May 2012 - 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

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review request: 4244896: (process) Provide System.getPid(), System.killProcess(String pid)
by Rob McKenna 26 Jun '12

26 Jun '12

Hi folks, I'm hoping for some feedback on the above. Webrev: http://cr.openjdk.java.net/~robm/4244896/webrev.00/ This bug adds two abstract methods to Process.java with the intention of: 1) providing a facility to implement a destroyForcibly method. In practical terms this means provide a method that will send a SIGKILL to the subprocess in *nix environments as opposed to the SIGTERM that is currently being sent. (Windows' destroy() method effectively behaves like this already) 2) providing a facility to check whether the subprocess is still alive. As per the bug report the toString/pid work has been left to be completed separately. Notes: a) It has been pointed out to me that the addition of abstract methods to java.lang.Process should be discussed, particularly as to whether default methods should be provided. b) In src/windows/native/java/lang/ProcessImpl_md.c I chose to use WaitForSingleObject on the off chance that a process may choose to return STILL_ACTIVE as an error code. Thanks! -Rob

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hg: jdk8/tl/jdk: 6924259: Remove offset and count fields from java.lang.String
by mike.duigou＠oracle.com 23 Jun '12

23 Jun '12

Changeset: 2c773daa825d Author: mduigou Date: 2012-05-17 10:06 -0700 URL: http://hg.openjdk.java.net/jdk8/tl/jdk/rev/2c773daa825d 6924259: Remove offset and count fields from java.lang.String Summary: Removes the use of shared character array buffers by String along with the two fields needed to support the use of shared buffers. Reviewed-by: alanb, mduigou, forax, briangoetz Contributed-by: brian.doherty(a)oracle.com ! src/share/classes/java/lang/Integer.java ! src/share/classes/java/lang/Long.java ! src/share/classes/java/lang/String.java ! src/share/classes/java/lang/StringCoding.java

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Review request for 7145913 CachedRowSetSwriter.insertNewRow() throws SQLException
by Lance Andersen - Oracle 14 Jun '12

14 Jun '12

Hi, Looking for a reviewer for 7145913. This addresses an issue with where a SQLException could be thrown when writing a row back with an auto-incremented column on some databases. Webrev is http://cr.openjdk.java.net/~lancea/7145913/webrev.00/. RowSet TCK, sqe tests and unit tests all pass with this fix. Best Lance Lance Andersen| Principal Member of Technical Staff | +1.781.442.2037 Oracle Java Engineering 1 Network Drive Burlington, MA 01803 Lance.Andersen(a)oracle.com

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Review request CR 7171918 XmlReaderContentHandler.endElement does not handle a Delete Tag properly
by Lance Andersen - Oracle 13 Jun '12

13 Jun '12

Attached is the fix for 7171918. If a WebRowSet is written to disk that contains a row marked for deletion and then read back into a WebRowSet, it was not marked again as a deleted row. Here is the fix to endElement() to address this. hg diff XmlReaderContentHandler.java diff -r 4580652d9828 src/share/classes/com/sun/rowset/internal/XmlReaderContentHandler.java --- a/src/share/classes/com/sun/rowset/internal/XmlReaderContentHandler.java Fri May 04 16:00:47 2012 -0400 +++ b/src/share/classes/com/sun/rowset/internal/XmlReaderContentHandler.java Fri May 25 15:52:21 2012 -0400 @@ -764,6 +764,7 @@ rs.next(); rs.setOriginalRow(); applyUpdates(); + rs.deleteRow(); } catch (SQLException ex) { throw new SAXException(MessageFormat.format(resBundle.handleGetObject("xmlrch.errdel").toString() , ex.getMessage())); Best, Lance Lance Andersen| Principal Member of Technical Staff | +1.781.442.2037 Oracle Java Engineering 1 Network Drive Burlington, MA 01803 Lance.Andersen(a)oracle.com

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Review request for CR 171917 CachedRowSetImpl.populate does not handle map properly
by Lance Andersen - Oracle 08 Jun '12

08 Jun '12

Attached is the fix for bug 171917 . The populate() method needs to check for a size of 0 for the map in case a webrowset xml file has an empty map tag, which would result in calling setobject specifying a map and not all databases/drivers support this. simple 1 line change: hg diff CachedRowSetImpl.java diff -r 4580652d9828 src/share/classes/com/sun/rowset/CachedRowSetImpl.java --- a/src/share/classes/com/sun/rowset/CachedRowSetImpl.java Fri May 04 16:00:47 2012 -0400 +++ b/src/share/classes/com/sun/rowset/CachedRowSetImpl.java Fri May 25 15:45:29 2012 -0400 @@ -659,7 +659,7 @@ * us work with drivers that do not support * getObject with a map in fairly sensible way */ - if (map == null) { + if (map == null || map.size() == 0) { obj = data.getObject(i); } else { obj = data.getObject(i, map); Best Lance Lance Andersen| Principal Member of Technical Staff | +1.781.442.2037 Oracle Java Engineering 1 Network Drive Burlington, MA 01803 Lance.Andersen(a)oracle.com

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[PATCH] 7164256 : EnumMap.clone doesn't clear the entrySet field, keeping a reference to the original Map
by Diego Belfer 07 Jun '12

07 Jun '12

Hello, I have reported a bug in the implementation of the clone method of the EnumMap class. The instance field entrySet is not being cleared, so the entrySet field of the new instance references to the original EnumMap. I am providing a simple patch for review. It adds one line to the clone method that sets the entrySet field to null. This is my first contribution to the OpenJDK, so let me know if I am missing some step of the process. Thanks, Diego Belfer [muralx]

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hg: jdk8/tl/jdk: 7126277: Alternative String hashing implementation
by mike.duigou＠oracle.com 04 Jun '12

04 Jun '12

Changeset: 43bd5ee0205e Author: mduigou Date: 2012-05-30 22:18 -0700 URL: http://hg.openjdk.java.net/jdk8/tl/jdk/rev/43bd5ee0205e 7126277: Alternative String hashing implementation Summary: All of the hashing based Map implementations: HashMap, Hashtable, LinkedHashMap, WeakHashMap and ConcurrentHashMap are modified to use an enhanced hashing algorithm for string keys when the capacity of the hash table has ever grown beyond 512 entries. The enhanced hashing implementation uses the murmur3 hashing algorithm along with random hash seeds and index masks. These enhancements mitigate cases where colliding String hash values could result in a performance bottleneck. Reviewed-by: alanb, forax, dl ! make/java/java/FILES_java.gmk ! src/share/classes/java/lang/String.java ! src/share/classes/java/util/HashMap.java ! src/share/classes/java/util/Hashtable.java ! src/share/classes/java/util/LinkedHashMap.java ! src/share/classes/java/util/WeakHashMap.java ! src/share/classes/java/util/concurrent/ConcurrentHashMap.java + src/share/classes/sun/misc/Hashing.java ! test/java/util/Collection/BiggernYours.java ! test/java/util/Hashtable/HashCode.java ! test/java/util/Hashtable/SimpleSerialization.java + test/java/util/Map/Collisions.java ! test/java/util/Map/Get.java + test/sun/misc/Hashing.java

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[doc]Small modification on the WeakHashMap doc
by Charles Lee 04 Jun '12

04 Jun '12

Hi guys, In the Implementation notes of WeakHashMap[1], says: /One way to deal with this is to wrap values themselves within WeakReferences before inserting, as in: m.put(key, new WeakReference(value)), and then unwrapping upon each get./ However, it is not concise and a little misleading. Because the value in the WeakReference can be GC'd if there are no strong reference to it. This behaviour surprises some customers. How about add a statement like [2]: /However, as the use of WeakReference in this manner will not prevent value objects from being GC'd, this approach is only useful when entries in the map are not relied upon for keeping the underlying value objects "live"./ [1]: http://docs.oracle.com/javase/7/docs/api/java/util/WeakHashMap.html [2]: http://cr.openjdk.java.net/~littlee/7166055/webrev.00/ <http://cr.openjdk.java.net/%7Elittlee/7166055/webrev.00/> -- Yours Charles

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hg: jdk8/tl/langtools: 7159016: Static import of member in processor-generated class fails in JDK 7
by james.holmlund＠oracle.com 31 May '12

31 May '12

Changeset: 844478076c25 Author: jjh Date: 2012-05-31 15:07 -0700 URL: http://hg.openjdk.java.net/jdk8/tl/langtools/rev/844478076c25 7159016: Static import of member in processor-generated class fails in JDK 7 Reviewed-by: jjg ! src/share/classes/com/sun/tools/javac/comp/MemberEnter.java + test/tools/javac/T7159016.java

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