☕【Java技術之旅】走進線程池的世界（基礎篇）

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而本文描述線程池是JDK中提供的","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"ThreadPoolExecutor","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"類。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 當然，使用線程池可以帶來一系列好處：","attrs":{}}]},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"降低資源消耗","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"：通過池化技術重複利用已創建的線程，降低線程創建和銷燬造成的損耗。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"提高響應速度","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"：任務到達時，無需等待線程創建即可立即執行。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"提高線程的可管理性","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"：線程是稀缺資源，如果無限制創建，不僅會消耗系統資源，還會因爲線程的不合理分佈導致資源調度失衡，降低系統的穩定性。使用線程池可以進行統一的分配、調優和監控。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"提供更多更強大的功能","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"：線程池具備可拓展性，允許開發人員向其中增加更多的功能。比如延時定時線程池","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"ScheduledThreadPoolExecutor","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"，就允許任務延期執行或定期執行。","attrs":{}}]}]}],"attrs":{}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"🚀 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流程描述沒有問題，但如果某些點未經過推敲，容易導致誤解，而且描述中的情境太理想化，如果配置時不考慮運行時環境，也會出現一些非常詭異的問題。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"📚 線程池的構造器","attrs":{}}]},{"type":"paragraph","attrs":{"indent":1,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"使用線程池離不開 ThreadPoolExecutor 類，該類實現了 ExecutorService 接口，其構造方法如下：","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public ThreadPoolExecutor(int corePoolSize,\t\n int maximumPoolSize,\t\n long keepAliveTime,\t\n TimeUnit unit,\t\n BlockingQueue workQueue,\t\n ThreadFactory threadFactory,\t\n RejectedExecutionHandler 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task)，有返回值","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 使用jstack 打印出線程棧並數一下線程池內線程數量，會發現線程池內的線程數會隨着任務的提交而逐漸增大，直到達到 coreSize。因爲核心池的設計初衷是想它能作爲常駐池，承載日常流量，所以它應該被儘快初始化，於是線程池的邏輯是在沒有達到 coreSize 之前，每一個任務都會創建一個新的線程，對應的源碼爲：","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":" public void execute(Runnable command) {\n ...\n int c = ctl.get();\n // workerCountOf() 方法是獲取線程池內線程數量\n if (workerCountOf(c) < corePoolSize) { \n if (addWorker(command, true))\n return;\n c = ctl.get();\n }\n ...\n }\n","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 我們也知道線程被創建後，會在一個 while 循環裏嘗試從 BlockingQueue 裏獲取並執行任務，說它正在 running 也不爲過。基於此，我們對一些高併發服務進行的預熱，","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"其實並不是期望 JVM 能對熱點代碼做 JIT 等優化，對線程池、連接池和本地緩存的預熱纔是重點","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"🚀 阻塞隊列","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" BlockingQueue是線程池內的另一個重要組件，首先它是線程池”生產者-消費者”模型的中間媒介，另外它也可以爲大量突發的流量做緩衝，但理解和配置它也經常會出錯。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 基於”生產者-消費者”模型，我們可能會認爲如果配置了足夠的消費者，線程池就不會有任何問題。其實不然，我們還必須考慮併發量這一因素。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 設想以下情況：有 1000 個任務要同時提交到線程池內併發執行，在線程池被初始化完成的情況下，它們都要被放到 BlockingQueue 內等待被消費，在極限情況下，消費線程一個任務也沒有執行完成，那麼這 1000 個請求需要同時存在於 BlockingQueue 內，如果配置的 BlockingQueue Size 小於 1000，多餘的請求就會被拒絕。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 那麼這種極限情況發生的概率有多大呢？答案是非常大，因爲操作系統對 I/O 線程的調度優先級是非常高的，一般我們的任務都是由 I/O 的準備或完成（如 tomcat 受理了 http 請求）開始的，所以很有可能被調度到的都是 tomcat 線程，它們在一直往線程池內提交請求，而消費者線程卻調度不到，導致請求堆積。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 我負責的服務就發生過這種請求被異常拒絕的情況，壓測時 QPS 2000，平均響應時間爲 20ms，正常情況下，40 個線程就可以平衡生產速度，不會堆積。但在 BlockingQueue Size 爲 50 時，即使線程池 coreSize 爲 1000，還會出現請求被線程池拒絕的情況。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 這種情況下，BlockingQueue 的重要的意義就是它是一個能長時間存儲任務的容器，能以很小的代價爲線程池提供緩衝。根據上文可知，線程池能支持","attrs":{}},{"type":"codeinline","content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"BlockingQueue Size","attrs":{}}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"個任務同時提交，我們把最大同時提交的任務個數，稱爲併發量，配置線程池時，瞭解併發量異常重要","attrs":{}},{"type":"text","text":"。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"🚀 GC回收問題","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"Full GC 是 Stop the World 的，但這裏的 World 指的是 JVM，而一個請求 I/O 的準備和完成是操作系統在進行的，JVM 停止了，但操作系統還是會正常受理請求，在 JVM 恢復後執行，所以 GC 是會堆積請求的。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"上文中提到的併發量計算一定要考慮到 GC 時間內堆積的請求同時被受理的情況，堆積的請求數可以通過 ","attrs":{}},{"type":"codeinline","content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"QPS*GC時間","attrs":{}}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" 來簡單得出，還有一定要記得留出冗餘。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"🚀 業務峯值考慮","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"除此之外，配置線程池參數時，一定要考慮業務場景。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"假如接口的流量大部分來自於一個定時程序，那麼平均 QPS 就沒有了任何意義，線程池設計時就要考慮給 BlockingQueue 的 Size 設置一個大一些的值；而如果流量非常不平均，一天內只有某一小段時間纔有高流量的話，而且線程資源緊張的情況下，就要考慮給線程池的 maxSize 留下較大的冗餘；在流量尖刺明顯而響應時間不那麼敏感時，也可以設置較大的 BlockingQueue，允許任務進行一定程度的堆積。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"當然除了經驗和計算外，對服務做定時的壓測無疑更能幫助掌握服務真實的情況。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"併發量的計算","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":" ","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"我們常用 QPS 來衡量服務壓力，所以配置線程池參數時也經常參考這個值，但有時候 QPS 和併發量有時候相關性並沒有那麼高，QPS 還要","attrs":{}},{"type":"codeinline","content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"搭配任務執行時間","attrs":{}}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"來","attrs":{}},{"type":"codeinline","content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"推算","attrs":{}}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":"峯值併發量。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":" 比如請求間隔嚴格相同的接口，平均 QPS 爲 1000，它的併發量峯值是多少呢？我們並沒有辦法估算，因爲如果任務 執行時間爲 1ms，那麼它的併發量只有 1；而如果任務執行時間爲 1s，那麼併發量峯值爲 1000。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"strong","attrs":{}}],"text":" 可是知道了任務執行時間，就能算出併發量了嗎？也不能，因爲如果請求的間隔不同，可能 1min 內的請求都在一秒內發過來，那這個併發量還要乘以 60，所以上面才說知道了 QPS 和任務執行時間，併發量也只能靠推算。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":" 計算併發量，我一般的經驗值是 ","attrs":{}},{"type":"codeinline","content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":"QPS*平均響應時間","attrs":{}}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":"，再留上一倍的冗餘，但如果業務重要的話，BlockingQueue Size 設置大一些也無妨（1000 或以上），畢竟每個任務佔用的內存量很有限。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"運行模型","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":" ","attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"明確的一點是線程池並沒有準確的調度功能，即它無法感知有哪些線程是處於空閒狀態的，並把提交的任務派發給空閒線程。線程池採用的是”生產者-消費者”模式，除了觸發線程創建的任務（線程的 firstTask）不會入 BlockingQueue 外，其他任務都要進入到 BlockingQueue，等待線程池內的線程消費，而任務會被哪個線程消費到完全取決於操作系統的調度。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}}],"text":"對應的生產者源碼如下：","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"    public void execute(Runnable command) {\n        ...\n        if (isRunning(c) && workQueue.offer(command)) { \n isRunning() 是判斷線程池處理戚狀態\n            int recheck = ctl.get();\n            if (! isRunning(recheck) && remove(command))\n                reject(command);\n            else if (workerCountOf(recheck) == 0)\n                addWorker(null, false);\n        }\n        ...\n    }","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對應的消費者源碼如下：","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"private Runnable getTask() {\n        for (;;) {\n            ...\n            Runnable r = timed ?\n                workQueue.poll(keepAliveTime, TimeUnit.NANOSECONDS) :\n                workQueue.take();\n            if (r != null)\n                return r;\n            ...\n        }\n    }","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"🚀 可選線程池模型","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":5},"content":[{"type":"text","text":"📚 newCachedThreadPool(會創建過多的線程任務)","attrs":{}}]},{"type":"codeblock","attrs":{"lang":null},"content":[{"type":"text","text":"new ThreadPoolExecutor(0,Integer.MAX_VALUE,60L, \n TimeUnit.SECONDS,new SynchronousQueue())","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":5},"content":[{"type":"text","text":"📚 newFixedThreadPool(會擠壓過多的任務到隊列裏)","attrs":{}}]},{"type":"codeblock","attrs":{"lang":null},"content":[{"type":"text","text":"new ThreadPoolExecutor(nThreads,nThreads,0L, \n TimeUnit.MILLISECONDS,new LinkedBlockingQueue())","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":5},"content":[{"type":"text","text":"📚 newSingleThreadPool(會擠壓過多的任務到隊列裏)","attrs":{}}]},{"type":"codeblock","attrs":{"lang":null},"content":[{"type":"text","text":"new ThreadPoolExecutor(1,1,0L,\n TimeUnit.MILLISECONDS,new LinkedBlockingQueue())","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":5},"content":[{"type":"text","text":"📚 newScheduledThreadPool(會創建過多的線程任務)","attrs":{}}]},{"type":"codeblock","attrs":{"lang":null},"content":[{"type":"text","text":"new ScheduledThreadPoolExecutor(nThreads,Integer.MAX_VALUE,0L,\n TimeUnit.NANOSECONDS,new DelayedWorkQueue())","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"user"}},{"type":"strong","attrs":{}}],"text":"調用實例：","attrs":{}},{"type":"codeinline","marks":[{"type":"strong"}],"attrs":{}},{"type":"text","marks":[{"type":"size","attrs":{"size":10}},{"type":"color","attrs":{"color":"#F5222D","name":"user"}}],"text":"構造線程池時優先選用線程池模型，如果這些模型不能滿足要求，再自定義 ThreadPoolExecutor 線程池","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"🚀 線程池的關閉","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":9}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":"shutdown()：不會立即關閉線程池，而是不再接受新的任務，等當前所有任務處理完之後關閉線程池","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"size","attrs":{"size":9}},{"type":"color","attrs":{"color":"#F5222D","name":"red"}},{"type":"strong","attrs":{}}],"text":"shutdownNow()：立即關閉線程池，打斷正在執行的任務，清空緩衝隊列，返回尚未執行的任務","attrs":{}}]}],"attrs":{}}]}