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• Performance considerations for common SQL queries

  - by Jim Giercyk

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

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• 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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• How to reference or vlookup a list of values based on a comma separated list of column references within a cell in excel?

  - by glallen

  I want to do a vlookup (or similar) against a column which is a list of values. This works fine for looking up a value from a single row, but I want to be able to look up multiple rows, sum the results, and divide by the number of rows referenced. For example: A B C D E F G [----given values----------------] [Work/Auth] [sum(vlookup(each(G),table,5)) /count(G)] [given vals] 1 Item Authorized OnHand Working Operational% DependencyOR% Dependencies 2 A 1 1 1 1 .55 B 3 B 10 5 5 .50 .55 C,D 4 C 100 75 50 .50 .60 D 5 D 10 10 6 .60 1 I want to be able to show an Operational Rate, and an operational rate of the systems each system depends on (F). In order to get a value for F, I want to sum over each value in column-E that was referenced by a dependency in column-G then divide by the number of dependencies in G. Column-G can have varying lengths, and will be a comma separated list of values from column-A. Is there any way to do this in excel?

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• Performing Aggregate Functions on Multi-Million Row Tables

  - by Daniel Short

  I'm having some serious performance issues with a multi-million row table that I feel I should be able to get results from fairly quick. Here's a run down of what I have, how I'm querying it, and how long it's taking: I'm running SQL Server 2008 Standard, so Partitioning isn't currently an option I'm attempting to aggregate all views for all inventory for a specific account over the last 30 days. All views are stored in the following table: CREATE TABLE [dbo].[LogInvSearches_Daily]( [ID] [bigint] IDENTITY(1,1) NOT NULL, [Inv_ID] [int] NOT NULL, [Site_ID] [int] NOT NULL, [LogCount] [int] NOT NULL, [LogDay] [smalldatetime] NOT NULL, CONSTRAINT [PK_LogInvSearches_Daily] PRIMARY KEY CLUSTERED ( [ID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON, FILLFACTOR = 90) ON [PRIMARY] ) ON [PRIMARY] This table has 132,000,000 records, and is over 4 gigs. A sample of 10 rows from the table: ID Inv_ID Site_ID LogCount LogDay -------------------- ----------- ----------- ----------- ----------------------- 1 486752 48 14 2009-07-21 00:00:00 2 119314 51 16 2009-07-21 00:00:00 3 313678 48 25 2009-07-21 00:00:00 4 298863 0 1 2009-07-21 00:00:00 5 119996 0 2 2009-07-21 00:00:00 6 463777 534 7 2009-07-21 00:00:00 7 339976 503 2 2009-07-21 00:00:00 8 333501 570 4 2009-07-21 00:00:00 9 453955 0 12 2009-07-21 00:00:00 10 443291 0 4 2009-07-21 00:00:00 (10 row(s) affected) I have the following index on LogInvSearches_Daily: /****** Object: Index [IX_LogInvSearches_Daily_LogDay] Script Date: 05/12/2010 11:08:22 ******/ CREATE NONCLUSTERED INDEX [IX_LogInvSearches_Daily_LogDay] ON [dbo].[LogInvSearches_Daily] ( [LogDay] ASC ) INCLUDE ( [Inv_ID], [LogCount]) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, SORT_IN_TEMPDB = OFF, IGNORE_DUP_KEY = OFF, DROP_EXISTING = OFF, ONLINE = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] I need to pull inventory only from the Inventory for a specific account id. I have an index on the Inventory as well. I'm using the following query to aggregate the data and give me the top 5 records. This query is currently taking 24 seconds to return the 5 rows: StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- SELECT TOP 5 Sum(LogCount) AS Views , DENSE_RANK() OVER(ORDER BY Sum(LogCount) DESC, Inv_ID DESC) AS Rank , Inv_ID FROM LogInvSearches_Daily D (NOLOCK) WHERE LogDay DateAdd(d, -30, getdate()) AND EXISTS( SELECT NULL FROM propertyControlCenter.dbo.Inventory (NOLOCK) WHERE Acct_ID = 18731 AND Inv_ID = D.Inv_ID ) GROUP BY Inv_ID (1 row(s) affected) StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |--Top(TOP EXPRESSION:((5))) |--Sequence Project(DEFINE:([Expr1007]=dense_rank)) |--Segment |--Segment |--Sort(ORDER BY:([Expr1006] DESC, [D].[Inv_ID] DESC)) |--Stream Aggregate(GROUP BY:([D].[Inv_ID]) DEFINE:([Expr1006]=SUM([LOALogs].[dbo].[LogInvSearches_Daily].[LogCount] as [D].[LogCount]))) |--Sort(ORDER BY:([D].[Inv_ID] ASC)) |--Nested Loops(Inner Join, OUTER REFERENCES:([D].[Inv_ID])) |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1011], [Expr1012], [Expr1010])) | |--Compute Scalar(DEFINE:(([Expr1011],[Expr1012],[Expr1010])=GetRangeWithMismatchedTypes(dateadd(day,(-30),getdate()),NULL,(6)))) | | |--Constant Scan | |--Index Seek(OBJECT:([LOALogs].[dbo].[LogInvSearches_Daily].[IX_LogInvSearches_Daily_LogDay] AS [D]), SEEK:([D].[LogDay] > [Expr1011] AND [D].[LogDay] < [Expr1012]) ORDERED FORWARD) |--Index Seek(OBJECT:([propertyControlCenter].[dbo].[Inventory].[IX_Inventory_Acct_ID]), SEEK:([propertyControlCenter].[dbo].[Inventory].[Acct_ID]=(18731) AND [propertyControlCenter].[dbo].[Inventory].[Inv_ID]=[LOA (13 row(s) affected) I tried using a CTE to pick up the rows first and aggregate them, but that didn't run any faster, and gives me essentially the same execution plan. (1 row(s) affected) StmtText ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- --SET SHOWPLAN_TEXT ON; WITH getSearches AS ( SELECT LogCount -- , DENSE_RANK() OVER(ORDER BY Sum(LogCount) DESC, Inv_ID DESC) AS Rank , D.Inv_ID FROM LogInvSearches_Daily D (NOLOCK) INNER JOIN propertyControlCenter.dbo.Inventory I (NOLOCK) ON Acct_ID = 18731 AND I.Inv_ID = D.Inv_ID WHERE LogDay DateAdd(d, -30, getdate()) -- GROUP BY Inv_ID ) SELECT Sum(LogCount) AS Views, Inv_ID FROM getSearches GROUP BY Inv_ID (1 row(s) affected) StmtText ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |--Stream Aggregate(GROUP BY:([D].[Inv_ID]) DEFINE:([Expr1004]=SUM([LOALogs].[dbo].[LogInvSearches_Daily].[LogCount] as [D].[LogCount]))) |--Sort(ORDER BY:([D].[Inv_ID] ASC)) |--Nested Loops(Inner Join, OUTER REFERENCES:([D].[Inv_ID])) |--Nested Loops(Inner Join, OUTER REFERENCES:([Expr1008], [Expr1009], [Expr1007])) | |--Compute Scalar(DEFINE:(([Expr1008],[Expr1009],[Expr1007])=GetRangeWithMismatchedTypes(dateadd(day,(-30),getdate()),NULL,(6)))) | | |--Constant Scan | |--Index Seek(OBJECT:([LOALogs].[dbo].[LogInvSearches_Daily].[IX_LogInvSearches_Daily_LogDay] AS [D]), SEEK:([D].[LogDay] > [Expr1008] AND [D].[LogDay] < [Expr1009]) ORDERED FORWARD) |--Index Seek(OBJECT:([propertyControlCenter].[dbo].[Inventory].[IX_Inventory_Acct_ID] AS [I]), SEEK:([I].[Acct_ID]=(18731) AND [I].[Inv_ID]=[LOALogs].[dbo].[LogInvSearches_Daily].[Inv_ID] as [D].[Inv_ID]) ORDERED FORWARD) (8 row(s) affected) (1 row(s) affected) So given that I'm getting good Index Seeks in my execution plan, what can I do to get this running faster? Thanks, Dan

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• Supporting Piping (A Useful Hello World)

  - by blastthisinferno

  I am trying to write a collection of simple C++ programs that follow the basic Unix philosophy by: Make each program do one thing well. Expect the output of every program to become the input to another, as yet unknown, program. I'm having an issue trying to get the output of one to be the input of the other, and getting the output of one be the input of a separate instance of itself. Very briefly, I have a program add which takes arguments and spits out the summation. I want to be able to pipe the output to another add instance. ./add 1 2 | ./add 3 4 That should yield 6 but currently yields 10. I've encountered two problems: The cin waits for user input from the console. I don't want this, and haven't been able to find a simple example showing a the use of standard input stream without querying the user in the console. If someone knows of an example please let me know. I can't figure out how to use standard input while supporting piping. Currently, it appears it does not work. If I issue the command ./add 1 2 | ./add 3 4 it results in 7. The relevant code is below: add.cpp snippet // ... COMMAND LINE PROCESSING ... std::vector<double> numbers = multi.getValue(); // using TCLAP for command line parsing if (numbers.size() > 0) { double sum = numbers[0]; double arg; for (int i=1; i < numbers.size(); i++) { arg = numbers[i]; sum += arg; } std::cout << sum << std::endl; } else { double input; // right now this is test code while I try and get standard input streaming working as expected while (std::cin) { std::cin >> input; std::cout << input << std::endl; } } // ... MORE IRRELEVANT CODE ... So, I guess my question(s) is does anyone see what is incorrect with this code in order to support piping standard input? Are there some well known (or hidden) resources that explain clearly how to implement an example application supporting the basic Unix philosophy? @Chris Lutz I've changed the code to what's below. The problem where cin still waits for user input on the console, and doesn't just take from the standard input passed from the pipe. Am I missing something trivial for handling this? I haven't tried Greg Hewgill's answer yet, but don't see how that would help since the issue is still with cin. // ... COMMAND LINE PROCESSING ... std::vector<double> numbers = multi.getValue(); // using TCLAP for command line parsing double sum = numbers[0]; double arg; for (int i=1; i < numbers.size(); i++) { arg = numbers[i]; sum += arg; } // right now this is test code while I try and get standard input streaming working as expected while (std::cin) { std::cin >> arg; std::cout << arg << std::endl; } std::cout << sum << std::endl; // ... MORE IRRELEVANT CODE ...

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• LINQ and Aggregate function

  - by vik20000in

  LINQ also provides with itself important aggregate function. Aggregate function are function that are applied over a sequence like and return only one value like Average, count, sum, Maximum etc…Below are some of the Aggregate functions provided with LINQ and example of their implementation. Count     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count();The below example provided count for only odd number.     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count(n => n%2 = 1);  Sum     int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 };        double numSum = numbers.Sum();  Minimum      int minNum = numbers.Min(); Maximum      int maxNum = numbers.Max();Average      double averageNum = numbers.Average();  Aggregate      double[] doubles = { 1.7, 2.3, 1.9, 4.1, 2.9 };     double product = doubles.Aggregate((runningProduct, nextFactor) => runningProduct * nextFactor);  Vikram

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• BizTalk 2009 - Scoped Record Counting in Maps

  - by StuartBrierley

  Within BizTalk there is a functoid called Record Count that will return the number of instances of a repeated record or repeated element that occur in a message instance. The input to this functoid is the record or element to be counted. As an example take the following Source schema, where the Source message has a repeated record called Box and each Box has a repeated element called Item: An instance of this Source schema may look as follows; 2 box records - one with 2 items and one with only 1 item. Our destination schema has a number of elements and a repeated box record.  The top level elements contain totals for the number of boxes and the overall number of items.  Each box record contains a single element representing the number of items in that box. Using the Record Count functoid it is easy to map the top level elements, producing the expected totals of 2 boxes and 3 items: We now need to map the total number of items per box, but how will we do this?  We have already seen that the record count functoid returns the total number of instances for the entire message, and unfortunately it does not allow you to specify a scoping parameter.  In order to acheive Scoped Record Counting we will need to make use of a combination of functoids. As you can see above, by linking to a Logical Existence functoid from the record/element to be counted we can then feed the output into a Value Mapping functoid.  Set the other Value Mapping parameter to "1" and link the output to a Cumulative Sum functoid. Set the other Cumulative Sum functoid parameter to "1" to limit the scope of the Cumulative Sum. This gives us the expected results of Items per Box of 2 and 1 respectively. I ran into this issue with a larger schema on a more complex map, but the eventual solution is still the same.  Hopefully this simplified example will act as a good reminder to me and save someone out there a few minutes of brain scratching.

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• how to architect this to make it unit testable

  - by SOfanatic

  I'm currently working on a project where I'm receiving an object via web service (WSDL). The overall process is the following: Receive object - add/delete/update parts (or all) of it - and return the object with the changes made. The thing is that sometimes these changes are complicated and there is some logic involved, other databases, other web services, etc. so to facilitate this I'm creating a custom object that mimics the original one but has some enhanced functionality to make some things easier. So I'm trying to have this process: Receive original object - convert/copy it to custom object - add/delete/update - convert/copy it back to original object - return original object. Example: public class Row { public List<Field> Fields { get; set; } public string RowId { get; set; } public Row() { this.Fields = new List<Field>(); } } public class Field { public string Number { get; set; } public string Value { get; set; } } So for example, one of the "actions" to perform on this would be to find all Fields in a Row that match a Value equal to something, and update them with some other value. I have a CustomRow class that represents the Row class, how can I make this class unit testable? Do I have to create an interface ICustomRow to mock it in the unit test? If one of the actions is to sum all of the Values in the Fields that have a Number equal to 10, like this function, how can design the custom class to facilitate unit tests. Sample function: public int Sum(FieldNumber number) { return row.Fields.Where(x => x.FieldNumber.Equals(number)).Sum(x => x.FieldValue); } Am I approaching this the wrong way?

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• how to organize rendering

  - by Irbis

  I use a deferred rendering. During g-buffer stage my rendering loop for a sponza model (obj format) looks like this: int i = 0; int sum = 0; map<string, mtlItem *>::const_iterator itrEnd = mtl.getIteratorEnd(); for(map<string, mtlItem *>::const_iterator itr = mtl.getIteratorBegin(); itr != itrEnd; ++itr) { glActiveTexture(GL_TEXTURE0 + 0); glBindTexture(GL_TEXTURE_2D, itr->second->map_KdId); glDrawElements(GL_TRIANGLES, indicesCount[i], GL_UNSIGNED_INT, (GLvoid*)(sum * 4)); sum += indicesCount[i]; ++i; glBindTexture(GL_TEXTURE_2D, 0); } I sorted faces based on materials. I switch only a diffuse texture but I can place there more material properties. Is it a good approach ? I also wonder how to handle a different kind of materials, for example: some material use a normal map, other doesn't use. Should I have a different shaders for them ?

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• Is Programming == Math?

  - by moffdub

  I've heard many times that all programming is really a subset of math. Some suggest that OO, at its roots, is mathematically based. I don't get the connection. Aside from some obvious examples: using induction to prove a recursive algorithm formal correctness proofs functional languages lambda calculus asymptotic complexity DFAs, NFAs, Turing Machines, and theoretical computation in general the fact that everything on the box is binary In what ways is programming really a subset of math? I'm looking for an explanation that might have relevance to enterprise/OO development (if there is a strong enough connection, that is). Thanks in advance. Edit: as I stated in a comment to an answer, math is uber important to programming, but what I struggle with is the "subset" argument.

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• An approximate algorithm for finding Steiner Forest.

  - by Tadeusz A. Kadlubowski

  Hello. Consider a weighted graph G=(V,E,w). We are given a family of subsets of vertices V_i. Those sets of vertices are not necessarily disjoint. A Steiner Forest is a forest that for each subset of vertices V_i connects all of the vertices in this subset with a tree. Example: only one subset V_1 = V. In this case a Steiner forest is a spanning tree of the whole graph. Enough theory. Finding such a forest with minimal weight is difficult (NP-complete). Do you know any quicker approximate algorithm to find such a forest with non-optimal weight?

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• Subsetting a data frame in a function using another data frame as parameter

  - by lecodesportif

  I would like to submit a data frame to a function and use it to subset another data frame. This is the basic data frame: foo <- data.frame(var1= c('1', '1', '1', '2', '2', '3'), var2=c('A', 'A', 'B', 'B', 'C', 'C')) I use the following function to find out the frequencies of var2 for specified values of var1. foobar <- function(x, y, z){ a <- subset(x, (x$var1 == y)) b <- subset(a, (a$var2 == z)) n=nrow(b) return(n) } Examples: foobar(foo, 1, "A") # returns 2 foobar(foo, 1, "B") # returns 1 foobar(foo, 3, "C") # returns 1 This works. But now I want to submit a data frame of values to foobar. Instead of the above examples, I would like to submit df to foobar and get the same results as above (2, 1, 1) df <- data.frame(var1=c('1','1','3'), var2=c("A", "B", "C")) When I change foobar to accept two arguments like foobar(foo, df) and use y[, c(var1)] and y[, c(var2)] instead of the two parameters x and y it still doesn't work. Which way is there to do this?

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• Finding a sequence in a List

  - by user113164

  I have a list of integers that I would like to search for a sequence. For example, if i have a master list: 1, 2, 3, 4, 9, 2, 39, 482, 19283, 19, 23, 1, 29 And I want to find sequence: 1, 2, 3, 4 I would like some easy way to fill a subset list with: 1, 2, 3, 4 + the next five integers in the master list And then remove the integers in the subset list from the master list so at the end of the operation, my lists would look like this: Master list: 19, 23, 1, 29 Subset list: 1, 2, 3, 4, 9, 2, 39, 482, 19283 Hope that makes sense. I'm guessing maybe linq would be good for something like this, but I've never used it before. Can anyone help?

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• Accessing 'data' argument of with() function?

  - by Ken Williams

  Is it possible, in the expr expression of the with() function, to access the data argument directly? Here's what I mean conceptually: > print(df) result qid f1 f2 f3 -1 1 0.0000 0.1253 0.0000 -1 1 0.0098 0.0000 0.0000 1 1 0.0000 0.0000 0.1941 -1 2 0.0000 0.2863 0.0948 1 2 0.0000 0.0000 0.0000 1 2 0.0000 0.7282 0.9087 > with(df, subset(.data, select=f1:f3)) # Doesn't work Of course the above example is kind of silly, but it would be handy for things like this: with(subset(df, f2>0), foo(qid, vars=subset(.data, select=f1:f3))) I tried to poke around with environment() and parent.frame() etc., but didn't come up with anything that worked. Maybe this is really a question about eval(), since that's how with.default() is implemented.

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• PrintingPermissionLevel, SafePrinting, and restrictions

  - by Steve Cooper

  There is a PrintingPermission attribute in the framework which takes a PrintingPermissionLevel enumeration with one of these values; NoPrinting: Prevents access to printers. NoPrinting is a subset of SafePrinting. SafePrinting: Provides printing only from a restricted dialog box. SafePrinting is a subset of DefaultPrinting. DefaultPrinting: Provides printing programmatically to the default printer, along with safe printing through semirestricted dialog box. DefaultPrinting is a subset of AllPrinting. AllPrinting: Provides full access to all printers. The documentation is really sparse, and I wondered if anyone can tell me more about the SafePrinting option. What does the documentation mean when it says "Provides printing only from a restricted dialog box." I have no idea what this means. Can anyone shed any light? This subject is touched in the MS certification 70-505: TS: Microsoft .NET Framework 3.5, Windows Forms Application Development and so I'm keen to find out more.

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• Exception in thread "main" java.lang.StackOverflowError

  - by Ray.R.Chua

  I have a piece of code and I could not figure out why it is giving me Exception in thread "main" java.lang.StackOverflowError. This is the question: Given a positive integer n, prints out the sum of the lengths of the Syracuse sequence starting in the range of 1 to n inclusive. So, for example, the call: lengths(3) will return the the combined length of the sequences: 1 2 1 3 10 5 16 8 4 2 1 which is the value: 11. lengths must throw an IllegalArgumentException if its input value is less than one. My Code: import java.util.HashMap; public class Test { HashMap<Integer,Integer> syraSumHashTable = new HashMap<Integer,Integer>(); public Test(){ } public int lengths(int n)throws IllegalArgumentException{ int sum =0; if(n < 1){ throw new IllegalArgumentException("Error!! Invalid Input!"); } else{ for(int i =1; i<=n;i++){ if(syraSumHashTable.get(i)==null) { syraSumHashTable.put(i, printSyra(i,1)); sum += (Integer)syraSumHashTable.get(i); } else{ sum += (Integer)syraSumHashTable.get(i); } } return sum; } } private int printSyra(int num, int count){ int n = num; if(n == 1){ return count; } else{ if(n%2==0){ return printSyra(n/2, ++count); } else{ return printSyra((n*3)+1, ++count) ; } } } } Driver code: public static void main(String[] args) { // TODO Auto-generated method stub Test s1 = new Test(); System.out.println(s1.lengths(90090249)); //System.out.println(s1.lengths(5)); } . I know the problem lies with the recursion. The error does not occur if the input is a small value, example: 5. But when the number is huge, like 90090249, I got the Exception in thread "main" java.lang.StackOverflowError. Thanks all for your help. :) I almost forgot the error msg: Exception in thread "main" java.lang.StackOverflowError at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:65) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:65) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60) at Test.printSyra(Test.java:60)

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• Stop running this script, IE7 using PHP

  - by Jomel Dicen

  I incorporate javascript in my PHP program: Try to check my codes. It loops depend on the number of records in database. for instance: $counter = 0; foreach($row_value as $data): echo $this->javascript($counter, $data->exrate, $data->tab); endforeach; private function javascript($counter=NULL, $exrate=NULL, $tab=NULL){ $js = " <script type='text/javascript'> $(function () { var textBox0 = $('input:text[id$=quantity{$counter}]').keyup(foo); var textBox1 = $('input:text[id$=mc{$counter}]').keyup(foo); var textBox2 = $('input:text[id$=lc{$counter}]').keyup(foo); function foo() { var value0 = textBox0.val(); var value1 = textBox1.val(); var value2 = textBox2.val(); var sum = add(value1, value2) * (value0 * {$exrate}); $('input:text[id$=result{$counter}]').val(parseFloat(sum).toFixed(2)); // Compute Total Quantity var qtotal = 0; $('.quantity{$tab}').each(function() { qtotal += Number($(this).val()); }); $('#tquantity{$tab}').text(qtotal); // Compute MC UNIT var mctotal = 0; $('.mc{$tab}').each(function() { mctotal += Number($(this).val()); }); $('#tmc{$tab}').text(mctotal); // Compute LC UNIT var lctotal = 0; $('.lc{$tab}').each(function() { lctotal += Number($(this).val()); }); $('#tlc{$tab}').text(lctotal); // Compute Result var result = 0; $('.result{$tab}').each(function() { result += Number($(this).val()); }); $('#tresult{$tab}').text(result); } function add() { var sum = 0; for (var i = 0, j = arguments.length; i < j; i++) { if (IsNumeric(arguments[i])) { sum += parseFloat(arguments[i]); } } return sum; } function IsNumeric(input) { return (input - 0) == input && input.length > 0; } }); </script> "; return $js; } When I running this on IE this message is always annoying me " Stop running this script? A script on this page is causing your web browser to run slowly. If it continues to run, your computer might become unresponsive." but in firefox it's functioning well.

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• SQL SERVER – How to Ignore Columnstore Index Usage in Query

  - by pinaldave

  Earlier I wrote about SQL SERVER – Fundamentals of Columnstore Index and very first question I received in email was as following. “We are using SQL Server 2012 CTP3 and so far so good. In our data warehouse solution we have created 1 non-clustered columnstore index on our large fact table. We have very unique situation but your article did not cover it. We are running few queries on our fact table which is working very efficiently but there is one query which earlier was running very fine but after creating this non-clustered columnstore index this query is running very slow. We dropped the columnstore index and suddenly this one query is running fast but other queries which were benefited by this columnstore index it is running slow. Any workaround in this situation?” In summary the question in simple words “How can we ignore using columnstore index in selective queries?” Very interesting question – you can use I can understand there may be the cases when columnstore index is not ideal and needs to be ignored the same. You can use the query hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX to ignore the columnstore index. SQL Server Engine will use any other index which is best after ignoring the columnstore index. Here is the quick script to prove the same. We will first create sample database and then create columnstore index on the same. Once columnstore index is created we will write simple query. This query will use columnstore index. We will then show the usage of the query hint. USE AdventureWorks GO -- Create New Table CREATE TABLE [dbo].[MySalesOrderDetail]( [SalesOrderID] [int] NOT NULL, [SalesOrderDetailID] [int] NOT NULL, [CarrierTrackingNumber] [nvarchar](25) NULL, [OrderQty] [smallint] NOT NULL, [ProductID] [int] NOT NULL, [SpecialOfferID] [int] NOT NULL, [UnitPrice] [money] NOT NULL, [UnitPriceDiscount] [money] NOT NULL, [LineTotal] [numeric](38, 6) NOT NULL, [rowguid] [uniqueidentifier] NOT NULL, [ModifiedDate] [datetime] NOT NULL ) ON [PRIMARY] GO -- Create clustered index CREATE CLUSTERED INDEX [CL_MySalesOrderDetail] ON [dbo].[MySalesOrderDetail] ( [SalesOrderDetailID]) GO -- Create Sample Data Table -- WARNING: This Query may run upto 2-10 minutes based on your systems resources INSERT INTO [dbo].[MySalesOrderDetail] SELECT S1.* FROM Sales.SalesOrderDetail S1 GO 100 -- Create ColumnStore Index CREATE NONCLUSTERED COLUMNSTORE INDEX [IX_MySalesOrderDetail_ColumnStore] ON [MySalesOrderDetail] (UnitPrice, OrderQty, ProductID) GO Now we have created columnstore index so if we run following query it will use for sure the same index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID GO We can specify Query Hint IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX as described in following query and it will not use columnstore index. -- Select Table with regular Index SELECT ProductID, SUM(UnitPrice) SumUnitPrice, AVG(UnitPrice) AvgUnitPrice, SUM(OrderQty) SumOrderQty, AVG(OrderQty) AvgOrderQty FROM [dbo].[MySalesOrderDetail] GROUP BY ProductID ORDER BY ProductID OPTION (IGNORE_NONCLUSTERED_COLUMNSTORE_INDEX) GO Let us clean up the database. -- Cleanup DROP INDEX [IX_MySalesOrderDetail_ColumnStore] ON [dbo].[MySalesOrderDetail] GO TRUNCATE TABLE dbo.MySalesOrderDetail GO DROP TABLE dbo.MySalesOrderDetail GO Again, make sure that you use hint sparingly and understanding the proper implication of the same. Make sure that you test it with and without hint and select the best option after review of your administrator. Here is the question for you – have you started to use SQL Server 2012 for your validation and development (not on production)? It will be interesting to know the answer. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Index, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Error codes for C++

  - by billy

  #include <iostream> #include <iomanip> using namespace std; //Global constant variable declaration const int MaxRows = 8, MaxCols = 10, SEED = 10325; //Functions Declaration void PrintNameHeader(ostream& out); void Fill2DArray(double ary[][MaxCols]); void Print2DArray(const double ary[][MaxCols]); double GetTotal(const double ary[][MaxCols]); double GetAverage(const double ary[][MaxCols]); double GetRowTotal(const double ary[][MaxCols], int theRow); double GetColumnTotal(const double ary[][MaxCols], int theRow); double GetHighestInRow(const double ary[][MaxCols], int theRow); double GetLowestInRow(const double ary[][MaxCols], int theRow); double GetHighestInCol(const double ary[][MaxCols], int theCol); double GetLowestInCol(const double ary[][MaxCols], int theCol); double GetHighest(const double ary[][MaxCols], int& theRow, int& theCol); double GetLowest(const double ary[][MaxCols], int& theRow, int& theCol); int main() { int theRow; int theCol; PrintNameHeader(cout); cout << fixed << showpoint << setprecision(1); srand(static_cast<unsigned int>(SEED)); double ary[MaxRows][MaxCols]; cout << "The seed value for random number generator is: " << SEED << endl; cout << endl; Fill2DArray(ary); Print2DArray(ary); cout << " The Total for all the elements in this array is: " << setw(7) << GetTotal(ary) << endl; cout << "The Average of all the elements in this array is: " << setw(7) << GetAverage(ary) << endl; cout << endl; cout << "The sum of each row is:" << endl; for(int index = 0; index < MaxRows; index++) { cout << "Row " << (index + 1) << ": " << GetRowTotal(ary, theRow) << endl; } cout << "The highest and lowest of each row is: " << endl; for(int index = 0; index < MaxCols; index++) { cout << "Row " << (index + 1) << ": " << GetHighestInRow(ary, theRow) << " " << GetLowestInRow(ary, theRow) << endl; } cout << "The highest and lowest of each column is: " << endl; for(int index = 0; index < MaxCols; index++) { cout << "Col " << (index + 1) << ": " << GetHighestInCol(ary, theRow) << " " << GetLowestInCol(ary, theRow) << endl; } cout << "The highest value in all the elements in this array is: " << endl; cout << GetHighest(ary, theRow, theCol) << "[" << theRow << "]" << "[" << theCol << "]" << endl; cout << "The lowest value in all the elements in this array is: " << endl; cout << GetLowest(ary, theRow, theCol) << "[" << theRow << "]" << "[" << theCol << "]" << endl; return 0; } //Define Functions void PrintNameHeader(ostream& out) { out << "*******************************" << endl; out << "* *" << endl; out << "* C.S M10A Spring 2010 *" << endl; out << "* Programming Assignment 10 *" << endl; out << "* Due Date: Thurs. Mar. 25 *" << endl; out << "*******************************" << endl; out << endl; } void Fill2DArray(double ary[][MaxCols]) { for(int index1 = 0; index1 < MaxRows; index1++) { for(int index2= 0; index2 < MaxCols; index2++) { ary[index1][index2] = (rand()%1000)/10; } } } void Print2DArray(const double ary[][MaxCols]) { cout << " Column "; for(int index = 0; index < MaxCols; index++) { int column = index + 1; cout << " " << column << " "; } cout << endl; cout << " "; for(int index = 0; index < MaxCols; index++) { int column = index +1; cout << "----- "; } cout << endl; for(int index1 = 0; index1 < MaxRows; index1++) { cout << "Row " << (index1 + 1) << ":"; for(int index2= 0; index2 < MaxCols; index2++) { cout << setw(6) << ary[index1][index2]; } } } double GetTotal(const double ary[][MaxCols]) { double total = 0; for(int theRow = 0; theRow < MaxRows; theRow++) { total = total + GetRowTotal(ary, theRow); } return total; } double GetAverage(const double ary[][MaxCols]) { double total = 0, average = 0; total = GetTotal(ary); average = total / (MaxRows * MaxCols); return average; } double GetRowTotal(const double ary[][MaxCols], int theRow) { double sum = 0; for(int index = 0; index < MaxCols; index++) { sum = sum + ary[theRow][index]; } return sum; } double GetColumTotal(const double ary[][MaxCols], int theCol) { double sum = 0; for(int index = 0; index < theCol; index++) { sum = sum + ary[index][theCol]; } return sum; } double GetHighestInRow(const double ary[][MaxCols], int theRow) { double highest = 0; for(int index = 0; index < MaxCols; index++) { if(ary[theRow][index] > highest) highest = ary[theRow][index]; } return highest; } double GetLowestInRow(const double ary[][MaxCols], int theRow) { double lowest = 0; for(int index = 0; index < MaxCols; index++) { if(ary[theRow][index] < lowest) lowest = ary[theRow][index]; } return lowest; } double GetHighestInCol(const double ary[][MaxCols], int theCol) { double highest = 0; for(int index = 0; index < MaxRows; index++) { if(ary[index][theCol] > highest) highest = ary[index][theCol]; } return highest; } double GetLowestInCol(const double ary[][MaxCols], int theCol) { double lowest = 0; for(int index = 0; index < MaxRows; index++) { if(ary[index][theCol] < lowest) lowest = ary[index][theCol]; } return lowest; } double GetHighest(const double ary[][MaxCols], int& theRow, int& theCol) { theRow = 0; theCol = 0; double highest = ary[theRow][theCol]; for(int index = 0; index < MaxRows; index++) { for(int index1 = 0; index1 < MaxCols; index1++) { double highest = 0; if(ary[index1][theCol] > highest) { highest = ary[index][index1]; theRow = index; theCol = index1; } } } return highest; } double Getlowest(const double ary[][MaxCols], int& theRow, int& theCol) { theRow = 0; theCol = 0; double lowest = ary[theRow][theCol]; for(int index = 0; index < MaxRows; index++) { for(int index1 = 0; index1 < MaxCols; index1++) { double lowest = 0; if(ary[index1][theCol] < lowest) { lowest = ary[index][index1]; theRow = index; theCol = index1; } } } return lowest; } . 1>------ Build started: Project: teddy lab 10, Configuration: Debug Win32 ------ 1>Compiling... 1>lab 10.cpp 1>c:\users\owner\documents\visual studio 2008\projects\teddy lab 10\teddy lab 10\ lab 10.cpp(46) : warning C4700: uninitialized local variable 'theRow' used 1>c:\users\owner\documents\visual studio 2008\projects\teddy lab 10\teddy lab 10\ lab 10.cpp(62) : warning C4700: uninitialized local variable 'theCol' used 1>Linking... 1> lab 10.obj : error LNK2028: unresolved token (0A0002E0) "double __cdecl GetLowest(double const (* const)[10],int &,int &)" (?GetLowest@@$$FYANQAY09$$CBNAAH1@Z) referenced in function "int __cdecl main(void)" (?main@@$$HYAHXZ) 1> lab 10.obj : error LNK2019: unresolved external symbol "double __cdecl GetLowest(double const (* const)[10],int &,int &)" (?GetLowest@@$$FYANQAY09$$CBNAAH1@Z) referenced in function "int __cdecl main(void)" (?main@@$$HYAHXZ) 1>C:\Users\owner\Documents\Visual Studio 2008\Projects\ lab 10\Debug\ lab 10.exe : fatal error LNK1120: 2 unresolved externals 1>Build log was saved at "file://c:\Users\owner\Documents\Visual Studio 2008\Projects\ lab 10\teddy lab 10\Debug\BuildLog.htm" 1>teddy lab 10 - 3 error(s), 2 warning(s) ========== Build: 0 succeeded, 1 failed, 0 up-to-date, 0 skipped ==========

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• Missing features from WebGL and OpenGL ES

  - by Chris Smith

  I've started using WebGL and am pleased with how easy it is to leverage my OpenGL (and by extension OpenGL ES) experience. However, my understanding is as follows: OpenGL ES is a subset of OpenGL WebGL is a subset of OpenGL ES Is this correct for both cases? If so, are there resources for detailing which features are missing? For example, one notable missing feature is glPushMatrix and glPopMatrix. I don't see those in WebGL, but in my searches I cannot find them referenced in OpenGL ES material either.

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• Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

  - by Mark Hornick

  The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3),         count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(cv.count = cv)   }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(year=x$year[1], cv.count = cv)   }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res)    year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   data.frame(year=x$year[1], mean.count = mean.count)   }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res)    year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) {   total.count <- sum(x$count)   data.frame(year=x$year[1], count=x$count, total.count = total.count)   }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res)    year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),       cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) {   mu <- mean(x$count)   sigma <- sd(x$count)   cv <- sigma/mu   data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv)   }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res)    year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize,            homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) {   data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr))   }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res)    year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),                  y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() +   geom_point(data = df, aes(x = gp, y = y)) +   geom_point(data = ds, aes(x = gp, y = mean),              colour = 'red', size = 3) +   geom_errorbar(data = ds, aes(x = gp, y = mean,                                ymin = mean - sd, ymax = mean + sd),              colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) {   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) {   df <- df[,1:2]   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4)   }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

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• Tell the kernel to strongly cache a particular directory

  - by silviot

  This question is a rephrasing of Optimizing EXT4 performance. I have a directory that contains build files, most very small, but totaling 5.6G. I usually access the same subset of files (some thousands, for some tens of megabytes) over and over again. The subset changes daily (different projects, different versions of libraries). What takes longer when I use it seem to be disk seeks. For example if I do a du twice the second time it takes as much time as the first, and disk activity is similar. Ideally I'd like to tell the kernel to allocate X Mb to the metadata and Y to data in the folder, like the options for nfs cache. Is it possible in some way, other than mounting nfs from localhost and caching it to a ramdisk?

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• Sample uniformly at random from an n-dimensional unit simplex.

  - by dreeves

  Sampling uniformly at random from an n-dimensional unit simplex is the fancy way to say that you want n random numbers such that they are all non-negative, they sum to one, and every possible vector of n non-negative numbers that sum to one are equally likely. In the n=2 case you want to sample uniformly from the segment of the line x+y=1 (ie, y=1-x) that is in the positive quadrant. In the n=3 case you're sampling from the triangle-shaped part of the plane x+y+z=1 that is in the positive octant of R3: (Image from http://en.wikipedia.org/wiki/Simplex.) Note that picking n uniform random numbers and then normalizing them so they sum to one does not work. You end up with a bias towards less extreme numbers. Similarly, picking n-1 uniform random numbers and then taking the nth to be one minus the sum of them also introduces bias. Wikipedia gives two algorithms to do this correctly: http://en.wikipedia.org/wiki/Simplex#Random_sampling (Though the second one currently claims to only be correct in practice, not in theory. I'm hoping to clean that up or clarify it when I understand this better. I initially stuck in a "WARNING: such-and-such paper claims the following is wrong" on that Wikipedia page and someone else turned it into the "works only in practice" caveat.) Finally, the question: What do you consider the best implementation of simplex sampling in Mathematica (preferably with empirical confirmation that it's correct)? Related questions http://stackoverflow.com/questions/2171074/generating-a-probability-distribution http://stackoverflow.com/questions/3007975/java-random-percentages

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• mysql query to dynamically convert row data to columns

  - by Anirudh Goel

  I am working on a pivot table query. The schema is as follows Sno, Name, District The same name may appear in many districts eg take the sample data for example 1 Mike CA 2 Mike CA 3 Proctor JB 4 Luke MN 5 Luke MN 6 Mike CA 7 Mike LP 8 Proctor MN 9 Proctor JB 10 Proctor MN 11 Luke MN As you see i have a set of 4 distinct districts (CA, JB, MN, LP). Now i wanted to get the pivot table generated for it by mapping the name against districts Name CA JB MN LP Mike 3 0 0 1 Proctor 0 2 2 0 Luke 0 0 3 0 i wrote the following query for this select name,sum(if(District="CA",1,0)) as "CA",sum(if(District="JB",1,0)) as "JB",sum(if(District="MN",1,0)) as "MN",sum(if(District="LP",1,0)) as "LP" from district_details group by name However there is a possibility that the districts may increase, in that case i will have to manually edit the query again and add the new district to it. I want to know if there is a query which can dynamically take the names of distinct districts and run the above query. I know i can do it with a procedure and generating the script on the fly, is there any other method too? I ask so because the output of the query "select distinct(districts) from district_details" will return me a single column having district name on each row, which i will like to be transposed to the column.

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• Porting Oracle Procedure to PostgreSQL

  - by Grasper

  I am porting an Oracle function into Postgres PGPLSQL.. I have been using this guide: http://www.postgresql.org/docs/8.1/static/plpgsql.html CREATE OR REPLACE PROCEDURE DATA_UPDATE (mission NUMBER, task NUMBER) AS BEGIN IF mission IS NOT NULL THEN UPDATE MISSION_OBJECTIVE MO SET (MO.MO_TKR_TOTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT NVL(SUM(RR.TRQ_FUEL_OFFLOAD),0), NVL(SUM(RR.TRQ_NUMBER_RECEIVERS),0) FROM REFUELING_REQUEST RR, MISSION_REQUEST_PAIRING MRP WHERE MO.MSN_INT_ID = MRP.MSN_INT_ID AND MO.MO_INT_ID = MRP.MO_INT_ID AND MRP.REQ_INT_ID = RR.REQ_INT_ID) WHERE MO.MSN_INT_ID = mission AND MO.MO_INT_ID = task ; END IF ; COMMIT ; END ; I've got it this far: CREATE OR REPLACE FUNCTION DATA_UPDATE (NUMERIC, NUMERIC) RETURNS integer as ' DECLARE mission ALIAS for $1; task ALIAS for $2; BEGIN IF mission IS NOT NULL THEN UPDATE MISSION_OBJECTIVE MO SET (MO.MO_TKR_TOTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT COALESCE(SUM(RR.TRQ_FUEL_OFFLOAD),0), COALESCE(SUM(RR.TRQ_NUMBER_RECEIVERS),0) FROM REFUELING_REQUEST RR, MISSION_REQUEST_PAIRING MRP WHERE MO.MSN_INT_ID = MRP.MSN_INT_ID AND MO.MO_INT_ID = MRP.MO_INT_ID AND MRP.REQ_INT_ID = RR.REQ_INT_ID) WHERE MO.MSN_INT_ID = mission AND MO.MO_INT_ID = task ; END IF; COMMIT; END; ' LANGUAGE plpgsql; This is the error I get: ERROR: syntax error at or near "SELECT" LINE 1: ...OTAL_OFF_SCHEDULED, MO.MO_TKR_TOTAL_RECEIVERS) = (SELECT COA... I do not know why this isn't working... any ideas?

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