Search Results

Search found 27001 results on 1081 pages for 'oracle applications strat'.

Page 584/1081 | < Previous Page | 580 581 582 583 584 585 586 587 588 589 590 591  | Next Page >

• ct.sym steals the ASM class

  - by Geertjan

  Some mild consternation on the Twittersphere yesterday. Marcus Lagergren not being able to find the ASM classes in JDK 8 in NetBeans IDE: And there's no such problem in Eclipse (and apparently in IntelliJ IDEA). Help, does NetBeans (despite being incredibly awesome) suck, after all? The truth of the matter is that there's something called "ct.sym" in the JDK. When javac is compiling code, it doesn't link against rt.jar. Instead, it uses a special symbol file lib/ct.sym with class stubs. Internal JDK classes are not put in that symbol file, since those are internal classes. You shouldn't want to use them, at all. However, what if you're Marcus Lagergren who DOES need these classes? I.e., he's working on the internal JDK classes and hence needs to have access to them. Fair enough that the general Java population can't access those classes, since they're internal implementation classes that could be changed anytime and one wouldn't want all unknown clients of those classes to start breaking once changes are made to the implementation, i.e., this is the rt.jar's internal class protection mechanism. But, again, we're now Marcus Lagergen and not the general Java population. For the solution, read Jan Lahoda, NetBeans Java Editor guru, here: https://netbeans.org/bugzilla/show_bug.cgi?id=186120 In particular, take note of this: AFAIK, the ct.sym is new in JDK6. It contains stubs for all classes that existed in JDK5 (for compatibility with existing programs that would use private JDK classes), but does not contain implementation classes that were introduced in JDK6 (only API classes). This is to prevent application developers to accidentally use JDK's private classes (as such applications would be unportable and may not run on future versions of JDK). Note that this is not really a NB thing - this is the behavior of javac from the JDK. I do not know about any way to disable this except deleting ct.sym or the option mentioned above. Regarding loading the classes: JVM uses two classpath's: classpath and bootclasspath. rt.jar is on the bootclasspath and has precedence over anything on the "custom" classpath, which is used by the application. The usual way to override classes on bootclasspath is to start the JVM with "-Xbootclasspath/p:" option, which prepends the given jars (and presumably also directories) to bootclasspath. Hence, let's take the first option, the simpler one, and simply delete the "ct.sym" file. Again, only because we need to work with those internal classes as developers of the JDK, not because we want to hack our way around "ct.sym", which would mean you'd not have portable code at the end of the day. Go to the JDK 8 lib folder and you'll find the file: Delete it. Start NetBeans IDE again, either on JDK 7 or JDK 8, doesn't make a difference for these purposes, create a new Java application (or use an existing one), make sure you have set the JDK above as the JDK of the application, and hey presto: The above obviously assumes you have a build of JDK 8 that actually includes the ASM package. And below you can see that not only are the classes found but my build succeeded, even though I'm using internal JDK classes. The yellow markings in the sidebar mean that the classes are imported but not used in the code, where normally, if I hadn't removed "ct.sym", I would have seen red error marking instead, and the code wouldn't have compiled. Note: I've tried setting "-XDignore.symbol.file" in "netbeans.conf" and in other places, but so far haven't got that to work. Simply deleting the "ct.sym" file (or back it up somewhere and put it back when needed) is quite clearly the most straightforward solution. Ultimately, if you want to be able to use those internal classes while still having portable code, do you know what you need to do? You need to create a JDK bug report stating that you need an internal class to be added to "ct.sym". Probably you'll get a motivation back stating WHY that internal class isn't supposed to be used externally. There must be a reason why those classes aren't available for external usage, otherwise they would have been added to "ct.sym". So, now the only remaining question is why the Eclipse compiler doesn't hide the internal JDK classes. Apparently the Eclipse compiler ignores the "ct.sym" file. In other words, at the end of the day, far from being a bug in NetBeans... we have now found a (pretty enormous, I reckon) bug in Eclipse. The Eclipse compiler does not protect you from using internal JDK classes and the code that you create in Eclipse may not work with future releases of the JDK, since the JDK team is simply going to be changing those classes that are not found in the "ct.sym" file while assuming (correctly, thanks to the presence of "ct.sym" mechanism) that no code in the world, other than JDK code, is tied to those classes.

  Read the article

• Creating an SMF service for mercurial web server

  - by Chris W Beal

  I'm working on a project at the moment, which has a number of contributers. We're managing the project gate (which is stand alone) with mercurial. We want to have an easy way of seeing the changelog, so we can show management what is going on.  Luckily mercurial provides a basic web server which allows you to see the changes, and drill in to change sets. This can be run as a daemon, but as it was running on our build server, every time it was rebooted, someone needed to remember to start the process again. This is of course a classic usage of SMF. Now I'm not an experienced person at writing SMF services, so it took me 1/2 an hour or so to figure it out the first time. But going forward I should know what I'm doing a bit better. I did reference this doc extensively. Taking a step back, the command to start the mercurial web server is $ hg serve -p <port number> -d So we somehow need to get SMF to run that command for us. In the simplest form, SMF services are really made up of two components. The manifest Usually lives in /var/svc/manifest somewhere Can be imported from any location The method Usually live in /lib/svc/method I simply put the script straight in that directory. Not very repeatable, but it worked Can take an argument of start, stop, or refresh Lets start with the manifest. This looks pretty complex, but all it's doing is describing the service name, the dependencies, the start and stop methods, and some properties. The properties can be by instance, that is to say I could have multiple hg serve processes handling different mercurial projects, on different ports simultaneously Here is the manifest I wrote. I stole extensively from the examples in the Documentation. So my manifest looks like this $ cat hg-serve.xml <?xml version="1.0"?> <!DOCTYPE service_bundle SYSTEM "/usr/share/lib/xml/dtd/service_bundle.dtd.1"> <service_bundle type='manifest' name='hg-serve'> <service name='application/network/hg-serve' type='service' version='1'> <dependency name='network' grouping='require_all' restart_on='none' type='service'> <service_fmri value='svc:/milestone/network:default' /> </dependency> <exec_method type='method' name='start' exec='/lib/svc/method/hg-serve %m' timeout_seconds='2' /> <exec_method type='method' name='stop' exec=':kill' timeout_seconds='2'> </exec_method> <instance name='project-gate' enabled='true'> <method_context> <method_credential user='root' group='root' /> </method_context> <property_group name='hg-serve' type='application'> <propval name='path' type='astring' value='/src/project-gate'/> <propval name='port' type='astring' value='9998' /> </property_group> </instance> <stability value='Evolving' /> <template> <common_name> <loctext xml:lang='C'>hg-serve</loctext> </common_name> <documentation> <manpage title='hg' section='1' /> </documentation> </template> </service> </service_bundle> So the only things I had to decide on in this are the service name "application/network/hg-serve" the start and stop methods (more of which later) and the properties. This is the information I need to pass to the start method script. In my case the port I want to start the web server on "9998", and the path to the source gate "/src/project-gate". These can be read in to the start method. So now lets look at the method scripts $ cat /lib/svc/method/hg-serve #!/sbin/sh # # # Copyright (c) 2012, Oracle and/or its affiliates. All rights reserved. # # Standard prolog # . /lib/svc/share/smf_include.sh if [ -z $SMF_FMRI ]; then echo "SMF framework variables are not initialized." exit $SMF_EXIT_ERR fi # # Build the command line flags # # Get the port and directory from the SMF properties port=`svcprop -c -p hg-serve/port $SMF_FMRI` dir=`svcprop -c -p hg-serve/path $SMF_FMRI` echo "$1" case "$1" in 'start') cd $dir /usr/bin/hg serve -d -p $port ;; *) echo "Usage: $0 {start|refresh|stop}" exit 1 ;; esac exit $SMF_EXIT_OK This is all pretty self explanatory, we read the port and directory using svcprop, and use those simply to run a command in the start case. We don't need to implement a stop case, as the manifest says to use "exec=':kill'for the stop method. Now all we need to do is import the manifest and start the service, but first verify the manifest # svccfg verify /path/to/hg-serve.xml If that doesn't give an error try importing it # svccfg import /path/to/hg-serve.xml If like me you originally put the hg-serve.xml file in /var/svc/manifest somewhere you'll get an error and told to restart the import service svccfg: Restarting svc:/system/manifest-import The manifest being imported is from a standard location and should be imported with the command : svcadm restart svc:/system/manifest-import # svcadm restart svc:/system/manifest-import and you're nearly done. You can look at the service using svcs -l # svcs -l hg-serve fmri svc:/application/network/hg-serve:project-gate name hg-serve enabled false state disabled next_state none state_time Thu May 31 16:11:47 2012 logfile /var/svc/log/application-network-hg-serve:project-gate.log restarter svc:/system/svc/restarter:default contract_id 15749 manifest /var/svc/manifest/network/hg/hg-serve.xml dependency require_all/none svc:/milestone/network:default (online) And look at the interesting properties # svcprop hg-serve hg-serve/path astring /src/project-gate hg-serve/port astring 9998 ...stuff deleted.... Then simply enable the service and if every things gone right, you can point your browser at http://server:9998 and get a nice graphical log of project activity. # svcadm enable hg-serve # svcs -l hg-serve fmri svc:/application/network/hg-serve:project-gate name hg-serve enabled true state online next_state none state_time Thu May 31 16:18:11 2012 logfile /var/svc/log/application-network-hg-serve:project-gate.log restarter svc:/system/svc/restarter:default contract_id 15858 manifest /var/svc/manifest/network/hg/hg-serve.xml dependency require_all/none svc:/milestone/network:default (online) None of this is rocket science, but a bit fiddly. Hence I thought I'd blog it. It might just be you see this in google and it clicks with you more than one of the many other blogs or how tos about it. Plus I can always refer back to it myself in 3 weeks, when I want to add another project to the server, and I've forgotten how to do it.

  Read the article

• How to Secure a Data Role by Multiple Business Units

  - by Elie Wazen

  In this post we will see how a Role can be data secured by multiple Business Units (BUs).  Separate Data Roles are generally created for each BU if a corresponding data template generates roles on the basis of the BU dimension. The advantage of creating a policy with a rule that includes multiple BUs is that while mapping these roles in HCM Role Provisioning Rules, fewer number of entires need to be made. This could facilitate maintenance for enterprises with a large number of Business Units. Note: The example below applies as well if the securing entity is Inventory Organization. Let us take for example the case of a user provisioned with the "Accounts Payable Manager - Vision Operations" Data Role in Fusion Applications. This user will be able to access Invoices in Vision Operations but will not be able to see Invoices in Vision Germany. Figure 1. A User with a Data Role restricting them to Data from BU: Vision Operations With the role granted above, this is what the user will see when they attempt to select Business Units while searching for AP Invoices. Figure 2.The List Of Values of Business Units is limited to single one. This is the effect of the Data Role granted to that user as can be seen in Figure 1 In order to create a data role that secures by multiple BUs,  we need to start by creating a condition that groups those Business Units we want to include in that data role. This is accomplished by creating a new condition against the BU View .  That Condition will later be used to create a data policy for our newly created Role.  The BU View is a Database resource and  is accessed from APM as seen in the search below Figure 3.Viewing a Database Resource in APM The next step is create a new condition,  in which we define a sql predicate that includes 2 BUs ( The ids below refer to Vision Operations and Vision Germany).  At this point we have simply created a standalone condition.  We have not used this condition yet, and security is therefore not affected. Figure 4. Custom Role that inherits the Purchase Order Overview Duty We are now ready to create our Data Policy.  in APM, we search for our newly Created Role and Navigate to “Find Global Policies”.  we query the Role we want to secure and navigate to view its global policies. Figure 5. The Job Role we plan on securing We can see that the role was not defined with a Data Policy . So will create one that uses the condition we created earlier.   Figure 6. Creating a New Data Policy In the General Information tab, we have to specify the DB Resource that the Security Policy applies to:  In our case this is the BU View Figure 7. Data Policy Definition - Selection of the DB Resource we will secure by In the Rules Tab, we  make the rule applicable to multiple values of the DB Resource we selected in the previous tab.  This is where we associate the condition we created against the BU view to this data policy by entering the Condition name in the Condition field Figure 8. Data Policy Rule The last step of Defining the Data Policy, consists of  explicitly selecting  the Actions that are goverened by this Data Policy.  In this case for example we select the Actions displayed below in the right pane. Once the record is saved , we are ready to use our newly secured Data Role. Figure 9. Data Policy Actions We can now see a new Data Policy associated with our Role.  Figure 10. Role is now secured by a Data Policy We now Assign that new Role to the User.  Of course this does not have to be done in OIM and can be done using a Provisioning Rule in HCM. Figure 11. Role assigned to the User who previously was granted the Vision Ops secured role. Once that user accesses the Invoices Workarea this is what they see: In the image below the LOV of Business Unit returns the two values defined in our data policy namely: Vision Operations and Vision Germany Figure 12. The List Of Values of Business Units now includes the two we included in our data policy. This is the effect of the data role granted to that user as can be seen in Figure 11

  Read the article

• Das T5-4 TPC-H Ergebnis naeher betrachtet

  - by Stefan Hinker

  Inzwischen haben vermutlich viele das neue TPC-H Ergebnis der SPARC T5-4 gesehen, das am 7. Juni bei der TPC eingereicht wurde.  Die wesentlichen Punkte dieses Benchmarks wurden wie gewohnt bereits von unserer Benchmark-Truppe auf  "BestPerf" zusammengefasst.  Es gibt aber noch einiges mehr, das eine naehere Betrachtung lohnt. Skalierbarkeit Das TPC raet von einem Vergleich von TPC-H Ergebnissen in unterschiedlichen Groessenklassen ab.  Aber auch innerhalb der 3000GB-Klasse ist es interessant: SPARC T4-4 mit 4 CPUs (32 Cores mit 3.0 GHz) liefert 205,792 QphH. SPARC T5-4 mit 4 CPUs (64 Cores mit 3.6 GHz) liefert 409,721 QphH. Das heisst, es fehlen lediglich 1863 QphH oder 0.45% zu 100% Skalierbarkeit, wenn man davon ausgeht, dass die doppelte Anzahl Kerne das doppelte Ergebnis liefern sollte.  Etwas anspruchsvoller, koennte man natuerlich auch einen Faktor von 2.4 erwarten, wenn man die hoehere Taktrate mit beruecksichtigt.  Das wuerde die Latte auf 493901 QphH legen.  Dann waere die SPARC T5-4 bei 83%.  Damit stellt sich die Frage: Was hat hier nicht skaliert?  Vermutlich der Plattenspeicher!  Auch hier lohnt sich eine naehere Betrachtung: Plattenspeicher Im Bericht auf BestPerf und auch im Full Disclosure Report der TPC stehen einige interessante Details zum Plattenspeicher und der Konfiguration.   In der Konfiguration der SPARC T4-4 wurden 12 2540-M2 Arrays verwendet, die jeweils ca. 1.5 GB/s Durchsatz liefert, insgesamt also eta 18 GB/s.  Dabei waren die Arrays offensichtlich mit jeweils 2 Kabeln pro Array direkt an die 24 8GBit FC-Ports des Servers angeschlossen.  Mit den 2x 8GBit Ports pro Array koennte man so ein theoretisches Maximum von 2GB/s erreichen.  Tatsaechlich wurden 1.5GB/s geliefert, was so ziemlich dem realistischen Maximum entsprechen duerfte. Fuer den Lauf mit der SPARC T5-4 wurden doppelt so viele Platten verwendet.  Dafuer wurden die 2540-M2 Arrays mit je einem zusaetzlichen Plattentray erweitert.  Mit dieser Konfiguration wurde dann (laut BestPerf) ein Maximaldurchsatz von 33 GB/s erreicht - nicht ganz das doppelte des SPARC T4-4 Laufs.  Um tatsaechlich den doppelten Durchsatz (36 GB/s) zu liefern, haette jedes der 12 Arrays 3 GB/s ueber seine 4 8GBit Ports liefern muessen.  Im FDR stehen nur 12 dual-port FC HBAs, was die Verwendung der Brocade FC Switches erklaert: Es wurden alle 4 8GBit ports jedes Arrays an die Switches angeschlossen, die die Datenstroeme dann in die 24 16GBit HBA ports des Servers buendelten.  Das theoretische Maximum jedes Storage-Arrays waere nun 4 GB/s.  Wenn man jedoch den Protokoll- und "Realitaets"-Overhead mit einrechnet, sind die tatsaechlich gelieferten 2.75 GB/s gar nicht schlecht.  Mit diesen Zahlen im Hinterkopf ist die Verdopplung des SPARC T4-4 Ergebnisses eine gute Leistung - und gleichzeitig eine gute Erklaerung, warum nicht bis zum 2.4-fachen skaliert wurde. Nebenbei bemerkt: Weder die SPARC T4-4 noch die SPARC T5-4 hatten in der gemessenen Konfiguration irgendwelche Flash-Devices. Mitbewerb Seit die T4 Systeme auf dem Markt sind, bemuehen sich unsere Mitbewerber redlich darum, ueberall den Eindruck zu hinterlassen, die Leistung des SPARC CPU-Kerns waere weiterhin mangelhaft.  Auch scheinen sie ueberzeugt zu sein, dass (ueber)grosse Caches und hohe Taktraten die einzigen Schluessel zu echter Server Performance seien.  Wenn ich mir nun jedoch die oeffentlichen TPC-H Ergebnisse ansehe, sehe ich dies: TPC-H @3000GB, Non-Clustered Systems System QphH SPARC T5-4 3.6 GHz SPARC T5 4/64 – 2048 GB 409,721.8 SPARC T4-4 3.0 GHz SPARC T4 4/32 – 1024 GB 205,792.0 IBM Power 780 4.1 GHz POWER7 8/32 – 1024 GB 192,001.1 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64 – 512 GB 162,601.7 Kurz zusammengefasst: Mit 32 Kernen (mit 3 GHz und 4MB L3 Cache), liefert die SPARC T4-4 mehr QphH@3000GB ab als IBM mit ihrer 32 Kern Power7 (bei 4.1 GHz und 32MB L3 Cache) und auch mehr als HP mit einem 64 Kern Intel Xeon System (2.27 GHz und 24MB L3 Cache).  Ich frage mich, wo genau SPARC hier mangelhaft ist? Nun koennte man natuerlich argumentieren, dass beide Ergebnisse nicht gerade neu sind.  Nun, in Ermangelung neuerer Ergebnisse kann man ja mal ein wenig spekulieren: IBMs aktueller Performance Report listet die o.g. IBM Power 780 mit einem rPerf Wert von 425.5.  Ein passendes Nachfolgesystem mit Power7+ CPUs waere die Power 780+ mit 64 Kernen, verfuegbar mit 3.72 GHz.  Sie wird mit einem rPerf Wert von  690.1 angegeben, also 1.62x mehr.  Wenn man also annimmt, dass Plattenspeicher nicht der limitierende Faktor ist (IBM hat mit 177 SSDs getestet, sie duerfen das gerne auf 400 erhoehen) und IBMs eigene Leistungsabschaetzung zugrunde legt, darf man ein theoretisches Ergebnis von 311398 QphH@3000GB erwarten.  Das waere dann allerdings immer noch weit von dem Ergebnis der SPARC T5-4 entfernt, und gerade in der von IBM so geschaetzen "per core" Metric noch weniger vorteilhaft. In der x86-Welt sieht es nicht besser aus.  Leider gibt es von Intel keine so praktischen rPerf-Tabellen.  Daher muss ich hier fuer eine Schaetzung auf SPECint_rate2006 zurueckgreifen.  (Ich bin kein grosser Fan von solchen Kreuz- und Querschaetzungen.  Insb. SPECcpu ist nicht besonders geeignet, um Datenbank-Leistung abzuschaetzen, da fast kein IO im Spiel ist.)  Das o.g. HP System wird bei SPEC mit 1580 CINT2006_rate gelistet.  Das bis einschl. 2013-06-14 beste Resultat fuer den neuen Intel Xeon E7-4870 mit 8 CPUs ist 2180 CINT2006_rate.  Das ist immerhin 1.38x besser.  (Wenn man nur die Taktrate beruecksichtigen wuerde, waere man bei 1.32x.)  Hier weiter zu rechnen, ist muessig, aber fuer die ungeduldigen Leser hier eine kleine tabellarische Zusammenfassung: TPC-H @3000GB Performance Spekulationen System QphH* Verbesserung gegenueber der frueheren Generation SPARC T4-4 32 cores SPARC T4 205,792 2x SPARC T5-464 cores SPARC T5 409,721 IBM Power 780 32 cores Power7 192,001 1.62x IBM Power 780+ 64 cores Power7+  311,398* HP ProLiant DL980 G764 cores Intel Xeon X7560 162,601 1.38x HP ProLiant DL980 G780 cores Intel Xeon E7-4870    224,348* * Keine echten Resultate  - spekulative Werte auf der Grundlage von rPerf (Power7+) oder SPECint_rate2006 (HP) Natuerlich sind IBM oder HP herzlich eingeladen, diese Werte zu widerlegen.  Aber stand heute warte ich noch auf aktuelle Benchmark Veroffentlichungen in diesem Datensegment. Was koennen wir also zusammenfassen? Es gibt einige Hinweise, dass der Plattenspeicher der begrenzende Faktor war, der die SPARC T5-4 daran hinderte, auf jenseits von 2x zu skalieren Der Mythos, dass SPARC Kerne keine Leistung bringen, ist genau das - ein Mythos.  Wie sieht es umgekehrt eigentlich mit einem TPC-H Ergebnis fuer die Power7+ aus? Cache ist nicht der magische Performance-Schalter, fuer den ihn manche Leute offenbar halten. Ein System, eine CPU-Architektur und ein Betriebsystem jenseits einer gewissen Grenze zu skalieren ist schwer.  In der x86-Welt scheint es noch ein wenig schwerer zu sein. Was fehlt?  Nun, das Thema Preis/Leistung ueberlasse ich gerne den Verkaeufern ;-) Und zu guter Letzt: Nein, ich habe mich nicht ins Marketing versetzen lassen.  Aber manchmal kann ich mich einfach nicht zurueckhalten... Disclosure Statements The views expressed on this blog are my own and do not necessarily reflect the views of Oracle. TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org, results as of 6/7/13. Prices are in USD. SPARC T5-4 409,721.8 QphH@3000GB, $3.94/QphH@3000GB, available 9/24/13, 4 processors, 64 cores, 512 threads; SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads. SPEC and the benchmark names SPECfp and SPECint are registered trademarks of the Standard Performance Evaluation Corporation. Results as of June 18, 2013 from www.spec.org. HP ProLiant DL980 G7 (2.27 GHz, Intel Xeon X7560): 1580 SPECint_rate2006; HP ProLiant DL980 G7 (2.4 GHz, Intel Xeon E7-4870): 2180 SPECint_rate2006,

  Read the article

• T4 Performance Counters explained

  - by user13346607

  Now that T4 is out for a few month some people might have wondered what details of the new pipeline you can monitor. A "cpustat -h" lists a lot of events that can be monitored, and only very few are self-explanatory. I will try to give some insight on all of them, some of these "PIC events" require an in-depth knowledge of T4 pipeline. Over time I will try to explain these, for the time being these events should simply be ignored. (Side note: some counters changed from tape-out 1.1 (*only* used in the T4 beta program) to tape-out 1.2 (used in the systems shipping today) The table only lists the tape-out 1.2 counters) 0 0 1 1058 6033 Oracle Microelectronics 50 14 7077 14.0 Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} pic name (cpustat) Prose Comment Sel-pipe-drain-cycles, Sel-0-[wait|ready], Sel-[1,2] Sel-0-wait counts cycles a strand waits to be selected. Some reasons can be counted in detail; these are: Sel-0-ready: Cycles a strand was ready but not selected, that can signal pipeline oversubscription Sel-1: Cycles only one instruction or µop was selected Sel-2: Cycles two instructions or µops were selected Sel-pipe-drain-cycles: cf. PRM footnote 8 to table 10.2 Pick-any, Pick-[0|1|2|3] Cycles one, two, three, no or at least one instruction or µop is picked Instr_FGU_crypto Number of FGU or crypto instructions executed on that vcpu Instr_ld dto. for load Instr_st dto. for store SPR_ring_ops dto. for SPR ring ops Instr_other dto. for all other instructions not listed above, PRM footnote 7 to table 10.2 lists the instructions Instr_all total number of instructions executed on that vcpu Sw_count_intr Nr of S/W count instructions on that vcpu (sethi %hi(fc000),%g0 (whatever that is))  Atomics nr of atomic ops, which are LDSTUB/a, CASA/XA, and SWAP/A SW_prefetch Nr of PREFETCH or PREFETCHA instructions Block_ld_st Block loads or store on that vcpu IC_miss_nospec, IC_miss_[L2_or_L3|local|remote]\ _hit_nospec Various I$ misses, distinguished by where they hit. All of these count per thread, but only primary events: T4 counts only the first occurence of an I$ miss on a core for a certain instruction. If one strand misses in I$ this miss is counted, but if a second strand on the same core misses while the first miss is being resolved, that second miss is not counted This flavour of I$ misses counts only misses that are caused by instruction that really commit (note the "_nospec") BTC_miss Branch target cache miss ITLB_miss ITLB misses (synchronously counted) ITLB_miss_asynch dto. but asynchronously [I|D]TLB_fill_\ [8KB|64KB|4MB|256MB|2GB|trap] H/W tablewalk events that fill ITLB or DTLB with translation for the corresponding page size. The “_trap” event occurs if the HWTW was not able to fill the corresponding TLB IC_mtag_miss, IC_mtag_miss_\ [ptag_hit|ptag_miss|\ ptag_hit_way_mismatch] I$ micro tag misses, with some options for drill down Fetch-0, Fetch-0-all fetch-0 counts nr of cycles nothing was fetched for this particular strand, fetch-0-all counts cycles nothing was fetched for all strands on a core Instr_buffer_full Cycles the instruction buffer for a strand was full, thereby preventing any fetch BTC_targ_incorrect Counts all occurences of wrongly predicted branch targets from the BTC [PQ|ROB|LB|ROB_LB|SB|\ ROB_SB|LB_SB|RB_LB_SB|\ DTLB_miss]\ _tag_wait ST_q_tag_wait is listed under sl=20. These counters monitor pipeline behaviour therefore they are not strand specific: PQ_...: cycles Rename stage waits for a Pick Queue tag (might signal memory bound workload for single thread mode, cf. Mail from Richard Smith) ROB_...: cycles Select stage waits for a ROB (ReOrderBuffer) tag LB_...: cycles Select stage waits for a Load Buffer tag SB_...: cycles Select stage waits for Store Buffer tag combinations of the above are allowed, although some of these events can overlap, the counter will only be incremented once per cycle if any of these occur DTLB_...: cycles load or store instructions wait at Pick stage for a DTLB miss tag [ID]TLB_HWTW_\ [L2_hit|L3_hit|L3_miss|all] Counters for HWTW accesses caused by either DTLB or ITLB misses. Canbe further detailed by where they hit IC_miss_L2_L3_hit, IC_miss_local_remote_remL3_hit, IC_miss I$ prefetches that were dropped because they either miss in L2$ or L3$ This variant counts misses regardless if the causing instruction commits or not DC_miss_nospec, DC_miss_[L2_L3|local|remote_L3]\ _hit_nospec D$ misses either in general or detailed by where they hit cf. the explanation for the IC_miss in two flavours for an explanation of _nospec and the reasoning for two DC_miss counters DTLB_miss_asynch counts all DTLB misses asynchronously, there is no way to count them synchronously DC_pref_drop_DC_hit, SW_pref_drop_[DC_hit|buffer_full] L1-D$ h/w prefetches that were dropped because of a D$ hit, counted per core. The others count software prefetches per strand [Full|Partial]_RAW_hit_st_[buf|q] Count events where a load wants to get data that has not yet been stored, i. e. it is still inside the pipeline. The data might be either still in the store buffer or in the store queue. If the load's data matches in the SB and in the store queue the data in buffer takes precedence of course since it is younger [IC|DC]_evict_invalid, [IC|DC|L1]_snoop_invalid, [IC|DC|L1]_invalid_all Counter for invalidated cache evictions per core St_q_tag_wait Number of cycles pipeline waits for a store queue tag, of course counted per core Data_pref_[drop_L2|drop_L3|\ hit_L2|hit_L3|\ hit_local|hit_remote] Data prefetches that can be further detailed by either why they were dropped or where they did hit St_hit_[L2|L3], St_L2_[local|remote]_C2C, St_local, St_remote Store events distinguished by where they hit or where they cause a L2 cache-to-cache transfer, i.e. either a transfer from another L2$ on the same die or from a different die DC_miss, DC_miss_\ [L2_L3|local|remote]_hit D$ misses either in general or detailed by where they hit cf. the explanation for the IC_miss in two flavours for an explanation of _nospec and the reasoning for two DC_miss counters L2_[clean|dirty]_evict Per core clean or dirty L2$ evictions L2_fill_buf_full, L2_wb_buf_full, L2_miss_buf_full Per core L2$ buffer events, all count number of cycles that this state was present L2_pipe_stall Per core cycles pipeline stalled because of L2$ Branches Count branches (Tcc, DONE, RETRY, and SIT are not counted as branches) Br_taken Counts taken branches (Tcc, DONE, RETRY, and SIT are not counted as branches) Br_mispred, Br_dir_mispred, Br_trg_mispred, Br_trg_mispred_\ [far_tbl|indir_tbl|ret_stk] Counter for various branch misprediction events.  Cycles_user counts cycles, attribute setting hpriv, nouser, sys controls addess space to count in Commit-[0|1|2], Commit-0-all, Commit-1-or-2 Number of times either no, one, or two µops commit for a strand. Commit-0-all counts number of times no µop commits for the whole core, cf. footnote 11 to table 10.2 in PRM for a more detailed explanation on how this counters interacts with the privilege levels

  Read the article

• OpenSSL Versions in Solaris

  - by darrenm

  Those of you have have installed Solaris 11 or have read some of the blogs by my colleagues will have noticed Solaris 11 includes OpenSSL 1.0.0, this is a different version to what we have in Solaris 10.  I hope the following explains why that is and how it fits with the expectations on binary compatibility between Solaris releases. Solaris 10 was the first release where we included OpenSSL libraries and headers (part of it was actually statically linked into the SSH client/server in Solaris 9).  At time we were building and releasing Solaris 10 the current train of OpenSSL was 0.9.7.  The OpenSSL libraries at that time were known to not always be completely API and ABI (binary) compatible between releases (some times even in the lettered patch releases) though mostly if you stuck with the documented high level APIs you would be fine.   For this reason OpenSSL was classified as a 'Volatile' interface and in Solaris 10 Volatile interfaces were not part of the default library search path which is why the OpenSSL libraries live in /usr/sfw/lib on Solaris 10.  Okay, but what does Volatile mean ? Quoting from the attributes(5) man page description of Volatile (which was called External in older taxonomy): Volatile interfaces can change at any time and for any reason. The Volatile interface stability level allows Sun pro- ducts to quickly track a fluid, rapidly evolving specif- ication. In many cases, this is preferred to providing additional stability to the interface, as it may better meet the expectations of the consumer. The most common application of this taxonomy level is to interfaces that are controlled by a body other than Sun, but unlike specifications controlled by standards bodies or Free or Open Source Software (FOSS) communities which value interface compatibility, it can not be asserted that an incompatible change to the interface specifica- tion would be exceedingly rare. It may also be applied to FOSS controlled software where it is deemed more important to track the community with minimal latency than to provide stability to our customers. It also common to apply the Volatile classification level to interfaces in the process of being defined by trusted or widely accepted organization. These are generically referred to as draft standards. An "IETF Internet draft" is a well understood example of a specification under development. Volatile can also be applied to experimental interfaces. No assertion is made regarding either source or binary compatibility of Volatile interfaces between any two releases, including patches. Applications containing these interfaces might fail to function properly in any future release. Note that last paragraph!  OpenSSL is only one example of the many interfaces in Solaris that are classified as Volatile.  At the other end of the scale we have Committed (Stable in Solaris 10 terminology) interfaces, these include things like the POSIX APIs or Solaris specific APIs that we have no intention of changing in an incompatible way.  There are also Private interfaces and things we declare as Not-an-Interface (eg command output not intended for scripting against only to be read by humans). Even if we had declared OpenSSL as a Committed/Stable interface in Solaris 10 there are allowed exceptions, again quoting from attributes(5): 4. An interface specification which isn't controlled by Sun has been changed incompatibly and the vast majority of interface consumers expect the newer interface. 5. Not making the incompatible change would be incomprehensible to our customers. In our opinion and that of our large and small customers keeping up with the OpenSSL community is important, and certainly both of the above cases apply. Our policy for dealing with OpenSSL on Solaris 10 was to stay at 0.9.7 and add fixes for security vulnerabilities (the version string includes the CVE numbers of fixed vulnerabilities relevant to that release train).  The last release of OpenSSL 0.9.7 delivered by the upstream community was more than 4 years ago in Feb 2007. Now lets roll forward to just before the release of Solaris 11 Express in 2010. By that point in time the current OpenSSL release was 0.9.8 with the 1.0.0 release known to be coming soon.  Two significant changes to OpenSSL were made between Solaris 10 and Solaris 11 Express.  First in Solaris 11 Express (and Solaris 11) we removed the requirement that Volatile libraries be placed in /usr/sfw/lib, that means OpenSSL is now in /usr/lib, secondly we upgraded it to the then current version stream of OpenSSL (0.9.8) as was expected by our customers. In between Solaris 11 Express in 2010 and the release of Solaris 11 in 2011 the OpenSSL community released version 1.0.0.  This was a huge milestone for a long standing and highly respected open source project.  It would have been highly negligent of Solaris not to include OpenSSL 1.0.0e in the Solaris 11 release. It is the latest best supported and best performing version.     In fact Solaris 11 isn't 'just' OpenSSL 1.0.0 but we have added our SPARC T4 engine and the AES-NI engine to support the on chip crypto acceleration. This gives us 4.3x better AES performance than OpenSSL 0.9.8 running on AIX on an IBM POWER7. We are now working with the OpenSSL community to determine how best to integrate the SPARC T4 changes into the mainline OpenSSL.  The OpenSSL 'pkcs11' engine we delivered in Solaris 10 to support the CA-6000 card and the SPARC T1/T2/T3 hardware is still included in Solaris 11. When OpenSSL 1.0.1 and 1.1.0 come out we will asses what is best for Solaris customers. It might be upgrade or it might be parallel delivery of more than one version stream.  At this time Solaris 11 still classifies OpenSSL as a Volatile interface, it is our hope that we will be able at some point in a future release to give it a higher interface stability level. Happy crypting! and thank-you OpenSSL community for all the work you have done that helps Solaris.

  Read the article

• NUMA-aware placement of communication variables

  - by Dave

  For classic NUMA-aware programming I'm typically most concerned about simple cold, capacity and compulsory misses and whether we can satisfy the miss by locally connected memory or whether we have to pull the line from its home node over the coherent interconnect -- we'd like to minimize channel contention and conserve interconnect bandwidth. That is, for this style of programming we're quite aware of where memory is homed relative to the threads that will be accessing it. Ideally, a page is collocated on the node with the thread that's expected to most frequently access the page, as simple misses on the page can be satisfied without resorting to transferring the line over the interconnect. The default "first touch" NUMA page placement policy tends to work reasonable well in this regard. When a virtual page is first accessed, the operating system will attempt to provision and map that virtual page to a physical page allocated from the node where the accessing thread is running. It's worth noting that the node-level memory interleaving granularity is usually a multiple of the page size, so we can say that a given page P resides on some node N. That is, the memory underlying a page resides on just one node. But when thinking about accesses to heavily-written communication variables we normally consider what caches the lines underlying such variables might be resident in, and in what states. We want to minimize coherence misses and cache probe activity and interconnect traffic in general. I don't usually give much thought to the location of the home NUMA node underlying such highly shared variables. On a SPARC T5440, for instance, which consists of 4 T2+ processors connected by a central coherence hub, the home node and placement of heavily accessed communication variables has very little impact on performance. The variables are frequently accessed so likely in M-state in some cache, and the location of the home node is of little consequence because a requester can use cache-to-cache transfers to get the line. Or at least that's what I thought. Recently, though, I was exploring a simple shared memory point-to-point communication model where a client writes a request into a request mailbox and then busy-waits on a response variable. It's a simple example of delegation based on message passing. The server polls the request mailbox, and having fetched a new request value, performs some operation and then writes a reply value into the response variable. As noted above, on a T5440 performance is insensitive to the placement of the communication variables -- the request and response mailbox words. But on a Sun/Oracle X4800 I noticed that was not the case and that NUMA placement of the communication variables was actually quite important. For background an X4800 system consists of 8 Intel X7560 Xeons . Each package (socket) has 8 cores with 2 contexts per core, so the system is 8x8x2. Each package is also a NUMA node and has locally attached memory. Every package has 3 point-to-point QPI links for cache coherence, and the system is configured with a twisted ladder "mobius" topology. The cache coherence fabric is glueless -- there's not central arbiter or coherence hub. The maximum distance between any two nodes is just 2 hops over the QPI links. For any given node, 3 other nodes are 1 hop distant and the remaining 4 nodes are 2 hops distant. Using a single request (client) thread and a single response (server) thread, a benchmark harness explored all permutations of NUMA placement for the two threads and the two communication variables, measuring the average round-trip-time and throughput rate between the client and server. In this benchmark the server simply acts as a simple transponder, writing the request value plus 1 back into the reply field, so there's no particular computation phase and we're only measuring communication overheads. In addition to varying the placement of communication variables over pairs of nodes, we also explored variations where both variables were placed on one page (and thus on one node) -- either on the same cache line or different cache lines -- while varying the node where the variables reside along with the placement of the threads. The key observation was that if the client and server threads were on different nodes, then the best placement of variables was to have the request variable (written by the client and read by the server) reside on the same node as the client thread, and to place the response variable (written by the server and read by the client) on the same node as the server. That is, if you have a variable that's to be written by one thread and read by another, it should be homed with the writer thread. For our simple client-server model that means using split request and response communication variables with unidirectional message flow on a given page. This can yield up to twice the throughput of less favorable placement strategies. Our X4800 uses the QPI 1.0 protocol with source-based snooping. Briefly, when node A needs to probe a cache line it fires off snoop requests to all the nodes in the system. Those recipients then forward their response not to the original requester, but to the home node H of the cache line. H waits for and collects the responses, adjudicates and resolves conflicts and ensures memory-model ordering, and then sends a definitive reply back to the original requester A. If some node B needed to transfer the line to A, it will do so by cache-to-cache transfer and let H know about the disposition of the cache line. A needs to wait for the authoritative response from H. So if a thread on node A wants to write a value to be read by a thread on node B, the latency is dependent on the distances between A, B, and H. We observe the best performance when the written-to variable is co-homed with the writer A. That is, we want H and A to be the same node, as the writer doesn't need the home to respond over the QPI link, as the writer and the home reside on the very same node. With architecturally informed placement of communication variables we eliminate at least one QPI hop from the critical path. Newer Intel processors use the QPI 1.1 coherence protocol with home-based snooping. As noted above, under source-snooping a requester broadcasts snoop requests to all nodes. Those nodes send their response to the home node of the location, which provides memory ordering, reconciles conflicts, etc., and then posts a definitive reply to the requester. In home-based snooping the snoop probe goes directly to the home node and are not broadcast. The home node can consult snoop filters -- if present -- and send out requests to retrieve the line if necessary. The 3rd party owner of the line, if any, can respond either to the home or the original requester (or even to both) according to the protocol policies. There are myriad variations that have been implemented, and unfortunately vendor terminology doesn't always agree between vendors or with the academic taxonomy papers. The key is that home-snooping enables the use of a snoop filter to reduce interconnect traffic. And while home-snooping might have a longer critical path (latency) than source-based snooping, it also may require fewer messages and less overall bandwidth. It'll be interesting to reprise these experiments on a platform with home-based snooping. While collecting data I also noticed that there are placement concerns even in the seemingly trivial case when both threads and both variables reside on a single node. Internally, the cores on each X7560 package are connected by an internal ring. (Actually there are multiple contra-rotating rings). And the last-level on-chip cache (LLC) is partitioned in banks or slices, which with each slice being associated with a core on the ring topology. A hardware hash function associates each physical address with a specific home bank. Thus we face distance and topology concerns even for intra-package communications, although the latencies are not nearly the magnitude we see inter-package. I've not seen such communication distance artifacts on the T2+, where the cache banks are connected to the cores via a high-speed crossbar instead of a ring -- communication latencies seem more regular.

  Read the article

• DTracing TCP congestion control

  - by user12820842

  In a previous post, I showed how we can use DTrace to probe TCP receive and send window events. TCP receive and send windows are in effect both about flow-controlling how much data can be received - the receive window reflects how much data the local TCP is prepared to receive, while the send window simply reflects the size of the receive window of the peer TCP. Both then represent flow control as imposed by the receiver. However, consider that without the sender imposing flow control, and a slow link to a peer, TCP will simply fill up it's window with sent segments. Dealing with multiple TCP implementations filling their peer TCP's receive windows in this manner, busy intermediate routers may drop some of these segments, leading to timeout and retransmission, which may again lead to drops. This is termed congestion, and TCP has multiple congestion control strategies. We can see that in this example, we need to have some way of adjusting how much data we send depending on how quickly we receive acknowledgement - if we get ACKs quickly, we can safely send more segments, but if acknowledgements come slowly, we should proceed with more caution. More generally, we need to implement flow control on the send side also. Slow Start and Congestion Avoidance From RFC2581, let's examine the relevant variables: "The congestion window (cwnd) is a sender-side limit on the amount of data the sender can transmit into the network before receiving an acknowledgment (ACK). Another state variable, the slow start threshold (ssthresh), is used to determine whether the slow start or congestion avoidance algorithm is used to control data transmission" Slow start is used to probe the network's ability to handle transmission bursts both when a connection is first created and when retransmission timers fire. The latter case is important, as the fact that we have effectively lost TCP data acts as a motivator for re-probing how much data the network can handle from the sending TCP. The congestion window (cwnd) is initialized to a relatively small value, generally a low multiple of the sending maximum segment size. When slow start kicks in, we will only send that number of bytes before waiting for acknowledgement. When acknowledgements are received, the congestion window is increased in size until cwnd reaches the slow start threshold ssthresh value. For most congestion control algorithms the window increases exponentially under slow start, assuming we receive acknowledgements. We send 1 segment, receive an ACK, increase the cwnd by 1 MSS to 2*MSS, send 2 segments, receive 2 ACKs, increase the cwnd by 2*MSS to 4*MSS, send 4 segments etc. When the congestion window exceeds the slow start threshold, congestion avoidance is used instead of slow start. During congestion avoidance, the congestion window is generally updated by one MSS for each round-trip-time as opposed to each ACK, and so cwnd growth is linear instead of exponential (we may receive multiple ACKs within a single RTT). This continues until congestion is detected. If a retransmit timer fires, congestion is assumed and the ssthresh value is reset. It is reset to a fraction of the number of bytes outstanding (unacknowledged) in the network. At the same time the congestion window is reset to a single max segment size. Thus, we initiate slow start until we start receiving acknowledgements again, at which point we can eventually flip over to congestion avoidance when cwnd ssthresh. Congestion control algorithms differ most in how they handle the other indication of congestion - duplicate ACKs. A duplicate ACK is a strong indication that data has been lost, since they often come from a receiver explicitly asking for a retransmission. In some cases, a duplicate ACK may be generated at the receiver as a result of packets arriving out-of-order, so it is sensible to wait for multiple duplicate ACKs before assuming packet loss rather than out-of-order delivery. This is termed fast retransmit (i.e. retransmit without waiting for the retransmission timer to expire). Note that on Oracle Solaris 11, the congestion control method used can be customized. See here for more details. In general, 3 or more duplicate ACKs indicate packet loss and should trigger fast retransmit . It's best not to revert to slow start in this case, as the fact that the receiver knew it was missing data suggests it has received data with a higher sequence number, so we know traffic is still flowing. Falling back to slow start would be excessive therefore, so fast recovery is used instead. Observing slow start and congestion avoidance The following script counts TCP segments sent when under slow start (cwnd ssthresh). #!/usr/sbin/dtrace -s #pragma D option quiet tcp:::connect-request / start[args[1]-cs_cid] == 0/ { start[args[1]-cs_cid] = 1; } tcp:::send / start[args[1]-cs_cid] == 1 && args[3]-tcps_cwnd tcps_cwnd_ssthresh / { @c["Slow start", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } tcp:::send / start[args[1]-cs_cid] == 1 && args[3]-tcps_cwnd args[3]-tcps_cwnd_ssthresh / { @c["Congestion avoidance", args[2]-ip_daddr, args[4]-tcp_dport] = count(); } As we can see the script only works on connections initiated since it is started (using the start[] associative array with the connection ID as index to set whether it's a new connection (start[cid] = 1). From there we simply differentiate send events where cwnd ssthresh (congestion avoidance). Here's the output taken when I accessed a YouTube video (where rport is 80) and from an FTP session where I put a large file onto a remote system. # dtrace -s tcp_slow_start.d ^C ALGORITHM RADDR RPORT #SEG Slow start 10.153.125.222 20 6 Slow start 138.3.237.7 80 14 Slow start 10.153.125.222 21 18 Congestion avoidance 10.153.125.222 20 1164 We see that in the case of the YouTube video, slow start was exclusively used. Most of the segments we sent in that case were likely ACKs. Compare this case - where 14 segments were sent using slow start - to the FTP case, where only 6 segments were sent before we switched to congestion avoidance for 1164 segments. In the case of the FTP session, the FTP data on port 20 was predominantly sent with congestion avoidance in operation, while the FTP session relied exclusively on slow start. For the default congestion control algorithm - "newreno" - on Solaris 11, slow start will increase the cwnd by 1 MSS for every acknowledgement received, and by 1 MSS for each RTT in congestion avoidance mode. Different pluggable congestion control algorithms operate slightly differently. For example "highspeed" will update the slow start cwnd by the number of bytes ACKed rather than the MSS. And to finish, here's a neat oneliner to visually display the distribution of congestion window values for all TCP connections to a given remote port using a quantization. In this example, only port 80 is in use and we see the majority of cwnd values for that port are in the 4096-8191 range. # dtrace -n 'tcp:::send { @q[args[4]-tcp_dport] = quantize(args[3]-tcps_cwnd); }' dtrace: description 'tcp:::send ' matched 10 probes ^C 80 value ------------- Distribution ------------- count -1 | 0 0 |@@@@@@ 5 1 | 0 2 | 0 4 | 0 8 | 0 16 | 0 32 | 0 64 | 0 128 | 0 256 | 0 512 | 0 1024 | 0 2048 |@@@@@@@@@ 8 4096 |@@@@@@@@@@@@@@@@@@@@@@@@@@ 23 8192 | 0

  Read the article

• 12c - SQL Text Expansion

  - by noreply(at)blogger.com (Thomas Kyte)

  Here is another small but very useful new feature in Oracle Database 12c - SQL Text Expansion.  It will come in handy in two cases:You are asked to tune what looks like a simple query - maybe a two table join with simple predicates.  But it turns out the two tables are each views of views of views and so on... In other words, you've been asked to 'tune' a 15 page query, not a two liner.You are asked to take a look at a query against tables with VPD (virtual private database) policies.  In order words, you have no idea what you are trying to 'tune'.A new function, EXPAND_SQL_TEXT, in the DBMS_UTILITY package makes seeing what the "real" SQL is quite easy. For example - take the common view ALL_USERS - we can now:ops$tkyte%ORA12CR1> variable x clobops$tkyte%ORA12CR1> begin  2          dbms_utility.expand_sql_text  3          ( input_sql_text => 'select * from all_users',  4            output_sql_text => :x );  5  end;  6  /PL/SQL procedure successfully completed.ops$tkyte%ORA12CR1> print xX--------------------------------------------------------------------------------SELECT "A1"."USERNAME" "USERNAME","A1"."USER_ID" "USER_ID","A1"."CREATED" "CREATED","A1"."COMMON" "COMMON" FROM  (SELECT "A4"."NAME" "USERNAME","A4"."USER#" "USER_ID","A4"."CTIME" "CREATED",DECODE(BITAND("A4"."SPARE1",128),128,'YES','NO') "COMMON" FROM "SYS"."USER$" "A4","SYS"."TS$" "A3","SYS"."TS$" "A2" WHERE "A4"."DATATS#"="A3"."TS#" AND "A4"."TEMPTS#"="A2"."TS#" AND "A4"."TYPE#"=1) "A1"Now it is easy to see what query is really being executed at runtime - regardless of how many views of views you might have.  You can see the expanded text - and that will probably lead you to the conclusion that maybe that 27 table join to 25 tables you don't even care about might better be written as a two table join.Further, if you've ever tried to figure out what a VPD policy might be doing to your SQL, you know it was hard to do at best.  Christian Antognini wrote up a way to sort of see it - but you never get to see the entire SQL statement: http://www.antognini.ch/2010/02/tracing-vpd-predicates/.  But now with this function - it becomes rather trivial to see the expanded SQL - after the VPD has been applied.  We can see this by setting up a small table with a VPD policy ops$tkyte%ORA12CR1> create table my_table  2  (  data        varchar2(30),  3     OWNER       varchar2(30) default USER  4  )  5  /Table created.ops$tkyte%ORA12CR1> create or replace  2  function my_security_function( p_schema in varchar2,  3                                 p_object in varchar2 )  4  return varchar2  5  as  6  begin  7     return 'owner = USER';  8  end;  9  /Function created.ops$tkyte%ORA12CR1> begin  2     dbms_rls.add_policy  3     ( object_schema   => user,  4       object_name     => 'MY_TABLE',  5       policy_name     => 'MY_POLICY',  6       function_schema => user,  7       policy_function => 'My_Security_Function',  8       statement_types => 'select, insert, update, delete' ,  9       update_check    => TRUE ); 10  end; 11  /PL/SQL procedure successfully completed.And then expanding a query against it:ops$tkyte%ORA12CR1> begin  2          dbms_utility.expand_sql_text  3          ( input_sql_text => 'select * from my_table',  4            output_sql_text => :x );  5  end;  6  /PL/SQL procedure successfully completed.ops$tkyte%ORA12CR1> print xX--------------------------------------------------------------------------------SELECT "A1"."DATA" "DATA","A1"."OWNER" "OWNER" FROM  (SELECT "A2"."DATA" "DATA","A2"."OWNER" "OWNER" FROM "OPS$TKYTE"."MY_TABLE" "A2" WHERE "A2"."OWNER"=USER@!) "A1"Not an earth shattering new feature - but extremely useful in certain cases.  I know I'll be using it when someone asks me to look at a query that looks simple but has a twenty page plan associated with it!

  Read the article

• CPU Usage in Very Large Coherence Clusters

  - by jpurdy

  When sizing Coherence installations, one of the complicating factors is that these installations (by their very nature) tend to be application-specific, with some being large, memory-intensive caches, with others acting as I/O-intensive transaction-processing platforms, and still others performing CPU-intensive calculations across the data grid. Regardless of the primary resource requirements, Coherence sizing calculations are inherently empirical, in that there are so many permutations that a simple spreadsheet approach to sizing is rarely optimal (though it can provide a good starting estimate). So we typically recommend measuring actual resource usage (primarily CPU cycles, network bandwidth and memory) at a given load, and then extrapolating from those measurements. Of course there may be multiple types of load, and these may have varying degrees of correlation -- for example, an increased request rate may drive up the number of objects "pinned" in memory at any point, but the increase may be less than linear if those objects are naturally shared by concurrent requests. But for most reasonably-designed applications, a linear resource model will be reasonably accurate for most levels of scale. However, at extreme scale, sizing becomes a bit more complicated as certain cluster management operations -- while very infrequent -- become increasingly critical. This is because certain operations do not naturally tend to scale out. In a small cluster, sizing is primarily driven by the request rate, required cache size, or other application-driven metrics. In larger clusters (e.g. those with hundreds of cluster members), certain infrastructure tasks become intensive, in particular those related to members joining and leaving the cluster, such as introducing new cluster members to the rest of the cluster, or publishing the location of partitions during rebalancing. These tasks have a strong tendency to require all updates to be routed via a single member for the sake of cluster stability and data integrity. Fortunately that member is dynamically assigned in Coherence, so it is not a single point of failure, but it may still become a single point of bottleneck (until the cluster finishes its reconfiguration, at which point this member will have a similar load to the rest of the members). The most common cause of scaling issues in large clusters is disabling multicast (by configuring well-known addresses, aka WKA). This obviously impacts network usage, but it also has a large impact on CPU usage, primarily since the senior member must directly communicate certain messages with every other cluster member, and this communication requires significant CPU time. In particular, the need to notify the rest of the cluster about membership changes and corresponding partition reassignments adds stress to the senior member. Given that portions of the network stack may tend to be single-threaded (both in Coherence and the underlying OS), this may be even more problematic on servers with poor single-threaded performance. As a result of this, some extremely large clusters may be configured with a smaller number of partitions than ideal. This results in the size of each partition being increased. When a cache server fails, the other servers will use their fractional backups to recover the state of that server (and take over responsibility for their backed-up portion of that state). The finest granularity of this recovery is a single partition, and the single service thread can not accept new requests during this recovery. Ordinarily, recovery is practically instantaneous (it is roughly equivalent to the time required to iterate over a set of backup backing map entries and move them to the primary backing map in the same JVM). But certain factors can increase this duration drastically (to several seconds): large partitions, sufficiently slow single-threaded CPU performance, many or expensive indexes to rebuild, etc. The solution of course is to mitigate each of those factors but in many cases this may be challenging. Larger clusters also lead to the temptation to place more load on the available hardware resources, spreading CPU resources thin. As an example, while we've long been aware of how garbage collection can cause significant pauses, it usually isn't viewed as a major consumer of CPU (in terms of overall system throughput). Typically, the use of a concurrent collector allows greater responsiveness by minimizing pause times, at the cost of reducing system throughput. However, at a recent engagement, we were forced to turn off the concurrent collector and use a traditional parallel "stop the world" collector to reduce CPU usage to an acceptable level. In summary, there are some less obvious factors that may result in excessive CPU consumption in a larger cluster, so it is even more critical to test at full scale, even though allocating sufficient hardware may often be much more difficult for these large clusters.

  Read the article

• A Patent for Workload Management Based on Service Level Objectives

  - by jsavit

  I'm very pleased to announce that after a tiny :-) wait of about 5 years, my patent application for a workload manager was finally approved. Background Many operating systems have a resource manager which lets you control machine resources. For example, Solaris provides controls for CPU with several options: shares for proportional CPU allocation. If you have twice as many shares as me, and we are competing for CPU, you'll get about twice as many CPU cycles), dedicated CPU allocation in which a number of CPUs are exclusively dedicated to an application's use. You can say that a zone or project "owns" 8 CPUs on a 32 CPU machine, for example. And, capped CPU in which you specify the upper bound, or cap, of how much CPU an application gets. For example, you can throttle an application to 0.125 of a CPU. (This isn't meant to be an exhaustive list of Solaris RM controls.) Workload management Useful as that is (and tragic that some other operating systems have little resource management and isolation, and frighten people into running only 1 app per OS instance - and wastefully size every server for the peak workload it might experience) that's not really workload management. With resource management one controls the resources, and hope that's enough to meet application service objectives. In fact, we hold resource distribution constant, see if that was good enough, and adjust resource distribution if that didn't meet service level objectives. Here's an example of what happens today: Let's try 30% dedicated CPU. Not enough? Let's try 80% Oh, that's too much, and we're achieving much better response time than the objective, but other workloads are starving. Let's back that off and try again. It's not the process I object to - it's that we to often do this manually. Worse, we sometimes identify and adjust the wrong resource and fiddle with that to no useful result. Back in my days as a customer managing large systems, one of my users would call me up to beg for a "CPU boost": Me: "it won't make any difference - there's plenty of spare CPU to be had, and your application is completely I/O bound." User: "Please do it anyway." Me: "oh, all right, but it won't do you any good." (I did, because he was a friend, but it didn't help.) Prior art There are some operating environments that take a stab about workload management (rather than resource management) but I find them lacking. I know of one that uses synthetic "service units" composed of the sum of CPU, I/O and memory allocations multiplied by weighting factors. A workload is set to make a target rate of service units consumed per second. But this seems to be missing a key point: what is the relationship between artificial 'service units' and actually meeting a throughput or response time objective? What if I get plenty of one of the components (so am getting enough service units), but not enough of the resource whose needed to remove the bottleneck? Actual workload management That's not really the answer either. What is needed is to specify a workload's service levels in terms of externally visible metrics that are meaningful to a business, such as response times or transactions per second, and have the workload manager figure out which resources are not being adequately provided, and then adjust it as needed. If an application is not meeting its service level objectives and the reason is that it's not getting enough CPU cycles, adjust its CPU resource accordingly. If the reason is that the application isn't getting enough RAM to keep its working set in memory, then adjust its RAM assignment appropriately so it stops swapping. Simple idea, but that's a task we keep dumping on system administrators. In other words - don't hold the number of CPU shares constant and watch the achievement of service level vary. Instead, hold the service level constant, and dynamically adjust the number of CPU shares (or amount of other resources like RAM or I/O bandwidth) in order to meet the objective. Instrumenting non-instrumented applications There's one little problem here: how do I measure application performance in a way relating to a service level. I don't want to do it based on internal resources like number of CPU seconds it received per minute - We need to make resource decisions based on externally visible and meaningful measures of performance, not synthetic items or internal resource counters. If I have a way of marking the beginning and end of a transaction, I can then measure whether or not the application is meeting an objective based on it. If I can observe the delay factors for an application, I can see which resource shortages are slowing an application enough to keep it from meeting its objectives. I can then adjust resource allocations to relieve those shortages. Fortunately, Solaris provides facilities for both marking application progress and determining what factors cause application latency. The Solaris DTrace facility let's me introspect on application behavior: in particular I can see events like "receive a web hit" and "respond to that web hit" so I can get transaction rate and response time. DTrace (and tools like prstat) let me see where latency is being added to an application, so I know which resource to adjust. Summary After a delay of a mere few years, I am the proud creator of a patent (advice to anyone interested in going through the process: don't hold your breath!). The fundamental idea is fairly simple: instead of holding resource constant and suffering variable levels of success meeting service level objectives, properly characterise the service level objective in meaningful terms, instrument the application to see if it's meeting the objective, and then have a workload manager change resource allocations to remove delays preventing service level attainment. I've done it by hand for a long time - I think that's what a computer should do for me.

  Read the article

• Take Two: Comparing JVMs on ARM/Linux

  - by user12608080

  Although the intent of the previous article, entitled Comparing JVMs on ARM/Linux, was to introduce and highlight the availability of the HotSpot server compiler (referred to as c2) for Java SE-Embedded ARM v7,  it seems, based on feedback, that everyone was more interested in the OpenJDK comparisons to Java SE-E.  In fact there were two main concerns: The fact that the previous article compared Java SE-E 7 against OpenJDK 6 might be construed as an unlevel playing field because version 7 is newer and therefore potentially more optimized. That the generic compiler settings chosen to build the OpenJDK implementations did not put those versions in a particularly favorable light. With those considerations in mind, we'll institute the following changes to this version of the benchmarking: In order to help alleviate an additional concern that there is some sort of benchmark bias, we'll use a different suite, called DaCapo.  Funded and supported by many prestigious organizations, DaCapo's aim is to benchmark real world applications.  Further information about DaCapo can be found at http://dacapobench.org. At the suggestion of Xerxes Ranby, who has been a great help through this entire exercise, a newer Linux distribution will be used to assure that the OpenJDK implementations were built with more optimal compiler settings.  The Linux distribution in this instance is Ubuntu 11.10 Oneiric Ocelot. Having experienced difficulties getting Ubuntu 11.10 to run on the original D2Plug ARMv7 platform, for these benchmarks, we'll switch to an embedded system that has a supported Ubuntu 11.10 release.  That platform is the Freescale i.MX53 Quick Start Board.  It has an ARMv7 Coretex-A8 processor running at 1GHz with 1GB RAM. We'll limit comparisons to 4 JVM implementations: Java SE-E 7 Update 2 c1 compiler (default) Java SE-E 6 Update 30 (c1 compiler is the only option) OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 CACAO build 1.1.0pre2 OpenJDK 6 IcedTea6 1.11pre 6b23~pre11-0ubuntu1.11.10.2 JamVM build-1.6.0-devel Certain OpenJDK implementations were eliminated from this round of testing for the simple reason that their performance was not competitive.  The Java SE 7u2 c2 compiler was also removed because although quite respectable, it did not perform as well as the c1 compilers.  Recall that c2 works optimally in long-lived situations.  Many of these benchmarks completed in a relatively short period of time.  To get a feel for where c2 shines, take a look at the first chart in this blog. The first chart that follows includes performance of all benchmark runs on all platforms.  Later on we'll look more at individual tests.  In all runs, smaller means faster.  The DaCapo aficionado may notice that only 10 of the 14 DaCapo tests for this version were executed.  The reason for this is that these 10 tests represent the only ones successfully completed by all 4 JVMs.  Only the Java SE-E 6u30 could successfully run all of the tests.  Both OpenJDK instances not only failed to complete certain tests, but also experienced VM aborts too. One of the first observations that can be made between Java SE-E 6 and 7 is that, for all intents and purposes, they are on par with regards to performance.  While it is a fact that successive Java SE releases add additional optimizations, it is also true that Java SE 7 introduces additional complexity to the Java platform thus balancing out any potential performance gains at this point.  We are still early into Java SE 7.  We would expect further performance enhancements for Java SE-E 7 in future updates. In comparing Java SE-E to OpenJDK performance, among both OpenJDK VMs, Cacao results are respectable in 4 of the 10 tests.  The charts that follow show the individual results of those four tests.  Both Java SE-E versions do win every test and outperform Cacao in the range of 9% to 55%. For the remaining 6 tests, Java SE-E significantly outperforms Cacao in the range of 114% to 311% So it looks like OpenJDK results are mixed for this round of benchmarks.  In some cases, performance looks to have improved.  But in a majority of instances, OpenJDK still lags behind Java SE-Embedded considerably. Time to put on my asbestos suit.  Let the flames begin...

  Read the article

• 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.

  Read the article

• OS8- AK8- The bad news...

  - by Steve Tunstall

  Ok I told you I would give you the bad news of AK8 to go along with all the cool new stuff, so here it is. It's not that bad, really, just things you need to be aware of. First, the 2013.1 code is being called OS8, AK8 and 2013.1 by different people. I mean different people INSIDE Oracle!! It was supposed to be easy, but it never is. So for the rest of this blog entry, I'm calling it AK8. AK8 is not compatible with the 7x10 series. Ever. The 7x10 series is not supported with AK8, and if you try to upgrade one, it will fail at the healthcheck. All 7x20 series, all of them regardless of age, are supported with AK8. Drive trays. Let's talk about drive trays and SAS cards. The older drive trays for the 7x20 series were called the "Riverwalk 2" or "DS2" trays. They were technically the "J4410" series JBODs that Sun used to sell a la carte before we stopped selling JBODs. Don't get me started on that, it still makes me mad. We used these for many years, and you can still buy them right now until December 15th, 2013, when they will no longer be sold. The DS2 tray only came as a 4u, 24 drive shelf. It held 3.5" drives, and you had a choice of 2TB, 3TB, 300GB or 600GB drives. The SAS HBA in the 7x20 series was called a "Thebe" card, with a part # of 7105394. The 7420, for example, came standard with two of these "Thebe" cards for connecting to the disk trays. Two Thebe cards could handle up to 12 trays, so one would add two more cards to go to 24 trays, or have up to six Thebe cards to handle 36 trays. This card was for external SAS only. It did not connect to the internal OS drives or the Readzillas, both of which used the internal SCSI controller of the server. These Riverwalk 2 trays ARE supported with AK8. You can upgrade your older 7420 or 7320, no problem, as-is. The much older Riverwalk 1 trays or J4400 trays are NOT supported by AK8. However, they were only used by the 7x10 series, and we already said that the 7x10 series was not supported. Here's where it gets tricky. Since last January, we have been selling the new style disk trays. We call them the "DE2-24P" and the "DE2-24C" trays. The "C" tray is for capacity drives, which are 3.5" 3TB or 4TB drives. The "P" trays are for performance drives, which are 2.5" 300GB and 900GB drives. These trays are NOT Riverwalk 2 trays, even though the "C" series may kind of look like it. Different manufacturer and different firmware. They are not new. Like I said, we've been selling them with the 7x20 series since last January. They are the only disk trays we will be selling going forward. Of course, AK8 supports them. So what's the problem? The problem is going to be for people who have to mix drive trays. Remember, your older 7x20 series has Thebe SAS2 HBAs. These have 2 SAS ports per card.  The new ZS3-2 and ZS3-4 systems, however, have the new "Thebe2" SAS2 HBAs. These Thebe2 cards have 4 ports per card. This is very cool, as we can now do more SAS channels with less cards. Instead of needing 4 SAS cards to grow to 24 trays like we did with the old Thebe cards, I can now do 24 trays with only 2 Thebe2 cards. This means more IO slots for fun things like Infiniband and 10G. So far, so good, right? These Thebe2 cards work with any disk tray. You can even mix older DS2 trays with the newer DE2 trays in the same system, as long as you have Thebe2 cards. Ah, there's your problem. You don't have Thebe2 cards in your old 7420, do you? Well, I told you the bad news wasn't that bad, right? We can take out your Thebe cards and replace them with Thebe2. You can then plug your older DS2 trays right back in, and also now get newer DE2 trays going forward. However, it's important that the trays are on different SAS channels. You can mix them in the same system, but not on the same channel. Ask your local SC if you need help with the new cable layout. By the way, the new ZS3-2 and ZS3-4 systems also include a new IO card called "Erie" cards. These are for INTERNAL SAS to the OS drives and the Readzillas. So those are now SAS2 instead of SATA like the older models. Yes, the Erie card uses an IO slot, but that's OK, because the Thebe2 cards allow us to use less SAS HBAs to grow the system, right? That's it. Not too much bad news and really not that bad. AK8 does not support the 7x10 series, and you may need new Thebe2 cards in your older systems if you want to add on newer DE2 trays. I think we can all agree that there are worse things out there. Like our Congress.   Next up.... More good news and cool AK8 tricks. Such as virtual NICS. 

  Read the article

• Making Those PanelBoxes Behave

  - by Duncan Mills

  I have a little problem to solve earlier this week - misbehaving <af:panelBox> components... What do I mean by that? Well here's the scenario, I have a page fragment containing a set of panelBoxes arranged vertically. As it happens, they are stamped out in a loop but that does not really matter. What I want to be able to do is to provide the user with a simple UI to close and open all of the panelBoxes in concert. This could also apply to showDetailHeader and similar items with a disclosed attrubute, but in this case it's good old panelBoxes.  Ok, so the basic solution to this should be self evident. I can set up a suitable scoped managed bean that the panelBoxes all refer to for their disclosed attribute state. Then the open all / close commandButtons in the UI can simply set the state of that bean for all the panelBoxes to pick up via EL on their disclosed attribute. Sound OK? Well that works basically without a hitch, but turns out that there is a slight problem and this is where the framework is attempting to be a little too helpful. The issue is that is the user manually discloses or hides a panelBox then that will override the value that the EL is setting. So for example. I start the page with all panelBoxes collapsed, all set by the EL state I'm storing on the session I manually disclose panelBox no 1. I press the Expand All button - all works as you would hope and all the panelBoxes are now disclosed, including of course panelBox 1 which I just expanded manually. Finally I press the Collapse All button and everything collapses except that first panelBox that I manually disclosed.  The problem is that the component remembers this manual disclosure and that overrides the value provided by the expression. If I change the viewId (navigate away and back) then the panelBox will start to behave again, until of course I touch it again! Now, the more astute amoungst you would think (as I did) Ah, sound like the MDS personalizaton stuff is getting in the way and the solution should simply be to set the dontPersist attribute to disclosed | ALL. Alas this does not fix the issue.  After a little noodling on the best way to approach this I came up with a solution that works well, although if you think of an alternative way do let me know. The principle is simple. In the disclosureListener for the panelBox I take a note of the clientID of the panelBox component that has been touched by the user along with the state. This all gets stored in a Map of Booleans in ViewScope which is keyed by clientID and stores the current disclosed state in the Boolean value.  The listener looks like this (it's held in a request scope backing bean for the page): public void handlePBDisclosureEvent(DisclosureEvent disclosureEvent) { String clientId = disclosureEvent.getComponent().getClientId(FacesContext.getCurrentInstance()); boolean state = disclosureEvent.isExpanded(); pbState.addTouchedPanelBox(clientId, state); } The pbState variable referenced here is a reference to the bean which will hold the state of the panelBoxes that lives in viewScope (recall that everything is re-set when the viewid is changed so keeping this in viewScope is just fine and cleans things up automatically). The addTouchedPanelBox() method looks like this: public void addTouchedPanelBox(String clientId, boolean state) { //create the cache if needed this is just a Map<String,Boolean> if (_touchedPanelBoxState == null) { _touchedPanelBoxState = new HashMap<String, Boolean>(); } // Simply put / replace _touchedPanelBoxState.put(clientId, state); } So that's the first part, we now have a record of every panelBox that the user has touched. So what do we do when the Collapse All or Expand All buttons are pressed? Here we do some JavaScript magic. Basically for each clientID that we have stored away, we issue a client side disclosure event from JavaScript - just as if the user had gone back and changed it manually. So here's the Collapse All button action: public String CloseAllAction() { submitDiscloseOverride(pbState.getTouchedClientIds(true), false); _uiManager.closeAllBoxes(); return null; }  The _uiManager.closeAllBoxes() method is just manipulating the master-state that all of the panelBoxes are bound to using EL. The interesting bit though is the line:  submitDiscloseOverride(pbState.getTouchedClientIds(true), false); To break that down, the first part is a call to that viewScoped state holder to ask for a list of clientIDs that need to be "tweaked": public String getTouchedClientIds(boolean targetState) { StringBuilder sb = new StringBuilder(); if (_touchedPanelBoxState != null && _touchedPanelBoxState.size() > 0) { for (Map.Entry<String, Boolean> entry : _touchedPanelBoxState.entrySet()) { if (entry.getValue() == targetState) { if (sb.length() > 0) { sb.append(','); } sb.append(entry.getKey()); } } } return sb.toString(); } You'll notice that this method only processes those panelBoxes that will be in the wrong state and returns those as a comma separated list. This is then processed by the submitDiscloseOverride() method: private void submitDiscloseOverride(String clientIdList, boolean targetDisclosureState) { if (clientIdList != null && clientIdList.length() > 0) { FacesContext fctx = FacesContext.getCurrentInstance(); StringBuilder script = new StringBuilder(); script.append("overrideDiscloseHandler('"); script.append(clientIdList); script.append("',"); script.append(targetDisclosureState); script.append(");"); Service.getRenderKitService(fctx, ExtendedRenderKitService.class).addScript(fctx, script.toString()); } } This method constructs a JavaScript command to call a routine called overrideDiscloseHandler() in a script attached to the page (using the standard <af:resource> tag). That method parses out the list of clientIDs and sends the correct message to each one: function overrideDiscloseHandler(clientIdList, newState) { AdfLogger.LOGGER.logMessage(AdfLogger.INFO, "Disclosure Hander newState " + newState + " Called with: " + clientIdList); //Parse out the list of clientIds var clientIdArray = clientIdList.split(','); for (var i = 0; i < clientIdArray.length; i++){ var panelBox = flipPanel = AdfPage.PAGE.findComponentByAbsoluteId(clientIdArray[i]); if (panelBox.getComponentType() == "oracle.adf.RichPanelBox"){ panelBox.broadcast(new AdfDisclosureEvent(panelBox, newState)); } }  }  So there you go. You can see how, with a few tweaks the same code could be used for other components with disclosure that might suffer from the same problem, although I'd point out that the behavior I'm working around here us usually desirable. You can download the running example (11.1.2.2) from here. 

  Read the article

• VNIC - New feature of AK8 - Working with VNICs

  - by Steve Tunstall

  One of the important new features of the AK8 code is the ability to use multiple IP addresses on the same physical network port. This feature is called VNICs, or Virtual NICs. This allows us to no longer "burn" a whole port in a cluster when one cluster peer owns a network port. Traditionally, we have had to leave Net0 empty on controller 2, because it was used for managing controller 1. Vise-versa for Net1 on Controller 1. Then, if you have data going over 10GigE ports, you probably only had half of your ports running at any given time, and the partner 10GigE port on the other controller just sat there, doing nothing, unless the first controller went down. What a waste. Those days are over.  I want to thank and give a big shout-out to our good partner, OnX Enterprise Solutions, for allowing me to come into their lab and play around with their 7320 to do this demo. They let me make a big mess of their lab for the day as I played around with VNICs. If you're looking for a partner who knows Oracle well and can also piece together a solution from multiple vendors to get you what you need, OnX is a good choice. If you would like to talk to your local OnX rep, you can contact Scott Gill at [email protected] and he can point you in the right direction for your area.  Here we go: Here is what your Datalinks window looks like BEFORE you upgrade to AK8. Here's what the same screen looks like after you upgrade. See the new box? So here is my current network setup. I have my 4 physical interfaces setup each with an IP address. If I ping them, no problems.  So I can ping 180, 181, 251, and 252. However, if I try to ping 240, it does not work, as the 240 address is not being used by any of these interfaces, right?Let's change that. Here, I'm going to make a new Datalink by clicking the Datalink "Plus sign" button. I will check the VNIC box and tell it to use igb2, even though another interface is already using it. Now, I will create a new Interface, and choose "v_dl2" for it's datalink. My new network screen looks like this. A few things to take note of here. First, when I click the "igb2" device, it only highlights dl2 and int2. It does not highlight v_dl2 or v_int2.I think it should, but OK, it looks like VNICs don't highlight when you click the device. Second, note how the underscore character in v_dl2 and v_int2 do not seem to show on this screen. You can see it plainly if you go in and edit them, but from here it looks like a space instead of an underscore. Just a cosmetic bug, but something to be aware of. Now, if I click the VNIC datalink "v_dl2", on the other hand, it DOES highlight the device it belongs to, as it should. Seen here: Note that it did not, however, highlight int2 with it, even though int2 is connected to igb2. That's because we clicked v_dl2, which int2 has nothing to do with. So I'm OK with that. So let's try pinging 240 now. Of course, it works great.  So I now make another VNIC, and call it v_dl3 using igb3, and v_int3 with an address of 241. I then setup three shares, using ports 251, 240, and 241.Remember that IP 251 and 240 both are using the same physical port of igb2, and IP 241 is using port igb3. Next, I copy a folder full of stuff over to all three shares at the same time. I have analytics going so I can see the traffic. My top chart is showing the logical interfaces, and the bottom chart is showing the physical ports.Sure enough, look at the igb2 and vnic1 interfaces. They equal the traffic going over the igb2 physical port on the second chart. VNIC2, on the other hand, gets igb3 all to itself. This would work the same way with 10Gig or Infiniband ports. You can now have multiple IP addresses and even completely different subnets sharing the same physical ports. You may need to make route table entries for that. This allows us to use all of the ports you paid for with no more waste.  Very, very cool.  One small "bug" I found when doing this. It's really not a bug, it was designed to do this when VNICs were not around. But now that we have NVIC capability, they should probably change this. I've alerted the engineering team about this and they're looking into it, so perhaps it will be fixed in a later code. Here it is. Remember when we made the new VNIC datalink, I specifically said to click on the "Plus Sign" button to create it? I don't always do that. I really like to use the drag-and-drop method to create my datalinks in the network screen.HOWEVER, if you were to do that for building a VNIC, it will mess you up a little. Watch this. Here, I'm dragging igb3 over to make a new datalink. igb3 is already being used by dl3, but I'm going to make this a VNIC, so who cares, right? Well, the ZFSSA does not KNOW you are going to make it a VNIC, now does it? So... it works as designed and REMOVES the igb3 device from the current dl3 datalink in the background. See how it's now missing? At the same time, the dl3 datalink choice is missing from my list of possible VNICs for me to choose from!!!! Hey!!! I wanted to pick dl3. Why isn't it on the list??? Well, it can't be on this list because dl3 no longer has a device associated with it. Bummer for you. When you click cancel, the device is still missing from dl3. The fix is easy. Just edit dl3 by clicking the pencil button, do absolutely nothing, and click "Apply". The device will magically come back. Now, make the VNIC datalink by clicking the "Plus Sign" button. Sure enough, once you check the VNIC box, dl3 is a valid choice. No problem.  That's it for now. Have fun with VNICs.

  Read the article

• How to Plug a Small Hole in NetBeans JSF (Join Table) Code Generation

  - by MarkH

  I was asked recently to provide an assist with designing and building a small-but-vital application that had at its heart some basic CRUD (Create, Read, Update, & Delete) functionality, built upon an Oracle database, to be accessible from various locations. Working from the stated requirements, I fleshed out the basic application and database designs and, once validated, set out to complete the first iteration for review. Using SQL Developer, I created the requisite tables, indices, and sequences for our first run. One of the tables was a many-to-many join table with three fields: one a primary key for that table, the other two being primary keys for the other tables, represented as foreign keys in the join table. Here is a simplified example of the trio of tables: Once the database was in decent shape, I fired up NetBeans to let it have first shot at the code. NetBeans does a great job of generating a mountain of essential code, saving developers what must be millions of hours of effort each year by building a basic foundation with a few clicks and keystrokes. Lest you think it (or any tool) can do everything for you, however, occasionally something tosses a paper clip into the delicate machinery and makes you open things up to fix them. Join tables apparently qualify.  :-) In the case above, the entity class generated for the join table (New Entity Classes from Database) included an embedded object consisting solely of the two foreign key fields as attributes, in addition to an object referencing each one of the "component" tables. The Create page generated (New JSF Pages from Entity Classes) worked well to a point, but when trying to save, we were greeted with an error: Transaction aborted. Hmm. A quick debugger session later and I'd identified the issue: when trying to persist the new join-table object, the embedded "foreign-keys-only" object still had null values for its two (required value) attributes...even though the embedded table objects had populated key attributes. Here's the simple fix: In the join-table controller class, find the public String create() method. It will look something like this:     public String create() {        try {            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } To restore balance to the force, modify the create() method as follows (changes in red):     public String create() {         try {            // Add the next two lines to resolve:            current.getJoinEntityPK().setTbl1id(current.getTbl1().getId().toBigInteger());            current.getJoinEntityPK().setTbl2id(current.getTbl2().getId().toBigInteger());            getFacade().create(current);            JsfUtil.addSuccessMessage(ResourceBundle.getBundle("/Bundle").getString("JoinEntityCreated"));            return prepareCreate();        } catch (Exception e) {            JsfUtil.addErrorMessage(e, ResourceBundle.getBundle("/Bundle").getString("PersistenceErrorOccured"));            return null;        }    } I'll be refactoring this code shortly, but for now, it works. Iteration one is complete and being reviewed, and we've met the milestone. Here's to happy endings (and customers)! All the best,Mark

  Read the article

• Implementing a Custom Coherence PartitionAssignmentStrategy

  - by jpurdy

  A recent A-Team engagement required the development of a custom PartitionAssignmentStrategy (PAS). By way of background, a PAS is an implementation of a Java interface that controls how a Coherence partitioned cache service assigns partitions (primary and backup copies) across the available set of storage-enabled members. While seemingly straightforward, this is actually a very difficult problem to solve. Traditionally, Coherence used a distributed algorithm spread across the cache servers (and as of Coherence 3.7, this is still the default implementation). With the introduction of the PAS interface, the model of operation was changed so that the logic would run solely in the cache service senior member. Obviously, this makes the development of a custom PAS vastly less complex, and in practice does not introduce a significant single point of failure/bottleneck. Note that Coherence ships with a default PAS implementation but it is not used by default. Further, custom PAS implementations are uncommon (this engagement was the first custom implementation that we know of). The particular implementation mentioned above also faced challenges related to managing multiple backup copies but that won't be discussed here. There were a few challenges that arose during design and implementation: Naive algorithms had an unreasonable upper bound of computational cost. There was significant complexity associated with configurations where the member count varied significantly between physical machines. Most of the complexity of a PAS is related to rebalancing, not initial assignment (which is usually fairly simple). A custom PAS may need to solve several problems simultaneously, such as: Ensuring that each member has a similar number of primary and backup partitions (e.g. each member has the same number of primary and backup partitions) Ensuring that each member carries similar responsibility (e.g. the most heavily loaded member has no more than one partition more than the least loaded). Ensuring that each partition is on the same member as a corresponding local resource (e.g. for applications that use partitioning across message queues, to ensure that each partition is collocated with its corresponding message queue). Ensuring that a given member holds no more than a given number of partitions (e.g. no member has more than 10 partitions) Ensuring that backups are placed far enough away from the primaries (e.g. on a different physical machine or a different blade enclosure) Achieving the above goals while ensuring that partition movement is minimized. These objectives can be even more complicated when the topology of the cluster is irregular. For example, if multiple cluster members may exist on each physical machine, then clearly the possibility exists that at certain points (e.g. following a member failure), the number of members on each machine may vary, in certain cases significantly so. Consider the case where there are three physical machines, with 3, 3 and 9 members each (respectively). This introduces complexity since the backups for the 9 members on the the largest machine must be spread across the other 6 members (to ensure placement on different physical machines), preventing an even distribution. For any given problem like this, there are usually reasonable compromises available, but the key point is that objectives may conflict under extreme (but not at all unlikely) circumstances. The most obvious general purpose partition assignment algorithm (possibly the only general purpose one) is to define a scoring function for a given mapping of partitions to members, and then apply that function to each possible permutation, selecting the most optimal permutation. This would result in N! (factorial) evaluations of the scoring function. This is clearly impractical for all but the smallest values of N (e.g. a partition count in the single digits). It's difficult to prove that more efficient general purpose algorithms don't exist, but the key take away from this is that algorithms will tend to either have exorbitant worst case performance or may fail to find optimal solutions (or both) -- it is very important to be able to show that worst case performance is acceptable. This quickly leads to the conclusion that the problem must be further constrained, perhaps by limiting functionality or by using domain-specific optimizations. Unfortunately, it can be very difficult to design these more focused algorithms. In the specific case mentioned, we constrained the solution space to very small clusters (in terms of machine count) with small partition counts and supported exactly two backup copies, and accepted the fact that partition movement could potentially be significant (preferring to solve that issue through brute force). We then used the out-of-the-box PAS implementation as a fallback, delegating to it for configurations that were not supported by our algorithm. Our experience was that the PAS interface is quite usable, but there are intrinsic challenges to designing PAS implementations that should be very carefully evaluated before committing to that approach.

  Read the article

• Solaris 11.1 changes building of code past the point of __NORETURN

  - by alanc

  While Solaris 11.1 was under development, we started seeing some errors in the builds of the upstream X.Org git master sources, such as: "Display.c", line 65: Function has no return statement : x_io_error_handler "hostx.c", line 341: Function has no return statement : x_io_error_handler from functions that were defined to match a specific callback definition that declared them as returning an int if they did return, but these were calling exit() instead of returning so hadn't listed a return value. These had been generating warnings for years which we'd been ignoring, but X.Org has made enough progress in cleaning up code for compiler warnings and static analysis issues lately, that the community turned up the default error levels, including the gcc flag -Werror=return-type and the equivalent Solaris Studio cc flags -v -errwarn=E_FUNC_HAS_NO_RETURN_STMT, so now these became errors that stopped the build. Yet on Solaris, gcc built this code fine, while Studio errored out. Investigation showed this was due to the Solaris headers, which during Solaris 10 development added a number of annotations to the headers when gcc was being used for the amd64 kernel bringup before the Studio amd64 port was ready. Since Studio did not support the inline form of these annotations at the time, but instead used #pragma for them, the definitions were only present for gcc. To resolve this, I fixed both sides of the problem, so that it would work for building new X.Org sources on older Solaris releases or with older Studio compilers, as well as fixing the general problem before it broke more software building on Solaris. To the X.Org sources, I added the traditional Studio #pragma does_not_return to recognize that functions like exit() don't ever return, in patches such as this Xserver patch. Adding a dummy return statement was ruled out as that introduced unreachable code errors from compilers and analyzers that correctly realized you couldn't reach that code after a return statement. And on the Solaris 11.1 side, I updated the annotation definitions in <sys/ccompile.h> to enable for Studio 12.0 and later compilers the annotations already existing in a number of system headers for functions like exit() and abort(). If you look in that file you'll see the annotations we currently use, though the forms there haven't gone through review to become a Committed interface, so may change in the future. Actually getting this integrated into Solaris though took a bit more work than just editing one header file. Our ELF binary build comparison tool, wsdiff, actually showed a large number of differences in the resulting binaries due to the compiler using this information for branch prediction, code path analysis, and other possible optimizations, so after comparing enough of the disassembly output to be comfortable with the changes, we also made sure to get this in early enough in the release cycle so that it would get plenty of test exposure before the release. It also required updating quite a bit of code to avoid introducing new lint or compiler warnings or errors, and people building applications on top of Solaris 11.1 and later may need to make similar changes if they want to keep their build logs similarly clean. Previously, if you had a function that was declared with a non-void return type, lint and cc would warn if you didn't return a value, even if you called a function like exit() or panic() that ended execution. For instance: #include <stdlib.h> int callback(int status) { if (status == 0) return status; exit(status); } would previously require a never executed return 0; after the exit() to avoid lint warning "function falls off bottom without returning value". Now the compiler & lint will both issue "statement not reached" warnings for a return 0; after the final exit(), allowing (or in some cases, requiring) it to be removed. However, if there is no return statement anywhere in the function, lint will warn that you've declared a function returning a value that never does so, suggesting you can declare it as void. Unfortunately, if your function signature is required to match a certain form, such as in a callback, you not be able to do so, and will need to add a /* LINTED */ to the end of the function. If you need your code to build on both a newer and an older release, then you will either need to #ifdef these unreachable statements, or, to keep your sources common across releases, add to your sources the corresponding #pragma recognized by both current and older compiler versions, such as: #pragma does_not_return(exit) #pragma does_not_return(panic) Hopefully this little extra work is paid for by the compilers & code analyzers being able to better understand your code paths, giving you better optimizations and more accurate errors & warning messages.

  Read the article

• Twitter ?? Nashorn ????(??)

  - by Homma

  ???? Nashorn ? Java ??????? Twitter ???????????????????? JavaFX ??????????????? ????? ??? jlaskey ??? Nashorn Blog ????????????? https://blogs.oracle.com/nashorn/entry/nashorn_in_the_twitterverse_continued ???????? ?? Twitter ???????????????????????? JavaFX ??????????????????????????????? Nashorn ?? JavaFX ??????????????JavaFX ???????????????????????????????????????Nashorn ? Java ????????????????????????????????????(JavaFX ?????????????????????)? ?????????????????????????????????????????????? Twitter ????????????????????????? var twitter4j = Packages.twitter4j; var TwitterFactory = twitter4j.TwitterFactory; var Query = twitter4j.Query; function getTrendingData() { var twitter = new TwitterFactory().instance; var query = new Query("nashorn OR nashornjs"); query.since("2012-11-21"); query.count = 100; var data = {}; do { var result = twitter.search(query); var tweets = result.tweets; for each (var tweet in tweets) { var date = tweet.createdAt; var key = (1900 + date.year) + "/" + (1 + date.month) + "/" + date.date; data[key] = (data[key] || 0) + 1; } } while (query = result.nextQuery()); return data; } ??????????????????getTrendingData() ??????????????(??????????Nashorn ???????? OpenJDK ?????? 2012 ? 11 ? 21 ???)??????????????????????????????????? ????JavaFX ? BarChart ??????????? var javafx = Packages.javafx; var Stage = javafx.stage.Stage var Scene = javafx.scene.Scene; var Group = javafx.scene.Group; var Chart = javafx.scene.chart.Chart; var FXCollections = javafx.collections.FXCollections; var ObservableList = javafx.collections.ObservableList; var CategoryAxis = javafx.scene.chart.CategoryAxis; var NumberAxis = javafx.scene.chart.NumberAxis; var BarChart = javafx.scene.chart.BarChart; var XYChart = javafx.scene.chart.XYChart; var Series = javafx.scene.chart.XYChart.Series; var Data = javafx.scene.chart.XYChart.Data; function graph(stage, data) { var root = new Group(); stage.scene = new Scene(root); var dates = Object.keys(data); var xAxis = new CategoryAxis(); xAxis.categories = FXCollections.observableArrayList(dates); var yAxis = new NumberAxis("Tweets", 0.0, 200.0, 50.0); var series = FXCollections.observableArrayList(); for (var date in data) { series.add(new Data(date, data[date])); } var tweets = new Series("Tweets", series); var barChartData = FXCollections.observableArrayList(tweets); var chart = new BarChart(xAxis, yAxis, barChartData, 25.0); root.children.add(chart); } ????????????????????????????????stage.scene = new Scene(root) ? stage.setScene(new Scene(root)) ????????????????????Nashorn ? stage ??????? scene ???????????????????(Dynalink ?????????)Java Beans ???????????????? (setScene()) ???????????????????????????????Nashorn ? FXCollections ??????????????????????????????observableArrayList(dates) ??????????Nashorn ? JavaScript ??? (dates) ? Java ???????????????????????????? JavaScript ?????????????????? Java ????????????????????????????????????????????????????????????? ????????????????????????????????? JavaFX ???????????????????????? JavaFX ??????????????javafx.application.Application ??????????????????????????? JavaFX ????????????????????????????????????????????????? import java.io.IOException; import java.io.InputStream; import java.io.InputStreamReader; import javafx.application.Application; import javafx.stage.Stage; import javax.script.ScriptEngine; import javax.script.ScriptEngineManager; import javax.script.ScriptException; public class TrendingMain extends Application { private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); private Trending trending; public static void main(String[] args) { launch(args); } @Override public void start(Stage stage) throws Exception { trending = (Trending) load("Trending.js"); trending.start(stage); } @Override public void stop() throws Exception { trending.stop(); } private Object load(String script) throws IOException, ScriptException { try (final InputStream is = TrendingMain.class.getResourceAsStream(script)) { return engine.eval(new InputStreamReader(is, "utf-8")); } } } ???? Nashorn ??????? JSR-223 ? javax.script ????????? private static final ScriptEngineManager MANAGER = new ScriptEngineManager(); private final ScriptEngine engine = MANAGER.getEngineByName("nashorn"); ????????? JavaScript ???????? Nashorn ???????????????????? load ???????????????????????engine ???????????????load ????????????? ???????????????Java ???????????????????????????????????????????????????? Java ????????????????JavaFX ???????? start ????? stop ?????????????????????????????????????? public interface Trending { public void start(Stage stage) throws Exception; public void stop() throws Exception; } ?????????????????????????????? function newTrending() { return new Packages.Trending() { start: function(stage) { var data = getTrendingData(); graph(stage, data); stage.show(); }, stop: function() { } } } newTrending(); ?????? Trending ?????????????????????start ????? stop ??????????????????????????????????? eval ???? Java ??????????????? trending = (Trending) load("Trending.js"); ????????????????Trending.js ??????? getTrendingData ???????????? newTrending ????????????????????? Java ?????????newTrending ????????? eval ????????? Trending ????????????????????????????????????????????????????????? trending.start(stage); ???????? ???? Nashorn ????????? http://www.myexpospace.com/JavaOne2012/SessionFiles/CON5251_PDF_5251_0001.pdf ???????? Dynalink ??????? https://github.com/szegedi/dynalink ????????

  Read the article

• How to escape or remove double quotes in rsyslog template

  - by Evgeny

  I want rsyslog to write log messages in JSON format, which requires to use double-quotes (") around strings. Problem is that values sometime include double-quotes themselves, and those need to be escaped - but I can't figure out how to do that. Currently my rsyslog.conf contains this format that I use (a bit simplified): $template JsonFormat,"{\"msg\":\"%msg%\",\"app-name\":\"%app-name%\"}\n",sql But when a msg arrives that contains double quotes, the JSON is broken, example: user pid=21214 uid=0 auid=4294967295 msg='PAM setcred: user="oracle" exe="/bin/su" (hostname=?, addr=?, terminal=? result=Success)' turns into: {"msg":"user pid=21214 uid=0 auid=4294967295 msg='PAM setcred: user="oracle" exe="/bin/su" (hostname=?, addr=?, terminal=? result=Success)'","app-name":"user"} but what I need it to become is: {"msg":"user pid=21214 uid=0 auid=4294967295 msg='PAM setcred: user=\"oracle\" exe=\"/bin/su\" (hostname=?, addr=?, terminal=? result=Success)'","app-name":"user"}

  Read the article

• How can I create an “su” only user (no SSH or SFTP) and limit who can “su” into that account in RHEL5? [closed]

  - by Beaming Mel-Bin

  Possible Duplicate: How can I allow one user to su to another without allowing root access? We have a user account that our DBAs use (oracle). I do not want to set a password on this account and want to only allow users in the dba group to su - oracle. How can I accomplish this? I was thinking of just giving them sudo access to the su - oracle command. However, I wouldn't be surprised if there was a more polished/elegant/secure way.

  Read the article

• Difficulty in running Tomcat v7.0 with Eclipse Juno

  - by user1673718

  I get the following error when I run my JSP file in Eclipse-Juno with Tomcat v7: 'starting Tomcat v7.0 server at localhost' has encountered a problem. Port 8080 required by Tomcat v7.0 server at localhost is already in use. The server may already be running in another process, or a system process may be using the port. To start this server you will need to stop the other process or change the port number(s). I have Oracle 10g installed in my System. When I type "http://localhost:8080" it opens the Oracle 10g license agreement so I think Oracle 10g is already running in that port. To change the port of Tomcat I tried Google, which said to change the port in the "C:\Program Files\Apache Software Foundation\Apache Tomcat 7.0.14\conf\httpd.conf" file But at "C:\Program Files\Apache Software Foundation\Apache Tomcat 7.0.14\conf" there was no httpd.conf file. I only have "catalina.policy,catalina.properties,context,logging.properties,server,tomcat-users,web" files in that conf folder. I use windows XP.

  Read the article

• Where is the central ZFS website now?

  - by Stefan Lasiewski

  Oracle dumped OpenSolaris in Fall 2010, and it is unclear if Oracle will continue to publicly release updates to ZFS, except maybe after they release their next major version of Solaris. FreeBSD now has ZFS v28 available for testing. But where did v28 come from? I notice that the main ZFS website does not show version 28 available. Has this website been abandoned? If so, where is the central website for the ZFS project, so that I can browse the repo, read the mailing lists, read the release notes, etc. (I realize that OpenSolaris has been dumped by Oracle, and that they are limiting their ZFS releases to the community).

  Read the article

• How to make VirtualBox headless answer on rdp port?

  - by stiv

  I'd like to run windows xp on RDP: $ VBoxManage modifyvm winxp32 --vrdeport 3389 $ VBoxHeadless -s winxp32 -v on Oracle VM VirtualBox Headless Interface 4.1.18_Debian (C) 2008-2012 Oracle Corporation All rights reserved. (waiting) in another window: $ telnet localhost 3389 Trying 127.0.0.1... telnet: Unable to connect to remote host: Connection refused Yes, I've read about extension: $ sudo VBoxManage extpack install Oracle_VM_VirtualBox_Extension_Pack-4.1.20-80170.vbox-extpack 0%... Progress state: NS_ERROR_FAILURE VBoxManage: error: Failed to install "Oracle_VM_VirtualBox_Extension_Pack-4.1.20- 80170.vbox-extpack": Extension pack 'Oracle VM VirtualBox Extension Pack' is already installed. In case of a reinstallation, please uninstall it first Looked through all manuals and all help requests. No success. What's wrong? Any ideas?

  Read the article

< Previous Page | 580 581 582 583 584 585 586 587 588 589 590 591  | Next Page >