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现在大部分项目都用kotlin了,协程也是绕不开的话题,今天我们来看看协程相关的问题
好了,废话不多说了,咱们继续来学习
#面试#android#kotlin#xi协程
源码定位(CoroutineScheduler.kt):
internal object DefaultIoScheduler : CoroutineDispatcher() {
private val default = UnlimitedIoScheduler.limitedParallelism(
SystemProp(IO_PARALLELISM_PROPERTY_NAME, 64.coerceAtLeast(AVAILABLE_PROCESSORS))
)
}
灾难现场:
优化方案:
// CPU密集型任务转用Default调度器
viewModelScope.launch(Dispatchers.Default) {
val data = parseLargeJson(response)
withContext(Dispatchers.Main) { updateUI(data) }
}
错误代码:
withContext(Dispatchers.IO) {
withContext(Dispatchers.Default) { heavyCalculation() } // 额外调度开销
}
源码解析(kotlinx-coroutines-core 1.6.4):
// 每次调度触发线程池任务提交
fun dispatch(context: CoroutineContext, block: Runnable) {
(context[ContinuationInterceptor] as CoroutineDispatcher)
.dispatch(context, block)
}
性能特征:
灾难案例:
val customDispatcher = Executors.newFixedThreadPool(8).asCoroutineDispatcher()
GlobalScope.launch(customDispatcher) {
processMessage() // 未关闭导致200+线程残留
}
内存特征:
根治方案:
val dispatcher = Executors.newFixedThreadPool(4).asCoroutineDispatcher()
try {
CoroutineScope(dispatcher).launch { /
*...*
/ }
} finally {
(dispatcher.executor as ExecutorService).shutdown()
}
错误实现:
fun onClick() {
runBlocking(Dispatchers.Main) { // 阻塞主线程
val result = withContext(Dispatchers.IO) { blockingCall() }
updateUI(result)
}
}
卡顿原理:
正确异步方案:
lifecycleScope.launch {
val result = withContext(Dispatchers.IO) { suspendCall() }
updateUI(result) // 自动切回主线程
}
class MonitorInterceptor : CoroutineContext.Element {
override val key = CoroutineName("Monitor")
override fun<T> interceptContinuation(continuation: Continuation<T>) {
Metrics.record("coroutine_start", Thread.currentThread().name)
continuation.resumeWith(...)
}
}
监控指标:
检测工具链:
StrictMode.setThreadPolicy(ThreadPolicy.Builder().detectResourceMismatches().penaltyDeath().build())
架构方案:
// 分层线程池配置
val cpuDispatcher = Dispatchers.Default.limitedParallelism(CPU_CORES)
val ioDispatcher = Dispatchers.IO.limitedParallelism(32)
val dbDispatcher = newSingleThreadContext("DBWriter")
// 动态扩容机制
object DynamicDispatcher {
privateval pool = ThreadPoolExecutor(
2, 32, 60L, TimeUnit.SECONDS,
SynchronousQueue(),
NamedThreadFactory("DynamicPool")
)
funadjustPool(queueSize: Int) {
if (queueSize > 10) pool.corePoolSize = (CPU_CORES × 1.5).toInt()
}
}
核心原理:
通过本文剖析,开发者应立即实施: