C# Priority Queue

Question

I'm looking for a priority queue with an interface like this:

class PriorityQueue<T>
{
    public void Enqueue(T item, int priority)
    {
    }

    public T Dequeue()
    {
    }
}

All the implementations I've seen assume that item is an IComparable but I don't like this approach; I want to specify the priority when I'm pushing it onto the queue.

If a ready-made implementation doesn't exist, what's the best way to go about doing this myself? What underlying data structure should I use? Some sort of self-balancing tree, or what? A standard C#.net structure would be nice.

Answer 1

Seems like you could roll your own with a seriews of Queues, one for each priority. Dictionary and just add it to the appropriate one.

Answer 2

You can add safety checks and what not, but here is a very simple implementation using SortedList:

class PriorityQueue<T> {
    SortedList<Pair<int>, T> _list;
    int count;

    public PriorityQueue() {
        _list = new SortedList<Pair<int>, T>(new PairComparer<int>());
    }

    public void Enqueue(T item, int priority) {
        _list.Add(new Pair<int>(priority, count), item);
        count++;
    }

    public T Dequeue() {
        T item = _list[_list.Keys[0]];
        _list.RemoveAt(0);
        return item;
    }
}

I'm assuming that smaller values of priority correspond to higher priority items (this is easy to modify).

If multiple threads will be accessing the queue you will need to add a locking mechanism too. This is easy, but let me know if you need guidance here.

SortedList is implemented internally as a binary tree.

The above implementation needs the following helper classes. This address Lasse V. Karlsen's comment that items with the same priority can not be added using the naive implementation using a SortedList.

class Pair<T> {
    public T First { get; private set; }
    public T Second { get; private set; }

    public Pair(T first, T second) {
        First = first;
        Second = second;
    }

    public override int GetHashCode() {
        return First.GetHashCode() ^ Second.GetHashCode();
    }

    public override bool Equals(object other) {
        Pair<T> pair = other as Pair<T>;
        if (pair == null) {
            return false;
        }
        return (this.First.Equals(pair.First) && this.Second.Equals(pair.Second));
    }
}

class PairComparer<T> : IComparer<Pair<T>> where T : IComparable {
    public int Compare(Pair<T> x, Pair<T> y) {
        if (x.First.CompareTo(y.First) < 0) {
            return -1;
        }
        else if (x.First.CompareTo(y.First) > 0) {
            return 1;
        }
        else {
            return x.Second.CompareTo(y.Second);
        }
    }
}

Answer 3

If you have an existing priority queue implementation based on IComparable, you can easily use that to build the structure you need:

public class CustomPriorityQueue<T>  // where T need NOT be IComparable
{
  private class PriorityQueueItem : IComparable<PriorityQueueItem>
  {
    private readonly T _item;
    private readonly int _priority:

    // obvious constructor, CompareTo implementation and Item accessor
  }

  // the existing PQ implementation where the item *does* need to be IComparable
  private readonly PriorityQueue<PriorityQueueItem> _inner = new PriorityQueue<PriorityQueueItem>();

  public void Enqueue(T item, int priority)
  {
    _inner.Enqueue(new PriorityQueueItem(item, priority));
  }

  public T Dequeue()
  {
    return _inner.Dequeue().Item;
  }
}

Answer 4

You could write a wrapper around one of the existing implementations that modifies the interface to your preference:

using System;

class PriorityQueueThatYouDontLike<T> where T: IComparable<T>
{
    public void Enqueue(T item) { throw new NotImplementedException(); }
    public T Dequeue() { throw new NotImplementedException(); }
}

class PriorityQueue<T>
{
    class ItemWithPriority : IComparable<ItemWithPriority>
    {
        public ItemWithPriority(T t, int priority)
        {
            Item = t;
            Priority = priority;
        }

        public T Item {get; private set;}
        public int Priority {get; private set;}

        public int CompareTo(ItemWithPriority other)
        {
            return Priority.CompareTo(other.Priority);
        }
    }

    PriorityQueueThatYouDontLike<ItemWithPriority> q = new PriorityQueueThatYouDontLike<ItemWithPriority>();

    public void Enqueue(T item, int priority)
    {
        q.Enqueue(new ItemWithPriority(item, priority));
    }

    public T Dequeue()
    {
        return q.Dequeue().Item;
    }
}

This is the same as itowlson's suggestion. I just took longer to write mine because I filled out more of the methods. :-s

Answer 5

Here's a very simple lightweight implementation that has O(log(n)) performance for both push and pop. It uses a heap data structure built on top of a List<T>.

/// <summary>Implements a priority queue of T, where T has an ordering.</summary>
/// Elements may be added to the queue in any order, but when we pull
/// elements out of the queue, they will be returned in 'ascending' order.
/// Adding new elements into the queue may be done at any time, so this is
/// useful to implement a dynamically growing and shrinking queue. Both adding
/// an element and removing the first element are log(N) operations. 
/// 
/// The queue is implemented using a priority-heap data structure. For more 
/// details on this elegant and simple data structure see "Programming Pearls"
/// in our library. The tree is implemented atop a list, where 2N and 2N+1 are
/// the child nodes of node N. The tree is balanced and left-aligned so there
/// are no 'holes' in this list. 
/// <typeparam name="T">Type T, should implement IComparable[T];</typeparam>
public class PriorityQueue<T> where T : IComparable<T> {
   /// <summary>Clear all the elements from the priority queue</summary>
   public void Clear () {
      mA.Clear ();
   }

   /// <summary>Add an element to the priority queue - O(log(n)) time operation.</summary>
   /// <param name="item">The item to be added to the queue</param>
   public void Add (T item) {
      // We add the item to the end of the list (at the bottom of the
      // tree). Then, the heap-property could be violated between this element
      // and it's parent. If this is the case, we swap this element with the 
      // parent (a safe operation to do since the element is known to be less
      // than it's parent). Now the element move one level up the tree. We repeat
      // this test with the element and it's new parent. The element, if lesser
      // than everybody else in the tree will eventually bubble all the way up
      // to the root of the tree (or the head of the list). It is easy to see 
      // this will take log(N) time, since we are working with a balanced binary
      // tree.
      int n = mA.Count; mA.Add (item);
      while (n != 0) {
         int p = n / 2;    // This is the 'parent' of this item
         if (mA[n].CompareTo (mA[p]) >= 0) break;  // Item >= parent
         T tmp = mA[n]; mA[n] = mA[p]; mA[p] = tmp; // Swap item and parent
         n = p;            // And continue
      }
   }

   /// <summary>Returns the number of elements in the queue.</summary>
   public int Count {
      get { return mA.Count; }
   }

   /// <summary>Returns true if the queue is empty.</summary>
   /// Trying to call Peek() or Next() on an empty queue will throw an exception.
   /// Check using Empty first before calling these methods.
   public bool Empty {
      get { return mA.Count == 0; }
   }

   /// <summary>Allows you to look at the first element waiting in the queue, without removing it.</summary>
   /// This element will be the one that will be returned if you subsequently call Next().
   public T Peek () {
      return mA[0];
   }

   /// <summary>Removes and returns the first element from the queue (least element)</summary>
   /// <returns>The first element in the queue, in ascending order.</returns>
   public T Next () {
      // The element to return is of course the first element in the array, 
      // or the root of the tree. However, this will leave a 'hole' there. We
      // fill up this hole with the last element from the array. This will 
      // break the heap property. So we bubble the element downwards by swapping
      // it with it's lower child until it reaches it's correct level. The lower
      // child (one of the orignal elements with index 1 or 2) will now be at the
      // head of the queue (root of the tree).
      T val = mA[0];
      int nMax = mA.Count - 1;
      mA[0] = mA[nMax]; mA.RemoveAt (nMax);  // Move the last element to the top

      int p = 0;
      while (true) {
         // c is the child we want to swap with. If there
         // is no child at all, then the heap is balanced
         int c = p * 2; if (c >= nMax) break;

         // If the second child is smaller than the first, that's the one
         // we want to swap with this parent.
         if (c + 1 < nMax && mA[c + 1].CompareTo (mA[c]) < 0) c++;
         // If the parent is already smaller than this smaller child, then
         // we are done
         if (mA[p].CompareTo (mA[c]) <= 0) break;

         // Othewise, swap parent and child, and follow down the parent
         T tmp = mA[p]; mA[p] = mA[c]; mA[c] = tmp;
         p = c;
      }
      return val;
   }

   /// <summary>The List we use for implementation.</summary>
   List<T> mA = new List<T> ();
}

Answer 6

What would be so terrible about something like this?

class PriorityQueue<TItem, TPriority> where TPriority : IComparable
{
    private SortedList<TPriority, Queue<TItem>> pq = new SortedList<TPriority, Queue<TItem>>();
    public int Count { get; private set; }

    public void Enqueue(TItem item, TPriority priority)
    {
        ++Count;
        if (!pq.ContainsKey(priority)) pq[priority] = new Queue<TItem>();
        pq[priority].Enqueue(item);
    }

    public TItem Dequeue()
    {
        --Count;
        var queue = pq.ElementAt(0).Value;
        if (queue.Count == 1) pq.RemoveAt(0);
        return queue.Dequeue();
    }
}

class PriorityQueue<TItem> : PriorityQueue<TItem, int> { }