协程可以借助 Dispatchers.Default 这样的多线程调度器并行执行，因此也会遇到通常的并行编程问题。其中最主要的问题，是如何同步访问共享可变状态。有些解决方法与普通多线程程序相同，有些则是协程特有的方法。
问题：共享计数器
先启动 100 个协程，让每个协程把同一个动作执行 1000 次；同时记录完成动作所花费的时间，以便比较：
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}最简单的动作是递增一个共享可变变量。下面把这些动作放到多线程的 Dispatchers.Default 上执行：
import kotlinx.coroutines.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
var counter = 0
fun main() = runBlocking {
withContext(Dispatchers.Default) {
massiveRun {
counter++
}
}
println("Counter = $counter")
}
//sampleEnd最后会输出什么？最终计数很可能不是 Counter = 100000，因为 100 个协程从多个线程同时递增 counter，没有任何同步措施。counter++ 涉及读取、计算和写回，动作之间可能相互覆盖。
volatile 不能解决复合操作的原子性
一个常见误解是，只要把变量声明为 volatile 就能解决并发问题。试试看：
import kotlinx.coroutines.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
@Volatile // in Kotlin `volatile` is an annotation
var counter = 0
fun main() = runBlocking {
withContext(Dispatchers.Default) {
massiveRun {
counter++
}
}
println("Counter = $counter")
}
//sampleEnd原文指出，这段代码可能更慢，但仍然不能保证最终计数为 100000。volatile 对对应变量的单次读写提供可线性化保证，却不能使递增这样的更大复合动作整体成为原子操作。这里必须解决的是整个递增过程的同步。
使用线程安全的数据结构
一种同时适用于普通线程和协程的通用办法，是使用线程安全的数据结构。它为共享状态所需的操作提供必要的同步或原子性。简单计数器可以使用 AtomicInteger，其 incrementAndGet 是原子递增操作：
import kotlinx.coroutines.*
import java.util.concurrent.atomic.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
val counter = AtomicInteger()
fun main() = runBlocking {
withContext(Dispatchers.Default) {
massiveRun {
counter.incrementAndGet()
}
}
println("Counter = $counter")
}
//sampleEnd对于本文的简单计数问题，原文将原子计数器列为最快的方法。类似方法适用于普通计数器、集合、队列及其已有的基本线程安全操作。不过，复杂状态或没有现成线程安全实现的复杂操作，未必能直接套用这个方法。
细粒度线程约束
线程约束指把某项共享状态的全部访问限定到一个线程。UI 应用经常采用这种方法，把界面状态限制在同一个事件分发线程或应用线程中。协程可通过单线程上下文实现它。
import kotlinx.coroutines.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
val counterContext = newSingleThreadContext("CounterContext")
var counter = 0
fun main() = runBlocking {
withContext(Dispatchers.Default) {
massiveRun {
// confine each increment to a single-threaded context
withContext(counterContext) {
counter++
}
}
}
println("Counter = $counter")
}
//sampleEnd这段代码采用细粒度线程约束，每一次递增都通过 withContext(counterContext)，从多线程的 Dispatchers.Default 切换到单线程上下文。原文指出，这种频繁切换使示例运行很慢。
粗粒度线程约束
实践中通常把一大段更新状态的业务逻辑放到同一个线程上，而不是每个小操作都切换线程。下面的示例从一开始就把全部协程放到单线程上下文中：
import kotlinx.coroutines.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
val counterContext = newSingleThreadContext("CounterContext")
var counter = 0
fun main() = runBlocking {
// confine everything to a single-threaded context
withContext(counterContext) {
massiveRun {
counter++
}
}
println("Counter = $counter")
}
//sampleEnd按原文的示例比较，这种方法明显更快，也会得到正确计数。原因在于共享计数器的访问始终限定在同一个线程，且避免了每次递增的跨上下文切换。
专用线程的生命周期：以上两段代码保留原始示例。当前 newSingleThreadContext 创建的线程资源需要按所用协程版本的 API 要求显式关闭，并可能需要相应 opt-in。生产代码不能长期泄漏该上下文；处理时应保证协程完成后再释放资源。此处未改写原文代码，也未编译或运行。
使用互斥保护共享状态
互斥方案让共享状态的修改发生在不会并发执行的临界区内。阻塞式程序通常使用 synchronized 或 ReentrantLock。协程对应的工具是 Mutex，它通过 lock 和 unlock 界定临界区。关键区别是 Mutex.lock() 可以挂起协程，等待锁时不必阻塞执行线程。
withLock 扩展函数封装了 mutex.lock(); try { ... } finally { mutex.unlock() } 这种常用结构：
import kotlinx.coroutines.*
import kotlinx.coroutines.sync.*
import kotlin.system.*
suspend fun massiveRun(action: suspend () -> Unit) {
val n = 100 // number of coroutines to launch
val k = 1000 // times an action is repeated by each coroutine
val time = measureTimeMillis {
coroutineScope { // scope for coroutines
repeat(n) {
launch {
repeat(k) { action() }
}
}
}
}
println("Completed ${n * k} actions in $time ms")
}
//sampleStart
val mutex = Mutex()
var counter = 0
fun main() = runBlocking {
withContext(Dispatchers.Default) {
massiveRun {
// protect each increment with lock
mutex.withLock {
counter++
}
}
}
println("Counter = $counter")
}
//sampleEnd这里的锁粒度很细，因此有加锁开销。不过，如果必须周期性地更新某项共享状态，又没有一个自然的所属线程，互斥仍然是合适的选择。
比较的边界：本文的正确性分析针对这个计数示例。原文的快慢结论不能推广成所有程序中的性能排名；单次 measureTimeMillis 输出也不是严谨的基准测试。本文未生成本机计数或耗时结果。
来源：JetBrains 与 Kotlin 协程文档贡献者，Shared mutable state and concurrency，页面日期 2024 年 9 月 27 日；实际内容源是 kotlinx.coroutines 文档仓库，正文及示例采用 Apache License 2.0。本文进行了中文翻译与排版，并补充线程资源和性能比较的边界说明；七个代码块保留原文。Copyright 2000-2020 JetBrains s.r.o. and Kotlin Programming Language contributors.
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