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  • trying to understand some codes related to window.onload in js

    - by user2507818
    <body> <script language="javascript"> window.tdiff = []; fred = function(a,b){return a-b;}; window.onload = function(e){ console.log("window.onload", e, Date.now() ,window.tdiff, (window.tdiff[1] = Date.now()) && window.tdiff.reduce(fred) ); } </script> </body> Above code is taken from a site. In firefox-console, it shows: window.onload load 1372646227664 [undefined, 1372646227664] 1372646227664 Question: For window.tdiff->[undefined, 1372646227664], why not:[], because when runs to code:window.tdiff, it is still an empty array? For window.tdiff.reduce(fred)->1372646227664, window.tdiff = [undefined, 1372646227664], undefined - 1372646227664, should be NaN, why it shows 1372646227664?

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  • Add Recaptcha and GridView to an ASP.NET 3.5 Guestbook using MS SQL Server and VB.NET

    This is the conclusion to a four-part ASP.NET 3.5 guest book application tutorial series. In this last part you will learn how to integrate Recaptcha which is used to prevent spam automatic bot submission. Also to be discussed is how to add a GridView web control which is used to display all guest book comments retrieved from the database.... Download a Free Trial of Windows 7 Reduce Management Costs and Improve Productivity with Windows 7

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  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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  • Merging /boot and rearranging grub2 entries

    - by Tobias Kienzler
    I have used 10.10 and now for testing purposes installed 10.04 to a separate partition. 10.10 is currently on a single partition, while for 10.04 I decided to separate /boot to a third partition. Now my questions: How can I move and merge 10.10's /boot on the new /boot partition What do I have to modify to rearrange the (automatic) entries? How can I have the entries contain the distribution name to reduce confusion? How can I make sure the grub configuration stays identical when either distribution updates?

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  • Merging /boot and rearring grub2 entries

    - by Tobias Kienzler
    I have used 10.10 and now for testing purposes installed 10.04 to a separate partition. 10.10 is currently on a single partition, while for 10.04 I decided to separate /boot to a third partition. Now my questions: How can I move and merge 10.10's /boot on the new /boot partition What do I have to modify to rearrange the (automatic) entries? How can I have the entries contain the distribution name to reduce confusion? How can I make sure the grub configuration stays identical?

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  • Interview with Al-Sorayai Group’s Managing Director on the Oracle Retail deployment

    - by user801960
    Recently, I had the opportunity to speak with Sheik Al Sorayai, Managing Director of the Saudi Arabian carpet and rug manufacturer, the Al-Sorayai Group. His business has recently implemented Oracle® Retail Merchandising and Stores applications in only six months to support the launch of its new furniture retail concept, HomeStyle. With an aggressive growth strategy for the new business in place, the Oracle Retail solutions are enabling Al-Sorayai to coordinate merchandising and store operations and improve decision-making and insight to optimise margins, reduce inventory costs and provide a consistent customer experience.

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  • NoSQL with RavenDB and ASP.NET MVC - Part 2

    - by shiju
    In my previous post, we have discussed on how to work with RavenDB document database in an ASP.NET MVC application. We have setup RavenDB for our ASP.NET MVC application and did basic CRUD operations against a simple domain entity. In this post, let’s discuss on domain entity with deep object graph and how to query against RavenDB documents using Indexes.Let's create two domain entities for our demo ASP.NET MVC appplication  public class Category {       public string Id { get; set; }     [Required(ErrorMessage = "Name Required")]     [StringLength(25, ErrorMessage = "Must be less than 25 characters")]     public string Name { get; set;}     public string Description { get; set; }     public List<Expense> Expenses { get; set; }       public Category()     {         Expenses = new List<Expense>();     } }    public class Expense {       public string Id { get; set; }     public Category Category { get; set; }     public string  Transaction { get; set; }     public DateTime Date { get; set; }     public double Amount { get; set; }   }  We have two domain entities - Category and Expense. A single category contains a list of expense transactions and every expense transaction should have a Category.Let's create  ASP.NET MVC view model  for Expense transaction public class ExpenseViewModel {     public string Id { get; set; }       public string CategoryId { get; set; }       [Required(ErrorMessage = "Transaction Required")]            public string Transaction { get; set; }       [Required(ErrorMessage = "Date Required")]            public DateTime Date { get; set; }       [Required(ErrorMessage = "Amount Required")]     public double Amount { get; set; }       public IEnumerable<SelectListItem> Category { get; set; } } Let's create a contract type for Expense Repository  public interface IExpenseRepository {     Expense Load(string id);     IEnumerable<Expense> GetExpenseTransactions(DateTime startDate,DateTime endDate);     void Save(Expense expense,string categoryId);     void Delete(string id);  } Let's create a concrete type for Expense Repository for handling CRUD operations. public class ExpenseRepository : IExpenseRepository {   private IDocumentSession session; public ExpenseRepository() {         session = MvcApplication.CurrentSession; } public Expense Load(string id) {     return session.Load<Expense>(id); } public IEnumerable<Expense> GetExpenseTransactions(DateTime startDate, DateTime endDate) {             //Querying using the Index name "ExpenseTransactions"     //filtering with dates     var expenses = session.LuceneQuery<Expense>("ExpenseTransactions")         .WaitForNonStaleResults()         .Where(exp => exp.Date >= startDate && exp.Date <= endDate)         .ToArray();     return expenses; } public void Save(Expense expense,string categoryId) {     var category = session.Load<Category>(categoryId);     if (string.IsNullOrEmpty(expense.Id))     {         //new expense transaction         expense.Category = category;         session.Store(expense);     }     else     {         //modifying an existing expense transaction         var expenseToEdit = Load(expense.Id);         //Copy values to  expenseToEdit         ModelCopier.CopyModel(expense, expenseToEdit);         //set category object         expenseToEdit.Category = category;       }     //save changes     session.SaveChanges(); } public void Delete(string id) {     var expense = Load(id);     session.Delete<Expense>(expense);     session.SaveChanges(); }   }  Insert/Update Expense Transaction The Save method is used for both insert a new expense record and modifying an existing expense transaction. For a new expense transaction, we store the expense object with associated category into document session object and load the existing expense object and assign values to it for editing a existing record.  public void Save(Expense expense,string categoryId) {     var category = session.Load<Category>(categoryId);     if (string.IsNullOrEmpty(expense.Id))     {         //new expense transaction         expense.Category = category;         session.Store(expense);     }     else     {         //modifying an existing expense transaction         var expenseToEdit = Load(expense.Id);         //Copy values to  expenseToEdit         ModelCopier.CopyModel(expense, expenseToEdit);         //set category object         expenseToEdit.Category = category;       }     //save changes     session.SaveChanges(); } Querying Expense transactions   public IEnumerable<Expense> GetExpenseTransactions(DateTime startDate, DateTime endDate) {             //Querying using the Index name "ExpenseTransactions"     //filtering with dates     var expenses = session.LuceneQuery<Expense>("ExpenseTransactions")         .WaitForNonStaleResults()         .Where(exp => exp.Date >= startDate && exp.Date <= endDate)         .ToArray();     return expenses; }  The GetExpenseTransactions method returns expense transactions using a LINQ query expression with a Date comparison filter. The Lucene Query is using a index named "ExpenseTransactions" for getting the result set. In RavenDB, Indexes are LINQ queries stored in the RavenDB server and would be  executed on the background and will perform query against the JSON documents. Indexes will be working with a lucene query expression or a set operation. Indexes are composed using a Map and Reduce function. Check out Ayende's blog post on Map/Reduce We can create index using RavenDB web admin tool as well as programmitically using its Client API. The below shows the screen shot of creating index using web admin tool. We can also create Indexes using Raven Cleint API as shown in the following code documentStore.DatabaseCommands.PutIndex("ExpenseTransactions",     new IndexDefinition<Expense,Expense>() {     Map = Expenses => from exp in Expenses                     select new { exp.Date } });  In the Map function, we used a Linq expression as shown in the following from exp in docs.Expensesselect new { exp.Date };We have not used a Reduce function for the above index. A Reduce function is useful while performing aggregate functions based on the results from the Map function. Indexes can be use with set operations of RavenDB.SET OperationsUnlike other document databases, RavenDB supports set based operations that lets you to perform updates, deletes and inserts to the bulk_docs endpoint of RavenDB. For doing this, you just pass a query to a Index as shown in the following commandDELETE http://localhost:8080/bulk_docs/ExpenseTransactions?query=Date:20100531The above command using the Index named "ExpenseTransactions" for querying the documents with Date filter and  will delete all the documents that match the query criteria. The above command is equivalent of the following queryDELETE FROM ExpensesWHERE Date='2010-05-31' Controller & ActionsWe have created Expense Repository class for performing CRUD operations for the Expense transactions. Let's create a controller class for handling expense transactions.   public class ExpenseController : Controller { private ICategoryRepository categoyRepository; private IExpenseRepository expenseRepository; public ExpenseController(ICategoryRepository categoyRepository, IExpenseRepository expenseRepository) {     this.categoyRepository = categoyRepository;     this.expenseRepository = expenseRepository; } //Get Expense transactions based on dates public ActionResult Index(DateTime? StartDate, DateTime? EndDate) {     //If date is not passed, take current month's first and last dte     DateTime dtNow;     dtNow = DateTime.Today;     if (!StartDate.HasValue)     {         StartDate = new DateTime(dtNow.Year, dtNow.Month, 1);         EndDate = StartDate.Value.AddMonths(1).AddDays(-1);     }     //take last date of startdate's month, if endate is not passed     if (StartDate.HasValue && !EndDate.HasValue)     {         EndDate = (new DateTime(StartDate.Value.Year, StartDate.Value.Month, 1)).AddMonths(1).AddDays(-1);     }       var expenses = expenseRepository.GetExpenseTransactions(StartDate.Value, EndDate.Value);     if (Request.IsAjaxRequest())     {           return PartialView("ExpenseList", expenses);     }     ViewData.Add("StartDate", StartDate.Value.ToShortDateString());     ViewData.Add("EndDate", EndDate.Value.ToShortDateString());             return View(expenses);            }   // GET: /Expense/Edit public ActionResult Edit(string id) {       var expenseModel = new ExpenseViewModel();     var expense = expenseRepository.Load(id);     ModelCopier.CopyModel(expense, expenseModel);     var categories = categoyRepository.GetCategories();     expenseModel.Category = categories.ToSelectListItems(expense.Category.Id.ToString());                    return View("Save", expenseModel);          }   // // GET: /Expense/Create   public ActionResult Create() {     var expenseModel = new ExpenseViewModel();               var categories = categoyRepository.GetCategories();     expenseModel.Category = categories.ToSelectListItems("-1");     expenseModel.Date = DateTime.Today;     return View("Save", expenseModel); }   // // POST: /Expense/Save // Insert/Update Expense Tansaction [HttpPost] public ActionResult Save(ExpenseViewModel expenseViewModel) {     try     {         if (!ModelState.IsValid)         {               var categories = categoyRepository.GetCategories();                 expenseViewModel.Category = categories.ToSelectListItems(expenseViewModel.CategoryId);                               return View("Save", expenseViewModel);         }           var expense=new Expense();         ModelCopier.CopyModel(expenseViewModel, expense);          expenseRepository.Save(expense, expenseViewModel.CategoryId);                       return RedirectToAction("Index");     }     catch     {         return View();     } } //Delete a Expense Transaction public ActionResult Delete(string id) {     expenseRepository.Delete(id);     return RedirectToAction("Index");     }     }     Download the Source - You can download the source code from http://ravenmvc.codeplex.com

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  • CRMIT Solution´s CRM++ Asterisk Telephony Connector Achieves Oracle Validated Integration with Oracle Sales Cloud

    - by Richard Lefebvre
    To achieve Oracle Validated Integration, Oracle partners are required to meet a stringent set of requirements that are based on the needs and priorities of the customers. Based on a Telephony Application Programming Interface (TAPI) framework the CRM++ Asterisk Telephony Connector integrates the Asterisk telephony solutions with Oracle® Sales Cloud. "The CRM++ Asterisk Telephony Connector for Oracle® Sales Cloud showcases CRMIT Solutions focus and commitment to extend the Customer Experience (CX) expertise to our existing and potential customers," said Vinod Reddy, Founder & CEO, CRMIT Solutions. "Oracle® Validated Integration applies a rigorous technical review and test process," said Kevin O’Brien, senior director, ISV and SaaS Strategy, Oracle®. "Achieving Oracle® Validated Integration through Oracle® PartnerNetwork gives our customers confidence that the CRM++ Asterisk Telephony Connector for Oracle® Sales Cloud has been validated and that the products work together as designed. This helps reduce deployment risk and improves the user experience for our joint customers." CRM++ is a suite of native Customer Experience solutions for Oracle® CRM On Demand, Oracle® Sales Cloud and Oracle® RightNow Cloud Service. With over 3000+ users the CRM++ framework helps extend the Customer Experience (CX) and the power of Customer Relations Management features including Email WorkBench, Self Service Portal, Mobile CRM, Social CRM and Computer Telephony Integration.. About CRMIT Solutions CRMIT Solutions is a pioneer in delivering SaaS-based customer experience (CX) consulting and solutions. With more than 200 certified customer relationship management (CRM) consultants and more than 175 successful CRM deployments globally, CRMIT Solutions offers a range of CRM++ applications for accelerated deployments including various rapid implementation and migration utilities for Oracle® Sales Cloud, Oracle® CRM On Demand, Oracle® Eloqua, Oracle® Social Relationship Management and Oracle® RightNow Cloud Service. About Oracle Validated Integration Oracle Validated Integration, available through the Oracle PartnerNetwork (OPN), gives customers confidence that the integration of complementary partner software products with Oracle Applications and specific Oracle Fusion Middleware solutions have been validated, and the products work together as designed. This can help customers reduce risk, improve system implementation cycles, and provide for smoother upgrades and simpler maintenance. Oracle Validated Integration applies a rigorous technical process to review partner integrations. Partners who have successfully completed the program are authorized to use the “Oracle Validated Integration” logo. For more information, please visit Oracle.com at http://www.oracle.com/us/partnerships/solutions/index.html.

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  • Why the Cave Troll in the ‘Mines of Moria’ was So Angry [Humorous LOTR Video]

    - by Asian Angel
    Did you ever wonder why the cave troll the Fellowship of the Ring met in the Mines of Moria was so angry? It all comes down to a certain Hobbit’s carelessness! LEGO The Cranky Cavetroll [via Geeks are Sexy] HTG Explains: What is the Windows Page File and Should You Disable It? How To Get a Better Wireless Signal and Reduce Wireless Network Interference How To Troubleshoot Internet Connection Problems

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  • Internet Explorer 9 Commercial (The Honest Version) [Parody Video]

    - by Asian Angel
    Internet Explorer 9 does well as a browser, but what if things did not run as smoothly as one liked? That is where this humorous parody video of the official Internet Explorer 9 commercial steps in… Internet Explorer 9 Commercial (The Honest Version) [via Softpedia] How To Get a Better Wireless Signal and Reduce Wireless Network Interference How To Troubleshoot Internet Connection Problems 7 Ways To Free Up Hard Disk Space On Windows

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  • Notify-osd -- Now with 70% Less Annoy :-)

    <b>SilverWav's Journal:</b> "Reduce the notify-osd time-out to 3 seconds, rather than the default 10. Its amazing how much this changed my appreciation of the notifications... I have found the annoyance is mainly based on them being on screen too long"

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  • The Difference Between .com, .net, .org and Why We’re About To See Many More Top-Level Domains

    - by Chris Hoffman
    .com, .net, .org and other website suffixes are known as “top-level domains” (TLDs). While we normally see only a few of these, there are hundreds of them – and there may be thousands more soon. Top-level domains are managed by the Internet Assigned Numbers Authority (IANA), which is run by the Internet Corporation for Assigned Names and Numbers (ICANN). HTG Explains: What is the Windows Page File and Should You Disable It? How To Get a Better Wireless Signal and Reduce Wireless Network Interference How To Troubleshoot Internet Connection Problems

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  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

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  • links for 2010-04-28

    - by Bob Rhubart
    Guido Schmutz: Oracle BPM11g available! Oracle ACE Director Guido Schmutz shares his impressions after attending a hands-on workshop conducted by Masons of SOA member Clemens Utschig-Utschig. (tags: oracle otn oracleace bpm soa soasuite) Elena Zannoni : 2010 Collaboration Summit Impressions Elena Zannoni has collected her thoughts on #C10 and shares them in this great blog post. (tags: oracle otn linux architecture collaborate2010) Hajo Normann: BPMN 2.0 in Oracle BPM Suite: The future of BPM starts now "The BPM Studio sets itself apart from pure play BPMN 2.0 tools by being seamlessly integrated inside a holistic SOA / BPM toolset: BPMN models are placed in SCA-Composites in SOA Suite 11g. This allows to abstract away the complexities of SOA integration aspects from business process aspects. For UIs in BPMN tasks, you have the richness of ADF 11g based Frontends." -- Oracle ACE Director and Masons of SOA member Hajo Normann (tags: oracle otn oracleace bpm soa sca) Brain Dirking: AIIM Best Practice Awards to Two Oracle Customers Brian Dirking's great write-up of the AIIM Awards Banquet, at which the Bureau of Indian Affairs and the Charles Town Police Department were among the winners of the 2010 Carl E. Nelson Best Practices Awards. (tags: oracle otn aiim bpm ecm enterprise2.0) Mark Wilcox: Upcoming Directory Services Live Webcast - Improve Time-to-Market and Reduce Cost with Oracle Directory Services Live Webcast: Improve Time-to-Market and Reduce Cost with Oracle Directory Services Event Date: Thursday, May 27, 2010 Event Time: 10:00 AM Pacific Standard Time / 1:00 Eastern Standard Time (tags: oracle otn webcast security identitymanagement) Celine Beck: Introducing AutoVue Document Print Service Celine Beck offers a detailed overview of Oracle AutoVue. (tags: oracle otn enatarch visualization printing) Vikas Jain: What's new in OWSM 11gR1 PS2 (11.1.1.3.0) ? Vikas Jain shares links to resources relevant to the recently releases patch set for Oracle Web Services Manager 11gR1. (tags: oracle otn soa webservices oswm) @theovanarem: Oracle SOA Suite 11g Release 1 Patch Set 2 Theo Van Arem shares links to several resources relevant to the release of the latest patch set for Oracle SOA Suite 11g. (tags: oracle otn soa soasuite middleware) @vambenepe: Analyzing the VMforce announcement "The new thing is that force.com now supports an additional runtime, in addition to Apex. That new runtime uses the Java language, with the constraint that it is used via the Spring framework. Which is familiar territory to many developers. That’s it." -- William Vambenepe (tags: oracle otn cloud paas)

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  • 1 Million IOPS

    - by GrumpyOldDBA
    As a keen follower of storage performance I couldn't help but be drawn to this article in The Register http://www.theregister.co.uk/2010/04/14/lsi_million_iops/ this morning. I gave my 5 year old laptop a new lease of life with a SSD and in combination with the old drive made external managed to reduce the time of a demo query from 50 odd mins down to 6 mins. I also have 4 Silicon Power 32GB SSDs set up as a raid 0 on my home server, an overblown PC. http://www.futurestorage.co.uk/index.asp?selmanuf...(read more)

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  • Viewing the Future Through the ‘Eyes of the Past’ [Humorous Image]

    - by Asian Angel
    Really makes you feel nostalgic, eh? You can access the full-size version to get a better view of the upper right corner here. O_O This is a close approximation of the original title of the post. [via Reddit - Tech Support Gore] How To Get a Better Wireless Signal and Reduce Wireless Network Interference How To Troubleshoot Internet Connection Problems 7 Ways To Free Up Hard Disk Space On Windows

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  • Building a Web Form in ASP.NET and PHP: a Comparison

    While there are important differences between PHP and ASP.NET both are used to build websites. Because of this both need to enable developers to build web forms among other tasks. This article compares building a web form in PHP with building the same form in ASP.NET to help those familiar with one set of tools to learn how to use the other set.... Download a Free Trial of Windows 7 Reduce Management Costs and Improve Productivity with Windows 7

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  • SQL SERVER – PREEMPTIVE and Non-PREEMPTIVE – Wait Type – Day 19 of 28

    - by pinaldave
    In this blog post, we are going to talk about a very interesting subject. I often get questions related to SQL Server 2008 Book-Online about various Preemptive wait types. I got a few questions asking what these wait types are and how they could be interpreted. To get current wait types of the system, you can read this article and run the script: SQL SERVER – DMV – sys.dm_os_waiting_tasks and sys.dm_exec_requests – Wait Type – Day 4 of 28. Before we continue understanding them, let us study first what PREEMPTIVE and Non-PREEMPTIVE waits in SQL Server mean. PREEMPTIVE: Simply put, this wait means non-cooperative. While SQL Server is executing a task, the Operating System (OS) interrupts it. This leads to SQL Server to involuntarily give up the execution for other higher priority tasks. This is not good for SQL Server as it is a particular external process which makes SQL Server to yield. This kind of wait can reduce the performance drastically and needs to be investigated properly. Non-PREEMPTIVE: In simple terms, this wait means cooperative. SQL Server manages the scheduling of the threads. When SQL Server manages the scheduling instead of the OS, it makes sure its own priority. In this case, SQL Server decides the priority and one thread yields to another thread voluntarily. In the earlier version of SQL Server, there was no preemptive wait types mentioned and the associated task status with them was marked as suspended. In SQL Server 2005, preemptive wait types were not listed as well, but their associated task status was marked as running. In SQL Server 2008, preemptive wait types are properly listed and their associated task status is also marked as running. Now, SQL Server is in Non-Preemptive mode by default and it works fine. When CLR, extended Stored Procedures and other external components run, they run in Preemptive mode, leading to the creation of these wait types. There are a wide variety of preemptive wait types. If you see consistent high value in the Preemptive wait types, I strongly suggest that you look into the wait type and try to know the root cause. If you are still not sure, you can send me an email or leave a comment about it and I will do my best to help you reduce this wait type. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Skechers Leverages Oracle Applications, Business Intelligence and On Demand Offerings to Drive Long-Term Growth

    - by user801960
    This month Oracle Retail in the USA announced that Skechers - a world leading lifestyle footwear retailer - would be adopting several Oracle Retail products as part of their global growth strategy and to maximise business efficiency.  While based primarily in the USA, Skechers is a respected retailer across the world and has been an Oracle customer since 1997.  The key information about the announcement is below.  To find out more about Skechers visit their website: http://www.skechers.com/  Skechers U.S.A. Inc., an award-winning global leader in the lifestyle footwear industry, has upgraded and expanded its Oracle® Applications investment, implemented Oracle Database and moved to Oracle On Demand, Oracle’s premier cloud service to support rapid growth across its retail and wholesale channels. The new business information systems are part of a larger initiative for the billion-dollar-plus footwear company to fuel growth, reduce total cost of ownership and enable the business to respond faster to market opportunities. With more than 3,000 styles of shoes to design, develop and market, Skechers upgraded to Oracle’s PeopleSoft Enterprise Financial Management and PeopleSoft Supply Chain Management to increase operational efficiencies and improve controls by establishing an integrated, industry-specific platform. An Oracle customer since 1997, Skechers implemented PeopleSoft Enterprise Real Estate Management to meet the rapid growth of its retail stores worldwide. The company is the first customer to go live on the Real Estate Management module and worked closely with Oracle to provide development insight. Skechers also implemented Oracle Fusion Governance, Risk, and Compliance applications. This deployment enabled the company to leverage its existing corporate governance and compliance efforts throughout the global enterprise and more effectively manage the audit processes across multiple business units, processes and systems while reducing audit costs. Next, Skechers leveraged Oracle Financial Analytics, a pre-built Oracle Business Intelligence Application and PeopleSoft Enterprise Project Costing and PeopleSoft Enterprise Contracts to develop a custom Royalty Management dashboard, providing managers with better financial visibility to the company’s licensing contracts. The company switched to Oracle Database and moved database hosting and management to Oracle On Demand to reduce maintenance, implementation and system administration costs. As a result, Skechers is also achieving a better response time and is delivering a higher level of 24x7 support. OSI Consulting, a Platinum partner in Oracle PartnerNetwork (OPN), provided implementation and integration services to Skechers.   To view the full announcement please click here

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  • Retrieving Data from Microsoft SQL Server 2008 Using ASP.NET 3.5

    Most of the web applications on the Internet require retrieving data from a database. Almost all websites today are database-driven so it is of primary importance for any developer to retrieve data from a website s database and display it on the web browser. This article illustrates basic ways of retrieving data from Microsoft SQL Server 2 8 using the ASP.NET 3.5 web platform.... Download a Free Trial of Windows 7 Reduce Management Costs and Improve Productivity with Windows 7

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  • SQL SERVER – Recompile Stored Procedure at Run Time

    - by pinaldave
    I recently received an email from reader after reading my previous article on SQL SERVER – Plan Recompilation and Reduce Recompilation – Performance Tuning regarding how to recompile any stored procedure at run time. There are multiple ways to do this. If you want your stored procedure to always recompile at run time, you can add [...]

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  • Google I/O 2011: High-performance GWT: best practices for writing smaller, faster apps

    Google I/O 2011: High-performance GWT: best practices for writing smaller, faster apps David Chandler The GWT compiler isn't just a Java to JavaScript transliterator. In this session, we'll show you compiler optimizations to shrink your app and make it compile and run faster. Learn common performance pitfalls, how to use lightweight cell widgets, how to use code splitting with Activities and Places, and compiler options to reduce your app's size and compile time. From: GoogleDevelopers Views: 4791 21 ratings Time: 01:01:32 More in Science & Technology

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  • Oracle Optimized Solutions at Oracle OpenWorld 2012

    - by ferhatSF
    Have you registered for Oracle OpenWorld 2012 in San Francisco from September 30 to October 4? Visit the Oracle OpenWorld 2012 site today for registration and more information. Come join us to hear how Oracle Optimized Solutions can help you save money, reduce integration risks, and improve user productivity. Oracle Optimized Solutions are designed, pre-tested, tuned and fully documented architectures for optimal performance and availability. They provide written guidelines to help size, configure, purchase and deploy enterprise solutions that address common IT problems. Built with flexibility in mind, Oracle Optimized Solutions can be deployed as complete solutions or easily tailored to meet your specific needs - they are proven to save money, reduce integration risks and improve user productivity. Here is a preview of the planned Oracle OpenWorld sessions(*) on Oracle Optimized Solutions. October 1, 2012 Monday Time Session ID Title Location 12:15 PM CON7916 Accelerate Oracle E-Business Suite Deployment with SPARC SuperCluster Moscone West - 2001 03:15 PM GEN9691 General Session: Accelerate Your Business with the Oracle Hardware Advantage Moscone North - Hall D 04:45 PM CON4821 Building a Flexible Enterprise Cloud Infrastructure on Oracle SPARC Systems Moscone West - 2001 October 2, 2012 Tuesday Time Session ID Title Location 10:15 AM CON4561 Backup-and-Recovery Best Practices with Oracle Engineered Systems Products Moscone South - 252 11:45 AM CON3851 Optimizing JD Edwards EnterpriseOne on SPARC T4 Servers for Best Performance Moscone West - 2000 01:15 PM GEN11472 General Session: Breakthrough Efficiency in Private Cloud Infrastructure Moscone West - 3014 01:15 PM CON4600 Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization Moscone South - 252 05:00 PM CON9465 Next-Generation Directory: Oracle Unified Directory Moscone West - 3008 05:00 PM CON4088 Accelerate Your SAP Landscape with the Oracle SPARC SuperCluster Moscone West - 2001 05:00 PM CON7743 High-Performance Security for Oracle Applications Using SPARC T4 Systems Moscone West - 2000 05:00 PM CON3857 Archive Strategies for 100 Percent Data Availability Moscone South - 270 October 3, 2012 Wednesday Time Session ID Title Location 10:15 AM CON6528 Configure Oracle Hybrid Columnar Compression to Optimize Query Database Performance up to 10x Moscone South - 252 11:45 AM CON2590 Breakthrough in Private Cloud Management on SPARC T-Series Servers Moscone South - 270 01:15 PM CON4289 Oracle Optimized Solution for Siebel CRM at ACCOR Moscone West - 2000 05:00 PM CON7570 Improve PeopleSoft HCM Performance and Reliability with SPARC SuperCluster Moscone South - 252 * Schedule subject to change In addition, there will be Oracle Optimized Solutions Hands-On-Labs sessions planned. Please enroll ahead of time as space is limited: Oracle Optimized Solutions: Hands on Labs in Oracle OpenWorld Place: Marriott Marquis - Salon 14/15 Date and Time Session ID Title Monday October 1, 2012 01:45 PM HOL9868 Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c Monday October 1, 2012 03:15 PM HOL9907 Oracle Virtual Desktop Infrastructure Performance and Tablet Mobility Wednesday October 3, 2012 05:00 PM HOL9870 x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance Thursday October 4, 2012 11:15 AM HOL9869 0 to Database Backup and Recovery in 60 Minutes Oracle Optimized Solutions executives and experts will also be at hand for discussions and follow ups. And don’t forget to catch live demonstrations of our complete Oracle Optimized Solutions while at Oracle OpenWorld 2012 in San Francisco. We recommend the use of the Schedule Builder tool to plan your visit to the conference and for pre-enrollment in sessions of your interest. We hope to see you there!

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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