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• MYSQL Optimizing recurring expressions?

  - by Johannes

  Does the Optimizer evaluate the avergae only one time, or two times? SELECT AVG(col1), 1/AVG(col1) FROM table Thanks

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• Basics of Join Predicate Pushdown in Oracle

  - by Maria Colgan

  Happy New Year to all of our readers! We hope you all had a great holiday season. We start the new year by continuing our series on Optimizer transformations. This time it is the turn of Predicate Pushdown. I would like to thank Rafi Ahmed for the content of this blog.Normally, a view cannot be joined with an index-based nested loop (i.e., index access) join, since a view, in contrast with a base table, does not have an index defined on it. A view can only be joined with other tables using three methods: hash, nested loop, and sort-merge joins. Introduction The join predicate pushdown (JPPD) transformation allows a view to be joined with index-based nested-loop join method, which may provide a more optimal alternative. In the join predicate pushdown transformation, the view remains a separate query block, but it contains the join predicate, which is pushed down from its containing query block into the view. The view thus becomes correlated and must be evaluated for each row of the outer query block. These pushed-down join predicates, once inside the view, open up new index access paths on the base tables inside the view; this allows the view to be joined with index-based nested-loop join method, thereby enabling the optimizer to select an efficient execution plan. The join predicate pushdown transformation is not always optimal. The join predicate pushed-down view becomes correlated and it must be evaluated for each outer row; if there is a large number of outer rows, the cost of evaluating the view multiple times may make the nested-loop join suboptimal, and therefore joining the view with hash or sort-merge join method may be more efficient. The decision whether to push down join predicates into a view is determined by evaluating the costs of the outer query with and without the join predicate pushdown transformation under Oracle's cost-based query transformation framework. The join predicate pushdown transformation applies to both non-mergeable views and mergeable views and to pre-defined and inline views as well as to views generated internally by the optimizer during various transformations. The following shows the types of views on which join predicate pushdown is currently supported. UNION ALL/UNION view Outer-joined view Anti-joined view Semi-joined view DISTINCT view GROUP-BY view Examples Consider query A, which has an outer-joined view V. The view cannot be merged, as it contains two tables, and the join between these two tables must be performed before the join between the view and the outer table T4. A: SELECT T4.unique1, V.unique3 FROM T_4K T4,            (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3) VWHERE T4.unique3 = V.hundred(+) AND       T4.ten = V.ten(+) AND       T4.thousand = 5; The following shows the non-default plan for query A generated by disabling join predicate pushdown. When query A undergoes join predicate pushdown, it yields query B. Note that query B is expressed in a non-standard SQL and shows an internal representation of the query. B: SELECT T4.unique1, V.unique3 FROM T_4K T4,           (SELECT T10.unique3, T10.hundred, T10.ten             FROM T_5K T5, T_10K T10             WHERE T5.unique3 = T10.unique3             AND T4.unique3 = V.hundred(+)             AND T4.ten = V.ten(+)) V WHERE T4.thousand = 5; The execution plan for query B is shown below. In the execution plan BX, note the keyword 'VIEW PUSHED PREDICATE' indicates that the view has undergone the join predicate pushdown transformation. The join predicates (shown here in red) have been moved into the view V; these join predicates open up index access paths thereby enabling index-based nested-loop join of the view. With join predicate pushdown, the cost of query A has come down from 62 to 32.  As mentioned earlier, the join predicate pushdown transformation is cost-based, and a join predicate pushed-down plan is selected only when it reduces the overall cost. Consider another example of a query C, which contains a view with the UNION ALL set operator.C: SELECT R.unique1, V.unique3 FROM T_5K R,            (SELECT T1.unique3, T2.unique1+T1.unique1             FROM T_5K T1, T_10K T2             WHERE T1.unique1 = T2.unique1             UNION ALL             SELECT T1.unique3, T2.unique2             FROM G_4K T1, T_10K T2             WHERE T1.unique1 = T2.unique1) V WHERE R.unique3 = V.unique3 and R.thousand < 1; The execution plan of query C is shown below. In the above, 'VIEW UNION ALL PUSHED PREDICATE' indicates that the UNION ALL view has undergone the join predicate pushdown transformation. As can be seen, here the join predicate has been replicated and pushed inside every branch of the UNION ALL view. The join predicates (shown here in red) open up index access paths thereby enabling index-based nested loop join of the view. Consider query D as an example of join predicate pushdown into a distinct view. We have the following cardinalities of the tables involved in query D: Sales (1,016,271), Customers (50,000), and Costs (787,766).  D: SELECT C.cust_last_name, C.cust_city FROM customers C,            (SELECT DISTINCT S.cust_id             FROM sales S, costs CT             WHERE S.prod_id = CT.prod_id and CT.unit_price > 70) V WHERE C.cust_state_province = 'CA' and C.cust_id = V.cust_id; The execution plan of query D is shown below. As shown in XD, when query D undergoes join predicate pushdown transformation, the expensive DISTINCT operator is removed and the join is converted into a semi-join; this is possible, since all the SELECT list items of the view participate in an equi-join with the outer tables. Under similar conditions, when a group-by view undergoes join predicate pushdown transformation, the expensive group-by operator can also be removed. With the join predicate pushdown transformation, the elapsed time of query D came down from 63 seconds to 5 seconds. Since distinct and group-by views are mergeable views, the cost-based transformation framework also compares the cost of merging the view with that of join predicate pushdown in selecting the most optimal execution plan. Summary We have tried to illustrate the basic ideas behind join predicate pushdown on different types of views by showing example queries that are quite simple. Oracle can handle far more complex queries and other types of views not shown here in the examples. Again many thanks to Rafi Ahmed for the content of this blog post.

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• Using Durandal to Create Single Page Apps

  - by Stephen.Walther

  A few days ago, I gave a talk on building Single Page Apps on the Microsoft Stack. In that talk, I recommended that people use Knockout, Sammy, and RequireJS to build their presentation layer and use the ASP.NET Web API to expose data from their server. After I gave the talk, several people contacted me and suggested that I investigate a new open-source JavaScript library named Durandal. Durandal stitches together Knockout, Sammy, and RequireJS to make it easier to use these technologies together. In this blog entry, I want to provide a brief walkthrough of using Durandal to create a simple Single Page App. I am going to demonstrate how you can create a simple Movies App which contains (virtual) pages for viewing a list of movies, adding new movies, and viewing movie details. The goal of this blog entry is to give you a sense of what it is like to build apps with Durandal. Installing Durandal First things first. How do you get Durandal? The GitHub project for Durandal is located here: https://github.com/BlueSpire/Durandal The Wiki — located at the GitHub project — contains all of the current documentation for Durandal. Currently, the documentation is a little sparse, but it is enough to get you started. Instead of downloading the Durandal source from GitHub, a better option for getting started with Durandal is to install one of the Durandal NuGet packages. I built the Movies App described in this blog entry by first creating a new ASP.NET MVC 4 Web Application with the Basic Template. Next, I executed the following command from the Package Manager Console: Install-Package Durandal.StarterKit As you can see from the screenshot of the Package Manager Console above, the Durandal Starter Kit package has several dependencies including: · jQuery · Knockout · Sammy · Twitter Bootstrap The Durandal Starter Kit package includes a sample Durandal application. You can get to the Starter Kit app by navigating to the Durandal controller. Unfortunately, when I first tried to run the Starter Kit app, I got an error because the Starter Kit is hard-coded to use a particular version of jQuery which is already out of date. You can fix this issue by modifying the App_Start\DurandalBundleConfig.cs file so it is jQuery version agnostic like this: bundles.Add( new ScriptBundle("~/scripts/vendor") .Include("~/Scripts/jquery-{version}.js") .Include("~/Scripts/knockout-{version}.js") .Include("~/Scripts/sammy-{version}.js") // .Include("~/Scripts/jquery-1.9.0.min.js") // .Include("~/Scripts/knockout-2.2.1.js") // .Include("~/Scripts/sammy-0.7.4.min.js") .Include("~/Scripts/bootstrap.min.js") ); The recommendation is that you create a Durandal app in a folder off your project root named App. The App folder in the Starter Kit contains the following subfolders and files: · durandal – This folder contains the actual durandal JavaScript library. · viewmodels – This folder contains all of your application’s view models. · views – This folder contains all of your application’s views. · main.js — This file contains all of the JavaScript startup code for your app including the client-side routing configuration. · main-built.js – This file contains an optimized version of your application. You need to build this file by using the RequireJS optimizer (unfortunately, before you can run the optimizer, you must first install NodeJS). For the purpose of this blog entry, I wanted to start from scratch when building the Movies app, so I deleted all of these files and folders except for the durandal folder which contains the durandal library. Creating the ASP.NET MVC Controller and View A Durandal app is built using a single server-side ASP.NET MVC controller and ASP.NET MVC view. A Durandal app is a Single Page App. When you navigate between pages, you are not navigating to new pages on the server. Instead, you are loading new virtual pages into the one-and-only-one server-side view. For the Movies app, I created the following ASP.NET MVC Home controller: public class HomeController : Controller { public ActionResult Index() { return View(); } } There is nothing special about the Home controller – it is as basic as it gets. Next, I created the following server-side ASP.NET view. This is the one-and-only server-side view used by the Movies app: @{ Layout = null; } <!DOCTYPE html> <html> <head> <title>Index</title> </head> <body> <div id="applicationHost"> Loading app.... </div> @Scripts.Render("~/scripts/vendor") <script type="text/javascript" src="~/App/durandal/amd/require.js" data-main="/App/main"></script> </body> </html> Notice that I set the Layout property for the view to the value null. If you neglect to do this, then the default ASP.NET MVC layout will be applied to the view and you will get the <!DOCTYPE> and opening and closing <html> tags twice. Next, notice that the view contains a DIV element with the Id applicationHost. This marks the area where virtual pages are loaded. When you navigate from page to page in a Durandal app, HTML page fragments are retrieved from the server and stuck in the applicationHost DIV element. Inside the applicationHost element, you can place any content which you want to display when a Durandal app is starting up. For example, you can create a fancy splash screen. I opted for simply displaying the text “Loading app…”: Next, notice the view above includes a call to the Scripts.Render() helper. This helper renders out all of the JavaScript files required by the Durandal library such as jQuery and Knockout. Remember to fix the App_Start\DurandalBundleConfig.cs as described above or Durandal will attempt to load an old version of jQuery and throw a JavaScript exception and stop working. Your application JavaScript code is not included in the scripts rendered by the Scripts.Render helper. Your application code is loaded dynamically by RequireJS with the help of the following SCRIPT element located at the bottom of the view: <script type="text/javascript" src="~/App/durandal/amd/require.js" data-main="/App/main"></script> The data-main attribute on the SCRIPT element causes RequireJS to load your /app/main.js JavaScript file to kick-off your Durandal app. Creating the Durandal Main.js File The Durandal Main.js JavaScript file, located in your App folder, contains all of the code required to configure the behavior of Durandal. Here’s what the Main.js file looks like in the case of the Movies app: require.config({ paths: { 'text': 'durandal/amd/text' } }); define(function (require) { var app = require('durandal/app'), viewLocator = require('durandal/viewLocator'), system = require('durandal/system'), router = require('durandal/plugins/router'); //>>excludeStart("build", true); system.debug(true); //>>excludeEnd("build"); app.start().then(function () { //Replace 'viewmodels' in the moduleId with 'views' to locate the view. //Look for partial views in a 'views' folder in the root. viewLocator.useConvention(); //configure routing router.useConvention(); router.mapNav("movies/show"); router.mapNav("movies/add"); router.mapNav("movies/details/:id"); app.adaptToDevice(); //Show the app by setting the root view model for our application with a transition. app.setRoot('viewmodels/shell', 'entrance'); }); }); There are three important things to notice about the main.js file above. First, notice that it contains a section which enables debugging which looks like this: //>>excludeStart(“build”, true); system.debug(true); //>>excludeEnd(“build”); This code enables debugging for your Durandal app which is very useful when things go wrong. When you call system.debug(true), Durandal writes out debugging information to your browser JavaScript console. For example, you can use the debugging information to diagnose issues with your client-side routes: (The funny looking //> symbols around the system.debug() call are RequireJS optimizer pragmas). The main.js file is also the place where you configure your client-side routes. In the case of the Movies app, the main.js file is used to configure routes for three page: the movies show, add, and details pages. //configure routing router.useConvention(); router.mapNav("movies/show"); router.mapNav("movies/add"); router.mapNav("movies/details/:id");   The route for movie details includes a route parameter named id. Later, we will use the id parameter to lookup and display the details for the right movie. Finally, the main.js file above contains the following line of code: //Show the app by setting the root view model for our application with a transition. app.setRoot('viewmodels/shell', 'entrance'); This line of code causes Durandal to load up a JavaScript file named shell.js and an HTML fragment named shell.html. I’ll discuss the shell in the next section. Creating the Durandal Shell You can think of the Durandal shell as the layout or master page for a Durandal app. The shell is where you put all of the content which you want to remain constant as a user navigates from virtual page to virtual page. For example, the shell is a great place to put your website logo and navigation links. The Durandal shell is composed from two parts: a JavaScript file and an HTML file. Here’s what the HTML file looks like for the Movies app: <h1>Movies App</h1> <div class="container-fluid page-host"> <!--ko compose: { model: router.activeItem, //wiring the router afterCompose: router.afterCompose, //wiring the router transition:'entrance', //use the 'entrance' transition when switching views cacheViews:true //telling composition to keep views in the dom, and reuse them (only a good idea with singleton view models) }--><!--/ko--> </div> And here is what the JavaScript file looks like: define(function (require) { var router = require('durandal/plugins/router'); return { router: router, activate: function () { return router.activate('movies/show'); } }; }); The JavaScript file contains the view model for the shell. This view model returns the Durandal router so you can access the list of configured routes from your shell. Notice that the JavaScript file includes a function named activate(). This function loads the movies/show page as the first page in the Movies app. If you want to create a different default Durandal page, then pass the name of a different age to the router.activate() method. Creating the Movies Show Page Durandal pages are created out of a view model and a view. The view model contains all of the data and view logic required for the view. The view contains all of the HTML markup for rendering the view model. Let’s start with the movies show page. The movies show page displays a list of movies. The view model for the show page looks like this: define(function (require) { var moviesRepository = require("repositories/moviesRepository"); return { movies: ko.observable(), activate: function() { this.movies(moviesRepository.listMovies()); } }; }); You create a view model by defining a new RequireJS module (see http://requirejs.org). You create a RequireJS module by placing all of your JavaScript code into an anonymous function passed to the RequireJS define() method. A RequireJS module has two parts. You retrieve all of the modules which your module requires at the top of your module. The code above depends on another RequireJS module named repositories/moviesRepository. Next, you return the implementation of your module. The code above returns a JavaScript object which contains a property named movies and a method named activate. The activate() method is a magic method which Durandal calls whenever it activates your view model. Your view model is activated whenever you navigate to a page which uses it. In the code above, the activate() method is used to get the list of movies from the movies repository and assign the list to the view model movies property. The HTML for the movies show page looks like this: <table> <thead> <tr> <th>Title</th><th>Director</th> </tr> </thead> <tbody data-bind="foreach:movies"> <tr> <td data-bind="text:title"></td> <td data-bind="text:director"></td> <td><a data-bind="attr:{href:'#/movies/details/'+id}">Details</a></td> </tr> </tbody> </table> <a href="#/movies/add">Add Movie</a> Notice that this is an HTML fragment. This fragment will be stuffed into the page-host DIV element in the shell.html file which is stuffed, in turn, into the applicationHost DIV element in the server-side MVC view. The HTML markup above contains data-bind attributes used by Knockout to display the list of movies (To learn more about Knockout, visit http://knockoutjs.com). The list of movies from the view model is displayed in an HTML table. Notice that the page includes a link to a page for adding a new movie. The link uses the following URL which starts with a hash: #/movies/add. Because the link starts with a hash, clicking the link does not cause a request back to the server. Instead, you navigate to the movies/add page virtually. Creating the Movies Add Page The movies add page also consists of a view model and view. The add page enables you to add a new movie to the movie database. Here’s the view model for the add page: define(function (require) { var app = require('durandal/app'); var router = require('durandal/plugins/router'); var moviesRepository = require("repositories/moviesRepository"); return { movieToAdd: { title: ko.observable(), director: ko.observable() }, activate: function () { this.movieToAdd.title(""); this.movieToAdd.director(""); this._movieAdded = false; }, canDeactivate: function () { if (this._movieAdded == false) { return app.showMessage('Are you sure you want to leave this page?', 'Navigate', ['Yes', 'No']); } else { return true; } }, addMovie: function () { // Add movie to db moviesRepository.addMovie(ko.toJS(this.movieToAdd)); // flag new movie this._movieAdded = true; // return to list of movies router.navigateTo("#/movies/show"); } }; }); The view model contains one property named movieToAdd which is bound to the add movie form. The view model also has the following three methods: 1. activate() – This method is called by Durandal when you navigate to the add movie page. The activate() method resets the add movie form by clearing out the movie title and director properties. 2. canDeactivate() – This method is called by Durandal when you attempt to navigate away from the add movie page. If you return false then navigation is cancelled. 3. addMovie() – This method executes when the add movie form is submitted. This code adds the new movie to the movie repository. I really like the Durandal canDeactivate() method. In the code above, I use the canDeactivate() method to show a warning to a user if they navigate away from the add movie page – either by clicking the Cancel button or by hitting the browser back button – before submitting the add movie form: The view for the add movie page looks like this: <form data-bind="submit:addMovie"> <fieldset> <legend>Add Movie</legend> <div> <label> Title: <input data-bind="value:movieToAdd.title" required /> </label> </div> <div> <label> Director: <input data-bind="value:movieToAdd.director" required /> </label> </div> <div> <input type="submit" value="Add" /> <a href="#/movies/show">Cancel</a> </div> </fieldset> </form> I am using Knockout to bind the movieToAdd property from the view model to the INPUT elements of the HTML form. Notice that the FORM element includes a data-bind attribute which invokes the addMovie() method from the view model when the HTML form is submitted. Creating the Movies Details Page You navigate to the movies details Page by clicking the Details link which appears next to each movie in the movies show page: The Details links pass the movie ids to the details page: #/movies/details/0 #/movies/details/1 #/movies/details/2 Here’s what the view model for the movies details page looks like: define(function (require) { var router = require('durandal/plugins/router'); var moviesRepository = require("repositories/moviesRepository"); return { movieToShow: { title: ko.observable(), director: ko.observable() }, activate: function (context) { // Grab movie from repository var movie = moviesRepository.getMovie(context.id); // Add to view model this.movieToShow.title(movie.title); this.movieToShow.director(movie.director); } }; }); Notice that the view model activate() method accepts a parameter named context. You can take advantage of the context parameter to retrieve route parameters such as the movie Id. In the code above, the context.id property is used to retrieve the correct movie from the movie repository and the movie is assigned to a property named movieToShow exposed by the view model. The movie details view displays the movieToShow property by taking advantage of Knockout bindings: <div> <h2 data-bind="text:movieToShow.title"></h2> directed by <span data-bind="text:movieToShow.director"></span> </div> Summary The goal of this blog entry was to walkthrough building a simple Single Page App using Durandal and to get a feel for what it is like to use this library. I really like how Durandal stitches together Knockout, Sammy, and RequireJS and establishes patterns for using these libraries to build Single Page Apps. Having a standard pattern which developers on a team can use to build new pages is super valuable. Once you get the hang of it, using Durandal to create new virtual pages is dead simple. Just define a new route, view model, and view and you are done. I also appreciate the fact that Durandal did not attempt to re-invent the wheel and that Durandal leverages existing JavaScript libraries such as Knockout, RequireJS, and Sammy. These existing libraries are powerful libraries and I have already invested a considerable amount of time in learning how to use them. Durandal makes it easier to use these libraries together without losing any of their power. Durandal has some additional interesting features which I have not had a chance to play with yet. For example, you can use the RequireJS optimizer to combine and minify all of a Durandal app’s code. Also, Durandal supports a way to create custom widgets (client-side controls) by composing widgets from a controller and view. You can download the code for the Movies app by clicking the following link (this is a Visual Studio 2012 project): Durandal Movie App

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• Partition Wise Joins II

  - by jean-pierre.dijcks

  One of the things that I did not talk about in the initial partition wise join post was the effect it has on resource allocation on the database server. When Oracle applies a different join method - e.g. not PWJ - what you will see in SQL Monitor (in Enterprise Manager) or in an Explain Plan is a set of producers and a set of consumers. The producers scan the tables in the the join. If there are two tables the producers first scan one table, then the other. The producers thus provide data to the consumers, and when the consumers have the data from both scans they do the join and give the data to the query coordinator. Now that behavior means that if you choose a degree of parallelism of 4 to run such query with, Oracle will allocate 8 parallel processes. Of these 8 processes 4 are producers and 4 are consumers. The consumers only actually do work once the producers are fully done with scanning both sides of the join. In the plan above you can see that the producers access table SALES [line 11] and then do a PX SEND [line 9]. That is the producer set of processes working. The consumers receive that data [line 8] and twiddle their thumbs while the producers go on and scan CUSTOMERS. The producers send that data to the consumer indicated by PX SEND [line 5]. After receiving that data [line 4] the consumers do the actual join [line 3] and give the data to the QC [line 2]. BTW, the myth that you see twice the number of processes due to the setting PARALLEL_THREADS_PER_CPU=2 is obviously not true. The above is why you will see 2 times the processes of the DOP. In a PWJ plan the consumers are not present. Instead of producing rows and giving those to different processes, a PWJ only uses a single set of processes. Each process reads its piece of the join across the two tables and performs the join. The plan here is notably different from the initial plan. First of all the hash join is done right on top of both table scans [line 8]. This query is a little more complex than the previous so there is a bit of noise above that bit of info, but for this post, lets ignore that (sort stuff). The important piece here is that the PWJ plan typically will be faster and from a PX process number / resources typically cheaper. You may want to look out for those plans and try to get those to appear a lot... CREDITS: credits for the plans and some of the info on the plans go to Maria, as she actually produced these plans and is the expert on plans in general... You can see her talk about explaining the explain plan and other optimizer stuff over here: ODTUG in Washington DC, June 27 - July 1 On the Optimizer blog At OpenWorld in San Francisco, September 19 - 23 Happy joining and hope to see you all at ODTUG and OOW...

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• ... i just avoid GUID

  - by Tomaz.tsql

  Our partner was explaining to me that they are using GUID as primary key on all the tables. My immediate reaction was - why? and couple of basic doubts were: - since I can read uniqueidentifier, it does not tell me absolutely anything - if I will use my relational table, i sure will use other columns to get the information out - SQL is terrible when setting up clustered index on GUID columns (and hence performance problems) - why not use INT? it will save you space on disk, optimizer will be able...(read more)

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• SQL SERVER – SSMS Automatically Generates TOP (100) PERCENT in Query Designer

  - by pinaldave

  Earlier this week, I was surfing various SQL forums to see what kind of help developer need in the SQL Server world. One of the question indeed caught my attention. I am here regenerating complete question as well scenario to illustrate the point in a precise manner. Additionally, I have added added second part of the question to give completeness. Question: I am trying to create a view in Query Designer (not in the New Query Window). Every time I am trying to create a view it always adds  TOP (100) PERCENT automatically on the T-SQL script. No matter what I do, it always automatically adds the TOP (100) PERCENT to the script. I have attempted to copy paste from notepad, build a query and a few other things – there is no success. I am really not sure what I am doing wrong with Query Designer. Here is my query script: (I use AdventureWorks as a sample database) SELECT Person.Address.AddressID FROM Person.Address INNER JOIN Person.AddressType ON Person.Address.AddressID = Person.AddressType.AddressTypeID ORDER BY Person.Address.AddressID This script automatically replaces by following query: SELECT TOP (100) PERCENT Person.Address.AddressID FROM Person.Address INNER JOIN Person.AddressType ON Person.Address.AddressID = Person.AddressType.AddressTypeID ORDER BY Person.Address.AddressID However, when I try to do the same from New Query Window it works totally fine. However, when I attempt to create a view of the same query it gives following error. Msg 1033, Level 15, State 1, Procedure myView, Line 6 The ORDER BY clause is invalid in views, inline functions, derived tables, subqueries, and common table expressions, unless TOP, OFFSET or FOR XML is also specified. It is pretty clear to me now that the script which I have written seems to need TOP (100) PERCENT, so Query . Why do I need it? Is there any work around to this issue. I particularly find this question pretty interesting as it really touches the fundamentals of the T-SQL query writing. Please note that the query which is automatically changed is not in New Query Editor but opened from SSMS using following way. Database >> Views >> Right Click >> New View (see the image below) Answer: The answer to the above question can be very long but I will keep it simple and to the point. There are three things to discuss in above script 1) Reason for Error 2) Reason for Auto generates TOP (100) PERCENT and 3) Potential solutions to the above error. Let us quickly see them in detail. 1) Reason for Error The reason for error is already given in the error. ORDER BY is invalid in the views and a few other objects. One has to use TOP or other keywords along with it. The way semantics of the query works where optimizer only follows(honors) the ORDER BY in the same scope or the same SELECT/UPDATE/DELETE statement. There is a possibility that one can order after the scope of the view again the efforts spend to order view will be wasted. The final resultset of the query always follows the final ORDER BY or outer query’s order and due to the same reason optimizer follows the final order of the query and not of the views (as view will be used in another query for further processing e.g. in SELECT statement). Due to same reason ORDER BY is now allowed in the view. For further accuracy and clear guidance I suggest you read this blog post by Query Optimizer Team. They have explained it very clear manner the same subject. 2) Reason for Auto Generated TOP (100) PERCENT One of the most popular workaround to above error is to use TOP (100) PERCENT in the view. Now TOP (100) PERCENT allows user to use ORDER BY in the query and allows user to overcome above error which we discussed. This gives the impression to the user that they have resolved the error and successfully able to use ORDER BY in the View. Well, this is incorrect as well. The way this works is when TOP (100) PERCENT is used the result is not guaranteed as well it is ignored in our the query where the view is used. Here is the blog post on this subject: Interesting Observation – TOP 100 PERCENT and ORDER BY. Now when you create a new view in the SSMS and build a query with ORDER BY to avoid the error automatically it adds the TOP 100 PERCENT. Here is the connect item for the same issue. I am sure there will be more connect items as well but I could not find them. 3) Potential Solutions If you are reading this post from the beginning in that case, it is clear by now that ORDER BY should not be used in the View as it does not serve any purpose unless there is a specific need of it. If you are going to use TOP 100 PERCENT with ORDER BY there is absolutely no need of using ORDER BY rather avoid using it all together. Here is another blog post of mine which describes the same subject ORDER BY Does Not Work – Limitation of the Views Part 1. It is valid to use ORDER BY in a view if there is a clear business need of using TOP with any other percentage lower than 100 (for example TOP 10 PERCENT or TOP 50 PERCENT etc). In most of the cases ORDER BY is not needed in the view and it should be used in the most outer query for present result in desired order. User can remove TOP 100 PERCENT and ORDER BY from the view before using the view in any query or procedure. In the most outer query there should be ORDER BY as per the business need. I think this sums up the concept in a few words. This is a very long topic and not easy to illustrate in one single blog post. I welcome your comments and suggestions. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, SQL View, T SQL, Technology

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• The blocking nature of aggregates

  - by Rob Farley

  I wrote a post recently about how query tuning isn’t just about how quickly the query runs – that if you have something (such as SSIS) that is consuming your data (and probably introducing a bottleneck), then it might be more important to have a query which focuses on getting the first bit of data out. You can read that post here.  In particular, we looked at two operators that could be used to ensure that a query returns only Distinct rows. and The Sort operator pulls in all the data, sorts it (discarding duplicates), and then pushes out the remaining rows. The Hash Match operator performs a Hashing function on each row as it comes in, and then looks to see if it’s created a Hash it’s seen before. If not, it pushes the row out. The Sort method is quicker, but has to wait until it’s gathered all the data before it can do the sort, and therefore blocks the data flow. But that was my last post. This one’s a bit different. This post is going to look at how Aggregate functions work, which ties nicely into this month’s T-SQL Tuesday. I’ve frequently explained about the fact that DISTINCT and GROUP BY are essentially the same function, although DISTINCT is the poorer cousin because you have less control over it, and you can’t apply aggregate functions. Just like the operators used for Distinct, there are different flavours of Aggregate operators – coming in blocking and non-blocking varieties. The example I like to use to explain this is a pile of playing cards. If I’m handed a pile of cards and asked to count how many cards there are in each suit, it’s going to help if the cards are already ordered. Suppose I’m playing a game of Bridge, I can easily glance at my hand and count how many there are in each suit, because I keep the pile of cards in order. Moving from left to right, I could tell you I have four Hearts in my hand, even before I’ve got to the end. By telling you that I have four Hearts as soon as I know, I demonstrate the principle of a non-blocking operation. This is known as a Stream Aggregate operation. It requires input which is sorted by whichever columns the grouping is on, and it will release a row as soon as the group changes – when I encounter a Spade, I know I don’t have any more Hearts in my hand. Alternatively, if the pile of cards are not sorted, I won’t know how many Hearts I have until I’ve looked through all the cards. In fact, to count them, I basically need to put them into little piles, and when I’ve finished making all those piles, I can count how many there are in each. Because I don’t know any of the final numbers until I’ve seen all the cards, this is blocking. This performs the aggregate function using a Hash Match. Observant readers will remember this from my Distinct example. You might remember that my earlier Hash Match operation – used for Distinct Flow – wasn’t blocking. But this one is. They’re essentially doing a similar operation, applying a Hash function to some data and seeing if the set of values have been seen before, but before, it needs more information than the mere existence of a new set of values, it needs to consider how many of them there are. A lot is dependent here on whether the data coming out of the source is sorted or not, and this is largely determined by the indexes that are being used. If you look in the Properties of an Index Scan, you’ll be able to see whether the order of the data is required by the plan. A property called Ordered will demonstrate this. In this particular example, the second plan is significantly faster, but is dependent on having ordered data. In fact, if I force a Stream Aggregate on unordered data (which I’m doing by telling it to use a different index), a Sort operation is needed, which makes my plan a lot slower. This is all very straight-forward stuff, and information that most people are fully aware of. I’m sure you’ve all read my good friend Paul White (@sql_kiwi)’s post on how the Query Optimizer chooses which type of aggregate function to apply. But let’s take a look at SQL Server Integration Services. SSIS gives us a Aggregate transformation for use in Data Flow Tasks, but it’s described as Blocking. The definitive article on Performance Tuning SSIS uses Sort and Aggregate as examples of Blocking Transformations. I’ve just shown you that Aggregate operations used by the Query Optimizer are not always blocking, but that the SSIS Aggregate component is an example of a blocking transformation. But is it always the case? After all, there are plenty of SSIS Performance Tuning talks out there that describe the value of sorted data in Data Flow Tasks, describing the IsSorted property that can be set through the Advanced Editor of your Source component. And so I set about testing the Aggregate transformation in SSIS, to prove for sure whether providing Sorted data would let the Aggregate transform behave like a Stream Aggregate. (Of course, I knew the answer already, but it helps to be able to demonstrate these things). A query that will produce a million rows in order was in order. Let me rephrase. I used a query which produced the numbers from 1 to 1000000, in a single field, ordered. The IsSorted flag was set on the source output, with the only column as SortKey 1. Performing an Aggregate function over this (counting the number of rows per distinct number) should produce an additional column with 1 in it. If this were being done in T-SQL, the ordered data would allow a Stream Aggregate to be used. In fact, if the Query Optimizer saw that the field had a Unique Index on it, it would be able to skip the Aggregate function completely, and just insert the value 1. This is a shortcut I wouldn’t be expecting from SSIS, but certainly the Stream behaviour would be nice. Unfortunately, it’s not the case. As you can see from the screenshots above, the data is pouring into the Aggregate function, and not being released until all million rows have been seen. It’s not doing a Stream Aggregate at all. This is expected behaviour. (I put that in bold, because I want you to realise this.) An SSIS transformation is a piece of code that runs. It’s a physical operation. When you write T-SQL and ask for an aggregation to be done, it’s a logical operation. The physical operation is either a Stream Aggregate or a Hash Match. In SSIS, you’re telling the system that you want a generic Aggregation, that will have to work with whatever data is passed in. I’m not saying that it wouldn’t be possible to make a sometimes-blocking aggregation component in SSIS. A Custom Component could be created which could detect whether the SortKeys columns of the input matched the Grouping columns of the Aggregation, and either call the blocking code or the non-blocking code as appropriate. One day I’ll make one of those, and publish it on my blog. I’ve done it before with a Script Component, but as Script components are single-use, I was able to handle the data knowing everything about my data flow already. As per my previous post – there are a lot of aspects in which tuning SSIS and tuning execution plans use similar concepts. In both situations, it really helps to have a feel for what’s going on behind the scenes. Considering whether an operation is blocking or not is extremely relevant to performance, and that it’s not always obvious from the surface. In a future post, I’ll show the impact of blocking v non-blocking and synchronous v asynchronous components in SSIS, using some of LobsterPot’s Script Components and Custom Components as examples. When I get that sorted, I’ll make a Stream Aggregate component available for download.

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• The blocking nature of aggregates

  - by Rob Farley

  I wrote a post recently about how query tuning isn’t just about how quickly the query runs – that if you have something (such as SSIS) that is consuming your data (and probably introducing a bottleneck), then it might be more important to have a query which focuses on getting the first bit of data out. You can read that post here.  In particular, we looked at two operators that could be used to ensure that a query returns only Distinct rows. and The Sort operator pulls in all the data, sorts it (discarding duplicates), and then pushes out the remaining rows. The Hash Match operator performs a Hashing function on each row as it comes in, and then looks to see if it’s created a Hash it’s seen before. If not, it pushes the row out. The Sort method is quicker, but has to wait until it’s gathered all the data before it can do the sort, and therefore blocks the data flow. But that was my last post. This one’s a bit different. This post is going to look at how Aggregate functions work, which ties nicely into this month’s T-SQL Tuesday. I’ve frequently explained about the fact that DISTINCT and GROUP BY are essentially the same function, although DISTINCT is the poorer cousin because you have less control over it, and you can’t apply aggregate functions. Just like the operators used for Distinct, there are different flavours of Aggregate operators – coming in blocking and non-blocking varieties. The example I like to use to explain this is a pile of playing cards. If I’m handed a pile of cards and asked to count how many cards there are in each suit, it’s going to help if the cards are already ordered. Suppose I’m playing a game of Bridge, I can easily glance at my hand and count how many there are in each suit, because I keep the pile of cards in order. Moving from left to right, I could tell you I have four Hearts in my hand, even before I’ve got to the end. By telling you that I have four Hearts as soon as I know, I demonstrate the principle of a non-blocking operation. This is known as a Stream Aggregate operation. It requires input which is sorted by whichever columns the grouping is on, and it will release a row as soon as the group changes – when I encounter a Spade, I know I don’t have any more Hearts in my hand. Alternatively, if the pile of cards are not sorted, I won’t know how many Hearts I have until I’ve looked through all the cards. In fact, to count them, I basically need to put them into little piles, and when I’ve finished making all those piles, I can count how many there are in each. Because I don’t know any of the final numbers until I’ve seen all the cards, this is blocking. This performs the aggregate function using a Hash Match. Observant readers will remember this from my Distinct example. You might remember that my earlier Hash Match operation – used for Distinct Flow – wasn’t blocking. But this one is. They’re essentially doing a similar operation, applying a Hash function to some data and seeing if the set of values have been seen before, but before, it needs more information than the mere existence of a new set of values, it needs to consider how many of them there are. A lot is dependent here on whether the data coming out of the source is sorted or not, and this is largely determined by the indexes that are being used. If you look in the Properties of an Index Scan, you’ll be able to see whether the order of the data is required by the plan. A property called Ordered will demonstrate this. In this particular example, the second plan is significantly faster, but is dependent on having ordered data. In fact, if I force a Stream Aggregate on unordered data (which I’m doing by telling it to use a different index), a Sort operation is needed, which makes my plan a lot slower. This is all very straight-forward stuff, and information that most people are fully aware of. I’m sure you’ve all read my good friend Paul White (@sql_kiwi)’s post on how the Query Optimizer chooses which type of aggregate function to apply. But let’s take a look at SQL Server Integration Services. SSIS gives us a Aggregate transformation for use in Data Flow Tasks, but it’s described as Blocking. The definitive article on Performance Tuning SSIS uses Sort and Aggregate as examples of Blocking Transformations. I’ve just shown you that Aggregate operations used by the Query Optimizer are not always blocking, but that the SSIS Aggregate component is an example of a blocking transformation. But is it always the case? After all, there are plenty of SSIS Performance Tuning talks out there that describe the value of sorted data in Data Flow Tasks, describing the IsSorted property that can be set through the Advanced Editor of your Source component. And so I set about testing the Aggregate transformation in SSIS, to prove for sure whether providing Sorted data would let the Aggregate transform behave like a Stream Aggregate. (Of course, I knew the answer already, but it helps to be able to demonstrate these things). A query that will produce a million rows in order was in order. Let me rephrase. I used a query which produced the numbers from 1 to 1000000, in a single field, ordered. The IsSorted flag was set on the source output, with the only column as SortKey 1. Performing an Aggregate function over this (counting the number of rows per distinct number) should produce an additional column with 1 in it. If this were being done in T-SQL, the ordered data would allow a Stream Aggregate to be used. In fact, if the Query Optimizer saw that the field had a Unique Index on it, it would be able to skip the Aggregate function completely, and just insert the value 1. This is a shortcut I wouldn’t be expecting from SSIS, but certainly the Stream behaviour would be nice. Unfortunately, it’s not the case. As you can see from the screenshots above, the data is pouring into the Aggregate function, and not being released until all million rows have been seen. It’s not doing a Stream Aggregate at all. This is expected behaviour. (I put that in bold, because I want you to realise this.) An SSIS transformation is a piece of code that runs. It’s a physical operation. When you write T-SQL and ask for an aggregation to be done, it’s a logical operation. The physical operation is either a Stream Aggregate or a Hash Match. In SSIS, you’re telling the system that you want a generic Aggregation, that will have to work with whatever data is passed in. I’m not saying that it wouldn’t be possible to make a sometimes-blocking aggregation component in SSIS. A Custom Component could be created which could detect whether the SortKeys columns of the input matched the Grouping columns of the Aggregation, and either call the blocking code or the non-blocking code as appropriate. One day I’ll make one of those, and publish it on my blog. I’ve done it before with a Script Component, but as Script components are single-use, I was able to handle the data knowing everything about my data flow already. As per my previous post – there are a lot of aspects in which tuning SSIS and tuning execution plans use similar concepts. In both situations, it really helps to have a feel for what’s going on behind the scenes. Considering whether an operation is blocking or not is extremely relevant to performance, and that it’s not always obvious from the surface. In a future post, I’ll show the impact of blocking v non-blocking and synchronous v asynchronous components in SSIS, using some of LobsterPot’s Script Components and Custom Components as examples. When I get that sorted, I’ll make a Stream Aggregate component available for download.

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• Oracle????????????????????????~????????????????????

  - by Yusuke.Yamamoto

  RDBMS ???????·????????????????????????????????????????????????????????????????????????? ????????Oracle ?????????????????????????????????? Oracle Database ???????????????????????????????? ????????????????????? ????Oracle???????????????????????????????????????????????????????????????????????????? ?????????????? Oracle Database ???????????????????????? ??????????????????????????????????2????????????? 1. ??????(Query Transformation) Query Transformation ???????SQL??????????????????SQL????????????????????? Query Transformation ???Predicate Transformation ? Common Sub-expression Elimination (CSE), Order-BY Elimination (OBYE), Outer Join Elimination (OJE), Simple View Meging (SVM), Predicate Move around (PM), Complex View Merging (CVM), Sub-query Unnesting (SU), Join Predicate Push Down (JPPD) ???? OR Expansion, Star Transformation (ST) ????????????? ···???????????????????????????????????????????????????? Predicate Transformation ?????? Transitive Predicate Generation ????????????? ?????????????SQL???deptno ? 10 ????????????????????????????? select e.ename, d.loc from emp e, dept d where e.deptno=d.deptno and e.deptno=10; ???????????????emp ??? deptno=10 ??????????????dept ??? d.deptno=10 ??????????????????? emp ?? deptno=10 ????????????????????emp ?? deptno=10 ??????10???????10? dept ????????????dept ??20???????????????????????10?*20?=200?????(??????????·?????????)? ??SQL?? Transitive Predicate Generation ??????SQL????????????????? select e.ename, d.location from emp e, dept d where e.deptno=d.deptno and e.deptno=10 and d.deptno=10; ^^^^^^^^^^^ ??????dept ?????? deptno=10 ??????????????????????????10?*1?=10(dept.deptno ?unique????)?1/20????????????????1/20????????????????10??????????30???????????????Query Transformation ???????????????????????????? ?:??????????? dept ?? 1-row table ??????dept ?? driving ???(Outer Table)??? emp ?? probe ???(Inner Table)????????????1?*10?=10 ????????????????????????????????????????????????????????1/20????????????? ?????? Query Transformation ??????SQL????????????????????????????????? Transformation ??????????????????????????????????? 2. ????·????(Access Path Analysis) Access Path Analysis ??Query Transformation ??SQL????????????(Access Path)?????????(Join Method)?????(Join Order)?????????? ??????????????????(FTS)?ROWID?????????????????????????????·?????(Nested Loop Join)???????(Hash Join)????/?????(Sort Merge Join)????????????????????????????????????????????????????????????????????????? Oracle Database ????????? Query Transformation ???? Logical Optimizer?Access Path Analysis ???? Physical Optimizer ????????? ??????????????????????????????????????????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????? Oracle Database ????????????????????? "Oracle ????????" ?????????? Sustaining Engineering?? ?(??? ???) ???????????????? Sustaining Engineering ????????????????????????Oracle Database ???????????????????????? ?????????????????????Ruby????????????????????????? Oracle????????????????????????! Oracle????????????? Oracle????????????????????????

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• Learn How SEO Experts Services Can Help You Reach More of Your Local Customers

  What is search engine optimization or SEO? SEO is an acronym for "search engine optimization" or "search engine optimizer." We know that choosing a search engine specialist can be risky and we've even heard about some SEO consultants that used unethical... or what is known as black hat tricks.

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• CLR JIT Bugs Found During IKVM.NET Development

  "It is actually fairly common that people notice that things fail under retail but not debug and tend to blame code generation. While a code generation bug is possible, as a matter of statistics, it is not likely." -- Vance MorrisonDateCLRArchTypeDescription2010-06-12 v4 x64 Incorrect code Optimizer incorrectly propagates invariants.2010-06-04 v2, v4 x86 Crash ...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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• Automatic Maintenance Jobs in every PDB? New SPM Evolve Advisor Task in Oracle 12.1.0.2

  - by Mike Dietrich

  A customer checking out our slides from the OTN Tour in August 2014 asked me a finicky question the other day: "According to the documentation the Automatic SQL Tuning Advisor maintenance task gets executed only within the CDB$ROOT, but not within each PDB - but the slides are not clear here. So what is the truth?" Ok, that's good question. In my understanding all tasks will get executed within each PDB - that's why we recommend (based on experience) to break up the default maintenance windows when using Oracle Multitenant. Otherwise all PDBs will have the same maintenance windows, and guess what will happen when 25 PDBs start gathering object statistics at the same time ... The documentation indeed says: Automatic SQL Tuning Advisor data is stored in the root. It might have results about SQL statements executed in a PDB that were analyzed by the advisor, but these results are not included if the PDB is unplugged. A common user whose current container is the root can run SQL Tuning Advisor manually for SQL statements from any PDB. When a statement is tuned, it is tuned in any container that runs the statement. This sounds reasonable. But when we have a look into our PDBs or into the CDB_AUTOTASK_CLIENT view the result is different from what the doc says. In my environment I did create just two fresh empty PDBs (CON_ID 3 and 4): SQL> select client_name, status, con_id from cdb_autotask_client; CLIENT_NAME                           STATUS         CON_ID------------------------------------- ---------- ----------auto optimizer stats collection       ENABLED             1sql tuning advisor                    ENABLED             1auto space advisor                    ENABLED             1auto optimizer stats collection       ENABLED             4sql tuning advisor                    ENABLED             4auto space advisor                    ENABLED             4auto optimizer stats collection       ENABLED             3sql tuning advisor                    ENABLED             3auto space advisor                    ENABLED             3 9 rows selected. I haven't verified the reason why this is different from the docs but it may have been related to one change in Oracle Database 12.1.0.2: The new SPM Evolve Advisor Task ( SYS_AUTO_SPM_EVOLVE_TASK) for automatic plan evolution for SQL Plan Management. This new task doesn't appear as a stand-alone job (client) in the maintenance window but runs as a sub-entity of the Automatic SQL Tuning Advisor task. And (I'm just guessing) this may be one of the reasons why every PDB will have to have its own Automatic SQL Tuning Advisor task  Here you'll find more information about how to enable, disable and configure the new Oracle 12.1.0.2 SPM Evolve Advisor Task: Oracle Database 12.1.0.2 SQL Tuning Guide:Managing the SPM Evolve Advisor Task -Mike

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• WSS - Server Error in "/" Application. Compilation Error Message: CS1006: Could not write to output

  - by ptahiliani

  I got the above errror when I tried to run WSS default site after installing and running the Advance System Optimizer 3.o. I resolve this by going to the following locations and adding permission for the admin users accounts (ASP.NET & IIS_WPG) I have set up for Sharepoint. C:\WINDOWS\Microsoft.NET\Framework\v2.0.50727\Temporary ASP.NET Files C:\WINDOWS\System 32\Log Files C:\WINDOWS\Temp After the correct permissions have been added, Sharepoint works as normal.

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• Defining Indexes with SQL Server 2005

  All about SQL Indexes - Query Optimizer, Index, Unique Index, Clustered Index, Full Text Index, XML Index and SQL commands to create, delete, and rename SQL indexes.

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• The Seven Sins against T-SQL Performance

  There are seven common antipatterns in T-SQL coding that make code perform badly, and three good habits which will generally ensure that your code runs fast. If you learn nothing else from this list of great advice from Grant, just keep in mind that you should 'write for the optimizer'. Compress live data by 73% Red Gate's SQL Storage Compress reduces the size of live SQL Server databases, saving you disk space and storage costs. Learn more.

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• My Oracle Suport?????

  - by Dongwei Wang

  ????????????????,??????MOS???????(????),????????????????????????????:Note 62143.1 - Troubleshooting: Tuning the Shared Pool and Tuning Library Cache Latch ContentionNote 376442.1 - * How To Collect 10046 Trace (SQL_TRACE) Diagnostics for Performance IssuesNote 749227.1 - * How to Gather Optimizer Statistics on 11gNote 1359094.1 - FAQ: How to Use AWR reports to Diagnose Database Performance IssuesNote 1320966.1 - Things to Consider Before Upgrading to 11.2.0.2 to Avoid Poor Performance or Wrong ResultsNote 1392633.1 - Things to Consider Before Upgrading to 11.2.0.3 to Avoid Poor Performance or Wrong Results????????????????”??“???,?????????????????(PDF??)???????????????”Rate this document“????

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• SEO For Lawyers

  There are a number of lawyers who have good websites too and if you want your own website to do well against theirs, you better get a good SEO professional to help you. It is not difficult to hire a search engine optimizer and once you do so, you will notice the positive difference.

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• Why the SQL Server FORCESCAN hint exists

  It is often generalized that seeks are better than scans in terms of retrieving data from SQL Server. The index hint FORCESCAN was recently introduced so that you could coerce the optimizer to perform a scan instead of a seek. Which might lead you to wonder: Why would I ever want a scan instead of a seek? 12 must-have SQL Server toolsThe award-winning SQL Developer Bundle contains 10 tools for faster, simpler SQL Server development. Download a free trial.

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• Effective SEO Strategies For Better Search Rankings

  Development of successful SEO campaign totally depends on having well researched and effective SEO strategies for the website. As a search engine optimizer you need to figure out how to progress with search engine optimization at various stages to gain optimal results.

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• SQL Server Prefetch and Query Performance

  Prefetching can make a surprising difference to SQL Server query execution times where there is a high incidence of waiting for disk i/o operations, but the benefits come at a cost. Mostly, the Query Optimizer gets it right, but occasionally there are queries that would benefit from tuning. Get smart with SQL Backup ProGet faster, smaller backups with integrated verification.Quickly and easily DBCC CHECKDB your backups. Learn more.

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• Search Engine Optimization For Hire

  The acronym for search engine optimization or search engine optimizer is SEO. Wanting to hire an SEO is a big decision and should not be taken lightly.

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• Need advice in setting up server. fastCGI, suExec, speed, security, etc.

  - by lewisqic

  I am running my own dedicated server with centOS 5 and WHM/cPanel. I would like to configure my server to meet my needs but I need a little help. It will only be my own websites being run on this server. I'm still a little green when it comes to server administration so please forgive my ignorance. What I Would Like to Have: I need some public directories to be writable (for user image uploads and things like that) but I don't want those directories to have 777 permissions. I need individual accounts to have the ability to set custom php settings for their own account without affecting other accounts, whether through a php.ini file or through .htaccess or any other method. I would like things to run as fast as possible, whether that means using a php optimizer or cacher, such as eaccelerator or xcache or anything else. I need things to be as secure as possible. Here Are My Questions What should I use for my php handler? DSO? CGI? fastCGI? suPHP? Other? Should I be using suEXEC? What are the benefits or downfalls of this? What php optimizer/cacher is best to use? Are there any other security tips I need to know about all of this? I'd appreciate any advice or direction that can be offered. Thanks!

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• Operator of the week - Assert

  - by Fabiano Amorim

  Well my friends, I was wondering how to help you in a practical way to understand execution plans. So I think I'll talk about the Showplan Operators. Showplan Operators are used by the Query Optimizer (QO) to build the query plan in order to perform a specified operation. A query plan will consist of many physical operators. The Query Optimizer uses a simple language that represents each physical operation by an operator, and each operator is represented in the graphical execution plan by an icon. I'll try to talk about one operator every week, but so as to avoid having to continue to write about these operators for years, I'll mention only of those that are more common: The first being the Assert. The Assert is used to verify a certain condition, it validates a Constraint on every row to ensure that the condition was met. If, for example, our DDL includes a check constraint which specifies only two valid values for a column, the Assert will, for every row, validate the value passed to the column to ensure that input is consistent with the check constraint. Assert  and Check Constraints: Let's see where the SQL Server uses that information in practice. Take the following T-SQL: IF OBJECT_ID('Tab1') IS NOT NULL   DROP TABLE Tab1 GO CREATE TABLE Tab1(ID Integer, Gender CHAR(1))  GO  ALTER TABLE TAB1 ADD CONSTRAINT ck_Gender_M_F CHECK(Gender IN('M','F'))  GO INSERT INTO Tab1(ID, Gender) VALUES(1,'X') GO To the command above the SQL Server has generated the following execution plan: As we can see, the execution plan uses the Assert operator to check that the inserted value doesn't violate the Check Constraint. In this specific case, the Assert applies the rule, 'if the value is different to "F" and different to "M" than return 0 otherwise returns NULL'. The Assert operator is programmed to show an error if the returned value is not NULL; in other words, the returned value is not a "M" or "F". Assert checking Foreign Keys Now let's take a look at an example where the Assert is used to validate a foreign key constraint. Suppose we have this  query: ALTER TABLE Tab1 ADD ID_Genders INT GO  IF OBJECT_ID('Tab2') IS NOT NULL   DROP TABLE Tab2 GO CREATE TABLE Tab2(ID Integer PRIMARY KEY, Gender CHAR(1))  GO  INSERT INTO Tab2(ID, Gender) VALUES(1, 'F') INSERT INTO Tab2(ID, Gender) VALUES(2, 'M') INSERT INTO Tab2(ID, Gender) VALUES(3, 'N') GO  ALTER TABLE Tab1 ADD CONSTRAINT fk_Tab2 FOREIGN KEY (ID_Genders) REFERENCES Tab2(ID) GO  INSERT INTO Tab1(ID, ID_Genders, Gender) VALUES(1, 4, 'X') Let's look at the text execution plan to see what these Assert operators were doing. To see the text execution plan just execute SET SHOWPLAN_TEXT ON before run the insert command. |--Assert(WHERE:(CASE WHEN NOT [Pass1008] AND [Expr1007] IS NULL THEN (0) ELSE NULL END))      |--Nested Loops(Left Semi Join, PASSTHRU:([Tab1].[ID_Genders] IS NULL), OUTER REFERENCES:([Tab1].[ID_Genders]), DEFINE:([Expr1007] = [PROBE VALUE]))           |--Assert(WHERE:(CASE WHEN [Tab1].[Gender]<>'F' AND [Tab1].[Gender]<>'M' THEN (0) ELSE NULL END))           |    |--Clustered Index Insert(OBJECT:([Tab1].[PK]), SET:([Tab1].[ID] = RaiseIfNullInsert([@1]),[Tab1].[ID_Genders] = [@2],[Tab1].[Gender] = [Expr1003]), DEFINE:([Expr1003]=CONVERT_IMPLICIT(char(1),[@3],0)))           |--Clustered Index Seek(OBJECT:([Tab2].[PK]), SEEK:([Tab2].[ID]=[Tab1].[ID_Genders]) ORDERED FORWARD) Here we can see the Assert operator twice, first (looking down to up in the text plan and the right to left in the graphical plan) validating the Check Constraint. The same concept showed above is used, if the exit value is "0" than keep running the query, but if NULL is returned shows an exception. The second Assert is validating the result of the Tab1 and Tab2 join. It is interesting to see the "[Expr1007] IS NULL". To understand that you need to know what this Expr1007 is, look at the Probe Value (green text) in the text plan and you will see that it is the result of the join. If the value passed to the INSERT at the column ID_Gender exists in the table Tab2, then that probe will return the join value; otherwise it will return NULL. So the Assert is checking the value of the search at the Tab2; if the value that is passed to the INSERT is not found  then Assert will show one exception. If the value passed to the column ID_Genders is NULL than the SQL can't show a exception, in that case it returns "0" and keeps running the query. If you run the INSERT above, the SQL will show an exception because of the "X" value, but if you change the "X" to "F" and run again, it will show an exception because of the value "4". If you change the value "4" to NULL, 1, 2 or 3 the insert will be executed without any error. Assert checking a SubQuery: The Assert operator is also used to check one subquery. As we know, one scalar subquery can't validly return more than one value: Sometimes, however, a  mistake happens, and a subquery attempts to return more than one value . Here the Assert comes into play by validating the condition that a scalar subquery returns just one value. Take the following query: INSERT INTO Tab1(ID_TipoSexo, Sexo) VALUES((SELECT ID_TipoSexo FROM Tab1), 'F')    INSERT INTO Tab1(ID_TipoSexo, Sexo) VALUES((SELECT ID_TipoSexo FROM Tab1), 'F')    |--Assert(WHERE:(CASE WHEN NOT [Pass1016] AND [Expr1015] IS NULL THEN (0) ELSE NULL END))        |--Nested Loops(Left Semi Join, PASSTHRU:([tempdb].[dbo].[Tab1].[ID_TipoSexo] IS NULL), OUTER REFERENCES:([tempdb].[dbo].[Tab1].[ID_TipoSexo]), DEFINE:([Expr1015] = [PROBE VALUE]))              |--Assert(WHERE:([Expr1017]))             |    |--Compute Scalar(DEFINE:([Expr1017]=CASE WHEN [tempdb].[dbo].[Tab1].[Sexo]<>'F' AND [tempdb].[dbo].[Tab1].[Sexo]<>'M' THEN (0) ELSE NULL END))              |         |--Clustered Index Insert(OBJECT:([tempdb].[dbo].[Tab1].[PK__Tab1__3214EC277097A3C8]), SET:([tempdb].[dbo].[Tab1].[ID_TipoSexo] = [Expr1008],[tempdb].[dbo].[Tab1].[Sexo] = [Expr1009],[tempdb].[dbo].[Tab1].[ID] = [Expr1003]))              |              |--Top(TOP EXPRESSION:((1)))              |                   |--Compute Scalar(DEFINE:([Expr1008]=[Expr1014], [Expr1009]='F'))              |                        |--Nested Loops(Left Outer Join)              |                             |--Compute Scalar(DEFINE:([Expr1003]=getidentity((1856985942),(2),NULL)))              |                             |    |--Constant Scan              |                             |--Assert(WHERE:(CASE WHEN [Expr1013]>(1) THEN (0) ELSE NULL END))              |                                  |--Stream Aggregate(DEFINE:([Expr1013]=Count(*), [Expr1014]=ANY([tempdb].[dbo].[Tab1].[ID_TipoSexo])))             |                                       |--Clustered Index Scan(OBJECT:([tempdb].[dbo].[Tab1].[PK__Tab1__3214EC277097A3C8]))              |--Clustered Index Seek(OBJECT:([tempdb].[dbo].[Tab2].[PK__Tab2__3214EC27755C58E5]), SEEK:([tempdb].[dbo].[Tab2].[ID]=[tempdb].[dbo].[Tab1].[ID_TipoSexo]) ORDERED FORWARD)  You can see from this text showplan that SQL Server as generated a Stream Aggregate to count how many rows the SubQuery will return, This value is then passed to the Assert which then does its job by checking its validity. Is very interesting to see that  the Query Optimizer is smart enough be able to avoid using assert operators when they are not necessary. For instance: INSERT INTO Tab1(ID_TipoSexo, Sexo) VALUES((SELECT ID_TipoSexo FROM Tab1 WHERE ID = 1), 'F') INSERT INTO Tab1(ID_TipoSexo, Sexo) VALUES((SELECT TOP 1 ID_TipoSexo FROM Tab1), 'F')  For both these INSERTs, the Query Optimiser is smart enough to know that only one row will ever be returned, so there is no need to use the Assert. Well, that's all folks, I see you next week with more "Operators". Cheers, Fabiano

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• Top 5 Developer Enabling Nuggets in MySQL 5.6

  - by Rob Young

  MySQL 5.6 is truly a better MySQL and reflects Oracle's commitment to the evolution of the most popular and widelyused open source database on the planet.  The feature-complete 5.6 release candidate was announced at MySQL Connect in late September and the production-ready, generally available ("GA") product should be available in early 2013.  While the message around 5.6 has been focused mainly on mass appeal, advanced topics like performance/scale, high availability, and self-healing replication clusters, MySQL 5.6 also provides many developer-friendly nuggets that are designed to enable those who are building the next generation of web-based and embedded applications and services. Boiling down the 5.6 feature set into a smaller set, of simple, easy to use goodies designed with developer agility in mind, these things deserve a quick look:Subquery Optimizations Using semi-JOINs and late materialization, the MySQL 5.6 Optimizer delivers greatly improved subquery performance. Specifically, the optimizer is now more efficient in handling subqueries in the FROM clause; materialization of subqueries in the FROM clause is now postponed until their contents are needed during execution. Additionally, the optimizer may add an index to derived tables during execution to speed up row retrieval. Internal tests run using the DBT-3 benchmark Query #13, shown below, demonstrate an order of magnitude improvement in execution times (from days to seconds) over previous versions. select c_name, c_custkey, o_orderkey, o_orderdate, o_totalprice, sum(l_quantity)from customer, orders, lineitemwhere o_orderkey in (                select l_orderkey                from lineitem                group by l_orderkey                having sum(l_quantity) > 313  )  and c_custkey = o_custkey  and o_orderkey = l_orderkeygroup by c_name, c_custkey, o_orderkey, o_orderdate, o_totalpriceorder by o_totalprice desc, o_orderdateLIMIT 100;What does this mean for developers?  For starters, simplified subqueries can now be coded instead of complex joins for cross table lookups: SELECT title FROM film WHERE film_id IN (SELECT film_id FROM film_actor GROUP BY film_id HAVING count(*) > 12); And even more importantly subqueries embedded in packaged applications no longer need to be re-written into joins.  This is good news for both ISVs and their customers who have access to the underlying queries and who have spent development cycles writing, testing and maintaining their own versions of re-written queries across updated versions of a packaged app.The details are in the MySQL 5.6 docs. Online DDL OperationsToday's web-based applications are designed to rapidly evolve and adapt to meet business and revenue-generationrequirements. As a result, development SLAs are now most often measured in minutes vs days or weeks. For example, when an application must quickly support new product lines or new products within existing product lines, the backend database schema must adapt in kind, and most commonly while the application remains available for normal business operations.  MySQL 5.6 supports this level of online schema flexibility and agility by providing the following new ALTER TABLE online DDL syntax additions:  CREATE INDEX DROP INDEX Change AUTO_INCREMENT value for a column ADD/DROP FOREIGN KEY Rename COLUMN Change ROW FORMAT, KEY_BLOCK_SIZE for a table Change COLUMN NULL, NOT_NULL Add, drop, reorder COLUMN Again, the details are in the MySQL 5.6 docs. Key-value access to InnoDB via Memcached APIMany of the next generation of web, cloud, social and mobile applications require fast operations against simple Key/Value pairs. At the same time, they must retain the ability to run complex queries against the same data, as well as ensure the data is protected with ACID guarantees. With the new NoSQL API for InnoDB, developers have allthe benefits of a transactional RDBMS, coupled with the performance capabilities of Key/Value store.MySQL 5.6 provides simple, key-value interaction with InnoDB data via the familiar Memcached API.  Implemented via a new Memcached daemon plug-in to mysqld, the new Memcached protocol is mapped directly to the native InnoDB API and enables developers to use existing Memcached clients to bypass the expense of query parsing and go directly to InnoDB data for lookups and transactional compliant updates.  The API makes it possible to re-use standard Memcached libraries and clients, while extending Memcached functionality by integrating a persistent, crash-safe, transactional database back-end.  The implementation is shown here:So does this option provide a performance benefit over SQL?  Internal performance benchmarks using a customized Java application and test harness show some very promising results with a 9X improvement in overall throughput for SET/INSERT operations:You can follow the InnoDB team blog for the methodology, implementation and internal test cases that generated these results here. How to get started with Memcached API to InnoDB is here. New Instrumentation in Performance SchemaThe MySQL Performance Schema was introduced in MySQL 5.5 and is designed to provide point in time metrics for key performance indicators.  MySQL 5.6 improves the Performance Schema in answer to the most common DBA and Developer problems.  New instrumentations include: Statements/Stages What are my most resource intensive queries? Where do they spend time? Table/Index I/O, Table Locks Which application tables/indexes cause the most load or contention? Users/Hosts/Accounts Which application users, hosts, accounts are consuming the most resources? Network I/O What is the network load like? How long do sessions idle? Summaries Aggregated statistics grouped by statement, thread, user, host, account or object. The MySQL 5.6 Performance Schema is now enabled by default in the my.cnf file with optimized and auto-tune settings that minimize overhead (< 5%, but mileage will vary), so using the Performance Schema ona production server to monitor the most common application use cases is less of an issue.  In addition, new atomic levels of instrumentation enable the capture of granular levels of resource consumption by users, hosts, accounts, applications, etc. for billing and chargeback purposes in cloud computing environments.The MySQL docs are an excellent resource for all that is available and that can be done with the 5.6 Performance Schema. Better Condition Handling - GET DIAGNOSTICSMySQL 5.6 enables developers to easily check for error conditions and code for exceptions by introducing the new MySQL Diagnostics Area and corresponding GET DIAGNOSTICS interface command. The Diagnostic Area can be populated via multiple options and provides 2 kinds of information:Statement - which provides affected row count and number of conditions that occurredCondition - which provides error codes and messages for all conditions that were returned by a previous operation The addressable items for each are: The new GET DIAGNOSTICS command provides a standard interface into the Diagnostics Area and can be used via the CLI or from within application code to easily retrieve and handle the results of the most recent statement execution.  An example of how it is used might be:mysql> DROP TABLE test.no_such_table; ERROR 1051 (42S02): Unknown table 'test.no_such_table' mysql> GET DIAGNOSTICS CONDITION 1 -> @p1 = RETURNED_SQLSTATE, @p2 = MESSAGE_TEXT; mysql> SELECT @p1, @p2; +-------+------------------------------------+| @p1   | @p2                                | +-------+------------------------------------+| 42S02 | Unknown table 'test.no_such_table' | +-------+------------------------------------+ Options for leveraging the MySQL Diagnotics Area and GET DIAGNOSTICS are detailed in the MySQL Docs.While the above is a summary of some of the key developer enabling 5.6 features, it is by no means exhaustive. You can dig deeper into what MySQL 5.6 has to offer by reading this developer zone article or checking out "What's New in MySQL 5.6" in the MySQL docs.BONUS ALERT!  If you are developing on Windows or are considering MySQL as an alternative to SQL Server for your next project, application or shipping product, you should check out the MySQL Installer for Windows.  The installer includes the MySQL 5.6 RC database, all drivers, Visual Studio and Excel plugins, tray monitor and development tools all a single download and GUI installer.   So what are your next steps? Register for Dec. 13 "MySQL 5.6: Building the Next Generation of Web-Based Applications and Services" live web event.  Hurry!  Seats are limited. Download the MySQL 5.6 Release Candidate (look under the Development Releases tab) Provide Feedback <link to http://bugs.mysql.com/> Join the Developer discussion on the MySQL Forums Explore all MySQL Products and Developer Tools As always, thanks for your continued support of MySQL!

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• Table Variables: an empirical approach.

  - by Phil Factor

  It isn’t entirely a pleasant experience to publish an article only to have it described on Twitter as ‘Horrible’, and to have it criticized on the MVP forum. When this happened to me in the aftermath of publishing my article on Temporary tables recently, I was taken aback, because these critics were experts whose views I respect. What was my crime? It was, I think, to suggest that, despite the obvious quirks, it was best to use Table Variables as a first choice, and to use local Temporary Tables if you hit problems due to these quirks, or if you were doing complex joins using a large number of rows. What are these quirks? Well, table variables have advantages if they are used sensibly, but this requires some awareness by the developer about the potential hazards and how to avoid them. You can be hit by a badly-performing join involving a table variable. Table Variables are a compromise, and this compromise doesn’t always work out well. Explicit indexes aren’t allowed on Table Variables, so one cannot use covering indexes or non-unique indexes. The query optimizer has to make assumptions about the data rather than using column distribution statistics when a table variable is involved in a join, because there aren’t any column-based distribution statistics on a table variable. It assumes a reasonably even distribution of data, and is likely to have little idea of the number of rows in the table variables that are involved in queries. However complex the heuristics that are used might be in determining the best way of executing a SQL query, and they most certainly are, the Query Optimizer is likely to fail occasionally with table variables, under certain circumstances, and produce a Query Execution Plan that is frightful. The experienced developer or DBA will be on the lookout for this sort of problem. In this blog, I’ll be expanding on some of the tests I used when writing my article to illustrate the quirks, and include a subsequent example supplied by Kevin Boles. A simplified example. We’ll start out by illustrating a simple example that shows some of these characteristics. We’ll create two tables filled with random numbers and then see how many matches we get between the two tables. We’ll forget indexes altogether for this example, and use heaps. We’ll try the same Join with two table variables, two table variables with OPTION (RECOMPILE) in the JOIN clause, and with two temporary tables. It is all a bit jerky because of the granularity of the timing that isn’t actually happening at the millisecond level (I used DATETIME). However, you’ll see that the table variable is outperforming the local temporary table up to 10,000 rows. Actually, even without a use of the OPTION (RECOMPILE) hint, it is doing well. What happens when your table size increases? The table variable is, from around 30,000 rows, locked into a very bad execution plan unless you use OPTION (RECOMPILE) to provide the Query Analyser with a decent estimation of the size of the table. However, if it has the OPTION (RECOMPILE), then it is smokin’. Well, up to 120,000 rows, at least. It is performing better than a Temporary table, and in a good linear fashion. What about mixed table joins, where you are joining a temporary table to a table variable? You’d probably expect that the query analyzer would throw up its hands and produce a bad execution plan as if it were a table variable. After all, it knows nothing about the statistics in one of the tables so how could it do any better? Well, it behaves as if it were doing a recompile. And an explicit recompile adds no value at all. (we just go up to 45000 rows since we know the bigger picture now)   Now, if you were new to this, you might be tempted to start drawing conclusions. Beware! We’re dealing with a very complex beast: the Query Optimizer. It can come up with surprises What if we change the query very slightly to insert the results into a Table Variable? We change nothing else and just measure the execution time of the statement as before. Suddenly, the table variable isn’t looking so much better, even taking into account the time involved in doing the table insert. OK, if you haven’t used OPTION (RECOMPILE) then you’re toast. Otherwise, there isn’t much in it between the Table variable and the temporary table. The table variable is faster up to 8000 rows and then not much in it up to 100,000 rows. Past the 8000 row mark, we’ve lost the advantage of the table variable’s speed. Any general rule you may be formulating has just gone for a walk. What we can conclude from this experiment is that if you join two table variables, and can’t use constraints, you’re going to need that Option (RECOMPILE) hint. Count Dracula and the Horror Join. These tables of integers provide a rather unreal example, so let’s try a rather different example, and get stuck into some implicit indexing, by using constraints. What unusual words are contained in the book ‘Dracula’ by Bram Stoker? Here we get a table of all the common words in the English language (60,387 of them) and put them in a table. We put them in a Table Variable with the word as a primary key, a Table Variable Heap and a Table Variable with a primary key. We then take all the distinct words used in the book ‘Dracula’ (7,558 of them). We then create a table variable and insert into it all those uncommon words that are in ‘Dracula’. i.e. all the words in Dracula that aren’t matched in the list of common words. To do this we use a left outer join, where the right-hand value is null. The results show a huge variation, between the sublime and the gorblimey. If both tables contain a Primary Key on the columns we join on, and both are Table Variables, it took 33 Ms. If one table contains a Primary Key, and the other is a heap, and both are Table Variables, it took 46 Ms. If both Table Variables use a unique constraint, then the query takes 36 Ms. If neither table contains a Primary Key and both are Table Variables, it took 116383 Ms. Yes, nearly two minutes!! If both tables contain a Primary Key, one is a Table Variables and the other is a temporary table, it took 113 Ms. If one table contains a Primary Key, and both are Temporary Tables, it took 56 Ms.If both tables are temporary tables and both have primary keys, it took 46 Ms. Here we see table variables which are joined on their primary key again enjoying a  slight performance advantage over temporary tables. Where both tables are table variables and both are heaps, the query suddenly takes nearly two minutes! So what if you have two heaps and you use option Recompile? If you take the rogue query and add the hint, then suddenly, the query drops its time down to 76 Ms. If you add unique indexes, then you've done even better, down to half that time. Here are the text execution plans.So where have we got to? Without drilling down into the minutiae of the execution plans we can begin to create a hypothesis. If you are using table variables, and your tables are relatively small, they are faster than temporary tables, but as the number of rows increases you need to do one of two things: either you need to have a primary key on the column you are using to join on, or else you need to use option (RECOMPILE) If you try to execute a query that is a join, and both tables are table variable heaps, you are asking for trouble, well- slow queries, unless you give the table hint once the number of rows has risen past a point (30,000 in our first example, but this varies considerably according to context). Kevin’s Skew In describing the table-size, I used the term ‘relatively small’. Kevin Boles produced an interesting case where a single-row table variable produces a very poor execution plan when joined to a very, very skewed table. In the original, pasted into my article as a comment, a column consisted of 100000 rows in which the key column was one number (1) . To this was added eight rows with sequential numbers up to 9. When this was joined to a single-tow Table Variable with a key of 2 it produced a bad plan. This problem is unlikely to occur in real usage, and the Query Optimiser team probably never set up a test for it. Actually, the skew can be slightly less extreme than Kevin made it. The following test showed that once the table had 54 sequential rows in the table, then it adopted exactly the same execution plan as for the temporary table and then all was well. Undeniably, real data does occasionally cause problems to the performance of joins in Table Variables due to the extreme skew of the distribution. We've all experienced Perfectly Poisonous Table Variables in real live data. As in Kevin’s example, indexes merely make matters worse, and the OPTION (RECOMPILE) trick does nothing to help. In this case, there is no option but to use a temporary table. However, one has to note that once the slight de-skew had taken place, then the plans were identical across a huge range. Conclusions Where you need to hold intermediate results as part of a process, Table Variables offer a good alternative to temporary tables when used wisely. They can perform faster than a temporary table when the number of rows is not great. For some processing with huge tables, they can perform well when only a clustered index is required, and when the nature of the processing makes an index seek very effective. Table Variables are scoped to the batch or procedure and are unlikely to hang about in the TempDB when they are no longer required. They require no explicit cleanup. Where the number of rows in the table is moderate, you can even use them in joins as ‘Heaps’, unindexed. Beware, however, since, as the number of rows increase, joins on Table Variable heaps can easily become saddled by very poor execution plans, and this must be cured either by adding constraints (UNIQUE or PRIMARY KEY) or by adding the OPTION (RECOMPILE) hint if this is impossible. Occasionally, the way that the data is distributed prevents the efficient use of Table Variables, and this will require using a temporary table instead. Tables Variables require some awareness by the developer about the potential hazards and how to avoid them. If you are not prepared to do any performance monitoring of your code or fine-tuning, and just want to pummel out stuff that ‘just runs’ without considering namby-pamby stuff such as indexes, then stick to Temporary tables. If you are likely to slosh about large numbers of rows in temporary tables without considering the niceties of processing just what is required and no more, then temporary tables provide a safer and less fragile means-to-an-end for you.

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