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  • Lazy non-modifiable list in Google Collections

    - by mindas
    I was looking for a decent implementation of a generic lazy non-modifiable list implementation to wrap my search result entries. The unmodifiable part of the task is easy as it can be achieved by Collections.unmodifiableList() so I only need to sort out the the lazy part. Surprisingly, google-collections doesn't have anything to offer; while LazyList from Apache Commons Collections does not support generics. I have found an attempt to build something on top of google-collections but it seems to be incomplete (e.g. does not support size()), outdated (does not compile with 1.0 final) and requiring some external classes, but could be used as a good starting point to build my own class. Is anybody aware of any good implementation of a LazyList? If not, which option do you think is better: write my own implementation, based on google-collections ForwardingList, similar to what Peter Maas did; write my own wrapper around Commons Collections LazyList (the wrapper would only add generics so I don't have to cast everywhere but only in the wrapper itself); just write something on top of java.util.AbstractList; Any other suggestions are welcome.

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  • C#/.NET Little Wonders: The Concurrent Collections (1 of 3)

    - by James Michael Hare
    Once again we consider some of the lesser known classes and keywords of C#.  In the next few weeks, we will discuss the concurrent collections and how they have changed the face of concurrent programming. This week’s post will begin with a general introduction and discuss the ConcurrentStack<T> and ConcurrentQueue<T>.  Then in the following post we’ll discuss the ConcurrentDictionary<T> and ConcurrentBag<T>.  Finally, we shall close on the third post with a discussion of the BlockingCollection<T>. For more of the "Little Wonders" posts, see the index here. A brief history of collections In the beginning was the .NET 1.0 Framework.  And out of this framework emerged the System.Collections namespace, and it was good.  It contained all the basic things a growing programming language needs like the ArrayList and Hashtable collections.  The main problem, of course, with these original collections is that they held items of type object which means you had to be disciplined enough to use them correctly or you could end up with runtime errors if you got an object of a type you weren't expecting. Then came .NET 2.0 and generics and our world changed forever!  With generics the C# language finally got an equivalent of the very powerful C++ templates.  As such, the System.Collections.Generic was born and we got type-safe versions of all are favorite collections.  The List<T> succeeded the ArrayList and the Dictionary<TKey,TValue> succeeded the Hashtable and so on.  The new versions of the library were not only safer because they checked types at compile-time, in many cases they were more performant as well.  So much so that it's Microsoft's recommendation that the System.Collections original collections only be used for backwards compatibility. So we as developers came to know and love the generic collections and took them into our hearts and embraced them.  The problem is, thread safety in both the original collections and the generic collections can be problematic, for very different reasons. Now, if you are only doing single-threaded development you may not care – after all, no locking is required.  Even if you do have multiple threads, if a collection is “load-once, read-many” you don’t need to do anything to protect that container from multi-threaded access, as illustrated below: 1: public static class OrderTypeTranslator 2: { 3: // because this dictionary is loaded once before it is ever accessed, we don't need to synchronize 4: // multi-threaded read access 5: private static readonly Dictionary<string, char> _translator = new Dictionary<string, char> 6: { 7: {"New", 'N'}, 8: {"Update", 'U'}, 9: {"Cancel", 'X'} 10: }; 11:  12: // the only public interface into the dictionary is for reading, so inherently thread-safe 13: public static char? Translate(string orderType) 14: { 15: char charValue; 16: if (_translator.TryGetValue(orderType, out charValue)) 17: { 18: return charValue; 19: } 20:  21: return null; 22: } 23: } Unfortunately, most of our computer science problems cannot get by with just single-threaded applications or with multi-threading in a load-once manner.  Looking at  today's trends, it's clear to see that computers are not so much getting faster because of faster processor speeds -- we've nearly reached the limits we can push through with today's technologies -- but more because we're adding more cores to the boxes.  With this new hardware paradigm, it is even more important to use multi-threaded applications to take full advantage of parallel processing to achieve higher application speeds. So let's look at how to use collections in a thread-safe manner. Using historical collections in a concurrent fashion The early .NET collections (System.Collections) had a Synchronized() static method that could be used to wrap the early collections to make them completely thread-safe.  This paradigm was dropped in the generic collections (System.Collections.Generic) because having a synchronized wrapper resulted in atomic locks for all operations, which could prove overkill in many multithreading situations.  Thus the paradigm shifted to having the user of the collection specify their own locking, usually with an external object: 1: public class OrderAggregator 2: { 3: private static readonly Dictionary<string, List<Order>> _orders = new Dictionary<string, List<Order>>(); 4: private static readonly _orderLock = new object(); 5:  6: public void Add(string accountNumber, Order newOrder) 7: { 8: List<Order> ordersForAccount; 9:  10: // a complex operation like this should all be protected 11: lock (_orderLock) 12: { 13: if (!_orders.TryGetValue(accountNumber, out ordersForAccount)) 14: { 15: _orders.Add(accountNumber, ordersForAccount = new List<Order>()); 16: } 17:  18: ordersForAccount.Add(newOrder); 19: } 20: } 21: } Notice how we’re performing several operations on the dictionary under one lock.  With the Synchronized() static methods of the early collections, you wouldn’t be able to specify this level of locking (a more macro-level).  So in the generic collections, it was decided that if a user needed synchronization, they could implement their own locking scheme instead so that they could provide synchronization as needed. The need for better concurrent access to collections Here’s the problem: it’s relatively easy to write a collection that locks itself down completely for access, but anything more complex than that can be difficult and error-prone to write, and much less to make it perform efficiently!  For example, what if you have a Dictionary that has frequent reads but in-frequent updates?  Do you want to lock down the entire Dictionary for every access?  This would be overkill and would prevent concurrent reads.  In such cases you could use something like a ReaderWriterLockSlim which allows for multiple readers in a lock, and then once a writer grabs the lock it blocks all further readers until the writer is done (in a nutshell).  This is all very complex stuff to consider. Fortunately, this is where the Concurrent Collections come in.  The Parallel Computing Platform team at Microsoft went through great pains to determine how to make a set of concurrent collections that would have the best performance characteristics for general case multi-threaded use. Now, as in all things involving threading, you should always make sure you evaluate all your container options based on the particular usage scenario and the degree of parallelism you wish to acheive. This article should not be taken to understand that these collections are always supperior to the generic collections. Each fills a particular need for a particular situation. Understanding what each container is optimized for is key to the success of your application whether it be single-threaded or multi-threaded. General points to consider with the concurrent collections The MSDN points out that the concurrent collections all support the ICollection interface. However, since the collections are already synchronized, the IsSynchronized property always returns false, and SyncRoot always returns null.  Thus you should not attempt to use these properties for synchronization purposes. Note that since the concurrent collections also may have different operations than the traditional data structures you may be used to.  Now you may ask why they did this, but it was done out of necessity to keep operations safe and atomic.  For example, in order to do a Pop() on a stack you have to know the stack is non-empty, but between the time you check the stack’s IsEmpty property and then do the Pop() another thread may have come in and made the stack empty!  This is why some of the traditional operations have been changed to make them safe for concurrent use. In addition, some properties and methods in the concurrent collections achieve concurrency by creating a snapshot of the collection, which means that some operations that were traditionally O(1) may now be O(n) in the concurrent models.  I’ll try to point these out as we talk about each collection so you can be aware of any potential performance impacts.  Finally, all the concurrent containers are safe for enumeration even while being modified, but some of the containers support this in different ways (snapshot vs. dirty iteration).  Once again I’ll highlight how thread-safe enumeration works for each collection. ConcurrentStack<T>: The thread-safe LIFO container The ConcurrentStack<T> is the thread-safe counterpart to the System.Collections.Generic.Stack<T>, which as you may remember is your standard last-in-first-out container.  If you think of algorithms that favor stack usage (for example, depth-first searches of graphs and trees) then you can see how using a thread-safe stack would be of benefit. The ConcurrentStack<T> achieves thread-safe access by using System.Threading.Interlocked operations.  This means that the multi-threaded access to the stack requires no traditional locking and is very, very fast! For the most part, the ConcurrentStack<T> behaves like it’s Stack<T> counterpart with a few differences: Pop() was removed in favor of TryPop() Returns true if an item existed and was popped and false if empty. PushRange() and TryPopRange() were added Allows you to push multiple items and pop multiple items atomically. Count takes a snapshot of the stack and then counts the items. This means it is a O(n) operation, if you just want to check for an empty stack, call IsEmpty instead which is O(1). ToArray() and GetEnumerator() both also take snapshots. This means that iteration over a stack will give you a static view at the time of the call and will not reflect updates. Pushing on a ConcurrentStack<T> works just like you’d expect except for the aforementioned PushRange() method that was added to allow you to push a range of items concurrently. 1: var stack = new ConcurrentStack<string>(); 2:  3: // adding to stack is much the same as before 4: stack.Push("First"); 5:  6: // but you can also push multiple items in one atomic operation (no interleaves) 7: stack.PushRange(new [] { "Second", "Third", "Fourth" }); For looking at the top item of the stack (without removing it) the Peek() method has been removed in favor of a TryPeek().  This is because in order to do a peek the stack must be non-empty, but between the time you check for empty and the time you execute the peek the stack contents may have changed.  Thus the TryPeek() was created to be an atomic check for empty, and then peek if not empty: 1: // to look at top item of stack without removing it, can use TryPeek. 2: // Note that there is no Peek(), this is because you need to check for empty first. TryPeek does. 3: string item; 4: if (stack.TryPeek(out item)) 5: { 6: Console.WriteLine("Top item was " + item); 7: } 8: else 9: { 10: Console.WriteLine("Stack was empty."); 11: } Finally, to remove items from the stack, we have the TryPop() for single, and TryPopRange() for multiple items.  Just like the TryPeek(), these operations replace Pop() since we need to ensure atomically that the stack is non-empty before we pop from it: 1: // to remove items, use TryPop or TryPopRange to get multiple items atomically (no interleaves) 2: if (stack.TryPop(out item)) 3: { 4: Console.WriteLine("Popped " + item); 5: } 6:  7: // TryPopRange will only pop up to the number of spaces in the array, the actual number popped is returned. 8: var poppedItems = new string[2]; 9: int numPopped = stack.TryPopRange(poppedItems); 10:  11: foreach (var theItem in poppedItems.Take(numPopped)) 12: { 13: Console.WriteLine("Popped " + theItem); 14: } Finally, note that as stated before, GetEnumerator() and ToArray() gets a snapshot of the data at the time of the call.  That means if you are enumerating the stack you will get a snapshot of the stack at the time of the call.  This is illustrated below: 1: var stack = new ConcurrentStack<string>(); 2:  3: // adding to stack is much the same as before 4: stack.Push("First"); 5:  6: var results = stack.GetEnumerator(); 7:  8: // but you can also push multiple items in one atomic operation (no interleaves) 9: stack.PushRange(new [] { "Second", "Third", "Fourth" }); 10:  11: while(results.MoveNext()) 12: { 13: Console.WriteLine("Stack only has: " + results.Current); 14: } The only item that will be printed out in the above code is "First" because the snapshot was taken before the other items were added. This may sound like an issue, but it’s really for safety and is more correct.  You don’t want to enumerate a stack and have half a view of the stack before an update and half a view of the stack after an update, after all.  In addition, note that this is still thread-safe, whereas iterating through a non-concurrent collection while updating it in the old collections would cause an exception. ConcurrentQueue<T>: The thread-safe FIFO container The ConcurrentQueue<T> is the thread-safe counterpart of the System.Collections.Generic.Queue<T> class.  The concurrent queue uses an underlying list of small arrays and lock-free System.Threading.Interlocked operations on the head and tail arrays.  Once again, this allows us to do thread-safe operations without the need for heavy locks! The ConcurrentQueue<T> (like the ConcurrentStack<T>) has some departures from the non-concurrent counterpart.  Most notably: Dequeue() was removed in favor of TryDequeue(). Returns true if an item existed and was dequeued and false if empty. Count does not take a snapshot It subtracts the head and tail index to get the count.  This results overall in a O(1) complexity which is quite good.  It’s still recommended, however, that for empty checks you call IsEmpty instead of comparing Count to zero. ToArray() and GetEnumerator() both take snapshots. This means that iteration over a queue will give you a static view at the time of the call and will not reflect updates. The Enqueue() method on the ConcurrentQueue<T> works much the same as the generic Queue<T>: 1: var queue = new ConcurrentQueue<string>(); 2:  3: // adding to queue is much the same as before 4: queue.Enqueue("First"); 5: queue.Enqueue("Second"); 6: queue.Enqueue("Third"); For front item access, the TryPeek() method must be used to attempt to see the first item if the queue.  There is no Peek() method since, as you’ll remember, we can only peek on a non-empty queue, so we must have an atomic TryPeek() that checks for empty and then returns the first item if the queue is non-empty. 1: // to look at first item in queue without removing it, can use TryPeek. 2: // Note that there is no Peek(), this is because you need to check for empty first. TryPeek does. 3: string item; 4: if (queue.TryPeek(out item)) 5: { 6: Console.WriteLine("First item was " + item); 7: } 8: else 9: { 10: Console.WriteLine("Queue was empty."); 11: } Then, to remove items you use TryDequeue().  Once again this is for the same reason we have TryPeek() and not Peek(): 1: // to remove items, use TryDequeue. If queue is empty returns false. 2: if (queue.TryDequeue(out item)) 3: { 4: Console.WriteLine("Dequeued first item " + item); 5: } Just like the concurrent stack, the ConcurrentQueue<T> takes a snapshot when you call ToArray() or GetEnumerator() which means that subsequent updates to the queue will not be seen when you iterate over the results.  Thus once again the code below will only show the first item, since the other items were added after the snapshot. 1: var queue = new ConcurrentQueue<string>(); 2:  3: // adding to queue is much the same as before 4: queue.Enqueue("First"); 5:  6: var iterator = queue.GetEnumerator(); 7:  8: queue.Enqueue("Second"); 9: queue.Enqueue("Third"); 10:  11: // only shows First 12: while (iterator.MoveNext()) 13: { 14: Console.WriteLine("Dequeued item " + iterator.Current); 15: } Using collections concurrently You’ll notice in the examples above I stuck to using single-threaded examples so as to make them deterministic and the results obvious.  Of course, if we used these collections in a truly multi-threaded way the results would be less deterministic, but would still be thread-safe and with no locking on your part required! For example, say you have an order processor that takes an IEnumerable<Order> and handles each other in a multi-threaded fashion, then groups the responses together in a concurrent collection for aggregation.  This can be done easily with the TPL’s Parallel.ForEach(): 1: public static IEnumerable<OrderResult> ProcessOrders(IEnumerable<Order> orderList) 2: { 3: var proxy = new OrderProxy(); 4: var results = new ConcurrentQueue<OrderResult>(); 5:  6: // notice that we can process all these in parallel and put the results 7: // into our concurrent collection without needing any external locking! 8: Parallel.ForEach(orderList, 9: order => 10: { 11: var result = proxy.PlaceOrder(order); 12:  13: results.Enqueue(result); 14: }); 15:  16: return results; 17: } Summary Obviously, if you do not need multi-threaded safety, you don’t need to use these collections, but when you do need multi-threaded collections these are just the ticket! The plethora of features (I always think of the movie The Three Amigos when I say plethora) built into these containers and the amazing way they acheive thread-safe access in an efficient manner is wonderful to behold. Stay tuned next week where we’ll continue our discussion with the ConcurrentBag<T> and the ConcurrentDictionary<TKey,TValue>. For some excellent information on the performance of the concurrent collections and how they perform compared to a traditional brute-force locking strategy, see this wonderful whitepaper by the Microsoft Parallel Computing Platform team here.   Tweet Technorati Tags: C#,.NET,Concurrent Collections,Collections,Multi-Threading,Little Wonders,BlackRabbitCoder,James Michael Hare

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  • Fastest way to write large STL vector to file using STL

    - by ljubak
    I have a large vector (10^9 elements) of chars, and I was wondering what is the fastest way to write such vector to a file. So far I've been using next code: vector<char> vs; // ... Fill vector with data ofstream outfile("nanocube.txt", ios::out | ios::binary); ostream_iterator<char> oi(outfile, '\0'); copy(vs.begin(), vs.end(), oi); For this code it takes approximately two minutes to write all data to file. The actual question is: "Can I make it faster using STL and how"?

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  • Using local classes with STL algorithms

    - by David Rodríguez - dribeas
    I have always wondered why you cannot use locally defined classes as predicates to STL algorithms. In the question: Approaching STL algorithms, lambda, local classes and other approaches, BubbaT mentions says that 'Since the C++ standard forbids local types to be used as arguments' Example code: int main() { int array[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; std::vector<int> v( array, array+10 ); struct pair : public std::unary_function<int,bool> { bool operator()( int x ) { return !( x % 2 ); } }; std::remove_if( v.begin(), v.end(), pair() ); // error } Does anyone know where in the standard is the restriction? What is the rationale for disallowing local types?

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  • Functional Methods on Collections

    - by GlenPeterson
    I'm learning Scala and am a little bewildered by all the methods (higher-order functions) available on the collections. Which ones produce more results than the original collection, which ones produce less, and which are most appropriate for a given problem? Though I'm studying Scala, I think this would pertain to most modern functional languages (Clojure, Haskell) and also to Java 8 which introduces these methods on Java collections. Specifically, right now I'm wondering about map with filter vs. fold/reduce. I was delighted that using foldRight() can yield the same result as a map(...).filter(...) with only one traversal of the underlying collection. But a friend pointed out that foldRight() may force sequential processing while map() is friendlier to being processed by multiple processors in parallel. Maybe this is why mapReduce() is so popular? More generally, I'm still sometimes surprised when I chain several of these methods together to get back a List(List()) or to pass a List(List()) and get back just a List(). For instance, when would I use: collection.map(a => a.map(b => ...)) vs. collection.map(a => ...).map(b => ...) The for/yield command does nothing to help this confusion. Am I asking about the difference between a "fold" and "unfold" operation? Am I trying to jam too many questions into one? I think there may be an underlying concept that, if I understood it, might answer all these questions, or at least tie the answers together.

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  • STL vector reserve() and copy()

    - by natersoz
    Greetings, I am trying to perform a copy from one vector (vec1) to another vector (vec2) using the following 2 abbreviated lines of code (full test app follows): vec2.reserve( vec1.size() ); copy(vec1.begin(), vec1.end(), vec2.begin()); While the call to vec2 sets the capacity of vector vec2, the copying of data to vec2 seems to not fill in the values from vec1 to vec2. Replacing the copy() function with calls to push_back() works as expected. What am I missing here? Thanks for your help. vectest.cpp test program followed by resulting output follows. Compiler: gcc 3.4.4 on cygwin. Nat /** * vectest.cpp */ #include <iostream> #include <vector> using namespace std; int main() { vector<int> vec1; vector<int> vec2; vec1.push_back(1); vec1.push_back(2); vec1.push_back(3); vec1.push_back(4); vec1.push_back(5); vec1.push_back(6); vec1.push_back(7); vec2.reserve( vec1.size() ); copy(vec1.begin(), vec1.end(), vec2.begin()); cout << "vec1.size() = " << vec1.size() << endl; cout << "vec1.capacity() = " << vec1.capacity() << endl; cout << "vec1: "; for( vector<int>::const_iterator iter = vec1.begin(); iter < vec1.end(); ++iter ) { cout << *iter << " "; } cout << endl; cout << "vec2.size() = " << vec2.size() << endl; cout << "vec2.capacity() = " << vec2.capacity() << endl; cout << "vec2: "; for( vector<int>::const_iterator iter = vec2.begin(); iter < vec2.end(); ++iter ) { cout << *iter << endl; } cout << endl; } output: vec1.size() = 7 vec1.capacity() = 8 vec1: 1 2 3 4 5 6 7 vec2.size() = 0 vec2.capacity() = 7 vec2:

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  • Inside the Concurrent Collections: ConcurrentBag

    - by Simon Cooper
    Unlike the other concurrent collections, ConcurrentBag does not really have a non-concurrent analogy. As stated in the MSDN documentation, ConcurrentBag is optimised for the situation where the same thread is both producing and consuming items from the collection. We'll see how this is the case as we take a closer look. Again, I recommend you have ConcurrentBag open in a decompiler for reference. Thread Statics ConcurrentBag makes heavy use of thread statics - static variables marked with ThreadStaticAttribute. This is a special attribute that instructs the CLR to scope any values assigned to or read from the variable to the executing thread, not globally within the AppDomain. This means that if two different threads assign two different values to the same thread static variable, one value will not overwrite the other, and each thread will see the value they assigned to the variable, separately to any other thread. This is a very useful function that allows for ConcurrentBag's concurrency properties. You can think of a thread static variable: [ThreadStatic] private static int m_Value; as doing the same as: private static Dictionary<Thread, int> m_Values; where the executing thread's identity is used to automatically set and retrieve the corresponding value in the dictionary. In .NET 4, this usage of ThreadStaticAttribute is encapsulated in the ThreadLocal class. Lists of lists ConcurrentBag, at its core, operates as a linked list of linked lists: Each outer list node is an instance of ThreadLocalList, and each inner list node is an instance of Node. Each outer ThreadLocalList is owned by a particular thread, accessible through the thread local m_locals variable: private ThreadLocal<ThreadLocalList<T>> m_locals It is important to note that, although the m_locals variable is thread-local, that only applies to accesses through that variable. The objects referenced by the thread (each instance of the ThreadLocalList object) are normal heap objects that are not specific to any thread. Thinking back to the Dictionary analogy above, if each value stored in the dictionary could be accessed by other means, then any thread could access the value belonging to other threads using that mechanism. Only reads and writes to the variable defined as thread-local are re-routed by the CLR according to the executing thread's identity. So, although m_locals is defined as thread-local, the m_headList, m_nextList and m_tailList variables aren't. This means that any thread can access all the thread local lists in the collection by doing a linear search through the outer linked list defined by these variables. Adding items So, onto the collection operations. First, adding items. This one's pretty simple. If the current thread doesn't already own an instance of ThreadLocalList, then one is created (or, if there are lists owned by threads that have stopped, it takes control of one of those). Then the item is added to the head of that thread's list. That's it. Don't worry, it'll get more complicated when we account for the other operations on the list! Taking & Peeking items This is where it gets tricky. If the current thread's list has items in it, then it peeks or removes the head item (not the tail item) from the local list and returns that. However, if the local list is empty, it has to go and steal another item from another list, belonging to a different thread. It iterates through all the thread local lists in the collection using the m_headList and m_nextList variables until it finds one that has items in it, and it steals one item from that list. Up to this point, the two threads had been operating completely independently. To steal an item from another thread's list, the stealing thread has to do it in such a way as to not step on the owning thread's toes. Recall how adding and removing items both operate on the head of the thread's linked list? That gives us an easy way out - a thread trying to steal items from another thread can pop in round the back of another thread's list using the m_tail variable, and steal an item from the back without the owning thread knowing anything about it. The owning thread can carry on completely independently, unaware that one of its items has been nicked. However, this only works when there are at least 3 items in the list, as that guarantees there will be at least one node between the owning thread performing operations on the list head and the thread stealing items from the tail - there's no chance of the two threads operating on the same node at the same time and causing a race condition. If there's less than three items in the list, then there does need to be some synchronization between the two threads. In this case, the lock on the ThreadLocalList object is used to mediate access to a thread's list when there's the possibility of contention. Thread synchronization In ConcurrentBag, this is done using several mechanisms: Operations performed by the owner thread only take out the lock when there are less than three items in the collection. With three or greater items, there won't be any conflict with a stealing thread operating on the tail of the list. If a lock isn't taken out, the owning thread sets the list's m_currentOp variable to a non-zero value for the duration of the operation. This indicates to all other threads that there is a non-locked operation currently occuring on that list. The stealing thread always takes out the lock, to prevent two threads trying to steal from the same list at the same time. After taking out the lock, the stealing thread spinwaits until m_currentOp has been set to zero before actually performing the steal. This ensures there won't be a conflict with the owning thread when the number of items in the list is on the 2-3 item borderline. If any add or remove operations are started in the meantime, and the list is below 3 items, those operations try to take out the list's lock and are blocked until the stealing thread has finished. This allows a thread to steal an item from another thread's list without corrupting it. What about synchronization in the collection as a whole? Collection synchronization Any thread that operates on the collection's global structure (accessing anything outside the thread local lists) has to take out the collection's global lock - m_globalListsLock. This single lock is sufficient when adding a new thread local list, as the items inside each thread's list are unaffected. However, what about operations (such as Count or ToArray) that need to access every item in the collection? In order to ensure a consistent view, all operations on the collection are stopped while the count or ToArray is performed. This is done by freezing the bag at the start, performing the global operation, and unfreezing at the end: The global lock is taken out, to prevent structural alterations to the collection. m_needSync is set to true. This notifies all the threads that they need to take out their list's lock irregardless of what operation they're doing. All the list locks are taken out in order. This blocks all locking operations on the lists. The freezing thread waits for all current lockless operations to finish by spinwaiting on each m_currentOp field. The global operation can then be performed while the bag is frozen, but no other operations can take place at the same time, as all other threads are blocked on a list's lock. Then, once the global operation has finished, the locks are released, m_needSync is unset, and normal concurrent operation resumes. Concurrent principles That's the essence of how ConcurrentBag operates. Each thread operates independently on its own local list, except when they have to steal items from another list. When stealing, only the stealing thread is forced to take out the lock; the owning thread only has to when there is the possibility of contention. And a global lock controls accesses to the structure of the collection outside the thread lists. Operations affecting the entire collection take out all locks in the collection to freeze the contents at a single point in time. So, what principles can we extract here? Threads operate independently Thread-static variables and ThreadLocal makes this easy. Threads operate entirely concurrently on their own structures; only when they need to grab data from another thread is there any thread contention. Minimised lock-taking Even when two threads need to operate on the same data structures (one thread stealing from another), they do so in such a way such that the probability of actually blocking on a lock is minimised; the owning thread always operates on the head of the list, and the stealing thread always operates on the tail. Management of lockless operations Any operations that don't take out a lock still have a 'hook' to force them to lock when necessary. This allows all operations on the collection to be stopped temporarily while a global snapshot is taken. Hopefully, such operations will be short-lived and infrequent. That's all the concurrent collections covered. I hope you've found it as informative and interesting as I have. Next, I'll be taking a closer look at ThreadLocal, which I came across while analyzing ConcurrentBag. As you'll see, the operation of this class deserves a much closer look.

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  • Inside the Concurrent Collections: ConcurrentDictionary

    - by Simon Cooper
    Using locks to implement a thread-safe collection is rather like using a sledgehammer - unsubtle, easy to understand, and tends to make any other tool redundant. Unlike the previous two collections I looked at, ConcurrentStack and ConcurrentQueue, ConcurrentDictionary uses locks quite heavily. However, it is careful to wield locks only where necessary to ensure that concurrency is maximised. This will, by necessity, be a higher-level look than my other posts in this series, as there is quite a lot of code and logic in ConcurrentDictionary. Therefore, I do recommend that you have ConcurrentDictionary open in a decompiler to have a look at all the details that I skip over. The problem with locks There's several things to bear in mind when using locks, as encapsulated by the lock keyword in C# and the System.Threading.Monitor class in .NET (if you're unsure as to what lock does in C#, I briefly covered it in my first post in the series): Locks block threads The most obvious problem is that threads waiting on a lock can't do any work at all. No preparatory work, no 'optimistic' work like in ConcurrentQueue and ConcurrentStack, nothing. It sits there, waiting to be unblocked. This is bad if you're trying to maximise concurrency. Locks are slow Whereas most of the methods on the Interlocked class can be compiled down to a single CPU instruction, ensuring atomicity at the hardware level, taking out a lock requires some heavy lifting by the CLR and the operating system. There's quite a bit of work required to take out a lock, block other threads, and wake them up again. If locks are used heavily, this impacts performance. Deadlocks When using locks there's always the possibility of a deadlock - two threads, each holding a lock, each trying to aquire the other's lock. Fortunately, this can be avoided with careful programming and structured lock-taking, as we'll see. So, it's important to minimise where locks are used to maximise the concurrency and performance of the collection. Implementation As you might expect, ConcurrentDictionary is similar in basic implementation to the non-concurrent Dictionary, which I studied in a previous post. I'll be using some concepts introduced there, so I recommend you have a quick read of it. So, if you were implementing a thread-safe dictionary, what would you do? The naive implementation is to simply have a single lock around all methods accessing the dictionary. This would work, but doesn't allow much concurrency. Fortunately, the bucketing used by Dictionary allows a simple but effective improvement to this - one lock per bucket. This allows different threads modifying different buckets to do so in parallel. Any thread making changes to the contents of a bucket takes the lock for that bucket, ensuring those changes are thread-safe. The method that maps each bucket to a lock is the GetBucketAndLockNo method: private void GetBucketAndLockNo( int hashcode, out int bucketNo, out int lockNo, int bucketCount) { // the bucket number is the hashcode (without the initial sign bit) // modulo the number of buckets bucketNo = (hashcode & 0x7fffffff) % bucketCount; // and the lock number is the bucket number modulo the number of locks lockNo = bucketNo % m_locks.Length; } However, this does require some changes to how the buckets are implemented. The 'implicit' linked list within a single backing array used by the non-concurrent Dictionary adds a dependency between separate buckets, as every bucket uses the same backing array. Instead, ConcurrentDictionary uses a strict linked list on each bucket: This ensures that each bucket is entirely separate from all other buckets; adding or removing an item from a bucket is independent to any changes to other buckets. Modifying the dictionary All the operations on the dictionary follow the same basic pattern: void AlterBucket(TKey key, ...) { int bucketNo, lockNo; 1: GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, m_buckets.Length); 2: lock (m_locks[lockNo]) { 3: Node headNode = m_buckets[bucketNo]; 4: Mutate the node linked list as appropriate } } For example, when adding another entry to the dictionary, you would iterate through the linked list to check whether the key exists already, and add the new entry as the head node. When removing items, you would find the entry to remove (if it exists), and remove the node from the linked list. Adding, updating, and removing items all follow this pattern. Performance issues There is a problem we have to address at this point. If the number of buckets in the dictionary is fixed in the constructor, then the performance will degrade from O(1) to O(n) when a large number of items are added to the dictionary. As more and more items get added to the linked lists in each bucket, the lookup operations will spend most of their time traversing a linear linked list. To fix this, the buckets array has to be resized once the number of items in each bucket has gone over a certain limit. (In ConcurrentDictionary this limit is when the size of the largest bucket is greater than the number of buckets for each lock. This check is done at the end of the TryAddInternal method.) Resizing the bucket array and re-hashing everything affects every bucket in the collection. Therefore, this operation needs to take out every lock in the collection. Taking out mutiple locks at once inevitably summons the spectre of the deadlock; two threads each hold a lock, and each trying to acquire the other lock. How can we eliminate this? Simple - ensure that threads never try to 'swap' locks in this fashion. When taking out multiple locks, always take them out in the same order, and always take out all the locks you need before starting to release them. In ConcurrentDictionary, this is controlled by the AcquireLocks, AcquireAllLocks and ReleaseLocks methods. Locks are always taken out and released in the order they are in the m_locks array, and locks are all released right at the end of the method in a finally block. At this point, it's worth pointing out that the locks array is never re-assigned, even when the buckets array is increased in size. The number of locks is fixed in the constructor by the concurrencyLevel parameter. This simplifies programming the locks; you don't have to check if the locks array has changed or been re-assigned before taking out a lock object. And you can be sure that when a thread takes out a lock, another thread isn't going to re-assign the lock array. This would create a new series of lock objects, thus allowing another thread to ignore the existing locks (and any threads controlling them), breaking thread-safety. Consequences of growing the array Just because we're using locks doesn't mean that race conditions aren't a problem. We can see this by looking at the GrowTable method. The operation of this method can be boiled down to: private void GrowTable(Node[] buckets) { try { 1: Acquire first lock in the locks array // this causes any other thread trying to take out // all the locks to block because the first lock in the array // is always the one taken out first // check if another thread has already resized the buckets array // while we were waiting to acquire the first lock 2: if (buckets != m_buckets) return; 3: Calculate the new size of the backing array 4: Node[] array = new array[size]; 5: Acquire all the remaining locks 6: Re-hash the contents of the existing buckets into array 7: m_buckets = array; } finally { 8: Release all locks } } As you can see, there's already a check for a race condition at step 2, for the case when the GrowTable method is called twice in quick succession on two separate threads. One will successfully resize the buckets array (blocking the second in the meantime), when the second thread is unblocked it'll see that the array has already been resized & exit without doing anything. There is another case we need to consider; looking back at the AlterBucket method above, consider the following situation: Thread 1 calls AlterBucket; step 1 is executed to get the bucket and lock numbers. Thread 2 calls GrowTable and executes steps 1-5; thread 1 is blocked when it tries to take out the lock in step 2. Thread 2 re-hashes everything, re-assigns the buckets array, and releases all the locks (steps 6-8). Thread 1 is unblocked and continues executing, but the calculated bucket and lock numbers are no longer valid. Between calculating the correct bucket and lock number and taking out the lock, another thread has changed where everything is. Not exactly thread-safe. Well, a similar problem was solved in ConcurrentStack and ConcurrentQueue by storing a local copy of the state, doing the necessary calculations, then checking if that state is still valid. We can use a similar idea here: void AlterBucket(TKey key, ...) { while (true) { Node[] buckets = m_buckets; int bucketNo, lockNo; GetBucketAndLockNo( key.GetHashCode(), out bucketNo, out lockNo, buckets.Length); lock (m_locks[lockNo]) { // if the state has changed, go back to the start if (buckets != m_buckets) continue; Node headNode = m_buckets[bucketNo]; Mutate the node linked list as appropriate } break; } } TryGetValue and GetEnumerator And so, finally, we get onto TryGetValue and GetEnumerator. I've left these to the end because, well, they don't actually use any locks. How can this be? Whenever you change a bucket, you need to take out the corresponding lock, yes? Indeed you do. However, it is important to note that TryGetValue and GetEnumerator don't actually change anything. Just as immutable objects are, by definition, thread-safe, read-only operations don't need to take out a lock because they don't change anything. All lockless methods can happily iterate through the buckets and linked lists without worrying about locking anything. However, this does put restrictions on how the other methods operate. Because there could be another thread in the middle of reading the dictionary at any time (even if a lock is taken out), the dictionary has to be in a valid state at all times. Every change to state has to be made visible to other threads in a single atomic operation (all relevant variables are marked volatile to help with this). This restriction ensures that whatever the reading threads are doing, they never read the dictionary in an invalid state (eg items that should be in the collection temporarily removed from the linked list, or reading a node that has had it's key & value removed before the node itself has been removed from the linked list). Fortunately, all the operations needed to change the dictionary can be done in that way. Bucket resizes are made visible when the new array is assigned back to the m_buckets variable. Any additions or modifications to a node are done by creating a new node, then splicing it into the existing list using a single variable assignment. Node removals are simply done by re-assigning the node's m_next pointer. Because the dictionary can be changed by another thread during execution of the lockless methods, the GetEnumerator method is liable to return dirty reads - changes made to the dictionary after GetEnumerator was called, but before the enumeration got to that point in the dictionary. It's worth listing at this point which methods are lockless, and which take out all the locks in the dictionary to ensure they get a consistent view of the dictionary: Lockless: TryGetValue GetEnumerator The indexer getter ContainsKey Takes out every lock (lockfull?): Count IsEmpty Keys Values CopyTo ToArray Concurrent principles That covers the overall implementation of ConcurrentDictionary. I haven't even begun to scratch the surface of this sophisticated collection. That I leave to you. However, we've looked at enough to be able to extract some useful principles for concurrent programming: Partitioning When using locks, the work is partitioned into independant chunks, each with its own lock. Each partition can then be modified concurrently to other partitions. Ordered lock-taking When a method does need to control the entire collection, locks are taken and released in a fixed order to prevent deadlocks. Lockless reads Read operations that don't care about dirty reads don't take out any lock; the rest of the collection is implemented so that any reading thread always has a consistent view of the collection. That leads us to the final collection in this little series - ConcurrentBag. Lacking a non-concurrent analogy, it is quite different to any other collection in the class libraries. Prepare your thinking hats!

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  • The C++ Standard Template Library as a BDB Database (part 1)

    - by Gregory Burd
    If you've used C++ you undoubtedly have used the Standard Template Libraries. Designed for in-memory management of data and collections of data this is a core aspect of all C++ programs. Berkeley DB is a database library with a variety of APIs designed to ease development, one of those APIs extends and makes use of the STL for persistent, transactional data storage. dbstl is an STL standard compatible API for Berkeley DB. You can make use of Berkeley DB via this API as if you are using C++ STL classes, and still make full use of Berkeley DB features. Being an STL library backed by a database, there are some important and useful features that dbstl can provide, while the C++ STL library can't. The following are a few typical use cases to use the dbstl extensions to the C++ STL for data storage. When data exceeds available physical memory.Berkeley DB dbstl can vastly improve performance when managing a dataset which is larger than available memory. Performance suffers when the data can't reside in memory because the OS is forced to use virtual memory and swap pages of memory to disk. Switching to BDB's dbstl improves performance while allowing you to keep using STL containers. When you need concurrent access to C++ STL containers.Few existing C++ STL implementations support concurrent access (create/read/update/delete) within a container, at best you'll find support for accessing different containers of the same type concurrently. With the Berkeley DB dbstl implementation you can concurrently access your data from multiple threads or processes with confidence in the outcome. When your objects are your database.You want to have object persistence in your application, and store objects in a database, and use the objects across different runs of your application without having to translate them to/from SQL. The dbstl is capable of storing complicated objects, even those not located on a continous chunk of memory space, directly to disk without any unnecessary overhead. These are a few reasons why you should consider using Berkeley DB's C++ STL support for your embedded database application. In the next few blog posts I'll show you a few examples of this approach, it's easy to use and easy to learn.

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  • is c++ STL algorithms and containers same across platforms and performance?

    - by Abhilash M
    After learning good amount of c++, i'm now into STL containers and algorithms template library, my major concerns are, 1) Is this library same across different platforms like MS, linux n other os? 2) will quality or efficiency of program c++ module decrease with more use of STL containers and algorithms, i think i can't customize it to all needs. 3) Is this template library good to use in linux system programming, kernel modules? 4) lastly can i use this in programming contests, because it relives a lot of coding and pressure off shoulders.

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  • Using static vs. member find method on a STL set?

    - by B Johnson
    I am using a set because, i want to use the quick look up property of a sorted container such as a set. I am wondering if I have to use the find member method to get the benefit of a sorted container, or can I also use the static find method in the STL algorithms? My hunch is that using the static version will use a linear search instead of a binary search like I want.

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  • Difference among STLPort and SGI STL

    - by Yan Cheng CHEOK
    Recently, I was buzzed by the following problem STL std::string class causes crashes and memory corruption on multi-processor machines while using VC6. I plan to use an alternative STL libraries instead of the one provided by VC6. I came across 2 libraries : STLPort and SGI STL I was wondering what is the difference between the 2. Which one I should use? Which one able to guarantee thread safety? Thanks.

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  • What is so great about STL?

    - by Zygrob
    Hello StackOverflow. I am a Java developer trying to learn C++. I have many times read over the web (including StackOverflow) that STL is the best collections library that you can get in *any* language. (Sorry, I do not have any citations atm) However after studying some STL, I am really failing to see what makes STL so special. Would you please shed some light on what sets STL apart from the collection libraries of other languages and make it the _best_ collection library? Thanks in advance, Zygr??b.

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  • Comparing two collections for equality

    - by Crossbrowser
    I would like to compare two collections (in C#), but I'm not sure of the best way to implement this efficiently. I've read the other thread about Enumerable.SequenceEqual, but it's not exactly what I'm looking for. In my case, two collections would be equal if they both contain the same items (no matter the order). Example: collection1 = {1, 2, 3, 4}; collection2 = {2, 4, 1, 3}; collection1 == collection2; // true What I usually do is to loop through each item of one collection and see if it exists in the other collection, then loop through each item of the other collection and see if it exists in the first collection. (I start by comparing the lengths). if (collection1.Count != collection2.Count) return false; // the collections are not equal foreach (Item item in collection1) { if (!collection2.Contains(item)) return false; // the collections are not equal } foreach (Item item in collection2) { if (!collection1.Contains(item)) return false; // the collections are not equal } return true; // the collections are equal However, this is not entirely correct, and it's probably not the most efficient way to do compare two collections for equality. An example I can think of that would be wrong is: collection1 = {1, 2, 3, 3, 4} collection2 = {1, 2, 2, 3, 4} Which would be equal with my implementation. Should I just count the number of times each item is found and make sure the counts are equal in both collections? The examples are in some sort of C# (let's call it pseudo-C#), but give your answer in whatever language you wish, it does not matter. Note: I used integers in the examples for simplicity, but I want to be able to use reference-type objects too (they do not behave correctly as keys because only the reference of the object is compared, not the content).

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  • Type-safe, generic, empty Collections with static generics

    - by Droo
    I return empty collections vs. null whenever possible. I switch between two methods for doing so using java.util.Collections: return Collections.EMPTY_LIST; return Collections.emptyList(); where emptyList() is supposed to be type-safe. But I recently discovered: return Collections.<ComplexObject> emptyList(); return Collections.<ComplexObject> singletonList(new ComplexObject()); etc. I see this method in Eclipse Package Explorer: <clinit> () : void but I don't see how this is done in the source code (1.5). How is this magic tomfoolerie happening!!

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  • Switch from Microsofts STL to STLport

    - by Laserallan
    Hi! I'm using quite much STL in performance critical C++ code under windows. One possible "cheap" way to get some extra performance would be to change to a faster STL library. According to this post STLport is faster and uses less memory, however it's a few years old. Has anyone made this change recently and what were your results?

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  • Version of STL optimized for compile time?

    - by anon
    Hi! I'm looking for a variant of the STL (it's okay if it doesn't have all the functionality) that's optimized for short compile times -- I get bothered by long compile times that delay my compile-debug-edit cycle. I'm mainly interested in the containers of the STL: vector/map, and not so much the algorithms. Thanks!

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  • Can I disable min, max in STL?

    - by P-P
    Hi, I use one library which is very important and untouchable. The problem is that library declare min, max function, so when I include STL header in project, they conflict. I want to disable min, max function in STL (like #define NOMNMAX) if I could. If I can't, what would be solution? Thanks, in advance.

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  • printing stl containers with gdb 7.0

    - by Andrei
    I have installed GDB 7.0 and python per the following instructions. In the same manual, there is a mention of this file stl-views-1.0.3.gdb. What confuses me, is where should it be placed in order to enable pretty printing of stl containers. Would someone also explain to me all of this work? Thanks

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