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• OS X contains heapsort in stdlib.h which conflicts with heapsort in sort library

  - by CryptoQuick

  I'm using Ariel Faigon's sort library, found here: http://www.yendor.com/programming/sort/ I was able to get all my code working on Linux, but unfortunately, when trying to compile with GCC on Mac, its default stdlib.h contains another heapsort, which unfortunately results in a conflicting types error. Here's the man page for Apple heapsort: http://developer.apple.com/library/mac/#documentation/Darwin/Reference/ManPages/man3/heapsort.3.html Commenting out the heapsort in the sort library header causes a whole heap of problems. (pardon the pun) I also briefly thought of commenting out my use of stdlib.h, but I use malloc and realloc, so that won't work at all. Any ideas?

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• Heapsort not working in Python for list of strings using heapq module

  - by VSN

  I was reading the python 2.7 documentation when I came across the heapq module. I was interested in the heapify() and the heappop() methods. So, I decided to write a simple heapsort program for integers: from heapq import heapify, heappop user_input = raw_input("Enter numbers to be sorted: ") data = map (int, user_input.split(",")) new_data = [] for i in range(len(data)): heapify(data) new_data.append(heappop(data)) print new_data This worked like a charm. To make it more interesting, I thought I would take away the integer conversion and leave it as a string. Logically, it should make no difference and the code should work as it did for integers: from heapq import heapify, heappop user_input = raw_input("Enter numbers to be sorted: ") data = user_input.split(",") new_data = [] for i in range(len(data)): heapify(data) print data new_data.append(heappop(data)) print new_data Note: I added a print statement in the for loop to see the heapified list. Here's the output when I ran the script: `$ python heapsort.py Enter numbers to be sorted: 4, 3, 1, 9, 6, 2 [' 1', ' 3', ' 2', ' 9', ' 6', '4'] [' 2', ' 3', '4', ' 9', ' 6'] [' 3', ' 6', '4', ' 9'] [' 6', ' 9', '4'] [' 9', '4'] ['4'] [' 1', ' 2', ' 3', ' 6', ' 9', '4']` The reasoning I applied was that since the strings are being compared, the tree should be the same if they were numbers. As is evident, the heapify didn't work correctly after the third iteration. Could someone help me figure out if I am missing something here? I'm running Python 2.4.5 on RedHat 3.4.6-9. Thanks, VSN

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• (1 2 3 . #<void>)- heapsort

  - by superguay

  Hello everybody: I tried to implement a "pairing heap" with all the regular operations (merge, delete-min etc.), then I've been requested to write a function that would sort a list using my newly constructed heap implementation. Unfortunately it seems that someting goes wrong... Here's the relevant code: (define (heap-merge h1 h2) (cond ((heap-empty? h1) h2) ((heap-empty? h2) h1) (else (let ((min1 (heap-get-min h1)) (min2 (heap-get-min h2))) (if ((heap-get-less h1) min1 min2) (make-pairing-heap (heap-get-less h1) min1 (cons h2 (heap-get-subheaps h1))) (make-pairing-heap (heap-get-less h1) min2 (cons h1 (heap-get-subheaps h2)))))))) (define (heap-insert element h) (heap-merge (make-pairing-heap (heap-get-less h) element '()) h)) (define (heap-delete-min h) (define (merge-in-pairs less? subheaps) (cond ((null? subheaps) (make-heap less?)) ((null? (cdr subheaps)) (car subheaps)) (else (heap-merge (heap-merge (car subheaps) (cadr subheaps)) (merge-in-pairs less? (cddr subheaps)))))) (if (heap-empty? h) (error "expected pairing-heap for first argument, got an empty heap ") (merge-in-pairs (heap-get-less h) (heap-get-subheaps h)))) (define (heapsort l less?) (let aux ((h (accumulate heap-insert (make-heap less?) l))) (if (not (heap-empty? h)) (cons (heap-get-min h) (aux (heap-delete-min h)))))) And these are some selectors that may help you to understand the code: (define (make-pairing-heap less? min subheaps) (cons less? (cons min subheaps))) (define (make-heap less?) (cons less? '())) (define (heap-get-less h) (car h)) (define (heap-empty? h) (if (null? (cdr h)) #t #f)) Now lets get to the problem: When i run 'heapsort' it returns the sorted list with "void", as you can see: (heapsort (list 1 2 3) <) (1 2 3 . #)..HOW CAN I FIX IT? Regards, Superguay

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• Quicksort Vs Heapsort

  - by avd

  Both do in place sorting.Which is better:quicksort or heap sort? What are the applications/cases in which either of them is preferred?

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• Efficient heaps in purely functional languages

  - by Kim

  As an exercise in Haskell, I'm trying to implement heapsort. The heap is usually implemented as an array in imperative languages, but this would be hugely inefficient in purely functional languages. So I've looked at binary heaps, but everything I found so far describes them from an imperative viewpoint and the algorithms presented are hard to translate to a functional setting. How to efficiently implement a heap in a purely functional language such as Haskell? Edit: By efficient I mean it should still be in O(n*log n), but it doesn't have to beat a C program. Also, I'd like to use purely functional programming. What else would be the point of doing it in Haskell?

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• Removing elements from heap

  - by user193138

  I made a heap. I am curious if there's something subtley wrong with my remove function: int Heap::remove() { if (n == 0) exit(1); int temp = arr[0]; arr[0] = arr[--n]; heapDown(0); arr[n] = 0; return temp; } void Heap::heapDown(int i) { int l = left(i); int r = right(i); // comparing parent to left/right child // each has an inner if to handle if the first swap causes a second swap // ie 1 -> 3 -> 5 // 3 5 1 5 1 3 if (l < n && arr[i] < arr[l]) { swap(arr[i], arr[l]); heapDown(l); if (r < n && arr[i] < arr[r]) { swap(arr[i], arr[r]); heapDown(r); } } else if (r < n && arr[i] < arr[r]) { swap(arr[i], arr[r]); heapDown(r); if (l < n && arr[i] < arr[l]) { swap(arr[i], arr[l]); heapDown(l); } } } Here's my output i1i2i3i4i5i6i7 p Active heap: 7 4 6 1 3 2 5 r Removed 7 r Removed 6 p Active heap: 5 3 4 1 2 Here's my teacher's sample output: p Active heap : 7 4 6 1 3 2 5 r Removed 7 r Removed 6 p Active heap : 5 4 2 1 3 s Heapsorted : 1 2 3 4 5 While our outputs are completely different, I do seem to hold maxheap principle of having everything left oriented and for all nodes parent child(in every case I tried). I try to do algs like this from scratch, so maybe I'm just doing something really weird and wrong (I would only consider it "wrong" if it's O(lg n), as removes are intended to be for heaps). Is there anything in particular "wrong" about my remove? Thanks, http://ideone.com/PPh4eQ

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• descending heap sort

  - by user1

  use heap sort to sort this in descending order and show the steps or explanation please below is the tree 79 33 57 8 25 48 below is the array 79 - 33 - 57 - 8 - 25 - 48 ok ascending is easy because the largest element is at the top we can exchange the last element and the first element and then use heapify as the sample code in wikipedia describes it.

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• Sorting Algorithms Codes in C#.net

  This Article Have the Codes of QuickSort,MergeSort,BubbleSort,HeapSort

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• Why is Quicksort called "Quicksort"?

  - by Darrel Hoffman

  The point of this question is not to debate the merits of this over any other sorting algorithm - certainly there are many other questions that do this. This question is about the name. Why is Quicksort called "Quicksort"? Sure, it's "quick", most of the time, but not always. The possibility of degenerating to O(N^2) is well known. There are various modifications to Quicksort that mitigate this problem, but the ones which bring the worst case down to a guaranteed O(n log n) aren't generally called Quicksort anymore. (e.g. Introsort). I just wonder why of all the well-known sorting algorithms, this is the only one deserving of the name "quick", which describes not how the algorithm works, but how fast it (usually) is. Mergesort is called that because it merges the data. Heapsort is called that because it uses a heap. Introsort gets its name from "Introspective", since it monitors its own performance to decide when to switch from Quicksort to Heapsort. Similarly for all the slower ones - Bubblesort, Insertion sort, Selection sort, etc. They're all named for how they work. The only other exception I can think of is "Bogosort", which is really just a joke that nobody ever actually uses in practice. Why isn't Quicksort called something more descriptive, like "Partition sort" or "Pivot sort", which describe what it actually does? It's not even a case of "got here first". Mergesort was developed 15 years before Quicksort. (1945 and 1960 respectively according to Wikipedia) I guess this is really more of a history question than a programming one. I'm just curious how it got the name - was it just good marketing?

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• Order of learning sort algorithms

  - by user619818

  I have already studied bubblesort, insertion sort and selection sort and can implement them in C pretty much from knowledge of the algorithm. I want to go on to learn shellsort, merge sort, heapsort and quicksort, which I guess are a lot harder to understand. What order should I take these other sort algos? I am assuming a simpler sort algo helps learn a more complex one. Don't mind taking on some others if it helps the learning process.

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• Sorting Algorithms

  - by MarkPearl

  General Every time I go back to university I find myself wading through sorting algorithms and their implementation in C++. Up to now I haven’t really appreciated their true value. However as I discovered this last week with Dictionaries in C# – having a knowledge of some basic programming principles can greatly improve the performance of a system and make one think twice about how to tackle a problem. I’m going to cover briefly in this post the following: Selection Sort Insertion Sort Shellsort Quicksort Mergesort Heapsort (not complete) Selection Sort Array based selection sort is a simple approach to sorting an unsorted array. Simply put, it repeats two basic steps to achieve a sorted collection. It starts with a collection of data and repeatedly parses it, each time sorting out one element and reducing the size of the next iteration of parsed data by one. So the first iteration would go something like this… Go through the entire array of data and find the lowest value Place the value at the front of the array The second iteration would go something like this… Go through the array from position two (position one has already been sorted with the smallest value) and find the next lowest value in the array. Place the value at the second position in the array This process would be completed until the entire array had been sorted. A positive about selection sort is that it does not make many item movements. In fact, in a worst case scenario every items is only moved once. Selection sort is however a comparison intensive sort. If you had 10 items in a collection, just to parse the collection you would have 10+9+8+7+6+5+4+3+2=54 comparisons to sort regardless of how sorted the collection was to start with. If you think about it, if you applied selection sort to a collection already sorted, you would still perform relatively the same number of iterations as if it was not sorted at all. Many of the following algorithms try and reduce the number of comparisons if the list is already sorted – leaving one with a best case and worst case scenario for comparisons. Likewise different approaches have different levels of item movement. Depending on what is more expensive, one may give priority to one approach compared to another based on what is more expensive, a comparison or a item move. Insertion Sort Insertion sort tries to reduce the number of key comparisons it performs compared to selection sort by not “doing anything” if things are sorted. Assume you had an collection of numbers in the following order… 10 18 25 30 23 17 45 35 There are 8 elements in the list. If we were to start at the front of the list – 10 18 25 & 30 are already sorted. Element 5 (23) however is smaller than element 4 (30) and so needs to be repositioned. We do this by copying the value at element 5 to a temporary holder, and then begin shifting the elements before it up one. So… Element 5 would be copied to a temporary holder 10 18 25 30 23 17 45 35 – T 23 Element 4 would shift to Element 5 10 18 25 30 30 17 45 35 – T 23 Element 3 would shift to Element 4 10 18 25 25 30 17 45 35 – T 23 Element 2 (18) is smaller than the temporary holder so we put the temporary holder value into Element 3. 10 18 23 25 30 17 45 35 – T 23   We now have a sorted list up to element 6. And so we would repeat the same process by moving element 6 to a temporary value and then shifting everything up by one from element 2 to element 5. As you can see, one major setback for this technique is the shifting values up one – this is because up to now we have been considering the collection to be an array. If however the collection was a linked list, we would not need to shift values up, but merely remove the link from the unsorted value and “reinsert” it in a sorted position. Which would reduce the number of transactions performed on the collection. So.. Insertion sort seems to perform better than selection sort – however an implementation is slightly more complicated. This is typical with most sorting algorithms – generally, greater performance leads to greater complexity. Also, insertion sort performs better if a collection of data is already sorted. If for instance you were handed a sorted collection of size n, then only n number of comparisons would need to be performed to verify that it is sorted. It’s important to note that insertion sort (array based) performs a number item moves – every time an item is “out of place” several items before it get shifted up. Shellsort – Diminishing Increment Sort So up to now we have covered Selection Sort & Insertion Sort. Selection Sort makes many comparisons and insertion sort (with an array) has the potential of making many item movements. Shellsort is an approach that takes the normal insertion sort and tries to reduce the number of item movements. In Shellsort, elements in a collection are viewed as sub-collections of a particular size. Each sub-collection is sorted so that the elements that are far apart move closer to their final position. Suppose we had a collection of 15 elements… 10 20 15 45 36 48 7 60 18 50 2 19 43 30 55 First we may view the collection as 7 sub-collections and sort each sublist, lets say at intervals of 7 10 60 55 – 20 18 – 15 50 – 45 2 – 36 19 – 48 43 – 7 30 10 55 60 – 18 20 – 15 50 – 2 45 – 19 36 – 43 48 – 7 30 (Sorted) We then sort each sublist at a smaller inter – lets say 4 10 55 60 18 – 20 15 50 2 – 45 19 36 43 – 48 7 30 10 18 55 60 – 2 15 20 50 – 19 36 43 45 – 7 30 48 (Sorted) We then sort elements at a distance of 1 (i.e. we apply a normal insertion sort) 10 18 55 60 2 15 20 50 19 36 43 45 7 30 48 2 7 10 15 18 19 20 30 36 43 45 48 50 55 (Sorted) The important thing with shellsort is deciding on the increment sequence of each sub-collection. From what I can tell, there isn’t any definitive method and depending on the order of your elements, different increment sequences may perform better than others. There are however certain increment sequences that you may want to avoid. An even based increment sequence (e.g. 2 4 8 16 32 …) should typically be avoided because it does not allow for even elements to be compared with odd elements until the final sort phase – which in a way would negate many of the benefits of using sub-collections. The performance on the number of comparisons and item movements of Shellsort is hard to determine, however it is considered to be considerably better than the normal insertion sort. Quicksort Quicksort uses a divide and conquer approach to sort a collection of items. The collection is divided into two sub-collections – and the two sub-collections are sorted and combined into one list in such a way that the combined list is sorted. The algorithm is in general pseudo code below… Divide the collection into two sub-collections Quicksort the lower sub-collection Quicksort the upper sub-collection Combine the lower & upper sub-collection together As hinted at above, quicksort uses recursion in its implementation. The real trick with quicksort is to get the lower and upper sub-collections to be of equal size. The size of a sub-collection is determined by what value the pivot is. Once a pivot is determined, one would partition to sub-collections and then repeat the process on each sub collection until you reach the base case. With quicksort, the work is done when dividing the sub-collections into lower & upper collections. The actual combining of the lower & upper sub-collections at the end is relatively simple since every element in the lower sub-collection is smaller than the smallest element in the upper sub-collection. Mergesort With quicksort, the average-case complexity was O(nlog2n) however the worst case complexity was still O(N*N). Mergesort improves on quicksort by always having a complexity of O(nlog2n) regardless of the best or worst case. So how does it do this? Mergesort makes use of the divide and conquer approach to partition a collection into two sub-collections. It then sorts each sub-collection and combines the sorted sub-collections into one sorted collection. The general algorithm for mergesort is as follows… Divide the collection into two sub-collections Mergesort the first sub-collection Mergesort the second sub-collection Merge the first sub-collection and the second sub-collection As you can see.. it still pretty much looks like quicksort – so lets see where it differs… Firstly, mergesort differs from quicksort in how it partitions the sub-collections. Instead of having a pivot – merge sort partitions each sub-collection based on size so that the first and second sub-collection of relatively the same size. This dividing keeps getting repeated until the sub-collections are the size of a single element. If a sub-collection is one element in size – it is now sorted! So the trick is how do we put all these sub-collections together so that they maintain their sorted order. Sorted sub-collections are merged into a sorted collection by comparing the elements of the sub-collection and then adjusting the sorted collection. Lets have a look at a few examples… Assume 2 sub-collections with 1 element each 10 & 20 Compare the first element of the first sub-collection with the first element of the second sub-collection. Take the smallest of the two and place it as the first element in the sorted collection. In this scenario 10 is smaller than 20 so 10 is taken from sub-collection 1 leaving that sub-collection empty, which means by default the next smallest element is in sub-collection 2 (20). So the sorted collection would be 10 20 Lets assume 2 sub-collections with 2 elements each 10 20 & 15 19 So… again we would Compare 10 with 15 – 10 is the winner so we add it to our sorted collection (10) leaving us with 20 & 15 19 Compare 20 with 15 – 15 is the winner so we add it to our sorted collection (10 15) leaving us with 20 & 19 Compare 20 with 19 – 19 is the winner so we add it to our sorted collection (10 15 19) leaving us with 20 & _ 20 is by default the winner so our sorted collection is 10 15 19 20. Make sense? Heapsort (still needs to be completed) So by now I am tired of sorting algorithms and trying to remember why they were so important. I think every year I go through this stuff I wonder to myself why are we made to learn about selection sort and insertion sort if they are so bad – why didn’t we just skip to Mergesort & Quicksort. I guess the only explanation I have for this is that sometimes you learn things so that you can implement them in future – and other times you learn things so that you know it isn’t the best way of implementing things and that you don’t need to implement it in future. Anyhow… luckily this is going to be the last one of my sorts for today. The first step in heapsort is to convert a collection of data into a heap. After the data is converted into a heap, sorting begins… So what is the definition of a heap? If we have to convert a collection of data into a heap, how do we know when it is a heap and when it is not? The definition of a heap is as follows: A heap is a list in which each element contains a key, such that the key in the element at position k in the list is at least as large as the key in the element at position 2k +1 (if it exists) and 2k + 2 (if it exists). Does that make sense? At first glance I’m thinking what the heck??? But then after re-reading my notes I see that we are doing something different – up to now we have really looked at data as an array or sequential collection of data that we need to sort – a heap represents data in a slightly different way – although the data is stored in a sequential collection, for a sequential collection of data to be in a valid heap – it is “semi sorted”. Let me try and explain a bit further with an example… Example 1 of Potential Heap Data Assume we had a collection of numbers as follows 1[1] 2[2] 3[3] 4[4] 5[5] 6[6] For this to be a valid heap element with value of 1 at position [1] needs to be greater or equal to the element at position [3] (2k +1) and position [4] (2k +2). So in the above example, the collection of numbers is not in a valid heap. Example 2 of Potential Heap Data Lets look at another collection of numbers as follows 6[1] 5[2] 4[3] 3[4] 2[5] 1[6] Is this a valid heap? Well… element with the value 6 at position 1 must be greater or equal to the element at position [3] and position [4]. Is 6 > 4 and 6 > 3? Yes it is. Lets look at element 5 as position 2. It must be greater than the values at [4] & [5]. Is 5 > 3 and 5 > 2? Yes it is. If you continued to examine this second collection of data you would find that it is in a valid heap based on the definition of a heap.

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• Why is quicksort better than other sorting algorithms in practice?

  - by Raphael

  This is a repost of a question on cs.SE by Janoma. Full credits and spoils to him or cs.SE. In a standard algorithms course we are taught that quicksort is O(n log n) on average and O(n²) in the worst case. At the same time, other sorting algorithms are studied which are O(n log n) in the worst case (like mergesort and heapsort), and even linear time in the best case (like bubblesort) but with some additional needs of memory. After a quick glance at some more running times it is natural to say that quicksort should not be as efficient as others. Also, consider that students learn in basic programming courses that recursion is not really good in general because it could use too much memory, etc. Therefore (and even though this is not a real argument), this gives the idea that quicksort might not be really good because it is a recursive algorithm. Why, then, does quicksort outperform other sorting algorithms in practice? Does it have to do with the structure of real-world data? Does it have to do with the way memory works in computers? I know that some memories are way faster than others, but I don't know if that's the real reason for this counter-intuitive performance (when compared to theoretical estimates).

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• List of generic algorithms and data structures

  - by Jake Petroules

  As part of a library project, I want to include a plethora of generic algorithms and data structures. This includes algorithms for searching and sorting, data structures like linked lists and binary trees, path-finding algorithms like A*... the works. Basically, any generic algorithm or data structure you can think of that you think might be useful in such a library, please post or add it to the list. Thanks! (NOTE: Because there is no single right answer I've of course placed this in community wiki... and also, please don't suggest algorithms which are too specialized to be provided by a generic library) The List: Data structures AVL tree B-tree B*-tree B+-tree Binary tree Binary heap Binary search tree Linked lists Singly linked list Doubly linked list Stack Queue Sorting algorithms Binary tree sort Bubble sort Heapsort Insertion sort Merge sort Quicksort Selection sort Searching algorithms

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• Java Generic Casting Type Mismatch

  - by Kay

  public class MaxHeap<T extends Comparable<T>> implements Heap<T>{ private T[] heap; private int lastIndex; public void main(String[] args){ int i; T[] arr = {1,3,4,5,2}; //ERROR HERE ******* foo } public T[] Heapsort(T[]anArray, int n){ // build initial heap T[]sortedArray = anArray; for (int i = n-1; i< 0; i--){ //assert: the tree rooted at index is a semiheap heapRebuild(anArray, i, n); //assert: the tree rooted at index is a heap } //sort the heap array int last = n-1; //invariant: Array[0..last] is a heap, //Array[last+1..n-1] is sorted for (int j=1; j<n-1;j++) { sortedArray[0]=sortedArray[last]; last--; heapRebuild(anArray, 0, last); } return sortedArray; } protected void heapRebuild(T[ ] items, int root, int size){ foo } } The error is on the line with "T[arr] = {1,3,4,5,2}" Eclispe complains that there is a: "Type mismatch: cannot convert from int to T" I've tried to casting nearly everywhere but to no avail.A simple way out would be to not use generics but instead just ints but that's sadly not an option. I've got to find a way to resolve the array of ints "{1,3,4,5,2}" into an array of T so that the rest of my code will work smoothly.

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• heap sort function explanation needed

  - by Codeguru

  Hello, Can someone clearly explain me how these functions of heap sort are working?? void heapSort(int numbers[], int array_size) { int i, temp; for (i = (array_size / 2)-1; i >= 0; i--) siftDown(numbers, i, array_size); for (i = array_size-1; i >= 1; i--) { temp = numbers[0]; numbers[0] = numbers[i]; numbers[i] = temp; siftDown(numbers, 0, i-1); } } void siftDown(int numbers[], int root, int bottom) { int done, maxChild, temp; done = 0; while ((root*2 <= bottom) && (!done)) { if (root*2 == bottom) maxChild = root * 2; else if (numbers[root * 2] > numbers[root * 2 + 1]) maxChild = root * 2; else maxChild = root * 2 + 1; if (numbers[root] < numbers[maxChild]) { temp = numbers[root]; numbers[root] = numbers[maxChild]; numbers[maxChild] = temp; root = maxChild; } else done = 1; } }

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• How to optimize my PostgreSQL DB for prefix search?

  - by asmaier

  I have a table called "nodes" with roughly 1.7 million rows in my PostgreSQL db =#\d nodes Table "public.nodes" Column | Type | Modifiers --------+------------------------+----------- id | integer | not null title | character varying(256) | score | double precision | Indexes: "nodes_pkey" PRIMARY KEY, btree (id) I want to use information from that table for autocompletion of a search field, showing the user a list of the ten titles having the highest score fitting to his input. So I used this query (here searching for all titles starting with "s") =# explain analyze select title,score from nodes where title ilike 's%' order by score desc; QUERY PLAN ----------------------------------------------------------------------------------------------------------------------- Sort (cost=64177.92..64581.38 rows=161385 width=25) (actual time=4930.334..5047.321 rows=161264 loops=1) Sort Key: score Sort Method: external merge Disk: 5712kB -> Seq Scan on nodes (cost=0.00..46630.50 rows=161385 width=25) (actual time=0.611..4464.413 rows=161264 loops=1) Filter: ((title)::text ~~* 's%'::text) Total runtime: 5260.791 ms (6 rows) This was much to slow for using it with autocomplete. With some information from Using PostgreSQL in Web 2.0 Applications I was able to improve that with a special index =# create index title_idx on nodes using btree(lower(title) text_pattern_ops); =# explain analyze select title,score from nodes where lower(title) like lower('s%') order by score desc limit 10; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------ Limit (cost=18122.41..18122.43 rows=10 width=25) (actual time=1324.703..1324.708 rows=10 loops=1) -> Sort (cost=18122.41..18144.60 rows=8876 width=25) (actual time=1324.700..1324.702 rows=10 loops=1) Sort Key: score Sort Method: top-N heapsort Memory: 17kB -> Bitmap Heap Scan on nodes (cost=243.53..17930.60 rows=8876 width=25) (actual time=96.124..1227.203 rows=161264 loops=1) Filter: (lower((title)::text) ~~ 's%'::text) -> Bitmap Index Scan on title_idx (cost=0.00..241.31 rows=8876 width=0) (actual time=90.059..90.059 rows=161264 loops=1) Index Cond: ((lower((title)::text) ~>=~ 's'::text) AND (lower((title)::text) ~<~ 't'::text)) Total runtime: 1325.085 ms (9 rows) So this gave me a speedup of factor 4. But can this be further improved? What if I want to use '%s%' instead of 's%'? Do I have any chance of getting a decent performance with PostgreSQL in that case, too? Or should I better try a different solution (Lucene?, Sphinx?) for implementing my autocomplete feature?

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• Parsing language for both binary and character files

  - by Thorsten S.

  The problem: You have some data and your program needs specified input. For example strings which are numbers. You are searching for a way to transform the original data in a format you need. And the problem is: The source can be anything. It can be XML, property lists, binary which contains the needed data deeply embedded in binary junk. And your output format may vary also: It can be number strings, float, doubles.... You don't want to program. You want routines which gives you commands capable to transform the data in a form you wish. Surely it contains regular expressions, but it is very good designed and it offers capabilities which are sometimes much more easier and more powerful. Something like a super-grep which you can access (!) as program routines, not only as tool. It allows: joining/grouping/merging of results inserting/deleting/finding/replacing write macros which allows to execute a command chain repeatedly meta-grouping (lists-tables-hypertables) Example (No, I am not looking for a solution to this, it is just an example): You want to read xml strings embedded in a binary file with variable length records. Your tool reads the record length and deletes the junk surrounding your text. Now it splits open the xml and extracts the strings. Being Indian number glyphs and containing decimal commas instead of decimal points, your tool transforms it into ASCII and replaces commas with points. Now the results must be stored into matrices of variable length....etc. etc. I am searching for a good language / language-design and if possible, an implementation. Which design do you like or even, if it does not fulfill the conditions, wouldn't you want to miss ? EDIT: The question is if a solution for the problem exists and if yes, which implementations are available. You DO NOT implement your own sorting algorithm if Quicksort, Mergesort and Heapsort is available. You DO NOT invent your own text parsing method if you have regular expressions. You DO NOT invent your own 3D language for graphics if OpenGL/Direct3D is available. There are existing solutions or at least papers describing the problem and giving suggestions. And there are people who may have worked and experienced such problems and who can give ideas and suggestions. The idea that this problem is totally new and I should work out and implement it myself without background knowledge seems for me, I must admit, totally off the mark.

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