextClassLoader();
}
this.schedSignaler
= signaler;
if (getLockHandler()
== null)
{
if (isClustered())
{
setUseDBLocks(true);
}
if (getUseDBLocks())
{
if(getDriverDelegateClass()
!= null &&
getDriverDelegateClass().equals(MSSQLDelegate.class.getName()))
{
if(getSelectWithLockSQL()
== null)
{
String
msSqlDflt = "SELECT
* FROM {0}LOCKS WITH (UPDLOCK,ROWLOCK) WHERE " +
COL_SCHEDULER_NAME + "
= {1} AND LOCK_NAME = ?";
getLog().info("Detected
usage of MSSQLDelegate class - defaulting 'selectWithLockSQL' to '" +
msSqlDflt + "'.");
setSelectWithLockSQL(msSqlDflt);
}
}
getLog().info("Using
db table-based data access locking (synchronization).");
setLockHandler(new StdRowLockSemaphore(getTablePrefix(),
getInstanceName(), getSelectWithLockSQL()));
} else {
getLog().info(
"Using
thread monitor-based data access locking (synchronization).");
setLockHandler(new SimpleSemaphore());
}
}
}
|
当调用sch.start();方法时,scheduler做了例如以下工作:
1.通知listener開始启动
2.启动调度器线程
3.启动plugin
4.通知listener启动完毕
public void start() throws SchedulerException
{
if (shuttingDown||
closed) {
throw new SchedulerException(
"The
Scheduler cannot be restarted after shutdown() has been called.");
}
notifySchedulerListenersStarting();
if (initialStart
== null)
{
initialStart
= new Date();
this.resources.getJobStore().schedulerStarted();
startPlugins();
} else {
resources.getJobStore().schedulerResumed();
}
schedThread.togglePause(false);
getLog().info(
"Scheduler
" +
resources.getUniqueIdentifier() + "
started.");
notifySchedulerListenersStarted();
}
|
调度过程
调度器启动后,调度器的线程就处于执行状态了,開始执行quartz的主要工作–调度任务.
前面已介绍过,任务的调度过程大致分为三步:
1.获取待触发trigger
2.触发trigger
3.实例化并运行Job
以下分别分析三个阶段的源代码.
QuartzSchedulerThread是调度器线程类,调度过程的三个步骤就承载在run()方法中,分析见代码凝视:
public void run()
{
boolean lastAcquireFailed
= false;
while (!halted.get())
{
try {
synchronized (sigLock)
{
while (paused
&& !halted.get()) {
try {
sigLock.wait(1000L);
} catch (InterruptedException
ignore) {
}
}
if (halted.get())
{
break;
}
}
/获取当前线程池中线程的数量
int availThreadCount
= qsRsrcs.getThreadPool().blockForAvailableThreads();
if(availThreadCount
> 0)
{
List<OperableTrigger>
triggers = null;
long now
= System.currentTimeMillis();
clearSignaledSchedulingChange();
try {
triggers
= qsRsrcs.getJobStore().acquireNextTriggers(
now
+ idleWaitTime, Math.min(availThreadCount, qsRsrcs.getMaxBatchSize()), qsRsrcs.getBatchTimeWindow());
lastAcquireFailed
= false;
if (log.isDebugEnabled())
log.debug("batch
acquisition of " +
(triggers == null ? 0 :
triggers.size()) + "
triggers");
} catch (JobPersistenceException
jpe) {
if(!lastAcquireFailed)
{
qs.notifySchedulerListenersError(
"An
error occurred while scanning for the next triggers to fire.",
jpe);
}
lastAcquireFailed
= true;
continue;
} catch (RuntimeException
e) {
if(!lastAcquireFailed)
{
getLog().error("quartzSchedulerThreadLoop:
RuntimeException "
+e.getMessage(),
e);
}
lastAcquireFailed
= true;
continue;
}
if (triggers
!= null &&
!triggers.isEmpty()) {
now
= System.currentTimeMillis();
long triggerTime
= triggers.get(0).getNextFireTime().getTime();
long timeUntilTrigger
= triggerTime - now;
while(timeUntilTrigger
> 2)
{
synchronized (sigLock)
{
if (halted.get())
{
break;
}
if (!isCandidateNewTimeEarlierWithinReason(triggerTime, false))
{
try {
now
= System.currentTimeMillis();
timeUntilTrigger
= triggerTime - now;
if(timeUntilTrigger
>= 1)
sigLock.wait(timeUntilTrigger);
} catch (InterruptedException
ignore) {
}
}
}
if(releaseIfScheduleChangedSignificantly(triggers,
triggerTime)) {
break;
}
now
= System.currentTimeMillis();
timeUntilTrigger
= triggerTime - now;
}
if(triggers.isEmpty())
continue;
List<TriggerFiredResult>
bndles = new ArrayList<TriggerFiredResult>();
boolean goAhead
= true;
synchronized(sigLock)
{
goAhead
= !halted.get();
}
if(goAhead)
{
try {
List<TriggerFiredResult>
res = qsRsrcs.getJobStore().triggersFired(triggers);
if(res
!= null)
bndles
= res;
} catch (SchedulerException
se) {
qs.notifySchedulerListenersError(
"An
error occurred while firing triggers '"
+
triggers + "'",
se);
for (int i
= 0;
i < triggers.size(); i++) {
qsRsrcs.getJobStore().releaseAcquiredTrigger(triggers.get(i));
}
continue;
}
}
for (int i
= 0;
i < bndles.size(); i++) {
TriggerFiredResult
result = bndles.get(i);
TriggerFiredBundle
bndle = result.getTriggerFiredBundle();
Exception
exception = result.getException();
if (exception instanceof RuntimeException)
{
getLog().error("RuntimeException
while firing trigger " +
triggers.get(i), exception);
qsRsrcs.getJobStore().releaseAcquiredTrigger(triggers.get(i));
continue;
}
if (bndle
== null)
{
qsRsrcs.getJobStore().releaseAcquiredTrigger(triggers.get(i));
continue;
}
JobRunShell
shell = null;
try {
shell
= qsRsrcs.getJobRunShellFactory().createJobRunShell(bndle);
shell.initialize(qs);
} catch (SchedulerException
se) {
qsRsrcs.getJobStore().triggeredJobComplete(triggers.get(i),
bndle.getJobDetail(), CompletedExecutionInstruction.SET_ALL_JOB_TRIGGERS_ERROR);
continue;
}
if (qsRsrcs.getThreadPool().runInThread(shell)
== false)
{
getLog().error("ThreadPool.runInThread()
return false!");
qsRsrcs.getJobStore().triggeredJobComplete(triggers.get(i),
bndle.getJobDetail(), CompletedExecutionInstruction.SET_ALL_JOB_TRIGGERS_ERROR);
}
}
continue;
}
} else {
continue;
}
long now
= System.currentTimeMillis();
long waitTime
= now + getRandomizedIdleWaitTime();
long timeUntilContinue
= waitTime - now;
synchronized(sigLock)
{
try {
if(!halted.get())
{
if (!isScheduleChanged())
{
sigLock.wait(timeUntilContinue);
}
}
} catch (InterruptedException
ignore) {
}
}
} catch(RuntimeException
re) {
getLog().error("Runtime
error occurred in main trigger firing loop.",
re);
}
}
qs
= null;
qsRsrcs
= null;
}
|
调度器每次获取到的trigger是30s内须要运行的,所以要等待一段时间至trigger运行前2ms.在等待过程中涉及到一个新加进来更紧急的trigger的处理逻辑.分析写在凝视中,不再赘述.
能够看到调度器的仅仅要在执行状态,就会不停地执行调度流程.值得注意的是,在流程的最后线程会等待一个随机的时间.这就是quartz自带的负载平衡机制.
下面是三个步骤的跟进:
触发器的获取
调度器调用:
triggers
= qsRsrcs.getJobStore().acquireNextTriggers(
now
+ idleWaitTime, Math.min(availThreadCount, qsRsrcs.getMaxBatchSize()), qsRsrcs.getBatchTimeWindow());
|
在数据库中查找一定时间范围内将会被触发的trigger.參数的意义例如以下:參数1:nolaterthan = now+3000ms,即未来30s内将会被触发.參数2 最大获取数量,大小取线程池线程剩余量与定义值得较小者.參数3 时间窗体 默觉得0,程序会在nolaterthan后加上窗体大小来选择trigger.quratz会在每次触发trigger后计算出trigger下次要运行的时间,并在数据库QRTZ2_TRIGGERS中的NEXT_FIRE_TIME字段中记录.查找时将当前毫秒数与该字段比較,就能找出下一段时间内将会触发的触发器.查找时,调用在JobStoreSupport类中的方法:
public List<OperableTrigger>
acquireNextTriggers(final long noLaterThan, final int maxCount, final long timeWindow)
throws JobPersistenceException
{
String
lockName;
if(isAcquireTriggersWithinLock()
|| maxCount > 1)
{
lockName
= LOCK_TRIGGER_ACCESS;
} else {
lockName
= null;
}
return executeInNonManagedTXLock(lockName,
new TransactionCallback<List<OperableTrigger>>()
{
public List<OperableTrigger>
execute(Connection conn) throws JobPersistenceException
{
return acquireNextTrigger(conn,
noLaterThan, maxCount, timeWindow);
}
},
new TransactionValidator<List<OperableTrigger>>()
{
public Boolean
validate(Connection conn, List<OperableTrigger> result) throws JobPersistenceException
{
}
});
}
|
该方法关键的一点在于运行了executeInNonManagedTXLock()方法,这一方法指定了一个锁名,两个回调函数.在開始运行时获得锁,在方法运行完成后随着事务的提交锁被释放.在该方法的底层,使用 for update语句,在数据库中增加行级锁,保证了在该方法运行过程中,其它的调度器对trigger进行获取时将会等待该调度器释放该锁.此方法是前面介绍的quartz集群策略的的详细实现,这一模板方法在后面的trigger触发过程还会被使用.
public static final String
SELECT_FOR_LOCK = "SELECT
* FROM "
+
TABLE_PREFIX_SUBST + TABLE_LOCKS + "
WHERE " +
COL_SCHEDULER_NAME + "
= " +
SCHED_NAME_SUBST
+ "
AND " +
COL_LOCK_NAME + "
= ? FOR UPDATE";
|
进一步解释:quratz在获取数据库资源之前,先要以for update方式訪问LOCKS表中对应LOCK_NAME数据将改行锁定.假设在此前该行已经被锁定,那么等待,假设没有被锁定,那么读取满足要求的trigger,并把它们的status置为STATE_ACQUIRED,假设有tirgger已被置为STATE_ACQUIRED,那么说明该trigger已被别的调度器实例认领,无需再次认领,调度器会忽略此trigger.调度器实例之间的间接通信就体如今这里.
JobStoreSupport.acquireNextTrigger()方法中:
int rowsUpdated = getDelegate().updateTriggerStateFromOtherState(conn, triggerKey, STATE_ACQUIRED, STATE_WAITING);
最后释放锁,这时假设下一个调度器在排队获取trigger的话,则仍会执行同样的步骤.这样的机制保证了trigger不会被反复获取.依照这样的算法正常执行状态下调度器每次读取的trigger中会有相当一部分已被标记为被获取.
获取trigger的过程进行完成.
触发trigger:
QuartzSchedulerThread line336:
List<TriggerFiredResult> res = qsRsrcs.getJobStore().triggersFired(triggers);
调用JobStoreSupport类的triggersFired()方法:
public List<TriggerFiredResult>
triggersFired(final List<OperableTrigger>
triggers) throws JobPersistenceException
{
return executeInNonManagedTXLock(LOCK_TRIGGER_ACCESS,
new TransactionCallback<List<TriggerFiredResult>>()
{
public List<TriggerFiredResult>
execute(Connection conn) throws JobPersistenceException
{
List<TriggerFiredResult>
results = new ArrayList<TriggerFiredResult>();
TriggerFiredResult
result;
for (OperableTrigger
trigger : triggers) {
try {
TriggerFiredBundle
bundle = triggerFired(conn, trigger);
result
= new TriggerFiredResult(bundle);
} catch (JobPersistenceException
jpe) {
result
= new TriggerFiredResult(jpe);
} catch(RuntimeException
re) {
result
= new TriggerFiredResult(re);
}
results.add(result);
}
return results;
}
},
new TransactionValidator<List<TriggerFiredResult>>()
{
@Override
public Boolean
validate(Connection conn, List<TriggerFiredResult> result) throws JobPersistenceException
{
}
});
}
|
此处再次用到了quratz的行为规范:executeInNonManagedTXLock()方法,在获取锁的情况下对trigger进行触发操作.当中的触发细节例如以下:
protected TriggerFiredBundle
triggerFired(Connection conn,
OperableTrigger
trigger)
throws JobPersistenceException
{
JobDetail
job;
Calendar
cal = null;
try {
String
state = getDelegate().selectTriggerState(conn,
trigger.getKey());
if (!state.equals(STATE_ACQUIRED))
{
return null;
}
} catch (SQLException
e) {
throw new JobPersistenceException("Couldn't
select trigger state: "
+
e.getMessage(), e);
}
try {
job
= retrieveJob(conn, trigger.getJobKey());
if (job
== null)
{ return null;
}
} catch (JobPersistenceException
jpe) {
try {
getLog().error("Error
retrieving job, setting trigger state to ERROR.",
jpe);
getDelegate().updateTriggerState(conn,
trigger.getKey(),
STATE_ERROR);
} catch (SQLException
sqle) {
getLog().error("Unable
to set trigger state to ERROR.",
sqle);
}
throw jpe;
}
if (trigger.getCalendarName()
!= null)
{
cal
= retrieveCalendar(conn, trigger.getCalendarName());
if (cal
== null)
{ return null;
}
}
try {
getDelegate().updateFiredTrigger(conn,
trigger, STATE_EXECUTING, job);
} catch (SQLException
e) {
throw new JobPersistenceException("Couldn't
insert fired trigger: "
+
e.getMessage(), e);
}
Date
prevFireTime = trigger.getPreviousFireTime();
trigger.triggered(cal);
String
state = STATE_WAITING;
boolean force
= true;
if (job.isConcurrentExectionDisallowed())
{
state
= STATE_BLOCKED;
force
= false;
try {
getDelegate().updateTriggerStatesForJobFromOtherState(conn,
job.getKey(),
STATE_BLOCKED,
STATE_WAITING);
getDelegate().updateTriggerStatesForJobFromOtherState(conn,
job.getKey(),
STATE_BLOCKED,
STATE_ACQUIRED);
getDelegate().updateTriggerStatesForJobFromOtherState(conn,
job.getKey(),
STATE_PAUSED_BLOCKED,
STATE_PAUSED);
} catch (SQLException
e) {
throw new JobPersistenceException(
"Couldn't
update states of blocked triggers: "
+
e.getMessage(), e);
}
}
if (trigger.getNextFireTime()
== null)
{
state
= STATE_COMPLETE;
force
= true;
}
storeTrigger(conn,
trigger, job, true,
state, force, false);
job.getJobDataMap().clearDirtyFlag();
return new TriggerFiredBundle(job,
trigger, cal, trigger.getKey().getGroup()
.equals(Scheduler.DEFAULT_RECOVERY_GROUP), new Date(),
trigger
.getPreviousFireTime(),
prevFireTime, trigger.getNextFireTime());
}
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该方法做了下面工作:
1.获取trigger当前状态
2.通过trigger中的JobKey读取trigger包括的Job信息
3.将trigger更新至触发状态
4.结合calendar的信息触发trigger,涉及多次状态更新
5.更新数据库中trigger的信息,包含更改状态至STATE_COMPLETE,及计算下一次触发时间.
6.返回trigger触发结果的传输数据类TriggerFiredBundle
从该方法返回后,trigger的运行过程已基本完成.回到运行quratz操作规范的executeInNonManagedTXLock方法,将数据库锁释放.
trigger触发操作完毕
Job运行过程:
再回到线程类QuartzSchedulerThread的 line353这时触发器都已出发完成,job的具体信息都已就位
QuartzSchedulerThread line:368
qsRsrcs.getJobStore().releaseAcquiredTrigger(triggers.get(i));
shell.initialize(qs);
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为每一个Job生成一个可执行的RunShell,并放入线程池执行.
在最后调度线程生成了一个随机的等待时间,进入短暂的等待,这使得其它节点的调度器都有机会获取数据库资源.如此就实现了quratz的负载平衡.
这样一次完整的调度过程就结束了.调度器线程进入下一次循环.
总结:
简单地说,quartz的分布式调度策略是以数据库为边界资源的一种异步策略.各个调度器都遵守一个基于数据库锁的操作规则保证了操作的唯一性.同一时候多个节点的异步执行保证了服务的可靠.但这样的策略有自己的局限性.摘录官方文档中对quratz集群特性的说明:
Only one node will fire the job for each firing. What I mean by that is, if the job has a repeating trigger that tells it to fire every 10 seconds, then at 12:00:00 exactly one node will run the job, and at 12:00:10 exactly
one node will run the job, etc. It won't necessarily be the same node each time - it will more or less be random which node runs it. The load balancing mechanism is near-random for busy schedulers (lots of triggers) but favors the same node for non-busy (e.g.
few triggers) schedulers.
The clustering feature works best for scaling out long-running and/or cpu-intensive jobs (distributing the work-load over multiple nodes). If you need to scale out to support thousands of short-running (e.g 1 second) jobs,
consider partitioning the set of jobs by using multiple distinct schedulers (including multiple clustered schedulers for HA). The scheduler makes use of a cluster-wide lock, a pattern that degrades performance as you add more nodes (when going beyond about
three nodes - depending upon your database's capabilities, etc.).
说明指出,集群特性对于高cpu使用率的任务效果非常好,可是对于大量的短任务,各个节点都会抢占数据库锁,这样就出现大量的线程等待资源.这样的情况随着节点的添加会越来越严重.
附：
通讯图中关键步骤的主要sql语句：
3.
select TRIGGER_ACCESS from QRTZ2_LOCKS for update
4.
SELECT TRIGGER_NAME,
TRIGGER_GROUP,
NEXT_FIRE_TIME,
PRIORITY
FROM QRTZ2_TRIGGERS
WHERE SCHEDULER_NAME
= 'CRMscheduler'
AND TRIGGER_STATE
= 'ACQUIRED'
AND NEXT_FIRE_TIME
<= '{timekey
30s latter}'
AND (
MISFIRE_INSTR = -1
OR (
MISFIRE_INSTR != -1
AND NEXT_FIRE_TIME
>= '{timekey
now}' )
)
ORDER BY NEXT_FIRE_TIME ASC,
PRIORITY DESC;
5.
SELECT *
FROM QRTZ2_JOB_DETAILS
WHERE SCHEDULER_NAME
= CRMscheduler
AND JOB_NAME
= ?
AND JOB_GROUP
= ?;
6.
UPDATE TQRTZ2_TRIGGERS
SET TRIGGER_STATE
= 'ACQUIRED'
WHERE SCHED_NAME
= 'CRMscheduler'
AND TRIGGER_NAME
= '{triggerName}'
AND TRIGGER_GROUP
= '{triggerGroup}'
AND TRIGGER_STATE
= 'waiting';
7.
INSERT INTO QRTZ2_FIRED_TRIGGERS
(SCHEDULER_NAME,
ENTRY_ID,
TRIGGER_NAME,
TRIGGER_GROUP,
INSTANCE_NAME,
FIRED_TIME,
SCHED_TIME,
STATE,
JOB_NAME,
JOB_GROUP,
IS_NONCONCURRENT,
REQUESTS_RECOVERY,
PRIORITY)
VALUES( 'CRMscheduler',
?, ?, ?, ?, ?, ?, ? , ? , ? , ? , ? , ?);
8.
commit;
12.
select STAT_ACCESS from QRTZ2_LOCKS for update
13.
SELECT TRIGGER_STATE FROM QRTZ2_TRIGGERS WHERE SCHEDULER_NAME
= 'CRMscheduler' AND TRIGGER_NAME
= ? AND TRIGGER_GROUP
= ?;
14.
SELECT TRIGGER_STATE
FROM QRTZ2_TRIGGERS
WHERE SCHEDULER_NAME
= 'CRMscheduler'
AND TRIGGER_NAME
= ?
AND TRIGGER_GROUP
= ?;
14.
SELECT *
FROM QRTZ2_JOB_DETAILS
WHERE SCHEDULER_NAME
= CRMscheduler
AND JOB_NAME
= ?
15.
SELECT *
FROM QRTZ2_CALENDARS
WHERE SCHEDULER_NAME
= 'CRMscheduler'
AND CALENDAR_NAME
= ?;
16.
UPDATE QRTZ2_FIRED_TRIGGERS
SET INSTANCE_NAME
= ?,
FIRED_TIME
= ?,
ENTRY_STATE
= ?,
JOB_NAME
= ?,
JOB_GROUP
= ?,
IS_NONCONCURRENT
= ?,
REQUESTS_RECOVERY
= ?
WHERE SCHEDULER_NAME
= 'CRMscheduler'
AND ENTRY_ID
= ?;
17.
UPDATE TQRTZ2_TRIGGERS
SET TRIGGER_STATE
= ?
WHERE SCHED_NAME
= 'CRMscheduler'
AND TRIGGER_NAME
= '{triggerName}'
AND TRIGGER_GROUP
= '{triggerGroup}'
AND TRIGGER_STATE
= ？;
18.
UPDATE QRTZ2_TRIGGERS
SET JOB_NAME
= ?,
DESCRIPTION
= ?,
NEXT_FIRE_TIME
= ?,
PREV_FIRE_TIME
= ?,
TRIGGER_STATE
= ?,
START_TIME
= ?,
END_TIME
= ?,
CALENDAR_NAME
= ?,
MISFIRE_INSTRUCTION
= ?,
WHERE SCHEDULER_NAME
= SCHED_NAME_SUBST
AND TRIGGER_GROUP
= ?;
19.
commit;
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