什么是线程池
线程池 我们都知道 是一种池化技术，主要解决了线程创建都额外资源消耗，线程监控 等问题 当然 线程池不适用于以下几种情况
- 依赖性任务
- 对响应时间敏感的任务
- 使用了ThreadLocal 且不remove的任务
线程池核心参数
/**
* Creates a new {@code ThreadPoolExecutor} with the given initial
* parameters and default rejected execution handler.
*
* @param corePoolSize the number of threads to keep in the pool, even
* if they are idle, unless {@code allowCoreThreadTimeOut} is set
* @param maximumPoolSize the maximum number of threads to allow in the
* pool
* @param keepAliveTime when the number of threads is greater than
* the core, this is the maximum time that excess idle threads
* will wait for new tasks before terminating.
* @param unit the time unit for the {@code keepAliveTime} argument
* @param workQueue the queue to use for holding tasks before they are
* executed. This queue will hold only the {@code Runnable}
* tasks submitted by the {@code execute} method.
* @param threadFactory the factory to use when the executor
* creates a new thread
* @throws IllegalArgumentException if one of the following holds:
* {@code corePoolSize < 0}
* {@code keepAliveTime < 0}
* {@code maximumPoolSize <= 0}
* {@code maximumPoolSize < corePoolSize}
* @throws NullPointerException if {@code workQueue}
* or {@code threadFactory} is null
*/
public ThreadPoolExecutor(int corePoolSize,
int maximumPoolSize,
long keepAliveTime,
TimeUnit unit,
BlockingQueue workQueue,
ThreadFactory threadFactory) {
this(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue,
threadFactory, defaultHandler);
}
翻译
- corePoolSize 正常情况下 线程池的工作线程数
- maximumPoolSize 等待队列已经排满的情况下 线程池的最大工作线程数
- keepAliveTime 如果工作线程数 大于corePoolSize 情况下 空闲线程的回收等待时间
- unit keepAliveTime的单位
- workQueue 线程池的线程为corePoolSize的情况下 线程的等待队列
- threadFactory 线程池 创建工作线程的线程工厂
常用线程池创建手段
我们常用的线程池框架 为Executor 创建的线程池方式大致有以下几种
newCachedThreadPool
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。
newCachedThreadPool
这种方式会导致max工作线程数是Integer.maxValue 极端情况下 可能会导致服务器奔溃。
newFixedThreadPool
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。
newFixedThreadPool
这种方式创建的核心工作线程数和最大工作线程数是较为合理的
newScheduledThreadPool
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 newSingleThreadScheduledExecutor() {
return new DelegatedScheduledExecutorService
(new ScheduledThreadPoolExecutor(1));
}
public ScheduledThreadPoolExecutor(int corePoolSize) {
super(corePoolSize, Integer.MAX_VALUE, 0, NANOSECONDS,
new DelayedWorkQueue());
}
很明显 newScheduledThreadPool 这种方式 虽然设置了一个延时队列 ，但是极端情况下 仍然会让服务奔溃
newSingleThreadExecutor
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()));
}
综上所述 我们最好是自己设置参数 来生成自己的线程池
private static ExecutorService taskExecutor = new ThreadPoolExecutor(
MAX_THREAD_COUNT, MAX_THREAD_COUNT, 60, TimeUnit.SECONDS, taskQueue);
重写一个属于我们的线程池
如果 我们希望能自定义一些aop的方法。那么 我们可以通过重写线程池来实现 例子如下 其中 before
package pool;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.RejectedExecutionHandler;
import java.util.concurrent.ThreadFactory;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
/**
* @description:
* @author: brave.chen
* @create: 2020-04-05 17:51
**/
public class MyDesignThreadPool extends ThreadPoolExecutor {
private final ThreadLocal startTime = new ThreadLocal<>();
private final Logger logger = LoggerFactory.getLogger(MyDesignThreadPool.class);
private final AtomicInteger numTasks = new AtomicInteger();
private final AtomicLong totalTime = new AtomicLong();
@Override
protected void beforeExecute(Thread t, Runnable r) {
logger.info("Thread " + t + " : start " + r);
startTime.set(System.nanoTime());
super.beforeExecute(t, r);
}
@Override
protected void afterExecute(Runnable r, Throwable t) {
try {
long endTime = System.nanoTime();
long taskTime = endTime - startTime.get();
numTasks.incrementAndGet();
totalTime.addAndGet(taskTime);
logger.info("Thread " + t + " : end " + endTime + ", time = " + taskTime);
} finally {
super.afterExecute(r, t);
}
}
@Override
protected void terminated() {
try {
logger.info("Terminated : avg time = " + totalTime.get()/numTasks.get() );
}finally {
super.terminated();
}
}
public MyDesignThreadPool(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit, BlockingQueue workQueue) {
super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue);
}
public MyDesignThreadPool(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit, BlockingQueue workQueue, ThreadFactory threadFactory) {
super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue, threadFactory);
}
public MyDesignThreadPool(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit, BlockingQueue workQueue, RejectedExecutionHandler handler) {
super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue, handler);
}
public MyDesignThreadPool(int corePoolSize, int maximumPoolSize, long keepAliveTime, TimeUnit unit, BlockingQueue workQueue, ThreadFactory threadFactory, RejectedExecutionHandler handler) {
super(corePoolSize, maximumPoolSize, keepAliveTime, unit, workQueue, threadFactory, handler);
}
}