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  • What's up with LDoms: Part 2 - Creating a first, simple guest

    - by Stefan Hinker
    Welcome back! In the first part, we discussed the basic concepts of LDoms and how to configure a simple control domain.  We saw how resources were put aside for guest systems and what infrastructure we need for them.  With that, we are now ready to create a first, very simple guest domain.  In this first example, we'll keep things very simple.  Later on, we'll have a detailed look at things like sizing, IO redundancy, other types of IO as well as security. For now,let's start with this very simple guest.  It'll have one core's worth of CPU, one crypto unit, 8GB of RAM, a single boot disk and one network port.  CPU and RAM are easy.  The network port we'll create by attaching a virtual network port to the vswitch we created in the primary domain.  This is very much like plugging a cable into a computer system on one end and a network switch on the other.  For the boot disk, we'll need two things: A physical piece of storage to hold the data - this is called the backend device in LDoms speak.  And then a mapping between that storage and the guest domain, giving it access to that virtual disk.  For this example, we'll use a ZFS volume for the backend.  We'll discuss what other options there are for this and how to chose the right one in a later article.  Here we go: root@sun # ldm create mars root@sun # ldm set-vcpu 8 mars root@sun # ldm set-mau 1 mars root@sun # ldm set-memory 8g mars root@sun # zfs create rpool/guests root@sun # zfs create -V 32g rpool/guests/mars.bootdisk root@sun # ldm add-vdsdev /dev/zvol/dsk/rpool/guests/mars.bootdisk \ mars.root@primary-vds root@sun # ldm add-vdisk root mars.root@primary-vds mars root@sun # ldm add-vnet net0 switch-primary mars That's all, mars is now ready to power on.  There are just three commands between us and the OK prompt of mars:  We have to "bind" the domain, start it and connect to its console.  Binding is the process where the hypervisor actually puts all the pieces that we've configured together.  If we made a mistake, binding is where we'll be told (starting in version 2.1, a lot of sanity checking has been put into the config commands themselves, but binding will catch everything else).  Once bound, we can start (and of course later stop) the domain, which will trigger the boot process of OBP.  By default, the domain will then try to boot right away.  If we don't want that, we can set "auto-boot?" to false.  Finally, we'll use telnet to connect to the console of our newly created guest.  The output of "ldm list" shows us what port has been assigned to mars.  By default, the console service only listens on the loopback interface, so using telnet is not a large security concern here. root@sun # ldm set-variable auto-boot\?=false mars root@sun # ldm bind mars root@sun # ldm start mars root@sun # ldm list NAME STATE FLAGS CONS VCPU MEMORY UTIL UPTIME primary active -n-cv- UART 8 7680M 0.5% 1d 4h 30m mars active -t---- 5000 8 8G 12% 1s root@sun # telnet localhost 5000 Trying 127.0.0.1... Connected to localhost. Escape character is '^]'. ~Connecting to console "mars" in group "mars" .... Press ~? for control options .. {0} ok banner SPARC T3-4, No Keyboard Copyright (c) 1998, 2011, Oracle and/or its affiliates. All rights reserved. OpenBoot 4.33.1, 8192 MB memory available, Serial # 87203131. Ethernet address 0:21:28:24:1b:50, Host ID: 85241b50. {0} ok We're done, mars is ready to install Solaris, preferably using AI, of course ;-)  But before we do that, let's have a little look at the OBP environment to see how our virtual devices show up here: {0} ok printenv auto-boot? auto-boot? = false {0} ok printenv boot-device boot-device = disk net {0} ok devalias root /virtual-devices@100/channel-devices@200/disk@0 net0 /virtual-devices@100/channel-devices@200/network@0 net /virtual-devices@100/channel-devices@200/network@0 disk /virtual-devices@100/channel-devices@200/disk@0 virtual-console /virtual-devices/console@1 name aliases We can see that setting the OBP variable "auto-boot?" to false with the ldm command worked.  Of course, we'd normally set this to "true" to allow Solaris to boot right away once the LDom guest is started.  The setting for "boot-device" is the default "disk net", which means OBP would try to boot off the devices pointed to by the aliases "disk" and "net" in that order, which usually means "disk" once Solaris is installed on the disk image.  The actual devices these aliases point to are shown with the command "devalias".  Here, we have one line for both "disk" and "net".  The device paths speak for themselves.  Note that each of these devices has a second alias: "net0" for the network device and "root" for the disk device.  These are the very same names we've given these devices in the control domain with the commands "ldm add-vnet" and "ldm add-vdisk".  Remember this, as it is very useful once you have several dozen disk devices... To wrap this up, in this part we've created a simple guest domain, complete with CPU, memory, boot disk and network connectivity.  This should be enough to get you going.  I will cover all the more advanced features and a little more theoretical background in several follow-on articles.  For some background reading, I'd recommend the following links: LDoms 2.2 Admin Guide: Setting up Guest Domains Virtual Console Server: vntsd manpage - This includes the control sequences and commands available to control the console session. OpenBoot 4.x command reference - All the things you can do at the ok prompt

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  • Are there any books that teach techniques for effective pair programming?

    - by Paul D. Waite
    I’ve just read the pair programming chapter of ‘Making Software’ by Andy Oram, and I’d like to try it when I next get an opportunity. The chapter mentions that in one of the studies, the subjects were initially given instruction on effective pair programming. Are there any books (or chapters of books) that I could read to get a good grounding in how to do pair programming effectively, so that I’m more prepared?

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  • 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.

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  • Silverlight Cream for March 14, 2011 -- #1060

    - by Dave Campbell
    In this Issue: Lazar Nikolov, Rudi Grobler, WindowsPhoneGeek, Jesse Liberty, Pete Brown, Jessica Foster, Chris Rouw, Andy Beaulieu, and Colin Eberhardt. Above the Fold: Silverlight: "A Silverlight Resizable TextBlock (and other resizable things)" Colin Eberhardt WP7: "Retrofitting the Trial API" Jessica Foster Shoutouts: Rudi Grobler has a post up that's not Silverlight, but it's cool stuff you may be interested in: WPF Themes now available on NuGet From SilverlightCream.com: Simulating rain in Silverlight Lazar Nikolov has a cool tutorial up at SilverlightShow... Simulating rain. Nice demo near to top, and source code plus a very nice tutorial on the entire process. Making the ApplicationBar bindable Rudi Grobler has a couple new posts up... first this one on making the WP7 AppBar bindable... he's created 2 simple wrappers that make it possible to bind to a method... with source as usual! All about Splash Screens in WP7 – Creating animated Splash Screen WindowsPhoneGeek's latest is about splash screens in WP7, but he goes one better with animated splash screens. Lots of good information including closing points in more of a FAQ-style listing. Testing Network Availability Jesse Liberty's latest is on testing for network availability in your WP7 app. This should be required reading for anyone building a WP7 app, since you never know when the network is going to be there or not. Lighting up on Windows 7 with Native Extensions for Microsoft Silverlight Pete Brown's latest post is about the Native Extensions for Microsoft Silverlight or NESL library. Pete describes what NESL is, a link to the library, installing it, and tons more information. If you wanna know or try NESL... this looks like the place to start. Retrofitting the Trial API Jessica Foster paused on the way to shipping her WP7 app to add in the trial API code. Check out what she went through to complete that task, as she explains the steps and directions she took. Good description, links, and code. WP7 Insights #2: Creating a Splash Screen in WP7 In the 2nd post in his series on WP7 development, Chris Rouw is discussing a WP7 splash screen. He gives some good external links for references then gets right into discussing his code. Air Hockey for Windows Phone 7 Andy Beaulieu shares a tutorial he wrote for the Expression Toolbox site, using the Physics Helper Library and Farseer Physics Engine -- an Air Hockey game for WP7. A Silverlight Resizable TextBlock (and other resizable things) I think Michael Washington had the best comment about Colin Eberhardt's latest outing: "Another WOW example" ... drop this in the pretty darn cool category: an attached behavior that uses a Thumb control within a popup to adorn any UI element to allow the user to resize it! Stay in the 'Light! Twitter SilverlightNews | Twitter WynApse | WynApse.com | Tagged Posts | SilverlightCream Join me @ SilverlightCream | Phoenix Silverlight User Group Technorati Tags: Silverlight    Silverlight 3    Silverlight 4    Windows Phone MIX10

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  • PASS Professional Development Virtual Chapter Reboot

    - by AjarnMark
    The Professional Development Virtual Chapter for PASS is holding its first virtual meeting on Thursday, May 13 at 1:00 PM Eastern / 10:00 AM Pacfic time.  Andy Warren (@sqlandy) will be the speaker.  Click here to attend via Live Meeting.  Also, check the http://prof-dev.sqlpass.org web site and RSS Feed for ongoing updates and details.

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  • Survey: How much data do you work with?

    - by James Luetkehoelter
    Andy isn't the only one that can ask a survey question. This is something I really curious about because many of the answers or recommendations or rants in blogs are not universably applicable to every database - small databases must sometimes be treated differently, and uber databases are just a pain (and fun at the same time). So, how would you classify most of the databases you work with: 1) Up to 50GB 2) 50-500GB 3) 500GB - 2TB 4) DEAR GOD THAT"S TOO MUCH INFORMATION! Share this post: email it!...(read more)

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  • Android : un demi-million d'appareils activés par jour avec une croissance de 4.4 % par semaine, son succès ne faiblit pas

    Android : un demi-million d'appareils activés par jour Avec une croissance de 4.4 % par semaine, son succès ne faiblit pas Mise à jour du 28/06/2011 par Idelways Contrairement à ce que pourraient faire croire certains indices, le succès d'Android ne faiblit pas, il est même plus fort que jamais puisqu'il vient de franchir la barre des 500 000 appareils activés par jour. Et contrairement aux milestones précédents, cette nouvelle n'a été annoncée jusque-là qu'à travers le compte Twitter du guru de l'OS chez Google et son vice-président de l'ingénierie Andy Rubin. Le nombre d'activations continue d'augmenter ...

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  • Presenting Designing an SSIS Execution Framework to Steel City SQL 18 Jan 2011!

    - by andyleonard
    I'm honored to present Designing an SSIS Execution Framework (Level 300) to Steel City SQL - the Birmingham Alabama chapter of PASS - on 18 Jan 2011! The meeting starts at 6:00 PM 18 Jan 2011 and will be held at: New Horizons Computer Learning Center 601 Beacon Pkwy. West Suite 106 Birmingham, Alabama, 35209 ( Map for directions ) Abstract In this “demo-tastic” presentation, SSIS trainer, author, and consultant Andy Leonard explains the what, why, and how of an SSIS framework that delivers metadata-driven...(read more)

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  • 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,

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  • 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

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  • Join the Authors of SSIS Design Patterns at the PASS Summit 2012!

    - by andyleonard
    My fellow authors and I will be presenting a day-long pre-conference session titled SSIS Design Patterns at the PASS Summit 2012 in Seattle Monday 5 Nov 2012! Register to learn patterns for: Package execution Package logging Loading flat file sources Loading XML sources Loading the cloud Dynamic package generation SSIS Frameworks Data warehouse ETL Data flow performance   Presenting this session: Matt Masson Tim Mitchell Jessica Moss Michelle Ufford Andy Leonard I hope to see you in Seattle!...(read more)

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  • Recap - SQL Saturday 151 in Orlando

    - by KKline
    It's always a feel-good experience for me to return to SQL Saturday in Orlando, the place where SQL Saturdays were started by Andy Warren ( Twitter | Blog ). On this trip, I delivered a full-day, pre-conference seminar on Troubleshooting and Performance Tuning SQL Server. I also delivered a session on SQL Server Internals and Architecture to a totally packed house. For those of you who emailed me directly, here's the link for the special SQL Sentry offer . I got to attend the extended events session...(read more)

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  • The Faces in the Crowdsourcing

    - by Applications User Experience
    By Jeff Sauro, Principal Usability Engineer, Oracle Imagine having access to a global workforce of hundreds of thousands of people who can perform tasks or provide feedback on a design quickly and almost immediately. Distributing simple tasks not easily done by computers to the masses is called "crowdsourcing" and until recently was an interesting concept, but due to practical constraints wasn't used often. Enter Amazon.com. For five years, Amazon has hosted a service called Mechanical Turk, which provides an easy interface to the crowds. The service has almost half a million registered, global users performing a quarter of a million human intelligence tasks (HITs). HITs are submitted by individuals and companies in the U.S. and pay from $.01 for simple tasks (such as determining if a picture is offensive) to several dollars (for tasks like transcribing audio). What do we know about the people who toil away in this digital crowd? Can we rely on the work done in this anonymous marketplace? A rendering of the actual Mechanical Turk (from Wikipedia) Knowing who is behind Amazon's Mechanical Turk is fitting, considering the history of the actual Mechanical Turk. In the late 1800's, a mechanical chess-playing machine awed crowds as it beat master chess players in what was thought to be a mechanical miracle. It turned out that the creator, Wolfgang von Kempelen, had a small person (also a chess master) hiding inside the machine operating the arms to provide the illusion of automation. The field of human computer interaction (HCI) is quite familiar with gathering user input and incorporating it into all stages of the design process. It makes sense then that Mechanical Turk was a popular discussion topic at the recent Computer Human Interaction usability conference sponsored by the Association for Computing Machinery in Atlanta. It is already being used as a source for input on Web sites (for example, Feedbackarmy.com) and behavioral research studies. Two papers shed some light on the faces in this crowd. One paper tells us about the shifting demographics from mostly stay-at-home moms to young men in India. The second paper discusses the reliability and quality of work from the workers. Just who exactly would spend time doing tasks for pennies? In "Who are the crowdworkers?" University of California researchers Ross, Silberman, Zaldivar and Tomlinson conducted a survey of Mechanical Turk worker demographics and compared it to a similar survey done two years before. The initial survey reported workers consisting largely of young, well-educated women living in the U.S. with annual household incomes above $40,000. The more recent survey reveals a shift in demographics largely driven by an influx of workers from India. Indian workers went from 5% to over 30% of the crowd, and this block is largely male (two-thirds) with a higher average education than U.S. workers, and 64% report an annual income of less than $10,000 (keeping in mind $1 has a lot more purchasing power in India). This shifting demographic certainly has implications as language and culture can play critical roles in the outcome of HITs. Of course, the demographic data came from paying Turkers $.10 to fill out a survey, so there is some question about both a self-selection bias (characteristics which cause Turks to take this survey may be unrepresentative of the larger population), not to mention whether we can really trust the data we get from the crowd. Crowds can perform tasks or provide feedback on a design quickly and almost immediately for usability testing. (Photo attributed to victoriapeckham Flikr While having immediate access to a global workforce is nice, one major problem with Mechanical Turk is the incentive structure. Individuals and companies that deploy HITs want quality responses for a low price. Workers, on the other hand, want to complete the task and get paid as quickly as possible, so that they can get on to the next task. Since many HITs on Mechanical Turk are surveys, how valid and reliable are these results? How do we know whether workers are just rushing through the multiple-choice responses haphazardly answering? In "Are your participants gaming the system?" researchers at Carnegie Mellon (Downs, Holbrook, Sheng and Cranor) set up an experiment to find out what percentage of their workers were just in it for the money. The authors set up a 30-minute HIT (one of the more lengthy ones for Mechanical Turk) and offered a very high $4 to those who qualified and $.20 to those who did not. As part of the HIT, workers were asked to read an email and respond to two questions that determined whether workers were likely rushing through the HIT and not answering conscientiously. One question was simple and took little effort, while the second question required a bit more work to find the answer. Workers were led to believe other factors than these two questions were the qualifying aspect of the HIT. Of the 2000 participants, roughly 1200 (or 61%) answered both questions correctly. Eighty-eight percent answered the easy question correctly, and 64% answered the difficult question correctly. In other words, about 12% of the crowd were gaming the system, not paying enough attention to the question or making careless errors. Up to about 40% won't put in more than a modest effort to get paid for a HIT. Young men and those that considered themselves in the financial industry tended to be the most likely to try to game the system. There wasn't a breakdown by country, but given the demographic information from the first article, we could infer that many of these young men come from India, which makes language and other cultural differences a factor. These articles raise questions about the role of crowdsourcing as a means for getting quick user input at low cost. While compensating users for their time is nothing new, the incentive structure and anonymity of Mechanical Turk raises some interesting questions. How complex of a task can we ask of the crowd, and how much should these workers be paid? Can we rely on the information we get from these professional users, and if so, how can we best incorporate it into designing more usable products? Traditional usability testing will still play a central role in enterprise software. Crowdsourcing doesn't replace testing; instead, it makes certain parts of gathering user feedback easier. One can turn to the crowd for simple tasks that don't require specialized skills and get a lot of data fast. As more studies are conducted on Mechanical Turk, I suspect we will see crowdsourcing playing an increasing role in human computer interaction and enterprise computing. References: Downs, J. S., Holbrook, M. B., Sheng, S., and Cranor, L. F. 2010. Are your participants gaming the system?: screening mechanical turk workers. In Proceedings of the 28th international Conference on Human Factors in Computing Systems (Atlanta, Georgia, USA, April 10 - 15, 2010). CHI '10. ACM, New York, NY, 2399-2402. Link: http://doi.acm.org/10.1145/1753326.1753688 Ross, J., Irani, L., Silberman, M. S., Zaldivar, A., and Tomlinson, B. 2010. Who are the crowdworkers?: shifting demographics in mechanical turk. In Proceedings of the 28th of the international Conference Extended Abstracts on Human Factors in Computing Systems (Atlanta, Georgia, USA, April 10 - 15, 2010). CHI EA '10. ACM, New York, NY, 2863-2872. Link: http://doi.acm.org/10.1145/1753846.1753873

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  • SSIS Training 15-19 Oct in Reston Virginia

    - by andyleonard
    Early bird registration is now open for Linchpin People ’s SSIS training course From Zero To SSIS scheduled for 15-19 Oct 2012 in Reston Virginia! Register today – the early bird discount ends 28 Sep 2012. Training Description From Zero to SSIS was developed by Andy Leonard to train technology professionals in the fine art of using SQL Server Integration Services (SSIS) to build data integration and Extract-Transform-Load (ETL) solutions. The training is focused around labs and emphasizes a hands-on...(read more)

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  • Androids development life cycle model query [closed]

    - by Andrew Rose
    I have been currently researching Google and their approach to marketing the Android OS. Primarily using an open source technique with the Open Hand Alliance and out souring through third-party developers. I'm now keen to investigate their approach using various development life cycle models in the form of waterfall, spiral, scrum, agile etc. And i'm just curious to have some feedback from professionals and what approach they think Google would use to have a positive effect on their business. Thanks for your time Andy Rose

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  • 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.

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  • 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

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  • Google: Numbers favor Android over iPhone

    <b>The Open Road:</b> "And according to Google VP Andy Rubin, the more the search giant blankets the industry with competing Android-droid based mobile handsets, the more likely Google is to hit its expected value of market dominance over Apple's iPhone."

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  • How can I easily retab html files according to some sane default?

    - by James
    I have some html files that I'd like to retab that look like this: <header> <div class="wrapper"> <img src="images/logo.png"> <div class="userbox"> <div class="welcome">Welcome Andy!</div> <div class="blackbox"> <ul> <li><a href="#">Invite Friends</a></li> <li><a href="#">My Account</a></li> <li><a href="#">Cart</a></li> <li><a href="#">Sign Out</a></li> </ul> </div> </div> </div> </header> And I want them to look something like this: <header> <div class="wrapper"> <img src="images/logo.png"> <div class="userbox"> <div class="welcome">Welcome Andy!</div> <div class="blackbox"> <ul> <li><a href="#">Invite Friends</a></li> <li><a href="#">My Account</a></li> <li><a href="#">Cart</a></li> <li><a href="#">Sign Out</a></li> </ul> </div> </div> </div> </header> Or some sane default. What's the easiest way to go about doing this from the terminal in ubuntu for all of the html files in the current directory?

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  • Presenting Loading Data Warehouse Partitions with SSIS 2012 at SQL Saturday DC!

    - by andyleonard
    Join Darryll Petrancuri and me as we present Loading Data Warehouse Partitions with SSIS 2012 Saturday 8 Dec 2012 at SQL Saturday 173 in DC ! SQL Server 2012 table partitions offer powerful Big Data solutions to the Data Warehouse ETL Developer. In this presentation, Darryll Petrancuri and Andy Leonard demonstrate one approach to loading partitioned tables and managing the partitions using SSIS 2012, and reporting partition metrics using SSRS 2012. Objectives A practical solution for loading Big...(read more)

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  • 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!

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  • Presenting Loading Data Warehouse Partitions with SSIS 2012 at SQL Saturday DC!

    - by andyleonard
    Join Darryll Petrancuri and me as we present Loading Data Warehouse Partitions with SSIS 2012 Saturday 8 Dec 2012 at SQL Saturday 173 in DC ! SQL Server 2012 table partitions offer powerful Big Data solutions to the Data Warehouse ETL Developer. In this presentation, Darryll Petrancuri and Andy Leonard demonstrate one approach to loading partitioned tables and managing the partitions using SSIS 2012, and reporting partition metrics using SSRS 2012. Objectives A practical solution for loading Big...(read more)

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  • Free Webinar - Using Enterprise Data Integration Dashboards

    - by andyleonard
    Join Kent Bradshaw and me as we present Using Enterprise Data Integration Dashboards Tuesday 11 Dec 2012 at 10:00 AM ET! If data is the life of the modern organization, data integration is the heart of an enterprise. Data circulation is vital. Data integration dashboards provide enterprise ETL (Extract, Transform, and Load) teams near-real-time status supported with historical performance analysis. Join Linchpins Kent Bradshaw and Andy Leonard as they demonstrate and discuss the benefits of data...(read more)

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