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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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  • Java -Xms initial size effects

    - by SyBer
    Hi. What is the benefit of setting the -Xms parameter, and having the initial memory larger for example, then the default calculated one (64 MB in my case, according to Java GC tunning: http://java.sun.com/javase/technologies/hotspot/gc/gc_tuning_6.html#par_gc.ergonomics.default_size)? Also, is there any good to setting both the initial and maximum memories to same size? Thanks.

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  • Jmap can't connect to to make a dump

    - by Jasper Floor
    We have an open beta of an app which occasionally causes the heapspace to overflow. The JVM reacts by going on a permanent vacation. To analyze this I would like to peek into the memory at the point where it failed. Java does not want me to do this. The process is still in memory but it doesn't seem to be recognized as a java process. The server in question is a debian Lenny server, Java 6u14 /opt/jdk/bin# ./jmap -F -dump:format=b,file=/tmp/apidump.hprof 11175 Attaching to process ID 11175, please wait... sun.jvm.hotspot.debugger.NoSuchSymbolException: Could not find symbol "gHotSpotVMTypeEntryTypeNameOffset" in any of the known library names (libjvm.so, libjvm_g.so, gamma_g) at sun.jvm.hotspot.HotSpotTypeDataBase.lookupInProcess(HotSpotTypeDataBase.java:390) at sun.jvm.hotspot.HotSpotTypeDataBase.getLongValueFromProcess(HotSpotTypeDataBase.java:371) at sun.jvm.hotspot.HotSpotTypeDataBase.readVMTypes(HotSpotTypeDataBase.java:102) at sun.jvm.hotspot.HotSpotTypeDataBase.<init>(HotSpotTypeDataBase.java:85) at sun.jvm.hotspot.bugspot.BugSpotAgent.setupVM(BugSpotAgent.java:568) at sun.jvm.hotspot.bugspot.BugSpotAgent.go(BugSpotAgent.java:494) at sun.jvm.hotspot.bugspot.BugSpotAgent.attach(BugSpotAgent.java:332) at sun.jvm.hotspot.tools.Tool.start(Tool.java:163) at sun.jvm.hotspot.tools.HeapDumper.main(HeapDumper.java:77) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at sun.tools.jmap.JMap.runTool(JMap.java:179) at sun.tools.jmap.JMap.main(JMap.java:110) Debugger attached successfully. sun.jvm.hotspot.tools.HeapDumper requires a java VM process/core!

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  • C String literals: Where do they go?

    - by Chris Cooper
    I have read a lot of posts about "string literals" on SO, most of which have been about best-practices, or where the literal is NOT located in memory. I am interested in where the string DOES get allocated/stored, etc. I did find one intriguing answer here, saying: Defining a string inline actually embeds the data in the program itself and cannot be changed (some compilers allow this by a smart trick, don't bother). but, it had to do with C++, not to mention that it says not to bother. I am bothering. =D So my question is, again, where and how is my string literal kept? Why should I not try to alter it? Does the implementation vary by platform? Does anyone care to elaborate on the "smart trick?" Thanks for any explanations.

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  • Basic Java Multi-Threading Question

    - by Veered
    When an object is instantiated in Java, is it bound to the thread that instantiated in? Because when I anonymously implement an interface in one thread, and pass it to another thread to be run, all of its methods are run in the original thread. If they are bound to their creation thread, is there anyway to create an object that will run in whatever thread calls it?

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  • java gui changing picture causes heapspace error

    - by pie154
    I have a java programme than when a button is clicked it updates the image on screen to the according image. this will work for the first 15 or so clicks then it causes a java heapspace error. I think it is because of the way I am updating the jpanel that contains the bufferedimage but not sure what the reason is. My code to get make the JPanel contain the new image is, public class extraScreenPanel { static JPanel screenPanel = new JPanel(new BorderLayout()); public static JPanel extraScreenPanel(int dispNum) { JLabel label = new JLabel("" + dispNum + ""); label.setPreferredSize(new Dimension(800, 600)); //label.setUI( new VerticalLabelUI(true) ); label.setVerticalAlignment( SwingConstants.TOP ); screenPanel = imgDisp(dispNum); label.setForeground(Color.white); label.setFont(new Font("Serif", Font.BOLD, 200)); screenPanel.add(label, BorderLayout.PAGE_END ); return screenPanel; } public static JPanel imgDisp(int picNum) { /* String url[] = new String[5000]; String part1; url[0] = "C:/PiPhotoPic/pic16.jpg"; for(Integer i=1;i<5000;i++){ if(i<10){part1 = "C:/temp/new0000000";} else if(i<100){part1 = "C:/temp/new000000";} else if(i<1000){part1 = "C:/temp/new00000";} else {part1 = "C:/temp/new00000";} String num = Integer.toString(i); url[i]= part1 + num + ".jpg"; } if(picNum<0){picNum=0;} String ref = url[picNum];*/ //this code is just to get specific ref for image location BufferedImage loadImg = loadImage(ref); JImagePanel panel = new JImagePanel(loadImg, 0, 0); panel.setPreferredSize(new Dimension(800, 600)); return panel; } public static class JImagePanel extends JPanel{ private BufferedImage image; int x, y; public JImagePanel(BufferedImage image, int x, int y) { super(); this.image = image; this.x = x; this.y = y; } @Override protected void paintComponent(Graphics g) { super.paintComponent(g); g.drawImage(image, x, y, null); } } public static BufferedImage loadImage(String ref) { BufferedImage bimg = null; try { bimg = javax.imageio.ImageIO.read(new File(ref)); } catch (Exception e) { e.printStackTrace(); } BufferedImage bimg2 = resize(bimg,800,600); return bimg2; } public static BufferedImage resize(BufferedImage img, int newW, int newH) { int w = img.getWidth(); int h = img.getHeight(); BufferedImage dimg = dimg = new BufferedImage(newW, newH, img.getType()); Graphics2D g = dimg.createGraphics(); g.setRenderingHint(RenderingHints.KEY_INTERPOLATION, RenderingHints.VALUE_INTERPOLATION_BILINEAR); g.drawImage(img, 0, 0, newW, newH, 0, 0, w, h, null); g.dispose(); return dimg; } } And the code that updates my gui is, it works by removing the panel from its containg panel and then readding it to it. picPanel = imgDisp.imgDisp(num); repaintPicPanel(); public static void repaintPicPanel() { picPanel.removeAll(); menuPanel.remove(picPanel);; menuPanel.add(picPanel, BorderLayout.LINE_START); }

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  • Avoid an "out of memory error" in Java(eclipse), when using large data structure?

    - by gnomed
    OK, so I am writing a program that unfortunately needs to use a huge data structure to complete its work, but it is failing with a "out of memory error" during its initialization. While I understand entirely what that means and why it is a problem, I am having trouble overcoming it, since my program needs to use this large structure and I don't know any other way to store it. The program first indexes a large corpus of text files that I provide. This works fine. Then it uses this index to initialize a large 2D array. This array will have nXn entries, where "n" is the number of unique words in the corpus of text. For the relatively small chunk I am testing it on(about 60 files) it needs to make approximately 30,000x30,000 entries. this will probably be bigger once I run it on my full intended corpus too. It consistently fails every time, after it indexes, while it is initializing the data structure(to be worked on later). Things I have done include: revamp my code to use a primitive "int[]" instead of a "TreeMap" eliminate redundant structures, etc... Also, I have run eclipse with "eclipse -vmargs -Xmx2g" to max out my allocated memory I am fairly confident this is not going to be a simple line of code solution, but is most likely going to require a very new approach. I am looking for what that approach is, any ideas? Thanks, B.

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  • Priority queue with dynamic item priorities.

    - by sean
    I need to implement a priority queue where the priority of an item in the queue can change and the queue adjusts itself so that items are always removed in the correct order. I have some ideas of how I could implement this but I'm sure this is quite a common data structure so I'm hoping I can use an implementation by someone smarter than me as a base. Can anyone tell me the name of this type of priority queue so I know what to search for or, even better, point me to an implementation?

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  • RAM Questions in Iphone

    - by senthilmuthu
    Hi, Every RAM must have stack and heap (like CS,ES,DS,SS 4 segments).but is there like stack size in iphone,is only heap available?some tutorial say when we increase stack size , heap will be decreased,when we increase heap size ,stack will be decreased ...is it true..? or fixed stack size or fixed heap size ? any help please? Based on processor,will be segments changed?is there no need to have 4 segements for all processors?IF RAM does not have stack ,heap, where is it? IF RAM does not have stack ,heap ,where the heap and stack is managed?

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  • When exactly would a DLL use a different heap than the executable?

    - by Milo
    I know that if your DLL static links against a different version of the runtime then it creates its own heap. It will also if it is instructed to make a heap. Under these circumstances, it is unsafe for the DLL to delete what the exe allocated. In what cases does this NOT apply (as in, it is safe for the DLL to delete what the exe allocated)? Is it safe if both the exe and the DLL static link against the same runtime library? Thanks

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  • Bizzare Java invalid Assignment Operator Error

    - by Kay
    public class MaxHeap<T extends Comparable<T>> implements Heap<T>{ private T[] heap; private int lastIndex; private static final int defaultInitialCapacity = 25; public void add(T newItem) throws HeapException{ if (lastIndex < Max_Heap){ heap[lastIndex] = newItem; int place = lastIndex; int parent = (place – 1)/2; //ERROR HERE********** while ( (parent >=0) && (heap[place].compareTo(heap[parent])>0)){ T temp = heap[place]; heap[place] = heap[parent]; heap[parent] = temp; place = parent; parent = (place-1)/2; }else { throw new HeapException(“HeapException: Heap full”); } } } Eclipse complains that there is a: "Syntax error on token "Invalid Character", invalid AssignmentOperator" With the red line beneath the '(place-1)' There shouldn't be an error at all since it's just straight-forward arithmetic. Or is it not that simple?

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  • Unit Testing Interfaces in Python

    - by Nicholas Mancuso
    I am currently learning python in preperation for a class over the summer and have gotten started by implementing different types of heaps and priority based data structures. I began to write a unit test suite for the project but ran into difficulties into creating a generic unit test that only tests the interface and is oblivious of the actual implementation. I am wondering if it is possible to do something like this.. suite = HeapTestSuite(BinaryHeap()) suite.run() suite = HeapTestSuite(BinomialHeap()) suite.run() What I am currently doing just feels... wrong (multiple inheritance? ACK!).. class TestHeap: def reset_heap(self): self.heap = None def test_insert(self): self.reset_heap() #test that insert doesnt throw an exception... for x in self.inseq: self.heap.insert(x) def test_delete(self): #assert we get the first value we put in self.reset_heap() self.heap.insert(5) self.assertEquals(5, self.heap.delete_min()) #harder test. put in sequence in and check that it comes out right self.reset_heap() for x in self.inseq: self.heap.insert(x) for x in xrange(len(self.inseq)): val = self.heap.delete_min() self.assertEquals(val, x) class BinaryHeapTest(TestHeap, unittest.TestCase): def setUp(self): self.inseq = range(99, -1, -1) self.heap = BinaryHeap() def reset_heap(self): self.heap = BinaryHeap() class BinomialHeapTest(TestHeap, unittest.TestCase): def setUp(self): self.inseq = range(99, -1, -1) self.heap = BinomialHeap() def reset_heap(self): self.heap = BinomialHeap() if __name__ == '__main__': unittest.main()

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  • AppEngine JRuby - OutOfMemoryError: Java heap space - can it be solved?

    - by elado
    I use AppEngine JRuby on Rails (SDK version 1.3.3.1) - a problem I encounter often is that after a few requests the server is getting really SLOW, until it dies and throws OutOfMemoryError on the terminal (OSX). The requests themselves are very lightweight, not more than looking for an entity or saving it, using DataMapper. On appspot, this problem is not happening. Is there any way to enlarge the heap space for JRuby? The exception log: Exception in thread "Timer-2" java.lang.OutOfMemoryError: Java heap space Apr 29, 2010 8:08:22 AM com.google.apphosting.utils.jetty.JettyLogger warn WARNING: Error for /users/close_users java.lang.OutOfMemoryError: Java heap space at org.jruby.RubyHash.internalPut(RubyHash.java:480) at org.jruby.RubyHash.internalPut(RubyHash.java:461) at org.jruby.RubyHash.fastASet(RubyHash.java:837) at org.jruby.RubyArray.makeHash(RubyArray.java:2998) at org.jruby.RubyArray.makeHash(RubyArray.java:2992) at org.jruby.RubyArray.op_diff(RubyArray.java:3103) at org.jruby.RubyArray$i_method_1_0$RUBYINVOKER$op_diff.call(org/jruby/RubyArray$i_method_1_0$RUBYINVOKER$op_diff.gen) at org.jruby.runtime.callsite.CachingCallSite.call(CachingCallSite.java:146) at org.jruby.ast.CallOneArgNode.interpret(CallOneArgNode.java:57) at org.jruby.ast.LocalAsgnNode.interpret(LocalAsgnNode.java:123) at org.jruby.ast.NewlineNode.interpret(NewlineNode.java:104) at org.jruby.ast.BlockNode.interpret(BlockNode.java:71) at org.jruby.runtime.InterpretedBlock.evalBlockBody(InterpretedBlock.java:373) at org.jruby.runtime.InterpretedBlock.yield(InterpretedBlock.java:346) at org.jruby.runtime.InterpretedBlock.yield(InterpretedBlock.java:303) at org.jruby.runtime.Block.yield(Block.java:194) at org.jruby.RubyArray.collect(RubyArray.java:2354) at org.jruby.RubyArray$i_method_0_0$RUBYFRAMEDINVOKER$collect.call(org/jruby/RubyArray$i_method_0_0$RUBYFRAMEDINVOKER$collect.gen) at org.jruby.runtime.callsite.CachingCallSite.callBlock(CachingCallSite.java:115) at org.jruby.runtime.callsite.CachingCallSite.call(CachingCallSite.java:122) at org.jruby.ast.CallNoArgBlockNode.interpret(CallNoArgBlockNode.java:64) at org.jruby.ast.CallNoArgNode.interpret(CallNoArgNode.java:61) at org.jruby.ast.LocalAsgnNode.interpret(LocalAsgnNode.java:123) at org.jruby.ast.NewlineNode.interpret(NewlineNode.java:104) at org.jruby.ast.BlockNode.interpret(BlockNode.java:71) at org.jruby.ast.EnsureNode.interpret(EnsureNode.java:98) at org.jruby.ast.BeginNode.interpret(BeginNode.java:83) at org.jruby.ast.NewlineNode.interpret(NewlineNode.java:104) at org.jruby.ast.BlockNode.interpret(BlockNode.java:71) at org.jruby.ast.EnsureNode.interpret(EnsureNode.java:96) at org.jruby.internal.runtime.methods.InterpretedMethod.call(InterpretedMethod.java:201) at org.jruby.internal.runtime.methods.DefaultMethod.call(DefaultMethod.java:183)

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  • Stack allocation fails and heap allocation succeeds!! Is it possible??

    - by Prabhu
    Hello All, I have the following piece of snippet Class Sample { Obj_Class1 o1; Obj_Class2 o2;}; But the size of Obj_Class1 and Obj_Class2 is huge so that the compiler shows a warning "Consider moving some space to heap". I was asked to replace Obj_Class1 o1 with Obj_Class1* o1 = new Obj_Class1(); But I feel that there is no use of making this change as heap allocation will also fail if stack allocation fails. Am I correct? Or does it make sense to make this change ( other than suppressing the compiler warning ).

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  • Load and Web Performance Testing using Visual Studio Ultimate 2010-Part 3

    - by Tarun Arora
    Welcome back once again, in Part 1 of Load and Web Performance Testing using Visual Studio 2010 I talked about why Performance Testing the application is important, the test tools available in Visual Studio Ultimate 2010 and various test rig topologies, in Part 2 of Load and Web Performance Testing using Visual Studio 2010 I discussed the details of web performance & load tests as well as why it’s important to follow a goal based pattern while performance testing your application. In part 3 I’ll be discussing Test Result Analysis, Test Result Drill through, Test Report Generation, Test Run Comparison, Asp.net Profiler and some closing thoughts. Test Results – I see some creepy worms! In Part 2 we put together a web performance test and a load test, lets run the test to see load test to see how the Web site responds to the load simulation. While the load test is running you will be able to see close to real time analysis in the Load Test Analyser window. You can use the Load Test Analyser to conduct load test analysis in three ways: Monitor a running load test - A condensed set of the performance counter data is maintained in memory. To prevent the results memory requirements from growing unbounded, up to 200 samples for each performance counter are maintained. This includes 100 evenly spaced samples that span the current elapsed time of the run and the most recent 100 samples.         After the load test run is completed - The test controller spools all collected performance counter data to a database while the test is running. Additional data, such as timing details and error details, is loaded into the database when the test completes. The performance data for a completed test is loaded from the database and analysed by the Load Test Analyser. Below you can see a screen shot of the summary view, this provides key results in a format that is compact and easy to read. You can also print the load test summary, this is generated after the test has completed or been stopped.         Analyse the load test results of a previously run load test – We’ll see this in the section where i discuss comparison between two test runs. The performance counters can be plotted on the graphs. You also have the option to highlight a selected part of the test and view details, drill down to the user activity chart where you can hover over to see more details of the test run.   Generate Report => Test Run Comparisons The level of reports you can generate using the Load Test Analyser is astonishing. You have the option to create excel reports and conduct side by side analysis of two test results or to track trend analysis. The tools also allows you to export the graph data either to MS Excel or to a CSV file. You can view the ASP.NET profiler report to conduct further analysis as well. View Data and Diagnostic Attachments opens the Choose Diagnostic Data Adapter Attachment dialog box to select an adapter to analyse the result type. For example, you can select an IntelliTrace adapter, click OK and open the IntelliTrace summary for the test agent that was used in the load test.   Compare results This creates a set of reports that compares the data from two load test results using tables and bar charts. I have taken these screen shots from the MSDN documentation, I would highly recommend exploring the wealth of knowledge available on MSDN. Leaving Thoughts While load testing the application with an excessive load for a longer duration of time, i managed to bring the IIS to its knees by piling up a huge queue of requests waiting to be processed. This clearly means that the IIS had run out of threads as all the threads were busy processing existing request, one easy way of fixing this is by increasing the default number of allocated threads, but this might escalate the problem. The better suggestion is to try and drill down to the actual root cause of the problem. When ever the garbage collection runs it stops processing any pages so all requests that come in during that period are queued up, but realistically the garbage collection completes in fraction of a a second. To understand this better lets look at the .net heap, it is divided into large heap and small heap, anything greater than 85kB in size will be allocated to the Large object heap, the Large object heap is non compacting and remember large objects are expensive to move around, so if you are allocating something in the large object heap, make sure that you really need it! The small object heap on the other hand is divided into generations, so all objects that are supposed to be short-lived are suppose to live in Gen-0 and the long living objects eventually move to Gen-2 as garbage collection goes through.  As you can see in the picture below all < 85 KB size objects are first assigned to Gen-0, when Gen-0 fills up and a new object comes in and finds Gen-0 full, the garbage collection process is started, the process checks for all the dead objects and assigns them as the valid candidate for deletion to free up memory and promotes all the remaining objects in Gen-0 to Gen-1. So in the future when ever you clean up Gen-1 you have to clean up Gen-0 as well. When you fill up Gen – 0 again, all of Gen – 1 dead objects are drenched and rest are moved to Gen-2 and Gen-0 objects are moved to Gen-1 to free up Gen-0, but by this time your Garbage collection process has started to take much more time than it usually takes. Now as I mentioned earlier when garbage collection is being run all page requests that come in during that period are queued up. Does this explain why possibly page requests are getting queued up, apart from this it could also be the case that you are waiting for a long running database process to complete.      Lets explore the heap a bit more… What is really a case of crisis is when the objects are living long enough to make it to Gen-2 and then dying, this is definitely a high cost operation. But sometimes you need objects in memory, for example when you cache data you hold on to the objects because you need to use them right across the user session, which is acceptable. But if you wanted to see what extreme caching can do to your server then write a simple application that chucks in a lot of data in cache, run a load test over it for about 10-15 minutes, forcing a lot of data in memory causing the heap to run out of memory. If you get to such a state where you start running out of memory the IIS as a mode of recovery restarts the worker process. It is great way to free up all your memory in the heap but this would clear the cache. The problem with this is if the customer had 10 items in their shopping basket and that data was stored in the application cache, the user basket will now be empty forcing them either to get frustrated and go to a competitor website or if the customer is really patient, give it another try! How can you address this, well two ways of addressing this; 1. Workaround – A x86 bit processor only allows a maximum of 4GB of RAM, this means the machine effectively has around 3.4 GB of RAM available, the OS needs about 1.5 GB of RAM to run efficiently, the IIS and .net framework also need their share of memory, leaving you a heap of around 800 MB to play with. Because Team builds by default build your application in ‘Compile as any mode’ it means the application is build such that it will run in x86 bit mode if run on a x86 bit processor and run in a x64 bit mode if run on a x64 but processor. The problem with this is not all applications are really x64 bit compatible specially if you are using com objects or external libraries. So, as a quick win if you compiled your application in x86 bit mode by changing the compile as any selection to compile as x86 in the team build, you will be able to run your application on a x64 bit machine in x86 bit mode (WOW – By running Windows on Windows) and what that means is, you could use 8GB+ worth of RAM, if you take away everything else your application will roughly get a heap size of at least 4 GB to play with, which is immense. If you need a heap size of more than 4 GB you have either build a software for NASA or there is something fundamentally wrong in your application. 2. Solution – Now that you have put a workaround in place the IIS will not restart the worker process that regularly, which means you can take a breather and start working to get to the root cause of this memory leak. But this begs a question “How do I Identify possible memory leaks in my application?” Well i won’t say that there is one single tool that can tell you where the memory leak is, but trust me, ‘Performance Profiling’ is a great start point, it definitely gets you started in the right direction, let’s have a look at how. Performance Wizard - Start the Performance Wizard and select Instrumentation, this lets you measure function call counts and timings. Before running the performance session right click the performance session settings and chose properties from the context menu to bring up the Performance session properties page and as shown in the screen shot below, check the check boxes in the group ‘.NET memory profiling collection’ namely ‘Collect .NET object allocation information’ and ‘Also collect the .NET Object lifetime information’.    Now if you fire off the profiling session on your pages you will notice that the results allows you to view ‘Object Lifetime’ which shows you the number of objects that made it to Gen-0, Gen-1, Gen-2, Large heap, etc. Another great feature about the profile is that if your application has > 5% cases where objects die right after making to the Gen-2 storage a threshold alert is generated to alert you. Since you have the option to also view the most expensive methods and by capturing the IntelliTrace data you can drill in to narrow down to the line of code that is the root cause of the problem. Well now that we have seen how crucial memory management is and how easy Visual Studio Ultimate 2010 makes it for us to identify and reproduce the problem with the best of breed tools in the product. Caching One of the main ways to improve performance is Caching. Which basically means you tell the web server that instead of going to the database for each request you keep the data in the webserver and when the user asks for it you serve it from the webserver itself. BUT that can have consequences! Let’s look at some code, trust me caching code is not very intuitive, I define a cache key for almost all searches made through the common search page and cache the results. The approach works fine, first time i get the data from the database and second time data is served from the cache, significant performance improvement, EXCEPT when two users try to do the same operation and run into each other. But it is easy to handle this by adding the lock as you can see in the snippet below. So, as long as a user comes in and finds that the cache is empty, the user locks and starts to get the cache no more concurrency issues. But lets say you are processing 10 requests per second, by the time i have locked the operation to get the results from the database, 9 other users came in and found that the cache key is null so after i have come out and populated the cache they will still go in to get the results again. The application will still be faster because the next set of 10 users and so on would continue to get data from the cache. BUT if we added another null check after locking to build the cache and before actual call to the db then the 9 users who follow me would not make the extra trip to the database at all and that would really increase the performance, but didn’t i say that the code won’t be very intuitive, may be you should leave a comment you don’t want another developer to come in and think what a fresher why is he checking for the cache key null twice !!! The downside of caching is, you are storing the data outside of the database and the data could be wrong because the updates applied to the database would make the data cached at the web server out of sync. So, how do you invalidate the cache? Well if you only had one way of updating the data lets say only one entry point to the data update you can write some logic to say that every time new data is entered set the cache object to null. But this approach will not work as soon as you have several ways of feeding data to the system or your system is scaled out across a farm of web servers. The perfect solution to this is Micro Caching which means you cache the query for a set time duration and invalidate the cache after that set duration. The advantage is every time the user queries for that data with in the time span for which you have cached the results there are no calls made to the database and the data is served right from the server which makes the response immensely quick. Now figuring out the appropriate time span for which you micro cache the query results really depends on the application. Lets say your website gets 10 requests per second, if you retain the cache results for even 1 minute you will have immense performance gains. You would reduce 90% hits to the database for searching. Ever wondered why when you go to e-bookers.com or xpedia.com or yatra.com to book a flight and you click on the book button because the fare seems too exciting and you get an error message telling you that the fare is not valid any more. Yes, exactly => That is a cache failure! These travel sites or price compare engines are not going to hit the database every time you hit the compare button instead the results will be served from the cache, because the query results are micro cached, its a perfect trade-off, by micro caching the results the site gains 100% performance benefits but every once in a while annoys a customer because the fare has expired. But the trade off works in the favour of these sites as they are still able to process up to 30+ page requests per second which means cater to the site traffic by may be losing 1 customer every once in a while to a competitor who is also using a similar caching technique what are the odds that the user will not come back to their site sooner or later? Recap   Resources Below are some Key resource you might like to review. I would highly recommend the documentation, walkthroughs and videos available on MSDN. You can always make use of Fiddler to debug Web Performance Tests. Some community test extensions and plug ins available on Codeplex might also be of interest to you. The Road Ahead Thank you for taking the time out and reading this blog post, you may also want to read Part I and Part II if you haven’t so far. If you enjoyed the post, remember to subscribe to http://feeds.feedburner.com/TarunArora. Questions/Feedback/Suggestions, etc please leave a comment. Next ‘Load Testing in the cloud’, I’ll be working on exploring the possibilities of running Test controller/Agents in the Cloud. See you on the other side! Thank You!   Share this post : CodeProject

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  • STS 2.5.2 showing error error occurred during initialization of vm could not reserve enough space for object heap eclipse

    - by user1460079
    I am using STS2.5.5 with jdk 1.6 in my system with 32 BIT winx Xp it was working fine for few HRs when i installed it for the first time, after that when i restart STS it started showing exception Failed Initialize when i start the TC it throws hte exception error occurred during initialization of vm could not reserve enough space for object heap eclipse It is working fine in another system with same config, to resolve this if i delete STS and reinstall STS it works fine but again throws same error if i close STS and open it can anybody help me, i am also not able to change the size in my sts.ini if i increase memory and start STS then it will not start.

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  • Where to set java heap options (e.g. -Xmx) for Tomcat 6 under Ubuntu 9.04?

    - by Chris
    I'm running Tomcat using the tomcat6 package from Ubuntu 9.04, which makes a daemon out of Tomcat using jsvc. I'd like to know the proper way to set Java heap options like -Xmx for Tomcat. I'd like to put the configuration wherever is most stylistically correct, and wherever is least likely to be overwritten by Ubuntu package updates. The options I see right now: Hard-code them somewhere in /etc/init.d/tomcat6. Hard-code them somewhere in /usr/share/tomcat6/bin/catalina.sh. Create a line in /usr/share/tomcat6/bin/startup.sh to set CATALINA_OPTS to have the desired flags, and then export CATALINA_OPTS as an environment variable. (This looks like it will get picked up by catalina.sh.) The last option sounds like the best one, and it's advocated (without explanation of why) at http://serverfault.com/questions/121038/increasing-javas-heapspace-in-tomcat-startup-script. But I wanted to get a second opinion. Anyone want to confirm that there isn't a better way?

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  • Error java.lang.OutOfMemoryError: getNewTla using Oracle EPM products

    - by Marc Schumacher
    Running into a Java out of memory error, it is very common behaviour in the field that the Java heap size will be increased. While this might help to solve a heap space out of memory error, it might not help to fix an out of memory error for the Thread Local Area (TLA). Increasing the available heap space from 1 GB to 16 GB might not even help in this situation. The Thread Local Area (TLA) is part of the Java heap, but as the name already indicates, this memory area is local to a specific thread so there is no need to synchronize with other threads using this memory area. For optimization purposes the TLA size is configurable using the Java command line option “-XXtlasize”. Depending on the JRockit version and the available Java heap, the default values vary. Using Oracle EPM System (mainly 11.1.2.x) the following setting was tested successfully: -XXtlasize:min=8k,preferred=128k More information about the “-XXtlasize” parameter can be found in the JRockit documentation: http://docs.oracle.com/cd/E13150_01/jrockit_jvm/jrockit/jrdocs/refman/optionXX.html

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