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  • Amazon EC2 performance vs desktop

    - by flashnik
    I'm wondering how to compare performance of EC2 instances with standard dedicated servers and desktop. I've found only comparance of defferent clouds. I need to find a solution to perform some computations which require CPU and memory (disc IO is not used). The choice is to use: EC2 (High-CPU) or Xeon 5620/5630 with DDR3 or Core i7-960/980 with DDR3 Can anybody help, how to compare their performance? I'm not speaking about reliability of alternatives, I want to understand pros and cons from the point of just performance.

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  • SQL Server Performance & Latching

    - by Colin
    I have a SQL server 2000 instance which runs several concurrent select statements on a group of 4 or 5 tables. Often the performance of the server during these queries becomes extremely diminished. The querys can take up to 10x as long as other runs of the same query, and it gets to the point where simple operations like getting the table list in object explorer or running sp_who can take several minutes. I've done my best to identify the cause of these issues, and the only performance metric which I've found to be off base is Average Latch Wait time. I've read that over 1 second wait time is bad, and mine ranges anywhere from 20 to 75 seconds under heavy use. So my question is, what could be the issue? Shouldn't SQL be able to handle multiple selects on a single table without losing so much performance? Can anyone suggest somewhere to go from here to investigate this problem? Thanks for the help.

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  • Flushing disk cache for performance benchmarks?

    - by Ido Hadanny
    I'm doing some performance benchmark on some heavy SQL script running on postgres 8.4 on a ubuntu box (natty). I'm experiencing some pretty un-stable performance, even though I'm supposed to be the only one running on the machine (the same script on the exact same data might run in 20m and then 40m for no specific reason). So, remembering my distant DBA training, I decided I should flush the postgres cache, using sudo /etc/init.d/postgresql restart, but it's still shaky! My question: maybe I'm missing some caches in my disk/os? I'm using a netapp appliance as my storage. Am I on the right track? Do I even want to make sure I get repeatable performance before I start tuning?

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  • Recommended website performance monitoring services? [closed]

    - by Dennis G.
    I'm looking for a good performance monitoring service for websites. I know about some of the available general monitoring services that check for uptime and notify you about unavailable services. But I'm specifically looking for a service with an emphasis on performance. I.e., I would like to see reports with detailed performance statistics from multiple locations world-wide, with a break-down on how long it took to fetch the different website resources, including third-party scripts such as Google Analytics and so on (the report should contain similar details such as the FireBug Net tab). Are there any such services and if so, which one is the best?

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  • Using HTML 5 SessionState to save rendered Page Content

    - by Rick Strahl
    HTML 5 SessionState and LocalStorage are very useful and super easy to use to manage client side state. For building rich client side or SPA style applications it's a vital feature to be able to cache user data as well as HTML content in order to swap pages in and out of the browser's DOM. What might not be so obvious is that you can also use the sessionState and localStorage objects even in classic server rendered HTML applications to provide caching features between pages. These APIs have been around for a long time and are supported by most relatively modern browsers and even all the way back to IE8, so you can use them safely in your Web applications. SessionState and LocalStorage are easy The APIs that make up sessionState and localStorage are very simple. Both object feature the same API interface which  is a simple, string based key value store that has getItem, setItem, removeitem, clear and  key methods. The objects are also pseudo array objects and so can be iterated like an array with  a length property and you have array indexers to set and get values with. Basic usage  for storing and retrieval looks like this (using sessionStorage, but the syntax is the same for localStorage - just switch the objects):// set var lastAccess = new Date().getTime(); if (sessionStorage) sessionStorage.setItem("myapp_time", lastAccess.toString()); // retrieve in another page or on a refresh var time = null; if (sessionStorage) time = sessionStorage.getItem("myapp_time"); if (time) time = new Date(time * 1); else time = new Date(); sessionState stores data that is browser session specific and that has a liftetime of the active browser session or window. Shut down the browser or tab and the storage goes away. localStorage uses the same API interface, but the lifetime of the data is permanently stored in the browsers storage area until deleted via code or by clearing out browser cookies (not the cache). Both sessionStorage and localStorage space is limited. The spec is ambiguous about this - supposedly sessionStorage should allow for unlimited size, but it appears that most WebKit browsers support only 2.5mb for either object. This means you have to be careful what you store especially since other applications might be running on the same domain and also use the storage mechanisms. That said 2.5mb worth of character data is quite a bit and would go a long way. The easiest way to get a feel for how sessionState and localStorage work is to look at a simple example. You can go check out the following example online in Plunker: http://plnkr.co/edit/0ICotzkoPjHaWa70GlRZ?p=preview which looks like this: Plunker is an online HTML/JavaScript editor that lets you write and run Javascript code and similar to JsFiddle, but a bit cleaner to work in IMHO (thanks to John Papa for turning me on to it). The sample has two text boxes with counts that update session/local storage every time you click the related button. The counts are 'cached' in Session and Local storage. The point of these examples is that both counters survive full page reloads, and the LocalStorage counter survives a complete browser shutdown and restart. Go ahead and try it out by clicking the Reload button after updating both counters and then shutting down the browser completely and going back to the same URL (with the same browser). What you should see is that reloads leave both counters intact at the counted values, while a browser restart will leave only the local storage counter intact. The code to deal with the SessionStorage (and LocalStorage not shown here) in the example is isolated into a couple of wrapper methods to simplify the code: function getSessionCount() { var count = 0; if (sessionStorage) { var count = sessionStorage.getItem("ss_count"); count = !count ? 0 : count * 1; } $("#txtSession").val(count); return count; } function setSessionCount(count) { if (sessionStorage) sessionStorage.setItem("ss_count", count.toString()); } These two functions essentially load and store a session counter value. The two key methods used here are: sessionStorage.getItem(key); sessionStorage.setItem(key,stringVal); Note that the value given to setItem and return by getItem has to be a string. If you pass another type you get an error. Don't let that limit you though - you can easily enough store JSON data in a variable so it's quite possible to pass complex objects and store them into a single sessionStorage value:var user = { name: "Rick", id="ricks", level=8 } sessionStorage.setItem("app_user",JSON.stringify(user)); to retrieve it:var user = sessionStorage.getItem("app_user"); if (user) user = JSON.parse(user); Simple! If you're using the Chrome Developer Tools (F12) you can also check out the session and local storage state on the Resource tab:   You can also use this tool to refresh or remove entries from storage. What we just looked at is a purely client side implementation where a couple of counters are stored. For rich client centric AJAX applications sessionStorage and localStorage provide a very nice and simple API to store application state while the application is running. But you can also use these storage mechanisms to manage server centric HTML applications when you combine server rendering with some JavaScript to perform client side data caching. You can both store some state information and data on the client (ie. store a JSON object and carry it forth between server rendered HTML requests) or you can use it for good old HTTP based caching where some rendered HTML is saved and then restored later. Let's look at the latter with a real life example. Why do I need Client-side Page Caching for Server Rendered HTML? I don't know about you, but in a lot of my existing server driven applications I have lists that display a fair amount of data. Typically these lists contain links to then drill down into more specific data either for viewing or editing. You can then click on a link and go off to a detail page that provides more concise content. So far so good. But now you're done with the detail page and need to get back to the list, so you click on a 'bread crumbs trail' or an application level 'back to list' button and… …you end up back at the top of the list - the scroll position, the current selection in some cases even filters conditions - all gone with the wind. You've left behind the state of the list and are starting from scratch in your browsing of the list from the top. Not cool! Sound familiar? This a pretty common scenario with server rendered HTML content where it's so common to display lists to drill into, only to lose state in the process of returning back to the original list. Look at just about any traditional forums application, or even StackOverFlow to see what I mean here. Scroll down a bit to look at a post or entry, drill in then use the bread crumbs or tab to go back… In some cases returning to the top of a list is not a big deal. On StackOverFlow that sort of works because content is turning around so quickly you probably want to actually look at the top posts. Not always though - if you're browsing through a list of search topics you're interested in and drill in there's no way back to that position. Essentially anytime you're actively browsing the items in the list, that's when state becomes important and if it's not handled the user experience can be really disrupting. Content Caching If you're building client centric SPA style applications this is a fairly easy to solve problem - you tend to render the list once and then update the page content to overlay the detail content, only hiding the list temporarily until it's used again later. It's relatively easy to accomplish this simply by hiding content on the page and later making it visible again. But if you use server rendered content, hanging on to all the detail like filters, selections and scroll position is not quite as easy. Or is it??? This is where sessionStorage comes in handy. What if we just save the rendered content of a previous page, and then restore it when we return to this page based on a special flag that tells us to use the cached version? Let's see how we can do this. A real World Use Case Recently my local ISP asked me to help out with updating an ancient classifieds application. They had a very busy, local classifieds app that was originally an ASP classic application. The old app was - wait for it: frames based - and even though I lobbied against it, the decision was made to keep the frames based layout to allow rapid browsing of the hundreds of posts that are made on a daily basis. The primary reason they wanted this was precisely for the ability to quickly browse content item by item. While I personally hate working with Frames, I have to admit that the UI actually works well with the frames layout as long as you're running on a large desktop screen. You can check out the frames based desktop site here: http://classifieds.gorge.net/ However when I rebuilt the app I also added a secondary view that doesn't use frames. The main reason for this of course was for mobile displays which work horribly with frames. So there's a somewhat mobile friendly interface to the interface, which ditches the frames and uses some responsive design tweaking for mobile capable operation: http://classifeds.gorge.net/mobile  (or browse the base url with your browser width under 800px)   Here's what the mobile, non-frames view looks like:   As you can see this means that the list of classifieds posts now is a list and there's a separate page for drilling down into the item. And of course… originally we ran into that usability issue I mentioned earlier where the browse, view detail, go back to the list cycle resulted in lost list state. Originally in mobile mode you scrolled through the list, found an item to look at and drilled in to display the item detail. Then you clicked back to the list and BAM - you've lost your place. Because there are so many items added on a daily basis the full list is never fully loaded, but rather there's a "Load Additional Listings"  entry at the button. Not only did we originally lose our place when coming back to the list, but any 'additionally loaded' items are no longer there because the list was now rendering  as if it was the first page hit. The additional listings, and any filters, the selection of an item all were lost. Major Suckage! Using Client SessionStorage to cache Server Rendered Content To work around this problem I decided to cache the rendered page content from the list in SessionStorage. Anytime the list renders or is updated with Load Additional Listings, the page HTML is cached and stored in Session Storage. Any back links from the detail page or the login or write entry forms then point back to the list page with a back=true query string parameter. If the server side sees this parameter it doesn't render the part of the page that is cached. Instead the client side code retrieves the data from the sessionState cache and simply inserts it into the page. It sounds pretty simple, and the overall the process is really easy, but there are a few gotchas that I'll discuss in a minute. But first let's look at the implementation. Let's start with the server side here because that'll give a quick idea of the doc structure. As I mentioned the server renders data from an ASP.NET MVC view. On the list page when returning to the list page from the display page (or a host of other pages) looks like this: https://classifieds.gorge.net/list?back=True The query string value is a flag, that indicates whether the server should render the HTML. Here's what the top level MVC Razor view for the list page looks like:@model MessageListViewModel @{ ViewBag.Title = "Classified Listing"; bool isBack = !string.IsNullOrEmpty(Request.QueryString["back"]); } <form method="post" action="@Url.Action("list")"> <div id="SizingContainer"> @if (!isBack) { @Html.Partial("List_CommandBar_Partial", Model) <div id="PostItemContainer" class="scrollbox" xstyle="-webkit-overflow-scrolling: touch;"> @Html.Partial("List_Items_Partial", Model) @if (Model.RequireLoadEntry) { <div class="postitem loadpostitems" style="padding: 15px;"> <div id="LoadProgress" class="smallprogressright"></div> <div class="control-progress"> Load additional listings... </div> </div> } </div> } </div> </form> As you can see the query string triggers a conditional block that if set is simply not rendered. The content inside of #SizingContainer basically holds  the entire page's HTML sans the headers and scripts, but including the filter options and menu at the top. In this case this makes good sense - in other situations the fact that the menu or filter options might be dynamically updated might make you only cache the list rather than essentially the entire page. In this particular instance all of the content works and produces the proper result as both the list along with any filter conditions in the form inputs are restored. Ok, let's move on to the client. On the client there are two page level functions that deal with saving and restoring state. Like the counter example I showed earlier, I like to wrap the logic to save and restore values from sessionState into a separate function because they are almost always used in several places.page.saveData = function(id) { if (!sessionStorage) return; var data = { id: id, scroll: $("#PostItemContainer").scrollTop(), html: $("#SizingContainer").html() }; sessionStorage.setItem("list_html",JSON.stringify(data)); }; page.restoreData = function() { if (!sessionStorage) return; var data = sessionStorage.getItem("list_html"); if (!data) return null; return JSON.parse(data); }; The data that is saved is an object which contains an ID which is the selected element when the user clicks and a scroll position. These two values are used to reset the scroll position when the data is used from the cache. Finally the html from the #SizingContainer element is stored, which makes for the bulk of the document's HTML. In this application the HTML captured could be a substantial bit of data. If you recall, I mentioned that the server side code renders a small chunk of data initially and then gets more data if the user reads through the first 50 or so items. The rest of the items retrieved can be rather sizable. Other than the JSON deserialization that's Ok. Since I'm using SessionStorage the storage space has no immediate limits. Next is the core logic to handle saving and restoring the page state. At first though this would seem pretty simple, and in some cases it might be, but as the following code demonstrates there are a few gotchas to watch out for. Here's the relevant code I use to save and restore:$( function() { … var isBack = getUrlEncodedKey("back", location.href); if (isBack) { // remove the back key from URL setUrlEncodedKey("back", "", location.href); var data = page.restoreData(); // restore from sessionState if (!data) { // no data - force redisplay of the server side default list window.location = "list"; return; } $("#SizingContainer").html(data.html); var el = $(".postitem[data-id=" + data.id + "]"); $(".postitem").removeClass("highlight"); el.addClass("highlight"); $("#PostItemContainer").scrollTop(data.scroll); setTimeout(function() { el.removeClass("highlight"); }, 2500); } else if (window.noFrames) page.saveData(null); // save when page loads $("#SizingContainer").on("click", ".postitem", function() { var id = $(this).attr("data-id"); if (!id) return true; if (window.noFrames) page.saveData(id); var contentFrame = window.parent.frames["Content"]; if (contentFrame) contentFrame.location.href = "show/" + id; else window.location.href = "show/" + id; return false; }); … The code starts out by checking for the back query string flag which triggers restoring from the client cache. If cached the cached data structure is read from sessionStorage. It's important here to check if data was returned. If the user had back=true on the querystring but there is no cached data, he likely bookmarked this page or otherwise shut down the browser and came back to this URL. In that case the server didn't render any detail and we have no cached data, so all we can do is redirect to the original default list view using window.location. If we continued the page would render no data - so make sure to always check the cache retrieval result. Always! If there is data the it's loaded and the data.html data is restored back into the document by simply injecting the HTML back into the document's #SizingContainer element:$("#SizingContainer").html(data.html); It's that simple and it's quite quick even with a fully loaded list of additional items and on a phone. The actual HTML data is stored to the cache on every page load initially and then again when the user clicks on an element to navigate to a particular listing. The former ensures that the client cache always has something in it, and the latter updates with additional information for the selected element. For the click handling I use a data-id attribute on the list item (.postitem) in the list and retrieve the id from that. That id is then used to navigate to the actual entry as well as storing that Id value in the saved cached data. The id is used to reset the selection by searching for the data-id value in the restored elements. The overall process of this save/restore process is pretty straight forward and it doesn't require a bunch of code, yet it yields a huge improvement in the usability of the site on mobile devices (or anybody who uses the non-frames view). Some things to watch out for As easy as it conceptually seems to simply store and retrieve cached content, you have to be quite aware what type of content you are caching. The code above is all that's specific to cache/restore cycle and it works, but it took a few tweaks to the rest of the script code and server code to make it all work. There were a few gotchas that weren't immediately obvious. Here are a few things to pay attention to: Event Handling Logic Timing of manipulating DOM events Inline Script Code Bookmarking to the Cache Url when no cache exists Do you have inline script code in your HTML? That script code isn't going to run if you restore from cache and simply assign or it may not run at the time you think it would normally in the DOM rendering cycle. JavaScript Event Hookups The biggest issue I ran into with this approach almost immediately is that originally I had various static event handlers hooked up to various UI elements that are now cached. If you have an event handler like:$("#btnSearch").click( function() {…}); that works fine when the page loads with server rendered HTML, but that code breaks when you now load the HTML from cache. Why? Because the elements you're trying to hook those events to may not actually be there - yet. Luckily there's an easy workaround for this by using deferred events. With jQuery you can use the .on() event handler instead:$("#SelectionContainer").on("click","#btnSearch", function() {…}); which monitors a parent element for the events and checks for the inner selector elements to handle events on. This effectively defers to runtime event binding, so as more items are added to the document bindings still work. For any cached content use deferred events. Timing of manipulating DOM Elements Along the same lines make sure that your DOM manipulation code follows the code that loads the cached content into the page so that you don't manipulate DOM elements that don't exist just yet. Ideally you'll want to check for the condition to restore cached content towards the top of your script code, but that can be tricky if you have components or other logic that might not all run in a straight line. Inline Script Code Here's another small problem I ran into: I use a DateTime Picker widget I built a while back that relies on the jQuery date time picker. I also created a helper function that allows keyboard date navigation into it that uses JavaScript logic. Because MVC's limited 'object model' the only way to embed widget content into the page is through inline script. This code broken when I inserted the cached HTML into the page because the script code was not available when the component actually got injected into the page. As the last bullet - it's a matter of timing. There's no good work around for this - in my case I pulled out the jQuery date picker and relied on native <input type="date" /> logic instead - a better choice these days anyway, especially since this view is meant to be primarily to serve mobile devices which actually support date input through the browser (unlike desktop browsers of which only WebKit seems to support it). Bookmarking Cached Urls When you cache HTML content you have to make a decision whether you cache on the client and also not render that same content on the server. In the Classifieds app I didn't render server side content so if the user comes to the page with back=True and there is no cached content I have to a have a Plan B. Typically this happens when somebody ends up bookmarking the back URL. The easiest and safest solution for this scenario is to ALWAYS check the cache result to make sure it exists and if not have a safe URL to go back to - in this case to the plain uncached list URL which amounts to effectively redirecting. This seems really obvious in hindsight, but it's easy to overlook and not see a problem until much later, when it's not obvious at all why the page is not rendering anything. Don't use <body> to replace Content Since we're practically replacing all the HTML in the page it may seem tempting to simply replace the HTML content of the <body> tag. Don't. The body tag usually contains key things that should stay in the page and be there when it loads. Specifically script tags and elements and possibly other embedded content. It's best to create a top level DOM element specifically as a placeholder container for your cached content and wrap just around the actual content you want to replace. In the app above the #SizingContainer is that container. Other Approaches The approach I've used for this application is kind of specific to the existing server rendered application we're running and so it's just one approach you can take with caching. However for server rendered content caching this is a pattern I've used in a few apps to retrofit some client caching into list displays. In this application I took the path of least resistance to the existing server rendering logic. Here are a few other ways that come to mind: Using Partial HTML Rendering via AJAXInstead of rendering the page initially on the server, the page would load empty and the client would render the UI by retrieving the respective HTML and embedding it into the page from a Partial View. This effectively makes the initial rendering and the cached rendering logic identical and removes the server having to decide whether this request needs to be rendered or not (ie. not checking for a back=true switch). All the logic related to caching is made on the client in this case. Using JSON Data and Client RenderingThe hardcore client option is to do the whole UI SPA style and pull data from the server and then use client rendering or databinding to pull the data down and render using templates or client side databinding with knockout/angular et al. As with the Partial Rendering approach the advantage is that there's no difference in the logic between pulling the data from cache or rendering from scratch other than the initial check for the cache request. Of course if the app is a  full on SPA app, then caching may not be required even - the list could just stay in memory and be hidden and reactivated. I'm sure there are a number of other ways this can be handled as well especially using  AJAX. AJAX rendering might simplify the logic, but it also complicates search engine optimization since there's no content loaded initially. So there are always tradeoffs and it's important to look at all angles before deciding on any sort of caching solution in general. State of the Session SessionState and LocalStorage are easy to use in client code and can be integrated even with server centric applications to provide nice caching features of content and data. In this post I've shown a very specific scenario of storing HTML content for the purpose of remembering list view data and state and making the browsing experience for lists a bit more friendly, especially if there's dynamically loaded content involved. If you haven't played with sessionStorage or localStorage I encourage you to give it a try. There's a lot of cool stuff that you can do with this beyond the specific scenario I've covered here… Resources Overview of localStorage (also applies to sessionStorage) Web Storage Compatibility Modernizr Test Suite© Rick Strahl, West Wind Technologies, 2005-2013Posted in JavaScript  HTML5  ASP.NET  MVC   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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  • I need help in inno setup custom page

    - by vinutavishal
    in inno setup i hav created a custom page with following code and has 3 text boxes now i want to validate that text box on custom form next button click if in text.text='2121212' something text is entered by user then only next button enabled pls help any one its urgent [CustomMessages] CustomForm_Caption=CustomForm Caption CustomForm_Description=CustomForm Description CustomForm_Label1_Caption0=College Name: CustomForm_Label2_Caption0=Product Type: CustomForm_Label3_Caption0=Product ID: [Code] var Label1: TLabel; Label2: TLabel; Label3: TLabel; Edit1: TEdit; Edit2: TEdit; Edit3: TEdit; Edit4: TEdit; Edit5: TEdit; Edit6: TEdit; { CustomForm_Activate } procedure CustomForm_Activate(Page: TWizardPage); begin // enter code here... end; { CustomForm_ShouldSkipPage } function CustomForm_ShouldSkipPage(Page: TWizardPage): Boolean; begin Result := False; end; { CustomForm_BackButtonClick } function CustomForm_BackButtonClick(Page: TWizardPage): Boolean; begin Result := True; end; { CustomForm_NextkButtonClick } function CustomForm_NextButtonClick(Page: TWizardPage): Boolean; begin RegWriteStringValue(HKEY_LOCAL_MACHINE, 'Software\SGS2.2\CS', 'College Name', Edit1.Text); RegWriteStringValue(HKEY_LOCAL_MACHINE, 'Software\SGS2.2\CS', 'Product Type', Edit2.Text); RegWriteStringValue(HKEY_LOCAL_MACHINE, 'Software\SGS2.2\CS', 'Product ID', Edit3.Text); Result := True; end; { CustomForm_CancelButtonClick } procedure CustomForm_CancelButtonClick(Page: TWizardPage; var Cancel, Confirm: Boolean); begin // enter code here... end; { CustomForm_CreatePage } function CustomForm_CreatePage(PreviousPageId: Integer): Integer; var Page: TWizardPage; begin Page := CreateCustomPage( PreviousPageId, ExpandConstant('{cm:CustomForm_Caption}'), ExpandConstant('{cm:CustomForm_Description}') ); { Label1 } Label1 := TLabel.Create(Page); with Label1 do begin Parent := Page.Surface; Caption := ExpandConstant('{cm:CustomForm_Label1_Caption0}'); Left := ScaleX(16); Top := ScaleY(24); Width := ScaleX(70); Height := ScaleY(13); end; { Label2 } Label2 := TLabel.Create(Page); with Label2 do begin Parent := Page.Surface; Caption := ExpandConstant('{cm:CustomForm_Label2_Caption0}'); Left := ScaleX(17); Top := ScaleY(56); Width := ScaleX(70); Height := ScaleY(13); end; { Label3 } Label3 := TLabel.Create(Page); with Label3 do begin Parent := Page.Surface; Caption := ExpandConstant('{cm:CustomForm_Label3_Caption0}'); Left := ScaleX(17); Top := ScaleY(88); Width := ScaleX(70); Height := ScaleY(13); end; { Edit1 } Edit1 := TEdit.Create(Page); with Edit1 do begin Parent := Page.Surface; Left := ScaleX(115); Top := ScaleY(24); Width := ScaleX(273); Height := ScaleY(21); TabOrder := 0; end; { Edit2 } Edit2 := TEdit.Create(Page); with Edit2 do begin Parent := Page.Surface; Left := ScaleX(115); Top := ScaleY(56); Width := ScaleX(273); Height := ScaleY(21); TabOrder := 1; end; { Edit3 } Edit3 := TEdit.Create(Page); with Edit3 do begin Parent := Page.Surface; Left := ScaleX(115); Top := ScaleY(88); Width := ScaleX(273); Height := ScaleY(21); TabOrder := 2; end; with Page do begin OnActivate := @CustomForm_Activate; OnShouldSkipPage := @CustomForm_ShouldSkipPage; OnBackButtonClick := @CustomForm_BackButtonClick; OnNextButtonClick := @CustomForm_NextButtonClick; OnCancelButtonClick := @CustomForm_CancelButtonClick; end; Result := Page.ID; end; { CustomForm_InitializeWizard } procedure InitializeWizard(); begin CustomForm_CreatePage(wpWelcome); end;

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  • Revisiting ANTS Performance Profiler 7.4

    - by James Michael Hare
    Last year, I did a small review on the ANTS Performance Profiler 6.3, now that it’s a year later and a major version number higher, I thought I’d revisit the review and revise my last post. This post will take the same examples as the original post and update them to show what’s new in version 7.4 of the profiler. Background A performance profiler’s main job is to keep track of how much time is typically spent in each unit of code. This helps when we have a program that is not running at the performance we expect, and we want to know where the program is experiencing issues. There are many profilers out there of varying capabilities. Red Gate’s typically seem to be the very easy to “jump in” and get started with very little training required. So let’s dig into the Performance Profiler. I’ve constructed a very crude program with some obvious inefficiencies. It’s a simple program that generates random order numbers (or really could be any unique identifier), adds it to a list, sorts the list, then finds the max and min number in the list. Ignore the fact it’s very contrived and obviously inefficient, we just want to use it as an example to show off the tool: 1: // our test program 2: public static class Program 3: { 4: // the number of iterations to perform 5: private static int _iterations = 1000000; 6: 7: // The main method that controls it all 8: public static void Main() 9: { 10: var list = new List<string>(); 11: 12: for (int i = 0; i < _iterations; i++) 13: { 14: var x = GetNextId(); 15: 16: AddToList(list, x); 17: 18: var highLow = GetHighLow(list); 19: 20: if ((i % 1000) == 0) 21: { 22: Console.WriteLine("{0} - High: {1}, Low: {2}", i, highLow.Item1, highLow.Item2); 23: Console.Out.Flush(); 24: } 25: } 26: } 27: 28: // gets the next order id to process (random for us) 29: public static string GetNextId() 30: { 31: var random = new Random(); 32: var num = random.Next(1000000, 9999999); 33: return num.ToString(); 34: } 35: 36: // add it to our list - very inefficiently! 37: public static void AddToList(List<string> list, string item) 38: { 39: list.Add(item); 40: list.Sort(); 41: } 42: 43: // get high and low of order id range - very inefficiently! 44: public static Tuple<int,int> GetHighLow(List<string> list) 45: { 46: return Tuple.Create(list.Max(s => Convert.ToInt32(s)), list.Min(s => Convert.ToInt32(s))); 47: } 48: } So let’s run it through the profiler and see what happens! Visual Studio Integration First, let’s look at how the ANTS profilers integrate with Visual Studio’s menu system. Once you install the ANTS profilers, you will get an ANTS menu item with several options: Notice that you can either Profile Performance or Launch ANTS Performance Profiler. These sound similar but achieve two slightly different actions: Profile Performance: this immediately launches the profiler with all defaults selected to profile the active project in Visual Studio. Launch ANTS Performance Profiler: this launches the profiler much the same way as starting it from the Start Menu. The profiler will pre-populate the application and path information, but allow you to change the settings before beginning the profile run. So really, the main difference is that Profile Performance immediately begins profiling with the default selections, where Launch ANTS Performance Profiler allows you to change the defaults and attach to an already-running application. Let’s Fire it Up! So when you fire up ANTS either via Start Menu or Launch ANTS Performance Profiler menu in Visual Studio, you are presented with a very simple dialog to get you started: Notice you can choose from many different options for application type. You can profile executables, services, web applications, or just attach to a running process. In fact, in version 7.4 we see two new options added: ASP.NET Web Application (IIS Express) SharePoint web application (IIS) So this gives us an additional way to profile ASP.NET applications and the ability to profile SharePoint applications as well. You can also choose your level of detail in the Profiling Mode drop down. If you choose Line-Level and method-level timings detail, you will get a lot more detail on the method durations, but this will also slow down profiling somewhat. If you really need the profiler to be as unintrusive as possible, you can change it to Sample method-level timings. This is performing very light profiling, where basically the profiler collects timings of a method by examining the call-stack at given intervals. Which method you choose depends a lot on how much detail you need to find the issue and how sensitive your program issues are to timing. So for our example, let’s just go with the line and method timing detail. So, we check that all the options are correct (if you launch from VS2010, the executable and path are filled in already), and fire it up by clicking the [Start Profiling] button. Profiling the Application Once you start profiling the application, you will see a real-time graph of CPU usage that will indicate how much your application is using the CPU(s) on your system. During this time, you can select segments of the graph and bookmark them, giving them mnemonic names. This can be useful if you want to compare performance in one part of the run to another part of the run. Notice that once you select a block, it will give you the call tree breakdown for that selection only, and the relative performance of those calls. Once you feel you have collected enough information, you can click [Stop Profiling] to stop the application run and information collection and begin a more thorough analysis. Analyzing Method Timings So now that we’ve halted the run, we can look around the GUI and see what we can see. By default, the times are shown in terms of percentage of time of the total run of the application, though you can change it in the View menu item to milliseconds, ticks, or seconds as well. This won’t affect the percentages of methods, it only affects what units the times are shown. Notice also that the major hotspot seems to be in a method without source, ANTS Profiler will filter these out by default, but you can right-click on the line and remove the filter to see more detail. This proves especially handy when a bottleneck is due to a method in the BCL. So now that we’ve removed the filter, we see a bit more detail: In addition, ANTS Performance Profiler gives you the ability to decompile the methods without source so that you can dive even deeper, though typically this isn’t necessary for our purposes. When looking at timings, there are generally two types of timings for each method call: Time: This is the time spent ONLY in this method, not including calls this method makes to other methods. Time With Children: This is the total of time spent in both this method AND including calls this method makes to other methods. In other words, the Time tells you how much work is being done exclusively in this method, and the Time With Children tells you how much work is being done inclusively in this method and everything it calls. You can also choose to display the methods in a tree or in a grid. The tree view is the default and it shows the method calls arranged in terms of the tree representing all method calls and the parent method that called them, etc. This is useful for when you find a hot-spot method, you can see who is calling it to determine if the problem is the method itself, or if it is being called too many times. The grid method represents each method only once with its totals and is useful for quickly seeing what method is the trouble spot. In addition, you can choose to display Methods with source which are generally the methods you wrote (as opposed to native or BCL code), or Any Method which shows not only your methods, but also native calls, JIT overhead, synchronization waits, etc. So these are just two ways of viewing the same data, and you’re free to choose the organization that best suits what information you are after. Analyzing Method Source If we look at the timings above, we see that our AddToList() method (and in particular, it’s call to the List<T>.Sort() method in the BCL) is the hot-spot in this analysis. If ANTS sees a method that is consuming the most time, it will flag it as a hot-spot to help call out potential areas of concern. This doesn’t mean the other statistics aren’t meaningful, but that the hot-spot is most likely going to be your biggest bang-for-the-buck to concentrate on. So let’s select the AddToList() method, and see what it shows in the source window below: Notice the source breakout in the bottom pane when you select a method (from either tree or grid view). This shows you the timings in this method per line of code. This gives you a major indicator of where the trouble-spot in this method is. So in this case, we see that performing a Sort() on the List<T> after every Add() is killing our performance! Of course, this was a very contrived, duh moment, but you’d be surprised how many performance issues become duh moments. Note that this one line is taking up 86% of the execution time of this application! If we eliminate this bottleneck, we should see drastic improvement in the performance. So to fix this, if we still wanted to maintain the List<T> we’d have many options, including: delay Sort() until after all Add() methods, using a SortedSet, SortedList, or SortedDictionary depending on which is most appropriate, or forgoing the sorting all together and using a Dictionary. Rinse, Repeat! So let’s just change all instances of List<string> to SortedSet<string> and run this again through the profiler: Now we see the AddToList() method is no longer our hot-spot, but now the Max() and Min() calls are! This is good because we’ve eliminated one hot-spot and now we can try to correct this one as well. As before, we can then optimize this part of the code (possibly by taking advantage of the fact the list is now sorted and returning the first and last elements). We can then rinse and repeat this process until we have eliminated as many bottlenecks as possible. Calls by Web Request Another feature that was added recently is the ability to view .NET methods grouped by the HTTP requests that caused them to run. This can be helpful in determining which pages, web services, etc. are causing hot spots in your web applications. Summary If you like the other ANTS tools, you’ll like the ANTS Performance Profiler as well. It is extremely easy to use with very little product knowledge required to get up and running. There are profilers built into the higher product lines of Visual Studio, of course, which are also powerful and easy to use. But for quickly jumping in and finding hot spots rapidly, Red Gate’s Performance Profiler 7.4 is an excellent choice. Technorati Tags: Influencers,ANTS,Performance Profiler,Profiler

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  • How to measure disk performance?

    - by Jakub Šturc
    I am going to "fix" a friend's computer this weekend. By the symptoms he describes it looks like he has a disk performance problem with his 5400 rpm disk. I want to be sure that disk is the problem so I want to "scientificaly" measure the performance. Which tools do you recommend me for this job? Is there any standard set of numbers I can compare the result of measurement with?

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  • Question about network topology and routing performance

    - by algorithms
    Hello I am currently working on a uni project about routing protocols and network performance, one of the criteria i was going to test under was to see what effect lan topology has, ie workstations arranged in mesh, star, ring etc, but i am having doubts as to whether that would have any affect on the routing performance thus would be useless to do, rather i'm thinking it would be better to test under the topology of the routers themselves, ie routers arranged in either star, mesh ring etc. I would appreciate some feedback on this as I am rather confused. Thank You

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  • VMWare Server - Writing files to virtual hard drive performance

    - by Ardman
    We have just moved our infrastructure from physical servers to virtual machines. Everything is running great and we are happy with the result of the move. We have identified one problem, and that is reading/writing performance. We have an application that compiles files and writes to disk. This is considerably slower on the new virtual machines compared to the physical machines. Is there a performance bottleneck when writing to a virtual hard drive compared to a physical hard drive?

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  • After writing SQL statements in MySQL, how to measure the speed / performance of them?

    - by Jian Lin
    I saw something from an "execution plan" article: 10 rows fetched in 0.0003s (0.7344s) How come there are 2 durations shown? What if I don't have large data set yet. For example, if I have only 20, 50, or even just 100 records, I can't really measure how faster 2 different SQL statements compare in term of speed in real life situation? In other words, there needs to be at least hundreds of thousands of records, or even a million records to accurately compares the performance of 2 different SQL statements?

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  • VMWare - Writing files to virtual hard drive performance

    - by Ardman
    We have just moved our infrastructure from physical servers to virtual machines. Everything is running great and we are happy with the result of the move. We have identified one problem, and that is reading/writing performance. We have an application that compiles files and writes to disk. This is considerably slower on the new virtual machines compared to the physical machines. Is there a performance bottleneck when writing to a virtual hard drive compared to a physical hard drive?

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  • Google indexing page with parameters but page is Disallowed in robots.txt

    - by Jakobud
    I have the following in robots.txt: User-agent: * Disallow: /refer.php User-agent: NinjaBot Allow: / Sitemap: http://www.mysite.com/sitemap.xml The refer.php file does various things depending on what GET parameters are passed to it. When I do a Google search, I see tons of results for pages like this: http://www.mysite.com/refer.php?o=23945 http://www.mysite.com/refer.php?o=39858 http://www.mysite.com/refer.php?o=9683 http://www.mysite.com/refer.php?o=10569 http://www.mysite.com/refer.php?o=58304 http://www.mysite.com/refer.php?o=69604 Is the reason that Google is indexing these because I don't have an asterisk * after refer.php in the robots.txt ? Should changing it to Disallow: /refer.php* fix the problem?

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  • Linux RAID-0 performance doesn't scale up over 1 GB/s

    - by wazoox
    I have trouble getting the max throughput out of my setup. The hardware is as follow : dual Quad-Core AMD Opteron(tm) Processor 2376 16 GB DDR2 ECC RAM dual Adaptec 52245 RAID controllers 48 1 TB SATA drives set up as 2 RAID-6 arrays (256KB stripe) + spares. Software : Plain vanilla 2.6.32.25 kernel, compiled for AMD-64, optimized for NUMA; Debian Lenny userland. benchmarks run : disktest, bonnie++, dd, etc. All give the same results. No discrepancy here. io scheduler used : noop. Yeah, no trick here. Up until now I basically assumed that striping (RAID 0) several physical devices should augment performance roughly linearly. However this is not the case here : each RAID array achieves about 780 MB/s write, sustained, and 1 GB/s read, sustained. writing to both RAID arrays simultaneously with two different processes gives 750 + 750 MB/s, and reading from both gives 1 + 1 GB/s. however when I stripe both arrays together, using either mdadm or lvm, the performance is about 850 MB/s writing and 1.4 GB/s reading. at least 30% less than expected! running two parallel writer or reader processes against the striped arrays doesn't enhance the figures, in fact it degrades performance even further. So what's happening here? Basically I ruled out bus or memory contention, because when I run dd on both drives simultaneously, aggregate write speed actually reach 1.5 GB/s and reading speed tops 2 GB/s. So it's not the PCIe bus. I suppose it's not the RAM. It's not the filesystem, because I get exactly the same numbers benchmarking against the raw device or using XFS. And I also get exactly the same performance using either LVM striping and md striping. What's wrong? What's preventing a process from going up to the max possible throughput? Is Linux striping defective? What other tests could I run?

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  • Optimize windows 2008 performance

    - by Giorgi
    Hello, I have windows server 2008 sp2 installed as virtual machine on my personal laptop. I use it only for source control (visual svn) and continuous integration (teamcity). As the virtual machine resources are limited I'd like to optimize it's performance by disabling services and features that are not necessary for my purposes. Can anyone recommend where to start or provide with tips for getting better performance. Thanks.

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  • Randomly poor 2D performance in Linux Mint 11 when using nvidia driver

    - by SDD
    I am using: - Linux Mint 11 - Geforce 560ti - nVidia driver (installed via helper programm, not from nvidia page) The third party nvidia drivers radomly cause very poor 2D performance. Radomly because the performance can be very great, but after the next reboot or login become very poor. After another reboot or login, this might change again to better or worse. I have no idea why and how and I need your help. Thank you.

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  • How to measure disk-performance under Windows?

    - by Alphager
    I'm trying to find out why my application is very slow on a certain machine (runs fine everywhere else). I think i have traced the performance-problems to hard-disk reads and writes and i think it's simply the very slow disk. What tool could i use to measure hd read and write performance under Windows 2003 in a non-destructive way (the partitions on the drives have to remain intact)?

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  • What percent of visitors should click on the next page before you enable prefetching?

    - by Kevin Burke
    Mozilla Firefox and Google Chrome support prefetching via an HTML tag: <!-- in chrome --> <link rel="prerender" href="http://example.org/index.html"> I suppose it is always worthwhile to include this tag if 100% of users on a page click on the "Next Page" button or similar, and never worthwhile to include it if only 2% or 3% of users visit the following page. At what percent of clicks should you turn on prefetching of the next page? 65%? Also, does the calculus change if the current page is HTTP and the next page is HTTPS?

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  • How can I most accurately calculate the execution time of an ASP.NET page while also displaying it o

    - by henningst
    I want to calculate the execution time of my ASP.NET pages and display it on the page. Currently I'm calculating the execution time using a System.Diagnostics.Stopwatch and then store the value in a log database. The stopwatch is started in OnInit and stopped in OnPreRenderComplete. This seems to be working quite fine, and it's giving a similar execution time as the one shown in the page trace. The problem now is that I'm not able to display the execution time on the page because the stopwatch is stopped too late in the life cycle. What is the best way to do this?

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • Benchmarking a file server

    - by Joel Coel
    I'm working on building a new file server... a simple Windows Server box with a few terabytes of disk space to share on the LAN. Pain for current hard drive prices aside :( -- I would like to get some benchmarks for this device under load compared to our old server. The old server was installed in 2005 and had 5 136GB 10K disks in RAID 5. The new server has 8 1TB disks in two RAID 10 volumes (plus a hot spare for each volume), but they're only 7.2K rpm, and of course with a much larger cache size. I'd like to get an idea of the performance expectations of the new server relative to the old. Where do I get started? I'd like to know both raw potential under different kinds of load for each server, as well an idea of what our real-world load looks like and how it will translate. Will disk load even matter, or will performance be more driven by the network connection? I could probably fumble through some disk i/o and wait counters in performance monitor, but I don't really know what to look for, which counters to watch, or for how long and when. FWIW, I'm expecting a nice improvement because of the benefits of having two different volumes and the better RAID 10 performance vs RAID 5, in spite of using slower disks... but I'd like to get an idea of how much.

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  • How can dev teams prevent slow performance in consumer apps?

    - by Crashworks
    When I previously asked what's responsible for slow software, a few answers I've received suggested it was a social and management problem: This isn't a technical problem, it's a marketing and management problem.... Utimately, the product mangers are responsible to write the specs for what the user is supposed to get. Lots of things can go wrong: The product manager fails to put button response in the spec ... The QA folks do a mediocre job of testing against the spec ... if the product management and QA staff are all asleep at the wheel, we programmers can't make up for that. —Bob Murphy People work on good-size apps. As they work, performance problems creep in, just like bugs. The difference is - bugs are "bad" - they cry out "find me, and fix me". Performance problems just sit there and get worse. Programmers often think "Well, my code wouldn't have a performance problem. Rather, management needs to buy me a newer/bigger/faster machine." The fact is, if developers periodically just hunt for performance problems (which is actually very easy) they could simply clean them out. —Mike Dunlavey So, if this is a social problem, what social mechanisms can an organization put into place to avoid shipping slow software to its customers?

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  • Nginx error page using django master page

    - by user835199
    I am using python django to develop a web app and using nginx and gunicorn as servers. I need to define nginx error page (for error codes like 500, 501 etc), but i want to keep the layout same as that in other site pages. For site pages, i use the include functionality of django but in this case, since django won't preprocess the page, i need to create a pure html page. Is there a way to reuse the master page that i created in django for creating this error page?

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