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  • JSR 360 and JSR 361: A Big Leap for Java ME 8

    - by terrencebarr
    It might have gone unnoticed to some, but Java ME took a big leap forward a couple of weeks ago with the filing of two new JSRs: JSR 360: “Connected Limited Device Configuration 8″ (aka CLDC 8) JSR 361: “Java ME Embedded Profile” (aka ME EP) Together, these two JSRs will significantly update, enhance, and modernize the Java ME platform, and specifically small embedded Java, with a host of new features and functionality. JSR 360 – Connected Limited Device Configuration 8 CLDC 8 is based on JSR 139 (CLDC 1.1) and updates the core Java ME VM, language support, libraries, and features to be aligned with Java SE 8. This will include: VM updated to comply with the JVM language specification version 2 Support for SE 7/8 language features like Generics, Assertions, Annotations, Try-with-Resources, and more New libraries such as Collections, NIO subset, Logging API subset A consolidated and enhanced Generic Connection Framework for multi-protocol I/O With CLDC 8, Java ME and Java SE are entering their next phase of alignment – making Java the only technology today that truly scales application development, code re-use, and tooling across the whole range of IT platforms, from small embedded to large enterprise. JSR 361 – Java ME Embedded Profile ME EP is based on JSR 228 (IMP-NG) and updates the specification in key areas to provide a powerful and flexible application environment for small embedded Java platforms, building on the features of CLDC 8:  A new, lightweight component and services model Shared libraries Multi-application concurrency, inter-application communication, and event system Application management API optionality, to address low-footprint use cases With ME EP, application developers will have a modern application environment which allows development and deployment of  modular, robust, sophisticated, and footprint-optimized solutions for a wide range of embedded use cases and devices. Summary While these JSRs are still under development, it’s clear that there are exciting new times ahead for Java ME – turning into a serious application platform while maintaining the focus on resource-constrained devices to address the expected explosion of small, smart, and connected embedded platforms. To learn more, click on the above links for JSR 360 and JSR 361. Or review the JavaOne 2012 online presentations on the topic: CON11300: Expanding the reach of the Java ME Platform CON5943: Java ME 8 Service Platform And stay tuned for more in this space! Cheers, – Terrence Filed under: Mobile & Embedded Tagged: "jsr 360", "jsr 361", "me 8", embedded, Embedded Java, JCP

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  • ANGLE wined3d in reverse

    <b>Wine-Reviews:</b> "Were happy to announce a new open source project called Almost Native Graphics Layer Engine, or ANGLE for short. The goal of ANGLE is to layer WebGLs subset of the OpenGL ES 2.0 API over DirectX 9.0c API calls."

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  • Thread placement policies on NUMA systems - update

    - by Dave
    In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

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  • Create Ubuntu repository on CentOS server with debmirror

    - by Wilco Groothand
    I want to create a UBUNTU repo mirror on my CentOS reposerver. I read about it and came to the conclusion that for our purpose debmirror was the correct solution, because I then could mirror a subset of the total repository. The problem is that with debmirror I run into the gpg key errors. Already solved in Ubuntu, but the apt-key solutions are not valid within CentOS. The command does not exists. I am totally stuck.

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  • Does GNC mean the death of Internet Explorer?

    - by Monika Michael
    From the wikipedia - Google Native Client (NaCl) is a sandboxing technology for running a subset of Intel x86 or ARM native code using software-based fault isolation. It is proposed for safely running native code from a web browser, allowing web-based applications to run at near-native speeds. (Emphasis mine) (Source) Compiled C++ code running in a browser? Are other companies working on a similar offering? What would it mean for the browser landscape?

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  • Building dynamic OLAP data marts on-the-fly

    - by DrJohn
    At the forthcoming SQLBits conference, I will be presenting a session on how to dynamically build an OLAP data mart on-the-fly. This blog entry is intended to clarify exactly what I mean by an OLAP data mart, why you may need to build them on-the-fly and finally outline the steps needed to build them dynamically. In subsequent blog entries, I will present exactly how to implement some of the techniques involved. What is an OLAP data mart? In data warehousing parlance, a data mart is a subset of the overall corporate data provided to business users to meet specific business needs. Of course, the term does not specify the technology involved, so I coined the term "OLAP data mart" to identify a subset of data which is delivered in the form of an OLAP cube which may be accompanied by the relational database upon which it was built. To clarify, the relational database is specifically create and loaded with the subset of data and then the OLAP cube is built and processed to make the data available to the end-users via standard OLAP client tools. Why build OLAP data marts? Market research companies sell data to their clients to make money. To gain competitive advantage, market research providers like to "add value" to their data by providing systems that enhance analytics, thereby allowing clients to make best use of the data. As such, OLAP cubes have become a standard way of delivering added value to clients. They can be built on-the-fly to hold specific data sets and meet particular needs and then hosted on a secure intranet site for remote access, or shipped to clients' own infrastructure for hosting. Even better, they support a wide range of different tools for analytical purposes, including the ever popular Microsoft Excel. Extension Attributes: The Challenge One of the key challenges in building multiple OLAP data marts based on the same 'template' is handling extension attributes. These are attributes that meet the client's specific reporting needs, but do not form part of the standard template. Now clearly, these extension attributes have to come into the system via additional files and ultimately be added to relational tables so they can end up in the OLAP cube. However, processing these files and filling dynamically altered tables with SSIS is a challenge as SSIS packages tend to break as soon as the database schema changes. There are two approaches to this: (1) dynamically build an SSIS package in memory to match the new database schema using C#, or (2) have the extension attributes provided as name/value pairs so the file's schema does not change and can easily be loaded using SSIS. The problem with the first approach is the complexity of writing an awful lot of complex C# code. The problem of the second approach is that name/value pairs are useless to an OLAP cube; so they have to be pivoted back into a proper relational table somewhere in the data load process WITHOUT breaking SSIS. How this can be done will be part of future blog entry. What is involved in building an OLAP data mart? There are a great many steps involved in building OLAP data marts on-the-fly. The key point is that all the steps must be automated to allow for the production of multiple OLAP data marts per day (i.e. many thousands, each with its own specific data set and attributes). Now most of these steps have a great deal in common with standard data warehouse practices. The key difference is that the databases are all built to order. The only permanent database is the metadata database (shown in orange) which holds all the metadata needed to build everything else (i.e. client orders, configuration information, connection strings, client specific requirements and attributes etc.). The staging database (shown in red) has a short life: it is built, populated and then ripped down as soon as the OLAP Data Mart has been populated. In the diagram below, the OLAP data mart comprises the two blue components: the Data Mart which is a relational database and the OLAP Cube which is an OLAP database implemented using Microsoft Analysis Services (SSAS). The client may receive just the OLAP cube or both components together depending on their reporting requirements.  So, in broad terms the steps required to fulfil a client order are as follows: Step 1: Prepare metadata Create a set of database names unique to the client's order Modify all package connection strings to be used by SSIS to point to new databases and file locations. Step 2: Create relational databases Create the staging and data mart relational databases using dynamic SQL and set the database recovery mode to SIMPLE as we do not need the overhead of logging anything Execute SQL scripts to build all database objects (tables, views, functions and stored procedures) in the two databases Step 3: Load staging database Use SSIS to load all data files into the staging database in a parallel operation Load extension files containing name/value pairs. These will provide client-specific attributes in the OLAP cube. Step 4: Load data mart relational database Load the data from staging into the data mart relational database, again in parallel where possible Allocate surrogate keys and use SSIS to perform surrogate key lookup during the load of fact tables Step 5: Load extension tables & attributes Pivot the extension attributes from their native name/value pairs into proper relational tables Add the extension attributes to the views used by OLAP cube Step 6: Deploy & Process OLAP cube Deploy the OLAP database directly to the server using a C# script task in SSIS Modify the connection string used by the OLAP cube to point to the data mart relational database Modify the cube structure to add the extension attributes to both the data source view and the relevant dimensions Remove any standard attributes that not required Process the OLAP cube Step 7: Backup and drop databases Drop staging database as it is no longer required Backup data mart relational and OLAP database and ship these to the client's infrastructure Drop data mart relational and OLAP database from the build server Mark order complete Start processing the next order, ad infinitum. So my future blog posts and my forthcoming session at the SQLBits conference will all focus on some of the more interesting aspects of building OLAP data marts on-the-fly such as handling the load of extension attributes and how to dynamically alter the structure of an OLAP cube using C#.

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  • Stairway to XML: Level 4 - Querying XML Data

    You can extract a subset of data from an XML instance by using the query() method, and you can use the value() method to retrieve individual element and attribute values from an XML instance. SQL Monitor v3 is even more powerfulUse custom metrics to monitor and alert on data that's most important for your environment, easily imported from our custom metrics site. Find out more.

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  • problems with my slotgame [delphi]

    - by Raiden2k
    hey guys im coding at the moment on a slotgame for the learning effect. here is the source code. my questions are below: unit Unit1; {$mode objfpc}{$H+} interface uses Classes, SysUtils, Windows, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, ExtCtrls, ComCtrls, Menus, ActnList, Spin, FileCtrl; type { TForm1 } TForm1 = class(TForm) FloatSpinEdit1: TFloatSpinEdit; Guthabenlb: TLabel; s4: TLabel; s5: TLabel; s6: TLabel; s7: TLabel; s8: TLabel; s9: TLabel; Timer3: TTimer; Winlb: TLabel; Loselb: TLabel; slotbn: TButton; s1: TLabel; s2: TLabel; s3: TLabel; Timer1: TTimer; Timer2: TTimer; procedure FormCreate(Sender: TObject); procedure slotbnClick(Sender: TObject); procedure Timer1Timer(Sender: TObject); procedure Timer2Timer(Sender: TObject); procedure Timer3Timer(Sender: TObject); private { private declarations } FRollen : array [0..2, 0..9] of String; public { public declarations } end; var Form1: TForm1; wins,loses : Integer; guthaben : Double = 10; implementation {$R *.lfm} { TForm1 } procedure TForm1.slotbnClick(Sender: TObject); begin Guthaben := Guthaben - 1.00; Guthabenlb.Caption := FloatToStr(guthaben) + (' €'); Timer1.Enabled := True; Timer2.Enabled := True; slotbn.Enabled := false; end; procedure TForm1.FormCreate(Sender: TObject); var i: integer; j: integer; n: integer; digits: TStringlist; begin Digits := TStringList.Create; try for i := low(FRollen) to high(FRollen) do begin for j := low(FRollen[i]) to high(FRollen[i]) do Digits.Add(IntToStr(j)); for j := low(FRollen[i]) to high(FRollen[i]) do begin n := Random(Digits.Count); FRollen[i, j] := Digits[n]; Digits.Delete(n); end; end finally Digits.Free; end; for i:=low(FRollen) to high(FRollen) do begin end; end; //==================================================================================================\\ // Drehen der Slots im Zufallsmodus //==================================================================================================// procedure TForm1.Timer1Timer(Sender: TObject); begin s1.Caption := IntToStr(Random(9)); s2.Caption := IntToStr(Random(9)); s3.Caption := IntToStr(Random(9)); s4.Caption := IntToStr(Random(9)); s5.Caption := IntToStr(Random(9)); s6.Caption := IntToStr(Random(9)); s7.Caption := IntToStr(Random(9)); s8.Caption := IntToStr(Random(9)); s9.Caption := IntToStr(Random(9)); end; //==================================================================================================// //===================================================================================================\\ // Gewonnen / Verloren abfrage //===================================================================================================// procedure TForm1.Timer2Timer(Sender: TObject); begin Timer1.Enabled := False; Timer2.Enabled := false; if (s1.Caption = s5.Caption) and (s1.Caption = s9.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s1.Caption = s4.Caption) and (s1.Caption = s7.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s2.Caption = s5.Caption) and (s2.Caption = s8.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s3.Caption = s6.Caption) and (s3.Caption = s9.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s3.Caption = s5.Caption) and (s3.Caption = s7.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else Inc(loses); slotbn.Enabled := True; Loselb.Caption := 'Loses: ' + IntToStr(loses); Winlb.Caption := 'Wins: ' + IntTostr(Wins); end; procedure TForm1.Timer3Timer(Sender: TObject); begin if (guthaben = 0) or (guthaben < 0) then begin Timer3.Enabled := False; MessageBox(handle,'Du hast verloren!','Verlierer!',MB_OK); close(); end; end; //======================================================================================================\\ end. How can i replace the labels through icons 16 x 16 pixels? How can i adjust the winning sum according to the icons.(for example 3 crowns give you 40 € and 3 apples only 10 €) How can i adhust the winning sum with a sum for every round?

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  • Problems with my slotgame

    - by Raiden2k
    I'm coding a slot game for learning. Here's the source code. My questions are below. unit Unit1; {$mode objfpc}{$H+} interface uses Classes, SysUtils, Windows, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, ExtCtrls, ComCtrls, Menus, ActnList, Spin, FileCtrl; type { TForm1 } TForm1 = class(TForm) FloatSpinEdit1: TFloatSpinEdit; Guthabenlb: TLabel; s4: TLabel; s5: TLabel; s6: TLabel; s7: TLabel; s8: TLabel; s9: TLabel; Timer3: TTimer; Winlb: TLabel; Loselb: TLabel; slotbn: TButton; s1: TLabel; s2: TLabel; s3: TLabel; Timer1: TTimer; Timer2: TTimer; procedure FormCreate(Sender: TObject); procedure slotbnClick(Sender: TObject); procedure Timer1Timer(Sender: TObject); procedure Timer2Timer(Sender: TObject); procedure Timer3Timer(Sender: TObject); private { private declarations } FRollen : array [0..2, 0..9] of String; public { public declarations } end; var Form1: TForm1; wins,loses : Integer; guthaben : Double = 10; implementation {$R *.lfm} { TForm1 } procedure TForm1.slotbnClick(Sender: TObject); begin Guthaben := Guthaben - 1.00; Guthabenlb.Caption := FloatToStr(guthaben) + (' €'); Timer1.Enabled := True; Timer2.Enabled := True; slotbn.Enabled := false; end; procedure TForm1.FormCreate(Sender: TObject); var i: integer; j: integer; n: integer; digits: TStringlist; begin Digits := TStringList.Create; try for i := low(FRollen) to high(FRollen) do begin for j := low(FRollen[i]) to high(FRollen[i]) do Digits.Add(IntToStr(j)); for j := low(FRollen[i]) to high(FRollen[i]) do begin n := Random(Digits.Count); FRollen[i, j] := Digits[n]; Digits.Delete(n); end; end finally Digits.Free; end; for i:=low(FRollen) to high(FRollen) do begin end; end; //==================================================================================================\\ // Drehen der Slots im Zufallsmodus //==================================================================================================// procedure TForm1.Timer1Timer(Sender: TObject); begin s1.Caption := IntToStr(Random(9)); s2.Caption := IntToStr(Random(9)); s3.Caption := IntToStr(Random(9)); s4.Caption := IntToStr(Random(9)); s5.Caption := IntToStr(Random(9)); s6.Caption := IntToStr(Random(9)); s7.Caption := IntToStr(Random(9)); s8.Caption := IntToStr(Random(9)); s9.Caption := IntToStr(Random(9)); end; //==================================================================================================// //===================================================================================================\\ // Gewonnen / Verloren abfrage //===================================================================================================// procedure TForm1.Timer2Timer(Sender: TObject); begin Timer1.Enabled := False; Timer2.Enabled := false; if (s1.Caption = s5.Caption) and (s1.Caption = s9.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s1.Caption = s4.Caption) and (s1.Caption = s7.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s2.Caption = s5.Caption) and (s2.Caption = s8.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s3.Caption = s6.Caption) and (s3.Caption = s9.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else if (s3.Caption = s5.Caption) and (s3.Caption = s7.Caption) then begin Guthaben := Guthaben + 5.00; Inc(wins); end else Inc(loses); slotbn.Enabled := True; Loselb.Caption := 'Loses: ' + IntToStr(loses); Winlb.Caption := 'Wins: ' + IntTostr(Wins); end; procedure TForm1.Timer3Timer(Sender: TObject); begin if (guthaben = 0) or (guthaben < 0) then begin Timer3.Enabled := False; MessageBox(handle,'Du hast verloren!','Verlierer!',MB_OK); close(); end; end; //======================================================================================================\\ end. How can I replace the labels through icons 16 x 16 pixels? How can I adjust the winning sum according to the icons? (for example 3 crowns give you 40 € and 3 apples only 10 €) How can I adjust the winning sum with a sum for every round?

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  • Nautilus and file command in 11.04 don't show metadata for WebM files

    - by Pili
    The file-name extension .webm is used for media files using the WebM multimedia format, which consists of the WebM container (a subset of the Matroska container) and audio and video streams with independet enconding and quality settings. Description of the issue: For files in the WebM format, the program file says that files are raw data, instead of determining and displaying the real file-format, which is WebM. Besides, Nautilus doesn't display the technical metadata of files in this format. Why is the file program not displaying the file format for WebM files?

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  • Does NaCl mean the death of Internet Explorer? [closed]

    - by Monika Michael
    From the wikipedia - Google Native Client (NaCl) is a sandboxing technology for running a subset of Intel x86 or ARM native code using software-based fault isolation. It is proposed for safely running native code from a web browser, allowing web-based applications to run at near-native speeds. (Emphasis mine) (Source) Compiled C++ code running in a browser? Are other companies working on a similar offering? What would it mean for the browser landscape?

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  • What are good design practices when working with Entity Framework

    - by AD
    This will apply mostly for an asp.net application where the data is not accessed via soa. Meaning that you get access to the objects loaded from the framework, not Transfer Objects, although some recommendation still apply. This is a community post, so please add to it as you see fit. Applies to: Entity Framework 1.0 shipped with Visual Studio 2008 sp1. Why pick EF in the first place? Considering it is a young technology with plenty of problems (see below), it may be a hard sell to get on the EF bandwagon for your project. However, it is the technology Microsoft is pushing (at the expense of Linq2Sql, which is a subset of EF). In addition, you may not be satisfied with NHibernate or other solutions out there. Whatever the reasons, there are people out there (including me) working with EF and life is not bad.make you think. EF and inheritance The first big subject is inheritance. EF does support mapping for inherited classes that are persisted in 2 ways: table per class and table the hierarchy. The modeling is easy and there are no programming issues with that part. (The following applies to table per class model as I don't have experience with table per hierarchy, which is, anyway, limited.) The real problem comes when you are trying to run queries that include one or many objects that are part of an inheritance tree: the generated sql is incredibly awful, takes a long time to get parsed by the EF and takes a long time to execute as well. This is a real show stopper. Enough that EF should probably not be used with inheritance or as little as possible. Here is an example of how bad it was. My EF model had ~30 classes, ~10 of which were part of an inheritance tree. On running a query to get one item from the Base class, something as simple as Base.Get(id), the generated SQL was over 50,000 characters. Then when you are trying to return some Associations, it degenerates even more, going as far as throwing SQL exceptions about not being able to query more than 256 tables at once. Ok, this is bad, EF concept is to allow you to create your object structure without (or with as little as possible) consideration on the actual database implementation of your table. It completely fails at this. So, recommendations? Avoid inheritance if you can, the performance will be so much better. Use it sparingly where you have to. In my opinion, this makes EF a glorified sql-generation tool for querying, but there are still advantages to using it. And ways to implement mechanism that are similar to inheritance. Bypassing inheritance with Interfaces First thing to know with trying to get some kind of inheritance going with EF is that you cannot assign a non-EF-modeled class a base class. Don't even try it, it will get overwritten by the modeler. So what to do? You can use interfaces to enforce that classes implement some functionality. For example here is a IEntity interface that allow you to define Associations between EF entities where you don't know at design time what the type of the entity would be. public enum EntityTypes{ Unknown = -1, Dog = 0, Cat } public interface IEntity { int EntityID { get; } string Name { get; } Type EntityType { get; } } public partial class Dog : IEntity { // implement EntityID and Name which could actually be fields // from your EF model Type EntityType{ get{ return EntityTypes.Dog; } } } Using this IEntity, you can then work with undefined associations in other classes // lets take a class that you defined in your model. // that class has a mapping to the columns: PetID, PetType public partial class Person { public IEntity GetPet() { return IEntityController.Get(PetID,PetType); } } which makes use of some extension functions: public class IEntityController { static public IEntity Get(int id, EntityTypes type) { switch (type) { case EntityTypes.Dog: return Dog.Get(id); case EntityTypes.Cat: return Cat.Get(id); default: throw new Exception("Invalid EntityType"); } } } Not as neat as having plain inheritance, particularly considering you have to store the PetType in an extra database field, but considering the performance gains, I would not look back. It also cannot model one-to-many, many-to-many relationship, but with creative uses of 'Union' it could be made to work. Finally, it creates the side effet of loading data in a property/function of the object, which you need to be careful about. Using a clear naming convention like GetXYZ() helps in that regards. Compiled Queries Entity Framework performance is not as good as direct database access with ADO (obviously) or Linq2SQL. There are ways to improve it however, one of which is compiling your queries. The performance of a compiled query is similar to Linq2Sql. What is a compiled query? It is simply a query for which you tell the framework to keep the parsed tree in memory so it doesn't need to be regenerated the next time you run it. So the next run, you will save the time it takes to parse the tree. Do not discount that as it is a very costly operation that gets even worse with more complex queries. There are 2 ways to compile a query: creating an ObjectQuery with EntitySQL and using CompiledQuery.Compile() function. (Note that by using an EntityDataSource in your page, you will in fact be using ObjectQuery with EntitySQL, so that gets compiled and cached). An aside here in case you don't know what EntitySQL is. It is a string-based way of writing queries against the EF. Here is an example: "select value dog from Entities.DogSet as dog where dog.ID = @ID". The syntax is pretty similar to SQL syntax. You can also do pretty complex object manipulation, which is well explained [here][1]. Ok, so here is how to do it using ObjectQuery< string query = "select value dog " + "from Entities.DogSet as dog " + "where dog.ID = @ID"; ObjectQuery<Dog> oQuery = new ObjectQuery<Dog>(query, EntityContext.Instance)); oQuery.Parameters.Add(new ObjectParameter("ID", id)); oQuery.EnablePlanCaching = true; return oQuery.FirstOrDefault(); The first time you run this query, the framework will generate the expression tree and keep it in memory. So the next time it gets executed, you will save on that costly step. In that example EnablePlanCaching = true, which is unnecessary since that is the default option. The other way to compile a query for later use is the CompiledQuery.Compile method. This uses a delegate: static readonly Func<Entities, int, Dog> query_GetDog = CompiledQuery.Compile<Entities, int, Dog>((ctx, id) => ctx.DogSet.FirstOrDefault(it => it.ID == id)); or using linq static readonly Func<Entities, int, Dog> query_GetDog = CompiledQuery.Compile<Entities, int, Dog>((ctx, id) => (from dog in ctx.DogSet where dog.ID == id select dog).FirstOrDefault()); to call the query: query_GetDog.Invoke( YourContext, id ); The advantage of CompiledQuery is that the syntax of your query is checked at compile time, where as EntitySQL is not. However, there are other consideration... Includes Lets say you want to have the data for the dog owner to be returned by the query to avoid making 2 calls to the database. Easy to do, right? EntitySQL string query = "select value dog " + "from Entities.DogSet as dog " + "where dog.ID = @ID"; ObjectQuery<Dog> oQuery = new ObjectQuery<Dog>(query, EntityContext.Instance)).Include("Owner"); oQuery.Parameters.Add(new ObjectParameter("ID", id)); oQuery.EnablePlanCaching = true; return oQuery.FirstOrDefault(); CompiledQuery static readonly Func<Entities, int, Dog> query_GetDog = CompiledQuery.Compile<Entities, int, Dog>((ctx, id) => (from dog in ctx.DogSet.Include("Owner") where dog.ID == id select dog).FirstOrDefault()); Now, what if you want to have the Include parametrized? What I mean is that you want to have a single Get() function that is called from different pages that care about different relationships for the dog. One cares about the Owner, another about his FavoriteFood, another about his FavotireToy and so on. Basicly, you want to tell the query which associations to load. It is easy to do with EntitySQL public Dog Get(int id, string include) { string query = "select value dog " + "from Entities.DogSet as dog " + "where dog.ID = @ID"; ObjectQuery<Dog> oQuery = new ObjectQuery<Dog>(query, EntityContext.Instance)) .IncludeMany(include); oQuery.Parameters.Add(new ObjectParameter("ID", id)); oQuery.EnablePlanCaching = true; return oQuery.FirstOrDefault(); } The include simply uses the passed string. Easy enough. Note that it is possible to improve on the Include(string) function (that accepts only a single path) with an IncludeMany(string) that will let you pass a string of comma-separated associations to load. Look further in the extension section for this function. If we try to do it with CompiledQuery however, we run into numerous problems: The obvious static readonly Func<Entities, int, string, Dog> query_GetDog = CompiledQuery.Compile<Entities, int, string, Dog>((ctx, id, include) => (from dog in ctx.DogSet.Include(include) where dog.ID == id select dog).FirstOrDefault()); will choke when called with: query_GetDog.Invoke( YourContext, id, "Owner,FavoriteFood" ); Because, as mentionned above, Include() only wants to see a single path in the string and here we are giving it 2: "Owner" and "FavoriteFood" (which is not to be confused with "Owner.FavoriteFood"!). Then, let's use IncludeMany(), which is an extension function static readonly Func<Entities, int, string, Dog> query_GetDog = CompiledQuery.Compile<Entities, int, string, Dog>((ctx, id, include) => (from dog in ctx.DogSet.IncludeMany(include) where dog.ID == id select dog).FirstOrDefault()); Wrong again, this time it is because the EF cannot parse IncludeMany because it is not part of the functions that is recognizes: it is an extension. Ok, so you want to pass an arbitrary number of paths to your function and Includes() only takes a single one. What to do? You could decide that you will never ever need more than, say 20 Includes, and pass each separated strings in a struct to CompiledQuery. But now the query looks like this: from dog in ctx.DogSet.Include(include1).Include(include2).Include(include3) .Include(include4).Include(include5).Include(include6) .[...].Include(include19).Include(include20) where dog.ID == id select dog which is awful as well. Ok, then, but wait a minute. Can't we return an ObjectQuery< with CompiledQuery? Then set the includes on that? Well, that what I would have thought so as well: static readonly Func<Entities, int, ObjectQuery<Dog>> query_GetDog = CompiledQuery.Compile<Entities, int, string, ObjectQuery<Dog>>((ctx, id) => (ObjectQuery<Dog>)(from dog in ctx.DogSet where dog.ID == id select dog)); public Dog GetDog( int id, string include ) { ObjectQuery<Dog> oQuery = query_GetDog(id); oQuery = oQuery.IncludeMany(include); return oQuery.FirstOrDefault; } That should have worked, except that when you call IncludeMany (or Include, Where, OrderBy...) you invalidate the cached compiled query because it is an entirely new one now! So, the expression tree needs to be reparsed and you get that performance hit again. So what is the solution? You simply cannot use CompiledQueries with parametrized Includes. Use EntitySQL instead. This doesn't mean that there aren't uses for CompiledQueries. It is great for localized queries that will always be called in the same context. Ideally CompiledQuery should always be used because the syntax is checked at compile time, but due to limitation, that's not possible. An example of use would be: you may want to have a page that queries which two dogs have the same favorite food, which is a bit narrow for a BusinessLayer function, so you put it in your page and know exactly what type of includes are required. Passing more than 3 parameters to a CompiledQuery Func is limited to 5 parameters, of which the last one is the return type and the first one is your Entities object from the model. So that leaves you with 3 parameters. A pitance, but it can be improved on very easily. public struct MyParams { public string param1; public int param2; public DateTime param3; } static readonly Func<Entities, MyParams, IEnumerable<Dog>> query_GetDog = CompiledQuery.Compile<Entities, MyParams, IEnumerable<Dog>>((ctx, myParams) => from dog in ctx.DogSet where dog.Age == myParams.param2 && dog.Name == myParams.param1 and dog.BirthDate > myParams.param3 select dog); public List<Dog> GetSomeDogs( int age, string Name, DateTime birthDate ) { MyParams myParams = new MyParams(); myParams.param1 = name; myParams.param2 = age; myParams.param3 = birthDate; return query_GetDog(YourContext,myParams).ToList(); } Return Types (this does not apply to EntitySQL queries as they aren't compiled at the same time during execution as the CompiledQuery method) Working with Linq, you usually don't force the execution of the query until the very last moment, in case some other functions downstream wants to change the query in some way: static readonly Func<Entities, int, string, IEnumerable<Dog>> query_GetDog = CompiledQuery.Compile<Entities, int, string, IEnumerable<Dog>>((ctx, age, name) => from dog in ctx.DogSet where dog.Age == age && dog.Name == name select dog); public IEnumerable<Dog> GetSomeDogs( int age, string name ) { return query_GetDog(YourContext,age,name); } public void DataBindStuff() { IEnumerable<Dog> dogs = GetSomeDogs(4,"Bud"); // but I want the dogs ordered by BirthDate gridView.DataSource = dogs.OrderBy( it => it.BirthDate ); } What is going to happen here? By still playing with the original ObjectQuery (that is the actual return type of the Linq statement, which implements IEnumerable), it will invalidate the compiled query and be force to re-parse. So, the rule of thumb is to return a List< of objects instead. static readonly Func<Entities, int, string, IEnumerable<Dog>> query_GetDog = CompiledQuery.Compile<Entities, int, string, IEnumerable<Dog>>((ctx, age, name) => from dog in ctx.DogSet where dog.Age == age && dog.Name == name select dog); public List<Dog> GetSomeDogs( int age, string name ) { return query_GetDog(YourContext,age,name).ToList(); //<== change here } public void DataBindStuff() { List<Dog> dogs = GetSomeDogs(4,"Bud"); // but I want the dogs ordered by BirthDate gridView.DataSource = dogs.OrderBy( it => it.BirthDate ); } When you call ToList(), the query gets executed as per the compiled query and then, later, the OrderBy is executed against the objects in memory. It may be a little bit slower, but I'm not even sure. One sure thing is that you have no worries about mis-handling the ObjectQuery and invalidating the compiled query plan. Once again, that is not a blanket statement. ToList() is a defensive programming trick, but if you have a valid reason not to use ToList(), go ahead. There are many cases in which you would want to refine the query before executing it. Performance What is the performance impact of compiling a query? It can actually be fairly large. A rule of thumb is that compiling and caching the query for reuse takes at least double the time of simply executing it without caching. For complex queries (read inherirante), I have seen upwards to 10 seconds. So, the first time a pre-compiled query gets called, you get a performance hit. After that first hit, performance is noticeably better than the same non-pre-compiled query. Practically the same as Linq2Sql When you load a page with pre-compiled queries the first time you will get a hit. It will load in maybe 5-15 seconds (obviously more than one pre-compiled queries will end up being called), while subsequent loads will take less than 300ms. Dramatic difference, and it is up to you to decide if it is ok for your first user to take a hit or you want a script to call your pages to force a compilation of the queries. Can this query be cached? { Dog dog = from dog in YourContext.DogSet where dog.ID == id select dog; } No, ad-hoc Linq queries are not cached and you will incur the cost of generating the tree every single time you call it. Parametrized Queries Most search capabilities involve heavily parametrized queries. There are even libraries available that will let you build a parametrized query out of lamba expressions. The problem is that you cannot use pre-compiled queries with those. One way around that is to map out all the possible criteria in the query and flag which one you want to use: public struct MyParams { public string name; public bool checkName; public int age; public bool checkAge; } static readonly Func<Entities, MyParams, IEnumerable<Dog>> query_GetDog = CompiledQuery.Compile<Entities, MyParams, IEnumerable<Dog>>((ctx, myParams) => from dog in ctx.DogSet where (myParams.checkAge == true && dog.Age == myParams.age) && (myParams.checkName == true && dog.Name == myParams.name ) select dog); protected List<Dog> GetSomeDogs() { MyParams myParams = new MyParams(); myParams.name = "Bud"; myParams.checkName = true; myParams.age = 0; myParams.checkAge = false; return query_GetDog(YourContext,myParams).ToList(); } The advantage here is that you get all the benifits of a pre-compiled quert. The disadvantages are that you most likely will end up with a where clause that is pretty difficult to maintain, that you will incur a bigger penalty for pre-compiling the query and that each query you run is not as efficient as it could be (particularly with joins thrown in). Another way is to build an EntitySQL query piece by piece, like we all did with SQL. protected List<Dod> GetSomeDogs( string name, int age) { string query = "select value dog from Entities.DogSet where 1 = 1 "; if( !String.IsNullOrEmpty(name) ) query = query + " and dog.Name == @Name "; if( age > 0 ) query = query + " and dog.Age == @Age "; ObjectQuery<Dog> oQuery = new ObjectQuery<Dog>( query, YourContext ); if( !String.IsNullOrEmpty(name) ) oQuery.Parameters.Add( new ObjectParameter( "Name", name ) ); if( age > 0 ) oQuery.Parameters.Add( new ObjectParameter( "Age", age ) ); return oQuery.ToList(); } Here the problems are: - there is no syntax checking during compilation - each different combination of parameters generate a different query which will need to be pre-compiled when it is first run. In this case, there are only 4 different possible queries (no params, age-only, name-only and both params), but you can see that there can be way more with a normal world search. - Noone likes to concatenate strings! Another option is to query a large subset of the data and then narrow it down in memory. This is particularly useful if you are working with a definite subset of the data, like all the dogs in a city. You know there are a lot but you also know there aren't that many... so your CityDog search page can load all the dogs for the city in memory, which is a single pre-compiled query and then refine the results protected List<Dod> GetSomeDogs( string name, int age, string city) { string query = "select value dog from Entities.DogSet where dog.Owner.Address.City == @City "; ObjectQuery<Dog> oQuery = new ObjectQuery<Dog>( query, YourContext ); oQuery.Parameters.Add( new ObjectParameter( "City", city ) ); List<Dog> dogs = oQuery.ToList(); if( !String.IsNullOrEmpty(name) ) dogs = dogs.Where( it => it.Name == name ); if( age > 0 ) dogs = dogs.Where( it => it.Age == age ); return dogs; } It is particularly useful when you start displaying all the data then allow for filtering. Problems: - Could lead to serious data transfer if you are not careful about your subset. - You can only filter on the data that you returned. It means that if you don't return the Dog.Owner association, you will not be able to filter on the Dog.Owner.Name So what is the best solution? There isn't any. You need to pick the solution that works best for you and your problem: - Use lambda-based query building when you don't care about pre-compiling your queries. - Use fully-defined pre-compiled Linq query when your object structure is not too complex. - Use EntitySQL/string concatenation when the structure could be complex and when the possible number of different resulting queries are small (which means fewer pre-compilation hits). - Use in-memory filtering when you are working with a smallish subset of the data or when you had to fetch all of the data on the data at first anyway (if the performance is fine with all the data, then filtering in memory will not cause any time to be spent in the db). Singleton access The best way to deal with your context and entities accross all your pages is to use the singleton pattern: public sealed class YourContext { private const string instanceKey = "On3GoModelKey"; YourContext(){} public static YourEntities Instance { get { HttpContext context = HttpContext.Current; if( context == null ) return Nested.instance; if (context.Items[instanceKey] == null) { On3GoEntities entity = new On3GoEntities(); context.Items[instanceKey] = entity; } return (YourEntities)context.Items[instanceKey]; } } class Nested { // Explicit static constructor to tell C# compiler // not to mark type as beforefieldinit static Nested() { } internal static readonly YourEntities instance = new YourEntities(); } } NoTracking, is it worth it? When executing a query, you can tell the framework to track the objects it will return or not. What does it mean? With tracking enabled (the default option), the framework will track what is going on with the object (has it been modified? Created? Deleted?) and will also link objects together, when further queries are made from the database, which is what is of interest here. For example, lets assume that Dog with ID == 2 has an owner which ID == 10. Dog dog = (from dog in YourContext.DogSet where dog.ID == 2 select dog).FirstOrDefault(); //dog.OwnerReference.IsLoaded == false; Person owner = (from o in YourContext.PersonSet where o.ID == 10 select dog).FirstOrDefault(); //dog.OwnerReference.IsLoaded == true; If we were to do the same with no tracking, the result would be different. ObjectQuery<Dog> oDogQuery = (ObjectQuery<Dog>) (from dog in YourContext.DogSet where dog.ID == 2 select dog); oDogQuery.MergeOption = MergeOption.NoTracking; Dog dog = oDogQuery.FirstOrDefault(); //dog.OwnerReference.IsLoaded == false; ObjectQuery<Person> oPersonQuery = (ObjectQuery<Person>) (from o in YourContext.PersonSet where o.ID == 10 select o); oPersonQuery.MergeOption = MergeOption.NoTracking; Owner owner = oPersonQuery.FirstOrDefault(); //dog.OwnerReference.IsLoaded == false; Tracking is very useful and in a perfect world without performance issue, it would always be on. But in this world, there is a price for it, in terms of performance. So, should you use NoTracking to speed things up? It depends on what you are planning to use the data for. Is there any chance that the data your query with NoTracking can be used to make update/insert/delete in the database? If so, don't use NoTracking because associations are not tracked and will causes exceptions to be thrown. In a page where there are absolutly no updates to the database, you can use NoTracking. Mixing tracking and NoTracking is possible, but it requires you to be extra careful with updates/inserts/deletes. The problem is that if you mix then you risk having the framework trying to Attach() a NoTracking object to the context where another copy of the same object exist with tracking on. Basicly, what I am saying is that Dog dog1 = (from dog in YourContext.DogSet where dog.ID == 2).FirstOrDefault(); ObjectQuery<Dog> oDogQuery = (ObjectQuery<Dog>) (from dog in YourContext.DogSet where dog.ID == 2 select dog); oDogQuery.MergeOption = MergeOption.NoTracking; Dog dog2 = oDogQuery.FirstOrDefault(); dog1 and dog2 are 2 different objects, one tracked and one not. Using the detached object in an update/insert will force an Attach() that will say "Wait a minute, I do already have an object here with the same database key. Fail". And when you Attach() one object, all of its hierarchy gets attached as well, causing problems everywhere. Be extra careful. How much faster is it with NoTracking It depends on the queries. Some are much more succeptible to tracking than other. I don't have a fast an easy rule for it, but it helps. So I should use NoTracking everywhere then? Not exactly. There are some advantages to tracking object. The first one is that the object is cached, so subsequent call for that object will not hit the database. That cache is only valid for the lifetime of the YourEntities object, which, if you use the singleton code above, is the same as the page lifetime. One page request == one YourEntity object. So for multiple calls for the same object, it will load only once per page request. (Other caching mechanism could extend that). What happens when you are using NoTracking and try to load the same object multiple times? The database will be queried each time, so there is an impact there. How often do/should you call for the same object during a single page request? As little as possible of course, but it does happens. Also remember the piece above about having the associations connected automatically for your? You don't have that with NoTracking, so if you load your data in multiple batches, you will not have a link to between them: ObjectQuery<Dog> oDogQuery = (ObjectQuery<Dog>)(from dog in YourContext.DogSet select dog); oDogQuery.MergeOption = MergeOption.NoTracking; List<Dog> dogs = oDogQuery.ToList(); ObjectQuery<Person> oPersonQuery = (ObjectQuery<Person>)(from o in YourContext.PersonSet select o); oPersonQuery.MergeOption = MergeOption.NoTracking; List<Person> owners = oPersonQuery.ToList(); In this case, no dog will have its .Owner property set. Some things to keep in mind when you are trying to optimize the performance. No lazy loading, what am I to do? This can be seen as a blessing in disguise. Of course it is annoying to load everything manually. However, it decreases the number of calls to the db and forces you to think about when you should load data. The more you can load in one database call the better. That was always true, but it is enforced now with this 'feature' of EF. Of course, you can call if( !ObjectReference.IsLoaded ) ObjectReference.Load(); if you want to, but a better practice is to force the framework to load the objects you know you will need in one shot. This is where the discussion about parametrized Includes begins to make sense. Lets say you have you Dog object public class Dog { public Dog Get(int id) { return YourContext.DogSet.FirstOrDefault(it => it.ID == id ); } } This is the type of function you work with all the time. It gets called from all over the place and once you have that Dog object, you will do very different things to it in different functions. First, it should be pre-compiled, because you will call that very often. Second, each different pages will want to have access to a different subset of the Dog data. Some will want the Owner, some the FavoriteToy, etc. Of course, you could call Load() for each reference you need anytime you need one. But that will generate a call to the database each time. Bad idea. So instead, each page will ask for the data it wants to see when it first request for the Dog object: static public Dog Get(int id) { return GetDog(entity,"");} static public Dog Get(int id, string includePath) { string query = "select value o " + " from YourEntities.DogSet as o " +

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  • Working with PivotTables in Excel

    - by Mark Virtue
    PivotTables are one of the most powerful features of Microsoft Excel.  They allow large amounts of data to be analyzed and summarized in just a few mouse clicks. In this article, we explore PivotTables, understand what they are, and learn how to create and customize them. Note:  This article is written using Excel 2010 (Beta).  The concept of a PivotTable has changed little over the years, but the method of creating one has changed in nearly every iteration of Excel.  If you are using a version of Excel that is not 2010, expect different screens from the ones you see in this article. A Little History In the early days of spreadsheet programs, Lotus 1-2-3 ruled the roost.  Its dominance was so complete that people thought it was a waste of time for Microsoft to bother developing their own spreadsheet software (Excel) to compete with Lotus.  Flash-forward to 2010, and Excel’s dominance of the spreadsheet market is greater than Lotus’s ever was, while the number of users still running Lotus 1-2-3 is approaching zero.  How did this happen?  What caused such a dramatic reversal of fortunes? Industry analysts put it down to two factors:  Firstly, Lotus decided that this fancy new GUI platform called “Windows” was a passing fad that would never take off.  They declined to create a Windows version of Lotus 1-2-3 (for a few years, anyway), predicting that their DOS version of the software was all anyone would ever need.  Microsoft, naturally, developed Excel exclusively for Windows.  Secondly, Microsoft developed a feature for Excel that Lotus didn’t provide in 1-2-3, namely PivotTables.  The PivotTables feature, exclusive to Excel, was deemed so staggeringly useful that people were willing to learn an entire new software package (Excel) rather than stick with a program (1-2-3) that didn’t have it.  This one feature, along with the misjudgment of the success of Windows, was the death-knell for Lotus 1-2-3, and the beginning of the success of Microsoft Excel. Understanding PivotTables So what is a PivotTable, exactly? Put simply, a PivotTable is a summary of some data, created to allow easy analysis of said data.  But unlike a manually created summary, Excel PivotTables are interactive.  Once you have created one, you can easily change it if it doesn’t offer the exact insights into your data that you were hoping for.  In a couple of clicks the summary can be “pivoted” – rotated in such a way that the column headings become row headings, and vice versa.  There’s a lot more that can be done, too.  Rather than try to describe all the features of PivotTables, we’ll simply demonstrate them… The data that you analyze using a PivotTable can’t be just any data – it has to be raw data, previously unprocessed (unsummarized) – typically a list of some sort.  An example of this might be the list of sales transactions in a company for the past six months. Examine the data shown below: Notice that this is not raw data.  In fact, it is already a summary of some sort.  In cell B3 we can see $30,000, which apparently is the total of James Cook’s sales for the month of January.  So where is the raw data?  How did we arrive at the figure of $30,000?  Where is the original list of sales transactions that this figure was generated from?  It’s clear that somewhere, someone must have gone to the trouble of collating all of the sales transactions for the past six months into the summary we see above.  How long do you suppose this took?  An hour?  Ten?  Probably. If we were to track down the original list of sales transactions, it might look something like this: You may be surprised to learn that, using the PivotTable feature of Excel, we can create a monthly sales summary similar to the one above in a few seconds, with only a few mouse clicks.  We can do this – and a lot more too! How to Create a PivotTable First, ensure that you have some raw data in a worksheet in Excel.  A list of financial transactions is typical, but it can be a list of just about anything:  Employee contact details, your CD collection, or fuel consumption figures for your company’s fleet of cars. So we start Excel… …and we load such a list… Once we have the list open in Excel, we’re ready to start creating the PivotTable. Click on any one single cell within the list: Then, from the Insert tab, click the PivotTable icon: The Create PivotTable box appears, asking you two questions:  What data should your new PivotTable be based on, and where should it be created?  Because we already clicked on a cell within the list (in the step above), the entire list surrounding that cell is already selected for us ($A$1:$G$88 on the Payments sheet, in this example).  Note that we could select a list in any other region of any other worksheet, or even some external data source, such as an Access database table, or even a MS-SQL Server database table.  We also need to select whether we want our new PivotTable to be created on a new worksheet, or on an existing one.  In this example we will select a new one: The new worksheet is created for us, and a blank PivotTable is created on that worksheet: Another box also appears:  The PivotTable Field List.  This field list will be shown whenever we click on any cell within the PivotTable (above): The list of fields in the top part of the box is actually the collection of column headings from the original raw data worksheet.  The four blank boxes in the lower part of the screen allow us to choose the way we would like our PivotTable to summarize the raw data.  So far, there is nothing in those boxes, so the PivotTable is blank.  All we need to do is drag fields down from the list above and drop them in the lower boxes.  A PivotTable is then automatically created to match our instructions.  If we get it wrong, we only need to drag the fields back to where they came from and/or drag new fields down to replace them. The Values box is arguably the most important of the four.  The field that is dragged into this box represents the data that needs to be summarized in some way (by summing, averaging, finding the maximum, minimum, etc).  It is almost always numerical data.  A perfect candidate for this box in our sample data is the “Amount” field/column.  Let’s drag that field into the Values box: Notice that (a) the “Amount” field in the list of fields is now ticked, and “Sum of Amount” has been added to the Values box, indicating that the amount column has been summed. If we examine the PivotTable itself, we indeed find the sum of all the “Amount” values from the raw data worksheet: We’ve created our first PivotTable!  Handy, but not particularly impressive.  It’s likely that we need a little more insight into our data than that. Referring to our sample data, we need to identify one or more column headings that we could conceivably use to split this total.  For example, we may decide that we would like to see a summary of our data where we have a row heading for each of the different salespersons in our company, and a total for each.  To achieve this, all we need to do is to drag the “Salesperson” field into the Row Labels box: Now, finally, things start to get interesting!  Our PivotTable starts to take shape….   With a couple of clicks we have created a table that would have taken a long time to do manually. So what else can we do?  Well, in one sense our PivotTable is complete.  We’ve created a useful summary of our source data.  The important stuff is already learned!  For the rest of the article, we will examine some ways that more complex PivotTables can be created, and ways that those PivotTables can be customized. First, we can create a two-dimensional table.  Let’s do that by using “Payment Method” as a column heading.  Simply drag the “Payment Method” heading to the Column Labels box: Which looks like this: Starting to get very cool! Let’s make it a three-dimensional table.  What could such a table possibly look like?  Well, let’s see… Drag the “Package” column/heading to the Report Filter box: Notice where it ends up…. This allows us to filter our report based on which “holiday package” was being purchased.  For example, we can see the breakdown of salesperson vs payment method for all packages, or, with a couple of clicks, change it to show the same breakdown for the “Sunseekers” package: And so, if you think about it the right way, our PivotTable is now three-dimensional.  Let’s keep customizing… If it turns out, say, that we only want to see cheque and credit card transactions (i.e. no cash transactions), then we can deselect the “Cash” item from the column headings.  Click the drop-down arrow next to Column Labels, and untick “Cash”: Let’s see what that looks like…As you can see, “Cash” is gone. Formatting This is obviously a very powerful system, but so far the results look very plain and boring.  For a start, the numbers that we’re summing do not look like dollar amounts – just plain old numbers.  Let’s rectify that. A temptation might be to do what we’re used to doing in such circumstances and simply select the whole table (or the whole worksheet) and use the standard number formatting buttons on the toolbar to complete the formatting.  The problem with that approach is that if you ever change the structure of the PivotTable in the future (which is 99% likely), then those number formats will be lost.  We need a way that will make them (semi-)permanent. First, we locate the “Sum of Amount” entry in the Values box, and click on it.  A menu appears.  We select Value Field Settings… from the menu: The Value Field Settings box appears. Click the Number Format button, and the standard Format Cells box appears: From the Category list, select (say) Accounting, and drop the number of decimal places to 0.  Click OK a few times to get back to the PivotTable… As you can see, the numbers have been correctly formatted as dollar amounts. While we’re on the subject of formatting, let’s format the entire PivotTable.  There are a few ways to do this.  Let’s use a simple one… Click the PivotTable Tools/Design tab: Then drop down the arrow in the bottom-right of the PivotTable Styles list to see a vast collection of built-in styles: Choose any one that appeals, and look at the result in your PivotTable:   Other Options We can work with dates as well.  Now usually, there are many, many dates in a transaction list such as the one we started with.  But Excel provides the option to group data items together by day, week, month, year, etc.  Let’s see how this is done. First, let’s remove the “Payment Method” column from the Column Labels box (simply drag it back up to the field list), and replace it with the “Date Booked” column: As you can see, this makes our PivotTable instantly useless, giving us one column for each date that a transaction occurred on – a very wide table! To fix this, right-click on any date and select Group… from the context-menu: The grouping box appears.  We select Months and click OK: Voila!  A much more useful table: (Incidentally, this table is virtually identical to the one shown at the beginning of this article – the original sales summary that was created manually.) Another cool thing to be aware of is that you can have more than one set of row headings (or column headings): …which looks like this…. You can do a similar thing with column headings (or even report filters). Keeping things simple again, let’s see how to plot averaged values, rather than summed values. First, click on “Sum of Amount”, and select Value Field Settings… from the context-menu that appears: In the Summarize value field by list in the Value Field Settings box, select Average: While we’re here, let’s change the Custom Name, from “Average of Amount” to something a little more concise.  Type in something like “Avg”: Click OK, and see what it looks like.  Notice that all the values change from summed totals to averages, and the table title (top-left cell) has changed to “Avg”: If we like, we can even have sums, averages and counts (counts = how many sales there were) all on the same PivotTable! Here are the steps to get something like that in place (starting from a blank PivotTable): Drag “Salesperson” into the Column Labels Drag “Amount” field down into the Values box three times For the first “Amount” field, change its custom name to “Total” and it’s number format to Accounting (0 decimal places) For the second “Amount” field, change its custom name to “Average”, its function to Average and it’s number format to Accounting (0 decimal places) For the third “Amount” field, change its name to “Count” and its function to Count Drag the automatically created field from Column Labels to Row Labels Here’s what we end up with: Total, average and count on the same PivotTable! Conclusion There are many, many more features and options for PivotTables created by Microsoft Excel – far too many to list in an article like this.  To fully cover the potential of PivotTables, a small book (or a large website) would be required.  Brave and/or geeky readers can explore PivotTables further quite easily:  Simply right-click on just about everything, and see what options become available to you.  There are also the two ribbon-tabs: PivotTable Tools/Options and Design.  It doesn’t matter if you make a mistake – it’s easy to delete the PivotTable and start again – a possibility old DOS users of Lotus 1-2-3 never had. We’ve included an Excel that should work with most versions of Excel, so you can download to practice your PivotTable skills. Download Our Practice Excel File Similar Articles Productive Geek Tips Magnify Selected Cells In Excel 2007Share Access Data with Excel in Office 2010Make Excel 2007 Print Gridlines In Workbook FileMake Excel 2007 Always Save in Excel 2003 FormatConvert Older Excel Documents to Excel 2007 Format TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 PCmover Professional Ben & Jerry’s Free Cone Day, 3/23/10 New Stinger from McAfee Helps Remove ‘FakeAlert’ Threats Google Apps Marketplace: Tools & Services For Google Apps Users Get News Quick and Precise With Newser Scan for Viruses in Ubuntu using ClamAV Replace Your Windows Task Manager With System Explorer

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  • Stored Procedures with SSRS? Hmm… not so much

    - by Rob Farley
    Little Bobby Tables’ mother says you should always sanitise your data input. Except that I think she’s wrong. The SQL Injection aspect is for another post, where I’ll show you why I think SQL Injection is the same kind of attack as many other attacks, such as the old buffer overflow, but here I want to have a bit of a whinge about the way that some people sanitise data input, and even have a whinge about people who insist on using stored procedures for SSRS reports. Let me say that again, in case you missed it the first time: I want to have a whinge about people who insist on using stored procedures for SSRS reports. Let’s look at the data input sanitisation aspect – except that I’m going to call it ‘parameter validation’. I’m talking about code that looks like this: create procedure dbo.GetMonthSummaryPerSalesPerson(@eomdate datetime) as begin     /* First check that @eomdate is a valid date */     if isdate(@eomdate) != 1     begin         select 'Please enter a valid date' as ErrorMessage;         return;     end     /* Then check that time has passed since @eomdate */     if datediff(day,@eomdate,sysdatetime()) < 5     begin         select 'Sorry - EOM is not complete yet' as ErrorMessage;         return;     end         /* If those checks have succeeded, return the data */     select SalesPersonID, count(*) as NumSales, sum(TotalDue) as TotalSales     from Sales.SalesOrderHeader     where OrderDate >= dateadd(month,-1,@eomdate)         and OrderDate < @eomdate     group by SalesPersonID     order by SalesPersonID; end Notice that the code checks that a date has been entered. Seriously??!! This must only be to check for NULL values being passed in, because anything else would have to be a valid datetime to avoid an error. The other check is maybe fair enough, but I still don’t like it. The two problems I have with this stored procedure are the result sets and the small fact that the stored procedure even exists in the first place. But let’s consider the first one of these problems for starters. I’ll get to the second one in a moment. If you read Jes Borland (@grrl_geek)’s recent post about returning multiple result sets in Reporting Services, you’ll be aware that Reporting Services doesn’t support multiple results sets from a single query. And when it says ‘single query’, it includes ‘stored procedure call’. It’ll only handle the first result set that comes back. But that’s okay – we have RETURN statements, so our stored procedure will only ever return a single result set.  Sometimes that result set might contain a single field called ErrorMessage, but it’s still only one result set. Except that it’s not okay, because Reporting Services needs to know what fields to expect. Your report needs to hook into your fields, so SSRS needs to have a way to get that information. For stored procs, it uses an option called FMTONLY. When Reporting Services tries to figure out what fields are going to be returned by a query (or stored procedure call), it doesn’t want to have to run the whole thing. That could take ages. (Maybe it’s seen some of the stored procedures I’ve had to deal with over the years!) So it turns on FMTONLY before it makes the call (and turns it off again afterwards). FMTONLY is designed to be able to figure out the shape of the output, without actually running the contents. It’s very useful, you might think. set fmtonly on exec dbo.GetMonthSummaryPerSalesPerson '20030401'; set fmtonly off Without the FMTONLY lines, this stored procedure returns a result set that has three columns and fourteen rows. But with FMTONLY turned on, those rows don’t come back. But what I do get back hurts Reporting Services. It doesn’t run the stored procedure at all. It just looks for anything that could be returned and pushes out a result set in that shape. Despite the fact that I’ve made sure that the logic will only ever return a single result set, the FMTONLY option kills me by returning three of them. It would have been much better to push these checks down into the query itself. alter procedure dbo.GetMonthSummaryPerSalesPerson(@eomdate datetime) as begin     select SalesPersonID, count(*) as NumSales, sum(TotalDue) as TotalSales     from Sales.SalesOrderHeader     where     /* Make sure that @eomdate is valid */         isdate(@eomdate) = 1     /* And that it's sufficiently past */     and datediff(day,@eomdate,sysdatetime()) >= 5     /* And now use it in the filter as appropriate */     and OrderDate >= dateadd(month,-1,@eomdate)     and OrderDate < @eomdate     group by SalesPersonID     order by SalesPersonID; end Now if we run it with FMTONLY turned on, we get the single result set back. But let’s consider the execution plan when we pass in an invalid date. First let’s look at one that returns data. I’ve got a semi-useful index in place on OrderDate, which includes the SalesPersonID and TotalDue fields. It does the job, despite a hefty Sort operation. …compared to one that uses a future date: You might notice that the estimated costs are similar – the Index Seek is still 28%, the Sort is still 71%. But the size of that arrow coming out of the Index Seek is a whole bunch smaller. The coolest thing here is what’s going on with that Index Seek. Let’s look at some of the properties of it. Glance down it with me… Estimated CPU cost of 0.0005728, 387 estimated rows, estimated subtree cost of 0.0044385, ForceSeek false, Number of Executions 0. That’s right – it doesn’t run. So much for reading plans right-to-left... The key is the Filter on the left of it. It has a Startup Expression Predicate in it, which means that it doesn’t call anything further down the plan (to the right) if the predicate evaluates to false. Using this method, we can make sure that our stored procedure contains a single query, and therefore avoid any problems with multiple result sets. If we wanted, we could always use UNION ALL to make sure that we can return an appropriate error message. alter procedure dbo.GetMonthSummaryPerSalesPerson(@eomdate datetime) as begin     select SalesPersonID, count(*) as NumSales, sum(TotalDue) as TotalSales, /*Placeholder: */ '' as ErrorMessage     from Sales.SalesOrderHeader     where     /* Make sure that @eomdate is valid */         isdate(@eomdate) = 1     /* And that it's sufficiently past */     and datediff(day,@eomdate,sysdatetime()) >= 5     /* And now use it in the filter as appropriate */     and OrderDate >= dateadd(month,-1,@eomdate)     and OrderDate < @eomdate     group by SalesPersonID     /* Now include the error messages */     union all     select 0, 0, 0, 'Please enter a valid date' as ErrorMessage     where isdate(@eomdate) != 1     union all     select 0, 0, 0, 'Sorry - EOM is not complete yet' as ErrorMessage     where datediff(day,@eomdate,sysdatetime()) < 5     order by SalesPersonID; end But still I don’t like it, because it’s now a stored procedure with a single query. And I don’t like stored procedures that should be functions. That’s right – I think this should be a function, and SSRS should call the function. And I apologise to those of you who are now planning a bonfire for me. Guy Fawkes’ night has already passed this year, so I think you miss out. (And I’m not going to remind you about when the PASS Summit is in 2012.) create function dbo.GetMonthSummaryPerSalesPerson(@eomdate datetime) returns table as return (     select SalesPersonID, count(*) as NumSales, sum(TotalDue) as TotalSales, '' as ErrorMessage     from Sales.SalesOrderHeader     where     /* Make sure that @eomdate is valid */         isdate(@eomdate) = 1     /* And that it's sufficiently past */     and datediff(day,@eomdate,sysdatetime()) >= 5     /* And now use it in the filter as appropriate */     and OrderDate >= dateadd(month,-1,@eomdate)     and OrderDate < @eomdate     group by SalesPersonID     union all     select 0, 0, 0, 'Please enter a valid date' as ErrorMessage     where isdate(@eomdate) != 1     union all     select 0, 0, 0, 'Sorry - EOM is not complete yet' as ErrorMessage     where datediff(day,@eomdate,sysdatetime()) < 5 ); We’ve had to lose the ORDER BY – but that’s fine, as that’s a client thing anyway. We can have our reports leverage this stored query still, but we’re recognising that it’s a query, not a procedure. A procedure is designed to DO stuff, not just return data. We even get entries in sys.columns that confirm what the shape of the result set actually is, which makes sense, because a table-valued function is the right mechanism to return data. And we get so much more flexibility with this. If you haven’t seen the simplification stuff that I’ve preached on before, jump over to http://bit.ly/SimpleRob and watch the video of when I broke a microphone and nearly fell off the stage in Wales. You’ll see the impact of being able to have a simplifiable query. You can also read the procedural functions post I wrote recently, if you didn’t follow the link from a few paragraphs ago. So if we want the list of SalesPeople that made any kind of sales in a given month, we can do something like: select SalesPersonID from dbo.GetMonthSummaryPerSalesPerson(@eomonth) order by SalesPersonID; This doesn’t need to look up the TotalDue field, which makes a simpler plan. select * from dbo.GetMonthSummaryPerSalesPerson(@eomonth) where SalesPersonID is not null order by SalesPersonID; This one can avoid having to do the work on the rows that don’t have a SalesPersonID value, pushing the predicate into the Index Seek rather than filtering the results that come back to the report. If we had joins involved, we might see some of those being simplified out. We also get the ability to include query hints in individual reports. We shift from having a single-use stored procedure to having a reusable stored query – and isn’t that one of the main points of modularisation? Stored procedures in Reporting Services are just a bit limited for my liking. They’re useful in plenty of ways, but if you insist on using stored procedures all the time rather that queries that use functions – that’s rubbish. @rob_farley

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  • Seeking on a Heap, and Two Useful DMVs

    - by Paul White
    So far in this mini-series on seeks and scans, we have seen that a simple ‘seek’ operation can be much more complex than it first appears.  A seek can contain one or more seek predicates – each of which can either identify at most one row in a unique index (a singleton lookup) or a range of values (a range scan).  When looking at a query plan, we will often need to look at the details of the seek operator in the Properties window to see how many operations it is performing, and what type of operation each one is.  As you saw in the first post in this series, the number of hidden seeking operations can have an appreciable impact on performance. Measuring Seeks and Scans I mentioned in my last post that there is no way to tell from a graphical query plan whether you are seeing a singleton lookup or a range scan.  You can work it out – if you happen to know that the index is defined as unique and the seek predicate is an equality comparison, but there’s no separate property that says ‘singleton lookup’ or ‘range scan’.  This is a shame, and if I had my way, the query plan would show different icons for range scans and singleton lookups – perhaps also indicating whether the operation was one or more of those operations underneath the covers. In light of all that, you might be wondering if there is another way to measure how many seeks of either type are occurring in your system, or for a particular query.  As is often the case, the answer is yes – we can use a couple of dynamic management views (DMVs): sys.dm_db_index_usage_stats and sys.dm_db_index_operational_stats. Index Usage Stats The index usage stats DMV contains counts of index operations from the perspective of the Query Executor (QE) – the SQL Server component that is responsible for executing the query plan.  It has three columns that are of particular interest to us: user_seeks – the number of times an Index Seek operator appears in an executed plan user_scans – the number of times a Table Scan or Index Scan operator appears in an executed plan user_lookups – the number of times an RID or Key Lookup operator appears in an executed plan An operator is counted once per execution (generating an estimated plan does not affect the totals), so an Index Seek that executes 10,000 times in a single plan execution adds 1 to the count of user seeks.  Even less intuitively, an operator is also counted once per execution even if it is not executed at all.  I will show you a demonstration of each of these things later in this post. Index Operational Stats The index operational stats DMV contains counts of index and table operations from the perspective of the Storage Engine (SE).  It contains a wealth of interesting information, but the two columns of interest to us right now are: range_scan_count – the number of range scans (including unrestricted full scans) on a heap or index structure singleton_lookup_count – the number of singleton lookups in a heap or index structure This DMV counts each SE operation, so 10,000 singleton lookups will add 10,000 to the singleton lookup count column, and a table scan that is executed 5 times will add 5 to the range scan count. The Test Rig To explore the behaviour of seeks and scans in detail, we will need to create a test environment.  The scripts presented here are best run on SQL Server 2008 Developer Edition, but the majority of the tests will work just fine on SQL Server 2005.  A couple of tests use partitioning, but these will be skipped if you are not running an Enterprise-equivalent SKU.  Ok, first up we need a database: USE master; GO IF DB_ID('ScansAndSeeks') IS NOT NULL DROP DATABASE ScansAndSeeks; GO CREATE DATABASE ScansAndSeeks; GO USE ScansAndSeeks; GO ALTER DATABASE ScansAndSeeks SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE ScansAndSeeks SET AUTO_CLOSE OFF, AUTO_SHRINK OFF, AUTO_CREATE_STATISTICS OFF, AUTO_UPDATE_STATISTICS OFF, PARAMETERIZATION SIMPLE, READ_COMMITTED_SNAPSHOT OFF, RESTRICTED_USER ; Notice that several database options are set in particular ways to ensure we get meaningful and reproducible results from the DMVs.  In particular, the options to auto-create and update statistics are disabled.  There are also three stored procedures, the first of which creates a test table (which may or may not be partitioned).  The table is pretty much the same one we used yesterday: The table has 100 rows, and both the key_col and data columns contain the same values – the integers from 1 to 100 inclusive.  The table is a heap, with a non-clustered primary key on key_col, and a non-clustered non-unique index on the data column.  The only reason I have used a heap here, rather than a clustered table, is so I can demonstrate a seek on a heap later on.  The table has an extra column (not shown because I am too lazy to update the diagram from yesterday) called padding – a CHAR(100) column that just contains 100 spaces in every row.  It’s just there to discourage SQL Server from choosing table scan over an index + RID lookup in one of the tests. The first stored procedure is called ResetTest: CREATE PROCEDURE dbo.ResetTest @Partitioned BIT = 'false' AS BEGIN SET NOCOUNT ON ; IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; IF @Partitioned = 'true' BEGIN -- Enterprise, Trial, or Developer -- required for partitioning tests IF SERVERPROPERTY('EngineEdition') = 3 BEGIN EXECUTE (' DROP TABLE dbo.Example ; IF EXISTS ( SELECT 1 FROM sys.partition_schemes WHERE name = N''PS'' ) DROP PARTITION SCHEME PS ; IF EXISTS ( SELECT 1 FROM sys.partition_functions WHERE name = N''PF'' ) DROP PARTITION FUNCTION PF ; CREATE PARTITION FUNCTION PF (INTEGER) AS RANGE RIGHT FOR VALUES (20, 40, 60, 80, 100) ; CREATE PARTITION SCHEME PS AS PARTITION PF ALL TO ([PRIMARY]) ; CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ON PS (key_col); '); END ELSE BEGIN RAISERROR('Invalid SKU for partition test', 16, 1); RETURN; END; END ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; END; GO The second stored procedure, ShowStats, displays information from the Index Usage Stats and Index Operational Stats DMVs: CREATE PROCEDURE dbo.ShowStats @Partitioned BIT = 'false' AS BEGIN -- Index Usage Stats DMV (QE) SELECT index_name = ISNULL(I.name, I.type_desc), scans = IUS.user_scans, seeks = IUS.user_seeks, lookups = IUS.user_lookups FROM sys.dm_db_index_usage_stats AS IUS JOIN sys.indexes AS I ON I.object_id = IUS.object_id AND I.index_id = IUS.index_id WHERE IUS.database_id = DB_ID(N'ScansAndSeeks') AND IUS.object_id = OBJECT_ID(N'dbo.Example', N'U') ORDER BY I.index_id ; -- Index Operational Stats DMV (SE) IF @Partitioned = 'true' SELECT index_name = ISNULL(I.name, I.type_desc), partitions = COUNT(IOS.partition_number), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; ELSE SELECT index_name = ISNULL(I.name, I.type_desc), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; END; The final stored procedure, RunTest, executes a query written against the example table: CREATE PROCEDURE dbo.RunTest @SQL VARCHAR(8000), @Partitioned BIT = 'false' AS BEGIN -- No execution plan yet SET STATISTICS XML OFF ; -- Reset the test environment EXECUTE dbo.ResetTest @Partitioned ; -- Previous call will throw an error if a partitioned -- test was requested, but SKU does not support it IF @@ERROR = 0 BEGIN -- IO statistics and plan on SET STATISTICS XML, IO ON ; -- Test statement EXECUTE (@SQL) ; -- Plan and IO statistics off SET STATISTICS XML, IO OFF ; EXECUTE dbo.ShowStats @Partitioned; END; END; The Tests The first test is a simple scan of the heap table: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example'; The top result set comes from the Index Usage Stats DMV, so it is the Query Executor’s (QE) view.  The lower result is from Index Operational Stats, which shows statistics derived from the actions taken by the Storage Engine (SE).  We see that QE performed 1 scan operation on the heap, and SE performed a single range scan.  Let’s try a single-value equality seek on a unique index next: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 32'; This time we see a single seek on the non-clustered primary key from QE, and one singleton lookup on the same index by the SE.  Now for a single-value seek on the non-unique non-clustered index: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32'; QE shows a single seek on the non-clustered non-unique index, but SE shows a single range scan on that index – not the singleton lookup we saw in the previous test.  That makes sense because we know that only a single-value seek into a unique index is a singleton seek.  A single-value seek into a non-unique index might retrieve any number of rows, if you think about it.  The next query is equivalent to the IN list example seen in the first post in this series, but it is written using OR (just for variety, you understand): EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32 OR data = 33'; The plan looks the same, and there’s no difference in the stats recorded by QE, but the SE shows two range scans.  Again, these are range scans because we are looking for two values in the data column, which is covered by a non-unique index.  I’ve added a snippet from the Properties window to show that the query plan does show two seek predicates, not just one.  Now let’s rewrite the query using BETWEEN: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data BETWEEN 32 AND 33'; Notice the seek operator only has one predicate now – it’s just a single range scan from 32 to 33 in the index – as the SE output shows.  For the next test, we will look up four values in the key_col column: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col IN (2,4,6,8)'; Just a single seek on the PK from the Query Executor, but four singleton lookups reported by the Storage Engine – and four seek predicates in the Properties window.  On to a more complex example: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WITH (INDEX([PK dbo.Example key_col])) WHERE key_col BETWEEN 1 AND 8'; This time we are forcing use of the non-clustered primary key to return eight rows.  The index is not covering for this query, so the query plan includes an RID lookup into the heap to fetch the data and padding columns.  The QE reports a seek on the PK and a lookup on the heap.  The SE reports a single range scan on the PK (to find key_col values between 1 and 8), and eight singleton lookups on the heap.  Remember that a bookmark lookup (RID or Key) is a seek to a single value in a ‘unique index’ – it finds a row in the heap or cluster from a unique RID or clustering key – so that’s why lookups are always singleton lookups, not range scans. Our next example shows what happens when a query plan operator is not executed at all: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 8 AND @@TRANCOUNT < 0'; The Filter has a start-up predicate which is always false (if your @@TRANCOUNT is less than zero, call CSS immediately).  The index seek is never executed, but QE still records a single seek against the PK because the operator appears once in an executed plan.  The SE output shows no activity at all.  This next example is 2008 and above only, I’m afraid: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WHERE key_col BETWEEN 1 AND 30', @Partitioned = 'true'; This is the first example to use a partitioned table.  QE reports a single seek on the heap (yes – a seek on a heap), and the SE reports two range scans on the heap.  SQL Server knows (from the partitioning definition) that it only needs to look at partitions 1 and 2 to find all the rows where key_col is between 1 and 30 – the engine seeks to find the two partitions, and performs a range scan seek on each partition. The final example for today is another seek on a heap – try to work out the output of the query before running it! EXECUTE dbo.RunTest @SQL = 'SELECT TOP (2) WITH TIES * FROM Example WHERE key_col BETWEEN 1 AND 50 ORDER BY $PARTITION.PF(key_col) DESC', @Partitioned = 'true'; Notice the lack of an explicit Sort operator in the query plan to enforce the ORDER BY clause, and the backward range scan. © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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  • VHDL - Problem with std_logic_vector

    - by wretrOvian
    Hi, i'm coding a 4-bit binary adder with accumulator: library ieee; use ieee.std_logic_1164.all; entity binadder is port(n,clk,sh:in bit; x,y:inout std_logic_vector(3 downto 0); co:inout bit; done:out bit); end binadder; architecture binadder of binadder is signal state: integer range 0 to 3; signal sum,cin:bit; begin sum<= (x(0) xor y(0)) xor cin; co<= (x(0) and y(0)) or (y(0) and cin) or (x(0) and cin); process begin wait until clk='0'; case state is when 0=> if(n='1') then state<=1; end if; when 1|2|3=> if(sh='1') then x<= sum & x(3 downto 1); y<= y(0) & y(3 downto 1); cin<=co; end if; if(state=3) then state<=0; end if; end case; end process; done<='1' when state=3 else '0'; end binadder; The output : -- Compiling architecture binadder of binadder ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(15): No feasible entries for infix operator "xor". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(15): Type error resolving infix expression "xor" as type std.standard.bit. ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): No feasible entries for infix operator "and". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): Bad expression in right operand of infix expression "or". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): No feasible entries for infix operator "and". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): Bad expression in left operand of infix expression "or". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): Bad expression in right operand of infix expression "or". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(16): Type error resolving infix expression "or" as type std.standard.bit. ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(28): No feasible entries for infix operator "&". ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(28): Type error resolving infix expression "&" as type ieee.std_logic_1164.std_logic_vector. ** Error: C:/Modeltech_pe_edu_6.5a/examples/binadder.vhdl(39): VHDL Compiler exiting I believe i'm not handling std_logic_vector's correctly. Please tell me how? :(

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  • Problem sub-total Matrix with rdlc report in vb.NET

    - by Keven
    Hi everyone, I have a matrix and I need to add the money earned this year and past years. However, I must remove the money spent in past years. I must have the separate amount per year and the total of these amounts. This is what gives my matrix: Year = Fields!Year.value =formatnumber((sum(Fields!Results.Value))-(sum(iif( Fields!Year.value & Parameters!choosedYear.Value, Fields!Moneyspent.value,0))), 2) & "$" However, the subtotal gives me an error. What should I do? P.S.: I already found that the subtotal gives me an error because it's not in the scope of the rowgroup1, but is there a way to get the scope in the subtotal? or can anybody find another way to do it?

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  • mysql query using jdbc

    - by S.PRATHIBA
    Hi all, I have the following table: Service_ID feedback 31 1 32 1 33 1 1 I have the sample code to find the sum: ResultSet res = st.executeQuery("SELECT Service_ID,SUM(consumer_feedback) FROM consumer5 group by Service_ID"); while (res.next()) { int data=res.getInt(1); System.out.println(data); System.out.println("\n\n"); int c1 = res.getInt(2); e[m]=res.getInt(2); System.out.println("\n \n m is "+m+" e[m] is "+e[m]); if(e[m]<0) e[m]=0; m++; System.out.print(c1); System.out.println("\t\t"); } i have to get the output as 31 1 32 1 33 1 I am getting it.But for my project i have 34,35 also.I should get theoutput as 31 1 32 1 33 1 34 0 35 0

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  • CONVERT(int, (datepart(month, @search)), (datepart(day, @search)), DateAdd(year, Years.Year - (datepart(year, @search)))

    - by MyHeadHurts
    In the query the top part is getting all the years that will run in the stored procedure. Works fine But at first i just wanted to run the queries for yesterdays date for all the years, but now i realized i want the user to select a date that will be in a parameter @search Booked <= CONVERT(int,DateAdd(year, Years.Year - Year(getdate()), DateAdd(day, DateDiff(day, 2, getdate()), 1))) this should be easy because normally it would just be Booked <= CONVERT(int,@search) but the problem is i want to do something like a Booked <= CONVERT(int, (datepart(month, @search)), (datepart(day, @search)), DateAdd(year, Years.Year - (datepart(year, @search))) would something like that work i dont need to worry about subtracting days but i still need to worry about the years WITH Years AS ( SELECT DATEPART(year, GETDATE()) [Year] UNION ALL SELECT [Year]-1 FROM Years WHERE [Year]>@YearToGet ), q_00 as ( select DIVISION , DYYYY , sum(PARTY) as asofPAX , sum(APRICE) as asofSales from dbo.B101BookingsDetails INNER JOIN Years ON B101BookingsDetails.DYYYY = Years.Year where Booked <= CONVERT(int,DateAdd(year, Years.Year - Year(getdate()), DateAdd(day, DateDiff(day, 2, getdate()), 1))) and DYYYY = Years.Year group by DIVISION, DYYYY, years.year having DYYYY = years.year ),

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  • jqGrid footer cells "inherits" CSS from cells in the main grid

    - by Tore
    I have a footerrow in my jqGrid where I sum up the values in some of the columns. I set the footer using the 'footerData' function when the grid has completed loading. This requires the 'footerrow' property in the grid-options to be set to 'true'. Some of the columns which I don't sum up have CSS applied to them (to show some icons in the cells), which is set using the 'classes' property in the colModel API. The problem is that these CSS-classes are also applied to the cells in the footerrow. I don't want them applied there, but I don't know how to prevent them from being shown. I tried to use jQuery to remove the 'class' property from the td elements after calling the 'footerData' function. The problem is that while the grid is loading, the icons are flashed to the user. How can I prevent the CSS from being applied in the first place?

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  • Combinatorics, probability, dice

    - by TarGz
    A friend of mine asked: if I have two dice and I throw both of them, what is the most frequent sum (of the two dice' numbers)? I wrote a small script: from random import randrange d = dict((i, 0) for i in range(2, 13)) for i in xrange(100000): d[randrange(1, 7) + randrange(1, 7)] += 1 print d Which prints: 2: 2770, 3: 5547, 4: 8379, 5: 10972, 6: 13911, 7: 16610, 8: 14010, 9: 11138, 10: 8372, 11: 5545, 12: 2746 The question I have, why is 11 more frequent than 12? In both cases there is only one way (or two, if you count reverse too) how to get such sum (5 + 6, 6 + 6), so I expected the same probability..?

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  • How to optimize this MySQL query

    - by James Simpson
    This query was working fine when the database was small, but now that there are millions of rows in the database, I am realizing I should have looked at optimizing this earlier. It is looking at over 600,000 rows and is Using where; Using temporary; Using filesort (which leads to an execution time of 5-10 seconds). It is using an index on the field 'battle_type.' SELECT username, SUM( outcome ) AS wins, COUNT( * ) - SUM( outcome ) AS losses FROM tblBattleHistory WHERE battle_type = '0' && outcome < '2' GROUP BY username ORDER BY wins DESC , losses ASC , username ASC LIMIT 0 , 50

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