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  • dpkg error when using apt-get install

    - by V-T
    I upgraded to Ubuntu 14.04 from 12.04 and every time I use apt-get install for any package it ends with a bunch of errors about processing some of my latex packages. Including a snippet below: Sometimes, not accepting conffile updates in /etc/texmf/updmap.d causes updmap-sys to fail. Please check for files with extension .dpkg-dist or .ucf-dist in this directory dpkg: error processing package tex-common (--configure): subprocess installed post-installation script returned error exit status 1 dpkg: dependency problems prevent configuration of lmodern: lmodern depends on tex-common (>= 3); however: Package tex-common is not configured yet. Reproduced by using sudo dpkg --configure -a and a total list of packages with this error is included here: Errors were encountered while processing: tex-common texlive-publishers tex-gyre texlive-latex-extra-doc texlive-fonts-extra-doc texlive-lang-english texlive-luatex texlive-generic-recommended texlive-pstricks-doc texlive-fonts-recommended latex2html latex-xcolor texlive-pictures texlive-fonts-extra texlive-pictures-doc asymptote texlive-bibtex-extra texlive-latex-recommended-doc texlive-latex-recommended doxygen-latex texlive-pstricks tipa texlive-latex-base texlive-fonts-recommended-doc latex-beamer texlive-font-utils texlive-latex-base-doc texlive-latex-extra texlive-extra-utils texlive texlive-publishers-doc lmodern Any ideas on how to fix this?

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  • Lifecycle of an ASP.NET MVC 5 Application

    Here you can download a PDF Document that charts the lifecycle of every ASP.NET MVC 5 application, from receiving the HTTP request to sending the HTTP response back to the client. It is designed both as an educational tool for those who are new to ASP.NET MVC and also as a reference for those who need to drill into specific aspects of the application. The PDF document has the following features: Relevant HttpApplication stages to help you understand where MVC integrates into the ASP.NET application lifecycle. A high-level view of the MVC application lifecycle, where you can understand the major stages that every MVC application passes through in the request processing pipeline. A detail view that shows drills down into the details of the request processing pipeline. You can compare the high-level view and the detail view to see how the lifecycles details are collected into the various stages. Placement and purpose of all overridable methods on the Controller object in the request processing pipeline. You may or may not have the need to override any one method, but it is important for you to understand their role in the application lifecycle so that you can write code at the appropriate life cycle stage for the effect you intend. Blown-up diagrams showing how each of the filter types (authentication, authorization, action, and result) is invoked. Link to a useful article or blog from each point of interest in the detail view. span.fullpost {display:none;}

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  • Sharing business logic between server-side and client-side of web application?

    - by thoughtpunch
    Quick question concerning shared code/logic in back and front ends of a web application. I have a web application (Rails + heavy JS) that parses metadata from HTML pages fetched via a user supplied URL (think Pinterest or Instapaper). Currently this processing takes place exclusively on the client-side. The code that fetches the URL and parses the DOM is in a fairly large set of JS scripts in our Rails app. Occasionally want to do this processing on the server-side of the app. For example, what if a user supplied a URL but they have JS disabled or have a non-standard compliant browser, etc. Ideally I'd like to be able to process these URLS in Ruby on the back-end (in asynchronous background jobs perhaps) using the same logic that our JS parsers use WITHOUT porting the JS to Ruby. I've looked at systems that allow you to execute JS scripts in the backend like execjs as well as Ruby-to-Javascript compilers like OpalRB that would hopefully allow "write-once, execute many", but I'm not sure that either is the right decision. Whats the best way to avoid business logic duplication for apps that need to do both client-side and server-side processing of similar data?

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  • "sha256sum mismatch jdk-7u3-linux-x64.tar.gz " error when trying to install Oracle Java

    - by Fawkes5
    i recently tried installed java 7 on ubuntu 12.04 and i think i screwed something up I followed the instructions given here. First you need to remove openjdk for this run the following command from your terminal sudo apt-get purge openjdk* Now you can install Java7 by adding the following repository: sudo add-apt-repository ppa:eugenesan/java sudo apt-get update sudo apt-get install oracle-java7-installer Now everytime i install a new program i get the following error: Download done. sha256sum mismatch jdk-7u3-linux-x64.tar.gz Oracle JDK 7 is NOT installed. dpkg: error processing oracle-java7-installer (--configure): subprocess installed post-installation script returned error exit status 1 Setting up python-central (0.6.17ubuntu1) ... Setting up python-eggtrayicon (2.25.3-11) ... Setting up gmail-notify (1.6.1.1-1ubuntu1) ... Processing triggers for python-central ... Errors were encountered while processing: oracle-java7-installer Error in function: However.The program seems to install and work just fine so it doesn't seem to be a problem preventing me from doing anything So then i reinstalled openjdk by going: sudo apt-get install openjdk* But i still get the same error. going: sudo apt-get install oracle-java7-installer gives me the same error. What is going on? Please let me know if this is clear or not and ill try to explain my issue better

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  • Are long methods always bad?

    - by wobbily_col
    So looking around earlier I noticed some comments about long methods being bad practice. I am not sure I always agree that long methods are bad (and would like opinions from others). For example I have some Django views that do a bit of processing of the objects before sending them to the view, a long method being 350 lines of code. I have my code written so that it deals with the paramaters - sorting / filtering the queryset, then bit by bit does some processing on the objects my query has returned. So the processing is mainly conditional aggregation, that has complex enough rules it can't easily be done in the database, so I have some variables declared outside the main loop then get altered during the loop. varaible_1 = 0 variable_2 = 0 for object in queryset : if object.condition_condition_a and variable_2 > 0 : variable 1+= 1 ..... ... . more conditions to alter the variables return queryset, and context So according to the theory I should factor out all the code into smaller methods, so That I have the view method as being maximum one page long. However having worked on various code bases in the past, I sometimes find it makes the code less readable, when you need to constantly jump from one method to the next figuring out all the parts of it, while keeping the outermost method in your head. I find that having a long method that is well formatted, you can see the logic more easily, as it isn't getting hidden away in inner methods. I could factor out the code into smaller methods, but often there is is an inner loop being used for two or three things, so it would result in more complex code, or methods that don't do one thing but two or three (alternatively I could repeat inner loops for each task, but then there will be a performance hit). So is there a case that long methods are not always bad? Is there always a case for writing methods, when they will only be used in one place?

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  • What to do when 'dpkg --configure -a' fails with too many errors?

    - by rudivonstaden
    During an upgrade from lucid (10.04) to precise (12.04), the X session froze, and I have been trying to recover the upgrade to get a stable system. I have performed the following steps: Used ssh to log in to the stalled system over the network. Checked the contents of the /var/log/dist-upgrade directory. There was no activity on main.log, apt.log or term.log. top showed that process 'precise' was using about 3% CPU, but I could find no evidence that the upgrade process was still doing anything. 'dpkg' did not show up in top, but it came up with pgrep dpkg | xargs ps Killed the 'dpkg' and 'precise' processes Tried to recover the upgrade by running sudo fuser -vki /var/lib/dpkg/lock;sudo dpkg --configure -a. This was partially successful (some packages were configured), but failed with the message Processing was halted because there were too many errors. I ran the same command a few times, and each time some packages were configured but others failed. Tried running sudo apt-get -f install. It fails with similar errors to dpkg. The current situation is that dpkg --configure -a and sudo apt-get -f install fails with two kinds of error: Dependency issues, e.g.: dpkg: dependency problems prevent configuration of cifs-utils: cifs-utils depends on samba-common; however: Package samba-common is not configured yet. dpkg: error processing cifs-utils (--configure): dependency problems - leaving unconfigured Resource conflict, e.g.: debconf: DbDriver "config": /var/cache/debconf/config.dat is locked by another process: Resource temporarily unavailable Additionally, it seems there's reference to potential boot problems, so I'm not keen to reboot without fixing the install first: dpkg: too many errors, stopping Processing triggers for initramfs-tools ... update-initramfs: Generating /boot/initrd.img-3.2.0-25-generic cryptsetup: WARNING: failed to detect canonical device of /dev/sda1 cryptsetup: WARNING: could not determine root device from /etc/fstab So my question is, how to get a working install when dpkg --configure -a fails?

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  • Why am I getting this error while installing gnome?

    - by Sreehari Rajendran
    i have raring ringtail. I installed gnome a couple days ago. I try to install extensions but the site says I don't have the latest version. I type the command sudo apt-get install gnome-shell and I get this error Reading package lists... Done Building dependency tree Reading state information... Done gnome-shell is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 138 not upgraded. 1 not fully installed or removed. After this operation, 0 B of additional disk space will be used. Do you want to continue [Y/n]? y Setting up initramfs-tools (0.103ubuntu0.7) ... update-initramfs: deferring update (trigger activated) Processing triggers for initramfs-tools ... update-initramfs: Generating /boot/initrd.img-3.8.0-25-generic cp: cannot stat ‘/module-files.d/libpango1.0-0.modules’: No such file or directory cp: cannot stat ‘/modules/pango-basic-fc.so’: No such file or directory E: /usr/share/initramfs-tools/hooks/plymouth failed with return 1. update-initramfs: failed for /boot/initrd.img-3.8.0-25-generic with 1. dpkg: error processing initramfs-tools (--configure): subprocess installed post-installation script returned error exit status 1 No apport report written because MaxReports is reached already Errors were encountered while processing: initramfs-tools E: Sub-process /usr/bin/dpkg returned an error code (1) why?

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  • State Changes in a Component Based Architecture [closed]

    - by Maxem
    I'm currently working on a game and using the naive component based architecture thingie (Entities are a bag of components, entity.Update() calls Update on each updateable component), while the addition of new features is really simple, it makes a few things really difficult: a) multithreading / currency b) networking c) unit testing. Multithreading / Concurrency is difficult because I basically have to do poor mans concurrency (running the entity updates in separate threads while locking only stuff that crashes (like lists) and ignoring the staleness of read state (some states are already updated, others aren't)) Networking: There are no explicit state changes that I could efficiently push over the net. Unit testing: All updates may or may not conflict, so automated testing is at least awkward. I was thinking about these issues a bit and would like your input on these changes / idea: Switch from the naive cba to a cba with sub systems that work on lists of components Make all state changes explicit Combine 1 and 2 :p Example world update: statePostProcessing.Wait() // ensure that post processing has finished Apply(postProcessedState) state = new StateBag() Concurrently( () => LifeCycleSubSystem.Update(state), // populates the state bag () => MovementSubSystem.Update(state), // populates the state bag .... }) statePostProcessing = Future(() => PostProcess(state)) statePostProcessing.Start() // Tick is finished, the post processing happens in the background So basically the changes are (consistently) based on the data for the last tick; the post processing can a) generate network packages and b) fix conflicts / remove useless changes (example: entity has been destroyed - ignore movement etc.). EDIT: To clarify the granularity of the state changes: If I save these post processed state bags and apply them to an empty world, I see exactly what has happened in the game these state bags originated from - "Free" replay capability. EDIT2: I guess I should have used the term Event instead of State Change and point out that I kind of want to use the Event Sourcing pattern

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  • how to solve this problem

    - by Surbir
    root@me-desktop:~# sudo apt-get install aircrack-ng Reading package lists... Done Building dependency tree Reading state information... Done The following NEW packages will be installed: aircrack-ng 0 upgraded, 1 newly installed, 0 to remove and 446 not upgraded. 1 not fully installed or removed. Need to get 1,579kB of archives. After this operation, 2,843kB of additional disk space will be used. Get:1 http://archive.ubuntu.com/ubuntu/ maverick/universe aircrack-ng i386 1:1.1-1 [1,579kB] Fetched 1,579kB in 1min 9s (22.7kB/s) Selecting previously deselected package aircrack-ng. (Reading database ... 520739 files and directories currently installed.) Unpacking aircrack-ng (from .../aircrack-ng_1%3a1.1-1_i386.deb) ... Processing triggers for man-db ... Setting up linux-image-3.0.1-030001-generic (3.0.1-030001.201108060905) ... Running depmod. update-initramfs: Generating /boot/initrd.img-3.0.1-030001-generic Warning: No support for locale: en_US.utf8 Examining /etc/kernel/postinst.d. run-parts: executing /etc/kernel/postinst.d/dkms 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/initramfs-tools 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/nvidia-common 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/pm-utils 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/update-notifier 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic run-parts: executing /etc/kernel/postinst.d/zz-update-grub 3.0.1-030001-generic /boot/vmlinuz-3.0.1-030001-generic exec: 15: update-grub: not found run-parts: /etc/kernel/postinst.d/zz-update-grub exited with return code 2 Failed to process /etc/kernel/postinst.d at /var/lib/dpkg/info/linux-image-3.0.1-030001-generic.postinst line 1010. dpkg: error processing linux-image-3.0.1-030001-generic (--configure): subprocess installed post-installation script returned error exit status 2 Setting up aircrack-ng (1:1.1-1) ... Errors were encountered while processing: linux-image-3.0.1-030001-generic E: Sub-process /usr/bin/dpkg returned an error code (1) root@me-desktop:~#

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  • C++ Parallel Asynchonous task

    - by Doodlemeat
    I am currently building a randomly generated terrain game where terrain is created automatically around the player. I am experiencing lag when the generated process is active, as I am running quite heavy tasks with post-processing and creating physics bodies. Then I came to mind using a parallel asynchronous task to do the post-processing for me. But I have no idea how I am going to do that. I have searched for C++ std::async but I believe that is not what I want. In the examples I found, a task returned something. I want the task to change objects in the main program. This is what I want: // Main program // Chunks that needs to be processed. // NOTE! These chunks are already generated, but need post-processing only! std::vector<Chunk*> unprocessedChunks; And then my task could look something like this, running like a loop constantly checking if there is chunks to process. // Asynced task if(unprocessedChunks.size() > 0) { processChunk(unprocessedChunks.pop()); } I know it's not far from easy as I wrote it, but it would be a huge help for me if you could push me at the right direction. In Java, I could type something like this: asynced_task = startAsyncTask(new PostProcessTask()); And that task would run until I do this: asynced_task.cancel();

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  • Customer Highlight: NTT DOCOMO

    - by jeckels
    NTT DOCOMO is the largest mobile operator in Japan, and serves over 13 million smartphone customers. Due to their growing data processing and scalability needs, they turned to Oracle's Cloud Application Foundation products for an integral soultion. At Oracle OpenWorld 2012, we first showcased NTT DOCOMO as a customer who was utilizing Oracle Coherence to process mobile data at a rate of 700,000 events per second (and then using Hadoop for distributed processing of big data). Overall, this Led to a 50% cost reduction due to the ultra-high velocity traffic processing of their customers' events. Recently, on October 7th, 2013, Oracle and NTT DOCOMO were proud to again announce a partnership around another key component of Oracle CAF: WebLogic Server. WebLogic was recently deployed as the application platform of choice to run DOCOMO's mission-critical data system ALADIN, which connects nationwide shops and information centers. ALADIN, which also utilizes Oracle Database and Oracle Tuxedo, is based on Java Platform, Enterprise Edition (Java EE), which has allowed the company to operate smoothly while minimizing additional development and modification associated with the migration of application server products. We look forward to continuing to partner with NTT DOCOMO, and are proud that Oracle Cloud Application Foundation products are providing the mission-critical solutions - at scale - that DOCOMO requires. Want to learn more about how CAF products are working in the real world? Join us for a FREE Virtual Developer Day on November 5th from 9am-1pm Pacific Time!REGISTER NOW

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  • "type not defined" exception with WF4 RC

    - by avi1234
    Hi, I`m gettin the following exception while invoking my workflow (dynamically): The following errors were encountered while processing the workflow tree: 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "TryCast(simplerule_out,OutputBase2)". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "Res". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "Res". Type 'OutputBase2' is not defined. 'DynamicActivity': The private implementation of activity '1: DynamicActivity' has the following validation error: Compiler error(s) encountered processing expression "New List(Of OutputBase2)". Type 'OutputBase2' is not defined. The workflow is very simple and worked fine on VS 2010 beta 2! All I`m trying to do is to create new list of my abstract custom type "OutputBase2". public class OutputBase2 { public OutputBase2() { } public bool Succeeded { get; set; } } class Example { public void Exec() { ActivityBuilder builder = new ActivityBuilder(); builder.Name = "act1"; var res = new DynamicActivityProperty { Name = "Res", Type = typeof(OutArgument<List<OutputBase2>>), Value = new OutArgument<List<OutputBase2>>() }; builder.Properties.Add(res); builder.Implementation = new Sequence(); ((Sequence)builder.Implementation).Activities.Add(new Assign<List<OutputBase2>> { To = new VisualBasicReference<List<OutputBase2>> { ExpressionText = res.Name }, Value = new VisualBasicValue<List<OutputBase2>>("New List(Of OutputBase2)") }); Activity act = getActivity(builder); var res2 = WorkflowInvoker.Invoke(act); } string getXamlStringFromActivityBuilder(ActivityBuilder activityBuilder) { string xamlString; StringBuilder stringBuilder = new StringBuilder(); System.IO.StringWriter stringWriter = new System.IO.StringWriter(stringBuilder); System.Xaml.XamlSchemaContext xamlSchemaContext = new System.Xaml.XamlSchemaContext(); System.Xaml.XamlXmlWriter xamlXmlWriter = new System.Xaml.XamlXmlWriter(stringWriter, xamlSchemaContext); System.Xaml.XamlWriter xamlWriter = System.Activities.XamlIntegration.ActivityXamlServices.CreateBuilderWriter(xamlXmlWriter); System.Xaml.XamlServices.Save(xamlWriter, activityBuilder); xamlString = stringBuilder.ToString(); return xamlString; } public Activity getActivity(ActivityBuilder t) { string xamlString = getXamlStringFromActivityBuilder(t); System.IO.StringReader stringReader = new System.IO.StringReader(xamlString); Activity activity = System.Activities.XamlIntegration.ActivityXamlServices.Load(stringReader); return activity; } } Thanks!

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  • Problems related to showing MessageBox from non-GUI threads

    - by Hans Løken
    I'm working on a heavily data-bound Win.Forms application where I've found some strange behavior. The app has separate I/O threads receiving updates through asynchronous web-requests which it then sends to the main/GUI thread for processing and updating of application-wide data-stores (which in turn may be data-bound to various GUI-elements, etc.). The server at the other end of the web-requests requires periodic requests or the session times out. I've gone through several attempted solutions of dealing with thread-issues etc. and I've observed the following behavior: If I use Control.Invoke for sending updates from I/O-thread(s) to main-thread and this update causes a MessageBox to be shown the main form's message pump stops until the user clicks the ok-button. This also blocks the I/O-thread from continuing eventually leading to timeouts on the server. If I use Control.BeginInvoke for sending updates from I/O-thread(s) to main-thread the main form's message pump does not stop, but if the processing of an update leads to a messagebox being shown, the processing of the rest of that update is halted until the user clicks ok. Since the I/O-threads keep running and the message pump keeps processing messages several BeginInvoke's for updates may be called before the one with the message box is finished. This leads to out-of-sequence updates which is unacceptable. I/O-threads add updates to a blocking queue (very similar to http://stackoverflow.com/questions/530211/creating-a-blocking-queuet-in-net/530228#530228). GUI-thread uses a Forms.Timer that periodically applies all updates in the blocking queue. This solution solves both the problem of blocking I/O threads and sequentiality of updates i.e. next update will be never be started until previous is finished. However, there is a small performance cost as well as introducing a latency in showing updates that is unacceptable in the long run. I would like update-processing in the main-thread to be event-driven rather than polling. So to my question. How should I do this to: avoid blocking the I/O-threads guarantee that updates are finished in-sequence keep the main message pump running while showing a message box as a result of an update.

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  • ASP.NET Client to Server communication

    - by Nelson
    Can you help me make sense of all the different ways to communicate from browser to client in ASP.NET? I have made this a community wiki so feel free to edit my post to improve it. Specifically, I'm trying to understand in which scenario to use each one by listing how each works. I'm a little fuzzy on UpdatePanel vs CallBack (with ViewState): I know UpdatePanel always returns HTML while CallBack can return JSON. Any other major differences? ...and CallBack (without ViewState) vs WebMethod. CallBack goes through most of the Page lifecycle, WebMethod doesn't. Any other major differences? IHttpHandler Custom handler for anything (page, image, etc.) Only does what you tell it to do (light server processing) Page is an implementation of IHttpHandler If you don't need what Page provides, create a custom IHttpHandler If you are using Page but overriding Render() and not generating HTML, you probably can do it with a custom IHttpHandler (e.g. writing binary data such as images) By default can use the .axd or .ashx file extensions -- both are functionally similar .ashx doesn't have any built-in endpoints, so it's preferred by convention Regular PostBack (System.Web.UI.Page : IHttpHandler) Inherits Page Full PostBack, including ViewState and HTML control values (heavy traffic) Full Page lifecycle (heavy server processing) No JavaScript required Webpage flickers/scrolls since everything is reloaded in browser Returns full page HTML (heavy traffic) UpdatePanel (Control) Control inside Page Full PostBack, including ViewState and HTML control values (heavy traffic) Full Page lifecycle (heavy server processing) Controls outside the UpdatePanel do Render(NullTextWriter) Must use ScriptManager If no client-side JavaScript, it can fall back to regular PostBack with no JavaScript (?) No flicker/scroll since it's an async call, unless it falls back to regular postback. Can be used with master pages and user controls Has built-in support for progress bar Returns HTML for controls inside UpdatePanel (medium traffic) Client CallBack (Page, System.Web.UI.ICallbackEventHandler) Inherits Page Most of Page lifecycle (heavy server processing) Takes only data you specify (light traffic) and optionally ViewState (?) (medium traffic) Client must support JavaScript and use ScriptManager No flicker/scroll since it's an async call Can be used with master pages and user controls Returns only data you specify in format you specify (e.g. JSON, XML...) (?) (light traffic) WebMethod Class implements System.Web.Service.WebService HttpContext available through this.Context Takes only data you specify (light traffic) Server only runs the called method (light server processing) Client must support JavaScript No flicker/scroll since it's an async call Can be used with master pages and user controls Returns only data you specify, typically JSON (light traffic) Can create instance of server control to render HTML and sent back as string, but events, paging in GridView, etc. won't work PageMethods Essentially a WebMethod contained in the Page class, so most of WebMethod's bullet's apply Method must be public static, therefore no Page instance accessible HttpContext available through HttpContext.Current Accessed directly by URL Page.aspx/MethodName (e.g. with XMLHttpRequest directly or with library such as jQuery) Setting ScriptManager property EnablePageMethods="True" generates a JavaScript proxy for each WebMethod Cannot be used directly in user controls with master pages and user controls Any others?

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  • .Net Remote Log Querying

    - by jlafay
    I have a Win Service that I'm working on that consists of the service, WF Service (using WorkflowServiceHost), a Workflow (WorkflowApplication) that queries/processes data from a SQL Server DB, and a Comm Marshall class that handles data flow between the service and the WF. The WF does a lot of heavy data processing and the original app (early VB6) logged all the processing and displayed the results on the screen of the host machine. Critical events will be committed to eventlog because I strongly believe that should be common practice because admins naturally will look there and because it already has support for remote viewing. The workflow will also need to write logging events as it processes and iterates according to our business logic. Such as: records queried, records returned, processed records, etc. The data is very critical and we need to log actions as they occur. The logs are currently kept as text files on disk and I think that is ok. Ideally I would like to record log events in XML so it's easier to query and because it is less costly than a DB, especially since our DB servers do a lot of heavy processing anyways. Since we are replacing essentially a VB6 application with a robust windows service (taking advantage of WF 4.0), it has been requested that a remote client also be created. It receives callbacks from the service after subscribing to it and being added to a collection of subscribers. Basic statistics and summaries are updated client side after receiving basic monitoring data of what is going on with the service. We would like to also provide a way to provide details when we need to examine what is going on further because this is a long running data processing service and issues need to be addressed immediately. What is the best way to implement some type of query from the client that is sent to the service and returned to the client? Would it be efficient to implement another method to expose on the service and then have that pass that off to some querying class/object to examine the XML files by whichever specification and then return it to the client? That's the main concern. I don't want the service to processing to bottleneck much while this occurs. It seems that WF already auto-magically threads well for the most part but I want to make sure this is the right way to go about it. Any suggestions/recommendations on how to architect and implement a small log querying framework for a remote service would be awesome.

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • E: Sub-process /usr/bin/dpkg returned an error code (1) seems to be choking on kde-runtime-data version issue

    - by BMT
    12.04 LTS, on a dell mini 10. Install stable until about a week ago. Updated about 1x a week, sometimes more often. Several days ago, I booted up and the system was no longer working correctly. All these symptoms occurred simultaneously: Cannot run (exit on opening, every time): Update manager, software center, ubuntuOne, libreOffice. Vinagre autostarts on boot, no explanation, not set to startup with Ubuntu. Using apt-get to fix install results in the following: maura@pandora:~$ sudo apt-get -f install Reading package lists... Done Building dependency tree Reading state information... Done Correcting dependencies... Done The following package was automatically installed and is no longer required: libtelepathy-farstream2 Use 'apt-get autoremove' to remove them. The following extra packages will be installed: gwibber gwibber-service kde-runtime-data software-center Suggested packages: gwibber-service-flickr gwibber-service-digg gwibber-service-statusnet gwibber-service-foursquare gwibber-service-friendfeed gwibber-service-pingfm gwibber-service-qaiku unity-lens-gwibber The following packages will be upgraded: gwibber gwibber-service kde-runtime-data software-center 4 upgraded, 0 newly installed, 0 to remove and 39 not upgraded. 20 not fully installed or removed. Need to get 0 B/5,682 kB of archives. After this operation, 177 kB of additional disk space will be used. Do you want to continue [Y/n]? debconf: Perl may be unconfigured (Can't locate Scalar/Util.pm in @INC (@INC contains: /etc/perl /usr/local/lib/perl/5.14.2 /usr/local/share/perl/5.14.2 /usr/lib/perl5 /usr/share/perl5 /usr/lib/perl/5.14 /usr/share/perl/5.14 /usr/local/lib/site_perl .) at /usr/lib/perl/5.14/Hash/Util.pm line 9. BEGIN failed--compilation aborted at /usr/lib/perl/5.14/Hash/Util.pm line 9. Compilation failed in require at /usr/share/perl/5.14/fields.pm line 122. Compilation failed in require at /usr/share/perl5/Debconf/Log.pm line 10. Compilation failed in require at (eval 1) line 4. BEGIN failed--compilation aborted at (eval 1) line 4. ) -- aborting (Reading database ... 242672 files and directories currently installed.) Preparing to replace gwibber 3.4.1-0ubuntu1 (using .../gwibber_3.4.2-0ubuntu1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace gwibber-service 3.4.1-0ubuntu1 (using .../gwibber-service_3.4.2-0ubuntu1_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace kde-runtime-data 4:4.8.3-0ubuntu0.1 (using .../kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb) ... Unpacking replacement kde-runtime-data ... dpkg: error processing /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb (--unpack): trying to overwrite '/usr/share/sounds', which is also in package sound-theme-freedesktop 0.7.pristine-2 dpkg-deb (subprocess): subprocess data was killed by signal (Broken pipe) dpkg-deb: error: subprocess <decompress> returned error exit status 2 Preparing to replace python-crypto 2.4.1-1 (using .../python-crypto_2.4.1-1_i386.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace software-center 5.2.2.2 (using .../software-center_5.2.4_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/software-center_5.2.4_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Preparing to replace xdiagnose 2.5 (using .../archives/xdiagnose_2.5_all.deb) ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: warning: subprocess old pre-removal script returned error exit status 1 dpkg - trying script from the new package instead ... Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pyclean", line 25, in <module> import logging ImportError: No module named logging dpkg: error processing /var/cache/apt/archives/xdiagnose_2.5_all.deb (--unpack): subprocess new pre-removal script returned error exit status 1 No apport report written because MaxReports is reached already Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging Error in sys.excepthook: Traceback (most recent call last): File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook from apport.fileutils import likely_packaged, get_recent_crashes File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 1, in <module> from apport.report import Report File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in <module> from xml.parsers.expat import ExpatError File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in <module> from pyexpat import * ImportError: No module named pyexpat Original exception was: Traceback (most recent call last): File "/usr/bin/pycompile", line 27, in <module> import logging ImportError: No module named logging dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Errors were encountered while processing: /var/cache/apt/archives/gwibber_3.4.2-0ubuntu1_i386.deb /var/cache/apt/archives/gwibber-service_3.4.2-0ubuntu1_all.deb /var/cache/apt/archives/kde-runtime-data_4%3a4.8.4-0ubuntu0.1_all.deb /var/cache/apt/archives/python-crypto_2.4.1-1_i386.deb /var/cache/apt/archives/software-center_5.2.4_all.deb /var/cache/apt/archives/xdiagnose_2.5_all.deb E: Sub-process /usr/bin/dpkg returned an error code (1) maura@pandora:~$ ^C maura@pandora:~$

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  • Running your SSMS client as a domain user even if you&rsquo;re not in a domain

    - by Luca Zavarella
    I wonder if it is possible to use the SQL Server Management Studio (SSMS) client on my machine with a specific domain user when my machine wasn’t in that domain. In fact, many developers use some SSMS add-ons installed on their machine (with appropriate licenses), which greatly simplify their daily work. For example, I’m a Red Gate SQL Prompt addicted , so it’d be convenient for me to work on customers’ SQL Server instances with this tool. After reading Davide Mauri’s post, a friend and collegue of mine, I created a batch file in order to specify a domain and a user for SSMS: @echo off echo *************************************** echo *** Run SSMS 2008 R2 as domain user *** echo *************************************** echo. set /P user="Type the domain\username: " C:\Windows\System32\runas.exe /netonly /user:%user% "C:\Program Files (x86)\Microsoft SQL Server\100\Tools\Binn\VSShell\Common7\IDE\Ssms.exe" Then, you can create on your desktop a shortcut to the file batch previously developed and you can also change the shortcut icon, using the same SSMS icon (get it from the Ssms.exe file). Now if you double-click on the shortcut, you can set domain and user for the SSMS client on-the-fly: So enjoy using your “personal” SSMS client on your preferred domain

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  • Cocos2d-x Spritebatch node animation appears to be broken? cocos2d-x 2.0.3

    - by George Host
    Hi I have spent aprox 2 days trying to get this to work doing a google searches left and right and I did get it working except for sprite batch nodes. So in my class I am able to load kuwalio_stand.png and I tested kuwalio_walk1.png and 2 and 3 from the FrameCache(). They work for sure 100%. I run this code and it does not animate does anyone else have the same issue with sprite batch nodes? cocos2d::CCSprite * player = Player::create(); player->setPosition(cocos2d::CCPointMake(0.0f,0.0f)); player->setDisplayFrame(cocos2d::CCSpriteFrameCache::sharedSpriteFrameCache()->spriteFrameByName("kuwalio_stand.png")); player->setTag(PlayerTag); cocos2d::CCAnimation * walk = cocos2d::CCAnimation::create(); cocos2d::CCSpriteFrame * walk1 = cocos2d::CCSpriteFrameCache::sharedSpriteFrameCache()->spriteFrameByName("kuwalio_walk1"); cocos2d::CCSpriteFrame * walk2 = cocos2d::CCSpriteFrameCache::sharedSpriteFrameCache()->spriteFrameByName("kuwalio_walk2"); cocos2d::CCSpriteFrame * walk3 = cocos2d::CCSpriteFrameCache::sharedSpriteFrameCache()->spriteFrameByName("kuwalio_walk3"); walk->addSpriteFrame(walk1); walk->addSpriteFrame(walk2); walk->addSpriteFrame(walk3); cocos2d::CCAnimate * actionWalk = cocos2d::CCAnimate::create(walk); cocos2d::CCRepeatForever * actionRepeat = cocos2d::CCRepeatForever::create(actionWalk); walk->setDelayPerUnit(0.1f); actionWalk->setDuration(10.1f); this->runAction(actionRepeat); // Change camera to a soft follow camera. this->runAction(cocos2d::CCFollow::create(player)); mSceneSpriteBatchNode->addChild(player); // Have the CCNode object run its virtual update function as fast as possible. // Every frame for this layer. this-scheduleUpdate(); Counter example without the sprite batch node... cocos2d::CCSprite * sprite = cocos2d::CCSprite::create("kuwalio_walk1.png"); this->addChild(sprite,0); sprite->setPosition(cocos2d::CCPointMake(60,60)); sprite->retain(); cocos2d::CCAnimation * actionAnimation = cocos2d::CCAnimation::create(); actionAnimation->setDelayPerUnit(0.01f); actionAnimation->retain(); actionAnimation->addSpriteFrameWithFileName("kuwalio_walk1.png"); actionAnimation->addSpriteFrameWithFileName("kuwalio_walk2.png"); actionAnimation->addSpriteFrameWithFileName("kuwalio_walk3.png"); cocos2d::CCAnimate * a = cocos2d::CCAnimate::create(actionAnimation); a->setDuration(0.10f); cocos2d::CCRepeatForever * actionRepeat = cocos2d::CCRepeatForever::create(a); sprite->runAction(actionRepeat);

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  • Performance considerations for common SQL queries

    - by Jim Giercyk
    Originally posted on: http://geekswithblogs.net/NibblesAndBits/archive/2013/10/16/performance-considerations-for-common-sql-queries.aspxSQL offers many different methods to produce the same results.  There is a never-ending debate between SQL developers as to the “best way” or the “most efficient way” to render a result set.  Sometimes these disputes even come to blows….well, I am a lover, not a fighter, so I decided to collect some data that will prove which way is the best and most efficient.  For the queries below, I downloaded the test database from SQLSkills:  http://www.sqlskills.com/sql-server-resources/sql-server-demos/.  There isn’t a lot of data, but enough to prove my point: dbo.member has 10,000 records, and dbo.payment has 15,554.  Our result set contains 6,706 records. The following queries produce an identical result set; the result set contains aggregate payment information for each member who has made more than 1 payment from the dbo.payment table and the first and last name of the member from the dbo.member table.   /*************/ /* Sub Query  */ /*************/ SELECT  a.[Member Number] ,         m.lastname ,         m.firstname ,         a.[Number Of Payments] ,         a.[Average Payment] ,         a.[Total Paid] FROM    ( SELECT    member_no 'Member Number' ,                     AVG(payment_amt) 'Average Payment' ,                     SUM(payment_amt) 'Total Paid' ,                     COUNT(Payment_No) 'Number Of Payments'           FROM      dbo.payment           GROUP BY  member_no           HAVING    COUNT(Payment_No) > 1         ) a         JOIN dbo.member m ON a.[Member Number] = m.member_no         /***************/ /* Cross Apply  */ /***************/ SELECT  ca.[Member Number] ,         m.lastname ,         m.firstname ,         ca.[Number Of Payments] ,         ca.[Average Payment] ,         ca.[Total Paid] FROM    dbo.member m         CROSS APPLY ( SELECT    member_no 'Member Number' ,                                 AVG(payment_amt) 'Average Payment' ,                                 SUM(payment_amt) 'Total Paid' ,                                 COUNT(Payment_No) 'Number Of Payments'                       FROM      dbo.payment                       WHERE     member_no = m.member_no                       GROUP BY  member_no                       HAVING    COUNT(Payment_No) > 1                     ) ca /********/                    /* CTEs  */ /********/ ; WITH    Payments           AS ( SELECT   member_no 'Member Number' ,                         AVG(payment_amt) 'Average Payment' ,                         SUM(payment_amt) 'Total Paid' ,                         COUNT(Payment_No) 'Number Of Payments'                FROM     dbo.payment                GROUP BY member_no                HAVING   COUNT(Payment_No) > 1              ),         MemberInfo           AS ( SELECT   p.[Member Number] ,                         m.lastname ,                         m.firstname ,                         p.[Number Of Payments] ,                         p.[Average Payment] ,                         p.[Total Paid]                FROM     dbo.member m                         JOIN Payments p ON m.member_no = p.[Member Number]              )     SELECT  *     FROM    MemberInfo /************************/ /* SELECT with Grouping   */ /************************/ SELECT  p.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         COUNT(Payment_No) 'Number Of Payments' ,         AVG(payment_amt) 'Average Payment' ,         SUM(payment_amt) 'Total Paid' FROM    dbo.payment p         JOIN dbo.member m ON m.member_no = p.member_no GROUP BY p.member_no ,         m.lastname ,         m.firstname HAVING  COUNT(Payment_No) > 1   We can see what is going on in SQL’s brain by looking at the execution plan.  The Execution Plan will demonstrate which steps and in what order SQL executes those steps, and what percentage of batch time each query takes.  SO….if I execute all 4 of these queries in a single batch, I will get an idea of the relative time SQL takes to execute them, and how it renders the Execution Plan.  We can settle this once and for all.  Here is what SQL did with these queries:   Not only did the queries take the same amount of time to execute, SQL generated the same Execution Plan for each of them.  Everybody is right…..I guess we can all finally go to lunch together!  But wait a second, I may not be a fighter, but I AM an instigator.     Let’s see how a table variable stacks up.  Here is the code I executed: /********************/ /*  Table Variable  */ /********************/ DECLARE @AggregateTable TABLE     (       member_no INT ,       AveragePayment MONEY ,       TotalPaid MONEY ,       NumberOfPayments MONEY     ) INSERT  @AggregateTable         SELECT  member_no 'Member Number' ,                 AVG(payment_amt) 'Average Payment' ,                 SUM(payment_amt) 'Total Paid' ,                 COUNT(Payment_No) 'Number Of Payments'         FROM    dbo.payment         GROUP BY member_no         HAVING  COUNT(Payment_No) > 1   SELECT  at.member_no 'Member Number' ,         m.lastname ,         m.firstname ,         at.NumberOfPayments 'Number Of Payments' ,         at.AveragePayment 'Average Payment' ,         at.TotalPaid 'Total Paid' FROM    @AggregateTable at         JOIN dbo.member m ON m.member_no = at.member_no In the interest of keeping things in groupings of 4, I removed the last query from the previous batch and added the table variable query.  Here’s what I got:     Since we first insert into the table variable, then we read from it, the Execution Plan renders 2 steps.  BUT, the combination of the 2 steps is only 22% of the batch.  It is actually faster than the other methods even though it is treated as 2 separate queries in the Execution Plan.  The argument I often hear against Table Variables is that SQL only estimates 1 row for the table size in the Execution Plan.  While this is true, the estimate does not come in to play until you read from the table variable.  In this case, the table variable had 6,706 rows, but it still outperformed the other queries.  People argue that table variables should only be used for hash or lookup tables.  The fact is, you have control of what you put IN to the variable, so as long as you keep it within reason, these results suggest that a table variable is a viable alternative to sub-queries. If anyone does volume testing on this theory, I would be interested in the results.  My suspicion is that there is a breaking point where efficiency goes down the tubes immediately, and it would be interesting to see where the threshold is. Coding SQL is a matter of style.  If you’ve been around since they introduced DB2, you were probably taught a little differently than a recent computer science graduate.  If you have a company standard, I strongly recommend you follow it.    If you do not have a standard, generally speaking, there is no right or wrong answer when talking about the efficiency of these types of queries, and certainly no hard-and-fast rule.  Volume and infrastructure will dictate a lot when it comes to performance, so your results may vary in your environment.  Download the database and try it!

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  • Using the Windows Explorer Context Menu to reset Umbraco Directory Permissions

    - by Vizioz Limited
    Hi All,As Umbraco matures I am assuming that needing to reset directory permissions might well become a thing of the past, but at the moment it is still something when I copy sites between machines that I often find myself doing.As it's 4:30am I thought, there must be a better way than having to open up a DOS prompt, navigate to a directory and then run a batch file passing in the IIS root folder location.Well.. there is :)I googled for adding a command to the context menu within Windows Explorer, I found a way of doing this for XP, but it seems the functionality was removed from Windows 7, however I found a very neat freeware application called File Menu Tools which does work perfectly!I have now added a command to my context menu that enables me to right click an IIS site root folder and then call my batch script and automatically pass in the directory.This will save me a bunch of time :)

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  • Oracle Utilities Application Framework future feature deprecation

    - by Paula Speranza-Hadley
    From time to time, existing functionality is replaced with alternative features to offer greater flexibility and standardization. In Oracle Utilities Application Framework V4.2.0.0.0 the following features are being announced for deprecation in the next release or have been previously announced and are not being delivered with this version of the Oracle Utilities Application Framework: ·         No SQL Server Support – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with any support for SQL Server. ·         No MPL Support – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with the Multi-Purpose Listener (MPL) component of the XML Application Integration (XAI) component. Customers using the MPL should migrate to Oracle Service Bus. ·         No provided Crystal Reports/Business Objects Interface – Oracle Utilities Application Framework V4.2.0.0.0 or above does not ship with a supported Crystal Reports/Business Objects Interface. This facility is now available as downloadable customization for existing or new customers. Responsibility for maintenance and new features is now individual customer's responsibility. ·         XAI Servlet deprecation – The XAI Servlet (xaiserver and classicxai) will be removed in the next release of the Oracle Utilities Application Framework. Customers are encouraged to migrate to the native Web Services Support as outlined in XAI Best Practices whitepaper available from My Oracle Support (Doc Id: 942074.1). ·         ConfigLab deprecation – The ConfigLab facility will be removed in the next release of Oracle Utilities Application Framework for products it is shipped with. Customers are recommended to migrate to the Configuration Migration Assistant which provides the same and more functionality.   ·         Archiving deprecation – The inbuilt Archiving has been removed from Oracle Utilities Application Framework V4.2.0.0.0 or above, for products it is shipped with. Customers considering Archiving solution should migrate to the Information Lifecycle Management based solution provided for your product. ·         DISTRIBUTED batch execution mode deprecation – The DISTRIBUTED execution mode used by the batch component of the Oracle Utilities Application Framework will be deprecated in the next release of the Oracle Utilities Application Framework. Customers using DISTRUBUTED mode should migrate to CLUSTERED mode as outlined in the Batch Best Practices For Oracle Utilities Application Framework Based Products whitepaper available from My Oracle Support (Doc Id: 836362.1). ·         XAI Schema Editor deprecation – The XAI Schema Editor which is a component of the Oracle Utilities Software Development Kit will be removed in the next release of the Oracle Utilities Application Framework. Customers should migrate their existing schemas to Business Object based schemas and use the browser based Schema Editor instead.  

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  • SQLAuthority News – Technical Review of Learning at Koenig Solutions

    - by pinaldave
    Yesterday I finished my 3 days fast track in person learning of course End to End SQL Server Business Intelligence at Koenig Solutions. You can read my previous article over here regarding why am I learning SQL Server. Yesterday I blogged about my experience of arriving to Training Center and my induction with the center. The Training Days I had enrolled for three days training so my routine each of the three days was very much same. However, the content every day was different as I was learning something new every day. Let me describe a few of the interesting details of my daily routine. A Single Student Batch The best part of my training was that in my training batch, I am single student. Koenig is known to smaller batches and often they have single student batches as well. I was very much delighted to know that I will have dedicated access and attention from my trainer in my batch as I will be single student in my batch. In most of the labs I have observed there are no more than 4 students at any time. Prakash and Pinal 7:30 AM Breakfast Talk We all students gather at 7:30 in breakfast area. The best time of the day. I was the only Indian student in the group. The other students were from USA, Canada, Nigeria, Bhutan, Tanzania, and a few others from other countries. I immediately become the source of information and reference manual. Though the distance between Delhi and Bangalore is 2000+ KM I was considered as a local guy. 8:30 AMHeading to Training Center Every day without fail at 8:30 the van started from our accommodation to the training center. As mentioned in an earlier blog post the distance is about 5 minutes and we were able to reach at the location before 8:45. This gave us some time settle in before our class starts at 9:00 AM. 9:00 AM Order Lunch Food Well it may sound funny that we just had breakfast 30 minutes but the first thing everybody has to do is to order lunch as soon as the class starts. There is an online training portal to order food for the day. Everybody has to place their order early during the day so the food arrives on time during lunch time. Everybody can order whatever they want to order using an online ordering system. The options are plenty and everybody can order what they like. 9:05 AM Learning Starts After deciding the lunch we started the learning. I was very fortunate to have a very experienced trainer - Prakash Chheatry. Though I have never met him before I have heard a lot about Prakash. He is known as the top most SQL Server Trainer in India. His student list contains some of the very well known SQL Server Experts of the world and few of SQL Server “best seller” book authors. Learning continues till 1:00 PM with one tea-coffee break in between. 1:00 PM Lunch The lunch time is again the fun time. We all students get together in the afternoon and tell the stories of the world. Indeed the best part of the day beside learning new stuff. 4:55 PM Ready to Return We stop at 4:55 as at precisely 5:00 PM the van stops by the institute which takes us back to our accommodation. Trust me seriously long long day always but the amount of the learning is the win of the day. 7:30 PM Dinner Time After coming back to the accommodation I study till 7:30 and then rush for dinner. Dinner is world cuisine and deserts are really delicious. After dinner every day I have written a blog and retired early as the next day is always going to be busier than the present day. What did I learn As I mentioned earlier I know SQL Server fairly well. I had expressed the same in my conversation as well. This is the reason I was assigned a fairly senior trainer and we learned everything quite quickly. As I know quite a few things we went pretty fast in many topics. There were a few things, I wanted to learn in detail as well practice on the labs. We slowed down where we wanted and rush through the concepts where I was very comfortable. Here is the list of the things which we covered in action pack three days. Introduction to Business Intelligence (Intro) SQL Server Analysis Service (Theory and Lab) SQL Server Integration Service  (Theory and Lab) SQL Server Reporting Service  (Theory and Lab) SQL Server PowerPivot (Lab) UDM (Theory) SharePoint Concepts (Theory) Power View (Demo) Business Intelligence and Security (Discussion) Well, I was delighted that I was able to refresh lots of concepts during these three days. Thanks to my trainer and my friend who helped me to have a good learning experience. I believe all the learning  will help me in my growth and future career. With this I end my this experience. I am planning to have another online learning experience later this month. I will blog about my experience as I begin it. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Training, T SQL, Technology

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  • Commit in SQL

    - by PRajkumar
    SQL Transaction Control Language Commands (TCL)                                           (COMMIT) Commit Transaction As a SQL language we use transaction control language very frequently. Committing a transaction means making permanent the changes performed by the SQL statements within the transaction. A transaction is a sequence of SQL statements that Oracle Database treats as a single unit. This statement also erases all save points in the transaction and releases transaction locks. Oracle Database issues an implicit COMMIT before and after any data definition language (DDL) statement. Oracle recommends that you explicitly end every transaction in your application programs with a COMMIT or ROLLBACK statement, including the last transaction, before disconnecting from Oracle Database. If you do not explicitly commit the transaction and the program terminates abnormally, then the last uncommitted transaction is automatically rolled back.   Until you commit a transaction: ·         You can see any changes you have made during the transaction by querying the modified tables, but other users cannot see the changes. After you commit the transaction, the changes are visible to other users' statements that execute after the commit ·         You can roll back (undo) any changes made during the transaction with the ROLLBACK statement   Note: Most of the people think that when we type commit data or changes of what you have made has been written to data files, but this is wrong when you type commit it means that you are saying that your job has been completed and respective verification will be done by oracle engine that means it checks whether your transaction achieved consistency when it finds ok it sends a commit message to the user from log buffer but not from data buffer, so after writing data in log buffer it insists data buffer to write data in to data files, this is how it works.   Before a transaction that modifies data is committed, the following has occurred: ·         Oracle has generated undo information. The undo information contains the old data values changed by the SQL statements of the transaction ·         Oracle has generated redo log entries in the redo log buffer of the System Global Area (SGA). The redo log record contains the change to the data block and the change to the rollback block. These changes may go to disk before a transaction is committed ·         The changes have been made to the database buffers of the SGA. These changes may go to disk before a transaction is committed   Note:   The data changes for a committed transaction, stored in the database buffers of the SGA, are not necessarily written immediately to the data files by the database writer (DBWn) background process. This writing takes place when it is most efficient for the database to do so. It can happen before the transaction commits or, alternatively, it can happen some times after the transaction commits.   When a transaction is committed, the following occurs: 1.      The internal transaction table for the associated undo table space records that the transaction has committed, and the corresponding unique system change number (SCN) of the transaction is assigned and recorded in the table 2.      The log writer process (LGWR) writes redo log entries in the SGA's redo log buffers to the redo log file. It also writes the transaction's SCN to the redo log file. This atomic event constitutes the commit of the transaction 3.      Oracle releases locks held on rows and tables 4.      Oracle marks the transaction complete   Note:   The default behavior is for LGWR to write redo to the online redo log files synchronously and for transactions to wait for the redo to go to disk before returning a commit to the user. However, for lower transaction commit latency application developers can specify that redo be written asynchronously and that transaction do not need to wait for the redo to be on disk.   The syntax of Commit Statement is   COMMIT [WORK] [COMMENT ‘your comment’]; ·         WORK is optional. The WORK keyword is supported for compliance with standard SQL. The statements COMMIT and COMMIT WORK are equivalent. Examples Committing an Insert INSERT INTO table_name VALUES (val1, val2); COMMIT WORK; ·         COMMENT Comment is also optional. This clause is supported for backward compatibility. Oracle recommends that you used named transactions instead of commit comments. Specify a comment to be associated with the current transaction. The 'text' is a quoted literal of up to 255 bytes that Oracle Database stores in the data dictionary view DBA_2PC_PENDING along with the transaction ID if a distributed transaction becomes in doubt. This comment can help you diagnose the failure of a distributed transaction. Examples The following statement commits the current transaction and associates a comment with it: COMMIT     COMMENT 'In-doubt transaction Code 36, Call (415) 555-2637'; ·         WRITE Clause Use this clause to specify the priority with which the redo information generated by the commit operation is written to the redo log. This clause can improve performance by reducing latency, thus eliminating the wait for an I/O to the redo log. Use this clause to improve response time in environments with stringent response time requirements where the following conditions apply: The volume of update transactions is large, requiring that the redo log be written to disk frequently. The application can tolerate the loss of an asynchronously committed transaction. The latency contributed by waiting for the redo log write to occur contributes significantly to overall response time. You can specify the WAIT | NOWAIT and IMMEDIATE | BATCH clauses in any order. Examples To commit the same insert operation and instruct the database to buffer the change to the redo log, without initiating disk I/O, use the following COMMIT statement: COMMIT WRITE BATCH; Note: If you omit this clause, then the behavior of the commit operation is controlled by the COMMIT_WRITE initialization parameter, if it has been set. The default value of the parameter is the same as the default for this clause. Therefore, if the parameter has not been set and you omit this clause, then commit records are written to disk before control is returned to the user. WAIT | NOWAIT Use these clauses to specify when control returns to the user. The WAIT parameter ensures that the commit will return only after the corresponding redo is persistent in the online redo log. Whether in BATCH or IMMEDIATE mode, when the client receives a successful return from this COMMIT statement, the transaction has been committed to durable media. A crash occurring after a successful write to the log can prevent the success message from returning to the client. In this case the client cannot tell whether or not the transaction committed. The NOWAIT parameter causes the commit to return to the client whether or not the write to the redo log has completed. This behavior can increase transaction throughput. With the WAIT parameter, if the commit message is received, then you can be sure that no data has been lost. Caution: With NOWAIT, a crash occurring after the commit message is received, but before the redo log record(s) are written, can falsely indicate to a transaction that its changes are persistent. If you omit this clause, then the transaction commits with the WAIT behavior. IMMEDIATE | BATCH Use these clauses to specify when the redo is written to the log. The IMMEDIATE parameter causes the log writer process (LGWR) to write the transaction's redo information to the log. This operation option forces a disk I/O, so it can reduce transaction throughput. The BATCH parameter causes the redo to be buffered to the redo log, along with other concurrently executing transactions. When sufficient redo information is collected, a disk write of the redo log is initiated. This behavior is called "group commit", as redo for multiple transactions is written to the log in a single I/O operation. If you omit this clause, then the transaction commits with the IMMEDIATE behavior. ·         FORCE Clause Use this clause to manually commit an in-doubt distributed transaction or a corrupt transaction. ·         In a distributed database system, the FORCE string [, integer] clause lets you manually commit an in-doubt distributed transaction. The transaction is identified by the 'string' containing its local or global transaction ID. To find the IDs of such transactions, query the data dictionary view DBA_2PC_PENDING. You can use integer to specifically assign the transaction a system change number (SCN). If you omit integer, then the transaction is committed using the current SCN. ·         The FORCE CORRUPT_XID 'string' clause lets you manually commit a single corrupt transaction, where string is the ID of the corrupt transaction. Query the V$CORRUPT_XID_LIST data dictionary view to find the transaction IDs of corrupt transactions. You must have DBA privileges to view the V$CORRUPT_XID_LIST and to specify this clause. ·         Specify FORCE CORRUPT_XID_ALL to manually commit all corrupt transactions. You must have DBA privileges to specify this clause. Examples Forcing an in doubt transaction. Example The following statement manually commits a hypothetical in-doubt distributed transaction. Query the V$CORRUPT_XID_LIST data dictionary view to find the transaction IDs of corrupt transactions. You must have DBA privileges to view the V$CORRUPT_XID_LIST and to issue this statement. COMMIT FORCE '22.57.53';

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  • How to I do install DB2 ODBC?

    - by Justin
    I have been trying, with no success, to install a IBM DB2 ODBC driver so that my PHP server can connect to a database. I've tried installing the db2_connect and get all sorts of problems, I tried install I Access for Linux and the RPM did not install right nor did using alien breed any useful results. I've also tried the DB2 Runtime v8.1, no success. If I attempt to run the rpm it claims I need dependencies that I can't find in apt-get. Yum is also not very helpful as it appears I don't have any repositories installed or lists... Running the simple RPM gives me this result in terminal: # rpm -ivh iSeriesAccess-7.1.0-1.0.x86_64.rpm rpm: RPM should not be used directly install RPM packages, use Alien instead! rpm: However assuming you know what you are doing... error: Failed dependencies: /bin/ln is needed by iSeriesAccess-7.1.0-1.0.x86_64 /sbin/ldconfig is needed by iSeriesAccess-7.1.0-1.0.x86_64 /bin/rm is needed by iSeriesAccess-7.1.0-1.0.x86_64 /bin/sh is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libc.so.6(GLIBC_2.3)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libdl.so.2()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libdl.so.2(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libgcc_s.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libm.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libm.so.6(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libodbcinst.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libodbc.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libpthread.so.0(GLIBC_2.3.2)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 librt.so.1()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 librt.so.1(GLIBC_2.2.5)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6()(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6(CXXABI_1.3)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 libstdc++.so.6(GLIBCXX_3.4)(64bit) is needed by iSeriesAccess-7.1.0-1.0.x86_64 Using alien and running the dkpg gives me thes headaque: $ alien iSeriesAccess-7.1.0-1.0.x86_64.rpm --scripts # dpkg -i iseriesaccess_7.1.0-2_amd64.deb (Reading database ... 127664 files and directories currently installed.) Preparing to replace iseriesaccess 7.1.0-2 (using iseriesaccess_7.1.0-2_amd64.deb) ... Unpacking replacement iseriesaccess ... post uninstall processing for iSeriesAccess 1.0...upgrade /var/lib/dpkg/info/iseriesaccess.postrm: line 8: [: upgrade: integer expression expected Setting up iseriesaccess (7.1.0-2) ... post install processing for iSeriesAccess 1.0...configure iSeries Access ODBC Driver has been deleted (if it existed at all) because its usage count became zero odbcinst: Driver installed. Usage count increased to 1. Target directory is /etc odbcinst: Driver installed. Usage count increased to 3. Target directory is /etc Processing triggers for libc-bin ... ldconfig deferred processing now taking place So it seems the files installed right, well my odbc driver shows up but db2cli.ini is no where to be found. So several questions. Is there a better alternative to connect php to db2, say an ubuntu package I can just install? Can someone direct me to the steps that makes my ubuntu server works well with the RPM so I can build my db2 instance? Also remember I'm connection to an I Series remotely. I'm not using the DB2 Express C thing, even if I did try it to get the db2 php functions to work. And I don't have zend but I think I have every other package on the ubuntu repositories. Help, thank you!

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