轉自集群調度機制調研及源碼分析
quartz2.2.1集群調度機制調研及源碼分析
引言
quartz集群架構
調度器實例化
調度過程
觸發(fā)器的獲取
觸發(fā)trigger:
Job執(zhí)行過程:
總結:
附:
引言
quratz是目前最為成熟,使用最廣泛的java任務調度框架,功能強大配置靈活.在企業(yè)應用中占重要地位.quratz在集群環(huán)境中的使用方式是每個企業(yè)級系統(tǒng)都要考慮的問題.早在2006年,在ITeye上就有一篇關于quratz集群方案的討論:http://www.iteye.com/topic/40970 ITeye創(chuàng)始人@Robbin在8樓給出了自己對quartz集群應用方案的意見.
后來有人總結了三種quratz集群方案:http://www.iteye.com/topic/114965
1.單獨啟動一個Job Server來跑job,不部署在web容器中.其他web節(jié)點當需要啟動異步任務的時候,可以通過種種方式(DB, JMS, Web Service, etc)通知Job Server,而Job Server收到這個通知之后,把異步任務加載到自己的任務隊列中去。
2.獨立出一個job server,這個server上跑一個spring+quartz的應用,這個應用專門用來啟動任務。在jobserver上加上hessain,得到業(yè)務接口,這樣jobserver就可以調用web container中的業(yè)務操作,也就是正真執(zhí)行任務的還是在cluster中的tomcat。在jobserver啟動定時任務之后,輪流調用各地址上的業(yè)務操作(類似apache分發(fā)tomcat一樣),這樣可以讓不同的定時任務在不同的節(jié)點上運行,減低了一臺某個node的壓力
3.quartz本身事實上也是支持集群的。在這種方案下,cluster上的每一個node都在跑quartz,然后也是通過數據中記錄的狀態(tài)來判斷這個操作是否正在執(zhí)行,這就要求cluster上所有的node的時間應該是一樣的。而且每一個node都跑應用就意味著每一個node都需要有自己的線程池來跑quartz.
總的來說,第一種方法,在單獨的server上執(zhí)行任務,對任務的適用范圍有很大的限制,要訪問在web環(huán)境中的各種資源非常麻煩.但是集中式的管理容易從架構上規(guī)避了分布式環(huán)境的種種同步問題.第二種方法在在第一種方法的基礎上減輕了jobserver的重量,只發(fā)送調用請求,不直接執(zhí)行任務,這樣解決了獨立server無法訪問web環(huán)境的問題,而且可以做到節(jié)點的輪詢.可以有效地均衡負載.第三種方案是quartz自身支持的集群方案,在架構上完全是分布式的,沒有集中的管理,quratz通過數據庫鎖以及標識字段保證多個節(jié)點對任務不重復獲取,并且有負載平衡機制和容錯機制,用少量的冗余,換取了高可用性(high avilable HA)和高可靠性.(個人認為和git的機制有異曲同工之處,分布式的冗余設計,換取可靠性和速度).
本文旨在研究quratz為解決分布式任務調度中存在的防止重復執(zhí)行和負載均衡等問題而建立的機制.以調度流程作為順序,配合源碼理解其中原理.
quratz的配置,及具體應用請參考CRM項目組的另一篇文章:CRM使用Quartz集群總結分享
quartz集群架構

quartz的分布式架構如上圖,可以看到數據庫是各節(jié)點上調度器的樞紐.各個節(jié)點并不感知其他節(jié)點的存在,只是通過數據庫來進行間接的溝通.
實際上,quartz的分布式策略就是一種以數據庫作為邊界資源的并發(fā)策略.每個節(jié)點都遵守相同的操作規(guī)范,使得對數據庫的操作可以串行執(zhí)行.而不同名稱的調度器又可以互不影響的并行運行.
組件間的通訊圖如下:(*注:主要的sql語句附在文章最后)

quartz運行時由QuartzSchedulerThread類作為主體,循環(huán)執(zhí)行調度流程。JobStore作為中間層,按照quartz的并發(fā)策略執(zhí)行數據庫操作,完成主要的調度邏輯。JobRunShellFactory負責實例化JobDetail對象,將其放入線程池運行。LockHandler負責獲取LOCKS表中的數據庫鎖。
整個quartz對任務調度的時序大致如下:

梳理一下其中的流程,可以表示為:
0.調度器線程run()
1.獲取待觸發(fā)trigger
1.1數據庫LOCKS表TRIGGER_ACCESS行加鎖
1.2讀取JobDetail信息
1.3讀取trigger表中觸發(fā)器信息并標記為"已獲取"
1.4commit事務,釋放鎖
2.觸發(fā)trigger
2.1數據庫LOCKS表STATE_ACCESS行加鎖
2.2確認trigger的狀態(tài)
2.3讀取trigger的JobDetail信息
2.4讀取trigger的Calendar信息
2.3更新trigger信息
2.3commit事務,釋放鎖
3實例化并執(zhí)行Job
3.1從線程池獲取線程執(zhí)行JobRunShell的run方法
可以看到,這個過程中有兩個相似的過程:同樣是對數據表的更新操作,同樣是在執(zhí)行操作前獲取鎖 操作完成后釋放鎖.這一規(guī)則可以看做是quartz解決集群問題的核心思想.
規(guī)則流程圖:

進一步解釋這條規(guī)則就是:一個調度器實例在執(zhí)行涉及到分布式問題的數據庫操作前,首先要獲取QUARTZ2_LOCKS表中對應當前調度器的行級鎖,獲取鎖后即可執(zhí)行其他表中的數據庫操作,隨著操作事務的提交,行級鎖被釋放,供其他調度器實例獲取.
集群中的每一個調度器實例都遵循這樣一種嚴格的操作規(guī)程,那么對于同一類調度器來說,每個實例對數據庫的操作只能是串行的.而不同名的調度器之間卻可以并行執(zhí)行.
下面我們深入源碼,從微觀上觀察quartz集群調度的細節(jié)
調度器實例化
一個最簡單的quartz helloworld應用如下:
public class HelloWorldMain { Log log = LogFactory.getLog(HelloWorldMain.class); public void run() { try { //取得Schedule對象 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(); } }
我們看到初始化一個調度器需要用工廠類獲取實例:
SchedulerFactory sf = new StdSchedulerFactory();
Scheduler sch = sf.getScheduler();
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然后啟動:
下面跟進StdSchedulerFactory的getScheduler()方法:
public Scheduler getScheduler() throws SchedulerException { if (cfg == null) { initialize(); } SchedulerRepository schedRep = SchedulerRepository.getInstance(); //從"調度器倉庫"中根據properties的SchedulerName配置獲取一個調度器實例 Scheduler sched = schedRep.lookup(getSchedulerName()); if (sched != null) { if (sched.isShutdown()) { schedRep.remove(getSchedulerName()); } else { return sched; } } //初始化調度器 sched = instantiate(); return sched; }
跟進初始化調度器方法sched = instantiate();發(fā)現是一個700多行的初始化方法,涉及到
- 讀取配置資源,
- 生成QuartzScheduler對象,
- 創(chuàng)建該對象的運行線程,并啟動線程;
- 初始化JobStore,QuartzScheduler,DBConnectionManager等重要組件,
至此,調度器的初始化工作已完成,初始化工作中quratz讀取了數據庫中存放的對應當前調度器的鎖信息,對應CRM中的表QRTZ2_LOCKS,中的STATE_ACCESS,TRIGGER_ACCESS兩個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 the user hasn't specified an explicit lock handler, then // choose one based on CMT/Clustered/UseDBLocks. if (getLockHandler() == null) { // If the user hasn't specified an explicit lock handler, // then we *must* use DB locks with clustering if (isClustered()) { setUseDBLocks(true); } if (getUseDBLocks()) { if(getDriverDelegateClass() != null && getDriverDelegateClass().equals(MSSQLDelegate.class.getName())) { if(getSelectWithLockSQL() == null) { //讀取數據庫LOCKS表中對應當前調度器的鎖信息 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()); } } }


當調用sch.start();方法時,scheduler做了如下工作:
1.通知listener開始啟動
2.啟動調度器線程
3.啟動plugin
4.通知listener啟動完成
public void start() throws SchedulerException { if (shuttingDown|| closed) { throw new SchedulerException( "The Scheduler cannot be restarted after shutdown() has been called."); } // QTZ-212 : calling new schedulerStarting() method on the listeners // right after entering start() //通知該調度器的listener啟動開始 notifySchedulerListenersStarting(); if (initialStart == null) { initialStart = new Date(); //啟動調度器的線程 this.resources.getJobStore().schedulerStarted(); //啟動plugins startPlugins(); } else { resources.getJobStore().schedulerResumed(); } schedThread.togglePause(false); getLog().info( "Scheduler " + resources.getUniqueIdentifier() + " started."); //通知該調度器的listener啟動完成 notifySchedulerListenersStarted(); }
調度過程
調度器啟動后,調度器的線程就處于運行狀態(tài)了,開始執(zhí)行quartz的主要工作–調度任務.
前面已介紹過,任務的調度過程大致分為三步:
1.獲取待觸發(fā)trigger
2.觸發(fā)trigger
3.實例化并執(zhí)行Job
下面分別分析三個階段的源碼.
QuartzSchedulerThread是調度器線程類,調度過程的三個步驟就承載在run()方法中,分析見代碼注釋:
按 Ctrl+C 復制代碼
按 Ctrl+C 復制代碼
調度器每次獲取到的trigger是30s內需要執(zhí)行的,所以要等待一段時間至trigger執(zhí)行前2ms.在等待過程中涉及到一個新加進來更緊急的trigger的處理邏輯.分析寫在注釋中,不再贅述.
可以看到調度器的只要在運行狀態(tài),就會不停地執(zhí)行調度流程.值得注意的是,在流程的最后線程會等待一個隨機的時間.這就是quartz自帶的負載平衡機制.
以下是三個步驟的跟進:
觸發(fā)器的獲取
調度器調用:
triggers = qsRsrcs.getJobStore().acquireNextTriggers(
now + idleWaitTime, Math.min(availThreadCount, qsRsrcs.getMaxBatchSize()), qsRsrcs.getBatchTimeWindow());
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在數據庫中查找一定時間范圍內將會被觸發(fā)的trigger.參數的意義如下:參數1:nolaterthan = now+3000ms,即未來30s內將會被觸發(fā).參數2 最大獲取數量,大小取線程池線程剩余量與定義值得較小者.參數3 時間窗口 默認為0,程序會在nolaterthan后加上窗口大小來選擇trigger.quratz會在每次觸發(fā)trigger后計算出trigger下次要執(zhí)行的時間,并在數據庫QRTZ2_TRIGGERS中的NEXT_FIRE_TIME字段中記錄.查找時將當前毫秒數與該字段比較,就能找出下一段時間內將會觸發(fā)的觸發(fā)器.查找時,調用在JobStoreSupport類中的方法:
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 { //...異常處理回調方法 } }); }
該方法關鍵的一點在于執(zhí)行了executeInNonManagedTXLock()方法,這一方法指定了一個鎖名,兩個回調函數.在開始執(zhí)行時獲得鎖,在方法執(zhí)行完畢后隨著事務的提交鎖被釋放.在該方法的底層,使用 for update語句,在數據庫中加入行級鎖,保證了在該方法執(zhí)行過程中,其他的調度器對trigger進行獲取時將會等待該調度器釋放該鎖.此方法是前面介紹的quartz集群策略的的具體實現,這一模板方法在后面的trigger觸發(fā)過程還會被使用.
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" ;
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進一步解釋:quratz在獲取數據庫資源之前,先要以for update方式訪問LOCKS表中相應LOCK_NAME數據將改行鎖定.如果在此前該行已經被鎖定,那么等待,如果沒有被鎖定,那么讀取滿足要求的trigger,并把它們的status置為STATE_ACQUIRED,如果有tirgger已被置為STATE_ACQUIRED,那么說明該trigger已被別的調度器實例認領,無需再次認領,調度器會忽略此trigger.調度器實例之間的間接通信就體現在這里.
JobStoreSupport.acquireNextTrigger()方法中:
int rowsUpdated = getDelegate().updateTriggerStateFromOtherState(conn, triggerKey, STATE_ACQUIRED, STATE_WAITING);
最后釋放鎖,這時如果下一個調度器在排隊獲取trigger的話,則仍會執(zhí)行相同的步驟.這種機制保證了trigger不會被重復獲取.按照這種算法正常運行狀態(tài)下調度器每次讀取的trigger中會有相當一部分已被標記為被獲取.
獲取trigger的過程進行完畢.
觸發(fā)trigger:
QuartzSchedulerThread line336:
List<TriggerFiredResult> res = qsRsrcs.getJobStore().triggersFired(triggers);
調用JobStoreSupport類的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 { //...異常處理回調方法 } }); }
此處再次用到了quratz的行為規(guī)范:executeInNonManagedTXLock()方法,在獲取鎖的情況下對trigger進行觸發(fā)操作.其中的觸發(fā)細節(jié)如下:


protected TriggerFiredBundle triggerFired(Connection conn, OperableTrigger trigger) throws JobPersistenceException { JobDetail job; Calendar cal = null; // Make sure trigger wasn't deleted, paused, or completed... try { // if trigger was deleted, state will be STATE_DELETED 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(); // call triggered - to update the trigger's next-fire-time state... 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()); }


該方法做了以下工作:
1.獲取trigger當前狀態(tài)
2.通過trigger中的JobKey讀取trigger包含的Job信息
3.將trigger更新至觸發(fā)狀態(tài)
4.結合calendar的信息觸發(fā)trigger,涉及多次狀態(tài)更新
5.更新數據庫中trigger的信息,包括更改狀態(tài)至STATE_COMPLETE,及計算下一次觸發(fā)時間.
6.返回trigger觸發(fā)結果的數據傳輸類TriggerFiredBundle
從該方法返回后,trigger的執(zhí)行過程已基本完畢.回到執(zhí)行quratz操作規(guī)范的executeInNonManagedTXLock方法,將數據庫鎖釋放.
trigger觸發(fā)操作完成
Job執(zhí)行過程:
再回到線程類QuartzSchedulerThread的 line353這時觸發(fā)器都已出發(fā)完畢,job的詳細信息都已就位
QuartzSchedulerThread line:368
qsRsrcs.getJobStore().releaseAcquiredTrigger(triggers.get(i));
shell.initialize(qs);
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為每個Job生成一個可運行的RunShell,并放入線程池運行.
在最后調度線程生成了一個隨機的等待時間,進入短暫的等待,這使得其他節(jié)點的調度器都有機會獲取數據庫資源.如此就實現了quratz的負載平衡.
這樣一次完整的調度過程就結束了.調度器線程進入下一次循環(huán).
總結:
簡單地說,quartz的分布式調度策略是以數據庫為邊界資源的一種異步策略.各個調度器都遵守一個基于數據庫鎖的操作規(guī)則保證了操作的唯一性.同時多個節(jié)點的異步運行保證了服務的可靠.但這種策略有自己的局限性.摘錄官方文檔中對quratz集群特性的說明:
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.).
說明指出,集群特性對于高cpu使用率的任務效果很好,但是對于大量的短任務,各個節(jié)點都會搶占數據庫鎖,這樣就出現大量的線程等待資源.這種情況隨著節(jié)點的增加會越來越嚴重.
附:
通訊圖中關鍵步驟的主要sql語句:


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 = ? AND JOB_GROUP = ?; 15. SELECT * FROM QRTZ2_CALENDARS WHERE SCHEDULER_NAME = 'CRMscheduler' AND CALENDAR_NAME = ?; 16. UPDATE QRTZ2_FIRED_TRIGGERS SET INSTANCE_NAME = ?, FIRED_TIME = ?, SCHED_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 = ?, JOB_GROUP = ?, DESCRIPTION = ?, NEXT_FIRE_TIME = ?, PREV_FIRE_TIME = ?, TRIGGER_STATE = ?, TRIGGER_TYPE = ?, START_TIME = ?, END_TIME = ?, CALENDAR_NAME = ?, MISFIRE_INSTRUCTION = ?, PRIORITY = ?, JOB_DATAMAP = ? WHERE SCHEDULER_NAME = SCHED_NAME_SUBST AND TRIGGER_NAME = ? AND TRIGGER_GROUP = ?; 19. commit;


原文地址:http://demo.netfoucs.com/gklifg/article/details/27090179