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  • wp e-commerce php modification

    - by user1015687
    Here are a few lines of code from a wordpress plugin (WP E-commerce) that calculate the price. Now what i want is that if 'cart_item' are more than 3 in quantity then the price should be (number of items * 4) else the function should work as it is listed below. Thnks for help. function calculate_subtotal($for_shipping = false) { 1047 global $wpdb; 1048 if($for_shipping == true ) { 1049 $total = 0; 1050 foreach($this->cart_items as $key => $cart_item) { 1051 if($cart_item->uses_shipping == 1) { 1052 $total += $cart_item->total_price; 1053 } 1054 } 1055 } else { 1056 $total = 0; 1057 if($this->subtotal == null) { 1058 foreach($this->cart_items as $key => $cart_item) { 1059 $total += $cart_item->total_price; 1060 } 1061 $this->subtotal = $total; 1062 } else { 1063 $total = $this->subtotal; 1064 } 1065 } 1066 return $total; 1067 }

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  • FAQ: GridView Calculation with JavaScript

    - by Vincent Maverick Durano
    In my previous post I wrote a simple demo on how to Calculate Totals in GridView and Display it in the Footer. Basically what it does is it calculates the total amount by typing into the TextBox and display the grand total in the footer of the GridView and basically it was a server side implemenation.  Many users in the forums are asking how to do the same thing without postbacks and how to calculate both amount and total amount together. In this post I will demonstrate how to do this using JavaScript. To get started let's go ahead and set up the form. Just for the simplicity of this demo I just set up the form like this:   <asp:gridview ID="GridView1" runat="server" ShowFooter="true" AutoGenerateColumns="false"> <Columns> <asp:BoundField DataField="RowNumber" HeaderText="Row Number" /> <asp:BoundField DataField="Description" HeaderText="Item Description" /> <asp:TemplateField HeaderText="Item Price"> <ItemTemplate> <asp:Label ID="LBLPrice" runat="server" Text='<%# Eval("Price") %>'></asp:Label> </ItemTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Quantity"> <ItemTemplate> <asp:TextBox ID="TXTQty" runat="server"></asp:TextBox> </ItemTemplate> <FooterTemplate> <b>Total Amount:</b> </FooterTemplate> </asp:TemplateField> <asp:TemplateField HeaderText="Sub-Total"> <ItemTemplate> <asp:Label ID="LBLSubTotal" runat="server"></asp:Label> </ItemTemplate> <FooterTemplate> <asp:Label ID="LBLTotal" runat="server" ForeColor="Green"></asp:Label> </FooterTemplate> </asp:TemplateField> </Columns> </asp:gridview>   As you can see there's no fancy about the mark up above. It just a standard GridView with BoundFields and TemplateFields on it. Now just for the purpose of this demo I just use a dummy data for populating the GridView. Here's the code below:   public partial class GridCalculation : System.Web.UI.Page { private void BindDummyDataToGrid() { DataTable dt = new DataTable(); DataRow dr = null; dt.Columns.Add(new DataColumn("RowNumber", typeof(string))); dt.Columns.Add(new DataColumn("Description", typeof(string))); dt.Columns.Add(new DataColumn("Price", typeof(string))); dr = dt.NewRow(); dr["RowNumber"] = 1; dr["Description"] = "Nike"; dr["Price"] = "1000"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 2; dr["Description"] = "Converse"; dr["Price"] = "800"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 3; dr["Description"] = "Adidas"; dr["Price"] = "500"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 4; dr["Description"] = "Reebok"; dr["Price"] = "750"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 5; dr["Description"] = "Vans"; dr["Price"] = "1100"; dt.Rows.Add(dr); dr = dt.NewRow(); dr["RowNumber"] = 6; dr["Description"] = "Fila"; dr["Price"] = "200"; dt.Rows.Add(dr); //Bind the Gridview GridView1.DataSource = dt; GridView1.DataBind(); } protected void Page_Load(object sender, EventArgs e) { if (!IsPostBack) { BindDummyDataToGrid(); } } }   Now try to run the page. The output should look something like below: The Client-Side Calculation Here's the code for the GridView calculation:   <script type="text/javascript"> function CalculateTotals() { var gv = document.getElementById("<%= GridView1.ClientID %>"); var tb = gv.getElementsByTagName("input"); var lb = gv.getElementsByTagName("span"); var sub = 0; var total = 0; var indexQ = 1; var indexP = 0; for (var i = 0; i < tb.length; i++) { if (tb[i].type == "text") { sub = parseFloat(lb[indexP].innerHTML) * parseFloat(tb[i].value); if (isNaN(sub)) { lb[i + indexQ].innerHTML = ""; sub = 0; } else { lb[i + indexQ].innerHTML = sub; } indexQ++; indexP = indexP + 2; total += parseFloat(sub); } } lb[lb.length -1].innerHTML = total; } </script>   The code above calculates the sub-total by multiplying the price and the quantity and at the same time calculates the total amount  by adding the sub-total values. Now you can simply call the JavaScript function above like this:   <ItemTemplate> <asp:TextBox ID="TXTQty" runat="server" onkeyup="CalculateTotals();"></asp:TextBox> </ItemTemplate>   Running the code above will display something like below: That's it! I hope someone find this post useful! Technorati Tags: ASP.NET,JavaScript,GridView,TipsTricks

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  • OS Development. Only Few Particular Questions

    - by Total Anime Immersion
    I am new to this site as a member but have consulted its answers quite a lot of times. Besides my questions regarding OS Development hasn't been answered in any forum. In OS Dev. we make a bootloader. The org point is 7C00H. Why so? Why not 0000h? What are the last two signatures in the bootloader used for? People on every forum have answered that it is important for the system to recognize it as a bootable media. But I want a specific answer. What do each of those signatures do. I have the basic concept of a kernel. Point is.. it relates to different files required in a system. It sort of binds up everything that is individually developed. Now the thing is that that I have floating ideas in my mind regarding different aspects like keyboard, mouse, etc.. how do I put them all together? Which should I start with first? If possible please provide a step by step procedure of the startups of the kernel. Suppose I have developed my language entirely in C and Assembly. Now questions is will exe files work on my system.. if it doesn't then I have to create my own files and publish them. Which is a bad idea.. next step would be for me to go for a compiler for a language which I have designed myself. Now the point is.. How do I implement the compiler into my OS? After all this my final question is that.. How do you go about multitasking and multithreading? and I don't want to use int 21h as its dos specific.. how do I go about making files, renaming them, etc. and all assembly books teach 16 but programming.. how do i go about doing 32 bit or 64 bit with the knowledge I have.. if the basics and instructions are the same.. I don't mind.. but how do i go about otherwise? Don't tell me to give up the idea because I WON'T. And don't tell me it's too complex because I have a sharp knowledge of working of a system, C, Java, Assembly, C++ and python, C#, visual basic.. and not just basics but full fledged api developments.. but I really want to go deep into the systems part.. so I want professional help.. And I have gone through many OS project files but I want help particularly from this site as there are people with knowledge depth who can guide me the right way. And please don't suggest any books above 20$ and they should be available on flipkart as amazon charges massively for shipping and I prefer free shipping from flipkart.

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  • What is the difference between Times and Dup in Assembly Language?

    - by Total Anime Immersion
    In a bootloader, the second last line is : TIMES 510-($-$$) db 0 Now, will this command also do the same : db 510-($-$$) DUP (0) If not why? I know what TIMES does, but its not mentioned in my x86 book by Mazidi (Pearson Publication). Any idea why? And what is the meaning of the $ sign exactly? Different sites have different information about $. And is the line TIMES 510-($-$$) db 0 absolutely necessary even if my bootloader code is of 512 bytes in size? So can anyone help me with these questions?

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  • Is Zend Framework a total waste of my time?

    - by Citizen
    Ok, I'm about 50% done with the "30 minute" quickstart guide from Zend. I must be missing something, because this seems like a total waste of time. The point of this quickguide is to create a guestbook, something I could do in 5 minutes with regular naked non-framework php. Here's my path to zend framework: c:/program files/wamp/www/_zend/ Here's my path to my quickstart project: c:/program files/wamp/www/_zend/bin/quickstart/ I have a number of questions at this point: http://framework.zend.com/docs/quickstart/create-a-model-and-database-table 1: I'm running the command line to run my database loading script. I get an error stating the it can't find the Zend/AutoLoader.php becuase my path to the zend library is wrong. I followed all of the steps. I defined the path to my zend library in the main config file, but for some reason, its defined again in my db loader. In all of these scripts that they have me load, it points the relative path to the zend library as being /../library Problem is, there's nothing in that folder. To get to my actual zend folder, you'd need to be (relatively) /../../../../library Which brings me to my 2nd question: 2: Where the #$#$ is the main Zend files supposed to be? The install directions were basically "put it wherever you want", when the real answer (after a bunch of errors and wasted time was) was "put it somewhere so that its really easy to type the full path a thousand times in command line" and "it also better be in a runnable place on your webserver since its going to create your quickstart application in a subdirectory within zend". Which brings us to the third question 3: Am I supposed to have this libary in both the parent core Zend (wamp/_zend/library) AND my application (quickstart/library)? 4: If that is the case, it seems like a ton of wasted files to be uploading. I'd like to use Zend to create products that my customers will download. 5 megs of overhead seems like a bit much. Zend claims you can use these library components separately, but it looks to me like I'm going to have to upload them every time. Which leads to the next question: 5: It appears that perhaps Zend is more for a single application that is not supposed to be distributed. Is this not the case? 6: According to their default file structure everything but my /public folder would be above public_html on my server if I wanted this to rest on my TLD. I would need to rename every reference of /public/ to /public_html/, or am I missing something else?

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  • High Load average threshold in linux

    - by user2481010
    My one of friend said that his server load average sometime goes above 500-1000, for me it is strange value because I never saw load average more than 10. I asked him give me some snapshot of top and memory usages, he gave following details: TOP USAGES top - 06:06:03 up 117 days, 23:02, 2 users, load average: 147.37, 44.57, 15.95 Tasks: 116 total, 2 running, 113 sleeping, 0 stopped, 1 zombie Cpu(s): 16.6%us, 6.9%sy, 0.0%ni, 9.2%id, 66.5%wa, 0.0%hi, 0.8%si, 0.0%st Mem: 8161648k total, 7779528k used, 382120k free, 3296k buffers Swap: 5242872k total, 1293072k used, 3949800k free, 168660k cached Free $ free -gt total used free shared buffers cached Mem: 7 6 1 0 0 4 -/+ buffers/cache: 1 5 Swap: 4 0 4 Total: 12 6 6 Total cpu $ nproc 8 my question is it possible load average more than 100 on 8 core,12 GB mem Server? because I read many tutorial,article on load average, it said that thumb rule is "number of cores = max load" according to thumb rule here is max load average 16 then how his server running with 147.37 load server? he said that it is least value (147.37) some time goes more than 500.

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  • Choosing Technology To Include In Software Design

    How many of us have been forced to select one technology over another when designing a new system? What factors do we and should we consider? How can we ensure the correct business decision is made? When faced with this type of decision it is important to gather as much information possible regarding each technology being considered as well as the project itself. Additionally, I tend to delay my decision about the technology until it is ultimately necessary to be made. The reason why I tend to delay such an important design decision is due to the fact that as the project progresses requirements and other factors can alter a decision for selecting the best technology for a project. Important factors to consider when making technology decisions: Time to Implement and Maintain Total Cost of Technology (including Implementation and maintenance) Adaptability of Technology Implementation Team’s Skill Sets Complexity of Technology (including Implementation and maintenance) orecasted Return On Investment (ROI) Forecasted Profit on Investment (POI) Of the factors to consider the ROI and POI weigh the heaviest because the take in to consideration the other factors when calculating the profitability and return on investments.For a real world example let us consider developing a web based lead management system for a new company. This system can either be hosted on Microsoft Windows based web server or on a Linux based web server. Important Factors for this Example Implementation Team’s Skill Sets Member 1  Skill Set: Classic ASP, ASP.Net, and MS SQL Server Experience: 10 years Member 2  Skill Set: PHP, MySQL, Photoshop and MS SQL Server Experience: 3 years Member 3  Skill Set: C++, VB6, ASP.Net, and MS SQL Server Experience: 12 years Total Cost of Technology (including Implementation and maintenance) Linux Initial Year: $5,000 (Random Value) Additional Years: $3,000 (Random Value) Windows Initial Year: $10,000 (Random Value) Additional Years: $3,000 (Random Value) Complexity of Technology Linux Large Learning Curve with user driven documentation Estimated learning cost: $30,000 Windows Minimal based on Teams skills with Microsoft based documentation Estimated learning cost: $5,000 ROI Linux Total Cost Initial Total Cost: $35,000 Additional Cost $3,000 per year Windows Total Cost Initial Total Cost: $15,000 Additional Cost $3,000 per year Based on the hypothetical numbers it would make more sense to select windows based web server because the initial investment of the technology is much lower initially compared to the Linux based web server.

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  • Help Creating a Google Analytics Funnel for Check out process

    - by Drew
    have a funnel question. I am currently working on tracking (through GA) guest and logged in member activity once they get to my sites shopping cart. But need help with setting up funnels. Specifically to see; Total sales Logged in member total sales List item Guest member sales The urls associated to the check out proces are: Logged in members /cart (arriving to checkout) /checkout (checking out as a logged in member) /checkout/confirmation (thank you - confirmed sale) Guest members - /cart (arriving to checkout) - /checkout-guest (checking out as a guest) - /checkout/confirmation (thanks you - confirmed sale) I've tested the funnels set up for the above with 9 transactions. But the end maths doesn't seem to line up. Total sales funnel shows 9 completed transactions when only tracking these to urls: - /cart - /checkout/confirmation Which is great - cause it's working Logged in member sales show a total of 9 completed transactions based on each step of the logged in url steps (above) being tracked in a funnel. Not good because this number should be 3. Guest check out funnel (see guest steps above) shows 9 as well. What the?!?!?!? The results I am looking for should reflect the following - total sales = 9, logged in members = 3, guest members = 6 Is there any way to set these urls up so that the funnels report the correct results - or do I need to changed the urls and provide logged in members and guest stand alone purchase confirmation pages (this would mean I can not track total sales which combine results from both streams)? Any knowledge in this area is welcome. Thanks.

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  • How to write PowerShell code part 2 (Using function)

    - by ybbest
    In the last post, I have showed you how to use external configuration file in your PowerShell script. In this post, I will show you how to create PowerShell function and call external PowerShell script.You can download the script here. 1. In the original script, I create the site directly using New-SPSite command. I will refactor it so that I will create a new function to create the site using New-SPSite. The PowerShell function is quite similar to a C# method. You put your function parameters in () and separate each parameter by a comma (,). Then you put your method body in {}. function add ([int] $num1 , [int] $num2){ $total=$num1+$num2 #Return $total $total } 2. The difference is you do not need semi-colon (;) at the end of each statement and when calling the method you do not need comma (,) to separate each parameter. function add ([int] $num1 , [int] $num2){ $total=$num1+$num2 #Return $total $total } #Calling the function [int] $num1=3 [int] $num2=4 $d= add $num1 $num2 Write-Host $d 3. If you like to return anything from the function, you just need to type in the object you like to return, not need to type return .e.g. $ObjectToReturn not return $ObjectToReturn

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  • How to write PowerShell code part 2 (Using function)

    - by ybbest
    In the last post, I have showed you how to use external configuration file in your PowerShell script. In this post, I will show you how to create PowerShell function and call external PowerShell script.You can download the script here. 1. In the original script, I create the site directly using New-SPSite command. I will refactor it so that I will create a new function to create the site using New-SPSite. The PowerShell function is quite similar to a C# method. You put your function parameters in () and separate each parameter by a comma (,). Then you put your method body in {}. function add ([int] $num1 , [int] $num2){ $total=$num1+$num2 #Return $total $total } 2. The difference is you do not need semi-colon (;) at the end of each statement and when calling the method you do not need comma (,) to separate each parameter. function add ([int] $num1 , [int] $num2){ $total=$num1+$num2 #Return $total $total } #Calling the function [int] $num1=3 [int] $num2=4 $d= add $num1 $num2 Write-Host $d 3. If you like to return anything from the function, you just need to type in the object you like to return, not need to type return .e.g. $ObjectToReturn not return $ObjectToReturn

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  • Advantage Database Server: slow stored procedure performance.

    - by ie
    I have a question about a performance of stored procedures in the ADS. I created a simple database with the following structure: CREATE TABLE MainTable ( Id INTEGER PRIMARY KEY, Name VARCHAR(50), Value INTEGER ); CREATE UNIQUE INDEX MainTableName_UIX ON MainTable ( Name ); CREATE TABLE SubTable ( Id INTEGER PRIMARY KEY, MainId INTEGER, Name VARCHAR(50), Value INTEGER ); CREATE INDEX SubTableMainId_UIX ON SubTable ( MainId ); CREATE UNIQUE INDEX SubTableName_UIX ON SubTable ( Name ); CREATE PROCEDURE CreateItems ( MainName VARCHAR ( 20 ), SubName VARCHAR ( 20 ), MainValue INTEGER, SubValue INTEGER, MainId INTEGER OUTPUT, SubId INTEGER OUTPUT ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @SubName VARCHAR ( 20 ); DECLARE @MainValue INTEGER; DECLARE @SubValue INTEGER; DECLARE @MainId INTEGER; DECLARE @SubId INTEGER; @MainName = (SELECT MainName FROM __input); @SubName = (SELECT SubName FROM __input); @MainValue = (SELECT MainValue FROM __input); @SubValue = (SELECT SubValue FROM __input); @MainId = (SELECT MAX(Id)+1 FROM MainTable); @SubId = (SELECT MAX(Id)+1 FROM SubTable ); INSERT INTO MainTable (Id, Name, Value) VALUES (@MainId, @MainName, @MainValue); INSERT INTO SubTable (Id, Name, MainId, Value) VALUES (@SubId, @SubName, @MainId, @SubValue); INSERT INTO __output SELECT @MainId, @SubId FROM system.iota; END; CREATE PROCEDURE UpdateItems ( MainName VARCHAR ( 20 ), MainValue INTEGER, SubValue INTEGER ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @MainValue INTEGER; DECLARE @SubValue INTEGER; DECLARE @MainId INTEGER; @MainName = (SELECT MainName FROM __input); @MainValue = (SELECT MainValue FROM __input); @SubValue = (SELECT SubValue FROM __input); @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = @MainName); UPDATE MainTable SET Value = @MainValue WHERE Id = @MainId; UPDATE SubTable SET Value = @SubValue WHERE MainId = @MainId; END; CREATE PROCEDURE SelectItems ( MainName VARCHAR ( 20 ), CalculatedValue INTEGER OUTPUT ) BEGIN DECLARE @MainName VARCHAR ( 20 ); @MainName = (SELECT MainName FROM __input); INSERT INTO __output SELECT m.Value * s.Value FROM MainTable m INNER JOIN SubTable s ON m.Id = s.MainId WHERE m.Name = @MainName; END; CREATE PROCEDURE DeleteItems ( MainName VARCHAR ( 20 ) ) BEGIN DECLARE @MainName VARCHAR ( 20 ); DECLARE @MainId INTEGER; @MainName = (SELECT MainName FROM __input); @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = @MainName); DELETE FROM SubTable WHERE MainId = @MainId; DELETE FROM MainTable WHERE Id = @MainId; END; Actually, the problem I had - even so light stored procedures work very-very slow (about 50-150 ms) relatively to plain queries (0-5ms). To test the performance, I created a simple test (in F# using ADS ADO.NET provider): open System; open System.Data; open System.Diagnostics; open Advantage.Data.Provider; let mainName = "main name #"; let subName = "sub name #"; // INSERT let cmdTextScriptInsert = " DECLARE @MainId INTEGER; DECLARE @SubId INTEGER; @MainId = (SELECT MAX(Id)+1 FROM MainTable); @SubId = (SELECT MAX(Id)+1 FROM SubTable ); INSERT INTO MainTable (Id, Name, Value) VALUES (@MainId, :MainName, :MainValue); INSERT INTO SubTable (Id, Name, MainId, Value) VALUES (@SubId, :SubName, @MainId, :SubValue); SELECT @MainId, @SubId FROM system.iota;"; let cmdTextProcedureInsert = "CreateItems"; // UPDATE let cmdTextScriptUpdate = " DECLARE @MainId INTEGER; @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = :MainName); UPDATE MainTable SET Value = :MainValue WHERE Id = @MainId; UPDATE SubTable SET Value = :SubValue WHERE MainId = @MainId;"; let cmdTextProcedureUpdate = "UpdateItems"; // SELECT let cmdTextScriptSelect = " SELECT m.Value * s.Value FROM MainTable m INNER JOIN SubTable s ON m.Id = s.MainId WHERE m.Name = :MainName;"; let cmdTextProcedureSelect = "SelectItems"; // DELETE let cmdTextScriptDelete = " DECLARE @MainId INTEGER; @MainId = (SELECT TOP 1 Id FROM MainTable WHERE Name = :MainName); DELETE FROM SubTable WHERE MainId = @MainId; DELETE FROM MainTable WHERE Id = @MainId;"; let cmdTextProcedureDelete = "DeleteItems"; let cnnStr = @"data source=D:\DB\test.add; ServerType=local; user id=adssys; password=***;"; let cnn = new AdsConnection(cnnStr); try cnn.Open(); let cmd = cnn.CreateCommand(); let parametrize ix prms = cmd.Parameters.Clear(); let addParam = function | "MainName" -> cmd.Parameters.Add(":MainName" , mainName + ix.ToString()) |> ignore; | "SubName" -> cmd.Parameters.Add(":SubName" , subName + ix.ToString() ) |> ignore; | "MainValue" -> cmd.Parameters.Add(":MainValue", ix * 3 ) |> ignore; | "SubValue" -> cmd.Parameters.Add(":SubValue" , ix * 7 ) |> ignore; | _ -> () prms |> List.iter addParam; let runTest testData = let (cmdType, cmdName, cmdText, cmdParams) = testData; let toPrefix cmdType cmdName = let prefix = match cmdType with | CommandType.StoredProcedure -> "Procedure-" | CommandType.Text -> "Script -" | _ -> "Unknown -" in prefix + cmdName; let stopWatch = new Stopwatch(); let runStep ix prms = parametrize ix prms; stopWatch.Start(); cmd.ExecuteNonQuery() |> ignore; stopWatch.Stop(); cmd.CommandText <- cmdText; cmd.CommandType <- cmdType; let startId = 1500; let count = 10; for id in startId .. startId+count do runStep id cmdParams; let elapsed = stopWatch.Elapsed; Console.WriteLine("Test '{0}' - total: {1}; per call: {2}ms", toPrefix cmdType cmdName, elapsed, Convert.ToInt32(elapsed.TotalMilliseconds)/count); let lst = [ (CommandType.Text, "Insert", cmdTextScriptInsert, ["MainName"; "SubName"; "MainValue"; "SubValue"]); (CommandType.Text, "Update", cmdTextScriptUpdate, ["MainName"; "MainValue"; "SubValue"]); (CommandType.Text, "Select", cmdTextScriptSelect, ["MainName"]); (CommandType.Text, "Delete", cmdTextScriptDelete, ["MainName"]) (CommandType.StoredProcedure, "Insert", cmdTextProcedureInsert, ["MainName"; "SubName"; "MainValue"; "SubValue"]); (CommandType.StoredProcedure, "Update", cmdTextProcedureUpdate, ["MainName"; "MainValue"; "SubValue"]); (CommandType.StoredProcedure, "Select", cmdTextProcedureSelect, ["MainName"]); (CommandType.StoredProcedure, "Delete", cmdTextProcedureDelete, ["MainName"])]; lst |> List.iter runTest; finally cnn.Close(); And I'm getting the following results: Test 'Script -Insert' - total: 00:00:00.0292841; per call: 2ms Test 'Script -Update' - total: 00:00:00.0056296; per call: 0ms Test 'Script -Select' - total: 00:00:00.0051738; per call: 0ms Test 'Script -Delete' - total: 00:00:00.0059258; per call: 0ms Test 'Procedure-Insert' - total: 00:00:01.2567146; per call: 125ms Test 'Procedure-Update' - total: 00:00:00.7442440; per call: 74ms Test 'Procedure-Select' - total: 00:00:00.5120446; per call: 51ms Test 'Procedure-Delete' - total: 00:00:01.0619165; per call: 106ms The situation with the remote server is much better, but still a great gap between plaqin queries and stored procedures: Test 'Script -Insert' - total: 00:00:00.0709299; per call: 7ms Test 'Script -Update' - total: 00:00:00.0161777; per call: 1ms Test 'Script -Select' - total: 00:00:00.0258113; per call: 2ms Test 'Script -Delete' - total: 00:00:00.0166242; per call: 1ms Test 'Procedure-Insert' - total: 00:00:00.5116138; per call: 51ms Test 'Procedure-Update' - total: 00:00:00.3802251; per call: 38ms Test 'Procedure-Select' - total: 00:00:00.1241245; per call: 12ms Test 'Procedure-Delete' - total: 00:00:00.4336334; per call: 43ms Is it any chance to improve the SP performance? Please advice. ADO.NET driver version - 9.10.2.9 Server version - 9.10.0.9 (ANSI - GERMAN, OEM - GERMAN) Thanks!

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  • Read ini from windows batch file

    - by Hintswen
    I'm trying to read a ini file with this format: [SectionName] total=4 [AnotherSectionName] total=7 [OtherSectionName] total=12 Basically I want to echo out certain values from the ini file(eg. the total under OtherSectionName followed by the total from AnotherSectionName).

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  • Access crosstab formula field in another crosstab column?

    - by Damien Joe
    How to access crosstab formula field in another column? I have report like with Dues & total both formula fields: Amount Dues(Done by a Formula) Total (Done by a Formula) ------ ------------------------- --------------------------- 500 20 % someAmount Formula for Dues: WhileReadingRecords; numberVar due:={Command.SomeField)/100; due Formula for Total: WhileReadingRecords; numberVar total:= {Command.Amount} - due; total How do I access due field inside the second formula for each row of record?

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  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

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  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

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  • Performance considerations for common SQL queries

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

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  • Need help with fixing Genetic Algorithm that's not evolving correctly

    - by EnderMB
    I am working on a maze solving application that uses a Genetic Algorithm to evolve a set of genes (within Individuals) to evolve a Population of Individuals that power an Agent through a maze. The majority of the code used appears to be working fine but when the code runs it's not selecting the best Individual's to be in the new Population correctly. When I run the application it outputs the following: Total Fitness: 380.0 - Best Fitness: 11.0 Total Fitness: 406.0 - Best Fitness: 15.0 Total Fitness: 344.0 - Best Fitness: 12.0 Total Fitness: 373.0 - Best Fitness: 11.0 Total Fitness: 415.0 - Best Fitness: 12.0 Total Fitness: 359.0 - Best Fitness: 11.0 Total Fitness: 436.0 - Best Fitness: 13.0 Total Fitness: 390.0 - Best Fitness: 12.0 Total Fitness: 379.0 - Best Fitness: 15.0 Total Fitness: 370.0 - Best Fitness: 11.0 Total Fitness: 361.0 - Best Fitness: 11.0 Total Fitness: 413.0 - Best Fitness: 16.0 As you can clearly see the fitnesses are not improving and neither are the best fitnesses. The main code responsible for this problem is here, and I believe the problem to be within the main method, most likely where the selection methods are called: package GeneticAlgorithm; import GeneticAlgorithm.Individual.Action; import Robot.Robot.Direction; import Maze.Maze; import Robot.Robot; import java.util.ArrayList; import java.util.Random; public class RunGA { protected static ArrayList tmp1, tmp2 = new ArrayList(); // Implementation of Elitism protected static int ELITISM_K = 5; // Population size protected static int POPULATION_SIZE = 50 + ELITISM_K; // Max number of Iterations protected static int MAX_ITERATIONS = 200; // Probability of Mutation protected static double MUTATION_PROB = 0.05; // Probability of Crossover protected static double CROSSOVER_PROB = 0.7; // Instantiate Random object private static Random rand = new Random(); // Instantiate Population of Individuals private Individual[] startPopulation; // Total Fitness of Population private double totalFitness; Robot robot = new Robot(); Maze maze; public void setElitism(int result) { ELITISM_K = result; } public void setPopSize(int result) { POPULATION_SIZE = result + ELITISM_K; } public void setMaxIt(int result) { MAX_ITERATIONS = result; } public void setMutProb(double result) { MUTATION_PROB = result; } public void setCrossoverProb(double result) { CROSSOVER_PROB = result; } /** * Constructor for Population */ public RunGA(Maze maze) { // Create a population of population plus elitism startPopulation = new Individual[POPULATION_SIZE]; // For every individual in population fill with x genes from 0 to 1 for (int i = 0; i < POPULATION_SIZE; i++) { startPopulation[i] = new Individual(); startPopulation[i].randGenes(); } // Evaluate the current population's fitness this.evaluate(maze, startPopulation); } /** * Set Population * @param newPop */ public void setPopulation(Individual[] newPop) { System.arraycopy(newPop, 0, this.startPopulation, 0, POPULATION_SIZE); } /** * Get Population * @return */ public Individual[] getPopulation() { return this.startPopulation; } /** * Evaluate fitness * @return */ public double evaluate(Maze maze, Individual[] newPop) { this.totalFitness = 0.0; ArrayList<Double> fitnesses = new ArrayList<Double>(); for (int i = 0; i < POPULATION_SIZE; i++) { maze = new Maze(8, 8); maze.fillMaze(); fitnesses.add(startPopulation[i].evaluate(maze, newPop)); //this.totalFitness += startPopulation[i].evaluate(maze, newPop); } //totalFitness = (Math.round(totalFitness / POPULATION_SIZE)); StringBuilder sb = new StringBuilder(); for(Double tmp : fitnesses) { sb.append(tmp + ", "); totalFitness += tmp; } // Progress of each Individual //System.out.println(sb.toString()); return this.totalFitness; } /** * Roulette Wheel Selection * @return */ public Individual rouletteWheelSelection() { // Calculate sum of all chromosome fitnesses in population - sum S. double randNum = rand.nextDouble() * this.totalFitness; int i; for (i = 0; i < POPULATION_SIZE && randNum > 0; ++i) { randNum -= startPopulation[i].getFitnessValue(); } return startPopulation[i-1]; } /** * Tournament Selection * @return */ public Individual tournamentSelection() { double randNum = rand.nextDouble() * this.totalFitness; // Get random number of population (add 1 to stop nullpointerexception) int k = rand.nextInt(POPULATION_SIZE) + 1; int i; for (i = 1; i < POPULATION_SIZE && i < k && randNum > 0; ++i) { randNum -= startPopulation[i].getFitnessValue(); } return startPopulation[i-1]; } /** * Finds the best individual * @return */ public Individual findBestIndividual() { int idxMax = 0; double currentMax = 0.0; double currentMin = 1.0; double currentVal; for (int idx = 0; idx < POPULATION_SIZE; ++idx) { currentVal = startPopulation[idx].getFitnessValue(); if (currentMax < currentMin) { currentMax = currentMin = currentVal; idxMax = idx; } if (currentVal > currentMax) { currentMax = currentVal; idxMax = idx; } } // Double check to see if this has the right one //System.out.println(startPopulation[idxMax].getFitnessValue()); // Maximisation return startPopulation[idxMax]; } /** * One Point Crossover * @param firstPerson * @param secondPerson * @return */ public static Individual[] onePointCrossover(Individual firstPerson, Individual secondPerson) { Individual[] newPerson = new Individual[2]; newPerson[0] = new Individual(); newPerson[1] = new Individual(); int size = Individual.SIZE; int randPoint = rand.nextInt(size); int i; for (i = 0; i < randPoint; ++i) { newPerson[0].setGene(i, firstPerson.getGene(i)); newPerson[1].setGene(i, secondPerson.getGene(i)); } for (; i < Individual.SIZE; ++i) { newPerson[0].setGene(i, secondPerson.getGene(i)); newPerson[1].setGene(i, firstPerson.getGene(i)); } return newPerson; } /** * Uniform Crossover * @param firstPerson * @param secondPerson * @return */ public static Individual[] uniformCrossover(Individual firstPerson, Individual secondPerson) { Individual[] newPerson = new Individual[2]; newPerson[0] = new Individual(); newPerson[1] = new Individual(); for(int i = 0; i < Individual.SIZE; ++i) { double r = rand.nextDouble(); if (r > 0.5) { newPerson[0].setGene(i, firstPerson.getGene(i)); newPerson[1].setGene(i, secondPerson.getGene(i)); } else { newPerson[0].setGene(i, secondPerson.getGene(i)); newPerson[1].setGene(i, firstPerson.getGene(i)); } } return newPerson; } public double getTotalFitness() { return totalFitness; } public static void main(String[] args) { // Initialise Environment Maze maze = new Maze(8, 8); maze.fillMaze(); // Instantiate Population //Population pop = new Population(); RunGA pop = new RunGA(maze); // Instantiate Individuals for Population Individual[] newPop = new Individual[POPULATION_SIZE]; // Instantiate two individuals to use for selection Individual[] people = new Individual[2]; Action action = null; Direction direction = null; String result = ""; /*result += "Total Fitness: " + pop.getTotalFitness() + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue();*/ // Print Current Population System.out.println("Total Fitness: " + pop.getTotalFitness() + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue()); // Instantiate counter for selection int count; for (int i = 0; i < MAX_ITERATIONS; i++) { count = 0; // Elitism for (int j = 0; j < ELITISM_K; ++j) { // This one has the best fitness newPop[count] = pop.findBestIndividual(); count++; } // Build New Population (Population size = Steps (28)) while (count < POPULATION_SIZE) { // Roulette Wheel Selection people[0] = pop.rouletteWheelSelection(); people[1] = pop.rouletteWheelSelection(); // Tournament Selection //people[0] = pop.tournamentSelection(); //people[1] = pop.tournamentSelection(); // Crossover if (rand.nextDouble() < CROSSOVER_PROB) { // One Point Crossover //people = onePointCrossover(people[0], people[1]); // Uniform Crossover people = uniformCrossover(people[0], people[1]); } // Mutation if (rand.nextDouble() < MUTATION_PROB) { people[0].mutate(); } if (rand.nextDouble() < MUTATION_PROB) { people[1].mutate(); } // Add to New Population newPop[count] = people[0]; newPop[count+1] = people[1]; count += 2; } // Make new population the current population pop.setPopulation(newPop); // Re-evaluate the current population //pop.evaluate(); pop.evaluate(maze, newPop); // Print results to screen System.out.println("Total Fitness: " + pop.totalFitness + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue()); //result += "\nTotal Fitness: " + pop.totalFitness + " - Best Fitness: " + pop.findBestIndividual().getFitnessValue(); } // Best Individual Individual bestIndiv = pop.findBestIndividual(); //return result; } } I have uploaded the full project to RapidShare if you require the extra files, although if needed I can add the code to them here. This problem has been depressing me for days now and if you guys can help me I will forever be in your debt.

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  • Find Control inside Grid Row

    - by arpan911
    i m using parent child grid and on child grid i m doing Show / hide threw java script. and child grid i bind run time with Templatecolumns like GridView NewDg = new GridView(); NewDg.ID = "dgdStoreWiseMenuStock"; TemplateField TOTAL = new TemplateField(); TOTAL.HeaderTemplate = new BusinessLogic.GridViewTemplateTextBox(ListItemType.Header, "TOTAL",e.Row.RowIndex ); TOTAL.HeaderStyle.Width = Unit.Percentage(5.00); TOTAL.ItemTemplate = new BusinessLogic.GridViewTemplateTextBox(ListItemType.Item, "TOTAL", e.Row.RowIndex); NewDg.Columns.Add(TOTAL); NewDg.DataSource = ds; NewDg.DataBind(); NewDg.Columns[1].Visible = false; NewDg.Columns[2].Visible = false; System.IO.StringWriter sw = new System.IO.StringWriter(); System.Web.UI.HtmlTextWriter htw = new System.Web.UI.HtmlTextWriter(sw); NewDg.RenderControl(htw); Now I have one TextBox inside Grid named "TOTAL" I want to Find This TextBox and wanna get its value. How can Get it ?

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  • Adding Mysql Columns in Rails Rake

    - by Gigg
    I have a rake file that does a series of calculations on a database. Basically it adds usage on equipment regularly. At the end of the day it needs to add that days total to a monthly total table and update that same table. i use the following simple concepts: To get data from the database is pretty simple: @usages = Usage.find(:all) time = Time.new for usage in @usages sql = ActiveRecord::Base.connection To insert it into the database: sql.execute "INSERT (or UPDATE) into usages ## add values and options as per MySQL But how do I: take a column from a database, add all of its values together that have a common value in another column, (i.e. if column x == value y) and then insert it into another column in another table, say dailyusages? I have tried these options: task (:monthly => :environment) do @dailyusages = Dailyusage.find(:all) for dailyusage in @dailyusages sql = ActiveRecord::Base.connection time = Time.new device = monthlyusages.device month = time.month if device == dailyusages.device ##&& month == dailyusages.month total = (dailyusage.total.sum.to_i) @monthlyusages = Monthlyusage.find(:all) for monthlyusage in @monthlyusages sql = ActiveRecord::Base.connection old_total = monthlyusage.total.to_i new_total = (old_total + total) sql.execute "UPDATE monthlyusages ( year, month, total, device ) values('#{time.year}', '#{time.month}', '#{total}', '#{dailyusage.device}' )" end I obviously have uncommented options and tried all sorts of things. Any help would really save me a load of trouble. Thanks in advance. (** BTW - I am new to rails, so go easy on me **)

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  • why does entity framework+mysql provider enumeration returns partial results with no exceptions

    - by Freddy Rios
    I'm trying to make sense of a situation I have using entity framework on .net 3.5 sp1 + MySQL 6.1.2.0 as the provider. It involves the following code: Response.Write("Products: " + plist.Count() + "<br />"); var total = 0; foreach (var p in plist) { //... some actions total++; //... other actions } Response.Write("Total Products Checked: " + total + "<br />"); Basically the total products is varying on each run, and it isn't matching the full total in plist. Its varies widely, from ~ 1/5th to half. There isn't any control flow code inside the foreach i.e. no break, continue, try/catch, conditions around total++, anything that could affect the count. As confirmation, there are other totals captured inside the loop related to the actions, and those match the lower and higher total runs. I don't find any reason to the above, other than something in entity framework or the mysql provider that causes it to end the foreach when retrieving an item. The body of the foreach can have some good variation in time, as the actions involve file & network access, my best shot at the time is that when the .net code takes beyond certain threshold there is some type of timeout in the underlying framework/provider and instead of causing an exception it is silently reporting no more items for enumeration. Can anyone give some light in the above scenario and/or confirm if the entity framework/mysql provider has the above behavior? Update: I can't reproduce the behavior by using Thread.Sleep in a simple foreach in a test project, not sure where else to look for this weird behavior :(.

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  • how to avoid if conditions,

    - by kumar kasimala
    Hi all, can anyone suggest best way to avoid most if conditions? I have below code, I want avoid most of cases if conditions, how to do it ? any solution is great help; if (adjustment.adjustmentAccount.isIncrease) { if (adjustment.increaseVATLine) { if (adjustment.vatItem.isSalesType) { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } else { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } } else { if (adjustment.vatItem.isSalesType) { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } else { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } } } else { if (adjustment.increaseVATLine) { if (adjustment.vatItem.isSalesType) { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } else { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } } else { if (adjustment.vatItem.isSalesType) { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } else { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } }

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  • does entity framework or mysql provider swallows timeout exceptions on enumeration of result?!

    - by Freddy Rios
    I'm trying to make sense of a situation I have using entity framework on .net 3.5 sp1 + MySQL 6.1.2.0 as the provider. It involves the following code: Response.Write("Products: " + plist.Count() + "<br />"); var total = 0; foreach (var p in plist) { //... some actions total++; //... other actions } Response.Write("Total Products Checked: " + total + "<br />"); Basically the total products is varying on each run, and it isn't matching the full total in plist. Its varies widely, from ~ 1/5th to half. There isn't any control flow code inside the foreach i.e. no break, continue, try/catch, conditions around total++, anything that could affect the count. As confirmation, there are other totals captured inside the loop related to the actions, and those match the lower and higher total runs. I don't find any reason to the above, other than something in entity framework or the mysql provider that causes it to end the foreach when retrieving an item. The body of the foreach can have some good variation in time, as the actions involve file & network access, my best shot at the time is that when it takes beyond certain threshold there is some type of timeout in the underlying framework/provider and instead of causing an exception it is silently reporting no more items for enumeration. Can anyone give some light in the above scenario and/or confirm if the entity framework/mysql provider has the above behavior?

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  • The program fails to display `cout` when it is run

    - by Jeff - FL
    Hello, I justed started a C++ course & I wrote, compiled, debugged & ran my first program: // This program calculates how much a little league team spent last year to purchase new baseballs. #include <iostream> using namespace std; int baseballs; int cost; int total; int main() { baseballs, cost, total; // Get the number of baseballs were purchased. cout << "How many baseballs were purchased? "; cin >> baseballs; // Get the cost of baseballs purchased. cout << "What was the cost of each baseball purchased? "; cin >> cost; // Calculate the total. total = baseballs * cost; // Display the total. cout << "The total amount spent $" << total << endl; return 0; } The only probelm that I encountered was that when I ran the program it failed to display the total amount spent (cout). Could someone please explain why? Thanks Jeff H - Sarasota, FL

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  • Refactoring a complicated if-condition

    - by kumar kasimala
    Hi all, Can anyone suggest best way to avoid most if conditions? I have below code, I want avoid most of cases if conditions, how to do it ? any solution is great help; if (adjustment.adjustmentAccount.isIncrease) { if (adjustment.increaseVATLine) { if (adjustment.vatItem.isSalesType) { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } else { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } } else { if (adjustment.vatItem.isSalesType) { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } else { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } } } else { if (adjustment.increaseVATLine) { if (adjustment.vatItem.isSalesType) { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } else { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } } else { if (adjustment.vatItem.isSalesType) { entry2.setDebit(adjustment.total); entry2.setCredit(0d); } else { entry2.setCredit(adjustment.total); entry2.setDebit(0d); } } }

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