先贴源码地址
.NET CORE很大一个好处就是代码的开源，你可以详细的查看你使用类的源代码，并学习微软的写法和实现思路。
这里我对.net core中ConcurrentDictionary源码进行了分析，里面采用了Volatile.Read和write（volatile作用：确保本条指令不会因编译器的优化而省略，且要求每次直接从内存地址读值，而不走寄存器），然后也使用了lock这种混合锁，而且还定义了更细颗粒度的锁。所以多线程使用ConcurrentDictionary集合还是比较好的选择。
本来想把本篇放到我的《C#异步编程系列》，不过后来感觉那个系列写的已经算是收尾了，而且以后还会有写更多core源码分析的文字，所以就单独新增一个系列把。
ConcurrentDictionary内部私有类
先上源码，再仔细聊
/// <summary> /// Tables that hold the internal state of the ConcurrentDictionary /// /// Wrapping the three tables in a single object allows us to atomically /// replace all tables at once. /// </summary> private sealed class Tables { // A singly-linked list for each bucket. // 单链表数据结构的桶，里面的节点就是对应字典值 internal readonly Node[] _buckets; // A set of locks, each guarding a section of the table. //锁的数组 internal readonly object[] _locks; // The number of elements guarded by each lock. internal volatile int[] _countPerLock; internal Tables(Node[] buckets, object[] locks, int[] countPerLock) { _buckets = buckets; _locks = locks; _countPerLock = countPerLock; } } /// <summary> /// A node in a singly-linked list representing a particular hash table bucket. /// 由Dictionary里的Entry改成Node，并且把next放到Node里 /// </summary> private sealed class Node { internal readonly TKey _key; internal TValue _value; internal volatile Node _next; internal readonly int _hashcode; internal Node(TKey key, TValue value, int hashcode, Node next) { _key = key; _value = value; _next = next; _hashcode = hashcode; } } private volatile Tables _tables; // Internal tables of the dictionary private IEqualityComparer<TKey> _comparer; // Key equality comparer // The maximum number of elements per lock before a resize operation is triggered // 每个锁对应的元素最大个数，如果超过，要重新进行resize tables private int _budget;
首先，内部类定义为私有且密封，这样就保证了无法从外部进行篡改，而且注意volatile关键字的使用，这确保了我们多线程操作的时候，最终都是去内存中读取对应地址的值和操作对应地址的值。
internal readonly object[] _locks; internal volatile int[] _countPerLock;
以上两个类是为了高性能及并发锁所建立的对象，实际方法上锁时，使用如下语句
lock (tables._locks[lockNo]) Monitor.Enter(tables._locks[lockNo], ref lockTaken);
以上两种调用方式是等价的，都会阻塞执行，直到获取到锁（对于Monitor我很多时候会尽可能使用TryEnter，毕竟不阻塞，不过这个类的实现一定要使用阻塞式的，这样程序逻辑才能继续往下走。更多关于Monitor我在 《C#异步编程（四）混合模式线程同步》里面有详细介绍）
这样，实现了颗粒化到每个单独的键值的锁，最大限度的保证了并发。
这里lockNo参数是通过GetBucketAndLockNo方法获取的，方法通过out变量返回值。
/// <summary> /// Computes the bucket and lock number for a particular key. ///这里获取桶的索引和锁的索引，注意，锁的索引和桶未必是同一个值。 /// </summary> private static void GetBucketAndLockNo(int hashcode, out int bucketNo, out int lockNo, int bucketCount, int lockCount) { bucketNo = (hashcode & 0x7fffffff) % bucketCount; lockNo = bucketNo % lockCount; }
上面方法中
hashcode 是通过private IEqualityComparer<TKey> _comparer对象的GetHashCode方法通过key获取到的。
bucketCount是整个table的长度。
lockCount是现有的锁的数组
TryAdd方法
我们从最简单的TryAdd方法开始介绍，这里ConcurrentDictionary类的封装非常合理，暴露出来的方法，很多是通过统一的内部方法进行执行，比如更新删除等操作等，都有类内部唯一的私有方法进行执行，然后通过向外暴漏各种参数不同的方法，来实现不同行为。
public bool TryAdd(TKey key, TValue value) { if (key == null) ThrowKeyNullException(); TValue dummy; return TryAddInternal(key, _comparer.GetHashCode(key), value, false, true, out dummy); }
上面TryAddInternal的参数对应如下
/// <summary> /// Shared internal implementation for inserts and updates. /// If key exists, we always return false; and if updateIfExists == true we force update with value; /// If key doesn't exist, we always add value and return true; /// </summary> private bool TryAddInternal(TKey key, int hashcode, TValue value, bool updateIfExists, bool acquireLock, out TValue resultingValue)
也就说说，updateIfExists为false，存在值的情况下，TryAdd不会更新原有值，而是直接返回false。我的多线程并发写库就是利用了这个特性，这个案例我会在本文最后介绍。现在我们来看TryAddInternal内部，废话不多说，上源码（大部分都注释过了，所以直接阅读即可）
//while包在外面，为了continue，如果发生了_tables私有变量在操作过程被其他线程修改的情况 while (true) { int bucketNo, lockNo; //变量复制到方法本地变量 判断tables是否在操作过程中被其他线程修改。 Tables tables = _tables; //提到过的获取桶的索引和锁的索引 GetBucketAndLockNo(hashcode, out bucketNo, out lockNo, tables._buckets.Length, tables._locks.Length); //是否要扩大tables bool resizeDesired = false; //是否成功获取锁，成功的话会在final块中进行退出 bool lockTaken = false; try { if (acquireLock) Monitor.Enter(tables._locks[lockNo], ref lockTaken); // If the table just got resized, we may not be holding the right lock, and must retry. // This should be a rare occurrence. if (tables != _tables) { continue; } // Try to find this key in the bucket Node prev = null; //这里如果找到对应地址为空，会直接跳出循环，说明对应的key没有添加锅 //不为空的时候，会进行返回false（具体是否更新根据updateIfExists）（当然也存在会有相同_hashcode值的情况，所以还要对key进行判定，key不同，继续往后找，直到找到相同key） for (Node node = tables._buckets[bucketNo]; node != null; node = node._next) { Debug.Assert((prev == null && node == tables._buckets[bucketNo]) || prev._next == node); //对hashcode和key进行判定，确保找到的就是要更新的 if (hashcode == node._hashcode && _comparer.Equals(node._key, key)) { // The key was found in the dictionary. If updates are allowed, update the value for that key. // We need to create a new node for the update, in order to support TValue types that cannot // be written atomically, since lock-free reads may be happening concurrently. if (updateIfExists) { if (s_isValueWriteAtomic) { node._value = value; } else { Node newNode = new Node(node._key, value, hashcode, node._next); if (prev == null) { Volatile.Write(ref tables._buckets[bucketNo], newNode); } else { prev._next = newNode; } } resultingValue = value; } else { resultingValue = node._value; } return false; } prev = node; } // The key was not found in the bucket. Insert the key-value pair. Volatile.Write<Node>(ref tables._buckets[bucketNo], new Node(key, value, hashcode, tables._buckets[bucketNo])); checked { tables._countPerLock[lockNo]++; } // // If the number of elements guarded by this lock has exceeded the budget, resize the bucket table. // It is also possible that GrowTable will increase the budget but won't resize the bucket table. // That happens if the bucket table is found to be poorly utilized due to a bad hash function. // if (tables._countPerLock[lockNo] > _budget) { resizeDesired = true; } } finally { if (lockTaken) Monitor.Exit(tables._locks[lockNo]); } // // The fact that we got here means that we just performed an insertion. If necessary, we will grow the table. // // Concurrency notes: // - Notice that we are not holding any locks at when calling GrowTable. This is necessary to prevent deadlocks. // - As a result, it is possible that GrowTable will be called unnecessarily. But, GrowTable will obtain lock 0 // and then verify that the table we passed to it as the argument is still the current table. // if (resizeDesired) { GrowTable(tables); } resultingValue = value; return true; }
ContainsKey和TryGetValue
ContainsKey和TryGetValue其实内部最后调用的都是私有TryGetValueInternal，这里ContainsKey调用TryGetValue。
ContainsKey方法
/// <summary> /// Determines whether the ConcurrentDictionary{TKey, TValue} contains the specified key. /// </summary> /// <param name="key">The key to locate in the</param> /// <returns>true if the ConcurrentDictionary{TKey, TValue} contains an element withthe specified key; otherwise, false.</returns> public bool ContainsKey(TKey key) { if (key == null) ThrowKeyNullException(); TValue throwAwayValue; return TryGetValue(key, out throwAwayValue); }
TryGetValue方法
/// <summary> /// Attempts to get the value associated with the specified key from the ConcurrentDictionary{TKey,TValue}. /// </summary> /// <param name="key">The key of the value to get.</param> /// <param name="value">When this method returns, <paramref name="value"/> contains the object from /// the ConcurrentDictionary{TKey,TValue} with the specified key or the default value of /// <returns>true if the key was found in the <see cref="ConcurrentDictionary{TKey,TValue}"/>; /// otherwise, false.</returns> public bool TryGetValue(TKey key, out TValue value) { if (key == null) ThrowKeyNullException(); return TryGetValueInternal(key, _comparer.GetHashCode(key), out value); }
TryGetValueInternal方法
private bool TryGetValueInternal(TKey key, int hashcode, out TValue value) { //用本地变量保存这个table的快照。 // We must capture the _buckets field in a local variable. It is set to a new table on each table resize. Tables tables = _tables; //获取key对应的桶位置 int bucketNo = GetBucket(hashcode, tables._buckets.Length); // We can get away w/out a lock here. // The Volatile.Read ensures that we have a copy of the reference to tables._buckets[bucketNo]. // This protects us from reading fields ('_hashcode', '_key', '_value' and '_next') of different instances. Node n = Volatile.Read<Node>(ref tables._buckets[bucketNo]); //如果key相符 ，赋值，不然继续寻找下一个。 while (n != null) { if (hashcode == n._hashcode && _comparer.Equals(n._key, key)) { value = n._value; return true; } n = n._next; } value = default(TValue);//没找到就赋默认值 return false; }
TryRemove
TryRemove方法
public bool TryRemove(TKey key, out TValue value) { if (key == null) ThrowKeyNullException(); return TryRemoveInternal(key, out value, false, default(TValue)); }
这个方法会调用内部私用的TryRemoveInternal
/// <summary> /// Removes the specified key from the dictionary if it exists and returns its associated value. /// If matchValue flag is set, the key will be removed only if is associated with a particular /// value. /// </summary> /// <param name="key">The key to search for and remove if it exists.</param> /// <param name="value">The variable into which the removed value, if found, is stored.</param> /// <param name="matchValue">Whether removal of the key is conditional on its value.</param> /// <param name="oldValue">The conditional value to compare against if <paramref name="matchValue"/> is true</param> /// <returns></returns> private bool TryRemoveInternal(TKey key, out TValue value, bool matchValue, TValue oldValue) { int hashcode = _comparer.GetHashCode(key); while (true) { Tables tables = _tables; int bucketNo, lockNo; //这里获取桶的索引和锁的索引，注意，锁的索引和桶未必是同一个值，具体算法看源码。 GetBucketAndLockNo(hashcode, out bucketNo, out lockNo, tables._buckets.Length, tables._locks.Length); //这里锁住的只是对应这个index指向的锁，而不是所有锁。 lock (tables._locks[lockNo]) { //这里table可能被重新分配，所以这里再次获取，看得到的是不是同一个table // If the table just got resized, we may not be holding the right lock, and must retry. // This should be a rare occurrence. if (tables != _tables) { continue; } Node prev = null; //这里同一个桶，可能因为连地址，有很多值，所以要对比key for (Node curr = tables._buckets[bucketNo]; curr != null; curr = curr._next) { Debug.Assert((prev == null && curr == tables._buckets[bucketNo]) || prev._next == curr); //对比是不是要删除的的那个元素 if (hashcode == curr._hashcode && _comparer.Equals(curr._key, key)) { if (matchValue) { bool valuesMatch = EqualityComparer<TValue>.Default.Equals(oldValue, curr._value); if (!valuesMatch) { value = default(TValue); return false; } } //执行删除，判断有没有上一个节点。然后修改节点指针或地址。 if (prev == null) { Volatile.Write<Node>(ref tables._buckets[bucketNo], curr._next); } else { prev._next = curr._next; } value = curr._value; tables._countPerLock[lockNo]--; return true; } prev = curr; } } value = default(TValue); return false; } }
我的使用实例
之前做项目时候，有个奇怪的场景，就是打电话的时候回调接口保存通话记录，这里通过CallId来唯一识别每次通话，但是前端程序是通过websocket跟通话服务建立连接（通话服务是另外一个公司做的）。客户是呼叫中心，一般在网页端都是多个页面操作，所以会有多个websocket连接，这时候每次通话，每个页面都会回调接口端，保存相同的通话记录，并发是同一时间的。
我们最早考虑使用消息队列来过滤重复的请求，但是我仔细考虑了下，发现使用ConcurrentDictionary方式的实现更简单，具体实现如下（我精简了下代码）：
private static ConcurrentDictionary<string,string> _strDic=new ConcurrentDictionary<string, string>(); public async Task<BaseResponse> AddUserByAccount(string callId) { if ( _strDic.ContainsKey(callId)) { return BaseResponse.GetBaseResponse(BusinessStatusType.Failed,"键值已存在"); } //成功写入 if (_strDic.TryAdd(callId,callId)) { var recordExist =await _userRepository.FirstOrDefaultAsync(c => c.CallId == callId); if (recordExist ==null) { Record record=new Record { CallId = callId, ………… ………… IsVerify=1 }; _userRepository.Insert(record); _userRepository.SaveChanges(); } return BaseResponse.GetBaseResponse(BusinessStatusType.OK); } //尝试竞争线程，写入失败 return BaseResponse.GetBaseResponse(BusinessStatusType.Failed,"写入失败"); }
这里如果进行同时的并发请求，最后请求都可以通过if ( _strDic.ContainsKey(callId))的判定，因为所有线程同时读取，都是未写入状态。但是多个线程会在TryAdd时有竞争，而且ConcurrentDictionary的实现保证了只有一个线程可以成功更新，其他的都返回失败。