前言
日常开发中,我们时常会听到什么IO密集型、CPU密集型任务...
那么这里提一个问题:大家知道什么样的任务或者代码会被认定为IO/CPU密集?又是用什么样的标准来认定IO/CPU密集?
如果你没有明确的答案,那么就随着这篇文章一起来聊一聊吧。
正文
最近团队里有基础技术的同学对项目中的线程池进行了重新设计,调整了IO线程池等线程池的优化。因此借助这个机会也就了解了一波开篇的那些问题。
一、宏观概念区分
这一部分经验丰富的同学都很熟悉。比如:
1.1、IO密集型任务
一般来说:文件读写、DB读写、网络请求等
1.2、CPU密集型任务
一般来说:计算型代码、Bitmap转换、Gson转换等
二、用代码区分
上一part都是咱们凭借经验划分的,这一part咱们就来用正经的指标来划分任务。
先看有哪些数据指标可以用来进行评估(以下方法以系统日志为准,加之开发经验为辅):
1. wallTime
任务的整体运行时长(包括了running + runnable + sleep等所有时长)。获取方案:
<code>run() { long start = System.currentTimeMillis(); // 业务代码 long wallTime = System.currentTimeMillis() - start; }/<code>
2. cpuTime
cputime是任务真正在cpu上跑的时长,即为running时长
获取方案1:
<code>run() { long start = SystemClock.currentThreadTimeMillis(); // 业务代码 long cpuTime = SystemClock.currentThreadTimeMillis() - start; }/<code>
获取方案2:
<code>/proc/pid/task/tid/schedse.sum_exec_runtime CPU上的运行时长/<code>
3. iowait time/count
指线程的iowait耗时。获取方案:
<code>/proc/pid/task/tid/sched se.statistics.iowait_sum IO等待累计时间 se.statistics.iowait_count IO等待累计次数/<code>
具体日志位置同上
4. runnable time
线程runnabel被调度的时长。获取方案:
<code>/proc/pid/task/tid/sched se.statistics.wait_sum 就绪队列等待累计时间/<code>
具体日志位置同上
5. sleep time
线程阻塞时长(包括Interruptible-sleep和Uninterruptible-sleep和iowait的时长)。获取方案:
<code>/proc/pid/task/tid/sched se.statistics.sum_sleep_runtime 阻塞累计时间/<code>
具体日志位置同上
6. utime/stime
utime是线程在用户态运行时长,stime是线程在内核态运行时长。获取方案:
<code>/proc/pid/task/tid/stat 第14个字段是utime,第15个字段是stime/<code>
7. rchar/wchar
wchar是write和pwrite函数写入的byte数。获取方案:
<code>/proc/pid/task/tid/io rchar: ...wchar: .../<code>
(没找到合适的日志,暂不讨论此情况)基于读写char数,我们可以将IO细分成读IO密集型和写IO密集型。
8. page_fault
缺页中断次数,分为major/minor fault。获取方案:
<code>/proc/pid/task/tid/stat 第10个字段是minor_fault,第12个字段是major_fault/<code>
9. ctx_switches
线程在用户/内核态的切换次数,分为voluntary和involuntary两种切换。获取方案:
<code>/proc/pid/task/tid/sched nr_switches 总共切换次数 nr_voluntary_switches 自愿切换次数 nr_involuntary_switches 非自愿切换次数/<code>
日志位置同上
10. percpuload
平均每个cpu的执行时长。获取方案:
<code>/proc/pid/task/tid/sched avg_per_cpu/<code>
日志位置同上
有了上述这些指标,我们就可以开始我们的任务确定了。
以下内容,大家可以自行测试加深印象。
2.1、IO密集型任务
比如这段代码:
<code>val br = BufferedReader(FileReader("xxxx"), 1024) try { while (br.readLine() != null) { } } finally { if (br != null) { br.close() } }/<code>
基于上述部分3. iowait time/count,我们可以在对应的日志文件中看出这段代码有明显的iowait 。
2.2、CPU密集型任务
比如这段代码:
<code>var n = 0.0 for (i in 0..9999999) { n = Math.cos(i.toDouble() )}/<code>
基于上述部分6. utime/stime的内容,看一看出这段代码utime会占比非常高,且几乎没有stime,此外没有io相关的耗时。
三、这玩意有啥用?
说白了,我们一切的优化手段都是为了服务于业务。对于业务开发来说:
为了不占用主线程 -> 所以启一个新线程 -> 频繁的new线程又会带来大量的开销 -> 所以使用线程池进行复用 -> 而不合理的线程池设计又会带来线程使用低效,甚至新加入的任务只能等待 -> 优化线程池
举个最简单的例子:线程池中放了最大允许俩个线程并行,那么假设运行中的俩个都是长IO的任务。那么新来的任务就只能等,哪怕它并不是特别耗时...
因此这玩意有啥用,还不是为更好的线程池设计做指导思想,更好的提升线程运行效率,降低业务上不必要的等待。
这里提供一些可供参考的工具方法和线程池设计:
3.1、判断任务类型
这里贴一些核心的思路,毕竟全部方案数据公司的代码,我也不方便全部贴出来:
<code>class TaskInfo { var cpuTimeStamp = 0.0 var timeStamp = 0.0 var iowaitTime = 0.0 var sleepTime = 0.0 var runnableTime = 0.0 var totalSwitches = 0.0 var voluntarySwitches = 0.0}/<code>
<code>object TaskInfoUtils { private const val SUM_SLEEP_RUNTIME = "se.statistics.sum_sleep_runtime" private const val WAIT_SUM = "se.statistics.wait_sum" private const val IOWAIT_SUM = "se.statistics.iowait_sum" private const val NR_SWITCHES = "nr_switches " private const val NR_VOLUNTARY_SWITCHES = "nr_voluntary_switches" private var schedPath = ThreadLocal() fun buildCurTaskInfo(): TaskInfo { val threadInfo = TaskInfo() threadInfo.timeStamp = System.currentTimeMillis().toDouble() threadInfo.cpuTimeStamp = SystemClock.currentThreadTimeMillis().toDouble() if (schedPath.get() == null) { schedPath.set("/proc/${android.os.Process.myPid()}/task/${getTid()}/sched") } BufferedReader(FileReader(schedPath.get()), READ_BUFFER_SIZE).use { br -> br.readLines().forEach { line -> when { line.startsWith(SUM_SLEEP_RUNTIME) -> threadInfo.sleepTime = line.split(":")[1].toDouble() line.startsWith(WAIT_SUM) -> threadInfo.runnableTime = line.split(":")[1].toDouble() line.startsWith(IOWAIT_SUM) -> threadInfo.iowaitTime = line.split(":")[1].toDouble() line.startsWith(NR_SWITCHES) -> threadInfo.totalSwitches = line.split(":")[1].toDouble() line.startsWith(NR_VOLUNTARY_SWITCHES) -> threadInfo.voluntarySwitches = line.split(":")[1].toDouble() } } } return threadInfo }}/<code>
<code>object TaskBoundJudge { private const val CPU_CPUTIME_INTERVAL = 0.8 private const val CPU_SWITCHES_INTERVAL = 0.1 private const val CPU_IOWAIT_INTERVAL = 0.01 private const val CPU_SLEEP_INTERVAL = 0.02 private const val CPU_CPUTIME_WEIGHTS = 0.1 private const val CPU_SWITCHES_WEIGHTS = 0.35 private const val CPU_IOWAIT_WEIGHTS = 0.15 private const val CPU_SLEEP_WEIGHTS = 0.40 private const val IO_CPUTIME_INTERVAL = 0.5 private const val IO_SWITCHES_INTERVAL = 0.4 private const val IO_IOWAIT_INTERVAL = 0.1 private const val IO_SLEEP_INTERVAL = 0.15 private const val IO_CPUTIME_WEIGHTS = 0.1 private const val IO_SWITCHES_WEIGHTS = 0.35 private const val IO_IOWAIT_WEIGHTS = 0.35 private const val IO_SLEEP_WEIGHTS = 0.2 fun isCpuTask(start: TaskInfo?, end: TaskInfo?): Boolean { if (start == null || end == null) { return false } val wallTime = end.timeStamp - start.timeStamp val cpuTime = end.cpuTimeStamp - start.cpuTimeStamp val runnableTime = end.runnableTime - start.runnableTime val totalSwitches = end.totalSwitches - start.totalSwitches val voluntarySwitches = end.voluntarySwitches - start.voluntarySwitches val iowaitTime = end.iowaitTime - start.iowaitTime val sleepTime = end.sleepTime - start.sleepTime var result = 0.0 if (cpuTime / (wallTime - runnableTime) > CPU_CPUTIME_INTERVAL) { result += CPU_CPUTIME_WEIGHTS } if (voluntarySwitches / totalSwitches < CPU_SWITCHES_INTERVAL) { result += CPU_SWITCHES_WEIGHTS } if (iowaitTime / sleepTime < CPU_IOWAIT_INTERVAL) { result += CPU_IOWAIT_WEIGHTS } if (sleepTime / (wallTime - runnableTime) < CPU_SLEEP_INTERVAL) { result += CPU_SLEEP_WEIGHTS } return result > 0.5 } fun isIOTask(start: TaskInfo?, end: TaskInfo?): Boolean { if (start == null || end == null) { return false } val wallTime = end.timeStamp - start.timeStamp val cpuTime = end.cpuTimeStamp - start.cpuTimeStamp val runnableTime = end.runnableTime - start.runnableTime val totalSwitches = end.totalSwitches - start.totalSwitches val voluntarySwitches = end.voluntarySwitches - start.voluntarySwitches val iowaitTime = end.iowaitTime - start.iowaitTime val sleepTime = end.sleepTime - start.sleepTime var result = 0.0 if (cpuTime / (wallTime - runnableTime) < IO_CPUTIME_INTERVAL) { result += IO_CPUTIME_WEIGHTS } if (voluntarySwitches / totalSwitches > IO_SWITCHES_INTERVAL) { result += IO_SWITCHES_WEIGHTS } if (iowaitTime / sleepTime > IO_IOWAIT_INTERVAL) { result += IO_IOWAIT_WEIGHTS } if (sleepTime / (wallTime - runnableTime) > IO_SLEEP_INTERVAL) { result += IO_SLEEP_WEIGHTS } return result > 0.5 } }/<code>
当我们想对某个方法进行计算是CPU还是IO。可以在这个方法的开始、结束调用 TaskInfoUtils.buildCurTaskInfo();然后调用 TaskBoundJudge.isCpuTask(start,end), TaskBoundJudge.isIOTask(start,end)即可。
3.2、线程池
IO密集型参考线程池:
<code>public static final ExecutorService IO_EXECUTOR = new ThreadPoolExecutor( 2, 128, 15, TimeUnit.SECONDS, new SynchronousQueue<>(), new CustomThreadFactory("MDove-IO", CustomThreadPriority.NORMAL), AbortPolicy() // 根据业务情况,自行定义拒绝实现。比如上报监控平台 );/<code>
CPU密集型参考线程池:
<code>public static final int CPU_COUNT = Runtime.getRuntime().availableProcessors(); public static final int MAXIMUM_POOL_SIZE = CPU_COUNT * 2 + 1; private static final int CPU_CORE_POOL_SIZE = Math.max(Math.min(MAXIMUM_POOL_SIZE, 4), Math.min(CPU_COUNT + 1, 9)); public static final ExecutorService CPU_EXECUTOR = new ThreadPoolExecutor( CPU_CORE_POOL_SIZE, CPU_COUNT * 2 + 1, 30, TimeUnit.SECONDS, new LinkedBlockingQueue<>(256), new SSThreadFactory("MDove-CPU", CustomThreadPriority.NORMAL), AbortPolicy() // 根据业务情况,自行定义拒绝实现。比如上报监控平台 );/<code>
上述线程池中设计的额外代码:
<code>class CustomThreadFactory : ThreadFactory { var name: String private set private var priority = CustomThreadPriority.NORMAL constructor(name: String, priority: CustomThreadPriority) { this.name = name this.priority = priority } override fun newThread(r: Runnable): Thread { val name = name + "-" + sCount.incrementAndGet() return object : Thread(r, name) { override fun run() { if (priority == CustomThreadPriority.LOW) { Process.setThreadPriority(Process.THREAD_PRIORITY_BACKGROUND) } else if (priority == CustomThreadPriority.HIGH) { Process.setThreadPriority(Process.THREAD_PRIORITY_DISPLAY) } super.run() } } } companion object { private val sCount = AtomicInteger(0) } } enum class CustomThreadPriority { LOW, NORMAL, HIGH, IMMEDIATE }/<code>
尾声
OK,这篇文章到这里就结束了。希望这篇文章能给大家在线程的使用和线程池的设计上带来帮助。
最后,让我们一起加油吧,“打工人”!