


This article will share with you an example of Swoole's high-concurrency aggregation request, and introduce how to make full use of the batch processing of the database to implement business functions more efficiently through aggregation of requests in high-concurrency scenarios. This example is only used as a starting point, hoping to stimulate more in-depth thinking.
Recommended related video courses: " Tens of millions of data concurrency solutions (theoretical and practical) "
Share some high-concurrency interview questions: 15 PHP interview questions about high concurrency (summary)
The background selected for this example is the concurrent order business. Under normal circumstances, creating an order on the backend is a insert
operation one by one. When concurrency is low, the database's insert
operation can indeed maintain good efficiency, but when the number of requests increases, the database frequently performs a single insert
This will make the overall efficiency of the order business lower (this article simply assumes that 1 order = 1insert
).
Through the above description, it is actually easy to think of the areas that need optimization. Analogy to the scene of taking an elevator in real life: an elevator is filled and then goes up, which can relieve the pressure of people flow as quickly as possible.
Let’s simply implement our idea with code:
<?php Swoole\Runtime::enableCoroutine($flags = SWOOLE_HOOK_ALL); // 最大等待次数 const MAX_TIMES = 10; // 按批处理时, 每一批的最大请求暂留数量 const MAX_REQUEST = 3; // 服务端最大超时时间, 避免客户端一直等待 const MAX_TIMEOUT = 5; Co\run(function () { // 请求传输的channel, 原因是不要在swoole的协程环境中, 使用多个协程修改同一个全局变量 // 如果是golang, 当然是可以不定义这里的$rqChannel // 只需要简单的将下面的$rqQueue和$times定义为全局变量即可达到一样的效果 // 但是最好的方式任然是是通过channel共享内存 $rqChannel = new Swoole\Coroutine\Channel(MAX_REQUEST); // 模拟创建订单 $createOrder = function () use ($rqChannel) { // 使用数组模拟请求暂留队列 $rqQueue = []; // 使用等待次数模拟tick效果 $times = MAX_TIMES; while (true) { $times--; // 必须带上timeout参数, 否则channel是阻塞的 $rq = $rqChannel->pop(1); // 保存1个正常的请求数据 if (!empty($rq)) { $rqQueue[] = $rq; } // 请求数量未达上限或者还有等待次数时, 提前进入下一次循环 if ($times > 0 && count($rqQueue) < MAX_REQUEST) { continue; } // 重置等待次数 $times = MAX_TIMES; // 初始化SQL $sql = "INSERT INTO orders VALUES "; $inserts = []; // 模拟数据验证 $validator = function ($input): bool { // 为了缩减代码, 没有真的做数据验证的处理 array_filter($input); return true; }; // $rqQueue在协程上下文是并发安全的, 所以遍历时不用担心 foreach ($rqQueue as $index => $rq) { list($data, $chan) = $rq; // 这里可以考虑后置执行, 原因是后面可以有一些补救逻辑 unset($rqQueue[$index]); // 判断$chan是否关闭å if ($chan->errCode === SWOOLE_CHANNEL_CLOSED) { $data = null; continue; } $bool = $validator($data); if ($bool) { $inserts[] = "({$data['user_name']}, {$data['amount']}, {$data['mobile']})"; $chan->push(['state' => 1]); } else { $chan->push(['state' => 0]); } // unset($rqQueue[$index]); } $sql .= (implode(',', $inserts) . ';'); // 模拟创建订单落库的逻辑 echo $sql; } }; // 新手要注意这一句代码的位置, 原因是 $server->start() 之后的代码不会执行 go($createOrder); // 路由处理器 $orderHandler = function ($rq, $res) use ($rqChannel) { $chan = new Swoole\Coroutine\Channel(1); // 使用timeout参数模拟超时 $bool = $rqChannel->push([$rq->post, $chan], MAX_TIMEOUT); if (!$bool) { // 关闭$chan $chan->close(); $res->end('timeout'); } if (!empty($data = $chan->pop())) { // 关闭$chan $chan->close(); // 区分成功或失败状态再输出响应 if ($data['state'] === 1) { $res->end(microtime()); } else { $res->end('error'); } } }; $server = new Co\Http\Server("0.0.0.0", 9502, false); $server->handle('/order/create', $orderHandler); // 当前协程容器的终点 $server->start(); });
The code is still very easy to understand as a whole, Variables$rqQueue
is the analogy of an elevator. The number of times a hold request has to wait for a certain period of time $times
is analogous to the need for an elevator to wait for the flow of people to enter one after another. Of course, the most important thing I hope readers will pay attention to is: In a coroutine environment, do not use shared memory for communication, but use communication to share memory.
Recommended learning: swoole tutorial
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