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• A crowded Extra-Solar system

  - by TATWORTH

  The orbiting Kepler telescope has found another unusual alien solar system. The Kepler telescope monitors star for changes in their brightness. The light resulting curves can be seen at http://www.planethunters.org.Recently an extra-solar system with 4 stars (planets orbiting two of the stars with the other two stars orbiting as a distant binary pair) was discovered using by two "arm-chair" astronomers using the above web site. Source SPACE.com: All about our solar system, outer space and exploration

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• Understanding and Using APPLY (Part 2)

  The second of a series by Paul White examining the APPLY operator. In this section learn how this operator compares with joins and about both the cross and outer options.

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• MYSQL - Multiple set values in one update statement [migrated]

  - by Maurzank

  MYSQL - MULTIPLE SET VALUES IN ONE UPDATE STATEMENT USING 2 TABLES AS REFERENCE AND STORING VALUES IN ONE OF THOSE TABLES WITH A SPECIFIC LOGIC. Hello people, A problem came up by making an UPDATE. The example issue is as follows: CURRENUSRTABLE +------------+-------+ | ID | STATE | +------------+-------+ | 123 | 3 | | 456 | 3 | | 789 | 3 | +------------+-------+ HISTORYTABLE +------------+------------+-----+ | ID | TRDATE | ACT | +------------+------------+-----+ | 123 | 2013-11-01 | 5 | | 456 | 2013-11-01 | 5 | | 789 | 2013-11-01 | 5 | | 123 | 2013-11-02 | 4 | | 456 | 2013-11-02 | 4 | | 789 | 2013-11-02 | 4 | | 123 | 2013-11-03 | 3 | | 456 | 2013-11-03 | 3 | | 789 | 2013-11-03 | 3 | +------------+------------+-----+ I'm using these variables: @BA=3, @DE=5, @BL=4, What I'm trying to do is an update on CURRENUSRTABLE.STATE using HISTORYTABLE.ACT with the following logic: STATE value will be updated as ACT value, except when STATE value is 4 and ACT is 3, then STATE will be 5 I made this statement: UPDATE CURRENUSRTABLE RIGHT OUTER JOIN HISTORYTABLE ON HISTORYTABLE.ID=CURRENUSRTABLE.ID SET CURRENUSRTABLE.STATE= ( SELECT CASE HISTORYTABLE.ACT WHEN @DE THEN @DE WHEN @BL THEN @BL WHEN @BA THEN CASE CURRENUSRTABLE.STATE WHEN @BL THEN @DE ELSE @BA END END ORDER BY HISTORYTABLE.TRDATE,FIELD(HISTORYTABLE.ACT,@DE,@BL,@BA) ) WHERE HISTORYTABLE.TRDATE BETWEEN '2013-11-01' AND '2013-11-01' I'm intentionally using "RIGHT OUTER JOIN" and "HISTORYTABLE.TRDATE BETWEEN" because I'd like to change the values in CURRENUSRTABLE using a timeframe of more than one day. If I execute this statement many times using only one day (i.e. "BETWEEN '2013-11-01' AND '2013-11-01'" and then "BETWEEN '2013-11-02' AND '2013-11-02'"... etc ) it works perfectly, but if it is executed using the dates "BETWEEN '2013-11-01' AND '2013-11-03'" the results on CURRENUSRTABLE.STATE are 3, which is wrong, it should be 5. I think the problem relies on "CASE CURRENUSRTABLE.STATE" when uses "HISTORYTABLE.TRDATE BETWEEN '2013-11-01' AND '2013-11-03'", because it reads the STATE 9 times which has not been commited yet until the statement ends. Query OK, 9 rows affected (0.00 sec) Rows matched: 9 Changed: 9 Warnings: 0 Maybe the solution is very simple, but unfortunately I've not much practice on MySQL since I've worked with it less than 2 months :) Is there any suggestions to solve this issue? PD: MySQL version is 4.1.22, I know is very old an EOL, unfortunately I have to make these statements on this version. Thanks!

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• What do I need to do to set my computer as Default Gateway?

  - by Vaibhav

  We are trying to put together a box with dual LAN cards (let's say Outer and Inner), where the Inner LAN card is supposed to act as a default gateway on the network it is connected to. This box is running Ubuntu. The basic purpose for this box is to take messages generated on the inner network, do some work with them and forward them out the Outer LAN card to a server. The inner network is completely isolated with simply a regular switch connecting the Inner LAN Card with two other boxes. These other boxes either throw out multi-cast messages (which the Inner LAN Card is listening to), or send out unicast messages meant for the server which is not on this inner network. So, we need the Inner LAN Card to act as a default gateway, where these unicast messages will then be sent, and the code on the dual-LAN Card box can then intercept and forward these messages to the server. Question: 1. How do we setup the LAN Card to be default gateway (does it need some configuration on Ubuntu)? 2. Once we have this setup, is it a simple matter of listening to the interface to intercept the incoming messages? Any help (pointers in the right direction) is appreciated. Thanks.

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• calling and killing a parent function with onmouseover and onmouseout events

  - by Zoolu

  I want to call the function upon the onmouseover="ParentFunction();" then kill it onmouseout="killParent();". Note: in my code the parent function is called initiate(); and the killer function is called reset(); which lies outside the parent function at the bottom of the script. I don't know how to kill the intitiate() function my first guess was: var reset = function(){ return initiate(); }; here's my source code: any suggestions and help appreciated. <!doctype html> <html> <head> <title> function/event prototype </title> <link rel="stylesheet" type="text/css" href="styling.css" /> </head> <body> <h2> <em>Fantastical place<br/>prototype</em> </h2> <div id="button-container"> <div id="button-box"> <button id="activate" onmouseover="initiate()" onmouseout="reset();" width="50px" height="50px" title="Activate"> </button> </div> <div id="text-box"> </div> </div> <div id="container"> <canvas id="playground" width="200px" height="250px"> </canvas> <canvas id="face" width="400px" height="200px"> </canvas> <!-- <div id="clear"> </div> --> </div> <script> alert("Welcome, there are x entries as of" +""+new Date().getHours()); //global scope var i=0; var c1 = []; //c is short for collect var c2 = []; var c3 = []; var c4 = []; var c5 = []; var c6 = []; var initiate = function(){ //the button that triggers the program var timer = setInterval(function(){clock()},90); //copy this block for ref. function clock(){ i+=1; var a = Math.round(Math.random()*200); var b = Math.round(Math.random()*250); var c = Math.round(Math.random()*200); var d = Math.round(Math.random()*250); var e = Math.round(Math.random()*200); var f = Math.round(Math.random()*250); c1.push(a); c2.push(b); c3.push(c); c4.push(d); c5.push(e); c6.push(f); // document.write(i); var c = document.getElementById("playground"); var ctx = c.getContext("2d"); ctx.beginPath(); ctx.moveTo(c3[i-2], c4[i-2]); ctx.bezierCurveTo(c1[i-2],c2[i-2],c5[i-2],c6[i-2],c3[i-1], c4[i-1]); // ctx.lineTo(c3[i-1], c4[i-1]); if(a<200){ ctx.strokeStyle="#FF33CC"; } else if(a<400){ ctx.strokeStyle="#FF33aa"; } else{ ctx.strokeStyle="#FF3388"; } ctx.stroke(); document.getElementById("text-box").innerHTML=i+"<p>Thoughts.</p>"; if(i===20){ //alert("15 reached"); clearInterval(timer);//to clearInterval must be using a global scoped variable. return; } }; //end of clock //setInterval(clock,150); var targetFace = document.getElementById("face"); var face = targetFace.getContext("2d"); var faceTimer = setInterval(function(){faceAnim()},80); //copy this block for ref. global scoped. function faceAnim(){ face.beginPath(); face.strokeStyle="#FF33CC"; face.moveTo(100,104); //eye line face.bezierCurveTo(150,125,250,125,300,104); face.moveTo(200,1); //centre line face.lineTo(200,400); face.moveTo(125,111);//left eye lid face.bezierCurveTo(160,135,170,130,185,120); face.moveTo(150,116);//left eye face.bezierCurveTo(155,125,165,125,170,118); face.moveTo(275,111);//right eye lid face.bezierCurveTo(240,135,230,130,215,120); face.moveTo(250,116);//right eye face.bezierCurveTo(245,125,235,125,230,118); face.moveTo(195, 118); //left nose face.lineTo(190, 160); face.lineTo(200,170); face.moveTo(190,160); //left nostroll face.lineTo(180,160); face.lineTo(191,154); face.moveTo(180,160); //left lower nostrol face.lineTo(200,170); face.moveTo(205, 118); //right nose face.lineTo(210, 160); face.lineTo(200,170); face.moveTo(210,160); //right nostroll face.lineTo(220,160); face.lineTo(209,154); face.moveTo(220,160); //right lower nostrol face.lineTo(200,170); face.moveTo(200,140); //outer triad face.lineTo(170, 100); face.lineTo(230, 100); face.lineTo(200, 140); face.moveTo(200,145); //outer triad drop shadow face.lineTo(170, 100); face.lineTo(230, 100); face.lineTo(200, 145); face.moveTo(200,130); //inner triad face.lineTo(180, 105); face.lineTo(220, 105); face.lineTo(200, 130); //face.lineWidth =0.6; face.moveTo(280,111);//outer right eye lid face.bezierCurveTo(240,140,230,135,210,120); face.moveTo(120,111);//outer left eye lid face.bezierCurveTo(160,140,170,135,190,120); face.moveTo(162,174); //upper mouth line face.bezierCurveTo(170,180,230,180,238,174); face.moveTo(165,175); //mouth line bottom face.bezierCurveTo(190,Math.floor(Math.random()*25+180),210,Math.floor(Math.random()*25+180),235,175); face.moveTo(232,204); //head shape face.lineTo(340, 20); face.moveTo(168,204); //head shape face.lineTo(60, 20); face.stroke(); //exicute all co-ords. }; //end of face anim var clearFace = function(){ document.getElementById('face').getContext('2d').clearRect(0, 0, 700, 750); }; setInterval(clearFace,90); }; //end of parent function var reset = function(){ document.getElementById('playground').getContext('2d').clearRect(0, 0, 700, 750); //clearInterval(faceTimer); //delete initiate(); }; </script> </body> </html>

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• F# - Facebook Hacker Cup - Double Squares

  - by Jacob

  I'm working on strengthening my F#-fu and decided to tackle the Facebook Hacker Cup Double Squares problem. I'm having some problems with the run-time and was wondering if anyone could help me figure out why it is so much slower than my C# equivalent. There's a good description from another post; Source: Facebook Hacker Cup Qualification Round 2011 A double-square number is an integer X which can be expressed as the sum of two perfect squares. For example, 10 is a double-square because 10 = 3^2 + 1^2. Given X, how can we determine the number of ways in which it can be written as the sum of two squares? For example, 10 can only be written as 3^2 + 1^2 (we don't count 1^2 + 3^2 as being different). On the other hand, 25 can be written as 5^2 + 0^2 or as 4^2 + 3^2. You need to solve this problem for 0 = X = 2,147,483,647. Examples: 10 = 1 25 = 2 3 = 0 0 = 1 1 = 1 My basic strategy (which I'm open to critique on) is to; Create a dictionary (for memoize) of the input numbers initialzed to 0 Get the largest number (LN) and pass it to count/memo function Get the LN square root as int Calculate squares for all numbers 0 to LN and store in dict Sum squares for non repeat combinations of numbers from 0 to LN If sum is in memo dict, add 1 to memo Finally, output the counts of the original numbers. Here is the F# code (See code changes at bottom) I've written that I believe corresponds to this strategy (Runtime: ~8:10); open System open System.Collections.Generic open System.IO /// Get a sequence of values let rec range min max = seq { for num in [min .. max] do yield num } /// Get a sequence starting from 0 and going to max let rec zeroRange max = range 0 max /// Find the maximum number in a list with a starting accumulator (acc) let rec maxNum acc = function | [] -> acc | p::tail when p > acc -> maxNum p tail | p::tail -> maxNum acc tail /// A helper for finding max that sets the accumulator to 0 let rec findMax nums = maxNum 0 nums /// Build a collection of combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) let rec combos range = seq { let count = ref 0 for inner in range do for outer in Seq.skip !count range do yield (inner, outer) count := !count + 1 } let rec squares nums = let dict = new Dictionary<int, int>() for s in nums do dict.[s] <- (s * s) dict /// Counts the number of possible double squares for a given number and keeps track of other counts that are provided in the memo dict. let rec countDoubleSquares (num: int) (memo: Dictionary<int, int>) = // The highest relevent square is the square root because it squared plus 0 squared is the top most possibility let maxSquare = System.Math.Sqrt((float)num) // Our relevant squares are 0 to the highest possible square; note the cast to int which shouldn't hurt. let relSquares = range 0 ((int)maxSquare) // calculate the squares up front; let calcSquares = squares relSquares // Build up our square combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) for (sq1, sq2) in combos relSquares do let v = calcSquares.[sq1] + calcSquares.[sq2] // Memoize our relevant results if memo.ContainsKey(v) then memo.[v] <- memo.[v] + 1 // return our count for the num passed in memo.[num] // Read our numbers from file. //let lines = File.ReadAllLines("test2.txt") //let nums = [ for line in Seq.skip 1 lines -> Int32.Parse(line) ] // Optionally, read them from straight array let nums = [1740798996; 1257431873; 2147483643; 602519112; 858320077; 1048039120; 415485223; 874566596; 1022907856; 65; 421330820; 1041493518; 5; 1328649093; 1941554117; 4225; 2082925; 0; 1; 3] // Initialize our memoize dictionary let memo = new Dictionary<int, int>() for num in nums do memo.[num] <- 0 // Get the largest number in our set, all other numbers will be memoized along the way let maxN = findMax nums // Do the memoize let maxCount = countDoubleSquares maxN memo // Output our results. for num in nums do printfn "%i" memo.[num] // Have a little pause for when we debug let line = Console.Read() And here is my version in C# (Runtime: ~1:40: using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; using System.Linq; using System.Text; namespace FBHack_DoubleSquares { public class TestInput { public int NumCases { get; set; } public List<int> Nums { get; set; } public TestInput() { Nums = new List<int>(); } public int MaxNum() { return Nums.Max(); } } class Program { static void Main(string[] args) { // Read input from file. //TestInput input = ReadTestInput("live.txt"); // As example, load straight. TestInput input = new TestInput { NumCases = 20, Nums = new List<int> { 1740798996, 1257431873, 2147483643, 602519112, 858320077, 1048039120, 415485223, 874566596, 1022907856, 65, 421330820, 1041493518, 5, 1328649093, 1941554117, 4225, 2082925, 0, 1, 3, } }; var maxNum = input.MaxNum(); Dictionary<int, int> memo = new Dictionary<int, int>(); foreach (var num in input.Nums) { if (!memo.ContainsKey(num)) memo.Add(num, 0); } DoMemoize(maxNum, memo); StringBuilder sb = new StringBuilder(); foreach (var num in input.Nums) { //Console.WriteLine(memo[num]); sb.AppendLine(memo[num].ToString()); } Console.Write(sb.ToString()); var blah = Console.Read(); //File.WriteAllText("out.txt", sb.ToString()); } private static int DoMemoize(int num, Dictionary<int, int> memo) { var highSquare = (int)Math.Floor(Math.Sqrt(num)); var squares = CreateSquareLookup(highSquare); var relSquares = squares.Keys.ToList(); Debug.WriteLine("Starting - " + num.ToString()); Debug.WriteLine("RelSquares.Count = {0}", relSquares.Count); int sum = 0; var index = 0; foreach (var square in relSquares) { foreach (var inner in relSquares.Skip(index)) { sum = squares[square] + squares[inner]; if (memo.ContainsKey(sum)) memo[sum]++; } index++; } if (memo.ContainsKey(num)) return memo[num]; return 0; } private static TestInput ReadTestInput(string fileName) { var lines = File.ReadAllLines(fileName); var input = new TestInput(); input.NumCases = int.Parse(lines[0]); foreach (var lin in lines.Skip(1)) { input.Nums.Add(int.Parse(lin)); } return input; } public static Dictionary<int, int> CreateSquareLookup(int maxNum) { var dict = new Dictionary<int, int>(); int square; foreach (var num in Enumerable.Range(0, maxNum)) { square = num * num; dict[num] = square; } return dict; } } } Thanks for taking a look. UPDATE Changing the combos function slightly will result in a pretty big performance boost (from 8 min to 3:45): /// Old and Busted... let rec combosOld range = seq { let rangeCache = Seq.cache range let count = ref 0 for inner in rangeCache do for outer in Seq.skip !count rangeCache do yield (inner, outer) count := !count + 1 } /// The New Hotness... let rec combos maxNum = seq { for i in 0..maxNum do for j in i..maxNum do yield i,j }

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• Using R to Analyze G1GC Log Files

  - by user12620111

  Using R to Analyze G1GC Log Files body, td { font-family: sans-serif; background-color: white; font-size: 12px; margin: 8px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } h1 { font-size:2.2em; } h2 { font-size:1.8em; } h3 { font-size:1.4em; } h4 { font-size:1.0em; } h5 { font-size:0.9em; } h6 { font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } pre { margin-top: 0; max-width: 95%; border: 1px solid #ccc; white-space: pre-wrap; } pre code { display: block; padding: 0.5em; } code.r, code.cpp { background-color: #F8F8F8; } table, td, th { border: none; } blockquote { color:#666666; margin:0; padding-left: 1em; border-left: 0.5em #EEE solid; } hr { height: 0px; border-bottom: none; border-top-width: thin; border-top-style: dotted; border-top-color: #999999; } @media print { * { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; } body { 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  Using R to Analyze G1GC Log Files   Using R to Analyze G1GC Log Files Introduction Working in Oracle Platform Integration gives an engineer opportunities to work on a wide array of technologies. My team’s goal is to make Oracle applications run best on the Solaris/SPARC platform. When looking for bottlenecks in a modern applications, one needs to be aware of not only how the CPUs and operating system are executing, but also network, storage, and in some cases, the Java Virtual Machine. I was recently presented with about 1.5 GB of Java Garbage First Garbage Collector log file data. If you’re not familiar with the subject, you might want to review Garbage First Garbage Collector Tuning by Monica Beckwith. The customer had been running Java HotSpot 1.6.0_31 to host a web application server. I was told that the Solaris/SPARC server was running a Java process launched using a commmand line that included the following flags: -d64 -Xms9g -Xmx9g -XX:+UseG1GC -XX:MaxGCPauseMillis=200 -XX:InitiatingHeapOccupancyPercent=80 -XX:PermSize=256m -XX:MaxPermSize=256m -XX:+PrintGC -XX:+PrintGCTimeStamps -XX:+PrintHeapAtGC -XX:+PrintGCDateStamps -XX:+PrintFlagsFinal -XX:+DisableExplicitGC -XX:+UnlockExperimentalVMOptions -XX:ParallelGCThreads=8 Several sources on the internet indicate that if I were to print out the 1.5 GB of log files, it would require enough paper to fill the bed of a pick up truck. Of course, it would be fruitless to try to scan the log files by hand. Tools will be required to summarize the contents of the log files. Others have encountered large Java garbage collection log files. There are existing tools to analyze the log files: IBM’s GC toolkit The chewiebug GCViewer gchisto HPjmeter Instead of using one of the other tools listed, I decide to parse the log files with standard Unix tools, and analyze the data with R. Data Cleansing The log files arrived in two different formats. I guess that the difference is that one set of log files was generated using a more verbose option, maybe -XX:+PrintHeapAtGC, and the other set of log files was generated without that option. Format 1 In some of the log files, the log files with the less verbose format, a single trace, i.e. the report of a singe garbage collection event, looks like this: {Heap before GC invocations=12280 (full 61): garbage-first heap total 9437184K, used 7499918K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 1 young (4096K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. 2014-05-14T07:24:00.988-0700: 60586.353: [GC pause (young) 7324M->7320M(9216M), 0.1567265 secs] Heap after GC invocations=12281 (full 61): garbage-first heap total 9437184K, used 7496533K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) region size 4096K, 0 young (0K), 0 survivors (0K) compacting perm gen total 262144K, used 144077K [0xffffffff40000000, 0xffffffff50000000, 0xffffffff50000000) the space 262144K, 54% used [0xffffffff40000000, 0xffffffff48cb3758, 0xffffffff48cb3800, 0xffffffff50000000) No shared spaces configured. } A simple grep can be used to extract a summary: $ grep "\[ GC pause (young" g1gc.log 2014-05-13T13:24:35.091-0700: 3.109: [GC pause (young) 20M->5029K(9216M), 0.0146328 secs] 2014-05-13T13:24:35.440-0700: 3.459: [GC pause (young) 9125K->6077K(9216M), 0.0086723 secs] 2014-05-13T13:24:37.581-0700: 5.599: [GC pause (young) 25M->8470K(9216M), 0.0203820 secs] 2014-05-13T13:24:42.686-0700: 10.704: [GC pause (young) 44M->15M(9216M), 0.0288848 secs] 2014-05-13T13:24:48.941-0700: 16.958: [GC pause (young) 51M->20M(9216M), 0.0491244 secs] 2014-05-13T13:24:56.049-0700: 24.066: [GC pause (young) 92M->26M(9216M), 0.0525368 secs] 2014-05-13T13:25:34.368-0700: 62.383: [GC pause (young) 602M->68M(9216M), 0.1721173 secs] But that format wasn't easily read into R, so I needed to be a bit more tricky. I used the following Unix command to create a summary file that was easy for R to read. $ echo "SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime" $ grep "\[GC pause (young" g1gc.log | grep -v mark | sed -e 's/[A-SU-z\(\),]/ /g' -e 's/->/ /' -e 's/: / /g' | more SecondsSinceLaunch BeforeSize AfterSize TotalSize RealTime 2014-05-13T13:24:35.091-0700 3.109 20 5029 9216 0.0146328 2014-05-13T13:24:35.440-0700 3.459 9125 6077 9216 0.0086723 2014-05-13T13:24:37.581-0700 5.599 25 8470 9216 0.0203820 2014-05-13T13:24:42.686-0700 10.704 44 15 9216 0.0288848 2014-05-13T13:24:48.941-0700 16.958 51 20 9216 0.0491244 2014-05-13T13:24:56.049-0700 24.066 92 26 9216 0.0525368 2014-05-13T13:25:34.368-0700 62.383 602 68 9216 0.1721173 Format 2 In some of the log files, the log files with the more verbose format, a single trace, i.e. the report of a singe garbage collection event, was more complicated than Format 1. Here is a text file with an example of a single G1GC trace in the second format. As you can see, it is quite complicated. It is nice that there is so much information available, but the level of detail can be overwhelming. I wrote this awk script (download) to summarize each trace on a single line. #!/usr/bin/env awk -f BEGIN { printf("SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize\n") } ###################### # Save count data from lines that are at the start of each G1GC trace. # Each trace starts out like this: # {Heap before GC invocations=14 (full 0): # garbage-first heap total 9437184K, used 325496K [0xfffffffd00000000, 0xffffffff40000000, 0xffffffff40000000) ###################### /{Heap.*full/{ gsub ( "\\)" , "" ); nf=split($0,a,"="); split(a[2],b," "); getline; if ( match($0, "first") ) { G1GC=1; IncrementalCount=b[1]; FullCount=substr( b[3], 1, length(b[3])-1 ); } else { G1GC=0; } } ###################### # Pull out time stamps that are in lines with this format: # 2014-05-12T14:02:06.025-0700: 94.312: [GC pause (young), 0.08870154 secs] ###################### /GC pause/ { DateTime=$1; SecondsSinceLaunch=substr($2, 1, length($2)-1); } ###################### # Heap sizes are in lines that look like this: # [ 4842M->4838M(9216M)] ###################### /\[ .*]$/ { gsub ( "\\[" , "" ); gsub ( "\ \]" , "" ); gsub ( "->" , " " ); gsub ( "\\( " , " " ); gsub ( "\ \)" , " " ); split($0,a," "); if ( split(a[1],b,"M") > 1 ) {BeforeSize=b[1]*1024;} if ( split(a[1],b,"K") > 1 ) {BeforeSize=b[1];} if ( split(a[2],b,"M") > 1 ) {AfterSize=b[1]*1024;} if ( split(a[2],b,"K") > 1 ) {AfterSize=b[1];} if ( split(a[3],b,"M") > 1 ) {TotalSize=b[1]*1024;} if ( split(a[3],b,"K") > 1 ) {TotalSize=b[1];} } ###################### # Emit an output line when you find input that looks like this: # [Times: user=1.41 sys=0.08, real=0.24 secs] ###################### /\[Times/ { if (G1GC==1) { gsub ( "," , "" ); split($2,a,"="); UserTime=a[2]; split($3,a,"="); SysTime=a[2]; split($4,a,"="); RealTime=a[2]; print DateTime,SecondsSinceLaunch,IncrementalCount,FullCount,UserTime,SysTime,RealTime,BeforeSize,AfterSize,TotalSize; G1GC=0; } } The resulting summary is about 25X smaller that the original file, but still difficult for a human to digest. SecondsSinceLaunch IncrementalCount FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ... 2014-05-12T18:36:34.669-0700: 3985.744 561 0 0.57 0.06 0.16 1724416 1720320 9437184 2014-05-12T18:36:34.839-0700: 3985.914 562 0 0.51 0.06 0.19 1724416 1720320 9437184 2014-05-12T18:36:35.069-0700: 3986.144 563 0 0.60 0.04 0.27 1724416 1721344 9437184 2014-05-12T18:36:35.354-0700: 3986.429 564 0 0.33 0.04 0.09 1725440 1722368 9437184 2014-05-12T18:36:35.545-0700: 3986.620 565 0 0.58 0.04 0.17 1726464 1722368 9437184 2014-05-12T18:36:35.726-0700: 3986.801 566 0 0.43 0.05 0.12 1726464 1722368 9437184 2014-05-12T18:36:35.856-0700: 3986.930 567 0 0.30 0.04 0.07 1726464 1723392 9437184 2014-05-12T18:36:35.947-0700: 3987.023 568 0 0.61 0.04 0.26 1727488 1723392 9437184 2014-05-12T18:36:36.228-0700: 3987.302 569 0 0.46 0.04 0.16 1731584 1724416 9437184 Reading the Data into R Once the GC log data had been cleansed, either by processing the first format with the shell script, or by processing the second format with the awk script, it was easy to read the data into R. g1gc.df = read.csv("summary.txt", row.names = NULL, stringsAsFactors=FALSE,sep="") str(g1gc.df) ## 'data.frame': 8307 obs. of 10 variables: ## $ row.names : chr "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ... ## $ SecondsSinceLaunch: num 1.16 1.47 1.97 3.83 6.1 ... ## $ IncrementalCount : int 0 1 2 3 4 5 6 7 8 9 ... ## $ FullCount : int 0 0 0 0 0 0 0 0 0 0 ... ## $ UserTime : num 0.11 0.05 0.04 0.21 0.08 0.26 0.31 0.33 0.34 0.56 ... ## $ SysTime : num 0.04 0.01 0.01 0.05 0.01 0.06 0.07 0.06 0.07 0.09 ... ## $ RealTime : num 0.02 0.02 0.01 0.04 0.02 0.04 0.05 0.04 0.04 0.06 ... ## $ BeforeSize : int 8192 5496 5768 22528 24576 43008 34816 53248 55296 93184 ... ## $ AfterSize : int 1400 1672 2557 4907 7072 14336 16384 18432 19456 21504 ... ## $ TotalSize : int 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 9437184 ... head(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount ## 1 2014-05-12T14:00:32.868-0700: 1.161 0 ## 2 2014-05-12T14:00:33.179-0700: 1.472 1 ## 3 2014-05-12T14:00:33.677-0700: 1.969 2 ## 4 2014-05-12T14:00:35.538-0700: 3.830 3 ## 5 2014-05-12T14:00:37.811-0700: 6.103 4 ## 6 2014-05-12T14:00:41.428-0700: 9.720 5 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 1 0 0.11 0.04 0.02 8192 1400 9437184 ## 2 0 0.05 0.01 0.02 5496 1672 9437184 ## 3 0 0.04 0.01 0.01 5768 2557 9437184 ## 4 0 0.21 0.05 0.04 22528 4907 9437184 ## 5 0 0.08 0.01 0.02 24576 7072 9437184 ## 6 0 0.26 0.06 0.04 43008 14336 9437184 Basic Statistics Once the data has been read into R, simple statistics are very easy to generate. All of the numbers from high school statistics are available via simple commands. For example, generate a summary of every column: summary(g1gc.df) ## row.names SecondsSinceLaunch IncrementalCount FullCount ## Length:8307 Min. : 1 Min. : 0 Min. : 0.0 ## Class :character 1st Qu.: 9977 1st Qu.:2048 1st Qu.: 0.0 ## Mode :character Median :12855 Median :4136 Median : 12.0 ## Mean :12527 Mean :4156 Mean : 31.6 ## 3rd Qu.:15758 3rd Qu.:6262 3rd Qu.: 61.0 ## Max. :55484 Max. :8391 Max. :113.0 ## UserTime SysTime RealTime BeforeSize ## Min. :0.040 Min. :0.0000 Min. : 0.0 Min. : 5476 ## 1st Qu.:0.470 1st Qu.:0.0300 1st Qu.: 0.1 1st Qu.:5137920 ## Median :0.620 Median :0.0300 Median : 0.1 Median :6574080 ## Mean :0.751 Mean :0.0355 Mean : 0.3 Mean :5841855 ## 3rd Qu.:0.920 3rd Qu.:0.0400 3rd Qu.: 0.2 3rd Qu.:7084032 ## Max. :3.370 Max. :1.5600 Max. :488.1 Max. :8696832 ## AfterSize TotalSize ## Min. : 1380 Min. :9437184 ## 1st Qu.:5002752 1st Qu.:9437184 ## Median :6559744 Median :9437184 ## Mean :5785454 Mean :9437184 ## 3rd Qu.:7054336 3rd Qu.:9437184 ## Max. :8482816 Max. :9437184 Q: What is the total amount of User CPU time spent in garbage collection? sum(g1gc.df$UserTime) ## [1] 6236 As you can see, less than two hours of CPU time was spent in garbage collection. Is that too much? To find the percentage of time spent in garbage collection, divide the number above by total_elapsed_time*CPU_count. In this case, there are a lot of CPU’s and it turns out the the overall amount of CPU time spent in garbage collection isn’t a problem when viewed in isolation. When calculating rates, i.e. events per unit time, you need to ask yourself if the rate is homogenous across the time period in the log file. Does the log file include spikes of high activity that should be separately analyzed? Averaging in data from nights and weekends with data from business hours may alias problems. If you have a reason to suspect that the garbage collection rates include peaks and valleys that need independent analysis, see the “Time Series” section, below. Q: How much garbage is collected on each pass? The amount of heap space that is recovered per GC pass is surprisingly low: At least one collection didn’t recover any data. (“Min.=0”) 25% of the passes recovered 3MB or less. (“1st Qu.=3072”) Half of the GC passes recovered 4MB or less. (“Median=4096”) The average amount recovered was 56MB. (“Mean=56390”) 75% of the passes recovered 36MB or less. (“3rd Qu.=36860”) At least one pass recovered 2GB. (“Max.=2121000”) g1gc.df$Delta = g1gc.df$BeforeSize - g1gc.df$AfterSize summary(g1gc.df$Delta) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0 3070 4100 56400 36900 2120000 Q: What is the maximum User CPU time for a single collection? The worst garbage collection (“Max.”) is many standard deviations away from the mean. The data appears to be right skewed. summary(g1gc.df$UserTime) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.040 0.470 0.620 0.751 0.920 3.370 sd(g1gc.df$UserTime) ## [1] 0.3966 Basic Graphics Once the data is in R, it is trivial to plot the data with formats including dot plots, line charts, bar charts (simple, stacked, grouped), pie charts, boxplots, scatter plots histograms, and kernel density plots. Histogram of User CPU Time per Collection I don't think that this graph requires any explanation. hist(g1gc.df$UserTime, main="User CPU Time per Collection", xlab="Seconds", ylab="Frequency") Box plot to identify outliers When the initial data is viewed with a box plot, you can see the one crazy outlier in the real time per GC. Save this data point for future analysis and drop the outlier so that it’s not throwing off our statistics. Now the box plot shows many outliers, which will be examined later, using times series analysis. Notice that the scale of the x-axis changes drastically once the crazy outlier is removed. par(mfrow=c(2,1)) boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(dominated by a crazy outlier)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") crazy.outlier.df=g1gc.df[g1gc.df$RealTime > 400,] g1gc.df=g1gc.df[g1gc.df$RealTime < 400,] boxplot(g1gc.df$UserTime,g1gc.df$SysTime,g1gc.df$RealTime, main="Box Plot of Time per GC\n(crazy outlier excluded)", names=c("usr","sys","elapsed"), xlab="Seconds per GC", ylab="Time (Seconds)", horizontal = TRUE, outcol="red") box(which = "outer", lty = "solid") Here is the crazy outlier for future analysis: crazy.outlier.df ## row.names SecondsSinceLaunch IncrementalCount ## 8233 2014-05-12T23:15:43.903-0700: 20741 8316 ## FullCount UserTime SysTime RealTime BeforeSize AfterSize TotalSize ## 8233 112 0.55 0.42 488.1 8381440 8235008 9437184 ## Delta ## 8233 146432 R Time Series Data To analyze the garbage collection as a time series, I’ll use Z’s Ordered Observations (zoo). “zoo is the creator for an S3 class of indexed totally ordered observations which includes irregular time series.” require(zoo) ## Loading required package: zoo ## ## Attaching package: 'zoo' ## ## The following objects are masked from 'package:base': ## ## as.Date, as.Date.numeric head(g1gc.df[,1]) ## [1] "2014-05-12T14:00:32.868-0700:" "2014-05-12T14:00:33.179-0700:" ## [3] "2014-05-12T14:00:33.677-0700:" "2014-05-12T14:00:35.538-0700:" ## [5] "2014-05-12T14:00:37.811-0700:" "2014-05-12T14:00:41.428-0700:" options("digits.secs"=3) times=as.POSIXct( g1gc.df[,1], format="%Y-%m-%dT%H:%M:%OS%z:") g1gc.z = zoo(g1gc.df[,-c(1)], order.by=times) head(g1gc.z) ## SecondsSinceLaunch IncrementalCount FullCount ## 2014-05-12 17:00:32.868 1.161 0 0 ## 2014-05-12 17:00:33.178 1.472 1 0 ## 2014-05-12 17:00:33.677 1.969 2 0 ## 2014-05-12 17:00:35.538 3.830 3 0 ## 2014-05-12 17:00:37.811 6.103 4 0 ## 2014-05-12 17:00:41.427 9.720 5 0 ## UserTime SysTime RealTime BeforeSize AfterSize ## 2014-05-12 17:00:32.868 0.11 0.04 0.02 8192 1400 ## 2014-05-12 17:00:33.178 0.05 0.01 0.02 5496 1672 ## 2014-05-12 17:00:33.677 0.04 0.01 0.01 5768 2557 ## 2014-05-12 17:00:35.538 0.21 0.05 0.04 22528 4907 ## 2014-05-12 17:00:37.811 0.08 0.01 0.02 24576 7072 ## 2014-05-12 17:00:41.427 0.26 0.06 0.04 43008 14336 ## TotalSize Delta ## 2014-05-12 17:00:32.868 9437184 6792 ## 2014-05-12 17:00:33.178 9437184 3824 ## 2014-05-12 17:00:33.677 9437184 3211 ## 2014-05-12 17:00:35.538 9437184 17621 ## 2014-05-12 17:00:37.811 9437184 17504 ## 2014-05-12 17:00:41.427 9437184 28672 Example of Two Benchmark Runs in One Log File The data in the following graph is from a different log file, not the one of primary interest to this article. I’m including this image because it is an example of idle periods followed by busy periods. It would be uninteresting to average the rate of garbage collection over the entire log file period. More interesting would be the rate of garbage collect in the two busy periods. Are they the same or different? Your production data may be similar, for example, bursts when employees return from lunch and idle times on weekend evenings, etc. Once the data is in an R Time Series, you can analyze isolated time windows. Clipping the Time Series data Flashing back to our test case… Viewing the data as a time series is interesting. You can see that the work intensive time period is between 9:00 PM and 3:00 AM. Lets clip the data to the interesting period:     par(mfrow=c(2,1)) plot(g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Complete Log File", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") clipped.g1gc.z=window(g1gc.z, start=as.POSIXct("2014-05-12 21:00:00"), end=as.POSIXct("2014-05-13 03:00:00")) plot(clipped.g1gc.z$UserTime, type="h", main="User Time per GC\nTime: Limited to Benchmark Execution", xlab="Time of Day", ylab="CPU Seconds per GC", col="#1b9e77") box(which = "outer", lty = "solid") Cumulative Incremental and Full GC count Here is the cumulative incremental and full GC count. When the line is very steep, it indicates that the GCs are repeating very quickly. Notice that the scale on the Y axis is different for full vs. incremental. plot(clipped.g1gc.z[,c(2:3)], main="Cumulative Incremental and Full GC count", xlab="Time of Day", col="#1b9e77") GC Analysis of Benchmark Execution using Time Series data In the following series of 3 graphs: The “After Size” show the amount of heap space in use after each garbage collection. Many Java objects are still referenced, i.e. alive, during each garbage collection. This may indicate that the application has a memory leak, or may indicate that the application has a very large memory footprint. Typically, an application's memory footprint plateau's in the early stage of execution. One would expect this graph to have a flat top. The steep decline in the heap space may indicate that the application crashed after 2:00. The second graph shows that the outliers in real execution time, discussed above, occur near 2:00. when the Java heap seems to be quite full. The third graph shows that Full GCs are infrequent during the first few hours of execution. The rate of Full GC's, (the slope of the cummulative Full GC line), changes near midnight.   plot(clipped.g1gc.z[,c("AfterSize","RealTime","FullCount")], xlab="Time of Day", col=c("#1b9e77","red","#1b9e77")) GC Analysis of heap recovered Each GC trace includes the amount of heap space in use before and after the individual GC event. During garbage coolection, unreferenced objects are identified, the space holding the unreferenced objects is freed, and thus, the difference in before and after usage indicates how much space has been freed. The following box plot and bar chart both demonstrate the same point - the amount of heap space freed per garbage colloection is surprisingly low. par(mfrow=c(2,1)) boxplot(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", horizontal = TRUE, col="red") hist(as.vector(clipped.g1gc.z$Delta), main="Amount of Heap Recovered per GC Pass", xlab="Size in KB", breaks=100, col="red") box(which = "outer", lty = "solid") This graph is the most interesting. The dark blue area shows how much heap is occupied by referenced Java objects. This represents memory that holds live data. The red fringe at the top shows how much data was recovered after each garbage collection. barplot(clipped.g1gc.z[,c("AfterSize","Delta")], col=c("#7570b3","#e7298a"), xlab="Time of Day", border=NA) legend("topleft", c("Live Objects","Heap Recovered on GC"), fill=c("#7570b3","#e7298a")) box(which = "outer", lty = "solid") When I discuss the data in the log files with the customer, I will ask for an explaination for the large amount of referenced data resident in the Java heap. There are two are posibilities: There is a memory leak and the amount of space required to hold referenced objects will continue to grow, limited only by the maximum heap size. After the maximum heap size is reached, the JVM will throw an “Out of Memory” exception every time that the application tries to allocate a new object. If this is the case, the aplication needs to be debugged to identify why old objects are referenced when they are no longer needed. The application has a legitimate requirement to keep a large amount of data in memory. The customer may want to further increase the maximum heap size. Another possible solution would be to partition the application across multiple cluster nodes, where each node has responsibility for managing a unique subset of the data. Conclusion In conclusion, R is a very powerful tool for the analysis of Java garbage collection log files. The primary difficulty is data cleansing so that information can be read into an R data frame. Once the data has been read into R, a rich set of tools may be used for thorough evaluation.

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• Heaps of Trouble?

  - by Paul White NZ

  If you’re not already a regular reader of Brad Schulz’s blog, you’re missing out on some great material.  In his latest entry, he is tasked with optimizing a query run against tables that have no indexes at all.  The problem is, predictably, that performance is not very good.  The catch is that we are not allowed to create any indexes (or even new statistics) as part of our optimization efforts. In this post, I’m going to look at the problem from a slightly different angle, and present an alternative solution to the one Brad found.  Inevitably, there’s going to be some overlap between our entries, and while you don’t necessarily need to read Brad’s post before this one, I do strongly recommend that you read it at some stage; he covers some important points that I won’t cover again here. The Example We’ll use data from the AdventureWorks database, copied to temporary unindexed tables.  A script to create these structures is shown below: CREATE TABLE #Custs ( CustomerID INTEGER NOT NULL, TerritoryID INTEGER NULL, CustomerType NCHAR(1) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, ); GO CREATE TABLE #Prods ( ProductMainID INTEGER NOT NULL, ProductSubID INTEGER NOT NULL, ProductSubSubID INTEGER NOT NULL, Name NVARCHAR(50) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, ); GO CREATE TABLE #OrdHeader ( SalesOrderID INTEGER NOT NULL, OrderDate DATETIME NOT NULL, SalesOrderNumber NVARCHAR(25) COLLATE SQL_Latin1_General_CP1_CI_AI NOT NULL, CustomerID INTEGER NOT NULL, ); GO CREATE TABLE #OrdDetail ( SalesOrderID INTEGER NOT NULL, OrderQty SMALLINT NOT NULL, LineTotal NUMERIC(38,6) NOT NULL, ProductMainID INTEGER NOT NULL, ProductSubID INTEGER NOT NULL, ProductSubSubID INTEGER NOT NULL, ); GO INSERT #Custs ( CustomerID, TerritoryID, CustomerType ) SELECT C.CustomerID, C.TerritoryID, C.CustomerType FROM AdventureWorks.Sales.Customer C WITH (TABLOCK); GO INSERT #Prods ( ProductMainID, ProductSubID, ProductSubSubID, Name ) SELECT P.ProductID, P.ProductID, P.ProductID, P.Name FROM AdventureWorks.Production.Product P WITH (TABLOCK); GO INSERT #OrdHeader ( SalesOrderID, OrderDate, SalesOrderNumber, CustomerID ) SELECT H.SalesOrderID, H.OrderDate, H.SalesOrderNumber, H.CustomerID FROM AdventureWorks.Sales.SalesOrderHeader H WITH (TABLOCK); GO INSERT #OrdDetail ( SalesOrderID, OrderQty, LineTotal, ProductMainID, ProductSubID, ProductSubSubID ) SELECT D.SalesOrderID, D.OrderQty, D.LineTotal, D.ProductID, D.ProductID, D.ProductID FROM AdventureWorks.Sales.SalesOrderDetail D WITH (TABLOCK); The query itself is a simple join of the four tables: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #Prods P JOIN #OrdDetail D ON P.ProductMainID = D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID JOIN #OrdHeader H ON D.SalesOrderID = H.SalesOrderID JOIN #Custs C ON H.CustomerID = C.CustomerID ORDER BY P.ProductMainID ASC OPTION (RECOMPILE, MAXDOP 1); Remember that these tables have no indexes at all, and only the single-column sampled statistics SQL Server automatically creates (assuming default settings).  The estimated query plan produced for the test query looks like this (click to enlarge): The Problem The problem here is one of cardinality estimation – the number of rows SQL Server expects to find at each step of the plan.  The lack of indexes and useful statistical information means that SQL Server does not have the information it needs to make a good estimate.  Every join in the plan shown above estimates that it will produce just a single row as output.  Brad covers the factors that lead to the low estimates in his post. In reality, the join between the #Prods and #OrdDetail tables will produce 121,317 rows.  It should not surprise you that this has rather dire consequences for the remainder of the query plan.  In particular, it makes a nonsense of the optimizer’s decision to use Nested Loops to join to the two remaining tables.  Instead of scanning the #OrdHeader and #Custs tables once (as it expected), it has to perform 121,317 full scans of each.  The query takes somewhere in the region of twenty minutes to run to completion on my development machine. A Solution At this point, you may be thinking the same thing I was: if we really are stuck with no indexes, the best we can do is to use hash joins everywhere. We can force the exclusive use of hash joins in several ways, the two most common being join and query hints.  A join hint means writing the query using the INNER HASH JOIN syntax; using a query hint involves adding OPTION (HASH JOIN) at the bottom of the query.  The difference is that using join hints also forces the order of the join, whereas the query hint gives the optimizer freedom to reorder the joins at its discretion. Adding the OPTION (HASH JOIN) hint results in this estimated plan: That produces the correct output in around seven seconds, which is quite an improvement!  As a purely practical matter, and given the rigid rules of the environment we find ourselves in, we might leave things there.  (We can improve the hashing solution a bit – I’ll come back to that later on). Faster Nested Loops It might surprise you to hear that we can beat the performance of the hash join solution shown above using nested loops joins exclusively, and without breaking the rules we have been set. The key to this part is to realize that a condition like (A = B) can be expressed as (A <= B) AND (A >= B).  Armed with this tremendous new insight, we can rewrite the join predicates like so: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #OrdDetail D JOIN #OrdHeader H ON D.SalesOrderID >= H.SalesOrderID AND D.SalesOrderID <= H.SalesOrderID JOIN #Custs C ON H.CustomerID >= C.CustomerID AND H.CustomerID <= C.CustomerID JOIN #Prods P ON P.ProductMainID >= D.ProductMainID AND P.ProductMainID <= D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID ORDER BY D.ProductMainID OPTION (RECOMPILE, LOOP JOIN, MAXDOP 1, FORCE ORDER); I’ve also added LOOP JOIN and FORCE ORDER query hints to ensure that only nested loops joins are used, and that the tables are joined in the order they appear.  The new estimated execution plan is: This new query runs in under 2 seconds. Why Is It Faster? The main reason for the improvement is the appearance of the eager Index Spools, which are also known as index-on-the-fly spools.  If you read my Inside The Optimiser series you might be interested to know that the rule responsible is called JoinToIndexOnTheFly. An eager index spool consumes all rows from the table it sits above, and builds a index suitable for the join to seek on.  Taking the index spool above the #Custs table as an example, it reads all the CustomerID and TerritoryID values with a single scan of the table, and builds an index keyed on CustomerID.  The term ‘eager’ means that the spool consumes all of its input rows when it starts up.  The index is built in a work table in tempdb, has no associated statistics, and only exists until the query finishes executing. The result is that each unindexed table is only scanned once, and just for the columns necessary to build the temporary index.  From that point on, every execution of the inner side of the join is answered by a seek on the temporary index – not the base table. A second optimization is that the sort on ProductMainID (required by the ORDER BY clause) is performed early, on just the rows coming from the #OrdDetail table.  The optimizer has a good estimate for the number of rows it needs to sort at that stage – it is just the cardinality of the table itself.  The accuracy of the estimate there is important because it helps determine the memory grant given to the sort operation.  Nested loops join preserves the order of rows on its outer input, so sorting early is safe.  (Hash joins do not preserve order in this way, of course). The extra lazy spool on the #Prods branch is a further optimization that avoids executing the seek on the temporary index if the value being joined (the ‘outer reference’) hasn’t changed from the last row received on the outer input.  It takes advantage of the fact that rows are still sorted on ProductMainID, so if duplicates exist, they will arrive at the join operator one after the other. The optimizer is quite conservative about introducing index spools into a plan, because creating and dropping a temporary index is a relatively expensive operation.  It’s presence in a plan is often an indication that a useful index is missing. I want to stress that I rewrote the query in this way primarily as an educational exercise – I can’t imagine having to do something so horrible to a production system. Improving the Hash Join I promised I would return to the solution that uses hash joins.  You might be puzzled that SQL Server can create three new indexes (and perform all those nested loops iterations) faster than it can perform three hash joins.  The answer, again, is down to the poor information available to the optimizer.  Let’s look at the hash join plan again: Two of the hash joins have single-row estimates on their build inputs.  SQL Server fixes the amount of memory available for the hash table based on this cardinality estimate, so at run time the hash join very quickly runs out of memory. This results in the join spilling hash buckets to disk, and any rows from the probe input that hash to the spilled buckets also get written to disk.  The join process then continues, and may again run out of memory.  This is a recursive process, which may eventually result in SQL Server resorting to a bailout join algorithm, which is guaranteed to complete eventually, but may be very slow.  The data sizes in the example tables are not large enough to force a hash bailout, but it does result in multiple levels of hash recursion.  You can see this for yourself by tracing the Hash Warning event using the Profiler tool. The final sort in the plan also suffers from a similar problem: it receives very little memory and has to perform multiple sort passes, saving intermediate runs to disk (the Sort Warnings Profiler event can be used to confirm this).  Notice also that because hash joins don’t preserve sort order, the sort cannot be pushed down the plan toward the #OrdDetail table, as in the nested loops plan. Ok, so now we understand the problems, what can we do to fix it?  We can address the hash spilling by forcing a different order for the joins: SELECT P.ProductMainID AS PID, P.Name, D.OrderQty, H.SalesOrderNumber, H.OrderDate, C.TerritoryID FROM #Prods P JOIN #Custs C JOIN #OrdHeader H ON H.CustomerID = C.CustomerID JOIN #OrdDetail D ON D.SalesOrderID = H.SalesOrderID ON P.ProductMainID = D.ProductMainID AND P.ProductSubID = D.ProductSubID AND P.ProductSubSubID = D.ProductSubSubID ORDER BY D.ProductMainID OPTION (MAXDOP 1, HASH JOIN, FORCE ORDER); With this plan, each of the inputs to the hash joins has a good estimate, and no hash recursion occurs.  The final sort still suffers from the one-row estimate problem, and we get a single-pass sort warning as it writes rows to disk.  Even so, the query runs to completion in three or four seconds.  That’s around half the time of the previous hashing solution, but still not as fast as the nested loops trickery. Final Thoughts SQL Server’s optimizer makes cost-based decisions, so it is vital to provide it with accurate information.  We can’t really blame the performance problems highlighted here on anything other than the decision to use completely unindexed tables, and not to allow the creation of additional statistics. I should probably stress that the nested loops solution shown above is not one I would normally contemplate in the real world.  It’s there primarily for its educational and entertainment value.  I might perhaps use it to demonstrate to the sceptical that SQL Server itself is crying out for an index. Be sure to read Brad’s original post for more details.  My grateful thanks to him for granting permission to reuse some of his material. Paul White Email: [email protected] Twitter: @PaulWhiteNZ

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• Inside the Concurrent Collections: ConcurrentBag

  - by Simon Cooper

  Unlike the other concurrent collections, ConcurrentBag does not really have a non-concurrent analogy. As stated in the MSDN documentation, ConcurrentBag is optimised for the situation where the same thread is both producing and consuming items from the collection. We'll see how this is the case as we take a closer look. Again, I recommend you have ConcurrentBag open in a decompiler for reference. Thread Statics ConcurrentBag makes heavy use of thread statics - static variables marked with ThreadStaticAttribute. This is a special attribute that instructs the CLR to scope any values assigned to or read from the variable to the executing thread, not globally within the AppDomain. This means that if two different threads assign two different values to the same thread static variable, one value will not overwrite the other, and each thread will see the value they assigned to the variable, separately to any other thread. This is a very useful function that allows for ConcurrentBag's concurrency properties. You can think of a thread static variable: [ThreadStatic] private static int m_Value; as doing the same as: private static Dictionary<Thread, int> m_Values; where the executing thread's identity is used to automatically set and retrieve the corresponding value in the dictionary. In .NET 4, this usage of ThreadStaticAttribute is encapsulated in the ThreadLocal class. Lists of lists ConcurrentBag, at its core, operates as a linked list of linked lists: Each outer list node is an instance of ThreadLocalList, and each inner list node is an instance of Node. Each outer ThreadLocalList is owned by a particular thread, accessible through the thread local m_locals variable: private ThreadLocal<ThreadLocalList<T>> m_locals It is important to note that, although the m_locals variable is thread-local, that only applies to accesses through that variable. The objects referenced by the thread (each instance of the ThreadLocalList object) are normal heap objects that are not specific to any thread. Thinking back to the Dictionary analogy above, if each value stored in the dictionary could be accessed by other means, then any thread could access the value belonging to other threads using that mechanism. Only reads and writes to the variable defined as thread-local are re-routed by the CLR according to the executing thread's identity. So, although m_locals is defined as thread-local, the m_headList, m_nextList and m_tailList variables aren't. This means that any thread can access all the thread local lists in the collection by doing a linear search through the outer linked list defined by these variables. Adding items So, onto the collection operations. First, adding items. This one's pretty simple. If the current thread doesn't already own an instance of ThreadLocalList, then one is created (or, if there are lists owned by threads that have stopped, it takes control of one of those). Then the item is added to the head of that thread's list. That's it. Don't worry, it'll get more complicated when we account for the other operations on the list! Taking & Peeking items This is where it gets tricky. If the current thread's list has items in it, then it peeks or removes the head item (not the tail item) from the local list and returns that. However, if the local list is empty, it has to go and steal another item from another list, belonging to a different thread. It iterates through all the thread local lists in the collection using the m_headList and m_nextList variables until it finds one that has items in it, and it steals one item from that list. Up to this point, the two threads had been operating completely independently. To steal an item from another thread's list, the stealing thread has to do it in such a way as to not step on the owning thread's toes. Recall how adding and removing items both operate on the head of the thread's linked list? That gives us an easy way out - a thread trying to steal items from another thread can pop in round the back of another thread's list using the m_tail variable, and steal an item from the back without the owning thread knowing anything about it. The owning thread can carry on completely independently, unaware that one of its items has been nicked. However, this only works when there are at least 3 items in the list, as that guarantees there will be at least one node between the owning thread performing operations on the list head and the thread stealing items from the tail - there's no chance of the two threads operating on the same node at the same time and causing a race condition. If there's less than three items in the list, then there does need to be some synchronization between the two threads. In this case, the lock on the ThreadLocalList object is used to mediate access to a thread's list when there's the possibility of contention. Thread synchronization In ConcurrentBag, this is done using several mechanisms: Operations performed by the owner thread only take out the lock when there are less than three items in the collection. With three or greater items, there won't be any conflict with a stealing thread operating on the tail of the list. If a lock isn't taken out, the owning thread sets the list's m_currentOp variable to a non-zero value for the duration of the operation. This indicates to all other threads that there is a non-locked operation currently occuring on that list. The stealing thread always takes out the lock, to prevent two threads trying to steal from the same list at the same time. After taking out the lock, the stealing thread spinwaits until m_currentOp has been set to zero before actually performing the steal. This ensures there won't be a conflict with the owning thread when the number of items in the list is on the 2-3 item borderline. If any add or remove operations are started in the meantime, and the list is below 3 items, those operations try to take out the list's lock and are blocked until the stealing thread has finished. This allows a thread to steal an item from another thread's list without corrupting it. What about synchronization in the collection as a whole? Collection synchronization Any thread that operates on the collection's global structure (accessing anything outside the thread local lists) has to take out the collection's global lock - m_globalListsLock. This single lock is sufficient when adding a new thread local list, as the items inside each thread's list are unaffected. However, what about operations (such as Count or ToArray) that need to access every item in the collection? In order to ensure a consistent view, all operations on the collection are stopped while the count or ToArray is performed. This is done by freezing the bag at the start, performing the global operation, and unfreezing at the end: The global lock is taken out, to prevent structural alterations to the collection. m_needSync is set to true. This notifies all the threads that they need to take out their list's lock irregardless of what operation they're doing. All the list locks are taken out in order. This blocks all locking operations on the lists. The freezing thread waits for all current lockless operations to finish by spinwaiting on each m_currentOp field. The global operation can then be performed while the bag is frozen, but no other operations can take place at the same time, as all other threads are blocked on a list's lock. Then, once the global operation has finished, the locks are released, m_needSync is unset, and normal concurrent operation resumes. Concurrent principles That's the essence of how ConcurrentBag operates. Each thread operates independently on its own local list, except when they have to steal items from another list. When stealing, only the stealing thread is forced to take out the lock; the owning thread only has to when there is the possibility of contention. And a global lock controls accesses to the structure of the collection outside the thread lists. Operations affecting the entire collection take out all locks in the collection to freeze the contents at a single point in time. So, what principles can we extract here? Threads operate independently Thread-static variables and ThreadLocal makes this easy. Threads operate entirely concurrently on their own structures; only when they need to grab data from another thread is there any thread contention. Minimised lock-taking Even when two threads need to operate on the same data structures (one thread stealing from another), they do so in such a way such that the probability of actually blocking on a lock is minimised; the owning thread always operates on the head of the list, and the stealing thread always operates on the tail. Management of lockless operations Any operations that don't take out a lock still have a 'hook' to force them to lock when necessary. This allows all operations on the collection to be stopped temporarily while a global snapshot is taken. Hopefully, such operations will be short-lived and infrequent. That's all the concurrent collections covered. I hope you've found it as informative and interesting as I have. Next, I'll be taking a closer look at ThreadLocal, which I came across while analyzing ConcurrentBag. As you'll see, the operation of this class deserves a much closer look.

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• Operator of the week - Assert

  - by Fabiano Amorim

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

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• How to eliminate NULL fields in TSQL

  - by salvationishere

  I am developing a TSQL query in SSMS 2008 R2. I am trying to develop this query to identify one record / client. Because some of these values are NULL, I am currently doing LEFT JOINS on most of the tables. But the problem with the LEFT JOINs is that now I get 1 record for some clients. But if I change this to INNER JOINs then some clients are excluded entirely because they have NULL values for these columns. How do I limit the query result to just one record / client regardless of NULL values? And if there are non-NULL values then I want it to choose the record with non-NULL values. Here is some of my current output: group_profile_id profile_name license_number is_accepting is_accepting_placement managing_office region vendor_name vendor_id applicant_type Office Address status_description Cert Date2 race ethnicity_desc religion 9CD932F1-6BE1-4F80-AB81-0CE32C565BCF Atreides Foster Home 1 Atreides1 1 Yes Manchester, NH Gulf Atlantic Atreides1 00000007 Treatment Foster Home 4042 Arrakis Avenue, Springfield, VT 05156 Open/Re-opened 2011-06-01 00:00:00.000 NULL NULL NULL DCE354D5-A7CC-409F-B5A3-89BF664B7718 Averitte, Leon and Sandra 00000044 1 Yes Birmingham, AL Gulf Atlantic AL Averitte, Leon and Sandra 00000044 Treatment Foster Home 3816 5th Avenue, Bessemer, AL 35020, (205)482-4307 Open/Re-opened 2011-08-05 00:00:00.000 NULL NULL NULL DCE354D5-A7CC-409F-B5A3-89BF664B7718 Averitte, Leon and Sandra 00000044 1 Yes Birmingham, AL Gulf Atlantic AL Averitte, Leon and Sandra 00000044 Treatment Foster Home 3816 5th Avenue, Bessemer, AL 35020, (205)482-4307 Open/Re-opened 2011-08-05 00:00:00.000 Caucasian/White Non Hispanic NULL AD02A43C-6F38-4F35-8C9E-E12422690BFB Bass, Matthew and Sarah 00000076 1 Yes Jacks on, MS Central Gulf Coast MS Bass, Matthew and Sarah 00000076 Treatment Foster Home 506 Eagelwood Drive, Florence, MS 39073, (601)665-7169 Open/Re-opened 2011-04-01 00:00:00.000 NULL NULL NULL AD02A43C-6F38-4F35-8C9E-E12422690BFB Bass, Matthew and Sarah 00000076 1 Yes Jackson, MS Central Gulf Coast MS Bass, Matthew and Sarah 00000076 Treatment Foster Home 506 Eagelwood Drive, Florence, MS 39073, (601)665-7169 Open/Re-opened 2011-04-01 00:00:00.000 Caucasian/White NULL Baptist You can see that both Averitte and Bass profile names have one record with NULL race, ethnicity, religion. How do I eliminate these rows (rows 2 and 4)? Here is my query currently: select distinct gp.group_profile_id, gp.profile_name, gp.license_number, gp.is_accepting, case when gp.is_accepting = 1 then 'Yes' when gp.is_accepting = 0 then 'No ' end as is_accepting_placement, mo.profile_name as managing_office, regions.[region_description] as region, pv.vendor_name, pv.id as vendor_id, at.description as applicant_type, dbo.GetGroupAddress(gp.group_profile_id, null, 0) as [Office Address], gsv.status_description, ri.[description] as race, ethnicity.description as ethnicity_desc, religion.description as religion from group_profile gp With (NoLock) --Office Information inner join group_profile_type gpt With (NoLock) on gp.group_profile_type_id = gpt.group_profile_type_id and gpt.type_code = 'FOSTERHOME' and gp.agency_id = @agency_id and gp.is_deleted = 0 inner join group_profile mo With (NoLock) on gp.managing_office_id = mo.group_profile_id left outer join payor_vendor pv With (NoLock) on gp.payor_vendor_id = pv.payor_vendor_id left outer join applicant_type at With (NoLock) on gp.applicant_type_id = at.applicant_type_id and at.is_foster_home = 1 inner join group_status_view gsv With (NoLock) on gp.group_profile_id = gsv.group_profile_id and gsv.status_value = 'OPEN' and gsv.effective_date = (Select max(b.effective_date) from group_status_view b With (NoLock) where gp.group_profile_id = b.group_profile_id) left outer join regions With (NoLock) on isnull(mo.regions_id, gp.regions_id) = regions.regions_id left join enrollment en on en.group_profile_id = gp.group_profile_id join event_log el on el.event_log_id = en.event_log_id left join people client on client.people_id = el.people_id left join race With (NoLock) on el.people_id = race.people_id left join group_profile_race gpr with (nolock) on gpr.race_info_id = race.race_info_id left join race_info ri with (nolock) on ri.race_info_id = gpr.race_info_id left join ethnicity With(NoLock) On client.ethnicity = ethnicity.ethnicity_id left join religion on client.religion = religion.religion_id

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• Issue in Creating an Insert Query See Description Below...

  - by Parth

  I am creating a Insert Query using PHP.. By fetching the data from a Audit table and iterating the values of it in loops.. table from which I am fetching the value has the snapshot below: The Code I am using to create is given below: mysql_select_db('information_schema'); $select = mysql_query("SELECT TABLE_NAME FROM TABLES WHERE TABLE_SCHEMA = 'pranav_test'"); $selectclumn = mysql_query("SELECT * FROM COLUMNS WHERE TABLE_SCHEMA = 'pranav_test'"); mysql_select_db('pranav_test'); $seletaudit = mysql_query("SELECT * FROM jos_audittrail WHERE live = 0"); $tables = array(); $i = 0; while($row = mysql_fetch_array($select)) { $tables[$i++] =$row['TABLE_NAME']; } while($row2 = mysql_fetch_array($seletaudit)) { $audit[] =$row2; } foreach($audit as $val) { if($val['operation'] == "INSERT") { if(in_array($val['table_name'],$tables)) { $insert = "INSERT INTO '".$val['table_name']."' ("; $selfld = mysql_query("SELECT field FROM jos_audittrail WHERE table_name = '".$val['table_name']."' AND operation = 'INSERT' AND trackid = '".$val['trackid']."'"); while($row3 = mysql_fetch_array($selfld)) { $values[] = $row3; } foreach($values as $field) { $insert .= "'".$field['field']."', "; } $insert .= "]"; $insert = str_replace(", ]",")",$insert); $insert .= " values ("; $selval = mysql_query("SELECT newvalue FROM jos_audittrail WHERE table_name = '".$val['table_name']."' AND operation = 'INSERT' AND trackid = '".$val['trackid']."' AND live = 0"); while($row4 = mysql_fetch_array($selval)) { $value[] = $row4; } /*echo "<pre>"; print_r($value);exit;*/ foreach($value as $data) { $insert .= "'".$data['newvalue']."', "; } $insert .= "["; $insert = str_replace(", [",")",$insert); } } } When I Echo the $insert out of the most outer for loop (for auditrail) The values get printed as many times as the records are found for the outer for loop..i.e 'orderby= show_noauth= show_title= link_titles= show_intro= show_section= link_section= show_category= link_category= show_author= show_create_date= show_modify_date= show_item_navigation= show_readmore= show_vote= show_icons= show_pdf_icon= show_print_icon= show_email_icon= show_hits= feed_summary= page_title= show_page_title=1 pageclass_sfx= menu_image=-1 secure=0 ', '0000-00-00 00:00:00', '13', '20', '1', '152', 'accmenu', 'IPL', 'ipl', 'index.php?option=com_content&view=archive', 'component' gets repeated , i.e. INSERT INTO 'jos_menu' ('params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type', 'params', 'checked_out_time', 'ordering', 'componentid', 'published', 'id', 'menutype', 'name', 'alias', 'link', 'type') values ('orderby= show_noauth= show_title= link_titles= show_intro= show_section= link_section= show_category= link_category= show_author= show_create_date= show_modify_date= show_item_navigation= show_readmore= show_vote= show_icons= show_pdf_icon= show_print_icon= show_email_icon= show_hits= feed_summary= page_title= show_page_title=1 pageclass_sfx= menu_image=-1 secure=0 ', '0000-00-00 00:00:00', '13', '20', '1', '152', 'accmenu', 'IPL', 'ipl', 'index.php?option=com_content&view=archive', 'component', 'orderby= show_noauth= show_title= link_titles= show_intro= show_section= link_section= show_category= link_category= show_author= show_create_date= show_modify_date= show_item_navigation= show_readmore= show_vote= show_icons= show_pdf_icon= show_print_icon= show_email_icon= show_hits= feed_summary= page_title= show_page_title=1 pageclass_sfx= menu_image=-1 secure=0 ', '0000-00-00 00:00:00', '13', '20', '1', '152', 'accmenu', 'IPL', 'ipl', 'index.php?option=com_content&view=archive', 'component', 'orderby= show_noauth= .. .. .. .. and so on What I want is I should get these Values for once, I know there is mistake using the outer Forloop, but I m not getting the idea of rectifying it.. Please help... please poke me for more clarification...

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• sql perfomance on new server

  - by Rapunzo

  My database is running on a pc (AMD Phenom x6, intel ssd disk, 8GB DDR3 RAM and windows 7 OS + sql server 2008 R2 sp3 ) and it started working hard, timeout problems and up to 30 seconds long queries after 200 mb of database And I also have an old server pc (IBM x-series 266: 72*3 15k rpm scsi discs with raid5, 4 gb ram and windows server 2003 + sql server 2008 R2 sp3 ) and same query start to give results in 100 seconds.. I tried query analyser tool for tuning my indexed. but not so much improvements. its a big dissapointment for me. because I thought even its an old server pc it should be more powerfull with 15k rpm discs with raid5. what should I do. do I need $10.000 new server to get a good performance for my sql server? cant I use that IBM server? Extra information: there is 50 sql users and its an ERP program. There is my query ALTER FUNCTION [dbo].[fnDispoTerbiye] ( ) RETURNS TABLE AS RETURN ( SELECT MD.dispoNo, SV.sevkNo, M1.musteriAdi AS musteri, SD.tipTurId, TT.tipTur, SD.tipNo, SD.desenNo, SD.varyantNo, SUM(T.topMetre) AS toplamSevkMetre, MD.dispoMetresi, DT.gelisMetresi, ISNULL(DT.fire, 0) AS fire, SV.sevkTarihi, DT.gelisTarihi, SP.mamulTermin, SD.miktar AS siparisMiktari, M.musteriAdi AS boyahane, MD.akisNotu AS islemler, --dbo.fnAkisIslemleri(MD.dispoNo) DT.partiNo, DT.iplikBoyaId, B.tanimAd AS BoyaTuru, MAX(HD.hamEn) AS hamEn, MAX(HD.hamGramaj) AS hamGramaj, TS.mamulEn, TS.mamulGramaj, DT.atkiCekmesi, DT.cozguCekmesi, DT.fiyat, DV.dovizCins, DT.dovizId, (SELECT CASE WHEN DT.dovizId = 2 THEN CAST(round(SUM(T .topMetre) * DT.fiyat * (SELECT TOP 1 satis FROM tblKur WHERE dovizId = 2 ORDER BY tarih DESC), 2) AS numeric(18, 2)) WHEN DT.dovizId = 3 THEN CAST(round(SUM(T .topMetre) * DT.fiyat * (SELECT TOP 1 satis FROM tblKur WHERE dovizId = 3 ORDER BY tarih DESC), 2) AS numeric(18, 2)) WHEN DT.dovizId = 1 THEN CAST(round(SUM(T .topMetre) * DT.fiyat * (SELECT TOP 1 satis FROM tblKur WHERE dovizId = 1 ORDER BY tarih DESC), 2) AS numeric(18, 2)) END AS Expr1) AS ToplamTLfiyat, DT.aciklama, MD.dispoNotu, SD.siparisId, SD.siparisDetayId, DT.sqlUserName, DT.kayitTarihi, O.orguAd, 'Çözgü=(' + (SELECT dbo.fnTipIplikler(SD.tipTurId, SD.tipNo, SD.desenNo, SD.varyantNo, 1) AS Expr1) + ')' + ' Atki=(' + (SELECT dbo.fnTipIplikler(SD.tipTurId, SD.tipNo, SD.desenNo, SD.varyantNo, 2) AS Expr1) + ')' AS iplikAciklama, DT.prosesOk, dbo.[fnYikamaTalimat](SP.siparisId) yikamaTalimati FROM tblDoviz AS DV WITH(NOLOCK) INNER JOIN tblDispoTerbiye AS DT WITH(NOLOCK) INNER JOIN tblTanimlar AS B WITH(NOLOCK) ON DT.iplikBoyaId = B.tanimId AND B.tanimTurId = 2 ON DV.id = DT.dovizId RIGHT OUTER JOIN tblMusteri AS M1 WITH(NOLOCK) INNER JOIN tblSiparisDetay AS SD WITH(NOLOCK) INNER JOIN tblDispo AS MD WITH(NOLOCK) ON SD.siparisDetayId = MD.siparisDetayId INNER JOIN tblTipTur AS TT WITH(NOLOCK) ON SD.tipTurId = TT.tipTurId INNER JOIN tblSiparis AS SP WITH(NOLOCK) ON SD.siparisId = SP.siparisId ON M1.musteriNo = SP.musteriNo INNER JOIN tblTip AS TP WITH(NOLOCK) ON SD.tipTurId = TP.tipTurId AND SD.tipNo = TP.tipNo AND SD.desenNo = TP.desen AND SD.varyantNo = TP.varyant INNER JOIN tblOrgu AS O WITH(NOLOCK) ON TP.orguId = O.orguId INNER JOIN tblMusteri AS M WITH(NOLOCK) INNER JOIN tblSevkiyat AS SV WITH(NOLOCK) ON M.musteriNo = SV.musteriNo INNER JOIN tblSevkDetay AS SVD WITH(NOLOCK) ON SV.sevkNo = SVD.sevkNo ON MD.mamulDispoHamSevkno = SV.sevkNo LEFT OUTER JOIN tblTop AS T WITH(NOLOCK) INNER JOIN tblDispo AS HD WITH(NOLOCK) ON T.dispoNo = HD.dispoNo AND T.dispoTuruId = HD.dispoTuruId ON SVD.dispoTuruId = T.dispoTuruId AND SVD.dispoNo = T.dispoNo AND SVD.topNo = T.topNo AND MD.siparisDetayId = HD.siparisDetayId ON DT.dispoTuruId = MD.dispoTuruId AND DT.dispoNo = MD.dispoNo LEFT OUTER JOIN tblDispoTerbiyeTest AS TS WITH(NOLOCK) ON DT.dispoTuruId = TS.dispoTuruId AND DT.dispoNo = TS.dispoNo --WHERE DT.gelisTarihi IS NULL -- OR DT.gelisTarihi > GETDATE()-30 GROUP BY MD.dispoNo, DT.partiNo, DT.iplikBoyaId, TS.mamulEn, TS.mamulGramaj, DT.gelisMetresi, DT.gelisTarihi, DT.atkiCekmesi, DT.cozguCekmesi, DT.fire, DT.fiyat, DT.aciklama, DT.sqlUserName, DT.kayitTarihi, SD.tipTurId, TT.tipTur, SD.tipNo, SD.desenNo, SD.varyantNo, SD.siparisId, SD.siparisDetayId, B.tanimAd, M.musteriAdi, M.musteriAdi, M1.musteriAdi, O.orguAd, TP.iplikAciklama, SD.miktar, MD.dispoNotu, SP.mamulTermin, DT.dovizId, DV.dovizCins, MD.dispoMetresi, MD.akisNotu, SV.sevkNo, SV.sevkTarihi, DT.prosesOk,SP.siparisId )

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• JSON Paring - How to show second Level ListView

  - by Sophie

  I am parsing JSON data into ListView, and successfully parsed first level of JSON in MainActivity.java, where i am showing list of Main Locations, like: Inner Locations Outer Locations Now i want whenever i do tap on Inner Locations then in SecondActivity it should show Delhi and NCR in a List, same goes for Outer Locations as well, in this case whenever user do tap need to show USA JSON look like: { "all": [ { "title": "Inner Locations", "maps": [ { "title": "Delhi", "markers": [ { "name": "Connaught Place", "latitude": 28.632777800000000000, "longitude": 77.219722199999980000 }, { "name": "Lajpat Nagar", "latitude": 28.565617900000000000, "longitude": 77.243389100000060000 } ] }, { "title": "NCR", "markers": [ { "name": "Gurgaon", "latitude": 28.440658300000000000, "longitude": 76.987347699999990000 }, { "name": "Noida", "latitude": 28.570000000000000000, "longitude": 77.319999999999940000 } ] } ] }, { "title": "Outer Locations", "maps": [ { "title": "United States", "markers": [ { "name": "Virgin Islands", "latitude": 18.335765000000000000, "longitude": -64.896335000000020000 }, { "name": "Vegas", "latitude": 36.114646000000000000, "longitude": -115.172816000000010000 } ] } ] } ] } Note: But whenever i do tap on any of the ListItem in first activity, not getting any list in SecondActivity, why ? MainActivity.java:- @Override protected Void doInBackground(Void... params) { // Create an array arraylist = new ArrayList<HashMap<String, String>>(); // Retrieve JSON Objects from the given URL address jsonobject = JSONfunctions .getJSONfromURL("http://10.0.2.2/locations.json"); try { // Locate the array name in JSON jsonarray = jsonobject.getJSONArray("all"); for (int i = 0; i < jsonarray.length(); i++) { HashMap<String, String> map = new HashMap<String, String>(); jsonobject = jsonarray.getJSONObject(i); // Retrieve JSON Objects map.put("title", jsonobject.getString("title")); arraylist.add(map); } } catch (JSONException e) { Log.e("Error", e.getMessage()); e.printStackTrace(); } return null; } @Override protected void onPostExecute(Void args) { // Locate the listview in listview_main.xml listview = (ListView) findViewById(R.id.listview); // Pass the results into ListViewAdapter.java adapter = new ListViewAdapter(MainActivity.this, arraylist); // Set the adapter to the ListView listview.setAdapter(adapter); // Close the progressdialog mProgressDialog.dismiss(); listview.setOnItemClickListener(new OnItemClickListener() { @Override public void onItemClick(AdapterView<?> parent, View view, int position, long id) { Toast.makeText(MainActivity.this, String.valueOf(position), Toast.LENGTH_LONG).show(); // TODO Auto-generated method stub Intent sendtosecond = new Intent(MainActivity.this, SecondActivity.class); // Pass all data rank sendtosecond.putExtra("title", arraylist.get(position).get(MainActivity.TITLE)); Log.d("Tapped Item::", arraylist.get(position).get(MainActivity.TITLE)); startActivity(sendtosecond); } }); } } } SecondActivity.java: @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); // Get the view from listview_main.xml setContentView(R.layout.listview_main); Intent in = getIntent(); strReceived = in.getStringExtra("title"); Log.d("Received Data::", strReceived); // Execute DownloadJSON AsyncTask new DownloadJSON().execute(); } // DownloadJSON AsyncTask private class DownloadJSON extends AsyncTask<Void, Void, Void> { @Override protected void onPreExecute() { super.onPreExecute(); } @Override protected Void doInBackground(Void... params) { // Create an array arraylist = new ArrayList<HashMap<String, String>>(); // Retrieve JSON Objects from the given URL address jsonobject = JSONfunctions .getJSONfromURL("http://10.0.2.2/locations.json"); try { // Locate the array name in JSON jsonarray = jsonobject.getJSONArray("maps"); for (int i = 0; i < jsonarray.length(); i++) { HashMap<String, String> map = new HashMap<String, String>(); jsonobject = jsonarray.getJSONObject(i); // Retrieve JSON Objects map.put("title", jsonobject.getString("title")); arraylist.add(map); } } catch (JSONException e) { Log.e("Error", e.getMessage()); e.printStackTrace(); } return null; }

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• How to bind to the sum of two data bound values in WPF?

  - by Sheridan

  I have designed an analog clock control. It uses the stroke from two ellipses to represent an outer border and an inner border to the clock face. I have exposed properties in the UserControl that allow a user to alter the thickness of these two borders. The Ellipse.StrokeThickness properties are then bound to these UserControl properties. At the moment, I am binding the UserControl property for the outer border thickness to the margins of the inner elements so that they are not hidden when the border size is increased. <Ellipse Name="OuterBorder" Panel.ZIndex="1" StrokeThickness="{Binding OuterBorderThickness, ElementName=This}" Stroke="{StaticResource OuterBorderBrush}" /> <Ellipse Name="InnerBorder" Panel.ZIndex="5" StrokeThickness="{Binding InnerBorderThickness, ElementName=This}" Margin="{Binding OuterBorderThickness, ElementName=This}" Stroke="{StaticResource InnerBorderBrush}"> ... <Ellipse Name="Face" Panel.ZIndex="1" Margin="{Binding OuterBorderThickness, ElementName=This}" Fill="{StaticResource FaceBackgroundBrush}" /> ... The problem is that if the inner border thickness is increased, this does not affect the margins and so the hour ticks and numbers can become partially obscured or hidden. So what I really need is to be able to bind the margin properties of the inner controls to the sum of the inner and outer border thickness values (they are of type double). I have done this successfully using 'DataContext = this;', but am trying to rewrite the control without this as I hear it is not recommended. I also thought about using a converter and passing the second value as the ConverterParameter, but didn't know how to bind to the ConverterParameter. Any tips would be greatly appreciated. EDIT Thanks to Kent's suggestion, I've created a simple MultiConverter to add the input values and return the result. I've hooked the SAME multibinding with converter XAML to both a TextBlock.Text property and the TextBlock.Margin property to test it. <TextBlock> <TextBlock.Text> <MultiBinding Converter="{StaticResource SumConverter}" ConverterParameter="Add"> <Binding Path="OuterBorderThickness" ElementName="This" /> <Binding Path="InnerBorderThickness" ElementName="This" /> </MultiBinding> </TextBlock.Text> <TextBlock.Margin> <MultiBinding Converter="{StaticResource SumConverter}" ConverterParameter="Add"> <Binding Path="OuterBorderThickness" ElementName="This" /> <Binding Path="InnerBorderThickness" ElementName="This" /> </MultiBinding> </TextBlock.Margin> </TextBlock> I can see the correct value displayed in the TexBlock, but the Margin is not set. Any ideas? EDIT Interestingly, the Margin property can be bound to a data property of type double, but this does not seem to apply within a MultiBinding. As advised by Kent, I changed the Converter to return the value as a Thickness object and now it works. Thanks Kent.

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• complex css image centering help?

  - by Tenshiko

  My problem is a bit more complex than the title says. Sorry, I don't know how to be more specific... I'm working on a website and I came across a part where I should display some thumbnails. The thing is, the thumbnails are not matching in dimensions. (I know, it sounds ridiculous, since this is thumbnails are for, right?) No, there is simply NO WAY to create them in the same dimensions!! I've managed to create a HTML+CSS structure to fix this problem, and the images are not stretching to fit their containers if they are smaller while keeping their aspect ratio. The only issue remaining, is to center the images. Since setting margin to "0 auto" or "auto 0" are not helping, I've tried setting up multiple containers and setting the margins to position the images. This is also not working: if I put a 120x120 picture in a 120x80 inner container, and I set the container's top and left margin to -50%, the margins become -60px both. Can this be fixed? Or is there yet another way to center images? I'm open to any suggestions! HTML: <div id="roll"> <div class="imgfix"> <div class="outer"> <div class="inner"> @if (ImageDimensionHelper.WhereToAlignImg(item.Width, item.Height, 120, 82) == ImgAlign.Width) <!-- ImageDimensionHelper tells me if the image should fit the container with its width or height. I set the class of the img accordingly. --> { <img class="width" src="@Url.Content(item.URL)" alt="@item.Name"/> } else { <img class="height" src="@Url.Content(item.URL)" alt="@item.Name"/> } </div> </div> </div> </div> CSS: .imgfix{ overflow:hidden; } .imgfix .outer { width:100%; height:100%;} .imgfix .inner { width:100%; height:100%; margin-top:-50%; margin-left:-50%; } /*This div (.inner) gets -60px for both margins every time, regardless of the size of itself, or the image inside it*/ #roll .imgfix { width:120px; height:82px; border: 1px #5b91ba solid; } #roll .imgfix .outer { margin-top:41px; margin-left:60px; } /*since I know specificly what these margins should be, I set them explicitly, because 50% got the wrong size.*/ #roll .imgfix img.width { width:120px; height:auto; margin: auto 0; } #roll .imgfix img.height { height:82px; width:auto; margin: 0 auto; }

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• How John Got 15x Improvement Without Really Trying

  - by rchrd

  The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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• Software Architecture verses Software Design

  Recently, I was asked what the differences between software architecture and software design are. At a very superficial level both architecture and design seem to mean relatively the same thing. However, if we examine both of these terms further we will find that they are in fact very different due to the level of details they encompass. Software Architecture can be defined as the essence of an application because it deals with high level concepts that do not include any details as to how they will be implemented. To me this gives stakeholders a view of a system or application as if someone was viewing the earth from outer space. At this distance only very basic elements of the earth can be detected like land, weather and water. As the viewer comes closer to earth the details in this view start to become more defined. Details about the earth’s surface will start to actually take form as well as mane made structures will be detected. The process of transitioning a view from outer space to inside our earth’s atmosphere is similar to how an architectural concept is transformed to an architectural design. From this vantage point stakeholders can start to see buildings and other structures as if they were looking out of a small plane window. This distance is still high enough to see a large area of the earth’s surface while still being able to see some details about the surface. This viewing point is very similar to the actual design process of an application in that it takes the very high level architectural concept or concepts and applies concrete design details to form a software design that encompasses the actual implementation details in the form of responsibilities and functions. Examples of these details include: interfaces, components, data, and connections. In review, software architecture deals with high level concepts without regard to any implementation details. Software design on the other hand takes high level concepts and applies concrete details so that software can be implemented. As part of the transition between software architecture to the creation of software design an evaluation on the architecture is recommended. There are several benefits to including this step as part of the transition process. It allows for projects to ensure that they are on the correct path as to meeting the stakeholder’s requirement goals, identifies possible cost savings and can be used to find missing or nonspecific requirements that cause ambiguity in a design. In the book “Evaluating Software Architectures: Methods and Case Studies”, they define key benefits to adding an architectural review process to ensure that an architecture is ready to move on to the design phase. Benefits to evaluating software architecture: Gathers all stakeholders to communicate about the project Goals are clearly defined in regards to the creation or validation of specific requirements Goals are prioritized so that when conflicts occur decisions will be made based on goal priority Defines a clear expectation of the architecture so that all stakeholders have a keen understanding of the project Ensures high quality documentation of the architecture Enables discoveries of architectural reuse  Increases the quality of architecture practices. I can remember a few projects that I worked on that could have really used an architectural review prior to being passed on to developers. This project was to create some new advertising space on the company’s website in order to sell space based on the location and some other criteria. I was one of the developer selected to lead this project and I was given a high level design concept and a long list of ever changing requirements due to the fact that sales department had no clear direction as to what exactly the project was going to do or how they were going to bill the clients once they actually agreed to purchase the Ad space. In my personal opinion IT should have pushed back to have the requirements further articulated instead of forcing programmers to code blindly attempting to build such an ambiguous project.  Unfortunately, we had to suffer with this project for about 4 months when it should have only taken 1.5 to complete due to the constantly changing and unclear requirements. References  Clements, P., Kazman, R., & Klein, M. (2002). Evaluating Software Architectures. Westford, Massachusetts: Courier Westford. 

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• Datapager in silverlight 4 -Nested datagrid visibility issue

  - by Archie

  I have a datagrid in silverlight with child datagrid nested in it. Also I have a DataPager on the outer datagrid. The code looks like this: <data:DataGrid x:Name="dgData" Width="600" ItemsSource="{Binding}" AutoGenerateColumns="False" IsReadOnly="True" HorizontalScrollBarVisibility="Hidden" CanUserSortColumns="False" RowDetailsVisibilityChanged="dgData_RowDetailsVisibilityChanged" Margin="20,0" Grid.RowSpan="2"> <data:DataGrid.Columns> <data:DataGridTextColumn Header="Item" Width="*" Binding="{Binding ItemName,Mode=TwoWay}"/> <data:DataGridTextColumn Header="Company" Width="*" Binding="{Binding Company,Mode=TwoWay}"/> </data:DataGrid.Columns> <data:DataGrid.RowDetailsTemplate> <DataTemplate> <data:DataGrid x:Name="dgRowDetail" Width="400" HorizontalScrollBarVisibility="Hidden" AutoGenerateColumns="False" Visibility="Collapsed"> <data:DataGrid.Columns> <data:DataGridTextColumn Header="Date" Width="*" Binding="{Binding Date,Mode=TwoWay}"/> <data:DataGridTextColumn Header="Price" Width="*" Binding="{Binding Price,Mode=TwoWay}"/> </data:DataGrid.Columns> </data:DataGrid> </DataTemplate> </data:DataGrid.RowDetailsTemplate> </data:DataGrid> <data:DataPager x:Name="dpData" HorizontalAlignment="Center" DisplayMode="FirstLastPreviousNextNumeric" Source="{Binding}"/> I have one PagedCollectionView pgv which is bound to outer datagrid as: DataContext = pgv; When the row is clicked I set the child datagrid's ItemsSource property to another PagedCollectionView. My problem is it works fine except for the first row for the first time. When I click on it, it doesn't fire the dgData_RowDetailsVisibilityChanged event. Also, when I click on second row, firstly first row fires the event and then the second row fires it and shows the nested grid. Please help.

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• entity framework 4 POCO's stored procedure error - "The FunctionImport could not be found in the container"

  - by user331884

  Entity Framework with POCO Entities generated by T4 template. Added Function Import named it "procFindNumber" specified complex collection named it "NumberResult". Here's what got generated in Context.cs file: public ObjectResult<NumberResult> procFindNumber(string lookupvalue) { ObjectParameter lookupvalueParameter; if (lookupvalue != null) { lookupvalueParameter = new ObjectParameter("lookupvalue", lookupvalue); } else { lookupvalueParameter = new ObjectParameter("lookupvalue", typeof(string)); } return base.ExecuteFunction<NumberResult>("procFindNumber", lookupvalueParameter); } Here's the stored procedure: ALTER PROCEDURE [dbo].[procFindNumber] @lookupvalue varchar(255) AS BEGIN SET NOCOUNT ON; DECLARE @sql nvarchar(MAX); IF @lookupvalue IS NOT NULL AND @lookupvalue <> '' BEGIN SELECT @sql = 'SELECT dbo.HBM_CLIENT.CLIENT_CODE, dbo.HBM_MATTER.MATTER_NAME, dbo.HBM_MATTER.CLIENT_MAT_NAME FROM dbo.HBM_MATTER INNER JOIN dbo.HBM_CLIENT ON dbo.HBM_MATTER.CLIENT_CODE = dbo.HBM_CLIENT.CLIENT_CODE LEFT OUTER JOIN dbo.HBL_CLNT_CAT ON dbo.HBM_CLIENT.CLNT_CAT_CODE = dbo.HBL_CLNT_CAT.CLNT_CAT_CODE LEFT OUTER JOIN dbo.HBL_CLNT_TYPE ON dbo.HBM_CLIENT.CLNT_TYPE_CODE = dbo.HBL_CLNT_TYPE.CLNT_TYPE_CODE WHERE (LTRIM(RTRIM(dbo.HBM_MATTER.CLIENT_CODE)) <> '''')' SELECT @sql = @sql + ' AND (dbo.HBM_MATTER.MATTER_NAME like ''%' + @lookupvalue + '%'')' SELECT @sql = @sql + ' OR (dbo.HBM_MATTER.CLIENT_MAT_NAME like ''%' + @lookupvalue + '%'')' SELECT @sql = @sql + ' ORDER BY dbo.HBM_MATTER.MATTER_NAME' -- Execute the SQL query EXEC sp_executesql @sql END END In my WCF service I try to execute the stored procedure: [WebGet(UriTemplate = "number/{value}/?format={format}")] public IEnumerable<NumberResult> GetNumber(string value, string format) { if (string.Equals("json", format, StringComparison.OrdinalIgnoreCase)) { WebOperationContext.Current.OutgoingResponse.Format = WebMessageFormat.Json; } using (var ctx = new MyEntities()) { ctx.ContextOptions.ProxyCreationEnabled = false; var results = ctx.procFindNumber(value); return results.ToList(); } } Error message says "The FunctionImport ... could not be found in the container ..." What am I doing wrong?

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• SQLite vs Firebird

  - by rwallace

  The scenario I'm looking at is "This program uses Postgres. Oh, you want to just use it single-user for the moment, and put off having to deal with installing a database server? Okay, in the meantime you can use it with the embedded single-user database." The question is then which embedded database is best. As I understand it, the two main contenders are SQLite and Firebird; so which is better? Criteria: Full SQL support, or as close as reasonably possible. Full text search. Easy to call from C# Locks, or allows you to lock, the database file to make sure nobody tries to run it multiuser and ends up six months down the road with intermittent data corruption in all their backups. Last but far from least, reliability. As I understand it, the disadvantages of SQLite are, No right outer join. Workaround: use left outer join instead. Not much integrity checking. Workaround: be really careful in the application code. No decimal numbers. Workaround: lots of aspirin. None of the above are showstoppers. Are there any others I'm missing? (I know it doesn't support some administrative and code-within-database SQL features, that aren't relevant for this kind of use case.) I don't know anything much about Firebird. What are its disadvantages?

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• Richfaces: rich:datatable rowspan using rich:subtable

  - by Markos Fragkakis

  Hi, I use Richfaces, Seam and JSF, and I want something like the following: and I have managed it to a degree using a rich:subtable like this: <rich:dataTable value="#{backingBean.companyList}" rows="100" var="company"> <f:facet name="header"> <rich:columnGroup> <rich:column>Company Name</rich:column> <rich:column>Company Email</rich:column> <rich:column>Product Name</rich:column> <rich:column>Product Email</rich:column> </rich:columnGroup> </f:facet> <rich:subTable value="#{company.products}" var="product" rowKeyVar="rowKey"> <rich:column rowspan="#{company.products.size()}" rendered="#{rowKey eq 0}"> #{company.name} </rich:column> <rich:column rowspan="#{company.products.size()}" rendered="#{rowKey eq 0}"> #{company.email} </rich:column> <rich:column> #{product.name} </rich:column> <rich:column> #{product.email} </rich:column> </rich:subTable> the problem is that companies that have NO products, do not get rendered at all. What I want would be for them to be rendered, and the remaining row (the product-specific columns) to be empty. Is there a way to do this? Note: I have also tried nested rich:datatables, but the internal columns do not overlap with the outer columns containing the header. With rich:subtable the inner columns overlap with the outer columns and show nice.

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• Binding from View-Model to View-Model of a child User Control in Silverlight? 2 sources - 1 target..

  - by andrej351

  Hi there, So i have a UserControl for one of my Views and have another 'child' UserControl inside that. The outer 'parent' UserControl has a Collection on its View-Model and a Grid control on it to display a list of Items. I want to place another UserControl inside this UserControl to display a form representing the details of one Item. The outer / parent UserControl's View-Model already has a property on it to hold the currently selected Item and i would like to bind this to a DependancyProperty on the inner / child UserControl. I would then like to bind that DependancyProperty to a property on the child UserControl's View-Model. I can then set the DependancyProperty once in XAML with a binding expression and have the child UserControl do all its work in its View-Model like it should. The code i have looks like this.. Parent UserControl: <UserControl x:Class="ItemsListView" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" DataContext="{Binding Source={StaticResource ServiceLocator}, Path=ItemsListViewModel}"> <!-- Grid Control here... --> <ItemDetailsView Item="{Binding Source={StaticResource ServiceLocator}, Path=ItemsListViewModel.SelectedItem}" /> </UserControl> Child UserControl: <UserControl x:Class="ItemDetailsView" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" DataContext="{Binding Source={StaticResource ServiceLocator}, Path=ItemDetailsViewModel}" ItemDetailsView.Item="{Binding Source={StaticResource ServiceLocator}, Path=ItemDetailsViewModel.Item, Mode=TwoWay}"> <!-- Form controls here... --> </UserControl> The selected Item is bound to the DependancyProperty fine. However from the DependancyProperty to the child View-Model does not. It appears to be a situation where there are two concurrent bindings which need to work but with the same target for two sources. Why won't the second (in the child UserControl) binding work?? Is there a way to acheive the behaviour I'm after?? Cheers.

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• Sql Query - Selecting rows where user can be both friend and user

  - by Gublooo

  Hey Sorry the title is not very clear. This is a follow up to my earlier question where one of the members helped me with a query. I have a following friends Table Friend friend_id - primary key user_id user_id_friend status The way the table is populated is - when I send a friend request to John - my userID appears in user_id and Johns userID appears in user_id_friend. Now another scenario is say Mike sends me a friend request - in this case mike's userID will appear in user_id and my userID will appear in user_id_friend So to find all my friends - I need to run a query to find where my userID appears in both user_id column as well as user_id_friend column What I am trying to do now is - when I search for user say John - I want all users Johns listed on my site to show up along with the status of whether they are my friend or not and if they are not - then show a "Add Friend" button. Based on the previous post - I got this query which does part of the job - My example user_id is 1: SELECT u.user_id, f.status FROM user u LEFT OUTER JOIN friend f ON f.user_id = u.user_id and f.user_id_friend = 1 where u.name like '%' So this only shows users with whom I am friends where they have sent me request ie my userID appears in user_id_friend. Although I am friends with others (where my userID appears in user_id column) - this query will return that as null To get those I need another query like this SELECT u.user_id, f.status FROM user u LEFT OUTER JOIN friend f ON f.user_id_friend = u.user_id and f.user_id = 1 where u.name like '%' So how do I combine these queries to return 1 set of users and what my friendship status with them is. I hope my question is clear Thanks

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• Complex query in mysql

  - by Satish

  I have two tables reports and holidays. reports: (username varchar(30),activity varchar(30),hours int(3),report_date date) holidays: (holiday_name varchar(30), holiday_date date) select * from reports gives +----------+-----------+---------+------------+ | username | activity | hours | date | +----------+-----------+---------+------------+ | prasoon | testing | 3 | 2009-01-01 | | prasoon | coding | 4 | 2009-01-03 | | gautam | coding | 1 | 2009-01-05 | | prasoon | coding | 4 | 2009-01-06 | | prasoon | coding | 4 | 2009-01-10 | | gautam | coding | 4 | 2009-01-10 | +----------+-----------+---------+------------+ select * from holidays gives +--------------+---------------+ | holiday_name | holiday_date | +--------------+---------------+ | Diwali | 2009-01-02 | | Holi | 2009-01-05 | +--------------+---------------+ When I used the following query SELECT dates.date AS date, CASE WHEN holiday_name IS NULL THEN COALESCE(reports.activity, 'Absent') WHEN holiday_name IS NOT NULL and reports.activity IS NOT NULL THEN reports.activity ELSE '' END AS activity, CASE WHEN holiday_name IS NULL THEN COALESCE(reports.hours, 'Absent') WHEN holiday_name IS NOT NULL and reports.hours IS NOT NULL THEN reports.hours ELSE '' END AS hours, CASE WHEN holiday_name IS NULL THEN COALESCE(holidays.holiday_name, '') ELSE holidays.holiday_name END AS holiday_name FROM dates LEFT OUTER JOIN reports ON dates.date = reports.date LEFT OUTER JOIN holidays ON dates.date = holidays.holiday_date where reports.username='gautam' and dates.date>='2009-01-01' and dates.date<='2009-01-10'; I got the following output +----------+-----------+---------+------------+ | date | activity | hours | holiday | +----------+-----------+---------+------------+ |2009-01-05| coding | 1 | Holi | +----------+-----------+---------+------------+ |2009-01-10| coding | 4 | | +----------+-----------+---------+------------+ but I expected this +----------+-----------+---------+------------+ | date | activity | hours | holiday | +----------+-----------+---------+------------+ |2009-01-01| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-02| | | Diwali | +----------+-----------+---------+------------+ |2009-01-03| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-04| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-05| Coding | 1 | Holi | +----------+-----------+---------+------------+ |2009-01-06| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-07| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-08| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-09| Absent | Absent | | +----------+-----------+---------+------------+ |2009-01-10| Coding | 4 | | +----------+-----------+---------+------------+ How can I modify the above query to get the desired output(for a particular user (gautam in this case))?

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