📅  最后修改于: 2023-12-03 15:39:07.395000             🧑  作者: Mango
FPS (Frames Per Second),即每秒帧数,是衡量电脑游戏、视频等每秒显示多少帧的一种性能指标。在程序员中,也有完整形式的FPS。
在程序员中,完整形式的FPS是指“Fiber Per Second”,即每秒纤程数。在多线程、异步编程中,一个独立子任务的纤程数,也可以称为该任务的一秒内完整形式的FPS。
完整形式的FPS作为衡量异步编程的性能指标,对于项目的性能优化、代码的优化有着重要作用。一般情况下,完整形式的FPS数值越高,表示该任务的性能越好,可以处理更多的并发请求。
基于不同语言,完整形式的FPS的实现可能有所不同,以下是一些常见的实现方式:
在Python中,可以使用asyncio库来实现异步编程,计算当前每秒纤程数的代码片段如下:
import asyncio
async def task():
await asyncio.sleep(0) # 模拟任务执行时间
return
async def main():
t1 = asyncio.create_task(task())
t2 = asyncio.create_task(task())
t3 = asyncio.create_task(task())
t4 = asyncio.create_task(task())
tasks = [t1, t2, t3, t4] # 假设有4个独立子任务
while True:
before = asyncio.all_tasks()
await asyncio.sleep(1)
after = asyncio.all_tasks()
fps = len(after) - len(before) # 计算一秒内新增的纤程数
print(f'FPS: {fps}')
asyncio.run(main())
在Java中,可以使用Spring框架的Fiber来实现异步编程,计算当前每秒纤程数的代码片段如下:
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.atomic.AtomicLong;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;
import org.springframework.stereotype.Service;
import com.alibaba.spring.util.CustomThreadPoolTaskExecutor;
import com.alibaba.spring.util.LogUtil;
import com.alibaba.spring.util.StatisticCountUtil;
import com.alibaba.spring.util.ThreadPoolStatisticCountUtil;
import com.alibaba.spring.util.ThreadPoolTaskExecutorUtil;
import com.alibaba.spring.util.TraceThreadLocalUtil;
import com.alibaba.spring.util.spring.FastBeanFactoryPostProcessor;
import reactor.core.publisher.Flux;
import reactor.core.scheduler.Schedulers;
@Service
public class FPS {
ExecutorService executor;
AtomicLong atomicLong = new AtomicLong();
public FPS() {
executor = Executors.newFixedThreadPool(10);
}
public void run1() {
System.out.println("FPS starting...");
static int nThread = 10;
ThreadPoolTaskExecutor executor = ThreadPoolTaskExecutorUtil
.getFixedThreadPoolTaskExecutor("111", nThread, nThread, 1);
StatisticCountUtil countUtil = new ThreadPoolStatisticCountUtil(executor.getThreadPoolExecutor());
LogUtil.log("test biz threadpool executor tasks");
CountDownLatch latch = new CountDownLatch(nThread);
for (int index = 1; index < nThread + 1; index++) {
executor.execute(new Runnable() {
@Override
public void run() {
Thread.currentThread().setName(TraceThreadLocalUtil.TRACE_ID + "-" + index);
try {
System.out.println("currentThread: " + Thread.currentThread());
Flux.range(0, Integer.MAX_VALUE).publishOn(Schedulers.single()).doOnNext(i -> {
StatisticCountUtil.increment(countUtil.getSubmittedCount());
try {
atomicLong.incrementAndGet();
Thread.sleep(10);
} catch (Exception e) {
}
}).subscribe();
} catch (Exception e) {
} finally {
latch.countDown();
}
}
});
}
try {
latch.await();
} catch (InterruptedException e) {
e.printStackTrace();
}
System.out.println("FPS: " + atomicLong.get());
}
}
在Node.js中,可以使用async库来实现异步编程,计算当前每秒纤程数的代码片段如下:
const async = require('async');
const tasks = [
cb => {
setTimeout(() => {
cb();
}, 500); // 模拟任务执行时间
},
cb => {
setTimeout(() => {
cb();
}, 800); // 模拟任务执行时间
},
cb => {
setTimeout(() => {
cb();
}, 700); // 模拟任务执行时间
}
];
let fps = 0;
setInterval(() => {
const before = async.queue().length();
async.parallel(tasks, () => {
const after = async.queue().length();
fps = after - before;
console.log(`FPS: ${fps}`);
});
}, 1000);
完整形式的FPS是异步编程中的一种性能指标,计算每秒纤程数的方法因语言而异。程序员可以根据完整形式的FPS数值,对项目的性能进行评估和优化。