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  • Groovy Refactoring in NetBeans

    - by Martin Janicek
    Hi guys, during the NetBeans 7.3 feature development, I spend quite a lot of time trying to get some basic Groovy refactoring to the game. I've implemented find usages and rename refactoring for some basic constructs (class types, fields, properties, variables and methods). It's certainly not perfect and it will definitely need a lot fixes and improvements to get it hundred percent reliable, but I need to start somehow :) I would like to ask all of you to test it as much as possible and file a new tickets to the cases where it doesn't work as expected (e.g. some occurrences which should be in usages isn't there etc.) ..it's really important for me because I don't have real Groovy project and thus I can test only some simple cases. I can promise, that with your help we can make it really useful for the next release. Also please be aware that the current version is focusing only on the .groovy files. That means it won't find any usages from the .java files (and the same applies for finding usages from java files - it won't find any groovy usages). I know it's not ideal, but as I said.. we have to start somehow and it wasn't possible to make it all-in-one, so only other option was to wait for the NetBeans 7.4. I'll focus on better Java-Groovy integration in the next release (not only in refactoring, but also in navigation, code completion etc.) BTW: I've created a new component with surprising name "Refactoring" in our bugzilla[1], so please put the reported issues into this category. [1] http://netbeans.org/bugzilla/buglist.cgi?product=groovy;component=Refactoring

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  • Exalogic 2.0.1 Tea Break Snippets - Creating and using Distribution Groups

    - by The Old Toxophilist
    By default running your Exalogic in a Virtual provides you with, what to Cloud Users, is a single large resource and they can just create vServers and not care about how they are laid down on the the underlying infrastructure. All the Cloud Users will know is that they can create vServers. For example if we have a Quarter Rack (8 Nodes) and our Cloud User creates 8 vServers those 8 vServers may run on 8 distinct nodes or may all run on the same node. Although in many cases we, as Cloud Users, may not be to worried how the Virtualisation Algorithm decides where to place our vServers there are cases where it is extremely important that vServers run on distinct physical compute nodes. For example if we have a Weblogic Cluster we will want the Servers with in the cluster to run on distinct physical node to cover for the situation where one physical node is lost. To achieve this the Exalogic Virtualised implementation provides Distribution Groups that define and anti-aliasing policy that the underlying Virtualisation Algorithm will take into account when placing vServers. It should be noted that Distribution Groups must be created before you create vServers because a vServer can only be added to a Distribution Group at creation time. Creating A Distribution Group To create a Distribution Groups we will first need to select the Account in which we want the Distribution Group to be created. Once we have selected the account we will see the Interface update and Account specific Actions will be displayed within the Action Panes. From the Action pane (or Right-Click on the Account) select the "Create Distribution Group" action. This will initiate the create wizard as follows. Distribution Group Details Within the first Step of the Wizard we can specify the name of the distribution group and this should be unique. In addition we can provide a detailed description of the group. Distribution Group Configuration The second step of the configuration wizard allows you to specify the number of elements that are required within this group and will specify a maximum of the number of nodes within you Exalogic. At this point it is always better to specify a group with spare capacity allowing for future expansion. As vServers are added to group the available slots decrease. Summary Finally the last step of the wizard display a summary of the information entered.

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  • Sources of NetBeans Gradle Plugin

    - by Geertjan
    Here is where you can find the sources of the latest and greatest NetBeans Gradle plugin: http://java.net/projects/nb-api-samples/sources/api-samples/show/versions/7.1/misc/GradleSupport To use it, download the sources above, open the sources into the IDE (which must be 7.1.1 or above), then you'll have a NetBeans module. Right-click it to run the module into a new instance of NetBeans IDE. In the Options window's Miscellaneous tab, there's a Gradle subtab for setting the Gradle location. In the New File dialog, in the Other category, you'll find a template named "Empty Gradle file". Make sure to name it "build" and to put it in the root directory of the application (by leaving the Folder field empty, you're specifying it should be created in the root directory). You'll then be able to expand the build.gradle file: Double-click a task to run it. When you open the file, it opens in the Groovy editor, if the Groovy editor is installed. When you make changes in the file, the list of tasks, shown above, is automatically recreated. It's at a really early stage of development and it would be great if developers out there would be interested in adding more features to it.

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  • Several New Hints

    - by Ondrej Brejla
    Hi all! Today we would like to introduce you some of our new experimental hints for NetBeans 7.2. They are called: Unused Use Statement and Immutable Variables. Unused Use Statement This hint is quite simple. It highlights (underlines) your use statements, which are not used. Typical use case is after some refactoring, when you forgot to remove some obsolete use statements. This hint warns you on them and allows you to remove them easily. Just click on the hint bulb in the gutter and select Remove Unused Use Statement. And of course, it works in multiple use statements combined too. Immutable Variables The next one is the hint which checks too many assignments into a variable. And why? That's simple. Mostly you should use just one assignment into one variable. But sometimes you are lazy and you do something like: But it's quite wrong, because what you really do is: And that's exactly the case, when our new hint warns you, that Too many assignments (2) into variable $foo occured. Nothing more. Yes, we know that there are some cases, where could be more assignments and no warning should occur, e.g.: Because maybe one likes longer increment syntax more than the short one. So we tried to handle these cases to don't bother you if it's not a need. Note: We are almost sure that this hint doesn't cover all your use cases, because there are a lot of them. So if you find something strange, write it into our bugzilla so we can handle it better for you. Thanks for your patience! And the last thing is, that you can set the number of allowed assignments in Tools -> Options -> Editor -> Hints -> PHP: Immutable Variables. Note: This hint works just for a common variables, not for fields. We have an enhancement request for that and it should be implemented in next version of NetBeans (probably 7.3). And that's all for today and as usual, please test it and if you find something strange, don't hesitate to file a new issue (product php, component Editor). Thanks.

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  • Add-ons for Firefox - Java Plugin has been blocked JRE versions below 1.6.0_31 or between 1.7.0 and 1.7.0_2

    - by user702295
    As Java 1.6u31 is not certified for use with EBS or Demantra, you may notice issues in relation to the Java plug-in.  Demantra Development is currently working to certify Java 1.6u31.  They are recommending that you upgrade to that version. EBS customers, should not be installing 1.6u31 as it is not certified.  If you do upgrade your browser, you will either need to downgrade to a lower release of Firefox or find a way of allowing Firefox to use the older version of the Java Plug-in.

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  • Pragmas and exceptions

    - by Darryl Gove
    The compiler pragmas: #pragma no_side_effect(routinename) #pragma does_not_write_global_data(routinename) #pragma does_not_read_global_data(routinename) are used to tell the compiler more about the routine being called, and enable it to do a better job of optimising around the routine. If a routine does not read global data, then global data does not need to be stored to memory before the call to the routine. If the routine does not write global data, then global data does not need to be reloaded after the call. The no side effect directive indicates that the routine does no I/O, does not read or write global data, and the result only depends on the input. However, these pragmas should not be used on routines that throw exceptions. The following example indicates the problem: #include <iostream extern "C" { int exceptional(int); #pragma no_side_effect(exceptional) } int exceptional(int a) { if (a==7) { throw 7; } else { return a+1; } } int a; int c=0; class myclass { public: int routine(); }; int myclass::routine() { for(a=0; a<1000; a++) { c=exceptional(c); } return 0; } int main() { myclass f; try { f.routine(); } catch(...) { std::cout << "Something happened" << a << c << std::endl; } } The routine "exceptional" is declared as having no side effects, however it can throw an exception. The no side effects directive enables the compiler to avoid storing global data back to memory, and retrieving it after the function call, so the loop containing the call to exceptional is quite tight: $ CC -O -S test.cpp ... .L77000061: /* 0x0014 38 */ call exceptional ! params = %o0 ! Result = %o0 /* 0x0018 36 */ add %i1,1,%i1 /* 0x001c */ cmp %i1,999 /* 0x0020 */ ble,pt %icc,.L77000061 /* 0x0024 */ nop However, when the program is run the result is incorrect: $ CC -O t.cpp $ ./a.out Something happend00 If the code had worked correctly, the output would have been "Something happened77" - the exception occurs on the seventh iteration. Yet, the current code produces a message that uses the original values for the variables 'a' and 'c'. The problem is that the exception handler reads global data, and due to the no side effects directive the compiler has not updated the global data before the function call. So these pragmas should not be used on routines that have the potential to throw exceptions.

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  • Skynet Big Data Demo Using Hexbug Spider Robot, Raspberry Pi, and Java SE Embedded (Part 3)

    - by hinkmond
    In Part 2, I described what connections you need to make for this demo using a Hexbug Spider Robot, a Raspberry Pi, and Java SE Embedded for programming. Here are some photos of me doing the soldering. Software engineers should not be afraid of a little soldering work. It's all good. See: Skynet Big Data Demo (Part 2) One thing to watch out for when you open the remote is that there may be some glue covering the contact points. Make sure to use an Exacto knife or small screwdriver to scrape away any glue or non-conductive material covering each place where you need to solder. And after you are done with your soldering and you gave the solder enough time to cool, make sure all your connections are marked so that you know which wire goes where. Give each wire a very light tug to make sure it is soldered correctly and is making good contact. There are lots of videos on the Web to help you if this is your first time soldering. Check out Laday Ada's (from adafruit.com) links on how to solder if you need some additional help: http://www.ladyada.net/learn/soldering/thm.html If everything looks good, zip everything back up and meet back here for how to connect these wires to your Raspberry Pi. That will be it for the hardware part of this project. See, that wasn't so bad. Hinkmond

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  • ZFS for Database Log Files

    - by user12620111
    I've been troubled by drop outs in CPU usage in my application server, characterized by the CPUs suddenly going from close to 90% CPU busy to almost completely CPU idle for a few seconds. Here is an example of a drop out as shown by a snippet of vmstat data taken while the application server is under a heavy workload. # vmstat 1  kthr      memory            page            disk          faults      cpu  r b w   swap  free  re  mf pi po fr de sr s3 s4 s5 s6   in   sy   cs us sy id  1 0 0 130160176 116381952 0 16 0 0 0 0  0  0  0  0  0 207377 117715 203884 70 21 9  12 0 0 130160160 116381936 0 25 0 0 0 0 0  0  0  0  0 200413 117162 197250 70 20 9  11 0 0 130160176 116381920 0 16 0 0 0 0 0  0  1  0  0 203150 119365 200249 72 21 7  8 0 0 130160176 116377808 0 19 0 0 0 0  0  0  0  0  0 169826 96144 165194 56 17 27  0 0 0 130160176 116377800 0 16 0 0 0 0  0  0  0  0  1 10245 9376 9164 2  1 97  0 0 0 130160176 116377792 0 16 0 0 0 0  0  0  0  0  2 15742 12401 14784 4 1 95  0 0 0 130160176 116377776 2 16 0 0 0 0  0  0  1  0  0 19972 17703 19612 6 2 92  14 0 0 130160176 116377696 0 16 0 0 0 0 0  0  0  0  0 202794 116793 199807 71 21 8  9 0 0 130160160 116373584 0 30 0 0 0 0  0  0 18  0  0 203123 117857 198825 69 20 11 This behavior occurred consistently while the application server was processing synthetic transactions: HTTP requests from JMeter running on an external machine. I explored many theories trying to explain the drop outs, including: Unexpected JMeter behavior Network contention Java Garbage Collection Application Server thread pool problems Connection pool problems Database transaction processing Database I/O contention Graphing the CPU %idle led to a breakthrough: Several of the drop outs were 30 seconds apart. With that insight, I went digging through the data again and looking for other outliers that were 30 seconds apart. In the database server statistics, I found spikes in the iostat "asvc_t" (average response time of disk transactions, in milliseconds) for the disk drive that was being used for the database log files. Here is an example:                     extended device statistics     r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 2053.6    0.0 8234.3  0.0  0.2    0.0    0.1   0  24 c3t60080E5...F4F6d0s0     0.0 2162.2    0.0 8652.8  0.0  0.3    0.0    0.1   0  28 c3t60080E5...F4F6d0s0     0.0 1102.5    0.0 10012.8  0.0  4.5    0.0    4.1   0  69 c3t60080E5...F4F6d0s0     0.0   74.0    0.0 7920.6  0.0 10.0    0.0  135.1   0 100 c3t60080E5...F4F6d0s0     0.0  568.7    0.0 6674.0  0.0  6.4    0.0   11.2   0  90 c3t60080E5...F4F6d0s0     0.0 1358.0    0.0 5456.0  0.0  0.6    0.0    0.4   0  55 c3t60080E5...F4F6d0s0     0.0 1314.3    0.0 5285.2  0.0  0.7    0.0    0.5   0  70 c3t60080E5...F4F6d0s0 Here is a little more information about my database configuration: The database and application server were running on two different SPARC servers. Storage for the database was on a storage array connected via 8 gigabit Fibre Channel Data storage and log file were on different physical disk drives Reliable low latency I/O is provided by battery backed NVRAM Highly available: Two Fibre Channel links accessed via MPxIO Two Mirrored cache controllers The log file physical disks were mirrored in the storage device Database log files on a ZFS Filesystem with cutting-edge technologies, such as copy-on-write and end-to-end checksumming Why would I be getting service time spikes in my high-end storage? First, I wanted to verify that the database log disk service time spikes aligned with the application server CPU drop outs, and they did: At first, I guessed that the disk service time spikes might be related to flushing the write through cache on the storage device, but I was unable to validate that theory. After searching the WWW for a while, I decided to try using a separate log device: # zpool add ZFS-db-41 log c3t60080E500017D55C000015C150A9F8A7d0 The ZFS log device is configured in a similar manner as described above: two physical disks mirrored in the storage array. This change to the database storage configuration eliminated the application server CPU drop outs: Here is the zpool configuration: # zpool status ZFS-db-41   pool: ZFS-db-41  state: ONLINE  scan: none requested config:         NAME                                     STATE         ZFS-db-41                                ONLINE           c3t60080E5...F4F6d0  ONLINE         logs           c3t60080E5...F8A7d0  ONLINE Now, the I/O spikes look like this:                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1053.5    0.0 4234.1  0.0  0.8    0.0    0.7   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1131.8    0.0 4555.3  0.0  0.8    0.0    0.7   0  76 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1167.6    0.0 4682.2  0.0  0.7    0.0    0.6   0  74 c3t60080E5...F8A7d0s0     0.0  162.2    0.0 19153.9  0.0  0.7    0.0    4.2   0  12 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1247.2    0.0 4992.6  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0     0.0   41.0    0.0   70.0  0.0  0.1    0.0    1.6   0   2 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1241.3    0.0 4989.3  0.0  0.8    0.0    0.6   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1193.2    0.0 4772.9  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0 We can see the steady flow of 4k writes to the ZIL device from O_SYNC database log file writes. The spikes are from flushing the transaction group. Like almost all problems that I run into, once I thoroughly understand the problem, I find that other people have documented similar experiences. Thanks to all of you who have documented alternative approaches. Saved for another day: now that the problem is obvious, I should try "zfs:zfs_immediate_write_sz" as recommended in the ZFS Evil Tuning Guide. References: The ZFS Intent Log Solaris ZFS, Synchronous Writes and the ZIL Explained ZFS Evil Tuning Guide: Cache Flushes ZFS Evil Tuning Guide: Tuning ZFS for Database Performance

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  • Integrating a Progress Bar into a Wizard

    - by Geertjan
    Normally, when you create a wizard, as described here, and you have your own iterator, you'll have a class signature like this: public final class MyWizardWizardIterator implements WizardDescriptor.InstantiatingIterator<WizardDescriptor> { Let's now imagine that you've got some kind of long running process your wizard needs to perform. Maybe the wizard needs to connect to something, which could take some time. Start by adding a new dependency on the Progress API, which gives you the classes that access the NetBeans Platform's progress functionality. Now all we need to do is change the class signature very slightly: public final class MyWizardWizardIterator implements WizardDescriptor.ProgressInstantiatingIterator<WizardDescriptor> { Take a look at the part of the signature above that is highlighted. I.e., use WizardDescriptor.ProgressInstantiatingIterator instead of WizardDescriptor.InstantiatingIterator. Now you will need to implement a new instantiate method, one that receives a ProgressHandle. The other instantiate method, i.e., the one that already existed, should never be accessed anymore, and so you can add an assert to that effect: @Override public Set<?> instantiate() throws IOException {     throw new AssertionError("instantiate(ProgressHandle) " //NOI18N             + "should have been called"); //NOI18N } @Override public Set instantiate(ProgressHandle ph) throws IOException {     return Collections.emptySet(); } OK. Let's now add some code to make our progress bar work: @Override public Set instantiate(ProgressHandle ph) throws IOException {     ph.start();     ph.progress("Processing...");     try {         //Simulate some long process:         Thread.sleep(2500);     } catch (InterruptedException ex) {         Exceptions.printStackTrace(ex);     }     ph.finish();     return Collections.emptySet(); } And, maybe even more impressive, you can also do this: @Override public Set instantiate(ProgressHandle ph) throws IOException {     ph.start(1000);     ph.progress("Processing...");     try {         //Simulate some long process:         ph.progress("1/4 complete...", 250);         Thread.sleep(2500);         ph.progress("1/2 complete...", 500);         Thread.sleep(5000);         ph.progress("3/4 complete...", 750);         Thread.sleep(7500);         ph.progress("Complete...", 1000);         Thread.sleep(1000);     } catch (InterruptedException ex) {         Exceptions.printStackTrace(ex);     }     ph.finish();     return Collections.emptySet(); } The screenshots above show you what you should see when the Finish button is clicked in each case.

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  • Dynamic Layout in BI Publisher

    - by Manoj Madhusoodanan
    This blog tells about how to set dynamic layout of a BI Publisher report.Lets take a simple business scenario.If user wants to view the output in either PDF or EXCEL.Two ways we can achieve this.  1) If the output type is not a program parameter the we can choose the layout type at the time of submission in the SRS window. 2) If the report output type is a parameter we need to choose the layout at the time of submission of the request using FND_REQUEST.ADD_LAYOUT. Here I am discussing the second approach.Following components I have created for this scenario. 1) XXCUST - Sample Report : This program actually generates the XML data. It is linked with a RTF template.Note: At the time of creating this program you can uncheck Use in SRS to prevent users from submitting from SRS window.Because we are submitting this program from another concurrent program.Also is not required to add it to request group.2) XXCUST - Report Wrapper : This program calls XXCUST - Sample Report and exposed to user through SRS.3) SAMPLE_REPORT_WRAPPER : Executable which calls xxcust_sample_rep.main4) SAMPLE_REPORT_EXE : Executable which calls xxcust_sample_rep.report_main5) XXCUST - Sample Report Data Definition : Data Definition linked to XXCUST - Sample Report6) XXCUST - Sample Report Template : Template linked to XXCUST - Sample Report Data Definition 7) Package 8) RTF

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  • JCP 2.9 & Transparency Spec Lead Call material is available

    - by Heather VanCura
    The JCP 2.9 & Transparency Spec Lead Call materials and recording from 9 November are now available on the JCP.org multimedia page.  Learn about changes introduced with JCP 2.9, effective Tuesday, 13 November, and a review of the JCP.Next reform efforts. Plus, a progress report on JCP 2.8, specifically around the areas of transparency, participation and agility, as well as suggestions for how you can get more involved in supporting these efforts with the current JCP program JSRs. 

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  • Smart Meter Management on the NetBeans Platform

    - by Geertjan
    Netinium® NCC is the operator console for the Netinium® AMM+ platform, a Head End system for multi-vendor smart meter and smart grid infrastructures. The role based NCC provides a uniform operations environment for grid operators and utilities to securely manage millions of smart meters, in-home displays and other smart devices using different types of communication networks such as IP, PLC, GPRS, CDMA and BPL. Based on the NetBeans Platform, the NCC offers the flexibility to easily extend the GUI with new functionality when new devices are added to the system.  For more information visit http://www.netinium.com.

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  • Transparency call for Spec Leads and EC materials posted

    - by heathervc
    The materials and recording from the February 2012 call for JCP program Spec Leads is now available.  This call features Martijn Verburg, alternate EC representative for the London Java Community and includes information on the Adopt-a-JSR program.  The materials and audio recording of the  "Leveraging the Community" call can be found on the multimedia page of jcp.org .  The EC meeting summaries from February and March 2012 have also been posted.  Following the April 2012 EC Meeting this morning (minutes and materials will be posted soon), there are now four EC Members that have lost their voting privileges--AT&T, SK Telecom, Samsung and Twitter.  In order to regain their privileges, these EC Members must attend two EC meeting in a row, as detailed in the EC Standing Rules.

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  • Online ALTER TABLE in MySQL 5.6

    - by Marko Mäkelä
    This is the low-level view of data dictionary language (DDL) operations in the InnoDB storage engine in MySQL 5.6. John Russell gave a more high-level view in his blog post April 2012 Labs Release – Online DDL Improvements. MySQL before the InnoDB Plugin Traditionally, the MySQL storage engine interface has taken a minimalistic approach to data definition language. The only natively supported operations were CREATE TABLE, DROP TABLE and RENAME TABLE. Consider the following example: CREATE TABLE t(a INT); INSERT INTO t VALUES (1),(2),(3); CREATE INDEX a ON t(a); DROP TABLE t; The CREATE INDEX statement would be executed roughly as follows: CREATE TABLE temp(a INT, INDEX(a)); INSERT INTO temp SELECT * FROM t; RENAME TABLE t TO temp2; RENAME TABLE temp TO t; DROP TABLE temp2; You could imagine that the database could crash when copying all rows from the original table to the new one. For example, it could run out of file space. Then, on restart, InnoDB would roll back the huge INSERT transaction. To fix things a little, a hack was added to ha_innobase::write_row for committing the transaction every 10,000 rows. Still, it was frustrating that even a simple DROP INDEX would make the table unavailable for modifications for a long time. Fast Index Creation in the InnoDB Plugin of MySQL 5.1 MySQL 5.1 introduced a new interface for CREATE INDEX and DROP INDEX. The old table-copying approach can still be forced by SET old_alter_table=0. This interface is used in MySQL 5.5 and in the InnoDB Plugin for MySQL 5.1. Apart from the ability to do a quick DROP INDEX, the main advantage is that InnoDB will execute a merge-sort algorithm before inserting the index records into each index that is being created. This should speed up the insert into the secondary index B-trees and potentially result in a better B-tree fill factor. The 5.1 ALTER TABLE interface was not perfect. For example, DROP FOREIGN KEY still invoked the table copy. Renaming columns could conflict with InnoDB foreign key constraints. Combining ADD KEY and DROP KEY in ALTER TABLE was problematic and not atomic inside the storage engine. The ALTER TABLE interface in MySQL 5.6 The ALTER TABLE storage engine interface was completely rewritten in MySQL 5.6. Instead of introducing a method call for every conceivable operation, MySQL 5.6 introduced a handful of methods, and data structures that keep track of the requested changes. In MySQL 5.6, online ALTER TABLE operation can be requested by specifying LOCK=NONE. Also LOCK=SHARED and LOCK=EXCLUSIVE are available. The old-style table copying can be requested by ALGORITHM=COPY. That one will require at least LOCK=SHARED. From the InnoDB point of view, anything that is possible with LOCK=EXCLUSIVE is also possible with LOCK=SHARED. Most ALGORITHM=INPLACE operations inside InnoDB can be executed online (LOCK=NONE). InnoDB will always require an exclusive table lock in two phases of the operation. The execution phases are tied to a number of methods: handler::check_if_supported_inplace_alter Checks if the storage engine can perform all requested operations, and if so, what kind of locking is needed. handler::prepare_inplace_alter_table InnoDB uses this method to set up the data dictionary cache for upcoming CREATE INDEX operation. We need stubs for the new indexes, so that we can keep track of changes to the table during online index creation. Also, crash recovery would drop any indexes that were incomplete at the time of the crash. handler::inplace_alter_table In InnoDB, this method is used for creating secondary indexes or for rebuilding the table. This is the ‘main’ phase that can be executed online (with concurrent writes to the table). handler::commit_inplace_alter_table This is where the operation is committed or rolled back. Here, InnoDB would drop any indexes, rename any columns, drop or add foreign keys, and finalize a table rebuild or index creation. It would also discard any logs that were set up for online index creation or table rebuild. The prepare and commit phases require an exclusive lock, blocking all access to the table. If MySQL times out while upgrading the table meta-data lock for the commit phase, it will roll back the ALTER TABLE operation. In MySQL 5.6, data definition language operations are still not fully atomic, because the data dictionary is split. Part of it is inside InnoDB data dictionary tables. Part of the information is only available in the *.frm file, which is not covered by any crash recovery log. But, there is a single commit phase inside the storage engine. Online Secondary Index Creation It may occur that an index needs to be created on a new column to speed up queries. But, it may be unacceptable to block modifications on the table while creating the index. It turns out that it is conceptually not so hard to support online index creation. All we need is some more execution phases: Set up a stub for the index, for logging changes. Scan the table for index records. Sort the index records. Bulk load the index records. Apply the logged changes. Replace the stub with the actual index. Threads that modify the table will log the operations to the logs of each index that is being created. Errors, such as log overflow or uniqueness violations, will only be flagged by the ALTER TABLE thread. The log is conceptually similar to the InnoDB change buffer. The bulk load of index records will bypass record locking. We still generate redo log for writing the index pages. It would suffice to log page allocations only, and to flush the index pages from the buffer pool to the file system upon completion. Native ALTER TABLE Starting with MySQL 5.6, InnoDB supports most ALTER TABLE operations natively. The notable exceptions are changes to the column type, ADD FOREIGN KEY except when foreign_key_checks=0, and changes to tables that contain FULLTEXT indexes. The keyword ALGORITHM=INPLACE is somewhat misleading, because certain operations cannot be performed in-place. For example, changing the ROW_FORMAT of a table requires a rebuild. Online operation (LOCK=NONE) is not allowed in the following cases: when adding an AUTO_INCREMENT column, when the table contains FULLTEXT indexes or a hidden FTS_DOC_ID column, or when there are FOREIGN KEY constraints referring to the table, with ON…CASCADE or ON…SET NULL option. The FOREIGN KEY limitations are needed, because MySQL does not acquire meta-data locks on the child or parent tables when executing SQL statements. Theoretically, InnoDB could support operations like ADD COLUMN and DROP COLUMN in-place, by lazily converting the table to a newer format. This would require that the data dictionary keep multiple versions of the table definition. For simplicity, we will copy the entire table, even for DROP COLUMN. The bulk copying of the table will bypass record locking and undo logging. For facilitating online operation, a temporary log will be associated with the clustered index of table. Threads that modify the table will also write the changes to the log. When altering the table, we skip all records that have been marked for deletion. In this way, we can simply discard any undo log records that were not yet purged from the original table. Off-page columns, or BLOBs, are an important consideration. We suspend the purge of delete-marked records if it would free any off-page columns from the old table. This is because the BLOBs can be needed when applying changes from the log. We have special logging for handling the ROLLBACK of an INSERT that inserted new off-page columns. This is because the columns will be freed at rollback.

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  • Stuff I learned at Innovate 2011

    - by David Dorf
    After returning from the NRF Innovate 2011 conference, I picked up few nuggets I thought I'd share here.  These thoughts are a bit random, but I hope they're useful nonetheless.Kevin Kelly opened the conference with six verbs that represent the future.  They were Screening, Interacting, Sharing, Accessing, Flowing, and Generating.  It struck me that these are all ways in which we merge the digital and physical worlds.  The internet of things continues to gain momentum.Some buzzwords:  deal economy, subscription commerce, discovery (instead of search), curationThat last one, curation, came up over and over.  Retailers, especially those in fashion, are finding value in helping their customers organize and present their own collections.  Social media has made sharing such collections easy, and mobile lets them take those ideas into the stores.  Mannequins are becoming less relevant.I heard from both HauteLook and Gilt Groupe (flash sale retailers) that a large percentage of their visits come from mobile devices, and most of those are iOS devices.  I find it interesting that even though Android has passed iPhone in units shipped (and will eventually pass iOS as a whole), its still the Apple crowd that leads the way.RadioShack mentioned their Holiday Heroes campaigned was very successful.  They asked their Foursquare users to check-in at a gym, coffee shop, and transportation hub as part of being a hero.  For this feat, customers were awarded a special badge that was worth 20% off at their next store visit. They claim a 3.5x increase in ticket size vs. regular check-in customers, and a 5x increase vs those that don't check-in at all.I also learned of RadioShack's #28 campaign, which is apparently one of the largest Twitter trends ever.  Their partnership with LIVESTRONG has gotten them followers, impressions, and credit for supporting the fight against cancer.The guys at Invodo showed the importance of video to e-commerce.  They gave compelling examples of how video can show customers the value of products better than just words.The highlight of the show was Guy Kawasaki's talk on innovation, which was not only informative but also peppered with humor and personality.  Back in the early days of the internet boom, Guy turned down the CEO position at Yahoo! because the commute was too long.  By his calculation, that was a $2B mistake.There are other good accounts of the conference at the NRF Blog.

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  • Java EE 8 update

    - by delabassee
    Planning for Java EE 8 is now well underway. As you know, a few weeks ago, we conducted a three part Java EE 8 Community Survey (you can find the final summary here). The data gathered have been very influential for the next steps. You can now expect over the coming weeks and months to see updates on the various specifications that compose the Java EE platform. Some Specification Leads are busy gathering additional feedback regarding what they should focus their efforts on (e.g. CDI 2 survey). Other Specification Leads have already publicly exposed what they think should be one of the focus for the evolution of the specification they lead.  For example, adding Server-Senet Events (SSE) support in JAX-RS is being discussed here and adding MVC support is being discussed here. Please remember that the fact we are now discussing any feature does not insure that it will be included in the proposal, nor in any particular update to Java EE. We can expect additional enhancements, changes and evolutions as we get closer to the finalisation of the different specifications... and there is still a long way to go with these specification proposals! Linda DeMichiel, Java EE Co-Specification Lead, has recently posted a draft proposal for the Java EE 8 Platform specification. Linda's goal is to recruit people and companies supporting this proposal before submitting it to the JCP.  This draft proposal is very interesting reading as it contains relevant information on the plans for Java EE 8 such as : The themes: Support for the latest web standards (eg. HTTP 2.0)  Continue to work on ease of development Improve the infrastructure for cloud support Alignment with Java SE 8 New JSRs to be added to the platform: J-Cache Java API for JSON Binding Java Configuration Plans for the Web Profile Plans on technologies to prune in Java EE 8, ... So if you haven't done it yet, I really encourage you to read the Java EE 8 draft proposal! Our goal for the Java EE 8 specification is for it to be finalized in the second half of 2016. It is important to note that we are in the early days of Java EE 8 and at this stage everything (themes, content, timing, etc.) is preliminary. Everything still needs to be discussed, challenged and agreed within the different Java Community Process (JCP) Experts Groups (EGs). Some EGs that still need to be formed! It could also means that the roadmap will have to be adjusted to follow the progress being made in the different EGs. This is also a good occasion to remind you that participation within those upcoming JCP Experts Groups is encouraged. Contributing in an EG is an effective lever to influence what Java EE 8 will become! Finally, as things get more concrete, we will share details on how to engage in the different Java EE 8 related Adopt-a-JSR initiatives, another way to contribute. You can also read other posts related to Java EE 8, here at The Aquarium blog. Just look for articles with the 'javaee8' tag.

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  • ANTLRWorks 2: Early Access Preview 10

    - by Geertjan
    I took a quick look at how the ANTLRWorks 2 project is getting on... and discovered that today, March 23, the new early access preview 10 has been released: http://www.antlr.org/wiki/display/ANTLR4/1.+Overview Downloaded it immediately and was impressed when browsing through the Java.g file that I also found on the Antlr site: (Click to enlarge the image above.) On the page above, the following enhancements are listed: Add tooltips for rule references Finally fixed the navigator update bug Major improvements to code completion Fix legacy mode Many performance and stability updates I've blogged before about how the developers on the above project consider their code completion to be "scary fast". Some discussions have taken place about how code developed by the ANTLRWorks team could be contributed to the NetBeans project, since NetBeans IDE and ANTLRWorks 2 are both based on the NetBeans Platform.

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  • Capgemini report - Business Cloud: The State of Play Shifts Rapidly

    - by Javier Puerta
    Capgemini has published a recent survey on the state of play of cloud adoption. The report indicates "clear evidence that the business, rather than purely IT, is becoming involved in driving Cloud strategy, and pioneering its use for ‘edge’ growth initiatives."  Ron Tolido, Senior Vice President and Chief Technology Officer of Applications Continental Europe at Capgemini, was one of the keynote speakers at our Exadata & Manageability Partner Community event in Istanbul in March. He is one of the drivers of this survey. Read his article "3 Key Cloud Insights for 2013". You an download the full report here:  "Business Cloud: The State of Play Shifts Rapidly - Fresh Insights into Cloud Adoption Trends"

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  • Performance triage

    - by Dave
    Folks often ask me how to approach a suspected performance issue. My personal strategy is informed by the fact that I work on concurrency issues. (When you have a hammer everything looks like a nail, but I'll try to keep this general). A good starting point is to ask yourself if the observed performance matches your expectations. Expectations might be derived from known system performance limits, prototypes, and other software or environments that are comparable to your particular system-under-test. Some simple comparisons and microbenchmarks can be useful at this stage. It's also useful to write some very simple programs to validate some of the reported or expected system limits. Can that disk controller really tolerate and sustain 500 reads per second? To reduce the number of confounding factors it's better to try to answer that question with a very simple targeted program. And finally, nothing beats having familiarity with the technologies that underlying your particular layer. On the topic of confounding factors, as our technology stacks become deeper and less transparent, we often find our own technology working against us in some unexpected way to choke performance rather than simply running into some fundamental system limit. A good example is the warm-up time needed by just-in-time compilers in Java Virtual Machines. I won't delve too far into that particular hole except to say that it's rare to find good benchmarks and methodology for java code. Another example is power management on x86. Power management is great, but it can take a while for the CPUs to throttle up from low(er) frequencies to full throttle. And while I love "turbo" mode, it makes benchmarking applications with multiple threads a chore as you have to remember to turn it off and then back on otherwise short single-threaded runs may look abnormally fast compared to runs with higher thread counts. In general for performance characterization I disable turbo mode and fix the power governor at "performance" state. Another source of complexity is the scheduler, which I've discussed in prior blog entries. Lets say I have a running application and I want to better understand its behavior and performance. We'll presume it's warmed up, is under load, and is an execution mode representative of what we think the norm would be. It should be in steady-state, if a steady-state mode even exists. On Solaris the very first thing I'll do is take a set of "pstack" samples. Pstack briefly stops the process and walks each of the stacks, reporting symbolic information (if available) for each frame. For Java, pstack has been augmented to understand java frames, and even report inlining. A few pstack samples can provide powerful insight into what's actually going on inside the program. You'll be able to see calling patterns, which threads are blocked on what system calls or synchronization constructs, memory allocation, etc. If your code is CPU-bound then you'll get a good sense where the cycles are being spent. (I should caution that normal C/C++ inlining can diffuse an otherwise "hot" method into other methods. This is a rare instance where pstack sampling might not immediately point to the key problem). At this point you'll need to reconcile what you're seeing with pstack and your mental model of what you think the program should be doing. They're often rather different. And generally if there's a key performance issue, you'll spot it with a moderate number of samples. I'll also use OS-level observability tools to lock for the existence of bottlenecks where threads contend for locks; other situations where threads are blocked; and the distribution of threads over the system. On Solaris some good tools are mpstat and too a lesser degree, vmstat. Try running "mpstat -a 5" in one window while the application program runs concurrently. One key measure is the voluntary context switch rate "vctx" or "csw" which reflects threads descheduling themselves. It's also good to look at the user; system; and idle CPU percentages. This can give a broad but useful understanding if your threads are mostly parked or mostly running. For instance if your program makes heavy use of malloc/free, then it might be the case you're contending on the central malloc lock in the default allocator. In that case you'd see malloc calling lock in the stack traces, observe a high csw/vctx rate as threads block for the malloc lock, and your "usr" time would be less than expected. Solaris dtrace is a wonderful and invaluable performance tool as well, but in a sense you have to frame and articulate a meaningful and specific question to get a useful answer, so I tend not to use it for first-order screening of problems. It's also most effective for OS and software-level performance issues as opposed to HW-level issues. For that reason I recommend mpstat & pstack as my the 1st step in performance triage. If some other OS-level issue is evident then it's good to switch to dtrace to drill more deeply into the problem. Only after I've ruled out OS-level issues do I switch to using hardware performance counters to look for architectural impediments.

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  • See you at OSCON!

    - by darcy
    In just under a month, I'll be speaking at the OSCON Java conference about various OpenJDK and JDK 7 matters: JDK 7 in a Nutshell The State of JDK and OpenJDK More detailed talks on those topics include Stuart's session on Coin in Action: Using New Java SE 7 Language Features in Real Code and Dalibor's OpenJDK – When And How To Contribute To The Java SE Reference Implementation. See you in Portland!

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  • Managing Custom Series

    - by user702295
    Custom series that have been added should be done with client Defined Prefix, ex. ACME Final Forecast, so they are can be identified as non-standard series.  With that said, it is not always done, so beginning in v7.3.0 there is a new column called Application_Id in the Computed_Fields table.  This is the table that stores the Series information.  Standard Series will have have a prefix similar to COMPUTED_FIELD, while a custom series will have an Application_Id value similar to 9041128B99FC454DB8E8A289E5E8F0C5. So a SQL that will return the list of custom series in your database might look something like this: select computed_title Series_Name, application_id from computed_fields where application_id not like '%COMPUTED_FIELD%' order by 1;

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