Priority queue

From Rosetta Code
Priority queue
You are encouraged to solve this task according to the task description, using any language you may know.

A priority queue is somewhat similar to a queue, with an important distinction: each item is added to a priority queue with a priority level, and will be later removed from the queue with the highest priority element first. That is, the items are (conceptually) stored in the queue in priority order instead of in insertion order.

Task: Create a priority queue. The queue must support at least two operations:

  1. Insertion. An element is added to the queue with a priority (a numeric value).
  2. Top item removal. Deletes the element or one of the elements with the current top priority and return it.

Optionally, other operations may be defined, such as peeking (find what current top priority/top element is), merging (combining two priority queues into one), etc.

To test your implementation, insert a number of elements into the queue, each with some random priority. Then dequeue them sequentially; now the elements should be sorted by priority. You can use the following task/priority items as input data:

Priority    Task
  3        Clear drains
  4        Feed cat
  5        Make tea
  1        Solve RC tasks
  2        Tax return

The implementation should try to be efficient. A typical implementation has O(log n) insertion and extraction time, where n is the number of items in the queue. You may choose to impose certain limits such as small range of allowed priority levels, limited capacity, etc. If so, discuss the reasons behind it.


Works with: Ada 2012

Ada 2012 includes container classes for priority queues.

with Ada.Containers.Synchronized_Queue_Interfaces;
with Ada.Containers.Unbounded_Priority_Queues;
with Ada.Strings.Unbounded;
procedure Priority_Queues is
use Ada.Containers;
use Ada.Strings.Unbounded;
type Queue_Element is record
Priority : Natural;
Content  : Unbounded_String;
end record;
function Get_Priority (Element : Queue_Element) return Natural is
return Element.Priority;
end Get_Priority;
function Before (Left, Right : Natural) return Boolean is
return Left > Right;
end Before;
package String_Queues is new Synchronized_Queue_Interfaces
(Element_Type => Queue_Element);
package String_Priority_Queues is new Unbounded_Priority_Queues
(Queue_Interfaces => String_Queues,
Queue_Priority => Natural);
My_Queue : String_Priority_Queues.Queue;
My_Queue.Enqueue (New_Item => (Priority => 3, Content => To_Unbounded_String ("Clear drains")));
My_Queue.Enqueue (New_Item => (Priority => 4, Content => To_Unbounded_String ("Feed cat")));
My_Queue.Enqueue (New_Item => (Priority => 5, Content => To_Unbounded_String ("Make tea")));
My_Queue.Enqueue (New_Item => (Priority => 1, Content => To_Unbounded_String ("Solve RC tasks")));
My_Queue.Enqueue (New_Item => (Priority => 2, Content => To_Unbounded_String ("Tax return")));
Element : Queue_Element;
while My_Queue.Current_Use > 0 loop
My_Queue.Dequeue (Element => Element);
Ada.Text_IO.Put_Line (Natural'Image (Element.Priority) & " => " & To_String (Element.Content));
end loop;
end Priority_Queues;


PQ_TopItem(Queue,Task:=""){ ; remove and return top priority item
TopPriority := PQ_TopPriority(Queue)
for T, P in Queue
if (P = TopPriority) && ((T=Task)||!Task)
return T , Queue.Remove(T)
return 0
PQ_AddTask(Queue,Task,Priority){ ; insert and return new task
for T, P in Queue
if (T=Task) || !(Priority && Task)
return 0
return Task, Queue[Task] := Priority
PQ_DelTask(Queue, Task){ ; delete and return task
for T, P in Queue
if (T = Task)
return Task, Queue.Remove(Task)
PQ_Peek(Queue){ ; peek and return top priority task(s)
TopPriority := PQ_TopPriority(Queue)
for T, P in Queue
if (P = TopPriority)
PeekList .= (PeekList?"`n":"") "`t" T
return PeekList
PQ_Check(Queue,Task){ ; check task and return its priority
for T, P in Queue
if (T = Task)
return P
return 0
PQ_Edit(Queue,Task,Priority){ ; Update task priority and return its new priority
for T, P in Queue
if (T = Task)
return Priority, Queue[T]:=Priority
return 0
PQ_View(Queue){ ; view current Queue
for T, P in Queue
Res .= P " : " T "`n"
Sort, Res, FMySort
return "Priority Queue=`n" Res
RegExMatch(a,"(\d+) : (.*)", x), RegExMatch(b,"(\d+) : (.*)", y)
return x1>y1?1:x1<y1?-1: x2>y2?1:x2<y2?-1: 0
PQ_TopPriority(Queue){ ; return queue's top priority
for T, P in Queue
TopPriority := TopPriority?TopPriority:P , TopPriority := TopPriority<P?TopPriority:P
return, TopPriority
data =
3 Clear drains
1 test
4 Feed cat
5 Make tea
1 Solve RC tasks
2 Tax return
PQ:=[] ; Create Priority Queue PQ[Task, Priority]
loop, parse, data, `n, `r
F:= StrSplit(A_LoopField, "`t") , PQ[F[2]] := F[1]
MsgBox, 262208,, % "Top Priority item(s)=`n" PQ_Peek(PQ) "`n`n" PQ_View(PQ)
MsgBox, 262208,, % "Add : " PQ_AddTask(PQ, "AutoHotkey", 2) "`n`n" PQ_View(PQ)
MsgBox, 262208,, % "Remove Top Item : " PQ_TopItem(PQ) "`n`n" PQ_View(PQ)
MsgBox, 262208,, % "Remove specific top item : " PQ_TopItem(PQ,"test") "`n`n" PQ_View(PQ)
MsgBox, 262208,, % "Delete Item : " PQ_DelTask(PQ, "Clear drains") "`n`n" PQ_View(PQ)
MsgBox, 262208,, % (Task:="Tax return") " new priority = " PQ_Edit(PQ,task, 7) "`n`n" PQ_View(PQ)
MsgBox, 262208,, % (Task:="Feed cat") " priority = " PQ_Check(PQ,task)"`n`n" PQ_View(PQ)


Axiom already has a heap domain for ordered sets. We define a domain for ordered key-entry pairs and then define a priority queue using the heap domain over the pairs:

)abbrev Domain ORDKE OrderedKeyEntry
OrderedKeyEntry(Key:OrderedSet,Entry:SetCategory): Exports == Implementation where
Exports == OrderedSet with
construct: (Key,Entry) -> %
elt: (%,"key") -> Key
elt: (%,"entry") -> Entry
Implementation == add
Rep := Record(k:Key,e:Entry)
x,y: %
construct(a,b) == construct(a,b)$Rep @ %
elt(x,"key"):Key == ([email protected]).k
elt(x,"entry"):Entry == ([email protected]).e
x < y == x.key < y.key
x = y == x.key = y.key
hash x == hash(x.key)
if Entry has CoercibleTo OutputForm then
coerce(x):OutputForm == bracket [(x.key)::OutputForm,(x.entry)::OutputForm]
)abbrev Domain PRIORITY PriorityQueue
S ==> OrderedKeyEntry(Key,Entry)
PriorityQueue(Key:OrderedSet,Entry:SetCategory): Exports == Implementation where
Exports == PriorityQueueAggregate S with
heap : List S -> %
setelt: (%,Key,Entry) -> Entry
Implementation == Heap(S) add
setelt(x:%,key:Key,entry:Entry) ==
For an example:
pq := empty()$PriorityQueue(Integer,String)
pq(3):="Clear drains";
pq(4):="Feed cat";
pq(5):="Make tea";
pq(1):="Solve RC tasks";
pq(2):="Tax return";
[extract!(pq) for i in 1..#pq]
   [[5,"Make tea"], [4,"Feed cat"], [3,"Clear drains"], [2,"Tax return"],
    [1,"Solve RC tasks"]]
                                  Type: List(OrderedKeyEntry(Integer,String))

Batch File[edit]

Batch has only a data structure, the environment that incidentally sorts itself automatically by key. The environment has a limit of 64K

@echo off
setlocal enabledelayedexpansion
call :push 10 "item ten"
call :push 2 "item two"
call :push 100 "item one hundred"
call :push 5 "item five"
call :pop & echo !order! !item!
call :pop & echo !order! !item!
call :pop & echo !order! !item!
call :pop & echo !order! !item!
call :pop & echo !order! !item!
set temp=000%1
set queu%temp:~-3%=%2
set queu >nul 2>nul
if %errorlevel% equ 1 (set order=-1&set item=no more items & goto:eof)
for /f "tokens=1,2 delims==" %%a in ('set queu') do set %%a=& set order=%%a& set item=%%~b& goto:next
set order= %order:~-3%
 002 item two
 005 item five
 010 item ten
 100 item one hundred
-1 no more items


Using a dynamic array as a binary heap. Stores integer priority and a character pointer. Supports push and pop.

#include <stdio.h>
#include <stdlib.h>
typedef struct {
int priority;
char *data;
} node_t;
typedef struct {
node_t *nodes;
int len;
int size;
} heap_t;
void push (heap_t *h, int priority, char *data) {
if (h->len + 1 >= h->size) {
h->size = h->size ? h->size * 2 : 4;
h->nodes = (node_t *)realloc(h->nodes, h->size * sizeof (node_t));
int i = h->len + 1;
int j = i / 2;
while (i > 1 && h->nodes[j].priority > priority) {
h->nodes[i] = h->nodes[j];
i = j;
j = j / 2;
h->nodes[i].priority = priority;
h->nodes[i].data = data;
char *pop (heap_t *h) {
int i, j, k;
if (!h->len) {
return NULL;
char *data = h->nodes[1].data;
h->nodes[1] = h->nodes[h->len];
i = 1;
while (1) {
k = i;
j = 2 * i;
if (j <= h->len && h->nodes[j].priority < h->nodes[k].priority) {
k = j;
if (j + 1 <= h->len && h->nodes[j + 1].priority < h->nodes[k].priority) {
k = j + 1;
if (k == i) {
h->nodes[i] = h->nodes[k];
i = k;
h->nodes[i] = h->nodes[h->len + 1];
return data;
int main () {
heap_t *h = (heap_t *)calloc(1, sizeof (heap_t));
push(h, 3, "Clear drains");
push(h, 4, "Feed cat");
push(h, 5, "Make tea");
push(h, 1, "Solve RC tasks");
push(h, 2, "Tax return");
int i;
for (i = 0; i < 5; i++) {
printf("%s\n", pop(h));
return 0;
Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea


The C++ standard library contains the std::priority_queue opaque data structure. It implements a max-heap.

#include <iostream>
#include <string>
#include <queue>
#include <utility>
int main() {
std::priority_queue<std::pair<int, std::string> > pq;
pq.push(std::make_pair(3, "Clear drains"));
pq.push(std::make_pair(4, "Feed cat"));
pq.push(std::make_pair(5, "Make tea"));
pq.push(std::make_pair(1, "Solve RC tasks"));
pq.push(std::make_pair(2, "Tax return"));
while (!pq.empty()) {
std::cout << << ", " << << std::endl;
return 0;
5, Make tea
4, Feed cat
3, Clear drains
2, Tax return
1, Solve RC tasks

Alternately, you can use a pre-existing container of yours and use the heap operations to manipulate it:

#include <iostream>
#include <string>
#include <vector>
#include <algorithm>
#include <utility>
int main() {
std::vector<std::pair<int, std::string> > pq;
pq.push_back(std::make_pair(3, "Clear drains"));
pq.push_back(std::make_pair(4, "Feed cat"));
pq.push_back(std::make_pair(5, "Make tea"));
pq.push_back(std::make_pair(1, "Solve RC tasks"));
// heapify
std::make_heap(pq.begin(), pq.end());
// enqueue
pq.push_back(std::make_pair(2, "Tax return"));
std::push_heap(pq.begin(), pq.end());
while (!pq.empty()) {
// peek
std::cout << pq[0].first << ", " << pq[0].second << std::endl;
// dequeue
std::pop_heap(pq.begin(), pq.end());
return 0;
5, Make tea
4, Feed cat
3, Clear drains
2, Tax return
1, Solve RC tasks


using System;
namespace PriorityQueue
class Program
static void Main(string[] args)
PriorityQueue PQ = new PriorityQueue();
PQ.push(3, "Clear drains");
PQ.push(4, "Feed cat");
PQ.push(5, "Make tea");
PQ.push(1, "Solve RC tasks");
PQ.push(2, "Tax return");
while (!PQ.Empty)
var Val = PQ.pop();
Console.WriteLine(Val[0] + " : " + Val[1]);
class PriorityQueue
private System.Collections.SortedList PseudoQueue;
public bool Empty
return PseudoQueue.Count == 0;
public PriorityQueue()
PseudoQueue = new System.Collections.SortedList();
public void push(object Priority, object Value)
PseudoQueue.Add(Priority, Value);
public object[] pop()
object[] ReturnValue = { null, null };
if (PseudoQueue.Count > 0)
ReturnValue[0] = PseudoQueue.GetKey(0);
ReturnValue[1] = PseudoQueue.GetByIndex(0);
return ReturnValue;

Min Heap Priority Queue

Works with: C# version 3.0+/DotNet 3.5+

The above code is not really a true Priority Queue as it does not allow duplicate keys; also, the SortedList on which it is based does not have O(log n) insertions and removals for random data as a true Priority Queue does. The below code implements a true Min Heap Priority Queue:

namespace PriorityQ {
using KeyT = UInt32;
using System;
using System.Collections.Generic;
using System.Linq;
class Tuple<K, V> { // for DotNet 3.5 without Tuple's
public K Item1; public V Item2;
public Tuple(K k, V v) { Item1 = k; Item2 = v; }
public override string ToString() {
return "(" + Item1.ToString() + ", " + Item2.ToString() + ")";
class MinHeapPQ<V> {
private struct HeapEntry {
public KeyT k; public V v;
public HeapEntry(KeyT k, V v) { this.k = k; this.v = v; }
private List<HeapEntry> pq;
private MinHeapPQ() { this.pq = new List<HeapEntry>(); }
private bool mt { get { return pq.Count == 0; } }
private int sz {
get {
var cnt = pq.Count;
return (cnt == 0) ? 0 : cnt - 1;
private Tuple<KeyT, V> pkmn {
get {
if (pq.Count == 0) return null;
else {
var mn = pq[0];
return new Tuple<KeyT, V>(mn.k, mn.v);
private void psh(KeyT k, V v) { // add extra very high item if none
if (pq.Count == 0) pq.Add(new HeapEntry(UInt32.MaxValue, v));
var i = pq.Count; pq.Add(pq[i - 1]); // copy bottom item...
for (var ni = i >> 1; ni > 0; i >>= 1, ni >>= 1) {
var t = pq[ni - 1];
if (t.k > k) pq[i - 1] = t; else break;
pq[i - 1] = new HeapEntry(k, v);
private void siftdown(KeyT k, V v, int ndx) {
var cnt = pq.Count - 1; var i = ndx;
for (var ni = i + i + 1; ni < cnt; ni = ni + ni + 1) {
var oi = i; var lk = pq[ni].k; var rk = pq[ni + 1].k;
var nk = k;
if (k > lk) { i = ni; nk = lk; }
if (nk > rk) { ni += 1; i = ni; }
if (i != oi) pq[oi] = pq[i]; else break;
pq[i] = new HeapEntry(k, v);
private void rplcmin(KeyT k, V v) {
if (pq.Count > 1) siftdown(k, v, 0);
private void dltmin() {
var lsti = pq.Count - 2;
if (lsti <= 0) pq.Clear();
else {
var lkv = pq[lsti];
pq.RemoveAt(lsti); siftdown(lkv.k, lkv.v, 0);
private void reheap(int i) {
var lfti = i + i + 1;
if (lfti < sz) {
var rghti = lfti + 1; reheap(lfti); reheap(rghti);
var ckv = pq[i]; siftdown(ckv.k, ckv.v, i);
private void bld(IEnumerable<Tuple<KeyT, V>> sq) {
var sqm = from e in sq
select new HeapEntry(e.Item1, e.Item2);
pq = sqm.ToList<HeapEntry>();
var sz = pq.Count;
if (sz > 0) {
var lkv = pq[sz - 1];
pq.Add(new HeapEntry(KeyT.MaxValue, lkv.v));
private IEnumerable<Tuple<KeyT, V>> sq() {
return from e in pq
where e.k != KeyT.MaxValue
select new Tuple<KeyT, V>(e.k, e.v); }
private void adj(Func<KeyT, V, Tuple<KeyT, V>> f) {
var cnt = pq.Count - 1;
for (var i = 0; i < cnt; ++i) {
var e = pq[i];
var r = f(e.k, e.v);
pq[i] = new HeapEntry(r.Item1, r.Item2);
public static MinHeapPQ<V> empty { get { return new MinHeapPQ<V>(); } }
public static bool isEmpty(MinHeapPQ<V> pq) { return; }
public static int size(MinHeapPQ<V> pq) { return; }
public static Tuple<KeyT, V> peekMin(MinHeapPQ<V> pq) { return pq.pkmn; }
public static MinHeapPQ<V> push(KeyT k, V v, MinHeapPQ<V> pq) {
pq.psh(k, v); return pq; }
public static MinHeapPQ<V> replaceMin(KeyT k, V v, MinHeapPQ<V> pq) {
pq.rplcmin(k, v); return pq; }
public static MinHeapPQ<V> deleteMin(MinHeapPQ<V> pq) { pq.dltmin(); return pq; }
public static MinHeapPQ<V> merge(MinHeapPQ<V> pq1, MinHeapPQ<V> pq2) {
return fromSeq(pq1.sq().Concat(pq2.sq())); }
public static MinHeapPQ<V> adjust(Func<KeyT, V, Tuple<KeyT, V>> f, MinHeapPQ<V> pq) {
pq.adj(f); return pq; }
public static MinHeapPQ<V> fromSeq(IEnumerable<Tuple<KeyT, V>> sq) {
var pq = new MinHeapPQ<V>(); pq.bld(sq); return pq; }
public static Tuple<Tuple<KeyT, V>, MinHeapPQ<V>> popMin(MinHeapPQ<V> pq) {
var rslt = pq.pkmn; if (rslt == null) return null;
pq.dltmin(); return new Tuple<Tuple<KeyT, V>, MinHeapPQ<V>>(rslt, pq); }
public static IEnumerable<Tuple<KeyT, V>> toSeq(MinHeapPQ<V> pq) {
for (; !; pq.dltmin()) yield return pq.pkmn; }
public static IEnumerable<Tuple<KeyT, V>> sort(IEnumerable<Tuple<KeyT, V>> sq) {
return toSeq(fromSeq(sq)); }

The above class code offers a full set of static methods and properties:

 1.  "empty" to create a new empty queue,
 2.  "isEmpty" to test if a queue is empty,
 3.  "size" to get the number of elements in the queue,
 4.  "peekMin" to retrieve the lowest priority key/value pair entry as a Tuple (possibly null for empty queues),
 5.  "push" to insert an entry,
 6.  "deleteMin" to remove the lowest priority entry,
 7.  "replaceMin" to replace the lowest priority and adjust the queue according to the value (faster than a "deleteMin" followed by a "push"), 
 8.  "adjust" to apply a function to every key/value entry pair and reheapify the result,
 9.  "merge" to merge two queues into a single reheapified result,
 10. "fromSeq" to build a queue from a sequence of key/value pair tuples,
 11. "popMin" which is a convenience function combining a "peekMin" with a "deleteMin", returning null if the queue is empty and a tuple of the result otherwise,
 12. "toSeq" to output an ordered sequence of the queue contents as Tuple's of the key/value pairs, and
 13. "sort" which is a convenience function combining "fromSeq" followed by "toSeq".

The first four are all O(1) and the remainder O(log n) except "adjust" and "fromSeq" are O(n), "merge" is O(m + n) where m and n are the sizes of the two queues, and "toSeq" and "sort" are O(n log n); "replaceMin" is still O(log n) but faster than a "deleteMin" followed by a "push" by a constant factor.

Note that the Key type "KeyT" is not generic in order to give better comparison efficiency than using generic comparison using the IComparible interface but can be changed to different numeric types using the "using KeyT = ???" type alias.

The above code can be tested as per the page specification by the following code:

    static void Main(string[] args) {
Tuple<uint, string>[] ins = { new Tuple<uint,string>(3u, "Clear drains"),
new Tuple<uint,string>(4u, "Feed cat"),
new Tuple<uint,string>(5u, "Make tea"),
new Tuple<uint,string>(1u, "Solve RC tasks"),
new Tuple<uint,string>(2u, "Tax return") };
var spq = ins.Aggregate(MinHeapPQ<string>.empty, (pq, t) => MinHeapPQ<string>.push(t.Item1, t.Item2, pq));
foreach (var e in MinHeapPQ<string>.toSeq(spq)) Console.WriteLine(e); Console.WriteLine();
foreach (var e in MinHeapPQ<string>.sort(ins)) Console.WriteLine(e); Console.WriteLine();
var npq = MinHeapPQ<string>.fromSeq(ins);
foreach (var e in MinHeapPQ<string>.toSeq(MinHeapPQ<string>.merge(npq, npq)))
Console.WriteLine(e); Console.WriteLine();
var npq = MinHeapPQ<string>.fromSeq(ins);
foreach (var e in MinHeapPQ<string>.toSeq(MinHeapPQ<string>.merge(npq, npq)))
foreach (var e in MinHeapPQ<string>.toSeq(MinHeapPQ<string>.adjust((k, v) => new Tuple<uint,string>(6u - k, v), npq)))
Console.WriteLine(e); Console.WriteLine();

It tests building the queue the slow way using repeated "push"'s - O(n log n), the faster "fromSeq" (included in the "sort") - O(n), and also tests the "merge" and "adjust" methods.

The output of the above test is as follows:

(1, Solve RC tasks)
(2, Tax return)
(3, Clear drains)
(4, Feed cat)
(5, Make tea)

(1, Solve RC tasks)
(2, Tax return)
(3, Clear drains)
(4, Feed cat)
(5, Make tea)

(1, Solve RC tasks)
(1, Solve RC tasks)
(2, Tax return)
(2, Tax return)
(3, Clear drains)
(3, Clear drains)
(4, Feed cat)
(4, Feed cat)
(5, Make tea)
(5, Make tea)

(1, Make tea)
(2, Feed cat)
(3, Clear drains)
(4, Tax return)
(5, Solve RC tasks)

Common Lisp[edit]

In this task were implemented to versions of the functions, the non-destructive ones that will not change the state of the priority queue and the destructive ones that will change. The destructive ones work very similarly to the 'pop' and 'push' functions.

;priority-queue's are implemented with association lists
(defun make-pq (alist)
(sort (copy-alist alist) (lambda (a b) (< (car a) (car b)))))
;Will change the state of pq
(define-modify-macro insert-pq (pair)
(lambda (pq pair) (sort-alist (cons pair pq))))
(define-modify-macro remove-pq-aux () cdr)
(defmacro remove-pq (pq)
`(let ((aux (copy-alist ,pq)))
(car aux)))
;Will not change the state of pq
(defun insert-pq-non-destructive (pair pq)
(sort-alist (cons pair pq)))
(defun remove-pq-non-destructive (pq)
(cdr pq))
(defparameter a (make-pq '((1 . "Solve RC tasks") (3 . "Clear drains") (2 . "Tax return") (5 . "Make tea"))))
(format t "~a~&" a)
(insert-pq a '(4 . "Feed cat"))
(format t "~a~&" a)
(format t "~a~&" (remove-pq a))
(format t "~a~&" a)
(format t "~a~&" (remove-pq a))
(format t "~a~&" a)
((1 . Solve RC tasks) (2 . Tax return) (3 . Clear drains) (5 . Make tea))
((1 . Solve RC tasks) (2 . Tax return) (3 . Clear drains) (4 . Feed cat) (5 . Make tea))
(1 . Solve RC tasks)
((2 . Tax return) (3 . Clear drains) (4 . Feed cat) (5 . Make tea))
(2 . Tax return)
((3 . Clear drains) (4 . Feed cat) (5 . Make tea))


user=> (use '
; priority-map can be used as a priority queue
user=> (def p (priority-map "Clear drains" 3, "Feed cat" 4, "Make tea" 5, "Solve RC tasks" 1))
user=> p
{"Solve RC tasks" 1, "Clear drains" 3, "Feed cat" 4, "Make tea" 5}
; You can use assoc or conj to add items
user=> (assoc p "Tax return" 2)
{"Solve RC tasks" 1, "Tax return" 2, "Clear drains" 3, "Feed cat" 4, "Make tea" 5}
; peek to get first item, pop to give you back the priority-map with the first item removed
user=> (peek p)
["Solve RC tasks" 1]
; Merge priority-maps together
user=> (into p [["Wax Car" 4]["Paint Fence" 1]["Sand Floor" 3]])
{"Solve RC tasks" 1, "Paint Fence" 1, "Clear drains" 3, "Sand Floor" 3, "Wax Car" 4, "Feed cat" 4, "Make tea" 5}


PriorityQueue = ->
# Use closure style for object creation (so no "new" required).
# Private variables are toward top.
h = []
better = (a, b) ->
h[a].priority < h[b].priority
swap = (a, b) ->
[h[a], h[b]] = [h[b], h[a]]
sift_down = ->
max = h.length
n = 0
while n < max
c1 = 2*n + 1
c2 = c1 + 1
best = n
best = c1 if c1 < max and better(c1, best)
best = c2 if c2 < max and better(c2, best)
return if best == n
swap n, best
n = best
sift_up = ->
n = h.length - 1
while n > 0
parent = Math.floor((n-1) / 2)
return if better parent, n
swap n, parent
n = parent
# now return the public interface, which is an object that only
# has functions on it
self =
size: ->
push: (priority, value) ->
elem =
priority: priority
value: value
h.push elem
pop: ->
throw Error("cannot pop from empty queue") if h.length == 0
value = h[0].value
last = h.pop()
if h.length > 0
h[0] = last
# test
do ->
pq = PriorityQueue()
pq.push 3, "Clear drains"
pq.push 4, "Feed cat"
pq.push 5, "Make tea"
pq.push 1, "Solve RC tasks"
pq.push 2, "Tax return"
while pq.size() > 0
console.log pq.pop()
# test high performance
for n in [1..100000]
priority = Math.random()
pq.push priority, priority
v = pq.pop()
console.log "First random element was #{v}"
while pq.size() > 0
new_v = pq.pop()
throw Error "Queue broken" if new_v < v
v = new_v
console.log "Final random element was #{v}"


> coffee
Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea
First random element was 0.00002744467929005623
Final random element was 0.9999718656763434

Component Pascal[edit]

BlackBox Component Builder

IMPORT StdLog,Boxes;
p-: LONGINT; (* Priority *)
value-: Boxes.Object
size-: LONGINT;
PROCEDURE NewRank*(p: LONGINT; v: Boxes.Object): Rank;
r: Rank;
NEW(r);r.p := p;r.value := v;
END NewRank;
PROCEDURE NewPQueue*(cap: LONGINT): PQueue;
pq: PQueue;
NEW(pq);pq.size := 0;
NEW(pq.a,cap);pq.a[0] := NewRank(MIN(INTEGER),NIL);
END NewPQueue;
PROCEDURE (pq: PQueue) Push*(r:Rank), NEW;
i := pq.size;
WHILE r.p < pq.a[i DIV 2].p DO
pq.a[i] := pq.a[i DIV 2];i := i DIV 2
pq.a[i] := r
END Push;
PROCEDURE (pq: PQueue) Pop*(): Rank,NEW;
r,y: Rank;
r := pq.a[1]; (* Priority object *)
y := pq.a[pq.size]; DEC(pq.size); i := 1; ok := FALSE;
WHILE (i <= pq.size DIV 2) & ~ok DO
j := i + 1;
IF (j < pq.size) & (pq.a[i].p > pq.a[j + 1].p) THEN INC(j) END;
IF y.p > pq.a[j].p THEN pq.a[i] := pq.a[j]; i := j ELSE ok := TRUE END
pq.a[i] := y;
END Pop;
PROCEDURE (pq: PQueue) IsEmpty*(): BOOLEAN,NEW;
RETURN pq.size = 0
END IsEmpty;
pq: PQueue;
r: Rank;
PROCEDURE ShowRank(r:Rank);
StdLog.Int(r.p);StdLog.String(":> ");StdLog.String(r.value.AsString());StdLog.Ln;
END ShowRank;
pq := NewPQueue(128);
pq.Push(NewRank(3,Boxes.NewString("Clear drains")));
pq.Push(NewRank(4,Boxes.NewString("Feed cat")));
pq.Push(NewRank(5,Boxes.NewString("Make tea")));
pq.Push(NewRank(1,Boxes.NewString("Solve RC tasks")));
pq.Push(NewRank(2,Boxes.NewString("Tax return")));
END Test;
END PQueues.

Interface extracted from the implementation

size-: LONGINT;
(pq: PQueue) IsEmpty (): BOOLEAN, NEW;
(pq: PQueue) Pop (): Rank, NEW;
(pq: PQueue) Push (r: Rank), NEW
value-: Boxes.Object
PROCEDURE NewPQueue (cap: LONGINT): PQueue;
PROCEDURE NewRank (p: LONGINT; v: Boxes.Object): Rank;
END PQueues.

Execute: ^Q PQueues.Test

 1:> Solve RC tasks
 2:> Tax return
 3:> Clear drains
 4:> Feed cat
 5:> Make tea


import std.stdio, std.container, std.array, std.typecons;
void main() {
alias tuple T;
auto heap = heapify([T(3, "Clear drains"),
T(4, "Feed cat"),
T(5, "Make tea"),
T(1, "Solve RC tasks"),
T(2, "Tax return")]);
while (!heap.empty) {
Tuple!(int,string)(5, "Make tea")
Tuple!(int,string)(4, "Feed cat")
Tuple!(int,string)(3, "Clear drains")
Tuple!(int,string)(2, "Tax return")
Tuple!(int,string)(1, "Solve RC tasks")


We use the built-in binary tree library. Each tree node has a datum (key . value). The functions (bin-tree-pop-first tree) and (bin-tree-pop-last tree) allow to extract the node with highest or lowest priority.

(lib 'tree)
(define tasks (make-bin-tree 3 "Clear drains"))
(bin-tree-insert tasks 2 "Tax return")
(bin-tree-insert tasks 5 "Make tea")
(bin-tree-insert tasks 1 "Solve RC tasks")
(bin-tree-insert tasks 4 "Feed 🐑")
(bin-tree-pop-first tasks) β†’ (1 . "Solve RC tasks")
(bin-tree-pop-first tasks) β†’ (2 . "Tax return")
(bin-tree-pop-first tasks) β†’ (3 . "Clear drains")
(bin-tree-pop-first tasks) β†’ (4 . "Feed 🐑")
(bin-tree-pop-first tasks) β†’ (5 . "Make tea")
(bin-tree-pop-first tasks) β†’ null
;; similarly
(bin-tree-pop-last tasks) β†’ (5 . "Make tea")
(bin-tree-pop-last tasks) β†’ (4 . "Feed 🐑")
; etc.


Translation of: Erlang
defmodule Priority do
def create, do: :gb_trees.empty
def insert( element, priority, queue ), do: :gb_trees.enter( priority, element, queue )
def peek( queue ) do
{_priority, element, _new_queue} = :gb_trees.take_smallest( queue )
def task do
items = [{3, "Clear drains"}, {4, "Feed cat"}, {5, "Make tea"}, {1, "Solve RC tasks"}, {2, "Tax return"}]
queue = Enum.reduce(items, create, fn({priority, element}, acc) -> insert( element, priority, acc ) end)
IO.puts "peek priority: #{peek( queue )}"
Enum.reduce(1..length(items), queue, fn(_n, q) -> write_top( q ) end)
def top( queue ) do
{_priority, element, new_queue} = :gb_trees.take_smallest( queue )
{element, new_queue}
defp write_top( q ) do
{element, new_queue} = top( q )
IO.puts "top priority: #{element}"
peek priority: Solve RC tasks
top priority: Solve RC tasks
top priority: Tax return
top priority: Clear drains
top priority: Feed cat
top priority: Make tea


Using built in gb_trees module, with the suggested interface for this task.

-module( priority_queue ).
-export( [create/0, insert/3, peek/1, task/0, top/1] ).
create() -> gb_trees:empty().
insert( Element, Priority, Queue ) -> gb_trees:enter( Priority, Element, Queue ).
peek( Queue ) ->
{_Priority, Element, _New_queue} = gb_trees:take_smallest( Queue ),
task() ->
Items = [{3, "Clear drains"}, {4, "Feed cat"}, {5, "Make tea"}, {1, "Solve RC tasks"}, {2, "Tax return"}],
Queue = lists:foldl( fun({Priority, Element}, Acc) -> insert( Element, Priority, Acc ) end, create(), Items ),
io:fwrite( "peek priority: ~p~n", [peek( Queue )] ),
lists:foldl( fun(_N, Q) -> write_top( Q ) end, Queue, lists:seq(1, erlang:length(Items)) ).
top( Queue ) ->
{_Priority, Element, New_queue} = gb_trees:take_smallest( Queue ),
{Element, New_queue}.
write_top( Q ) ->
{Element, New_queue} = top( Q ),
io:fwrite( "top priority: ~p~n", [Element] ),
12> priority_queue:task(). 
peek priority: "Solve RC tasks"
top priority: "Solve RC tasks"
top priority: "Tax return"
top priority: "Clear drains"
top priority: "Feed cat"
top priority: "Make tea"


The below codes all provide the standard priority queue functions of "peekMin", "push", and "deleteMin"; as well, "replaceMin" which can be much more efficient that a "deleteMin" followed by a "push" for some types of queues), "popMin" (generally a convenience function for "peekMin" followed by "deleteMin"), "adjust" for applying a function to all queue entries and reheapifying, "fromSeq" for building a queue from a sequence, "toSeq" for outputting a sorted sequence of the queue contents, and "sort" which is a convenience function combining the latter two functions are provided. Finally, the queue's all provide a "merge" function to combine two queues into one, and an "adjust" function which applies a function to every heap element and reheapifies.


Binomial Heap Priority Queue

The following Binomial Heap Priority Queue code has been adapted from a version by "DeeJay" updated for changes in F# over the intervening years, and implementing the O(1) "peekMin" mentioned in that post; in addition to the above standard priority queue functions, it also implements the "merge" function for which the Binomial Heap is particularly suited, taking O(log n) time rather than the usual O(n) (or worse) time:

module PriorityQ =
// type 'a treeElement = Element of uint32 * 'a
type 'a treeElement = struct val k:uint32 val v:'a new(k,v) = { k=k;v=v } end
type 'a tree = Node of uint32 * 'a treeElement * 'a tree list
type '
a heap = 'a tree list
[<NoEquality; NoComparison>]
type '
a outerheap = | HeapEmpty | HeapNotEmpty of 'a treeElement * 'a heap
let empty = HeapEmpty
let isEmpty = function | HeapEmpty -> true | _ -> false
let inline private rank (Node(r,_,_)) = r
let inline private root (Node(_,x,_)) = x
exception Empty_Heap
let peekMin = function | HeapEmpty -> None
| HeapNotEmpty(min, _) -> Some (min.k, min.v)
let rec private findMin heap =
match heap with | [] -> raise Empty_Heap //guarded so should never happen
| [node] -> root node,[]
| topnode::heap' ->
let min,subheap = findMin heap'
in let rtn = root topnode
match subheap with
| [] -> if rtn.k > min.k then min,[] else rtn,[]
| minnode::heap'' ->
let rmn = root minnode
if rtn.k <= rmn.k then rtn,heap
else rmn,minnode::topnode::heap''
let private mergeTree (Node(r,kv1,ts1) as tree1) (Node (_,kv2,ts2) as tree2) =
if kv1.k > kv2.k then Node(r+1u,kv2,tree1::ts2)
else Node(r+1u,kv1,tree2::ts1)
let rec private insTree (newnode: 'a tree) heap =
match heap with
| [] -> [newnode]
| topnode::heap'
-> if (rank newnode) < (rank topnode) then newnode::heap
else insTree (mergeTree newnode topnode) heap'
let push k v = let kv = treeElement(k,v) in let nn = Node(0u,kv,[])
function | HeapEmpty -> HeapNotEmpty(kv,[nn])
| HeapNotEmpty(min,heap) -> let nmin = if k > min.k then min else kv
HeapNotEmpty(nmin,insTree nn heap)
let rec private merge'
heap1 heap2 = //doesn't guaranty minimum tree node as head!!!
match heap1,heap2 with
| _,[] -> heap1
| [],_ -> heap2
| topheap1::heap1',topheap2::heap2' ->
match compare (rank topheap1) (rank topheap2) with
| -1 -> topheap1::merge' heap1' heap2
| 1 -> topheap2::merge' heap1 heap2'
| _ -> insTree (mergeTree topheap1 topheap2) (merge' heap1' heap2')
let merge oheap1 oheap2 = match oheap1,oheap2 with
| _,HeapEmpty -> oheap1
| HeapEmpty,_ -> oheap2
| HeapNotEmpty(min1,heap1),HeapNotEmpty(min2,heap2) ->
let min = if min1.k > min2.k then min2 else min1
heap1 heap2)
let rec private removeMinTree = function
| [] -> raise Empty_Heap // will never happen as already guarded
| [node] -> node,[]
| t::ts -> let t',ts' = removeMinTree ts
if (root t).k <= (root t').k then t,ts else t',t::ts'
let deleteMin =
function | HeapEmpty -> HeapEmpty
| HeapNotEmpty(_,heap) ->
match heap with
| [] -> HeapEmpty // should never occur: non empty heap with no elements
| [Node(_,_,heap'
)] -> match heap' with
| [] -> HeapEmpty
| _ -> let min,_ = findMin heap'

| _::_ -> let Node(_,_,ts1),ts2 = removeMinTree heap
let nheap = merge'
(List.rev ts1) ts2 in let min,_ = findMin nheap
let replaceMin k v pq = push k v (deleteMin pq)
let fromSeq sq = Seq.fold (fun pq (k, v) -> push k v pq) empty sq
let popMin pq = match peekMin pq with
| None -> None
| Some(kv) -> Some(kv, deleteMin pq)
let toSeq pq = Seq.unfold popMin pq
let sort sq = sq |> fromSeq |> toSeq
let adjust f pq = pq |> toSeq |> (fun (k, v) -> f k v) |> fromSeq

"isEmpty", "empty", and "peekMin" all have O(1) performance, "push" is O(1) amortized performance with O(log n) worst case, and the rest are O(log n) except for "fromSeq" (and thus "sort" and "adjust") which have O(n log n) performance since they use repeated "deleteMin" with one per entry.

No "size" function is provided, but it would be implemented by summing the total size of all the nested tree lists, which each have a "Count" property and thus would be quite fast.

Note that the current "adjust" function is horribly inefficient as it outputs the original queue as a sorted sequence (O(n log n) time complexity), maps the adjusting function to each element, and rebuilds the queue be repeated "push" operations of the resulting sequence. This could be improved by re-writing to output the sequence in unsorted order (using an internal function that doesn't use repeated "deleteMin" operations) and then rebuilding from the adjusted sequence; doing this would make the "adjust" operation take O(n) amortized time.

The "sort" function also uses a similar technique of building a queue from a sequence by repeated "push" operations (however, those only take O(n) amortized time for the Binomial Heap), then outputting a sorted sequence by repeated "popMin" operations for a combined O(n log n) time complexity.

Min Heap Priority Queue

The following code implementing a Min Heap Priority Queue is adapted from the ML PRIORITY_QUEUE code by Lawrence C. Paulson including separating the key/value pairs as separate entries in the data structure for better comparison efficiency; it implements an efficient "fromSeq" function using reheapify for which the Min Heap is particularly suited as it has only O(n) instead of O(n log n) computational time complexity, which method is also used for the "adjust" and "merge" functions:

module PriorityQ =
type HeapEntry<'V> = struct val k:uint32 val v:'V new(k,v) = {k=k;v=v} end
[<NoEquality; NoComparison>]
type PQ<'V> =
| Mt
| Br of HeapEntry<'
V> * PQ<'V> * PQ<'V>
let empty = Mt
let isEmpty = function | Mt -> true
| _ -> false
// Return number of elements in the priority queue.
// /O(log(n)^2)/
let rec size = function
| Mt -> 0
| Br(_, ll, rr) ->
let n = size rr
// rest n p q, where n = size ll, and size ll - size rr = 0 or 1
// returns 1 + size ll - size rr.
let rec rest n pl pr =
match pl with
| Mt -> 1
| Br(_, pll, plr) ->
match pr with
| Mt -> 2
| Br(_, prl, prr) ->
let nm1 = n - 1 in let d = nm1 >>> 1
if (nm1 &&& 1) = 0
then rest d pll prl // subtree sizes: (d or d+1), d; d, d
else rest d plr prr // subtree sizes: d+1, (d or d+1); d+1, d
2 * n + rest n ll rr
let peekMin = function | Br(kv, _, _) -> Some(kv.k, kv.v)
| _ -> None
let rec push wk wv =
function | Mt -> Br(HeapEntry(wk, wv), Mt, Mt)
| Br(vkv, ll, rr) ->
if wk <= vkv.k then
Br(HeapEntry(wk, wv), push vkv.k vkv.v rr, ll)
else Br(vkv, push wk wv rr, ll)
let inline private siftdown wk wv pql pqr =
let rec sift pl pr =
match pl with
| Mt -> Br(HeapEntry(wk, wv), Mt, Mt)
| Br(vkvl, pll, plr) ->
match pr with
| Mt -> if wk <= vkvl.k then Br(HeapEntry(wk, wv), pl, Mt)
else Br(vkvl, Br(HeapEntry(wk, wv), Mt, Mt), Mt)
| Br(vkvr, prl, prr) ->
if wk <= vkvl.k && wk <= vkvr.k then Br(HeapEntry(wk, wv), pl, pr)
elif vkvl.k <= vkvr.k then Br(vkvl, sift pll plr, pr)
else Br(vkvr, pl, sift prl prr)
sift pql pqr
let replaceMin wk wv = function | Mt -> Mt
| Br(_, ll, rr) -> siftdown wk wv ll rr
let deleteMin = function
| Mt -> Mt
| Br(_, ll, Mt) -> ll
| Br(vkv, ll, rr) ->
let rec leftrem = function | Mt -> vkv, Mt // should never happen
| Br(kvd, Mt, _) -> kvd, Mt
| Br(vkv, Br(kvd, _, _), Mt) ->
kvd, Br(vkv, Mt, Mt)
| Br(vkv, pl, pr) -> let kvd, pqd = leftrem pl
kvd, Br(vkv, pr, pqd)
let (kvd, pqd) = leftrem ll
siftdown kvd.k kvd.v rr pqd;
let adjust f pq =
let rec adj = function
| Mt -> Mt
| Br(vkv, ll, rr) -> let nk, nv = f vkv.k vkv.v
siftdown nk nv (adj ll) (adj rr)
adj pq
let fromSeq sq =
if Seq.isEmpty sq then Mt
else let nmrtr = sq.GetEnumerator()
let rec build lvl = if lvl = 0 || not (nmrtr.MoveNext()) then Mt
else let ck, cv = nmrtr.Current
let lft = lvl >>> 1
let rght = (lvl - 1) >>> 1
siftdown ck cv (build lft) (build rght)
build (sq |> Seq.length)
let merge (pq1:PQ<_>) (pq2:PQ<_>) = // merges without using a sequence
match pq1 with
| Mt -> pq2
| _ ->
match pq2 with
| Mt -> pq1
| _ ->
let rec zipper lvl pq rest =
if lvl = 0 then Mt, pq, rest else
let lft = lvl >>> 1 in let rght = (lvl - 1) >>> 1
match pq with
| Mt ->
match rest with
| [] | Mt :: _ -> Mt, pq, [] // Mt in list never happens
| Br(kv, ll, Mt) :: tl ->
let pl, pql, rstl = zipper lft ll tl
let pr, pqr, rstr = zipper rght pql rstl
siftdown kv.k kv.v pl pr, pqr, rstr
| Br(kv, ll, rr) :: tl ->
let pl, pql, rstl = zipper lft ll (rr :: tl)
let pr, pqr, rstr = zipper rght pql rstl
siftdown kv.k kv.v pl pr, pqr, rstr
| Br(kv, ll, Mt) ->
let pl, pql, rstl = zipper lft ll rest
let pr, pqr, rstr = zipper rght pql rstl
siftdown kv.k kv.v pl pr, pqr, rstr
| Br(kv, ll, rr) ->
let pl, pql, rstl = zipper lft ll (rr :: rest)
let pr, pqr, rstr = zipper rght pql rstl
siftdown kv.k kv.v pl pr, pqr, rstr
let sz = size pq1 + size pq2
let pq, _, _ = zipper sz pq1 [pq2] in pq
let popMin pq = match peekMin pq with
| None -> None
| Some(kv) -> Some(kv, deleteMin pq)
let toSeq pq = Seq.unfold popMin pq
let sort sq = sq |> fromSeq |> toSeq

The above code implements a "merge" function so that no internal sequence generation is necessary as generation of sequence iterators is quite inefficient in F# for a combined O(n) computational time complexity but a considerable reduction in the constant factor overhead.

Other than the "merge" function, the Min Heap Priority Queue has the same time complexity as for the Binomial Heap Priority Queue above except that "push" has O(log n) performance rather than the amortized O(1) performance; however, the Binomial Heap Priority Queue is generally a constant factor slower due to more complex operations. The Binomial Heap Priority Queue is generally more suited when used where merging of large queues or frequent "push" operations are used; the Min Heap Priority Queue is more suitable for use when replacing the value at the minimum entry of the queue is frequently required, especially when the adjusted value is not displaced very far down the queue on average.


Min Heap Priority Queue

As the Min Heap is usually implemented as a mutable array binary heap after a genealogical tree based model invented by Michael Eytzinger over 400 years ago, the following "ugly imperative" code implements the Min Heap Priority Queue this way; note that the code could be implemented not using "ugly" mutable state variables other than the contents of the array (DotNet List which implements a growable array) but in this case the code would be considerably slower as in not much faster or slower than the functional version since using mutable side effects greatly reduces the number of operations:

module PriorityQ =
type HeapEntry<'T> = struct val k:uint32 val v:'T new(k,v) = { k=k;v=v } end
type MinHeapTree<'T> = ResizeArray<HeapEntry<'T>>
let empty<'T> = MinHeapTree<HeapEntry<'T>>()
let isEmpty (pq: MinHeapTree<_>) = pq.Count = 0
let size (pq: MinHeapTree<_>) = let cnt = pq.Count
if cnt = 0 then 0 else cnt - 1
let peekMin (pq:MinHeapTree<_>) = if pq.Count > 1 then let kv = pq.[0]
Some (kv.k, kv.v) else None
let push k v (pq:MinHeapTree<_>) =
if pq.Count = 0 then pq.Add(HeapEntry(0xFFFFFFFFu,v)) //add an extra entry so there's always a right max node
let mutable nxtlvl = pq.Count in let mutable lvl = nxtlvl <<< 1 //1 past index of value added times 2
pq.Add(pq.[nxtlvl - 1]) //copy bottom entry then do bubble up while less than next level up
while ((lvl <- lvl >>> 1); nxtlvl <- nxtlvl >>> 1; nxtlvl <> 0) do
let t = pq.[nxtlvl - 1] in if t.k > k then pq.[lvl - 1] <- t else lvl <- lvl <<< 1; nxtlvl <- 0 //causes loop break
pq.[lvl - 1] <- HeapEntry(k,v); pq
let inline private siftdown k v ndx (pq: MinHeapTree<_>) =
let mutable i = ndx in let mutable ni = i in let cnt = pq.Count - 1
while (ni <- ni + ni + 1; ni < cnt) do
let lk = pq.[ni].k in let rk = pq.[ni + 1].k in let oi = i
let k = if k > lk then i <- ni; lk else k in if k > rk then ni <- ni + 1; i <- ni
if i <> oi then pq.[oi] <- pq.[i] else ni <- cnt //causes loop break
pq.[i] <- HeapEntry(k,v)
let replaceMin k v (pq:MinHeapTree<_>) = siftdown k v 0 pq; pq
let deleteMin (pq:MinHeapTree<_>) =
let lsti = pq.Count - 2
if lsti <= 0 then pq.Clear(); pq else
let lstkv = pq.[lsti]
siftdown lstkv.k lstkv.v 0 pq; pq
let adjust f (pq:MinHeapTree<_>) = //adjust all the contents using the function, then re-heapify
let cnt = pq.Count - 1
let rec adj i =
let lefti = i + i + 1 in let righti = lefti + 1
let ckv = pq.[i] in let (nk, nv) = f ckv.k ckv.v
if righti < cnt then adj righti
if lefti < cnt then adj lefti; siftdown nk nv i pq
else pq.[i] <- HeapEntry(nk, nv)
adj 0; pq
let fromSeq sq =
if Seq.isEmpty sq then empty
else let pq = new MinHeapTree<_>(sq |> (fun (k, v) -> HeapEntry(k, v)))
let sz = pq.Count in let lkv = pq.[sz - 1]
pq.Add(HeapEntry(UInt32.MaxValue, lkv.v))
let rec build i =
let lefti = i + i + 1
if lefti < sz then
let righti = lefti + 1 in build lefti; build righti
let ckv = pq.[i] in siftdown ckv.k ckv.v i pq
build 0; pq
let merge (pq1:MinHeapTree<_>) (pq2:MinHeapTree<_>) =
if pq2.Count = 0 then pq1 else
if pq1.Count = 0 then pq2 else
let pq = empty
pq.AddRange(pq1); pq.RemoveAt(pq.Count - 1)
let sz = pq.Count - 1
let rec build i =
let lefti = i + i + 1
if lefti < sz then
let righti = lefti + 1 in build lefti; build righti
let ckv = pq.[i] in siftdown ckv.k ckv.v i pq
build 0; pq
let popMin pq = match peekMin pq with
| None -> None
| Some(kv) -> Some(kv, deleteMin pq)
let toSeq pq = Seq.unfold popMin pq
let sort sq = sq |> fromSeq |> toSeq

The comments for the above code are the same as for the functional version; the main difference is that the imperative code takes about two thirds of the time on average.

All of the above codes can be tested under the F# REPL using the following:

> let testseq = [| (3u, "Clear drains");
(4u, "Feed cat");
(5u, "Make tea");
(1u, "Solve RC tasks");
(2u, "Tax return") |] |> Array.toSeq
let testpq = testseq |> MinHeap.fromSeq
testseq |> Seq.fold (fun pq (k, v) -> MinHeap.push k v pq) MinHeap.empty
|> MinHeap.toSeq |> Seq.iter (printfn "%A") // test slow build
printfn ""
testseq |> MinHeap.fromSeq |> MinHeap.toSeq // test fast build
|> Seq.iter (printfn "%A")
printfn ""
testseq |> MinHeap.sort |> Seq.iter (printfn "%A") // convenience function
printfn ""
MinHeap.merge testpq testpq // test merge
|> MinHeap.toSeq |> Seq.iter (printfn "%A")
printfn ""
testpq |> MinHeap.adjust (fun k v -> uint32 (MinHeap.size testpq) - k, v)
|> MinHeap.toSeq |> Seq.iter (printfn "%A") // test adjust;;

to produce the following output:

(1u, "Solve RC tasks")
(2u, "Tax return")
(3u, "Clear drains")
(4u, "Feed cat")
(5u, "Make tea")

(1u, "Solve RC tasks")
(2u, "Tax return")
(3u, "Clear drains")
(4u, "Feed cat")
(5u, "Make tea")

(1u, "Solve RC tasks")
(2u, "Tax return")
(3u, "Clear drains")
(4u, "Feed cat")
(5u, "Make tea")

(1u, "Solve RC tasks")
(1u, "Solve RC tasks")
(2u, "Tax return")
(2u, "Tax return")
(3u, "Clear drains")
(3u, "Clear drains")
(4u, "Feed cat")
(4u, "Feed cat")
(5u, "Make tea")
(5u, "Make tea")

(0u, "Make tea")
(1u, "Feed cat")
(2u, "Clear drains")
(3u, "Tax return")
(4u, "Solve RC tasks")
val it : unit = ()

Note that the code using "fromSeq" instead of repeated "push" operations to build a queue is considerably faster for large random-order entry sequences.

Also note that the imperative version modifies the state of the "testpq" binding for modification operations such as "push" and "deleteMin" or operations derived from those; this means that if the last two tests were reversed then the "merge" would be passed zero sized queues since the "testpq" would have been reduced by the "toSeq" operation (which effectively uses repeated "deleteMin" functions).


Factor has priority queues implemented in the library: documentation is available at (or by typing "heaps" help interactively in the listener).

<min-heap> [ {
{ 3 "Clear drains" }
{ 4 "Feed cat" }
{ 5 "Make tea" }
{ 1 "Solve RC tasks" }
{ 2 "Tax return" }
} swap heap-push-all
] [
[ print ] slurp-heap
] bi


Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea


module priority_queue_mod
implicit none
type node
character (len=100) :: task
integer :: priority
end type
type queue
type(node), allocatable :: buf(:)
integer :: n = 0
procedure :: top
procedure :: enqueue
procedure :: siftdown
end type
subroutine siftdown(this, a)
class (queue) :: this
integer :: a, parent, child
associate (x => this%buf)
parent = a
do while(parent*2 <= this%n)
child = parent*2
if (child + 1 <= this%n) then
if (x(child+1)%priority > x(child)%priority ) then
child = child +1
end if
end if
if (x(parent)%priority < x(child)%priority) then
x([child, parent]) = x([parent, child])
parent = child
end if
end do
end associate
end subroutine
function top(this) result (res)
class(queue) :: this
type(node) :: res
res = this%buf(1)
this%buf(1) = this%buf(this%n)
this%n = this%n - 1
call this%siftdown(1)
end function
subroutine enqueue(this, priority, task)
class(queue), intent(inout) :: this
integer :: priority
character(len=*) :: task
type(node) :: x
type(node), allocatable :: tmp(:)
integer :: i
x%priority = priority
x%task = task
this%n = this%n +1
if (.not.allocated(this%buf)) allocate(this%buf(1))
if (size(this%buf)<this%n) then
tmp(1:this%n-1) = this%buf
call move_alloc(tmp, this%buf)
end if
this%buf(this%n) = x
i = this%n
i = i / 2
if (i==0) exit
call this%siftdown(i)
end do
end subroutine
end module
program main
use priority_queue_mod
type (queue) :: q
type (node) :: x
call q%enqueue(3, "Clear drains")
call q%enqueue(4, "Feed cat")
call q%enqueue(5, "Make Tea")
call q%enqueue(1, "Solve RC tasks")
call q%enqueue(2, "Tax return")
do while (q%n >0)
x = q%top()
print "(g0,a,a)", x%priority, " -> ", trim(x%task)
end do
end program
! Output:
! 5 -> Make Tea
! 4 -> Feed cat
! 3 -> Clear drains
! 2 -> Tax return
! 1 -> Solve RC tasks


import util.ordering
native scala.collection.mutable.PriorityQueue
data Task( priority, description )
def comparator( Task(a, _), Task(b, _) )
| a > b = -1
| a < b = 1
| otherwise = 0
q = PriorityQueue( ordering(comparator) )
Task(3, 'Clear drains'),
Task(4, 'Feed cat'),
Task(5, 'Make tea'),
Task(1, 'Solve RC tasks'),
Task(2, 'Tax return')
while not q.isEmpty()
println( q.dequeue() )
Task(1, Solve RC tasks)
Task(2, Tax return)
Task(3, Clear drains)
Task(4, Feed cat)
Task(5, Make tea)


Go's standard library contains the container/heap package, which which provides operations to operate as a heap any data structure that contains the Push, Pop, Len, Less, and Swap methods.

package main
import (
type Task struct {
priority int
name string
type TaskPQ []Task
func (self TaskPQ) Len() int { return len(self) }
func (self TaskPQ) Less(i, j int) bool {
return self[i].priority < self[j].priority
func (self TaskPQ) Swap(i, j int) { self[i], self[j] = self[j], self[i] }
func (self *TaskPQ) Push(x interface{}) { *self = append(*self, x.(Task)) }
func (self *TaskPQ) Pop() (popped interface{}) {
popped = (*self)[len(*self)-1]
*self = (*self)[:len(*self)-1]
func main() {
pq := &TaskPQ{{3, "Clear drains"},
{4, "Feed cat"},
{5, "Make tea"},
{1, "Solve RC tasks"}}
// heapify
// enqueue
heap.Push(pq, Task{2, "Tax return"})
for pq.Len() != 0 {
// dequeue


{1 Solve RC tasks}
{2 Tax return}
{3 Clear drains}
{4 Feed cat}
{5 Make tea}


Groovy can use the built in java PriorityQueue class

import groovy.transform.Canonical
class Task implements Comparable<Task> {
int priority
String name
int compareTo(Task o) { priority <=> o?.priority }
new PriorityQueue<Task>().with {
add new Task(priority: 3, name: 'Clear drains')
add new Task(priority: 4, name: 'Feed cat')
add new Task(priority: 5, name: 'Make tea')
add new Task(priority: 1, name: 'Solve RC tasks')
add new Task(priority: 2, name: 'Tax return')
while (!empty) { println remove() }


Task(1, Solve RC tasks)
Task(2, Tax return)
Task(3, Clear drains)
Task(4, Feed cat)
Task(5, Make tea)


One of the best Haskell implementations of priority queues (of which there are many) is pqueue, which implements a binomial heap.

import Data.PQueue.Prio.Min
main = print (toList (fromList [(3, "Clear drains"),(4, "Feed cat"),(5, "Make tea"),(1, "Solve RC tasks"), (2, "Tax return")]))

Although Haskell's standard library does not have a dedicated priority queue structure, one can (for most purposes) use the built-in Data.Set data structure as a priority queue, as long as no two elements compare equal (since Set does not allow duplicate elements). This is the case here since no two tasks should have the same name. The complexity of all basic operations is still O(log n) although that includes the "elemAt 0" function to retrieve the first element of the ordered sequence if that were required; "fromList" takes O(n log n) and "toList" takes O(n) time complexity. Alternatively, a Data.Map.Lazy or Data.Map.Strict can be used in the same way with the same limitations.

import qualified Data.Set as S
main = print (S.toList (S.fromList [(3, "Clear drains"),(4, "Feed cat"),(5, "Make tea"),(1, "Solve RC tasks"), (2, "Tax return")]))
[(1,"Solve RC tasks"),(2,"Tax return"),(3,"Clear drains"),(4,"Feed cat"),(5,"Make tea")]

Alternatively, a homemade min heap implementation:

data MinHeap a = Nil | MinHeap { v::a, cnt::Int, l::MinHeap a, r::MinHeap a }
deriving (Show, Eq)
hPush :: (Ord a) => a -> MinHeap a -> MinHeap a
hPush x Nil = MinHeap {v = x, cnt = 1, l = Nil, r = Nil}
hPush x h = if x < vv -- insert element, try to keep the tree balanced
then if hLength (l h) <= hLength (r h)
then MinHeap { v=x, cnt=cc, l=hPush vv ll, r=rr }
else MinHeap { v=x, cnt=cc, l=ll, r=hPush vv rr }
else if hLength (l h) <= hLength (r h)
then MinHeap { v=vv, cnt=cc, l=hPush x ll, r=rr }
else MinHeap { v=vv, cnt=cc, l=ll, r=hPush x rr }
where (vv, cc, ll, rr) = (v h, 1 + cnt h, l h, r h)
hPop :: (Ord a) => MinHeap a -> (a, MinHeap a)
hPop h = (v h, pq) where -- just pop, heed not the tree balance
pq | l h == Nil = r h
| r h == Nil = l h
| v (l h) <= v (r h) = let (vv,hh) = hPop (l h) in
MinHeap {v = vv, cnt = hLength hh + hLength (r h),
l = hh, r = r h}
| otherwise = let (vv,hh) = hPop (r h) in
MinHeap {v = vv, cnt = hLength hh + hLength (l h),
l = l h, r = hh}
hLength :: (Ord a) => MinHeap a -> Int
hLength Nil = 0
hLength h = cnt h
hFromList :: (Ord a) => [a] -> MinHeap a
hFromList = foldl (flip hPush) Nil
hToList :: (Ord a) => MinHeap a -> [a]
hToList = unfoldr f where
f Nil = Nothing
f h = Just $ hPop h
main = mapM_ print $ hToList $ hFromList [
(3, "Clear drains"),
(4, "Feed cat"),
(5, "Make tea"),
(1, "Solve RC tasks"),
(2, "Tax return")]

The above code is a Priority Queue but isn't a Min Heap based on a Binary Heap for the following reasons: 1) it does not preserve the standard tree structure of the binary heap and 2) the tree balancing can be completely destroyed by some combinations of "pop" operations. The following code is a true purely functional Min Heap implementation and as well implements the extra optional features of Min Heap's that it can build a new Min Heap from a list in O(n) amortized time rather than the O(n log n) amortized time (for a large number of randomly ordered entries) by simply using repeated "push" operations; as well as the standard "push", "peek", "delete" and "pop" (combines the previous two). As well as the "fromList", "toList", and "sort" functions (the last combines the first two), it also has an "isEmpty" function to test for the empty queue, an "adjust" function that applies a function to every entry in the queue and reheapifies in O(n) amortized time and also the "replaceMin" function which is about twice as fast on the average as combined "delete" followed by "push" operations:

data MinHeap kv = MinHeapEmpty
| MinHeapLeaf !kv
| MinHeapNode !kv {-# UNPACK #-} !Int !(MinHeap a) !(MinHeap a)
deriving (Show, Eq)
emptyPQ :: MinHeap kv
emptyPQ = MinHeapEmpty
isEmptyPQ :: PriorityQ kv -> Bool
isEmptyPQ Mt = True
isEmptyPQ _ = False
sizePQ :: (Ord kv) => MinHeap kv -> Int
sizePQ MinHeapEmpty = 0
sizePQ (MinHeapLeaf _) = 1
sizePQ (MinHeapNode _ cnt _ _) = cnt
peekMinPQ :: MinHeap kv -> Maybe kv
peekMinPQ MinHeapEmpty = Nothing
peekMinPQ (MinHeapLeaf v) = Just v
peekMinPQ (MinHeapNode v _ _ _) = Just v
pushPQ :: (Ord kv) => kv -> MinHeap kv -> MinHeap kv
pushPQ kv pq = insert kv 0 pq where -- insert element, keeping the tree balanced
insert kv _ MinHeapEmpty = MinHeapLeaf kv
insert kv _ (MinHeapLeaf vv) = if kv <= vv
then MinHeapNode kv 2 (MinHeapLeaf vv) MinHeapEmpty
else MinHeapNode vv 2 (MinHeapLeaf kv) MinHeapEmpty
insert kv msk (MinHeapNode vv cc ll rr) = if kv <= vv
then if nmsk >= 0
then MinHeapNode kv nc (insert vv nmsk ll) rr
else MinHeapNode kv nc ll (insert vv nmsk rr)
else if nmsk >= 0
then MinHeapNode vv nc (insert kv nmsk ll) rr
else MinHeapNode vv nc ll (insert kv nmsk rr)
where nc = cc + 1
nmsk = if msk /= 0 then msk `shiftL` 1 -- walk path to next
else let s = floor $ (log $ fromIntegral nc) / log 2 in
(nc `shiftL` ((finiteBitSize cc) - s)) .|. 1 --never 0 again
siftdown :: (Ord kv) => kv -> Int -> MinHeap kv -> MinHeap kv -> MinHeap kv
siftdown kv cnt lft rght = replace cnt lft rght where
replace cc ll rr = case rr of -- adj to put kv in current left/right
MinHeapEmpty -> -- means left is a MinHeapLeaf
case ll of { (MinHeapLeaf vl) ->
if kv <= vl
then MinHeapNode kv 2 ll MinHeapEmpty
else MinHeapNode vl 2 (MinHeapLeaf kv) MinHeapEmpty }
MinHeapLeaf vr ->
case ll of
MinHeapLeaf vl -> if vl <= vr
then if kv <= vl then MinHeapNode kv cc ll rr
else MinHeapNode vl cc (MinHeapLeaf kv) rr
else if kv <= vr then MinHeapNode kv cc ll rr
else MinHeapNode vr cc ll (MinHeapLeaf kv)
MinHeapNode vl ccl lll rrl -> if vl <= vr
then if kv <= vl then MinHeapNode kv cc ll rr
else MinHeapNode vl cc (replace ccl lll rrl) rr
else if kv <= vr then MinHeapNode kv cc ll rr
else MinHeapNode vr cc ll (MinHeapLeaf kv)
MinHeapNode vr ccr llr rrr -> case ll of
(MinHeapNode vl ccl lll rrl) -> -- right is node, so is left
if vl <= vr then
if kv <= vl then MinHeapNode kv cc ll rr
else MinHeapNode vl cc (replace ccl lll rrl) rr
else if kv <= vr then MinHeapNode kv cc ll rr
else MinHeapNode vr cc ll (replace ccr llr rrr)
replaceMinPQ :: (Ord kv) => a -> MinHeap kv -> MinHeap kv
replaceMinPQ _ MinHeapEmpty = MinHeapEmpty
replaceMinPQ kv (MinHeapLeaf _) = MinHeapLeaf kv
replaceMinPQ kv (MinHeapNode _ cc ll rr) = siftdown kv cc ll rr where
deleteMinPQ :: (Ord kv) => MinHeap kv -> MinHeap kv
deleteMinPQ MinHeapEmpty = MinHeapEmpty -- remove min keeping tree balanced
deleteMinPQ pq = let (dkv, npq) = delete 0 pq in
replaceMinPQ dkv npq where
delete _ (MinHeapLeaf vv) = (vv, MinHeapEmpty)
delete msk (MinHeapNode vv cc ll rr) =
if rr == MinHeapEmpty -- means left is MinHeapLeaf
then case ll of (MinHeapLeaf vl) -> (vl, MinHeapLeaf vv)
else if nmsk >= 0 -- means only deal with left
then let (dv, npq) = delete nmsk ll in
(dv, MinHeapNode vv (cc - 1) npq rr)
else let (dv, npq) = delete nmsk rr in
(dv, MinHeapNode vv (cc - 1) ll npq)
where nmsk = if msk /= 0 then msk `shiftL` 1 -- walk path to last
else let s = floor $ (log $ fromIntegral cc) / log 2 in
(cc `shiftL` ((finiteBitSize cc) - s)) .|. 1 --never 0 again
adjustPQ :: (Ord kv) => (kv -> kv) -> MinHeap kv -> MinHeap kv
adjustPQ f pq = adjust pq where -- applies function to every element and reheapifies
adjust MinHeapEmpty = MinHeapEmpty
adjust (MinHeapLeaf v) = MinHeapLeaf (f v)
adjust (MinHeapNode vv cc ll rr) = siftdown (f vv) cc (adjust ll) (adjust rr)
fromListPQ :: (Ord kv) => [kv] -> MinHeap kv
-- fromListPQ = foldl (flip pushPQ) MinHeapEmpty -- O(n log n) time; slow
fromListPQ [] = MinHeapEmpty -- O(n) time using "adjust id" which is O(n)
fromListPQ xs = let (_, pq) = build 1 xs in pq where
sz = length xs
szd2 = sz `div` 2
build _ [] = ([], MinHeapEmpty)
build lvl (x:xs') = if lvl > szd2 then (xs', MinHeapLeaf x)
else let nlvl = lvl + lvl in
let (xrl, pql) = build nlvl xs' in
let (xrr, pqr) = if nlvl >= sz
then (xrl, MinHeapEmpty) -- no right leaf
else build (nlvl + 1) xrl in
let cnt = sizePQ pql + sizePQ pqr + 1 in
(xrr, siftdown x cnt pql pqr)
popMinPQ :: (Ord kv) => MinHeap kv -> Maybe (kv, MinHeap kv)
popMinPQ pq = case peekMinPQ pq of
Nothing -> Nothing
Just v -> Just (v, deleteMinPQ pq)
toListPQ :: (Ord kv) => MinHeap kv -> [kv]
toListPQ = unfoldr f where
f MinHeapEmpty = Nothing
f pq = popMinPQ pq
sortPQ :: (Ord kv) => [kv] -> [kv]
sortPQ ls = toListPQ $ fromListPQ ls

If one is willing to forgo the fast O(1) "size" function and to give up strict conformance to the Heap tree structure (where rather than building each new level until each left node is full to that level before increasing level to the right, a new level is built by promoting leaves to branches only containing left leaves until all branches have left leaves before filling any right leaves of that level) although having even better tree balancing and therefore at least as high efficiency, one can use the following code adapted from the ML PRIORITY_QUEUE code by Lawrence C. Paulson including separating the key/value pairs as separate entries in the data structure for better comparison efficiency; as noted in the code comments, a "size" function to output the number of elements in the queue (fairly quickly in O((log n)^2)), an "adjust" function to apply a function to all elements and reheapify in O(n) time, and a "merge" function to merge two queues has been added to the ML code:

data PriorityQ k v = Mt
| Br !k v !(PriorityQ k v) !(PriorityQ k v)
deriving (Eq, Ord, Read, Show)
emptyPQ :: PriorityQ k v
emptyPQ = Mt
isEmptyPQ :: PriorityQ k v -> Bool
isEmptyPQ Mt = True
isEmptyPQ _ = False
-- The size function isn't from the ML code, but an implementation was
-- suggested by Bertram Felgenhauer on Haskell Cafe, so it is included.
-- Return number of elements in the priority queue.
-- /O(log(n)^2)/
sizePQ :: PriorityQ k v -> Int
sizePQ Mt = 0
sizePQ (Br _ _ pl pr) = 2 * n + rest n pl pr where
n = sizePQ pr
-- rest n p q, where n = sizePQ q, and sizePQ p - sizePQ q = 0 or 1
-- returns 1 + sizePQ p - sizePQ q.
rest :: Int -> PriorityQ k v -> PriorityQ k v -> Int
rest 0 Mt _ = 1
rest 0 _ _ = 2
rest n (Br _ _ ll lr) (Br _ _ rl rr) = case r of
0 -> rest d ll rl -- subtree sizes: (d or d+1), d; d, d
1 -> rest d lr rr -- subtree sizes: d+1, (d or d+1); d+1, d
where m1 = n - 1
d = m1 `shiftR` 1
r = m1 .&. 1
peekMinPQ :: PriorityQ k v -> Maybe (k, v)
peekMinPQ Mt = Nothing
peekMinPQ (Br k v _ _) = Just (k, v)
pushPQ :: Ord k => k -> v -> PriorityQ k v -> PriorityQ k v
pushPQ wk wv Mt = Br wk wv Mt Mt
pushPQ wk wv (Br vk vv pl pr)
| wk <= vk = Br wk wv (pushPQ vk vv pr) pl
| otherwise = Br vk vv (pushPQ wk wv pr) pl
siftdown :: Ord k => k -> v -> PriorityQ k v -> PriorityQ k v -> PriorityQ k v
siftdown wk wv Mt _ = Br wk wv Mt Mt
siftdown wk wv (pl @ (Br vk vv _ _)) Mt
| wk <= vk = Br wk wv pl Mt
| otherwise = Br vk vv (Br wk wv Mt Mt) Mt
siftdown wk wv (pl @ (Br vkl vvl pll plr)) (pr @ (Br vkr vvr prl prr))
| wk <= vkl && wk <= vkr = Br wk wv pl pr
| vkl <= vkr = Br vkl vvl (siftdown wk wv pll plr) pr
| otherwise = Br vkr vvr pl (siftdown wk wv prl prr)
replaceMinPQ :: Ord k => k -> v -> PriorityQ k v -> PriorityQ k v
replaceMinPQ wk wv Mt = Mt
replaceMinPQ wk wv (Br _ _ pl pr) = siftdown wk wv pl pr
deleteMinPQ :: (Ord k) => PriorityQ k v -> PriorityQ k v
deleteMinPQ Mt = Mt
deleteMinPQ (Br _ _ pr Mt) = pr
deleteMinPQ (Br _ _ pl pr) = let (k, v, npl) = leftrem pl in
siftdown k v pr npl where
leftrem (Br k v Mt Mt) = (k, v, Mt)
leftrem (Br vk vv (Br k v _ _) Mt) = (k, v, Br vk vv Mt Mt)
leftrem (Br vk vv pl pr) = let (k, v, npl) = leftrem pl in
(k, v, Br vk vv pr npl)
-- the following function has been added to the ML code to apply a function
-- to all the entries in the queue and reheapify in O(n) time
adjustPQ :: (Ord k) => (k -> v -> (k, v)) -> PriorityQ k v -> PriorityQ k v
adjustPQ f pq = adjust pq where -- applies function to every element and reheapifies
adjust Mt = Mt
adjust (Br vk vv pl pr) = let (k, v) = f vk vv in
siftdown k v (adjust pl) (adjust pr)
fromListPQ :: (Ord k) => [(k, v)] -> PriorityQ k v
-- fromListPQ = foldl (flip pushPQ) Mt -- O(n log n) time; slow
fromListPQ [] = Mt -- O(n) time using adjust-from-bottom which is O(n)
fromListPQ xs = let (pq, _) = build (length xs) xs in pq where
build 0 xs = (Mt, xs)
build lvl ((k, v):xs') = let (pl, xrl) = build (lvl `shiftR` 1) xs'
(pr, xrr) = build ((lvl - 1) `shiftR` 1) xrl in
(siftdown k v pl pr, xrr)
-- the following function has been added to merge two queues in O(m + n) time
-- where m and n are the sizes of the two queues
mergePQ :: (Ord k) => PriorityQ k v -> PriorityQ k v -> PriorityQ k v
mergePQ pq1 Mt = pq1 -- from concatenated "dumb" list
mergePQ Mt pq2 = pq2 -- in O(m + n) time where m,n are sizes pq1,pq2
mergePQ pq1 pq2 = fromListPQ (zipper pq1 $ zipper pq2 []) where
zipper (Br wk wv Mt _) appndlst = (wk, wv) : appndlst
zipper (Br wk wv pl Mt) appndlst = (wk, wv) : zipper pl appndlst
zipper (Br wk wv pl pr) appndlst = (wk, wv) : zipper pl (zipper pr appndlst)
popMinPQ :: (Ord k) => PriorityQ k v -> Maybe ((k, v), PriorityQ k v)
popMinPQ pq = case peekMinPQ pq of
Nothing -> Nothing
Just kv -> Just (kv, deleteMinPQ pq)
toListPQ :: (Ord k) => PriorityQ k v -> [(k, v)]
toListPQ Mt = [] -- unfoldr popMinPQ
toListPQ pq @ (Br vk vv _ _) = (vk, vv) : (toListPQ $ deleteMinPQ pq)
sortPQ :: (Ord k) => [(k, v)] -> [(k, v)]
sortPQ ls = toListPQ $ fromListPQ ls

The above codes compile but do not run with GHC Haskell version 7.8.3 using the LLVM back end with LLVM version 3.4 and full optimization turned on under Windows 32; they were tested under Windows 64 and 32 using the Native Code Generator back end with full optimization. With GHC Haskell version 7.10.1 they compile and run with or without LLVM 3.5.1 for 32-bit Windows (64-bit GHC Haskell under Windows does not run with LLVM for version 7.10.1), with a slight execution speed advantage to using LLVM.

Min Heaps are faster than Priority Queue's based on Binomial Heaps (or Leftist or Skewed Heaps) when one mainly requires fast replacement of the head of the queue without many fresh "push" operations; Binomial Heap based versions (or Leftist or Skewed Heap based versions) are faster for merging of a series of large queues into one and for algorithms that have a lot of "push" operations of random entries. Both have O(log n) average "push" and "pop" time complexity with O(1) for "peek", but Binomial Heap based queues (and the others) tend to be somewhat slower by a constant factor due to more complex operations.

Min Heaps are also faster than the use of balanced tree Set's or Map's where many references are made to the next element in the queue (O(1) complexity rather than O(log n)) or where frequent modification and reinsertion of the next element in the queue is required (still O(log n) but faster by a constant factor greater than two on average) and generally faster by a constant factor as operations near the top of the queue don't have to traverse the entire tree structure; O(log n) is worst case time complexity for "replace" operations not average.

The above codes when tested with the following "main" function (with a slight modification for the first test when the combined kv entry is used):

testList = [ (3, "Clear drains"),
(4, "Feed cat"),
(5, "Make tea"),
(1, "Solve RC tasks"),
(2, "Tax return") ]
testPQ = fromListPQ testList
main = do -- slow build
mapM_ print $ toListPQ $ foldl (\pq (k, v) -> pushPQ k v pq) emptyPQ testList
putStrLn "" -- fast build
mapM_ print $ toListPQ $ fromListPQ testList
putStrLn "" -- combined fast sort
mapM_ print $ sortPQ testList
putStrLn "" -- test merge
mapM_ print $ toListPQ $ mergePQ testPQ testPQ
putStrLn "" -- test adjust
mapM_ print $ toListPQ $ adjustPQ (\x y -> (x * (-1), y)) testPQ

has the output as follows:

(1,"Solve RC tasks")
(2,"Tax return")
(3,"Clear drains")
(4,"Feed cat")
(5,"Make tea")

(1,"Solve RC tasks")
(2,"Tax return")
(3,"Clear drains")
(4,"Feed cat")
(5,"Make tea")

(1,"Solve RC tasks")
(2,"Tax return")
(3,"Clear drains")
(4,"Feed cat")
(5,"Make tea")

(1,"Solve RC tasks")
(1,"Solve RC tasks")
(2,"Tax return")
(2,"Tax return")
(3,"Clear drains")
(3,"Clear drains")
(4,"Feed cat")
(4,"Feed cat")
(5,"Make tea")
(5,"Make tea")

(-5,"Make tea")
(-4,"Feed cat")
(-3,"Clear drains")
(-2,"Tax return")
(-1,"Solve RC tasks")

but the first method uses the slower way of building a queue.

Icon and Unicon[edit]

This solution uses classes provided by the UniLib package. Heap is an implementation of a priority queue and Closure is used to allow the queue to order lists based on their first element. The solution only works in Unicon.

import Utils   # For Closure class
import Collections # For Heap (dense priority queue) class
procedure main()
pq := Heap(, Closure("[]",Arg,1) )
pq.add([3, "Clear drains"])
pq.add([4, "Feed cat"])
pq.add([5, "Make tea"])
pq.add([1, "Solve RC tasks"])
pq.add([2, "Tax return"])
while task := pq.get() do write(task[1]," -> ",task[2])

Output when run:

1 -> Solve RC tasks
2 -> Tax return
3 -> Clear drains
4 -> Feed cat
5 -> Make tea



coclass 'priorityQueue'
PRI=: ''
QUE=: ''
insert=:4 :0
p=. PRI,x
q=. QUE,y
assert. p -:&$ q
assert. 1 = #$q
ord=: \: p
QUE=: ord { q
PRI=: ord { p
i.0 0
topN=:3 :0
assert y<:#PRI
r=. y{.QUE
PRI=: y}.PRI
QUE=: y}.QUE

Efficiency is obtained by batching requests. Size of batch for insert is determined by size of arguments. Size of batch for topN is its right argument.


   Q=: conew'priorityQueue'
3 4 5 1 2 insert__Q 'clear drains';'feed cat';'make tea';'solve rc task';'tax return'
>topN__Q 1
make tea
>topN__Q 4
feed cat
clear drains
tax return
solve rc task


Java has a PriorityQueue class. It requires either the elements implement Comparable, or you give it a custom Comparator to compare the elements.

import java.util.PriorityQueue;
class Task implements Comparable<Task> {
final int priority;
final String name;
public Task(int p, String n) {
priority = p;
name = n;
public String toString() {
return priority + ", " + name;
public int compareTo(Task other) {
return priority < other.priority ? -1 : priority > other.priority ? 1 : 0;
public static void main(String[] args) {
PriorityQueue<Task> pq = new PriorityQueue<Task>();
pq.add(new Task(3, "Clear drains"));
pq.add(new Task(4, "Feed cat"));
pq.add(new Task(5, "Make tea"));
pq.add(new Task(1, "Solve RC tasks"));
pq.add(new Task(2, "Tax return"));
while (!pq.isEmpty())
1, Solve RC tasks
2, Tax return
3, Clear drains
4, Feed cat
5, Make tea


Since jq is a functional language, the priority queue must be represented explicitly as data; in the following, we use a JSON object with keys as priorities (strings). Since a given priority level may have more than task, we use arrays to hold the values.

The special key "priorities" is used to store the priorities in a sorted array. Since "sort" is fast we will use that rather than optimizing insertion in the priorities array.

We assume that if an item of a given priority is already in the priority queue, there is no need to add it again.
# In the following, pq stands for "priority queue".  
# Add an item with the given priority (an integer,
# or a string representing an integer)
# Input: a pq
def pq_add(priority; item):
(priority|tostring) as $p
| if .priorities|index($p) then
if (.[$p] | index(item)) then . else .[$p] += [item] end
else .[$p] = [item] | .priorities = (.priorities + [$p] | sort)
end ;
# emit [ item, pq ]
# Input: a pq
def pq_pop:
.priorities as $keys
| if ($keys|length) == 0 then [ null, . ]
if (.[$keys[0]] | length) == 1
then .priorities = .priorities[1:]
else .
| [ (.[$keys[0]])[0], (.[$keys[0]] = .[$keys[0]][1:]) ]
end ;
# Emit the item that would be popped, or null if there is none
# Input: a pq
def pq_peep:
.priorities as $keys
| if ($keys|length) == 0 then null
else (.[$keys[0]])[0]
end ;
# Add a bunch of tasks, presented as an array of arrays
# Input: a pq
def pq_add_tasks(list):
reduce list[] as $pair (.; . + pq_add( $pair[0]; $pair[1]) ) ;
# Pop all the tasks, producing a stream
# Input: a pq
def pq_pop_tasks:
pq_pop as $pair
| if $pair[0] == null then empty
else $pair[0], ( $pair[1] | pq_pop_tasks )
end ;
# Input: a bunch of tasks, presented as an array of arrays
def prioritize:
. as $list | {} | pq_add_tasks($list) | pq_pop_tasks ;

The specific task:

[ [3, "Clear drains"],
[4, "Feed cat"],
[5, "Make tea"],
[1, "Solve RC tasks"],
[2, "Tax return"]
] | prioritize
"Solve RC tasks"
"Tax return"
"Clear drains"
"Feed cat"
"Make tea"


Julia has built-in support for priority queues, though the PriorityQueue type is not exported by default. Priority queues are a specialization of the Dictionary type having ordered values, which serve as the priority. In addition to all of the methods of standard dictionaries, priority queues support: enqueue!, which adds an item to the queue, dequeue! which removes the lowest priority item from the queue, returning its key, and peek, which returns the (key, priority) of the lowest priority entry in the queue. The ordering behavior of the queue, which by default is its value sort order (typically low to high), can be set by passing an order directive to its constructor. For this task, Base.Order.Reverse is used to set-up the task queue to return tasks from high to low priority.

using Base.Collections
test = ["Clear drains" 3;
"Feed cat" 4;
"Make tea" 5;
"Solve RC tasks" 1;
"Tax return" 2]
task = PriorityQueue(Base.Order.Reverse)
for i in 1:size(test)[1]
enqueue!(task, test[i,1], test[i,2])
println("Tasks, completed according to priority:")
while !isempty(task)
(t, p) = peek(task)
println(" \"", t, "\" has priority ", p)
Tasks, completed according to priority:
    "Make tea" has priority 5
    "Feed cat" has priority 4
    "Clear drains" has priority 3
    "Tax return" has priority 2
    "Solve RC tasks" has priority 1


Translation of: Java
import java.util.PriorityQueue
internal data class Task(val priority: Int, val name: String) : Comparable<Task> {
override fun compareTo(other: Task) = when {
priority < other.priority -> -1
priority > other.priority -> 1
else -> 0
private infix fun String.priority(priority: Int) = Task(priority, this)
fun main(args: Array<String>) {
val q = PriorityQueue(listOf("Clear drains" priority 3,
"Feed cat" priority 4,
"Make tea" priority 5,
"Solve RC tasks" priority 1,
"Tax return" priority 2))
while (q.any()) println(q.remove())
Task(priority=1, name=Solve RC tasks)
Task(priority=2, name=Tax return)
Task(priority=3, name=Clear drains)
Task(priority=4, name=Feed cat)
Task(priority=5, name=Make tea)


define priorityQueue => type {
store = map,
cur_priority = void
public push(priority::integer, value) => {
local(store) = .`store`->find(#priority)
if(#store->isA(::array)) => {
.`cur_priority`->isA(::void) or #priority < .`cur_priority`
 ? .`cur_priority` = #priority
public pop => {
.`cur_priority` == void
 ? return void
local(store) = .`store`->find(.`cur_priority`)
local(retVal) = #store->first
#store->removeFirst&size > 0
 ? return #retVal
// Need to find next priority
if(.`store`->size == 0) => {
.`cur_priority` = void
// There are better / faster ways to do this
// The keys are actually already sorted, but the order of
// storage in a map is not actually defined, can't rely on it
.`cur_priority` = .`store`->keys->asArray->sort&first
return #retVal
public isEmpty => (.`store`->size == 0)
local(test) = priorityQueue
while(not #test->isEmpty) => {


This implementation uses a table with priorities as keys and queues as values. Queues for each priority are created when putting items as needed and are shrunk as necessary when popping items and removed when they are empty. Instead of using a plain array table for each queue, the technique shown in the Lua implementation from the Queue task is used. This avoids having to use table.remove(t, 1) to get and remove the first queue element, which is rather slow for big tables.

PriorityQueue = {
__index = {
put = function(self, p, v)
local q = self[p]
if not q then
q = {first = 1, last = 0}
self[p] = q
q.last = q.last + 1
q[q.last] = v
pop = function(self)
for p, q in pairs(self) do
if q.first <= q.last then
local v = q[q.first]
q[q.first] = nil
q.first = q.first + 1
return p, v
self[p] = nil
__call = function(cls)
return setmetatable({}, cls)
setmetatable(PriorityQueue, PriorityQueue)
-- Usage:
pq = PriorityQueue()
tasks = {
{3, 'Clear drains'},
{4, 'Feed cat'},
{5, 'Make tea'},
{1, 'Solve RC tasks'},
{2, 'Tax return'}
for _, task in ipairs(tasks) do
print(string.format("Putting: %d - %s", unpack(task)))
for prio, task in pq.pop, pq do
print(string.format("Popped: %d - %s", prio, task))


   Putting: 3 - Clear drains
   Putting: 4 - Feed cat
   Putting: 5 - Make tea
   Putting: 1 - Solve RC tasks
   Putting: 2 - Tax return
   Popped: 1 - Solve RC tasks
   Popped: 2 - Tax return
   Popped: 3 - Clear drains
   Popped: 4 - Feed cat
   Popped: 5 - Make tea

The implementation is faster than the Python implementations below using queue.PriorityQueue or heapq, even when comparing the standard Lua implementation against PyPy and millions of tasks are added to the queue. With LuaJIT it is yet faster. The following code measures the time needed to add 107 tasks with a random priority between 1 and 1000 and to retrieve them from the queue again in order.

-- Use socket.gettime() for benchmark measurements
-- since it has millisecond precision on most systems
local socket = require("socket")
n = 10000000 -- number of tasks added (10^7)
m = 1000 -- number different priorities
local pq = PriorityQueue()
print(string.format("Adding %d tasks with random priority 1-%d ...", n, m))
start = socket.gettime()
for i = 1, n do
pq:put(math.random(m), i)
print(string.format("Elapsed: %.3f ms.", (socket.gettime() - start) * 1000))
print("Retrieving all tasks in order...")
start = socket.gettime()
local pp = 0
local pv = 0
for i = 1, n do
local p, task = pq:pop()
-- check that tasks are popped in ascending priority
assert(p >= pp)
if pp == p then
-- check that tasks within one priority maintain the insertion order
assert(task > pt)
pp = p
pt = task
print(string.format("Elapsed: %.3f ms.", (socket.gettime() - start) * 1000))


push = Function[{queue, priority, item}, 
queue = SortBy[Append[queue, {priority, item}], First], HoldFirst];
pop = Function[queue,
If[[email protected] == 0, Null,
With[{item = queue[[-1, 2]]}, queue = [email protected]; item]],
peek = Function[queue,
If[[email protected] == 0, Null, Max[queue[[All, 1]]]], HoldFirst];
merge = Function[{queue1, queue2},
SortBy[Join[queue1, queue2], First], HoldAll];


queue = {};
push[queue, 3, "Clear drains"];
push[queue, 4, "Feed cat"];
push[queue, 5, "Make tea"];
push[queue, 1, "Solve RC tasks"];
push[queue, 2, "Tax return"];
queue1 = {};
push[queue1, 6, "Drink tea"];
Print[merge[queue, queue1]];



Make tea

{{1,Solve RC tasks},{2,Tax return},{3,Clear drains},{4,Feed cat},{6,Drink tea}}


/* Naive implementation using a sorted list of pairs [key, [item[1], ..., item[n]]].
The key may be any number (integer or not). Items are extracted in FIFO order. */
defstruct(pqueue(q = []))$
/* Binary search */
find_key(q, p) := block(
[i: 1, j: length(q), k, c],
if j = 0 then false
elseif (c: q[i][1]) >= p then
(if c = p then i else false)
elseif (c: q[j][1]) <= p then
(if c = p then j else false)
else catch(
while j >= i do (
k: quotient(i + j, 2),
if (c: q[k][1]) = p then throw(k)
elseif c < p then i: k + 1 else j: k - 1
pqueue_push(pq, x, p) := block(
[q: [email protected], k],
k: find_key(q, p),
if integerp(k) then q[k][2]: endcons(x, q[k][2])
else [email protected]: sort(cons([p, [x]], q)),
pqueue_pop(pq) := block(
[q: [email protected], v, x],
if emptyp(q) then 'fail else (
p: q[1][1],
v: q[1][2],
x: v[1],
if length(v) > 1 then q[1][2]: rest(v) else [email protected]: rest(q),
pqueue_print(pq) := block([t], while (t: pqueue_pop(pq)) # 'fail do disp(t))$
/* An example */
a: new(pqueue)$
pqueue_push(a, "take milk", 4)$
pqueue_push(a, "take eggs", 4)$
pqueue_push(a, "take wheat flour", 4)$
pqueue_push(a, "take salt", 4)$
pqueue_push(a, "take oil", 4)$
pqueue_push(a, "carry out crepe recipe", 5)$
pqueue_push(a, "savour !", 6)$
pqueue_push(a, "add strawberry jam", 5 + 1/2)$
pqueue_push(a, "call friends", 5 + 2/3)$
pqueue_push(a, "go to the supermarket and buy food", 3)$
pqueue_push(a, "take a shower", 2)$
pqueue_push(a, "get dressed", 2)$
pqueue_push(a, "wake up", 1)$
pqueue_push(a, "serve cider", 5 + 3/4)$
pqueue_push(a, "buy also cider", 3)$
"wake up"
"take a shower"
"get dressed"
"go to the supermarket and buy food"
"buy also cider"
"take milk"
"take butter"
"take flour"
"take salt"
"take oil"
"carry out recipe"
"add strawberry jam"
"call friends"
"serve cider"
"savour !"


Mercury comes with an efficient, albeit simple, priority queue in its standard library. The build_pqueue/2 predicate in the code below inserts the test data in arbitrary order. display_pqueue/3, in turn, removes one K/V pair at a time, displaying the value. Compiling and running the supplied program results in all tasks being displayed in priority order as expected.

:- module test_pqueue.
:- interface.
:- import_module io.
:- pred main(io::di, io::uo) is det.
:- implementation.
:- import_module int.
:- import_module list.
:- import_module pqueue.
:- import_module string.
:- pred build_pqueue(pqueue(int,string)::in, pqueue(int,string)::out) is det.
build_pqueue(!PQ) :-
pqueue.insert(3, "Clear drains",  !PQ),
pqueue.insert(4, "Feed cat",  !PQ),
pqueue.insert(5, "Make tea",  !PQ),
pqueue.insert(1, "Solve RC tasks", !PQ),
pqueue.insert(2, "Tax return",  !PQ).
:- pred display_pqueue(pqueue(int, string)::in, io::di, io::uo) is det.
display_pqueue(PQ, !IO) :-
( pqueue.remove(K, V, PQ, PQO) ->
io.format("Key = %d, Value = %s\n", [i(K), s(V)], !IO),
display_pqueue(PQO, !IO)
main(!IO) :-
build_pqueue(pqueue.init, PQO),
display_pqueue(PQO, !IO).


Translation of: C
PriElem[T] = tuple
data: T
pri: int
PriQueue[T] = object
buf: seq[PriElem[T]]
count: int
# first element not used to simplify indices
proc initPriQueue[T](initialSize = 4): PriQueue[T] =
result.count = 0
proc add[T](q: var PriQueue[T], data: T, pri: int) =
var n = q.buf.len
var m = n div 2
q.buf.setLen(n + 1)
# append at end, then up heap
while m > 0 and pri < q.buf[m].pri:
q.buf[n] = q.buf[m]
n = m
m = m div 2
q.buf[n] = (data, pri)
q.count = q.buf.len - 1
proc pop[T](q: var PriQueue[T]): PriElem[T] =
assert q.buf.len > 1
result = q.buf[1]
var qn = q.buf.len - 1
var n = 1
var m = 2
while m < qn:
if m + 1 < qn and q.buf[m].pri > q.buf[m+1].pri:
inc m
if q.buf[qn].pri <= q.buf[m].pri:
q.buf[n] = q.buf[m]
n = m
m = m * 2
q.buf[n] = q.buf[qn]
q.buf.setLen(q.buf.len - 1)
q.count = q.buf.len - 1
var p = initPriQueue[string]()
p.add("Clear drains", 3)
p.add("Feed cat", 4)
p.add("Make tea", 5)
p.add("Solve RC tasks", 1)
p.add("Tax return", 2)
while p.count > 0:
echo p.pop()
(data: Solve RC tasks, pri: 1)
(data: Tax return, pri: 2)
(data: Clear drains, pri: 3)
(data: Feed cat, pri: 4)
(data: Make tea, pri: 5)

Using Nim tables

import tables
pq = initTable[int, string]()
proc main() =
pq.add(3, "Clear drains")
pq.add(4, "Feed cat")
pq.add(5, "Make tea")
pq.add(1, "Solve RC tasks")
pq.add(2, "Tax return")
for i in countUp(1,5):
if pq.hasKey(i):
echo i, ": ", pq[i]
1: Solve RC tasks
2: Tax return
3: Clear drains
4: Feed cat
5: Make tea


Works with: Cocoa

The priority queue used in this example is not actually written in Objective-C. It is part of Apple's (C-based) Core Foundation library, which is included with in Cocoa on Mac OS X and iOS. Its interface is a C function interface, which makes the code very ugly. Core Foundation is not included in GNUStep or other Objective-C APIs.

#import <Foundation/Foundation.h>
const void *PQRetain(CFAllocatorRef allocator, const void *ptr) {
return (__bridge_retained const void *)(__bridge id)ptr;
void PQRelease(CFAllocatorRef allocator, const void *ptr) {
(void)(__bridge_transfer id)ptr;
CFComparisonResult PQCompare(const void *ptr1, const void *ptr2, void *unused) {
return [(__bridge id)ptr1 compare:(__bridge id)ptr2];
@interface Task : NSObject {
int priority;
NSString *name;
- (instancetype)initWithPriority:(int)p andName:(NSString *)n;
- (NSComparisonResult)compare:(Task *)other;
@implementation Task
- (instancetype)initWithPriority:(int)p andName:(NSString *)n {
if ((self = [super init])) {
priority = p;
name = [n copy];
return self;
- (NSString *)description {
return [NSString stringWithFormat:@"%d, %@", priority, name];
- (NSComparisonResult)compare:(Task *)other {
if (priority == other->priority)
return NSOrderedSame;
else if (priority < other->priority)
return NSOrderedAscending;
return NSOrderedDescending;
int main (int argc, const char *argv[]) {
@autoreleasepool {
CFBinaryHeapCallBacks callBacks = {0, PQRetain, PQRelease, NULL, PQCompare};
CFBinaryHeapRef pq = CFBinaryHeapCreate(NULL, 0, &callBacks, NULL);
CFBinaryHeapAddValue(pq, [[Task alloc] initWithPriority:3 andName:@"Clear drains"]);
CFBinaryHeapAddValue(pq, [[Task alloc] initWithPriority:4 andName:@"Feed cat"]);
CFBinaryHeapAddValue(pq, [[Task alloc] initWithPriority:5 andName:@"Make tea"]);
CFBinaryHeapAddValue(pq, [[Task alloc] initWithPriority:1 andName:@"Solve RC tasks"]);
CFBinaryHeapAddValue(pq, [[Task alloc] initWithPriority:2 andName:@"Tax return"]);
while (CFBinaryHeapGetCount(pq) != 0) {
Task *task = (id)CFBinaryHeapGetMinimum(pq);
NSLog(@"%@", task);
return 0;


2011-08-22 07:46:19.250 Untitled[563:903] 1, Solve RC tasks
2011-08-22 07:46:19.255 Untitled[563:903] 2, Tax return
2011-08-22 07:46:19.256 Untitled[563:903] 3, Clear drains
2011-08-22 07:46:19.257 Untitled[563:903] 4, Feed cat
2011-08-22 07:46:19.258 Untitled[563:903] 5, Make tea


Holger Arnold's OCaml base library provides a PriorityQueue module.

module PQ = Base.PriorityQueue
let () =
let tasks = [
3, "Clear drains";
4, "Feed cat";
5, "Make tea";
1, "Solve RC tasks";
2, "Tax return";
] in
let pq = PQ.make (fun (prio1, _) (prio2, _) -> prio1 > prio2) in
List.iter (PQ.add pq) tasks;
while not (PQ.is_empty pq) do
let _, task = PQ.first pq in
PQ.remove_first pq;
print_endline task


$ ocaml -I +pcre pcre.cma base.cma
Make tea
Feed cat
Clear drains
Tax return
Solve RC tasks

Although OCaml's standard library does not have a dedicated priority queue structure, one can (for most purposes) use the built-in Set data structure as a priority queue, as long as no two elements compare equal (since Set does not allow duplicate elements). This is the case here since no two tasks should have the same name. Note that Set is a functional, persistent data structure, so we derive new priority queues from the old ones functionally, rather than modifying them imperatively; the complexity is still O(log n).

Works with: OCaml version 4.02+
module PQSet = Set.Make
type t = int * string (* pair of priority and task name *)
let compare = compare
let () =
let tasks = [
3, "Clear drains";
4, "Feed cat";
5, "Make tea";
1, "Solve RC tasks";
2, "Tax return";
] in
let pq = PQSet.of_list tasks in
let rec aux pq' =
if not (PQSet.is_empty pq') then begin
let prio, name as task = PQSet.min_elt pq' in
Printf.printf "%d, %s\n" prio name;
aux (PQSet.remove task pq')
in aux pq
1, Solve RC tasks
2, Tax return
3, Clear drains
4, Feed cat
5, Make tea


There are a few implementations on CPAN. Following uses Heap::Priority[1]

use 5.10.0;
use strict;
use Heap::Priority;
my $h = new Heap::Priority;
$h->highest_first(); # higher or lower number is more important
$h->add(@$_) for ["Clear drains", 3],
["Feed cat", 4],
["Make tea", 5],
["Solve RC tasks", 1],
["Tax return", 2];
say while ($_ = $h->pop);
Make tea
Feed cat
Clear drains
Tax return
Solve RC tasks

Perl 6[edit]

This is a rather simple implementation. It requires the priority to be a positive integer value, with lower values being higher priority. There isn't a hard limit on how many priority levels you can have, though more than a few dozen is probably not practical.

The tasks are stored internally as an array of FIFO buffers, so multiple tasks of the same priority level will be returned in the order they were stored.

class PriorityQueue {
has @!tasks;
method insert (Int $priority where * >= 0, $task) {
@!tasks[$priority].push: $task;
method get { @!tasks.first(?*).shift }
method is-empty { ?none @!tasks }
my $pq =;
for (
3, 'Clear drains',
4, 'Feed cat',
5, 'Make tea',
9, 'Sleep',
3, 'Check email',
1, 'Solve RC tasks',
9, 'Exercise',
2, 'Do taxes'
) -> $priority, $task {
$pq.insert( $priority, $task );
say $pq.get until $;
Solve RC tasks
Do taxes
Clear drains
Check email
Feed cat
Make tea


Dictionary based solution. Allows duplicate tasks, FIFO within priority, and uses a callback-style method of performing tasks.
Assumes 5 is the highest priority and should be done first, for 1 first just delete the ",true" on traverse_dict calls.

integer tasklist = new_dict()
procedure add_task(integer priority, string desc)
integer k = getd_index(priority,tasklist)
if k=0 then
sequence descs = getd_by_index(k,tasklist)
end if
end procedure
function list_task_visitor(integer priority, sequence descs, integer /*user_data*/)
return 1
end function
procedure list_tasks()
traverse_dict(routine_id("list_task_visitor"), 0, tasklist,true)
end procedure
function pop_task_visitor(integer priority, sequence descs, integer rid)
string desc = descs[1]
descs = descs[2..$]
if length(descs)=0 then
end if
return 0
end function
procedure pop_task(integer rid)
if dict_size(tasklist)!=0 then
traverse_dict(routine_id("pop_task_visitor"), rid, tasklist,true)
end if
end procedure
add_task(3,"Clear drains")
add_task(4,"Feed cat")
add_task(5,"Make tea")
add_task(1,"Solve RC tasks")
add_task(2,"Tax return")
procedure do_task(integer priority, string desc)
end procedure
{5,{"Make tea"}}
{4,{"Feed cat"}}
{3,{"Clear drains"}}
{2,{"Tax return"}}
{1,{"Solve RC tasks"}}
{5,"Make tea"}
{4,{"Feed cat"}}
{3,{"Clear drains"}}
{2,{"Tax return"}}
{1,{"Solve RC tasks"}}


Works with: PHP version 5.3+

PHP's SplPriorityQueue class implements a max-heap. PHP also separately has SplHeap, SplMinHeap, and SplMaxHeap classes.

$pq = new SplPriorityQueue;
$pq->insert('Clear drains', 3);
$pq->insert('Feed cat', 4);
$pq->insert('Make tea', 5);
$pq->insert('Solve RC tasks', 1);
$pq->insert('Tax return', 2);
// This line causes extract() to return both the data and priority (in an associative array),
// Otherwise it would just return the data
while (!$pq->isEmpty()) {


    [data] => Make tea
    [priority] => 5
    [data] => Feed cat
    [priority] => 4
    [data] => Clear drains
    [priority] => 3
    [data] => Tax return
    [priority] => 2
    [data] => Solve RC tasks
    [priority] => 1
Works with: PHP version 5.3+

The difference between SplHeap and SplPriorityQueue is that SplPriorityQueue takes the data and the priority as two separate arguments, and the comparison is only made on the priority; whereas SplHeap takes only one argument (the element), and the comparison is made on that directly. In all of these classes it is possible to provide a custom comparator by subclassing the class and overriding its compare method.

$pq = new SplMinHeap;
$pq->insert(array(3, 'Clear drains'));
$pq->insert(array(4, 'Feed cat'));
$pq->insert(array(5, 'Make tea'));
$pq->insert(array(1, 'Solve RC tasks'));
$pq->insert(array(2, 'Tax return'));
while (!$pq->isEmpty()) {


    [0] => 1
    [1] => Solve RC tasks
    [0] => 2
    [1] => Tax return
    [0] => 3
    [1] => Clear drains
    [0] => 4
    [1] => Feed cat
    [0] => 5
    [1] => Make tea


The following implementation imposes no limits. It uses a binary tree for storage. The priority levels may be numeric, or of any other type.

# Insert item into priority queue
(de insertPQ (Queue Prio Item)
(idx Queue (cons Prio Item) T) )
# Remove and return top item from priority queue
(de removePQ (Queue)
(cdar (idx Queue (peekPQ Queue) NIL)) )
# Find top element in priority queue
(de peekPQ (Queue)
(let V (val Queue)
(while (cadr V)
(setq V @) )
(car V) ) )
# Merge second queue into first
(de mergePQ (Queue1 Queue2)
(balance Queue1 (sort (conc (idx Queue1) (idx Queue2)))) )


# Two priority queues
(off Pq1 Pq2)
# Insert into first queue
(insertPQ 'Pq1 3 '(Clear drains))
(insertPQ 'Pq1 4 '(Feed cat))
# Insert into second queue
(insertPQ 'Pq2 5 '(Make tea))
(insertPQ 'Pq2 1 '(Solve RC tasks))
(insertPQ 'Pq2 2 '(Tax return))
# Merge second into first queue
(mergePQ 'Pq1 'Pq2)
# Remove and print all items from first queue
(while Pq1
(println (removePQ 'Pq1)) )


(Solve RC tasks)
(Tax return)
(Clear drains)
(Feed cat)
(Make tea)


SWI-Prolog has a library, written by Lars Buitinck that implements priority queues.
Informations here :

Example of use :

priority-queue :-
TL0 = [3-'Clear drains',
4-'Feed cat'],
% we can create a priority queue from a list
list_to_heap(TL0, Heap0),
% alternatively we can start from an empty queue
% get from empty_heap/1.
% now we add the other elements
add_to_heap(Heap0, 5, 'Make tea', Heap1),
add_to_heap(Heap1, 1, 'Solve RC tasks', Heap2),
add_to_heap(Heap2, 2, 'Tax return', Heap3),
% we list the content of the heap:
heap_to_list(Heap3, TL1),
writeln('Content of the queue'), maplist(writeln, TL1),
% now we retrieve the minimum-priority pair
get_from_heap(Heap3, Priority, Key, Heap4),
format('Retrieve top of the queue : Priority ~w, Element ~w~n', [Priority, Key]),
% we list the content of the heap:
heap_to_list(Heap4, TL2),
writeln('Content of the queue'), maplist(writeln, TL2).

The output :

1 ?- priority-queue.
Content of the queue
1-Solve RC tasks
2-Tax return
3-Clear drains
4-Feed cat
5-Make tea

Retrieve top of the queue : Priority 1, Element Solve RC tasks

Content of the queue
2-Tax return
3-Clear drains
4-Feed cat
5-Make tea


The priority queue is implemented using a binary heap array and a map. The map stores the elements of a given priority in a FIFO list. Priorities can be any signed 32 value.

Structure taskList
List description.s() ;implements FIFO queue
Structure task
*tl.tList ;pointer to a list of task descriptions
Priority.i ;tasks priority, lower value has more priority
Structure priorityQueue
maxHeapSize.i ;increases as needed
heapItemCount.i ;number of elements currently in heap
Array heap.task(0) ;elements hold FIFO queues ordered by priorities, lowest first
map heapMap.taskList() ;holds lists of tasks with the same priority that are FIFO queues
Procedure insertPQ(*PQ.priorityQueue, description.s, p)
If FindMapElement(*PQ\heapMap(), Str(p))
*PQ\heapMap()\description() = description
Protected *tl.taskList = AddMapElement(*PQ\heapMap(), Str(p))
*tl\description() = description
Protected pos = *PQ\heapItemCount
*PQ\heapItemCount + 1
If *PQ\heapItemCount > *PQ\maxHeapSize
Select *PQ\maxHeapSize
Case 0
*PQ\maxHeapSize = 128
*PQ\maxHeapSize * 2
Redim *PQ\heap.task(*PQ\maxHeapSize)
While pos > 0 And p < *PQ\heap((pos - 1) / 2)\Priority
*PQ\heap(pos) = *PQ\heap((pos - 1) / 2)
pos = (pos - 1) / 2
*PQ\heap(pos)\tl = *tl
*PQ\heap(pos)\Priority = p
Procedure.s removePQ(*PQ.priorityQueue)
Protected *tl.taskList = *PQ\heap(0)\tl, description.s
description = *tl\description()
If ListSize(*tl\description()) > 1
DeleteMapElement(*PQ\heapMap(), Str(*PQ\heap(0)\Priority))
*PQ\heapItemCount - 1
*PQ\heap(0) = *PQ\heap(*PQ\heapItemCount)
Protected pos
Protected child1 = 2 * pos + 1
Protected child2 = 2 * pos + 2
If child1 >= *PQ\heapItemCount
Protected smallestChild
If child2 >= *PQ\heapItemCount
smallestChild = child1
ElseIf *PQ\heap(child1)\Priority <= *PQ\heap(child2)\Priority
smallestChild = child1
smallestChild = child2
If (*PQ\heap(smallestChild)\Priority >= *PQ\heap(pos)\Priority)
Swap *PQ\heap(pos)\tl, *PQ\heap(smallestChild)\tl
Swap *PQ\heap(pos)\Priority, *PQ\heap(smallestChild)\Priority
pos = smallestChild
ProcedureReturn description
Procedure isEmptyPQ(*PQ.priorityQueue) ;returns 1 if empty, otherwise returns 0
If *PQ\heapItemCount
ProcedureReturn 0
ProcedureReturn 1
If OpenConsole()
Define PQ.priorityQueue
insertPQ(PQ, "Clear drains", 3)
insertPQ(PQ, "Answer Phone 1", 8)
insertPQ(PQ, "Feed cat", 4)
insertPQ(PQ, "Answer Phone 2", 8)
insertPQ(PQ, "Make tea", 5)
insertPQ(PQ, "Sleep", 9)
insertPQ(PQ, "Check email", 3)
insertPQ(PQ, "Solve RC tasks", 1)
insertPQ(PQ, "Answer Phone 3", 8)
insertPQ(PQ, "Exercise", 9)
insertPQ(PQ, "Answer Phone 4", 8)
insertPQ(PQ, "Tax return", 2)
While Not isEmptyPQ(PQ)
Print(#CRLF$ + #CRLF$ + "Press ENTER to exit"): Input()
Solve RC tasks
Tax return
Clear drains
Check email
Feed cat
Make tea
Answer Phone 1
Answer Phone 2
Answer Phone 3
Answer Phone 4


Using PriorityQueue[edit]

Python has the class queue.PriorityQueue in its standard library.

The data structures in the "queue" module are synchronized multi-producer, multi-consumer queues for multi-threaded use. They can however handle this task:

>>> import queue
>>> pq = queue.PriorityQueue()
>>> for item in ((3, "Clear drains"), (4, "Feed cat"), (5, "Make tea"), (1, "Solve RC tasks"), (2, "Tax return")):
>>> while not pq.empty():
(1, 'Solve RC tasks')
(2, 'Tax return')
(3, 'Clear drains')
(4, 'Feed cat')
(5, 'Make tea')
Help text for queue.PriorityQueue
>>> import queue
>>> help(queue.PriorityQueue)
Help on class PriorityQueue in module queue:
class PriorityQueue(Queue)
| Variant of Queue that retrieves open entries in priority order (lowest first).
| Entries are typically tuples of the form: (priority number, data).
| Method resolution order:
| PriorityQueue
| Queue
| builtins.object
| Methods inherited from Queue:
| __init__(self, maxsize=0)
| empty(self)
| Return True if the queue is empty, False otherwise (not reliable!).
| This method is likely to be removed at some point. Use qsize() == 0
| as a direct substitute, but be aware that either approach risks a race
| condition where a queue can grow before the result of empty() or
| qsize() can be used.
| To create code that needs to wait for all queued tasks to be
| completed, the preferred technique is to use the join() method.
| full(self)
| Return True if the queue is full, False otherwise (not reliable!).
| This method is likely to be removed at some point. Use qsize() >= n
| as a direct substitute, but be aware that either approach risks a race
| condition where a queue can shrink before the result of full() or
| qsize() can be used.
| get(self, block=True, timeout=None)
| Remove and return an item from the queue.
| If optional args 'block' is true and 'timeout' is None (the default),
| block if necessary until an item is available. If 'timeout' is
| a positive number, it blocks at most 'timeout' seconds and raises
| the Empty exception if no item was available within that time.
| Otherwise ('block' is false), return an item if one is immediately
| available, else raise the Empty exception ('timeout' is ignored
| in that case).
| get_nowait(self)
| Remove and return an item from the queue without blocking.
| Only get an item if one is immediately available. Otherwise
| raise the Empty exception.
| join(self)
| Blocks until all items in the Queue have been gotten and processed.
| The count of unfinished tasks goes up whenever an item is added to the
| queue. The count goes down whenever a consumer thread calls task_done()
| to indicate the item was retrieved and all work on it is complete.
| When the count of unfinished tasks drops to zero, join() unblocks.
| put(self, item, block=True, timeout=None)
| Put an item into the queue.
| If optional args 'block' is true and 'timeout' is None (the default),
| block if necessary until a free slot is available. If 'timeout' is
| a positive number, it blocks at most 'timeout' seconds and raises
| the Full exception if no free slot was available within that time.
| Otherwise ('block' is false), put an item on the queue if a free slot
| is immediately available, else raise the Full exception ('timeout'
| is ignored in that case).
| put_nowait(self, item)
| Put an item into the queue without blocking.
| Only enqueue the item if a free slot is immediately available.
| Otherwise raise the Full exception.
| qsize(self)
| Return the approximate size of the queue (not reliable!).
| task_done(self)
| Indicate that a formerly enqueued task is complete.
| Used by Queue consumer threads. For each get() used to fetch a task,
| a subsequent call to task_done() tells the queue that the processing
| on the task is complete.
| If a join() is currently blocking, it will resume when all items
| have been processed (meaning that a task_done() call was received
| for every item that had been put() into the queue).
| Raises a ValueError if called more times than there were items
| placed in the queue.
| ----------------------------------------------------------------------
| Data descriptors inherited from Queue:
| __dict__
| dictionary for instance variables (if defined)
| __weakref__
| list of weak references to the object (if defined)

Using heapq[edit]

Python has the heapq module in its standard library.

Although one can use the heappush method to add items individually to a heap similar to the method used in the PriorityQueue example above, we will instead transform the list of items into a heap in one go then pop them off one at a time as before.

>>> from heapq import heappush, heappop, heapify
>>> items = [(3, "Clear drains"), (4, "Feed cat"), (5, "Make tea"), (1, "Solve RC tasks"), (2, "Tax return")]
>>> heapify(items)
>>> while items:
(1, 'Solve RC tasks')
(2, 'Tax return')
(3, 'Clear drains')
(4, 'Feed cat')
(5, 'Make tea')
Help text for module heapq
>>> help('heapq')
Help on module heapq:
heapq - Heap queue algorithm (a.k.a. priority queue).
Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for
all k, counting elements from 0. For the sake of comparison,
non-existing elements are considered to be infinite. The interesting
property of a heap is that a[0] is always its smallest element.
heap = [] # creates an empty heap
heappush(heap, item) # pushes a new item on the heap
item = heappop(heap) # pops the smallest item from the heap
item = heap[0] # smallest item on the heap without popping it
heapify(x) # transforms list into a heap, in-place, in linear time
item = heapreplace(heap, item) # pops and returns smallest item, and adds
# new item; the heap size is unchanged
Our API differs from textbook heap algorithms as follows:
- We use 0-based indexing. This makes the relationship between the
index for a node and the indexes for its children slightly less
obvious, but is more suitable since Python uses 0-based indexing.
- Our heappop() method returns the smallest item, not the largest.
These two make it possible to view the heap as a regular Python list
without surprises: heap[0] is the smallest item, and heap.sort()
maintains the heap invariant!
Transform list into a heap, in-place, in O(len(heap)) time.
Pop the smallest item off the heap, maintaining the heap invariant.
Push item onto heap, maintaining the heap invariant.
Push item on the heap, then pop and return the smallest item
from the heap. The combined action runs more efficiently than
heappush() followed by a separate call to heappop().
Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be
more appropriate when using a fixed-size heap. Note that the value
returned may be larger than item! That constrains reasonable uses of
this routine unless written as part of a conditional replacement:
if item > heap[0]:
item = heapreplace(heap, item)
Merge multiple sorted inputs into a single sorted output.
Similar to sorted(itertools.chain(*iterables)) but returns a generator,
does not pull the data into memory all at once, and assumes that each of
the input streams is already sorted (smallest to largest).
>>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))
[0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]
nlargest(n, iterable, key=None)
Find the n largest elements in a dataset.
Equivalent to: sorted(iterable, key=key, reverse=True)[:n]
nsmallest(n, iterable, key=None)
Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
__about__ = 'Heap queues\n\n[explanation by François Pinard]\n\nH... t...
__all__ = ['
heappush', 'heappop', 'heapify', 'heapreplace', 'merge', '...


Using closures:

PriorityQueue <- function() {
keys <- values <- NULL
insert <- function(key, value) {
ord <- findInterval(key, keys)
keys <<- append(keys, key, ord)
values <<- append(values, value, ord)
pop <- function() {
head <- list(key=keys[1],value=values[[1]])
values <<- values[-1]
keys <<- keys[-1]
empty <- function() length(keys) == 0
pq <- PriorityQueue()
pq$insert(3, "Clear drains")
pq$insert(4, "Feed cat")
pq$insert(5, "Make tea")
pq$insert(1, "Solve RC tasks")
pq$insert(2, "Tax return")
while(!pq$empty()) {
with(pq$pop(), cat(key,":",value,"\n"))
With output:
1 : Solve RC tasks 
2 : Tax return
3 : Clear drains
4 : Feed cat
5 : Make tea
A similar implementation using R5 classes:
PriorityQueue <-
fields = list(keys = "numeric", values = "list"),
methods = list(
insert = function(key,value) {
insert.order <- findInterval(key, keys)
keys <<- append(keys, key, insert.order)
values <<- append(values, value, insert.order)
pop = function() {
head <- list(key=keys[1],value=values[[1]])
keys <<- keys[-1]
values <<- values[-1]
empty = function() length(keys) == 0
The only change in the example would be in the instantiation:
pq <- PriorityQueue$new()


This solution implements priority queues on top of heaps.

#lang racket
(require data/heap)
(define pq (make-heap (Ξ»(x y) (<= (second x) (second y)))))
(define (insert! x pri)
(heap-add! pq (list pri x)))
(define (remove-min!)
(first (heap-min pq))
(heap-remove-min! pq)))
(insert! 3 "Clear drains")
(insert! 4 "Feed cat")
(insert! 5 "Make tea")
(insert! 1 "Solve RC tasks")
(insert! 2 "Tax return")


"Solve RC tasks"
"Tax return"
"Clear drains"
"Feed cat"
"Make tea"


version 1[edit]

Programming note:   this REXX version allows any number (with or without decimals, say, 5.7) for the priority, including negative numbers.

/*REXX program implements a priority queue;  with  insert/display/delete  the top task. */
#=0; @.= /*0 tasks; nullify the priority queue.*/
say '══════ inserting tasks.'; call .ins 3 "Clear drains"
call .ins 4 "Feed cat"
call .ins 5 "Make tea"
call .ins 1 "Solve RC tasks"
call .ins 2 "Tax return"
call .ins 6 "Relax"
call .ins 6 "Enjoy"
say '══════ showing tasks.'; call .show
say '══════ deletes top task.'; say .del() /*delete the top task. */
exit /*stick a fork in it, we're all done. */
.del: procedure expose @. #; parse arg p; if p=='' then; x=@.p; @.p=; return x
/*delete the top task entry. */
.ins: procedure expose @. #; #=#+1; @.#=arg(1); return # /*entry, P, task.*/
.show: procedure expose @. #; do j=1 for #; _=@.j; if _=='' then iterate; say _; end
return /* [↑] show whole list or just one. */
.top: procedure expose @. #; top=; top#=
do j=1 for #; _=word(@.j,1); if _=='' then iterate
if top=='' | _>top then do; top=_; top#=j; end
end /*j*/
return top#


══════ inserting tasks.
══════ showing tasks.
3 Clear drains
4 Feed cat
5 Make tea
1 Solve RC tasks
2 Tax return
6 Relax
6 Enjoy
══════ deletes top task.
6 Relax

version 2[edit]

/*REXX pgm implements a priority queue; with insert/show/delete top task*/
task.=0 /* for the sake of task.0done.* */
say '------ inserting tasks.'; call ins_task 3 'Clear drains'
call ins_task 4 'Feed cat'
call ins_task 5 'Make tea'
call ins_task 1 'Solve RC tasks'
call ins_task 2 'Tax return'
call ins_task 6 'Relax'
call ins_task 6 'Enjoy'
say '------ Showing tasks.'; call show_tasks
say '------ Show and delete top task.'
todo=n /* tasks to be done */
do While todo>0
Say top()
ins_task: procedure expose n task.
Parse Arg task.0pri.n task.0txt.n
show_tasks: procedure expose task. n
do i=1 To n
Say task.0pri.i task.0txt.i
top: procedure expose n task. todo /* get top task and mark it 'done' */
Do i=1 To n
If task.0pri.i>high &,
task.0done.i=0 Then Do
res=task.0pri.j task.0txt.j
return res
------ inserting tasks.
------ Showing tasks.
3 Clear drains
4 Feed cat
5 Make tea
1 Solve RC tasks
2 Tax return
6 Relax
6 Enjoy
------ Show and delete top task.
6 Relax
6 Enjoy
5 Make tea
4 Feed cat
3 Clear drains
2 Tax return
1 Solve RC tasks


A naive, inefficient implementation

class PriorityQueueNaive
def initialize(data=nil)
@q = {|h, k| h[k] = []}
data.each {|priority, item| @q[priority] << item} if data
@priorities = @q.keys.sort
def push(priority, item)
@q[priority] << item
@priorities = @q.keys.sort
def pop
p = @priorities[0]
item = @q[p].shift
if @q[p].empty?
def peek
unless empty?
def empty?
def each
@q.each do |priority, items|
items.each {|item| yield priority, item}
def dup
@q.each_with_object( do |(priority, items), obj|
items.each {|item| obj.push(priority, item)}
def merge(other)
raise TypeError unless self.class == other.class
pq = dup
other.each {|priority, item| pq.push(priority, item)}
pq # return a new object
def inspect
test = [
[6, "drink tea"],
[3, "Clear drains"],
[4, "Feed cat"],
[5, "Make tea"],
[6, "eat biscuit"],
[1, "Solve RC tasks"],
[2, "Tax return"],
pq =
test.each {|pr, str| pq.push(pr, str) }
until pq.empty?
puts pq.pop
test2 = test.shift(3)
p pq1 =
p pq2 =
p pq3 = pq1.merge(pq2)
puts "peek : #{pq3.peek}"
until pq3.empty?
puts pq3.pop
puts "peek : #{pq3.peek}"
Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea
drink tea
eat biscuit

{5=>["Make tea"], 6=>["eat biscuit"], 1=>["Solve RC tasks"], 2=>["Tax return"]}
{6=>["drink tea"], 3=>["Clear drains"], 4=>["Feed cat"]}
{5=>["Make tea"], 6=>["eat biscuit", "drink tea"], 1=>["Solve RC tasks"], 2=>["Tax return"], 3=>["Clear drains"], 4=>["Feed cat"]}
peek : Solve RC tasks
Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea
eat biscuit
drink tea
peek : 

Run BASIC[edit]

sqliteconnect #mem, ":memory:"
#mem execute("CREATE TABLE queue (priority float,descr text)")
' --------------------------------------------------------------
' Insert items into the que
' --------------------------------------------------------------
#mem execute("INSERT INTO queue VALUES (3,'Clear drains')")
#mem execute("INSERT INTO queue VALUES (4,'Feed cat')")
#mem execute("INSERT INTO queue VALUES (5,'Make tea')")
#mem execute("INSERT INTO queue VALUES (1,'Solve RC tasks')")
#mem execute("INSERT INTO queue VALUES (2,'Tax return')")
'--------------- insert priority between 4 and 5 -----------------
#mem execute("INSERT INTO queue VALUES (4.5,'My Special Project')")
what$ = " -------------- Find first priority ---------------------"
mem$ = "SELECT * FROM queue ORDER BY priority LIMIT 1"
gosub [getQueue]
what$ = " -------------- Find last priority ---------------------"
mem$ = "SELECT * FROM queue ORDER BY priority desc LIMIT 1"
gosub [getQueue]
what$ = " -------------- Delete Highest Priority ---------------------"
mem$ = "DELETE FROM queue WHERE priority = (select max(q.priority) FROM queue as q)"
#mem execute(mem$)
what$ = " -------------- List Priority Sequence ---------------------"
mem$ = "SELECT * FROM queue ORDER BY priority"
gosub [getQueue]
print what$
#mem execute(mem$)
rows = #mem ROWCOUNT()
print "Priority Description"
for i = 1 to rows
#row = #mem #nextrow()
priority = #row priority()
descr$ = #row descr$()
print priority;" ";descr$
next i
 -------------- Find first priority ---------------------
Priority    Description
1.0         Solve RC tasks
 -------------- Find last priority ---------------------
Priority    Description
5.0         Make tea
 -------------- List Priority Sequence ---------------------
Priority    Description
1.0         Solve RC tasks
2.0         Tax return
3.0         Clear drains
4.0         Feed cat
4.5         My Special Project


use std::collections::BinaryHeap;
use std::cmp::Ordering;
use std::borrow::Cow;
#[derive(Eq, PartialEq)]
struct Item<'a> {
priority: usize,
task: Cow<'a, str>, // Takes either borrowed or owned string
impl<'a> Item<'a> {
fn new<T>(p: usize, t: T) -> Self
where T: Into<Cow<'a, str>>
Item {
priority: p,
task: t.into(),
// Manually implpement Ord so we have a min heap
impl<'a> Ord for Item<'a> {
fn cmp(&self, other: &Self) -> Ordering {
// PartialOrd is required by Ord
impl<'a> PartialOrd for Item<'a> {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
fn main() {
let mut queue = BinaryHeap::with_capacity(5);
queue.push(Item::new(3, "Clear drains"));
queue.push(Item::new(4, "Feed cat"));
queue.push(Item::new(5, "Make tea"));
queue.push(Item::new(1, "Solve RC tasks"));
queue.push(Item::new(2, "Tax return"));
for item in queue {
println!("{}", item.task);
Solve RC tasks
Tax return
Make tea
Feed cat
Clear drains


Scala has a class PriorityQueue in its standard library.

import scala.collection.mutable.PriorityQueue
case class Task(prio:Int, text:String) extends Ordered[Task] {
def compare(that: Task)=that.prio compare this.prio
var q=PriorityQueue[Task]() ++ Seq(Task(3, "Clear drains"), Task(4, "Feed cat"),
Task(5, "Make tea"), Task(1, "Solve RC tasks"), Task(2, "Tax return"))
while(q.nonEmpty) println(q dequeue)


Task(1,Solve RC tasks)
Task(2,Tax return)
Task(3,Clear drains)
Task(4,Feed cat)
Task(5,Make tea)

Instead of deriving the class from Ordering an implicit conversion could be provided.

case class Task(prio:Int, text:String)
implicit def taskOrdering=new Ordering[Task] {
def compare(t1:Task, t2:Task):Int=t2.prio compare t1.prio


Translation of: Perl 6
class PriorityQueue {
has tasks = []
method insert (Number priority { _ >= 0 }, task) {
for n in range(tasks.len, priority) {
tasks[n] = []
method get { tasks.first { !.is_empty } -> shift }
method is_empty { tasks.all { .is_empty } }
var pq = PriorityQueue()
[3, 'Clear drains'],
[4, 'Feed cat'],
[5, 'Make tea'],
[9, 'Sleep'],
[3, 'Check email'],
[1, 'Solve RC tasks'],
[9, 'Exercise'],
[2, 'Do taxes'],
].each { |pair|
say pq.get while !pq.is_empty
Solve RC tasks
Do taxes
Clear drains
Check email
Feed cat
Make tea

Standard ML[edit]

Works with: SML/NJ

Note: this is a max-heap

structure TaskPriority = struct
type priority = int
val compare =
type item = int * string
val priority : item -> int = #1
structure PQ = LeftPriorityQFn (TaskPriority)
val tasks = [
(3, "Clear drains"),
(4, "Feed cat"),
(5, "Make tea"),
(1, "Solve RC tasks"),
(2, "Tax return")]
val pq = foldl PQ.insert PQ.empty tasks
(* or val pq = PQ.fromList tasks *)
fun aux pq' =
case pq' of
NONE => ()
| SOME ((prio, name), pq'') => (
print (Int.toString prio ^ ", " ^ name ^ "\n");
aux pq''
aux pq


5, Make tea
4, Feed cat
3, Clear drains
2, Tax return
1, Solve RC tasks


You can use CFBinaryHeap from Core Foundation, but it is super ugly due to the fact that CFBinaryHeap operates on generic pointers, and you need to convert back and forth between that and objects.

Works with: Swift version 2.x
class Task : Comparable, CustomStringConvertible {
var priority : Int
var name: String
init(priority: Int, name: String) {
self.priority = priority = name
var description: String {
return "\(priority), \(name)"
func ==(t1: Task, t2: Task) -> Bool {
return t1.priority == t2.priority
func <(t1: Task, t2: Task) -> Bool {
return t1.priority < t2.priority
struct TaskPriorityQueue {
let heap : CFBinaryHeapRef = {
var callBacks = CFBinaryHeapCallBacks(version: 0, retain: {
}, release: {
}, copyDescription: nil, compare: { (ptr1, ptr2, _) in
let t1 : Task = Unmanaged<Task>.fromOpaque(COpaquePointer(ptr1)).takeUnretainedValue()
let t2 : Task = Unmanaged<Task>.fromOpaque(COpaquePointer(ptr2)).takeUnretainedValue()
return t1 == t2 ? CFComparisonResult.CompareEqualTo : t1 < t2 ? CFComparisonResult.CompareLessThan : CFComparisonResult.CompareGreaterThan
return CFBinaryHeapCreate(nil, 0, &callBacks, nil)
var count : Int { return CFBinaryHeapGetCount(heap) }
mutating func push(t: Task) {
CFBinaryHeapAddValue(heap, UnsafePointer(Unmanaged.passUnretained(t).toOpaque()))
func peek() -> Task {
return Unmanaged<Task>.fromOpaque(COpaquePointer(CFBinaryHeapGetMinimum(heap))).takeUnretainedValue()
mutating func pop() -> Task {
let result = Unmanaged<Task>.fromOpaque(COpaquePointer(CFBinaryHeapGetMinimum(heap))).takeUnretainedValue()
return result
var pq = TaskPriorityQueue()
pq.push(Task(priority: 3, name: "Clear drains"))
pq.push(Task(priority: 4, name: "Feed cat"))
pq.push(Task(priority: 5, name: "Make tea"))
pq.push(Task(priority: 1, name: "Solve RC tasks"))
pq.push(Task(priority: 2, name: "Tax return"))
while pq.count != 0 {
1, Solve RC tasks
2, Tax return
3, Clear drains
4, Feed cat
5, Make tea


Library: Tcllib (Package: struct::prioqueue)
package require struct::prioqueue
set pq [struct::prioqueue]
foreach {priority task} {
3 "Clear drains"
4 "Feed cat"
5 "Make tea"
1 "Solve RC tasks"
2 "Tax return"
} {
# Insert into the priority queue
$pq put $task $priority
# Drain the queue, in priority-sorted order
while {[$pq size]} {
# Remove the front-most item from the priority queue
puts [$pq get]

Which produces this output:

Make tea
Feed cat
Clear drains
Tax return
Solve RC tasks


This solution uses a [hopefully small] fixed number of priorities, each of which has an unordered list of tasks. This allows O(1) insertions, O(p) for retrievals (p is the number of priorities).

class PQ{
fcn init(numLevels=10){ // 0..numLevels, bigger # == lower priorty
var [const] queue=(1).pump(numLevels+1,List.createLong(numLevels).write,L().copy);
fcn add(item,priorty){ queue[priorty].append(item); }
fcn peek{ if(q:=queue.filter1()) return(q[-1]); Void }// -->Void if empty
fcn pop { if(q:=queue.filter1()) return(q.pop()); Void }// -->Void if empty
var [private] state=L();
fcn [private] next{ // iterate
foreach n in ([qi..queue.len()-1]){
if(ii>=q.len()) ii=0;
else{ state.clear().append(n,ii+1); return(q[ii]) }
fcn walker{ state.clear().append(0,0); Walker(next) } // iterator front end
fcn toString{ "PQ(%d) items".fmt(queue.reduce(fcn(sum,q){ sum+q.len() },0)) }
foreach x in
(T("Clear drains",3, "Feed cat",4, "Make tea",5, "Solve RC tasks",1, "Tax return",2,
"Clean room",10,"Wash cat",10)){
println("Number 1 thing to do: ",pq.peek());
println("Top 2 things to do: ",pq.walker().walk(2));
println("Do this next year: ",pq.walker().walk()[-1]);
println("ToDo list:");
foreach item in (pq){ item.println() }
PQ(7) items
Number 1 thing to do: Solve RC tasks
Top 2 things to do: L("Solve RC tasks","Tax return")
Do this next year: Wash cat
ToDo list:
Solve RC tasks
Tax return
Clear drains
Feed cat
Make tea
Clean room
Wash cat
PQ(7) items