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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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  • SQL Server and Hyper-V Dynamic Memory Part 2

    - by SQLOS Team
    Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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  • Framework 4 Features: Support for Timed Jobs

    - by Anthony Shorten
    One of the new features of the Oracle Utilities Application Framework V4 is the ability for the batch framework to support Timed Batch. Traditionally batch is associated with set processing in the background in a fixed time frame. For example, billing customers. Over the last few versions their has been functionality required by the products required a more monitoring style batch process. The monitor is a batch process that looks for specific business events based upon record status or other pieces of data. For example, the framework contains a fact monitor (F1-FCTRN) that can be configured to look for specific status's or other conditions. The batch process then uses the instructions on the object to determine what to do. To support monitor style processing, you need to run the process regularly a number of times a day (for example, every ten minutes). Traditional batch could support this but it was not as optimal as expected (if you are a site using the old Workflow subsystem, you understand what I mean). The Batch framework was extended to add additional facilities to support times (and continuous batch which is another new feature for another blog entry). The new facilities include: The batch control now defines the job as Timed or Not Timed. Non-Timed batch are traditional batch jobs. The timer interval (the interval between executions) can be specified The timer can be made active or inactive. Only active timers are executed. Setting the Timer Active to inactive will stop the job at the next time interval. Setting the Timer Active to Active will start the execution of the timed job. You can specify the credentials, language to view the messages and an email address to send the a summary of the execution to. The email address is optional and requires an email server to be specified in the relevant feature configuration. You can specify the thread limits and commit intervals to be sued for the multiple executions. Once a timer job is defined it will be executed automatically by the Business Application Server process if the DEFAULT threadpool is active. This threadpool can be started using the online batch daemon (for non-production) or externally using the threadpoolworker utility. At that time any batch process with the Timer Active set to Active and Batch Control Type of Timed will begin executing. As Timed jobs are executed automatically then they do not appear in any external schedule or are managed by an external scheduler (except via the DEFAULT threadpool itself of course). Now, if the job has no work to do as the timer interval is being reached then that instance of the job is stopped and the next instance started at the timer interval. If there is still work to complete when the interval interval is reached, the instance will continue processing till the work is complete, then the instance will be stopped and the next instance scheduled for the next timer interval. One of the key ways of optimizing this processing is to set the timer interval correctly for the expected workload. This is an interesting new feature of the batch framework and we anticipate it will come in handy for specific business situations with the monitor processes.

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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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  • Oracle TimesTen In-Memory Database Performance on SPARC T4-2

    - by Brian
    The Oracle TimesTen In-Memory Database is optimized to run on Oracle's SPARC T4 processor platforms running Oracle Solaris 11 providing unsurpassed scalability, performance, upgradability, protection of investment and return on investment. The following demonstrate the value of combining Oracle TimesTen In-Memory Database with SPARC T4 servers and Oracle Solaris 11: On a Mobile Call Processing test, the 2-socket SPARC T4-2 server outperforms: Oracle's SPARC Enterprise M4000 server (4 x 2.66 GHz SPARC64 VII+) by 34%. Oracle's SPARC T3-4 (4 x 1.65 GHz SPARC T3) by 2.7x, or 5.4x per processor. Utilizing the TimesTen Performance Throughput Benchmark (TPTBM), the SPARC T4-2 server protects investments with: 2.1x the overall performance of a 4-socket SPARC Enterprise M4000 server in read-only mode and 1.5x the performance in update-only testing. This is 4.2x more performance per processor than the SPARC64 VII+ 2.66 GHz based system. 10x more performance per processor than the SPARC T2+ 1.4 GHz server. 1.6x better performance per processor than the SPARC T3 1.65 GHz based server. In replication testing, the two socket SPARC T4-2 server is over 3x faster than the performance of a four socket SPARC Enterprise T5440 server in both asynchronous replication environment and the highly available 2-Safe replication. This testing emphasizes parallel replication between systems. Performance Landscape Mobile Call Processing Test Performance System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 218,400 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 162,900 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 80,400 TimesTen Performance Throughput Benchmark (TPTBM) Read-Only System Processor Sockets/Cores/Threads Tps SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 7.9M SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 6.5M M4000 SPARC64 VII+, 2.66 GHz 4 16 32 3.1M T5440 SPARC T2+, 1.4 GHz 4 32 256 3.1M TimesTen Performance Throughput Benchmark (TPTBM) Update-Only System Processor Sockets/Cores/Threads Tps SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 547,800 M4000 SPARC64 VII+, 2.66 GHz 4 16 32 363,800 SPARC T3-4 SPARC T3, 1.65 GHz 4 64 512 240,500 TimesTen Replication Tests System Processor Sockets/Cores/Threads Asynchronous 2-Safe SPARC T4-2 SPARC T4, 2.85 GHz 2 16 128 38,024 13,701 SPARC T5440 SPARC T2+, 1.4 GHz 4 32 256 11,621 4,615 Configuration Summary Hardware Configurations: SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 256 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 4 x 300 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head SPARC T3-4 server 4 x SPARC T3 processors, 1.6 GHz 512 GB memory 1 x 8 Gbs FC Qlogic HBA 8 x 146 GB internal disks 1 x Sun Fire X4275 server configured as COMSTAR head SPARC Enterprise M4000 server 4 x SPARC64 VII+ processors, 2.66 GHz 128 GB memory 1 x 8 Gbs FC Qlogic HBA 1 x 6 Gbs SAS HBA 2 x 146 GB internal disks Sun Storage F5100 Flash Array (40 x 24 GB flash modules) 1 x Sun Fire X4275 server configured as COMSTAR head Software Configuration: Oracle Solaris 11 11/11 Oracle TimesTen 11.2.2.4 Benchmark Descriptions TimesTen Performance Throughput BenchMark (TPTBM) is shipped with TimesTen and measures the total throughput of the system. The workload can test read-only, update-only, delete and insert operations as required. Mobile Call Processing is a customer-based workload for processing calls made by mobile phone subscribers. The workload has a mixture of read-only, update, and insert-only transactions. The peak throughput performance is measured from multiple concurrent processes executing the transactions until a peak performance is reached via saturation of the available resources. Parallel Replication tests using both asynchronous and 2-Safe replication methods. For asynchronous replication, transactions are processed in batches to maximize the throughput capabilities of the replication server and network. In 2-Safe replication, also known as no data-loss or high availability, transactions are replicated between servers immediately emphasizing low latency. For both environments, performance is measured in the number of parallel replication servers and the maximum transactions-per-second for all concurrent processes. See Also SPARC T4-2 Server oracle.com OTN Oracle TimesTen In-Memory Database oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

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  • Using MVP, how to create a view from another view, linked with the same model object

    - by Dinaiz
    Background We use the Model-View-Presenter design pattern along with the abstract factory pattern and the "signal/slot" pattern in our application, to fullfill 2 main requirements Enhance testability (very lightweight GUI, every action can be simulated in unit tests) Make the "view" totally independant from the rest, so we can change the actual view implementation, without changing anything else In order to do so our code is divided in 4 layers : Core : which holds the model Presenter : which manages interactions between the view interfaces (see bellow) and the core View Interfaces : they define the signals and slots for a View, but not the implementation Views : the actual implementation of the views When the presenter creates or deals with views, it uses an abstract factory and only knows about the view interfaces. It does the signal/slot binding between views interfaces. It doesn't care about the actual implementation. In the "views" layer, we have a concrete factory which deals with implementations. The signal/slot mechanism is implemented using a custom framework built upon boost::function. Really, what we have is something like that : http://martinfowler.com/eaaDev/PassiveScreen.html Everything works fine. The problem However, there's a problem I don't know how to solve. Let's take for example a very simple drag and drop example. I have two ContainersViews (ContainerView1, ContainerView2). ContainerView1 has an ItemView1. I drag the ItemView1 from ContainerView1 to ContainerView2. ContainerView2 must create an ItemView2, of a different type, but which "points" to the same model object as ItemView1. So the ContainerView2 gets a callback called for the drop action with ItemView1 as a parameter. It calls ContainerPresenterB passing it ItemViewB In this case we are only dealing with views. In MVP-PV, views aren't supposed to know anything about the presenter nor the model, right ? How can I create the ItemView2 from the ItemView1, not knowing which model object is ItemView1 representing ? I thought about adding an "itemId" to every view, this id being the id of the core object the view represents. So in pseudo code, ContainerPresenter2 would do something like itemView2=abstractWidgetFactory.createItemView2(); this.add(itemView2,itemView1.getCoreObjectId()) I don't get too much into details. That just work. The problem I have here is that those itemIds are just like pointers. And pointers can be dangling. Imagine that by mistake, I delete itemView1, and this deletes coreObject1. The itemView2 will have a coreObjectId which represents an invalid coreObject. Isn't there a more elegant and "bulletproof" solution ? Even though I never did ObjectiveC or macOSX programming, I couldn't help but notice that our framework is very similar to Cocoa framework. How do they deal with this kind of problem ? Couldn't find more in-depth information about that on google. If someone could shed some light on this. I hope this question isn't too confusing ...

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  • Oracle Announces Oracle Insurance Policy Administration for Life and Annuity 9.4

    - by helen.pitts(at)oracle.com
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Today's global insurers require the ability to provide higher levels of service and quickly bring to market life insurance and annuity products that not only help them stand out from the competition, but also stay current with local legislation. To succeed, they require agile and flexible core systems that enable them to meet the unique localization requirements of the markets in which they operate, whether in North America, Asia Pacific or the Pan-European Region. The release of Oracle Insurance Policy Administration for Life and Annuity 9.4, announced today, helps insurers meet this need with expanded international market capabilities that enable them to reduce risk and profitably compete wherever their business takes them. It offers expanded multi-language along with unit-linked product and fund processing capabilities that enable regional and global insurers to rapidly configure and deliver localized products – along with providing better service for end users through a single policy admin solution. Key enhancements include: Kanji/Kana language support, pre-defined content, and imperial date processing for the Japanese market New localization flexibility for configuring and managing international mailing addresses along with regional variations for client information Enhanced capability to calculate unit-linked pricing and valuation, in addition to market-based processing and pre-configured unit linked content Expanded role-based security and masking capability to further protect sensitive customer data Enhanced capability to restrict processing specified activities based on time of day and user role, reducing exposure to market timing risks Further capability to eliminate duplicate client records, helping to reduce underwriting risks and enhance servicing through a single view of the client "The ability to leverage a single, rules-driven policy administration system for multiple global operation centers can help insurers realize significant improvements in speed to market, customer service, compliance with regional regulations, and consolidation efforts,” noted Celent's Craig Weber, senior vice president, Insurance. “We believe such initiatives are necessary to help the industry address service and distribution imperatives." Helping our customers meet these mission-critical business imperatives is a key objective for Oracle Insurance. Active, ongoing dialogue with our customers is an important part of the process to help understand how our solutions are and can continue to help them achieve success in the marketplace. I had the opportunity to meet with several of our insurance customers at the Oracle Insurance Policy Administration Client Advisory Board meeting last week in Philadelphia, Penn. (View photos on the Oracle Insurance Facebook page.)   It was a great forum for Oracle Insurance and our clients. Discussion centered on the latest business and IT trends, with opportunities to learn more about the latest release of Oracle Insurance Policy Administration for Life and Annuity and other Oracle Insurance solutions such as data warehousing / business intelligence, while exchanging best practices for product innovation and servicing customers and sales channels. Helen Pitts is senior product marketing manager for Oracle Insurance's life and annuities solutions.

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  • Organizations &amp; Architecture UNISA Studies &ndash; Chap 7

    - by MarkPearl
    Learning Outcomes Name different device categories Discuss the functions and structure of I/.O modules Describe the principles of Programmed I/O Describe the principles of Interrupt-driven I/O Describe the principles of DMA Discuss the evolution characteristic of I/O channels Describe different types of I/O interface Explain the principles of point-to-point and multipoint configurations Discuss the way in which a FireWire serial bus functions Discuss the principles of InfiniBand architecture External Devices An external device attaches to the computer by a link to an I/O module. The link is used to exchange control, status, and data between the I/O module and the external device. External devices can be classified into 3 categories… Human readable – e.g. video display Machine readable – e.g. magnetic disk Communications – e.g. wifi card I/O Modules An I/O module has two major functions… Interface to the processor and memory via the system bus or central switch Interface to one or more peripheral devices by tailored data links Module Functions The major functions or requirements for an I/O module fall into the following categories… Control and timing Processor communication Device communication Data buffering Error detection I/O function includes a control and timing requirement, to coordinate the flow of traffic between internal resources and external devices. Processor communication involves the following… Command decoding Data Status reporting Address recognition The I/O device must be able to perform device communication. This communication involves commands, status information, and data. An essential task of an I/O module is data buffering due to the relative slow speeds of most external devices. An I/O module is often responsible for error detection and for subsequently reporting errors to the processor. I/O Module Structure An I/O module functions to allow the processor to view a wide range of devices in a simple minded way. The I/O module may hide the details of timing, formats, and the electro mechanics of an external device so that the processor can function in terms of simple reads and write commands. An I/O channel/processor is an I/O module that takes on most of the detailed processing burden, presenting a high-level interface to the processor. There are 3 techniques are possible for I/O operations Programmed I/O Interrupt[t I/O DMA Access Programmed I/O When a processor is executing a program and encounters an instruction relating to I/O it executes that instruction by issuing a command to the appropriate I/O module. With programmed I/O, the I/O module will perform the requested action and then set the appropriate bits in the I/O status register. The I/O module takes no further actions to alert the processor. I/O Commands To execute an I/O related instruction, the processor issues an address, specifying the particular I/O module and external device, and an I/O command. There are four types of I/O commands that an I/O module may receive when it is addressed by a processor… Control – used to activate a peripheral and tell it what to do Test – Used to test various status conditions associated with an I/O module and its peripherals Read – Causes the I/O module to obtain an item of data from the peripheral and place it in an internal buffer Write – Causes the I/O module to take an item of data form the data bus and subsequently transmit that data item to the peripheral The main disadvantage of this technique is it is a time consuming process that keeps the processor busy needlessly I/O Instructions With programmed I/O there is a close correspondence between the I/O related instructions that the processor fetches from memory and the I/O commands that the processor issues to an I/O module to execute the instructions. Typically there will be many I/O devices connected through I/O modules to the system – each device is given a unique identifier or address – when the processor issues an I/O command, the command contains the address of the address of the desired device, thus each I/O module must interpret the address lines to determine if the command is for itself. When the processor, main memory and I/O share a common bus, two modes of addressing are possible… Memory mapped I/O Isolated I/O (for a detailed explanation read page 245 of book) The advantage of memory mapped I/O over isolated I/O is that it has a large repertoire of instructions that can be used, allowing more efficient programming. The disadvantage of memory mapped I/O over isolated I/O is that valuable memory address space is sued up. Interrupts driven I/O Interrupt driven I/O works as follows… The processor issues an I/O command to a module and then goes on to do some other useful work The I/O module will then interrupts the processor to request service when is is ready to exchange data with the processor The processor then executes the data transfer and then resumes its former processing Interrupt Processing The occurrence of an interrupt triggers a number of events, both in the processor hardware and in software. When an I/O device completes an I/O operations the following sequence of hardware events occurs… The device issues an interrupt signal to the processor The processor finishes execution of the current instruction before responding to the interrupt The processor tests for an interrupt – determines that there is one – and sends an acknowledgement signal to the device that issues the interrupt. The acknowledgement allows the device to remove its interrupt signal The processor now needs to prepare to transfer control to the interrupt routine. To begin, it needs to save information needed to resume the current program at the point of interrupt. The minimum information required is the status of the processor and the location of the next instruction to be executed. The processor now loads the program counter with the entry location of the interrupt-handling program that will respond to this interrupt. It also saves the values of the process registers because the Interrupt operation may modify these The interrupt handler processes the interrupt – this includes examination of status information relating to the I/O operation or other event that caused an interrupt When interrupt processing is complete, the saved register values are retrieved from the stack and restored to the registers Finally, the PSW and program counter values from the stack are restored. Design Issues Two design issues arise in implementing interrupt I/O Because there will be multiple I/O modules, how does the processor determine which device issued the interrupt? If multiple interrupts have occurred, how does the processor decide which one to process? Addressing device recognition, 4 general categories of techniques are in common use… Multiple interrupt lines Software poll Daisy chain Bus arbitration For a detailed explanation of these approaches read page 250 of the textbook. Interrupt driven I/O while more efficient than simple programmed I/O still requires the active intervention of the processor to transfer data between memory and an I/O module, and any data transfer must traverse a path through the processor. Thus is suffers from two inherent drawbacks… The I/O transfer rate is limited by the speed with which the processor can test and service a device The processor is tied up in managing an I/O transfer; a number of instructions must be executed for each I/O transfer Direct Memory Access When large volumes of data are to be moved, an efficient technique is direct memory access (DMA) DMA Function DMA involves an additional module on the system bus. The DMA module is capable of mimicking the processor and taking over control of the system from the processor. It needs to do this to transfer data to and from memory over the system bus. DMA must the bus only when the processor does not need it, or it must force the processor to suspend operation temporarily (most common – referred to as cycle stealing). When the processor wishes to read or write a block of data, it issues a command to the DMA module by sending to the DMA module the following information… Whether a read or write is requested using the read or write control line between the processor and the DMA module The address of the I/O device involved, communicated on the data lines The starting location in memory to read from or write to, communicated on the data lines and stored by the DMA module in its address register The number of words to be read or written, communicated via the data lines and stored in the data count register The processor then continues with other work, it delegates the I/O operation to the DMA module which transfers the entire block of data, one word at a time, directly to or from memory without going through the processor. When the transfer is complete, the DMA module sends an interrupt signal to the processor, this the processor is involved only at the beginning and end of the transfer. I/O Channels and Processors Characteristics of I/O Channels As one proceeds along the evolutionary path, more and more of the I/O function is performed without CPU involvement. The I/O channel represents an extension of the DMA concept. An I/O channel ahs the ability to execute I/O instructions, which gives it complete control over I/O operations. In a computer system with such devices, the CPU does not execute I/O instructions – such instructions are stored in main memory to be executed by a special purpose processor in the I/O channel itself. Two types of I/O channels are common A selector channel controls multiple high-speed devices. A multiplexor channel can handle I/O with multiple characters as fast as possible to multiple devices. The external interface: FireWire and InfiniBand Types of Interfaces One major characteristic of the interface is whether it is serial or parallel parallel interface – there are multiple lines connecting the I/O module and the peripheral, and multiple bits are transferred simultaneously serial interface – there is only one line used to transmit data, and bits must be transmitted one at a time With new generation serial interfaces, parallel interfaces are becoming less common. In either case, the I/O module must engage in a dialogue with the peripheral. In general terms the dialog may look as follows… The I/O module sends a control signal requesting permission to send data The peripheral acknowledges the request The I/O module transfers data The peripheral acknowledges receipt of data For a detailed explanation of FireWire and InfiniBand technology read page 264 – 270 of the textbook

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  • SQL SERVER – Partition Parallelism Support in expressor 3.6

    - by pinaldave
    I am very excited to learn that there is a new version of expressor’s data integration platform coming out in March of this year.  It will be version 3.6, and I look forward to using it and telling everyone about it.  Let me describe a little bit more about what will be so great in expressor 3.6: Greatly enhanced user interface Parallel Processing Bulk Artifact Upgrading The User Interface First let me cover the most obvious enhancements. The expressor Studio user interface (UI) has had some significant work done. Kudos to the expressor Engineering team; the entire UI is a visual masterpiece that is very responsive and intuitive. The improvements are more than just eye candy; they provide significant productivity gains when developing expressor Dataflows. Operator shape icons now include a description that identifies the function of each operator, instead of having to guess at the function by the icon. Operator shapes and highlighting depict the current function and status: Disabled, enabled, complete, incomplete, and error. Each status displays an appropriate message in the message panel with correction suggestions. Floating or docking property panels provide descriptive tool tips for each property as well as auto resize when adjusting the canvas, without having to search Help or the need to scroll around to get access to the property. Progress and status indicators let you know when an operation is working. “No limit” canvas with snap-to-grid allows automatic sizing and accurate positioning when you have numerous operators in the Dataflow. The inline tool bar offers quick access to pan, zoom, fit and overview functions. Selecting multiple artifacts with a right click context allows you to easily manage your workspace more efficiently. Partitioning and Parallel Processing Partitioning allows each operator to process multiple subsets of records in parallel as opposed to processing all records that flow through that operator in a single sequential set. This capability allows the user to configure the expressor Dataflow to run in a way that most efficiently utilizes the resources of the hardware where the Dataflow is running. Partitions can exist in most individual operators. Using partitions increases the speed of an expressor data integration application, therefore improving performance and load times. With the expressor 3.6 Enterprise Edition, expressor simplifies enabling parallel processing by adding intuitive partition settings that are easy to configure. Bulk Artifact Upgrading Bulk Artifact Upgrading sounds a bit intimidating, but it actually is not and it is a welcome addition to expressor Studio. In past releases, users were prompted to confirm that they wanted to upgrade their individual artifacts only when opened. This was a cumbersome and repetitive process. Now with bulk artifact upgrading, a user can easily select what artifact or group of artifacts to upgrade all at once. As you can see, there are many new features and upgrade options that will prove to make expressor Studio quicker and more efficient.  I hope I’m not the only one who is excited about all these new upgrades, and that I you try expressor and share your experience with me. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • Lubuntu: neither shut-down nor restart works

    - by Rantanplan
    aI have a freshly installed Lubuntu 14.04.1 (installed with forcepae option on a laptop with Pentium M processor). The only problem that I have found so far is that I cannot shut-down or restart the laptop. It always continues showing "Lubuntu" and some dots. Pressing Esc it says wait-for-state stop/waiting * Stopping rsync daemon rsync [OK] * Asking all remaining processes to terminate… [OK] * Killing all remaining processes… [fail] ModemManager [597] : <info> Caught signal, shutting down… ModemManager [597] : <info> ModemManager is shut down nm-dispatcher.action: Could not get the system bus. Make sure the message bus daemon is running! Message: Did not receive a reply. Possible causes: the remote application did not send a reply, the message bus security policy blocked the reply, the reply timeout expired, or the network connection was broken. * Deactivating swap… [OK] * Will now halt The cursor remains blinking but the only way to switch it off is to hold the power-off key pressed for some seconds. I tried sudo shutdown -h now, sudo halt and sudo poweroff resulting in the same problem. I also tried to add acpi=force in GRUB_CMDLINE_LINUX_DEFAULT="quiet splash" in /etc/default/grub and run sudo update-grub; then, using the taskbar's shot-down button lead to a direct stop of the laptop equal to holding the power-off key pressed for some seconds. Next I followed the answer http://askubuntu.com/a/202481/288322. Now, I directly receive some messages during shut-down starting wait-for-state stop/waiting * Stopping rsync daemon rsync [OK] * Asking all remaining processes to terminate… [OK] [ 240.944277] INFO: task kworker/0:2:24: block for more than 120 seconds. [ 240.944461] Tainted: G S 3.13.0-34-generic #60-Ubuntu [ 240.944623] "echo 0 > /proc/sys/kernel/hung_tasks_timeout_secs" disables this message. followed by some more similar lines and then: * Killing all remaining processes… [fail] ModemManager [576] : <info> Caught signal, shutting down… nm-dispatcher.action: Caught signal 15, shutting down... ModemManager [576] : <info> ModemManager is shut down * Deactivating swap… [OK] * Will now halt [ 600.944276] INFO: task kworker/0:2:24: block for more than 120 seconds. [ 600.944458] Tainted: G S 3.13.0-34-generic #60-Ubuntu [ 600.944619] "echo 0 > /proc/sys/kernel/hung_tasks_timeout_secs" disables this message. Then, nothing more was coming during the next 5 minutes. If you know where can I find relevant error information, I will be happy to search for them.

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  • Le Bluetooth 4.0 dans les starting-blocks, plus puissant il rend possible le développement d'applica

    Le Bluetooth 4.0 dans les starting-blocks Il rendrait possible le développement d'applications sportives, médicales et domotiques Le Bluetooth, dans sa version 4, serait prêt à bondir. C'est le message qui vient d'être donné sur le site officiel de la technologie. Le Bluetooth 4 marque en tout cas une réelle évolution. La portée du signal pourra à présent dépasser les 60 mètres (et être modulable). Sa consommation électrique sera plus réduite. Et le Bluetooth inclura à présent la norme radio 802.11 pour le transfert haut débit de fichiers. Ces évolutions, notamment la modularité du signal, permettront d'élargir la gamme de produits qui peuvent être int...

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