September 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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javax.sql.rowset.serial.SerialBlob doesn't support free and getBinaryStream
by Deven You 10 Jan '13

10 Jan '13

Hi All, First of all, if the jdbc problem has a better mailing list to post please tell me. I find that javax.sql.rowset.serial.SerialBlob is not fully implemented in OpenJDK 8. Methods public InputStream getBinaryStream(long pos,long length) throws SQLException public void free() throws SQLException only throw UnsupportedOperationException. I have made a patch[1] to implement these 2 methods. Could anyone take a look to review it. BTW: I think the spec for SerialBlob is not very clear like it doesn't mention if all method rather than free() need throw any exception after free() is invoked. However that behavior seems reasonable. [1] http://cr.openjdk.java.net/~littlee/OJDK-576/webrev Thanks a lot. -- Best Regards, Deven

3 14

Code Review Request: 7197662: (prefs) java/util/prefs/AddNodeChangeListener.java fails by timeout or by "couldn't get file lock"
by Kurchi Hazra 30 Nov '12

30 Nov '12

Hi, The tests in java/util/prefs creates new nodes under the user's home directory. This causes the tests to start out with pre-existing preferences and sometimes leads to interference between the tests. This fix is to change the userRoot property for each of these tests so these tests create nodes only under the current directory. Bug: http://bugs.sun.com/bugdatabase/view_bug.do?bug_id=7197662 Webrev: http://cr.openjdk.java.net/~khazra/7197662/webrev.00/ Thanks, - Kurchi

6 15

RFR: 7159567 - inconsistent configuration of MemoryHandler
by Jim Gish 25 Oct '12

25 Oct '12

Please review http://cr.openjdk.java.net/~jgish/Bug7159567-set-MemoryHandler-target/ <http://cr.openjdk.java.net/%7Ejgish/Bug7159567-set-MemoryHandler-target/> Overview - currently you can sub-class java.util.logging.MemoryHandler and specify its configuration in a logging.properties file via the classname. For example, if com.foo.MyMemoryHandler extends MemoryHandler, you can have: logging.properties: com.foo.MyMemoryHandler.push=WARNING com.foo.MyMemoryHandler.level=FINEST .... etc. However, the only way to specify target is by using the global property, java.util.logging.MemoryHandler.target. For example, java.util.logging.MemoryHandler.target=java.util.logging.ConsoleHandler This is clearly broken. This bug fix allows you to say: com.foo.MyMemoryHandler.target=java.util.logging.ConsoleHandler 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

3 17

RFR: 6206780 (str) Forwarding append methods in String{Buffer, Builder} are inconsistent
by Jim Gish 10 Oct '12

10 Oct '12

Please review the change at http://cr.openjdk.java.net/~jgish/Bug6206780-sb-append-forward Overview -- * introduced consistent forwarding to AbstractStringBuilder from StringBuffer and StringBuilder for append and other methods per the bug report. * Added missing @Override annotations. * Got rid of knowledge of the sub-classes from ASB (smelled too bad to leave in). * Retained all methods to maintain compatibility (even though some like append(StringBuffer) could go away because append(CharSequence) would do). * To further maintain compatibility, used sychronized(this) in place of adding synchronized methods to those methods in StringBuffer that now need it (because of type-based dispatch being done in super rather than in /both/ StringBuffer and StringBuilder with CharSequence args). * Ensured that StringBuffer.append(StringBuilder) and StringBuilder.append(StringBuffer) are both handled properly. 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

6 16

ServiceLoader.load(Class, ClassLoader) bug
by Peter Levart 02 Oct '12

02 Oct '12

Hi, The javadoc for java.util.ServiceLoader.load(Class service, ClassLoader loader) method says about it's parameters: * @param service * The interface or abstract class representing the service * * @param loader * The class loader to be used to load provider-configuration files * and provider classes, or <tt>null</tt> if the system class * loader (or, failing that, the bootstrap class loader) is to be * used So one might think that calling: ServiceLoader.load(service, null); is equivalent to: ServiceLoader.load(service, ClassLoader.getSystemClassLoader()); But it is not. Either this is a bug in ServiceLoader or javadoc has to be changed. If null is specified as ClassLoader then ServiceLoader locates META-INF/services/interface.name resources using system ClassLoader (using ClassLoader.getSystemResources), but loads implementation classes using bootstrap ClassLoader (using Class.forName(className, true, null)). If this is not the expected behaviour then the fix is simple (in the private constructor of ServiceLoader[217]): - loader = cl; + loader = cl == null ? ClassLoader.getSystemClassLoader() : cl; Regards, Peter

2 4

8000269: Cleanup javadoc warnings
by Alan Bateman 01 Oct '12

01 Oct '12

A small number of javadoc warnings have sneaked into the jdk8 builds lately. They are easily fixed but it's a good opportunity to try out Jon's doccheck tool [1], built on the DocTree API, and get a better workout. I decided to mostly focus on -Xmsgs:reference, which covers references like @param and @throws. I fixed a small number of html issues along the way but that was not my focus. The webrev, which fixes up most of the reference issues in roughly the core area, is here: http://cr.openjdk.java.net/~alanb/8000269/webrev/index.html When I say "most" then I excluded j.u.c and java.lang.invoke. The reason that so many files are changed is because doccheck looks at private and package-private classes and methods that wouldn't normally be in the javadoc. Also, no foray into the *Permission classes is possible without fixing up at least a few formatting issues. Note that there is one non-javadoc change in this webrev; in java.net.Inet4Address I have removed a private static field that has not been used for several releases. The changes are trivial and easy to review. At it covers many areas then partial reviews are okay too. Thanks, Alan. [1] http://mail.openjdk.java.net/pipermail/compiler-dev/2012-September/004800.h…

7 15

Review request for 7197395
by Lance Andersen - Oracle 29 Sep '12

29 Sep '12

Hi, This is a review request for adding @Deprecated to the remaining JDBC methods to suppress compiler warnings. The CCC was approved as this annotation results in the signatures changing (though they do not effect the execution of any applications). The webrev is http://cr.openjdk.java.net/~lancea/7197395/webrev.00/ 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

2 1

hg: jdk8/tl/langtools: 7198582: (java) Minor refactor of JavacParser
by kumar.x.srinivasan＠oracle.com 29 Sep '12

29 Sep '12

Changeset: 20e4a54b1629 Author: ksrini Date: 2012-09-29 09:00 -0700 URL: http://hg.openjdk.java.net/jdk8/tl/langtools/rev/20e4a54b1629 7198582: (java) Minor refactor of JavacParser Reviewed-by: jjg, ksrini Contributed-by: jan.lahoda(a)oracle.com ! src/share/classes/com/sun/tools/javac/comp/Resolve.java ! src/share/classes/com/sun/tools/javac/parser/JavacParser.java ! src/share/classes/com/sun/tools/javap/CodeWriter.java

1 0

hg: jdk8/tl/jdk: 7186723: TEST_BUG: Race condition in sun/management/jmxremote/startstop/JMXStartStopTest.sh
by dmitry.samersoff＠oracle.com 29 Sep '12

29 Sep '12

Changeset: 0c1c4b185451 Author: dsamersoff Date: 2012-09-29 15:44 +0400 URL: http://hg.openjdk.java.net/jdk8/tl/jdk/rev/0c1c4b185451 7186723: TEST_BUG: Race condition in sun/management/jmxremote/startstop/JMXStartStopTest.sh Summary: Make test self-terminating and independent to cygwin/mks kill behaviour Reviewed-by: sspitsyn, alanb ! test/ProblemList.txt ! test/sun/management/jmxremote/startstop/JMXStartStopDoSomething.java ! test/sun/management/jmxremote/startstop/JMXStartStopTest.java ! test/sun/management/jmxremote/startstop/JMXStartStopTest.sh ! test/sun/management/jmxremote/startstop/REMOTE_TESTING.txt

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