为什么要使用线程池？
创建线程和销毁线程的花销是比较大的，这些时间有可能比处理业务的时间还要长。这样频繁的创建线程和销毁线程，再加上业务工作线程，消耗系统资源的时间，可能导致系统资源不足。（我们可以把创建和销毁的线程的过程去掉）
线程池有什么作用？
1、提高效率 创建好一定数量的线程放在池中，等需要使用的时候就从池中拿一个，这要比需要的时候创建一个线程对象要快的多。
2、方便管理 可以编写线程池管理代码对池中的线程同一进行管理，比如说启动时有该程序创建100个线程，每当有请求的时候，就分配一个线程去工作，如果刚好并发有101个请求，那多出的这一个请求可以排队等候，避免因无休止的创建线程导致系统崩溃。
说说几种常见的线程池及使用场景
1、newSingleThreadExecutor
创建一个单线程化的线程池，它只会用唯一的工作线程来执行任务，保证所有任务按照指定顺序(FIFO, LIFO, 优先级)执行。
public static ExecutorService newSingleThreadExecutor() {
return new FinalizableDelegatedExecutorService
(new ThreadPoolExecutor(1, 1,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>()));
}
2、newFixedThreadPool
创建一个定长线程池，可控制线程最大并发数，超出的线程会在队列中等待。
public static ExecutorService newFixedThreadPool(int nThreads) {
return new ThreadPoolExecutor(nThreads, nThreads,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>());
}
3、newCachedThreadPool
创建一个可缓存线程池，如果线程池长度超过处理需要，可灵活回收空闲线程，若无可回收，则新建线程。
public static ExecutorService newCachedThreadPool() {
return new ThreadPoolExecutor(0, Integer.MAX_VALUE,
60L, TimeUnit.SECONDS,
new SynchronousQueue<Runnable>());
}
4、newScheduledThreadPool
创建一个定长线程池，支持定时及周期性任务执行。
public static ScheduledExecutorService newScheduledThreadPool(int corePoolSize) {
return new ScheduledThreadPoolExecutor(corePoolSize);
}
线程池不允许使用Executors去创建，而是通过ThreadPoolExecutor的方式，这样的处理方式让写的同学更加明确线程池的运行规则，规避资源耗尽的风险。 说明：Executors各个方法的弊端：
1）newFixedThreadPool和newSingleThreadExecutor:
主要问题是堆积的请求处理队列可能会耗费非常大的内存，甚至OOM。
2）newCachedThreadPool和newScheduledThreadPool:
主要问题是线程数最大数是Integer.MAX_VALUE，可能会创建数量非常多的线程，甚至OOM。
Positive example 1：
//org.apache.commons.lang3.concurrent.BasicThreadFactory
ScheduledExecutorService executorService =
new ScheduledThreadPoolExecutor(1,
new BasicThreadFactory.Builder().namingPattern("example-schedule-pool-%d").daemon(true).build()
);
Positive example 2：
ThreadFactory namedThreadFactory = new ThreadFactoryBuilder()
.setNameFormat("demo-pool-%d").build();
//Common Thread Pool
ExecutorService pool = new ThreadPoolExecutor(5, 200,
0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<Runnable>(1024), namedThreadFactory, new ThreadPoolExecutor.AbortPolicy());
pool.execute(()-> System.out.println(Thread.currentThread().getName()));
pool.shutdown();//gracefully shutdown
Positive example 3：
<bean id="userThreadPool"
class="org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor">
<property name="corePoolSize" value="10" />
<property name="maxPoolSize" value="100" />
<property name="queueCapacity" value="2000" />
<property name="threadFactory" value= threadFactory />
<property name="rejectedExecutionHandler">
<ref local="rejectedExecutionHandler" />
</property>
</bean>
//in code
userThreadPool.execute(thread);
个人在项目中用到的是第三种，业务需求，每天会有调度服务器会通过http协议请求
<bean id="xxDataThreadPool"
class="org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor">
<!-- 核心线程数 -->
<property name="corePoolSize" value="50"/>
<!-- 最大线程数 -->
<property name="maxPoolSize" value="500"/>
<!-- 队列最大长度 >=mainExecutor.maxSize -->
<property name="queueCapacity" value="10"/>
<!-- 线程池维护线程所允许的空闲时间 -->
<property name="keepAliveSeconds" value="1"/>
<!-- 线程池对拒绝任务(无线程可用)的处理策略 如果已经超过了限制丢弃消息，不进行处理 -->
<property name="rejectedExecutionHandler">
<bean class="java.util.concurrent.ThreadPoolExecutor$DiscardPolicy"/>
</property>
</bean>
@Controller
@RequestMapping("/windData")
public class WindDataListener {
private final static ThLogger logger = ThLoggerFactory.getLogger("WindDataDispatcher");
@Autowired
private ThreadPoolTaskExecutor controlerThreadPool;
@Autowired
private ThreadPoolTaskExecutor windDataThreadPool;
@Autowired
private WindDataRuntimeService runtimeService;
@Autowired
private MaintainAlarmSender maintainAlarmSender;
/**
* 启动调度
*/
@RequestMapping(value = "/receiveMsg", method = RequestMethod.GET)
@ResponseBody
public void receiveMsg() {
final String paramLog = LogConst.BUSSINESS_NAME + LogConst.HTTP_API;
logger.info("[{}][接收到调度消息]", paramLog);
//定时调度，可能有多个http请求，把请求都放在controlerThreadPool里面
controlerThreadPool.execute(new WindDataDispatcher(windDataThreadPool, runtimeService, maintainAlarmSender,
MDC.getCopyOfContextMap()));
logger.info("[{}][响应给调度系统]", paramLog);
}
}
public class WindDataDispatcher implements Runnable {
private final static ThLogger logger = ThLoggerFactory.getLogger("WindDataDispatcher");
private ThreadPoolTaskExecutor taskThreadPool;
private WindDataRuntimeService runtimeService;
private MaintainAlarmSender maintainAlarmSender;
private Map<Object, Object> mdcMap;
public WindDataDispatcher(ThreadPoolTaskExecutor taskThreadPool, WindDataRuntimeService runtimeService, MaintainAlarmSender maintainAlarmSender, Map<Object, Object> mdcMap) {
this.taskThreadPool = taskThreadPool;
this.runtimeService = runtimeService;
this.maintainAlarmSender = maintainAlarmSender;
this.mdcMap = mdcMap;
}
@Override
public void run() {
if (null != mdcMap) {
MDC.setContextMap(mdcMap);
}
final String paramLog = LogConst.BUSSINESS_NAME + LogConst.DISPATCHER;
logger.info("[{}启动]", paramLog);
taskThreadPool.execute(new WindDataExecutor(runtimeService, maintainAlarmSender, mdcMap));
logger.info("[{}结束]", paramLog);
}
}
public class WindDataExecutor implements Runnable {
private final static ThLogger logger = ThLoggerFactory.getLogger("WindDataDispatcher");
private WindDataRuntimeService runtimeService;
private MaintainAlarmSender maintainAlarmSender;
private Map<Object, Object> mdcMap;
public WindDataExecutor(WindDataRuntimeService runtimeService, MaintainAlarmSender maintainAlarmSender, Map<Object, Object> mdcMap) {
this.runtimeService = runtimeService;
this.maintainAlarmSender = maintainAlarmSender;
this.mdcMap = mdcMap;
}
@Override
public void run() {
if (null != mdcMap) {
MDC.setContextMap(mdcMap);
}
final String paramLog = LogConst.BUSSINESS_NAME + LogConst.EXECUTOR;
logger.info("[{}启动]", paramLog);
try {
runtimeService.groundWindData();
} catch (Exception e) {
logger.error("[{}异常]{}", new Object[]{paramLog, e});
maintainAlarmSender.sendMail(MaintainAlarmSender.DEFAULT_MAIL_SUB, paramLog + "异常:" + e);
}
logger.info("[{}结束]", paramLog);
}
}
线程池都有哪几种工作队列
1、ArrayBlockingQueue
是一个基于数组结构的有界阻塞队列，此队列按 FIFO（先进先出）原则对元素进行排序。
2、LinkedBlockingQueue
一个基于链表结构的阻塞队列，此队列按FIFO （先进先出） 排序元素，吞吐量通常要高于ArrayBlockingQueue。静态工厂方法Executors.newFixedThreadPool()使用了这个队列
3、SynchronousQueue
一个不存储元素的阻塞队列。每个插入操作必须等到另一个线程调用移除操作，否则插入操作一直处于阻塞状态，吞吐量通常要高于LinkedBlockingQueue，静态工厂方法Executors.newCachedThreadPool使用了这个队列。
4、PriorityBlockingQueue
一个具有优先级的无限阻塞队列。
线程池中的几种重要的参数及流程说明
public ThreadPoolExecutor(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
BlockingQueue<Runnable> workQueue,
ThreadFactory threadFactory,
RejectedExecutionHandler handler) {
if (corePoolSize < 0 ||
maximumPoolSize <= 0 ||
maximumPoolSize < corePoolSize ||
keepAliveTime < 0)
throw new IllegalArgumentException();
if (workQueue == null || threadFactory == null || handler == null)
throw new NullPointerException();
this.corePoolSize = corePoolSize;
this.maximumPoolSize = maximumPoolSize;
this.workQueue = workQueue;
this.keepAliveTime = unit.toNanos(keepAliveTime);
this.threadFactory = threadFactory;
this.handler = handler;
}
corePoolSize：核心池的大小，这个参数跟后面讲述的线程池的实现原理有非常大的关系。在创建了线程池后，默认情况下，线程池中并没有任何线程，而是等待有任务到来才创建线程去执行任务，除非调用了prestartAllCoreThreads()或者prestartCoreThread()方法，从这2个方法的名字就可以看出，是预创建线程的意思，即在没有任务到来之前就创建corePoolSize个线程或者一个线程。默认情况下，在创建了线程池后，线程池中的线程数为0，当有任务来之后，就会创建一个线程去执行任务，当线程池中的线程数目达到corePoolSize后，就会把到达的任务放到缓存队列当中；
maximumPoolSize：线程池最大线程数，这个参数也是一个非常重要的参数，它表示在线程池中最多能创建多少个线程；
keepAliveTime：表示线程没有任务执行时最多保持多久时间会终止。默认情况下，只有当线程池中的线程数大于corePoolSize时，keepAliveTime才会起作用，直到线程池中的线程数不大于corePoolSize，即当线程池中的线程数大于corePoolSize时，如果一个线程空闲的时间达到keepAliveTime，则会终止，直到线程池中的线程数不超过corePoolSize。但是如果调用了allowCoreThreadTimeOut(boolean)方法，在线程池中的线程数不大于corePoolSize时，keepAliveTime参数也会起作用，直到线程池中的线程数为0；
unit：参数keepAliveTime的时间单位，有7种取值，在TimeUnit类中有7种静态属性：
TimeUnit.DAYS; //天
TimeUnit.HOURS; //小时
TimeUnit.MINUTES; //分钟
TimeUnit.SECONDS; //秒
TimeUnit.MILLISECONDS; //毫秒
TimeUnit.MICROSECONDS; //微妙
TimeUnit.NANOSECONDS; //纳秒
workQueue：一个阻塞队列，用来存储等待执行的任务，这个参数的选择也很重要，会对线程池的运行过程产生重大影响，一般来说，这里的阻塞队列有以下几种选择：
ArrayBlockingQueue
LinkedBlockingQueue
SynchronousQueue
PriorityBlockingQueue
ArrayBlockingQueue和PriorityBlockingQueue使用较少，一般使用LinkedBlockingQueue和SynchronousQueue。线程池的排队策略与BlockingQueue有关。
threadFactory：用于设置创建线程的工厂，可以通过线程工厂给每个创建出来的线程做些更有意义的事情，比如设置daemon和优先级等等
handler：表示当拒绝处理任务时的策略，有以下四种取值：
1、AbortPolicy：直接抛出异常。
2、CallerRunsPolicy：只用调用者所在线程来运行任务。
3、DiscardOldestPolicy：丢弃队列里最近的一个任务，并执行当前任务。
4、DiscardPolicy：不处理，丢弃掉。
5、也可以根据应用场景需要来实现RejectedExecutionHandler接口自定义策略。如记录日志或持久化不能处理的任务。
/**
* A handler for rejected tasks that runs the rejected task
* directly in the calling thread of the {@code execute} method,
* unless the executor has been shut down, in which case the task
* is discarded.
*/
public static class CallerRunsPolicy implements RejectedExecutionHandler {
/**
* Creates a {@code CallerRunsPolicy}.
*/
public CallerRunsPolicy() { }
/**
* Executes task r in the caller's thread, unless the executor
* has been shut down, in which case the task is discarded.
*
* @param r the runnable task requested to be executed
* @param e the executor attempting to execute this task
*/
public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
if (!e.isShutdown()) {
r.run();
}
}
}
/**
* A handler for rejected tasks that throws a
* {@code RejectedExecutionException}.
*/
public static class AbortPolicy implements RejectedExecutionHandler {
/**
* Creates an {@code AbortPolicy}.
*/
public AbortPolicy() { }
/**
* Always throws RejectedExecutionException.
*
* @param r the runnable task requested to be executed
* @param e the executor attempting to execute this task
* @throws RejectedExecutionException always
*/
public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
throw new RejectedExecutionException("Task " + r.toString() +
" rejected from " +
e.toString());
}
}
/**
* A handler for rejected tasks that silently discards the
* rejected task.
*/
public static class DiscardPolicy implements RejectedExecutionHandler {
/**
* Creates a {@code DiscardPolicy}.
*/
public DiscardPolicy() { }
/**
* Does nothing, which has the effect of discarding task r.
*
* @param r the runnable task requested to be executed
* @param e the executor attempting to execute this task
*/
public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
}
}
/**
* A handler for rejected tasks that discards the oldest unhandled
* request and then retries {@code execute}, unless the executor
* is shut down, in which case the task is discarded.
*/
public static class DiscardOldestPolicy implements RejectedExecutionHandler {
/**
* Creates a {@code DiscardOldestPolicy} for the given executor.
*/
public DiscardOldestPolicy() { }
/**
* Obtains and ignores the next task that the executor
* would otherwise execute, if one is immediately available,
* and then retries execution of task r, unless the executor
* is shut down, in which case task r is instead discarded.
*
* @param r the runnable task requested to be executed
* @param e the executor attempting to execute this task
*/
public void rejectedExecution(Runnable r, ThreadPoolExecutor e) {
if (!e.isShutdown()) {
e.getQueue().poll();
e.execute(r);
}
}
}
ThreadPoolExecutor 源码理解 https://www.cnblogs.com/dolphin0520/p/3932921.html
public static void test(int size) {
ThreadPoolExecutor poolExecutor = new ThreadPoolExecutor(5, 20, 2, TimeUnit.SECONDS, new LinkedBlockingQueue<>(5));
for (int i = 0; i < size; i++) {
poolExecutor.execute(new DemoTask(i));
Console.log("poolSize:" + poolExecutor.getPoolSize());
Console.log("corePoolSize:" + poolExecutor.getCorePoolSize());
Console.log("maximumPoolSize:" + poolExecutor.getMaximumPoolSize());
Console.log("queue:" + poolExecutor.getQueue().size());
Console.log("completedTaskCount:" + poolExecutor.getCompletedTaskCount());
Console.log("largestPoolSize:" + poolExecutor.getLargestPoolSize());
Console.log("keepAliveTime:" + poolExecutor.getKeepAliveTime(TimeUnit.SECONDS));
}
poolExecutor.shutdown();
}
class DemoTask implements Runnable {
private int taskNum;
public DemoTask(int taskNum) {
this.taskNum = taskNum;
}
@Override
public void run() {
Console.log(StringUtils.center("正在执行" + taskNum, 20, "="));
try {
Thread.sleep(2000);
} catch (InterruptedException e) {
e.printStackTrace();
}
Console.log(StringUtils.center("执行完毕" + taskNum, 20, "="));
}
}
=======正在执行0========
poolSize:1
corePoolSize:5
maximumPoolSize:20
queue:0
completedTaskCount:0
largestPoolSize:1
keepAliveTime:2
poolSize:2
corePoolSize:5
maximumPoolSize:20
queue:0
completedTaskCount:0
=======正在执行1========
largestPoolSize:2
keepAliveTime:2
poolSize:3
corePoolSize:5
maximumPoolSize:20
=======正在执行2========
queue:0
completedTaskCount:0
largestPoolSize:3
keepAliveTime:2
poolSize:4
corePoolSize:5
maximumPoolSize:20
queue:0
=======正在执行3========
completedTaskCount:0
largestPoolSize:4
keepAliveTime:2
poolSize:5
corePoolSize:5
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maximumPoolSize:20
queue:0
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:5
corePoolSize:5
maximumPoolSize:20
queue:1
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:5
corePoolSize:5
maximumPoolSize:20
queue:2
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:5
corePoolSize:5
maximumPoolSize:20
queue:3
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:5
corePoolSize:5
maximumPoolSize:20
queue:4
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:5
corePoolSize:5
maximumPoolSize:20
queue:5
completedTaskCount:0
largestPoolSize:5
keepAliveTime:2
poolSize:6
corePoolSize:5
maximumPoolSize:20
queue:5
completedTaskCount:0
largestPoolSize:6
keepAliveTime:2
poolSize:7
corePoolSize:5
maximumPoolSize:20
queue:5
completedTaskCount:0
largestPoolSize:7
keepAliveTime:2
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poolSize:8
corePoolSize:5
maximumPoolSize:20
queue:5
completedTaskCount:0
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largestPoolSize:8
keepAliveTime:2
poolSize:9
corePoolSize:5
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maximumPoolSize:20
queue:5
completedTaskCount:0
largestPoolSize:9
keepAliveTime:2
poolSize:10
corePoolSize:5
maximumPoolSize:20
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queue:5
completedTaskCount:0
largestPoolSize:10
keepAliveTime:2
poolSize:11
corePoolSize:5
maximumPoolSize:20
queue:5
=======正在执行15=======
completedTaskCount:0
largestPoolSize:11
keepAliveTime:2
poolSize:12
corePoolSize:5
maximumPoolSize:20
queue:5
completedTaskCount:0
=======正在执行16=======
largestPoolSize:12
keepAliveTime:2
poolSize:13
corePoolSize:5
maximumPoolSize:20
=======正在执行17=======
queue:5
completedTaskCount:0
largestPoolSize:13
keepAliveTime:2
poolSize:14
corePoolSize:5
maximumPoolSize:20
queue:5
=======正在执行18=======
completedTaskCount:0
largestPoolSize:14
keepAliveTime:2
poolSize:15
corePoolSize:5
maximumPoolSize:20
=======正在执行19=======
queue:5
completedTaskCount:0
largestPoolSize:15
keepAliveTime:2
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怎么理解无界队列和有界队列
有界队列
1.初始的poolSize < corePoolSize，提交的runnable任务，会直接做为new一个Thread的参数，立马执行 。
2.当提交的任务数超过了corePoolSize，会将当前的runable提交到一个block queue中。
3.有界队列满了之后，如果poolSize < maximumPoolsize时，会尝试new 一个Thread的进行救急处理，立马执行对应的runnable任务。
4.如果3中也无法处理了，就会走到第四步执行reject操作。
无界队列
与有界队列相比，除非系统资源耗尽，否则无界的任务队列不存在任务入队失败的情况。当有新的任务到来，系统的线程数小于corePoolSize时，则新建线程执行任务。当达到corePoolSize后，就不会继续增加，若后续仍有新的任务加入，而没有空闲的线程资源，则任务直接进入队列等待。若任务创建和处理的速度差异很大，无界队列会保持快速增长，直到耗尽系统内存。
当线程池的任务缓存队列已满并且线程池中的线程数目达到maximumPoolSize，如果还有任务到来就会采取任务拒绝策略。
