April 2013 - core-libs-dev

Replacement of Quicksort in java.util.Arrays with new Dual-Pivot Quicksort
by Vladimir Yaroslavskiy 27 Feb '16

27 Feb '16

Hello All, I'd like to share with you new Dual-Pivot Quicksort which is faster than the known implementations (theoretically and experimental). I'd like to propose to replace the JDK's Quicksort implementation by new one. Description ----------- The classical Quicksort algorithm uses the following scheme: 1. Pick an element P, called a pivot, from the array. 2. Reorder the array so that all elements, which are less than the pivot, come before the pivot and all elements greater than the pivot come after it (equal values can go either way). After this partitioning, the pivot element is in its final position. 3. Recursively sort the sub-array of lesser elements and the sub-array of greater elements. The invariant of classical Quicksort is: [ <= p | >= p ] There are several modifications of the schema: [ < p | = p | > p ] or [ = p | < p | > p | = p ] But all of them use *one* pivot. The new Dual-Pivot Quicksort uses *two* pivots elements in this manner: 1. Pick an elements P1, P2, called pivots from the array. 2. Assume that P1 <= P2, otherwise swap it. 3. Reorder the array into three parts: those less than the smaller pivot, those larger than the larger pivot, and in between are those elements between (or equal to) the two pivots. 4. Recursively sort the sub-arrays. The invariant of the Dual-Pivot Quicksort is: [ < P1 | P1 <= & <= P2 } > P2 ] The new Quicksort is faster than current implementation of Quicksort in JDK (L. Bentley and M. Douglas McIlroy) and classical Quicksort. The full description of the Dual-Pivot Quicksort you can find on my page: http://iaroslavski.narod.ru/quicksort Performance tests ----------------- Here is result of running on different types of input data: Client VM all 85% organ 0..1 0..4 random ascend descend equal equal pipes random 010101 random Dual-Pivot: 16.83 5.31 5.47 0.35 0.68 10.59 1.06 1.02 2.18 Bentley's: 19.77 9.08 10.13 0.63 1.12 13.22 1.63 1.08 2.49 Server VM all 85% organ 0..1 0..4 random ascend descend equal equal pipes random 010101 random Dual-Pivot: 23.94 6.68 6.63 0.43 0.62 17.14 1.42 1.96 3.41 Bentley's: 25.20 10.18 10.32 2.07 1.33 16.72 2.95 1.82 3.39 The a lot of other tests have been run under client and server mode. The most interesting is BentleyBasher test framework. It runs battery of tests for all cases: { 100, 1000, 10000, 1000000 } x { sawtooth, rand, stagger, plateau, shuffle } x { ident, reverse, reverse_front, reverse_back, sort, dither} where 100, ... , 1000000 - array length sawtooth: x[i] =i%m rand: x[i] = rand() % m stagger: x[i] = (i*m + i) % n plateau: x[i] = min(i, m) shuffle: x[i] = rand()%m? (j+=2): (k+=2) ident(x) - a copy of x reverse(x, 0, n) - reversed copy reverse_front(x, 0, n/2) - front half reversed reverse_back(x, n/2, n) - back half reversed sort(x) - an ordered copy dither(x) - add i%5 to x[i] Here is the result of execution: Server VM: http://spreadsheets.google.com/pub?key=t_EAWUkQ4mD3BIbOv8Fa-AQ&output=html Client VM: http://spreadsheets.google.com/pub?key=tdiMo8xleTxd23nKUObcz0Q&single=true&… Mathematical investigations --------------------------- It is proved that for the Dual-Pivot Quicksort the average number of comparisons is 2*n*ln(n), the average number of swaps is 0.8*n*ln(n), whereas classical Quicksort algorithm has 2*n*ln(n) and 1*n*ln(n) respectively. Full mathematical proof see in attached proof.txt and proof_add.txt files. Theoretical results are also confirmed by experimental counting of the operations. Diff between current and new implementation of Quicksort -------------------------------------------------------- Here is the link to the diff for java.util.Arrays class: http://cr.openjdk.java.net/~alanb/6880672/webrev.00 If you like to look and play with new algorithm, please, take attached class DualPivotQuicksort.java Feedback -------- Also I'd like to share a feedback from Joshua Bloch and Jon Bentley who spent a lot of time investigating this algorithm, who gave me many advices and tips how to make new Quicksort better. -------- Original Message -------- Subject: Re: Integration of new Dual-Pivot Quicksort into JDK 7 Date: Thu, 10 Sep 2009 07:20:11 -0700 From: Joshua Bloch <jjb(a)google.com> Jon also says that Vladimir should make every reasonable improvement to the basic method before checking in the code. In his words, "It would be horrible to put the new code into the library, and then have someone else come along and speed it up by another 20% by using standard techniques." I believe it's not unlikely that this code may end up getting ported to many languages and widely deployed in much the manner of Bentley and McIlroy's fine sort (which is nearing 20 successful years in the field). Jon will help Vladimir do this. -------- Original Message -------- Subject: Dual-Pivot Quicksort: Next Steps Date: Wed, 09 Sep 2009 15:02:25 -0400 From: Jon Bentley <jbentley(a)avaya.com> Vladimir, Josh, I *finally* feel like I understand what is going on. Now that I (think that) I see it, it seems straightforward and obvious. Tony Hoare developed Quicksort in the early 1960s. I was very proud to make minor contributions to a particularly clean (binary) quicksort in the mid 1980s, to a relatively straightforward, industrial strength Quicksort with McIlroy in the early 1990s, and then to algorithms and data structures for strings with Sedgewick in the mid 1990s. I think that Vladimir's contributions to Quicksort go way beyond anything that I've ever done, and rank up there with Hoare's original design and Sedgewick's analysis. I feel so privileged to play a very, very minor role in helping Vladimir with the most excellent work! ----------------------------------------------- Let me know, if you have any questions/comments. Thank you, Vladimir Here is the details between steps (1) and (2) in the case of Dual-Pivot Quicksort: ----------------------------------------------------------------------------- >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = 1 + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + 1*i + C_{j-i-1} + 2*(j-i- 1) + C_{n-j-1} + 2*(n-j-1)} Equation (1) means that total number is the sum of the comparison numbers of all cases of partitions into 3 parts plus number of comparisons for elements from left part (one comparison), center and right parts (2 comparisons). !!! It can be rewritten in other way: !!! (2) C_n = 1 + R*(n-2) + 2/(n(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} where R is the average number of comparisons during one iteration. This constant R can be found as average value (1 + 2 + 2) / 3 = 5/3 ~ 1.6666. ----------------------------------------------------------------------------- How it can be rewritten: We should show that 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i + 2*(j-i- 1) + 2*(n-j-1)} equals to 5/3 * (n-2). Let's consider the double sum: sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i + 2*(j-i- 1) + 2*(n-j-1)} = = sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {2*n - 4} - sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i} = = 2*(n-2)*sum_{i=0}^{n-2} {n-1-i} - sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {i} = = 2*(n-2)*((n-1)^2 - (n-1)*(n-2)/2) - (n-1)*sum_{i=0}^{n-2} {i} + sum_{i=0}^{n-2} {i*i} = ------------------------------------------------------------------ here we use the property: sum_{k=1}{n} {k^2} = n^3/3 + n^2/2 + n/6 ------------------------------------------------------------------ = 2*(n-2)*((n-1)^2 - (n-1)*(n-2)/2) - (n-1)*(n-1)*(n-2)/2 + (n-2)^3/3 + (n-2)^2/2 + (n-2)/6 = = 1/6 * (12*(n-1)^2*(n-2) - 6*(n-1)*(n-2)^2 - 3*(n-1)^2*(n-2) + 2*(n-2)^3 + 3*(n-2)^2 + (n-2)) = = 1/6 * (3*(n-1)*(n-2)*(3*(n-1) - 2*(n-2)) + (n-2)*(2*(n-2)^2 + 3*(n-2) + 1))) = = 1/6 * (3*(n-1)*(n-2)*(n+1) + (n-2)*(2*n^2 - 5*n + 3)) = = 1/6 * (n-2)*(5*n^2 - 5*n) = 5/6 * n*(n-1)*(n-2) Substitute the result into equation (1): (1.1) C_n = 1 + 2/(n*(n-1)) * 5/6 * n*(n-1)*(n-2) + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} or (1.2) C_n = 1 + 5/3*(n-2) + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} We see that (1.2) is same as (2). /** * @author Vladimir Yaroslavskiy * @version 2009.09.10 m765 */ public class DualPivotQuicksort { public static void sort(int[] a) { sort(a, 0, a.length); } public static void sort(int[] a, int fromIndex, int toIndex) { rangeCheck(a.length, fromIndex, toIndex); dualPivotQuicksort(a, fromIndex, toIndex - 1, 3); } private static void rangeCheck(int length, int fromIndex, int toIndex) { if (fromIndex > toIndex) { throw new IllegalArgumentException("fromIndex(" + fromIndex + ") > toIndex(" + toIndex + ")"); } if (fromIndex < 0) { throw new ArrayIndexOutOfBoundsException(fromIndex); } if (toIndex > length) { throw new ArrayIndexOutOfBoundsException(toIndex); } } private static void swap(int[] a, int i, int j) { int temp = a[i]; a[i] = a[j]; a[j] = temp; } private static void dualPivotQuicksort(int[] a, int left, int right, int div) { int len = right - left; if (len < 27) { // insertion sort for tiny array for (int i = left + 1; i <= right; i++) { for (int j = i; j > left && a[j] < a[j - 1]; j--) { swap(a, j, j - 1); } } return; } int third = len / div; // "medians" int m1 = left + third; int m2 = right - third; if (m1 <= left) { m1 = left + 1; } if (m2 >= right) { m2 = right - 1; } if (a[m1] < a[m2]) { swap(a, m1, left); swap(a, m2, right); } else { swap(a, m1, right); swap(a, m2, left); } // pivots int pivot1 = a[left]; int pivot2 = a[right]; // pointers int less = left + 1; int great = right - 1; // sorting for (int k = less; k <= great; k++) { if (a[k] < pivot1) { swap(a, k, less++); } else if (a[k] > pivot2) { while (k < great && a[great] > pivot2) { great--; } swap(a, k, great--); if (a[k] < pivot1) { swap(a, k, less++); } } } // swaps int dist = great - less; if (dist < 13) { div++; } swap(a, less - 1, left); swap(a, great + 1, right); // subarrays dualPivotQuicksort(a, left, less - 2, div); dualPivotQuicksort(a, great + 2, right, div); // equal elements if (dist > len - 13 && pivot1 != pivot2) { for (int k = less; k <= great; k++) { if (a[k] == pivot1) { swap(a, k, less++); } else if (a[k] == pivot2) { swap(a, k, great--); if (a[k] == pivot1) { swap(a, k, less++); } } } } // subarray if (pivot1 < pivot2) { dualPivotQuicksort(a, less, great, div); } } } At first consider the classic Quicksort scheme and find the average number of comparisons and swaps for it. We assume that input data is random permutation of n numbers from the range [1..n]. Classic Quicksort ================= 1. Choose a pivot element (take random), 2. Compare each (n-1) elements with the pivot 3. and swap it, if necessary, to have the partitions: [ <= pivot | >= pivot ] 4. Sort recursively left and right parts. >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = (n-1) + 1/n * sum_{k=0}^{n-1} {C_k + C_{n-k-1}} and last sum can be rewritten: (2) C_n = (n-1) + 2/n * sum_{k=0}^{n-1} C_k Write formula (2) for n+1: (3) C_{n+1} = n + 2/(n+1) * sum_{k=0}^{n} C_k Multiply (2) by n and (3) by (n+1) and subtract one from other, we have: (4) (n+1)*C_{n+1} - n*C_n = 2*n + 2*C_n Sorting an array of n elements may be considered as selecting one permutation of the n elements among all possible permutations. The number of possible permutations of n elements is n!, so the task for any sorting algorithm is to determine the one permutation out of n! possibilities. The minimum number of operations (swap and comparisons) for sorting n elements is const*ln(n!). >From the Stirling's formula the approximation of the number of operations is A*n*ln(n) + B*n + C, where A, B and C are constant coefficients. The coefficients B and C are not important for large n. Therefore, the function C_n may be approximated by the equation: (5) C_n = A*n*ln(n) The function C_n is substituted from equation (5) into equation (4), which yields the following equation: (6) (n+1)*A*(n+1)*ln(n+1) - n*A*n*ln(n) = 2*n + 2*A*n*ln(n) Using the properties of logarithms, equation (6) can then be reduced to: (7) n*ln(1+1/n) + 2*ln(1+1/n) + (1/n) * ln(n+1) = 2/A Using a property of logarithm: ln(1 + x) -> x, if x -> 0, and other property: ln(n) / n -> 0, when n -> +oo, equation (7) will be approximated by: (8) 1 + 0 + 0 = 2/A So, the coefficient A is equal to 2 and the average number of comparisons in sorting of n size arrays is (9) C_n = 2*n*ln(n). To find the approximation of the average number of swaps, we use the similar approach as in the case of comparisons. The average number of swaps S_n as a function of the number of elements may be represented by the equation: (10) S_n = 1/2*(n-1) + 2/n * sum_{k=0}^{n-1} S_k We assume that average number of swaps during one iteration is 1/2*(n-1). It means that in average one half of elements is swapped only. Using the same approach, we find that the coefficient A equals to 1. Therefore, the function S_n may be approximated by the equation: (11) S_n = n*ln(n) ------------------------------------------------------------------------------- Now consider the Dual-Pivot Quicksort scheme and find the average number of comparisons and swaps for it. We assume that input data is random permutation of n numbers from the range [1..n]. Dual-Pivot Quicksort ==================== 1. Choose 2 pivot elements pivot1 and pivot2 (take random), 2. pivot1 must be less or equal than pivot2, otherwise they are swapped 3. Compare each (n-2) elements with the pivots 4. and swap it, if necessary, to have the partitions: [ <= p1 | p1 <= & <= p2 | >= p2 ] 5. Sort recursively left, center and right parts. >From the algorithm above, the average number of comparisons C_n as a function of the number of elements may be represented by the equation: (1) C_n = 1 + 2/(n*(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + 1*i + C_{j-i-1} + 2*(j-i-1) + C_{n-j-1} + 2*(n-j-1)} Equation (1) means that total number is the sum of the comparison numbers of all cases of partitions into 3 parts plus number of comparisons for elements from left part (one comparison), center and right parts (2 comparisons). It can be rewritten in other way: (2) C_n = 1 + R*(n-2) + 2/(n(n-1)) * sum_{i=0}^{n-2} sum_{j=i+1}^{n-1} {C_i + C_{j-i-1} + C_{n-j-1}} where R is the average number of comparisons during one iteration. This constant R can be found as average value (1 + 2 + 2) / 3 = 5/3, which means that elements from left part are required only one comparison, and elements form center and right parts - two comparisons. The double sum in equation (2) can be reduced: (3) C_n = 1 + 5/3*(n-2) + 2/(n*(n-1)) * sum_{k=0}^{n-2} {3 * (n-k-1) * C_k} Denote 1 + 5/3*(n-2) by f_n and multiply to n*(n-1): (4) n*(n-1)*C_n = n*(n-1)*f_n + 6 * sum_{k=0}^{n-2} {(n-k-1) * C_k} Write formula (4) for n+1: (5) n*(n+1)*C_{n+1} = n*(n+1)*f_n + 6 * sum_{k=0}^{n-1} {(n-k) * C_k} Subtract (4) from (5), we have: (6) n*(n+1)*C_{n+1} - n*(n-1)*C_n = n*(n+1)*f_n - n*(n-1)*f_n + 6 * sum_{k=0}^{n-2} C_k + 6*C_{n-1} Denote n*(n+1)*C_{n+1} - n*(n-1)*C_n by X_n and n*(n+1)*f_n - n*(n-1)*f_n by F_n: (7) X_n = F_n + 6 * sum_{k=0}^{n-2} C_k + 6*C_{n-1} Write formula (7) for n+1: (8) X_{n+1} = F_{n+1} + 6 * sum_{k=0}^{n-1} C_k + 6*C_n Subtract (7) from (8), we have: (9) X_{n+1} - X_n = F_{n+1} - F_n + 6*C_n Resolving of F_{n+1} - F_n gives: (10) X_{n+1} - X_n = 2 + 10*n + 6*C_n The function X_n is substituted into equation (10), which yields the following equation: (11) (n+1)*(n+2)C_{n+2} -2*n(n+1)*C_{n+1} + (n*(n-1) - 6)*C_n = 2 + 10*n We will find the function C_n approximated by the equation: (12) C_n = A*n*ln(n) The function C_n is substituted from equation (12) into equation (11), which yields the following equation: (13) (n^3+5*n^2+8*n+4)*ln(n+2) - (2*n^3+4*n^2+2*n)*ln(n+1) + (n^3-n^2-6*n)*ln(n) = (10*n+2) / A Using the properties of logarithms, equation (13) can then be reduced to: (14) n^3*(ln(n+2)-2*ln(n+1)+ln(n)) + n^2*(5*ln(n+2)-4*ln(n+1)-ln(n)) + n*(8*ln(n+2)-2*ln(n+1)-6*ln(n)) + 4*ln(n+2) = (10*n+2) / A Using a property of logarithm: ln(1 + x) -> x, if x -> 0, and other property: ln(n) / n -> 0, when n -> +oo, equation (15) will be approximated by: (15) -1 + 4 + 2 + 0 + 0 = 10 / A So, the coefficient A is equal to 2 and the average number of comparisons in sorting of n size arrays is (9) C_n = 2*n*ln(n). To find the approximation of the average number of swaps, we use the similar approach as in the case of comparisons. The average number of swaps S_n as a function of the number of elements may be represented by the equation: (10) S_n = 4 + 2/3*(n-2) + 2/(n*(n-1)) * sum_{k=0}^{n-2} {(n-k-1)*S_k} We assume that average number of swaps during one iteration is 2/3*(n-2). It means that in average one third of elements is swapped only. Using the same approach, we find that the coefficient A equals to 0.8. Therefore, the function S_n may be approximated by the equation: (11) S_n = 0.8*n*ln(n) ------------------------------------------------------------------------- And as summary: The value of the coefficient A: dual-pivot classic comparison: 2.0 2.0 swap: 0.8 1.0

14 20

RFR: String.join(), StringJoiner additions
by Jim Gish 05 Feb '14

05 Feb '14

Please review http://cr.openjdk.java.net/~jgish/Bugs-5015163-7175206-7172553/ <http://cr.openjdk.java.net/%7Ejgish/Bugs-5015163-7175206-7172553/> These are changes that we made in lambda that we're now bringing into JDK8. I've made a couple of additions - making StringJoiner final and adding a couple of constructors to set the emptyOutput chars. Thanks, Jim -- Jim Gish | Consulting Member of Technical Staff | +1.781.442.0304 Oracle Java Platform Group | Core Libraries Team 35 Network Drive Burlington, MA 01803 jim.gish(a)oracle.com

12 48

RFR : 8008632 : Additional JavaDoc tags @apiNote, @implSpec and @implNote for JDK API Docs
by Mike Duigou 10 Dec '13

10 Dec '13

Hello All; Some of you have noticed that the lambda streams libraries patches currently going in to the TL repo make use of special javadoc tags. There are three tags being used: @apiNote : Non-normative notes about the API. Usually used for examples. @implSpec : Describes required behaviour of conforming implementations. Key is that the description is not inherited. @implNote : Non-normative notes about the implementation. Typically used for descriptions of the behaviour. Also not inherited. The tags are used primarily by default method implementations but will be used elsewhere as well. These tags are enabled by use of the -tag feature on the javadoc tool command line. They are not proposed as standard javadoc tags and may be implemented differently in future Java releases. Since they are implemented as custom tags just for the JDK API documentation you can't automatically use them in your own code. (You can, of course, add the same command line options to your javadoc invocations if you like these tags). http://cr.openjdk.java.net/~mduigou/JDK-8008632/0/webrev/ The patch enumerates the new custom tags in addition to the standard tags to ensure correct output order.

4 15

RFR: 8009681: TEST_BUG: MethodExitReturnValuesTest.java fails with when there are unexpected background threads
by Mikael Auno 22 Oct '13

22 Oct '13

Hi, I'd like some reviews on http://cr.openjdk.java.net/~nloodin/8009681/webrev.01/ for JDK-8009681 (http://bugs.sun.com/view_bug.do?bug_id=8009681) The issue here is that when MethodExitReturnValuesTest hooks into MethodExit events through JDI it uses an exclude list to filter out classes from which it is not interested in these events. This is bound to break over and over again as new features are added to the JDK. I've changed the test to use an include list instead, containing only the handful of classes the test is actually interested in. Thanks, Mikael

4 6

8008662: Add @jdk.Supported to JDK-specific/supported API
by Alan Bateman 11 Sep '13

11 Sep '13

Joe Darcy recently added @jdk.Supported [1] to make it possible to identify JDK-specific APIs. I'd like to add this to a number of APIs in the com.sun namespace to make it obvious these are "supported". Specifically I'm proposing to add it to: - Java Debug Interface (com.sun.jdi) - Attach API (com.sun.tools.attach) - SCTP API (com.sun.nio.sctp) - HTTP server API (com.sun.net.httpserver) - Management extensions (com.sun.management) - JDK-specific API to JAAS (com.sun.security.auth) - JDK-specific JGSS API (com.sun.security.jgss) The javadoc for all of these is generated as part of the regular JDK "docs" build and so shouldn't be controversial. There are a number of other candidates in com.sun with murkier status that I've stayed clear of for now. The webrev with the changes is here: http://cr.openjdk.java.net/~alanb/8008662/webrev/ In a couple of cases the package description is legacy package.html so I've had to move/convert them to package-info.java. In all but one case I've added the annotation to the package-info, the one exception is com.sun.management where there is at least one type that is documented as "not supported". Joe Darcy might have suggestions on that. Otherwise this is mostly mechanical and the patch file is easier to review that the webrev. -Alan [1] http://hg.openjdk.java.net/jdk8/tl/langtools/rev/55cca2f38ee6

13 39

Request for Review: 5049299: (process) Use posix_spawn, not fork, on S10 to avoid swap exhaustion
by Rob McKenna 12 Aug '13

12 Aug '13

Hi folks, Looking for a review for the webrev below, which also resolves: 7175692: (process) Process.exec should use posix_spawn [macosx] For additional context and a brief description it would be well worth looking at the following thread started by Michael McMahon, who did the brunt of the work for this fix: http://mail.openjdk.java.net/pipermail/core-libs-dev/2009-May/thread.html#1… Basically the fix aims to swap fork for posix_spawn as the default process launch mechanism on Solaris and Mac OSX in order to avoid swap exhaustion issues encountered with fork()/exec(). It also offers a flag (java.lang.useFork) to allow a user to revert to the old behaviour. I'm having trouble seeing the wood for the trees at this point so I'm anticipating plenty of feedback. In particular I'd appreciate some discussion on: - The binary launcher name & property name may need some work. A more general property ("java.lang.launchMechanism") to allow a user to specify a particular call was mooted too. It may be more future proof and I'm completely open to that. (e.g. launchMechanism=spawn|fork|vfork|clone - we would obviously ignore inapplicable values on a per-platform basis) - I'd like a more robust way of checking that someone isn't trying to use jprochelper outside of the context for which it is meant. - The decision around which call to use getting moved to the java level and away from the native preprocessor. The webrev is at: http://cr.openjdk.java.net/~robm/5049299/webrev.01/ <http://cr.openjdk.java.net/%7Erobm/5049299/webrev.01/> Thanks a lot, -Rob

5 22

RFR : 8013712 : (XS) Add Objects.nonNull and Objects.isNull
by Mike Duigou 25 Jun '13

25 Jun '13

Hello all; Another changeset coming from the lambda libraries effort. This one is two small additions to the Objects class. The introduced methods are not really intended to be used directly, comparison operators work better in imperative logic, but the methods will be very useful as Predicates. http://cr.openjdk.java.net/~mduigou/JDK-8013712/0/webrev/ Thanks, Mike

8 11

RFR 8009581: Xpathexception does not honor initcause()
by Aleksej Efimov 05 Jun '13

05 Jun '13

Hi all, Can I have a reviews for the following change: http://cr.openjdk.java.net/~dmeetry/8009581/webrev.0/ <http://cr.openjdk.java.net/%7Edmeetry/8009581/webrev.0/> Summary: There is an erroneous behavior in 'initCause' method of javax.xml.xpath.XPathException class. Lets look at the following situation: XPathException is created with 'XPathException(String )' constructor and then the cause is initialized with 'initCause' method. Such initialization sequence of actions isn't restricted by XPathException [1] and Throwable [2] docs. After that a cause is retrieved by 'getCause()' method: this call returns incorrect cause = 'null'. It should return the same cause as was used in 'initCause'. And this is the erroneous behavior. Suggested fix (with regression test) is applicable both for JDK 8 and 7. Regards, -Aleksej [1] http://download.java.net/jdk8/docs/api/javax/xml/xpath/XPathException.html [2] http://download.java.net/jdk8/docs/api/java/lang/Throwable.html#initCause(j… <http://download.java.net/jdk8/docs/api/java/lang/Throwable.html#initCause%2…> [3] http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=8009581

5 31

RFR: 8012665: CharSequence.chars, CharSequence.codePoints
by Henry Jen 31 May '13

31 May '13

Hi, Please review two default methods add to CharSequence returns IntStream of char value or code point value. http://cr.openjdk.java.net/~henryjen/tl/8012665.0/webrev/ The synchronization test is relieved so lambda and other synthetic method is not tested. If synchronization is needed for those two default methods, subclass should override the methods. With charAt and codePointAt properly synchronized, the default implementation is sufficient. However as noted, if the sequence is mutated while the stream is being read, the result is undefined. Cheers, Henry

4 8

Re: RFR JDK-8003245
by John Zavgren 30 May '13

30 May '13

Florian: Yes, the uninitialized memory will be accessed in some cases, for example: @@ -1733,10 +1747,12 @@ CK_X9_42_DH1_DERIVE_PARAMS ckParam; jfieldID fieldID; jlong jKdf; jobject jOtherInfo, jPublicData; + memset(&ckParam, 0, sizeof(CK_X9_42_DH1_DERIVE_PARAMS));<--- added initialization + /* get kdf */ jX942Dh1DeriveParamsClass = (*env)->FindClass(env, CLASS_X9_42_DH1_DERIVE_PARAMS); if (jX942Dh1DeriveParamsClass == NULL) { return ckParam; } fieldID = (*env)->GetFieldID(env, jX942Dh1DeriveParamsClass, "kdf", "J"); if (fieldID == NULL) { return ckParam; } ----- Original Message ----- From: fweimer(a)redhat.com To: john.zavgren(a)oracle.com Cc: core-libs-dev(a)openjdk.java.net Sent: Wednesday, March 27, 2013 11:48:57 AM GMT -05:00 US/Canada Eastern Subject: Re: RFR JDK-8003245 On 03/20/2013 04:27 PM, John Zavgren wrote: > Please consider the following changes that eliminate the use of uninitialized memory. > http://cr.openjdk.java.net/~jzavgren/8003245/webrev.01/ Is the uninitialized memory accessed on the error paths? -- Florian Weimer / Red Hat Product Security Team

3 2