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• Is it faster to compute values in a query, call a Scalar Function (decimal(28,2) datatype) 4 times,

  - by Pulsehead

  I have a handful of queries I need to write in SQL Server 2005. Each Query will be calculating 4 unit cost values based on a handful of (up to 11) fields. Any time I want 1 of these 4 unit cost values, I'll want all 4. Which is quicker? Computing in the SQL Query ((a+b+c+d+e+f+g+h+i)/(j+k)), calling ComputeScalarUnitCost(datapoint.ID) 4 times, or joining to ComputeUnitCostTable(datapoint.ID) one time?

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• value types in the vm

  - by john.rose

  value types in the vm p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} p.p2 {margin: 0.0px 0.0px 14.0px 0.0px; font: 14.0px Times} p.p3 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times} p.p4 {margin: 0.0px 0.0px 15.0px 0.0px; font: 14.0px Times} p.p5 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier} p.p6 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Courier; min-height: 17.0px} p.p7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p8 {margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 14.0px Times; min-height: 18.0px} p.p9 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; min-height: 18.0px} p.p10 {margin: 0.0px 0.0px 12.0px 0.0px; font: 14.0px Times; color: #000000} li.li1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times} li.li7 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Times; min-height: 18.0px} span.s1 {font: 14.0px Courier} span.s2 {color: #000000} span.s3 {font: 14.0px Courier; color: #000000} ol.ol1 {list-style-type: decimal} Or, enduring values for a changing world. Introduction A value type is a data type which, generally speaking, is designed for being passed by value in and out of methods, and stored by value in data structures. The only value types which the Java language directly supports are the eight primitive types. Java indirectly and approximately supports value types, if they are implemented in terms of classes. For example, both Integer and String may be viewed as value types, especially if their usage is restricted to avoid operations appropriate to Object. In this note, we propose a definition of value types in terms of a design pattern for Java classes, accompanied by a set of usage restrictions. We also sketch the relation of such value types to tuple types (which are a JVM-level notion), and point out JVM optimizations that can apply to value types. This note is a thought experiment to extend the JVM’s performance model in support of value types. The demonstration has two phases.  Initially the extension can simply use design patterns, within the current bytecode architecture, and in today’s Java language. But if the performance model is to be realized in practice, it will probably require new JVM bytecode features, changes to the Java language, or both.  We will look at a few possibilities for these new features. An Axiom of Value In the context of the JVM, a value type is a data type equipped with construction, assignment, and equality operations, and a set of typed components, such that, whenever two variables of the value type produce equal corresponding values for their components, the values of the two variables cannot be distinguished by any JVM operation. Here are some corollaries: A value type is immutable, since otherwise a copy could be constructed and the original could be modified in one of its components, allowing the copies to be distinguished. Changing the component of a value type requires construction of a new value. The equals and hashCode operations are strictly component-wise. If a value type is represented by a JVM reference, that reference cannot be successfully synchronized on, and cannot be usefully compared for reference equality. A value type can be viewed in terms of what it doesn’t do. We can say that a value type omits all value-unsafe operations, which could violate the constraints on value types.  These operations, which are ordinarily allowed for Java object types, are pointer equality comparison (the acmp instruction), synchronization (the monitor instructions), all the wait and notify methods of class Object, and non-trivial finalize methods. The clone method is also value-unsafe, although for value types it could be treated as the identity function. Finally, and most importantly, any side effect on an object (however visible) also counts as an value-unsafe operation. A value type may have methods, but such methods must not change the components of the value. It is reasonable and useful to define methods like toString, equals, and hashCode on value types, and also methods which are specifically valuable to users of the value type. Representations of Value Value types have two natural representations in the JVM, unboxed and boxed. An unboxed value consists of the components, as simple variables. For example, the complex number x=(1+2i), in rectangular coordinate form, may be represented in unboxed form by the following pair of variables: /*Complex x = Complex.valueOf(1.0, 2.0):*/ double x_re = 1.0, x_im = 2.0; These variables might be locals, parameters, or fields. Their association as components of a single value is not defined to the JVM. Here is a sample computation which computes the norm of the difference between two complex numbers: double distance(/*Complex x:*/ double x_re, double x_im,         /*Complex y:*/ double y_re, double y_im) {     /*Complex z = x.minus(y):*/     double z_re = x_re - y_re, z_im = x_im - y_im;     /*return z.abs():*/     return Math.sqrt(z_re*z_re + z_im*z_im); } A boxed representation groups component values under a single object reference. The reference is to a ‘wrapper class’ that carries the component values in its fields. (A primitive type can naturally be equated with a trivial value type with just one component of that type. In that view, the wrapper class Integer can serve as a boxed representation of value type int.) The unboxed representation of complex numbers is practical for many uses, but it fails to cover several major use cases: return values, array elements, and generic APIs. The two components of a complex number cannot be directly returned from a Java function, since Java does not support multiple return values. The same story applies to array elements: Java has no ’array of structs’ feature. (Double-length arrays are a possible workaround for complex numbers, but not for value types with heterogeneous components.) By generic APIs I mean both those which use generic types, like Arrays.asList and those which have special case support for primitive types, like String.valueOf and PrintStream.println. Those APIs do not support unboxed values, and offer some problems to boxed values. Any ’real’ JVM type should have a story for returns, arrays, and API interoperability. The basic problem here is that value types fall between primitive types and object types. Value types are clearly more complex than primitive types, and object types are slightly too complicated. Objects are a little bit dangerous to use as value carriers, since object references can be compared for pointer equality, and can be synchronized on. Also, as many Java programmers have observed, there is often a performance cost to using wrapper objects, even on modern JVMs. Even so, wrapper classes are a good starting point for talking about value types. If there were a set of structural rules and restrictions which would prevent value-unsafe operations on value types, wrapper classes would provide a good notation for defining value types. This note attempts to define such rules and restrictions. Let’s Start Coding Now it is time to look at some real code. Here is a definition, written in Java, of a complex number value type. @ValueSafe public final class Complex implements java.io.Serializable {     // immutable component structure:     public final double re, im;     private Complex(double re, double im) {         this.re = re; this.im = im;     }     // interoperability methods:     public String toString() { return "Complex("+re+","+im+")"; }     public List<Double> asList() { return Arrays.asList(re, im); }     public boolean equals(Complex c) {         return re == c.re && im == c.im;     }     public boolean equals(@ValueSafe Object x) {         return x instanceof Complex && equals((Complex) x);     }     public int hashCode() {         return 31*Double.valueOf(re).hashCode()                 + Double.valueOf(im).hashCode();     }     // factory methods:     public static Complex valueOf(double re, double im) {         return new Complex(re, im);     }     public Complex changeRe(double re2) { return valueOf(re2, im); }     public Complex changeIm(double im2) { return valueOf(re, im2); }     public static Complex cast(@ValueSafe Object x) {         return x == null ? ZERO : (Complex) x;     }     // utility methods and constants:     public Complex plus(Complex c)  { return new Complex(re+c.re, im+c.im); }     public Complex minus(Complex c) { return new Complex(re-c.re, im-c.im); }     public double abs() { return Math.sqrt(re*re + im*im); }     public static final Complex PI = valueOf(Math.PI, 0.0);     public static final Complex ZERO = valueOf(0.0, 0.0); } This is not a minimal definition, because it includes some utility methods and other optional parts.  The essential elements are as follows: The class is marked as a value type with an annotation. The class is final, because it does not make sense to create subclasses of value types. The fields of the class are all non-private and final.  (I.e., the type is immutable and structurally transparent.) From the supertype Object, all public non-final methods are overridden. The constructor is private. Beyond these bare essentials, we can observe the following features in this example, which are likely to be typical of all value types: One or more factory methods are responsible for value creation, including a component-wise valueOf method. There are utility methods for complex arithmetic and instance creation, such as plus and changeIm. There are static utility constants, such as PI. The type is serializable, using the default mechanisms. There are methods for converting to and from dynamically typed references, such as asList and cast. The Rules In order to use value types properly, the programmer must avoid value-unsafe operations.  A helpful Java compiler should issue errors (or at least warnings) for code which provably applies value-unsafe operations, and should issue warnings for code which might be correct but does not provably avoid value-unsafe operations.  No such compilers exist today, but to simplify our account here, we will pretend that they do exist. A value-safe type is any class, interface, or type parameter marked with the @ValueSafe annotation, or any subtype of a value-safe type.  If a value-safe class is marked final, it is in fact a value type.  All other value-safe classes must be abstract.  The non-static fields of a value class must be non-public and final, and all its constructors must be private. Under the above rules, a standard interface could be helpful to define value types like Complex.  Here is an example: @ValueSafe public interface ValueType extends java.io.Serializable {     // All methods listed here must get redefined.     // Definitions must be value-safe, which means     // they may depend on component values only.     List<? extends Object> asList();     int hashCode();     boolean equals(@ValueSafe Object c);     String toString(); } //@ValueSafe inherited from supertype: public final class Complex implements ValueType { … The main advantage of such a conventional interface is that (unlike an annotation) it is reified in the runtime type system.  It could appear as an element type or parameter bound, for facilities which are designed to work on value types only.  More broadly, it might assist the JVM to perform dynamic enforcement of the rules for value types. Besides types, the annotation @ValueSafe can mark fields, parameters, local variables, and methods.  (This is redundant when the type is also value-safe, but may be useful when the type is Object or another supertype of a value type.)  Working forward from these annotations, an expression E is defined as value-safe if it satisfies one or more of the following: The type of E is a value-safe type. E names a field, parameter, or local variable whose declaration is marked @ValueSafe. E is a call to a method whose declaration is marked @ValueSafe. E is an assignment to a value-safe variable, field reference, or array reference. E is a cast to a value-safe type from a value-safe expression. E is a conditional expression E0 ? E1 : E2, and both E1 and E2 are value-safe. Assignments to value-safe expressions and initializations of value-safe names must take their values from value-safe expressions. A value-safe expression may not be the subject of a value-unsafe operation.  In particular, it cannot be synchronized on, nor can it be compared with the “==” operator, not even with a null or with another value-safe type. In a program where all of these rules are followed, no value-type value will be subject to a value-unsafe operation.  Thus, the prime axiom of value types will be satisfied, that no two value type will be distinguishable as long as their component values are equal. More Code To illustrate these rules, here are some usage examples for Complex: Complex pi = Complex.valueOf(Math.PI, 0); Complex zero = pi.changeRe(0);  //zero = pi; zero.re = 0; ValueType vtype = pi; @SuppressWarnings("value-unsafe")   Object obj = pi; @ValueSafe Object obj2 = pi; obj2 = new Object();  // ok List<Complex> clist = new ArrayList<Complex>(); clist.add(pi);  // (ok assuming List.add param is @ValueSafe) List<ValueType> vlist = new ArrayList<ValueType>(); vlist.add(pi);  // (ok) List<Object> olist = new ArrayList<Object>(); olist.add(pi);  // warning: "value-unsafe" boolean z = pi.equals(zero); boolean z1 = (pi == zero);  // error: reference comparison on value type boolean z2 = (pi == null);  // error: reference comparison on value type boolean z3 = (pi == obj2);  // error: reference comparison on value type synchronized (pi) { }  // error: synch of value, unpredictable result synchronized (obj2) { }  // unpredictable result Complex qq = pi; qq = null;  // possible NPE; warning: “null-unsafe" qq = (Complex) obj;  // warning: “null-unsafe" qq = Complex.cast(obj);  // OK @SuppressWarnings("null-unsafe")   Complex empty = null;  // possible NPE qq = empty;  // possible NPE (null pollution) The Payoffs It follows from this that either the JVM or the java compiler can replace boxed value-type values with unboxed ones, without affecting normal computations.  Fields and variables of value types can be split into their unboxed components.  Non-static methods on value types can be transformed into static methods which take the components as value parameters. Some common questions arise around this point in any discussion of value types. Why burden the programmer with all these extra rules?  Why not detect programs automagically and perform unboxing transparently?  The answer is that it is easy to break the rules accidently unless they are agreed to by the programmer and enforced.  Automatic unboxing optimizations are tantalizing but (so far) unreachable ideal.  In the current state of the art, it is possible exhibit benchmarks in which automatic unboxing provides the desired effects, but it is not possible to provide a JVM with a performance model that assures the programmer when unboxing will occur.  This is why I’m writing this note, to enlist help from, and provide assurances to, the programmer.  Basically, I’m shooting for a good set of user-supplied “pragmas” to frame the desired optimization. Again, the important thing is that the unboxing must be done reliably, or else programmers will have no reason to work with the extra complexity of the value-safety rules.  There must be a reasonably stable performance model, wherein using a value type has approximately the same performance characteristics as writing the unboxed components as separate Java variables. There are some rough corners to the present scheme.  Since Java fields and array elements are initialized to null, value-type computations which incorporate uninitialized variables can produce null pointer exceptions.  One workaround for this is to require such variables to be null-tested, and the result replaced with a suitable all-zero value of the value type.  That is what the “cast” method does above. Generically typed APIs like List<T> will continue to manipulate boxed values always, at least until we figure out how to do reification of generic type instances.  Use of such APIs will elicit warnings until their type parameters (and/or relevant members) are annotated or typed as value-safe.  Retrofitting List<T> is likely to expose flaws in the present scheme, which we will need to engineer around.  Here are a couple of first approaches: public interface java.util.List<@ValueSafe T> extends Collection<T> { … public interface java.util.List<T extends Object|ValueType> extends Collection<T> { … (The second approach would require disjunctive types, in which value-safety is “contagious” from the constituent types.) With more transformations, the return value types of methods can also be unboxed.  This may require significant bytecode-level transformations, and would work best in the presence of a bytecode representation for multiple value groups, which I have proposed elsewhere under the title “Tuples in the VM”. But for starters, the JVM can apply this transformation under the covers, to internally compiled methods.  This would give a way to express multiple return values and structured return values, which is a significant pain-point for Java programmers, especially those who work with low-level structure types favored by modern vector and graphics processors.  The lack of multiple return values has a strong distorting effect on many Java APIs. Even if the JVM fails to unbox a value, there is still potential benefit to the value type.  Clustered computing systems something have copy operations (serialization or something similar) which apply implicitly to command operands.  When copying JVM objects, it is extremely helpful to know when an object’s identity is important or not.  If an object reference is a copied operand, the system may have to create a proxy handle which points back to the original object, so that side effects are visible.  Proxies must be managed carefully, and this can be expensive.  On the other hand, value types are exactly those types which a JVM can “copy and forget” with no downside. Array types are crucial to bulk data interfaces.  (As data sizes and rates increase, bulk data becomes more important than scalar data, so arrays are definitely accompanying us into the future of computing.)  Value types are very helpful for adding structure to bulk data, so a successful value type mechanism will make it easier for us to express richer forms of bulk data. Unboxing arrays (i.e., arrays containing unboxed values) will provide better cache and memory density, and more direct data movement within clustered or heterogeneous computing systems.  They require the deepest transformations, relative to today’s JVM.  There is an impedance mismatch between value-type arrays and Java’s covariant array typing, so compromises will need to be struck with existing Java semantics.  It is probably worth the effort, since arrays of unboxed value types are inherently more memory-efficient than standard Java arrays, which rely on dependent pointer chains. It may be sufficient to extend the “value-safe” concept to array declarations, and allow low-level transformations to change value-safe array declarations from the standard boxed form into an unboxed tuple-based form.  Such value-safe arrays would not be convertible to Object[] arrays.  Certain connection points, such as Arrays.copyOf and System.arraycopy might need additional input/output combinations, to allow smooth conversion between arrays with boxed and unboxed elements. Alternatively, the correct solution may have to wait until we have enough reification of generic types, and enough operator overloading, to enable an overhaul of Java arrays. Implicit Method Definitions The example of class Complex above may be unattractively complex.  I believe most or all of the elements of the example class are required by the logic of value types. If this is true, a programmer who writes a value type will have to write lots of error-prone boilerplate code.  On the other hand, I think nearly all of the code (except for the domain-specific parts like plus and minus) can be implicitly generated. Java has a rule for implicitly defining a class’s constructor, if no it defines no constructors explicitly.  Likewise, there are rules for providing default access modifiers for interface members.  Because of the highly regular structure of value types, it might be reasonable to perform similar implicit transformations on value types.  Here’s an example of a “highly implicit” definition of a complex number type: public class Complex implements ValueType {  // implicitly final     public double re, im;  // implicitly public final     //implicit methods are defined elementwise from te fields:     //  toString, asList, equals(2), hashCode, valueOf, cast     //optionally, explicit methods (plus, abs, etc.) would go here } In other words, with the right defaults, a simple value type definition can be a one-liner.  The observant reader will have noticed the similarities (and suitable differences) between the explicit methods above and the corresponding methods for List<T>. Another way to abbreviate such a class would be to make an annotation the primary trigger of the functionality, and to add the interface(s) implicitly: public @ValueType class Complex { … // implicitly final, implements ValueType (But to me it seems better to communicate the “magic” via an interface, even if it is rooted in an annotation.) Implicitly Defined Value Types So far we have been working with nominal value types, which is to say that the sequence of typed components is associated with a name and additional methods that convey the intention of the programmer.  A simple ordered pair of floating point numbers can be variously interpreted as (to name a few possibilities) a rectangular or polar complex number or Cartesian point.  The name and the methods convey the intended meaning. But what if we need a truly simple ordered pair of floating point numbers, without any further conceptual baggage?  Perhaps we are writing a method (like “divideAndRemainder”) which naturally returns a pair of numbers instead of a single number.  Wrapping the pair of numbers in a nominal type (like “QuotientAndRemainder”) makes as little sense as wrapping a single return value in a nominal type (like “Quotient”).  What we need here are structural value types commonly known as tuples. For the present discussion, let us assign a conventional, JVM-friendly name to tuples, roughly as follows: public class java.lang.tuple.$DD extends java.lang.tuple.Tuple {      double $1, $2; } Here the component names are fixed and all the required methods are defined implicitly.  The supertype is an abstract class which has suitable shared declarations.  The name itself mentions a JVM-style method parameter descriptor, which may be “cracked” to determine the number and types of the component fields. The odd thing about such a tuple type (and structural types in general) is it must be instantiated lazily, in response to linkage requests from one or more classes that need it.  The JVM and/or its class loaders must be prepared to spin a tuple type on demand, given a simple name reference, $xyz, where the xyz is cracked into a series of component types.  (Specifics of naming and name mangling need some tasteful engineering.) Tuples also seem to demand, even more than nominal types, some support from the language.  (This is probably because notations for non-nominal types work best as combinations of punctuation and type names, rather than named constructors like Function3 or Tuple2.)  At a minimum, languages with tuples usually (I think) have some sort of simple bracket notation for creating tuples, and a corresponding pattern-matching syntax (or “destructuring bind”) for taking tuples apart, at least when they are parameter lists.  Designing such a syntax is no simple thing, because it ought to play well with nominal value types, and also with pre-existing Java features, such as method parameter lists, implicit conversions, generic types, and reflection.  That is a task for another day. Other Use Cases Besides complex numbers and simple tuples there are many use cases for value types.  Many tuple-like types have natural value-type representations. These include rational numbers, point locations and pixel colors, and various kinds of dates and addresses. Other types have a variable-length ‘tail’ of internal values. The most common example of this is String, which is (mathematically) a sequence of UTF-16 character values. Similarly, bit vectors, multiple-precision numbers, and polynomials are composed of sequences of values. Such types include, in their representation, a reference to a variable-sized data structure (often an array) which (somehow) represents the sequence of values. The value type may also include ’header’ information. Variable-sized values often have a length distribution which favors short lengths. In that case, the design of the value type can make the first few values in the sequence be direct ’header’ fields of the value type. In the common case where the header is enough to represent the whole value, the tail can be a shared null value, or even just a null reference. Note that the tail need not be an immutable object, as long as the header type encapsulates it well enough. This is the case with String, where the tail is a mutable (but never mutated) character array. Field types and their order must be a globally visible part of the API.  The structure of the value type must be transparent enough to have a globally consistent unboxed representation, so that all callers and callees agree about the type and order of components  that appear as parameters, return types, and array elements.  This is a trade-off between efficiency and encapsulation, which is forced on us when we remove an indirection enjoyed by boxed representations.  A JVM-only transformation would not care about such visibility, but a bytecode transformation would need to take care that (say) the components of complex numbers would not get swapped after a redefinition of Complex and a partial recompile.  Perhaps constant pool references to value types need to declare the field order as assumed by each API user. This brings up the delicate status of private fields in a value type.  It must always be possible to load, store, and copy value types as coordinated groups, and the JVM performs those movements by moving individual scalar values between locals and stack.  If a component field is not public, what is to prevent hostile code from plucking it out of the tuple using a rogue aload or astore instruction?  Nothing but the verifier, so we may need to give it more smarts, so that it treats value types as inseparable groups of stack slots or locals (something like long or double). My initial thought was to make the fields always public, which would make the security problem moot.  But public is not always the right answer; consider the case of String, where the underlying mutable character array must be encapsulated to prevent security holes.  I believe we can win back both sides of the tradeoff, by training the verifier never to split up the components in an unboxed value.  Just as the verifier encapsulates the two halves of a 64-bit primitive, it can encapsulate the the header and body of an unboxed String, so that no code other than that of class String itself can take apart the values. Similar to String, we could build an efficient multi-precision decimal type along these lines: public final class DecimalValue extends ValueType {     protected final long header;     protected private final BigInteger digits;     public DecimalValue valueOf(int value, int scale) {         assert(scale >= 0);         return new DecimalValue(((long)value << 32) + scale, null);     }     public DecimalValue valueOf(long value, int scale) {         if (value == (int) value)             return valueOf((int)value, scale);         return new DecimalValue(-scale, new BigInteger(value));     } } Values of this type would be passed between methods as two machine words. Small values (those with a significand which fits into 32 bits) would be represented without any heap data at all, unless the DecimalValue itself were boxed. (Note the tension between encapsulation and unboxing in this case.  It would be better if the header and digits fields were private, but depending on where the unboxing information must “leak”, it is probably safer to make a public revelation of the internal structure.) Note that, although an array of Complex can be faked with a double-length array of double, there is no easy way to fake an array of unboxed DecimalValues.  (Either an array of boxed values or a transposed pair of homogeneous arrays would be reasonable fallbacks, in a current JVM.)  Getting the full benefit of unboxing and arrays will require some new JVM magic. Although the JVM emphasizes portability, system dependent code will benefit from using machine-level types larger than 64 bits.  For example, the back end of a linear algebra package might benefit from value types like Float4 which map to stock vector types.  This is probably only worthwhile if the unboxing arrays can be packed with such values. More Daydreams A more finely-divided design for dynamic enforcement of value safety could feature separate marker interfaces for each invariant.  An empty marker interface Unsynchronizable could cause suitable exceptions for monitor instructions on objects in marked classes.  More radically, a Interchangeable marker interface could cause JVM primitives that are sensitive to object identity to raise exceptions; the strangest result would be that the acmp instruction would have to be specified as raising an exception. @ValueSafe public interface ValueType extends java.io.Serializable,         Unsynchronizable, Interchangeable { … public class Complex implements ValueType {     // inherits Serializable, Unsynchronizable, Interchangeable, @ValueSafe     … It seems possible that Integer and the other wrapper types could be retro-fitted as value-safe types.  This is a major change, since wrapper objects would be unsynchronizable and their references interchangeable.  It is likely that code which violates value-safety for wrapper types exists but is uncommon.  It is less plausible to retro-fit String, since the prominent operation String.intern is often used with value-unsafe code. We should also reconsider the distinction between boxed and unboxed values in code.  The design presented above obscures that distinction.  As another thought experiment, we could imagine making a first class distinction in the type system between boxed and unboxed representations.  Since only primitive types are named with a lower-case initial letter, we could define that the capitalized version of a value type name always refers to the boxed representation, while the initial lower-case variant always refers to boxed.  For example: complex pi = complex.valueOf(Math.PI, 0); Complex boxPi = pi;  // convert to boxed myList.add(boxPi); complex z = myList.get(0);  // unbox Such a convention could perhaps absorb the current difference between int and Integer, double and Double. It might also allow the programmer to express a helpful distinction among array types. As said above, array types are crucial to bulk data interfaces, but are limited in the JVM.  Extending arrays beyond the present limitations is worth thinking about; for example, the Maxine JVM implementation has a hybrid object/array type.  Something like this which can also accommodate value type components seems worthwhile.  On the other hand, does it make sense for value types to contain short arrays?  And why should random-access arrays be the end of our design process, when bulk data is often sequentially accessed, and it might make sense to have heterogeneous streams of data as the natural “jumbo” data structure.  These considerations must wait for another day and another note. More Work It seems to me that a good sequence for introducing such value types would be as follows: Add the value-safety restrictions to an experimental version of javac. Code some sample applications with value types, including Complex and DecimalValue. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. A staggered roll-out like this would decouple language changes from bytecode changes, which is always a convenient thing. A similar investigation should be applied (concurrently) to array types.  In this case, it seems to me that the starting point is in the JVM: Add an experimental unboxing array data structure to a production JVM, perhaps along the lines of Maxine hybrids.  No bytecode or language support is required at first; everything can be done with encapsulated unsafe operations and/or method handles. Create an experimental JVM which internally unboxes value types but does not require new bytecodes to do so.  Ensure the feasibility of the performance model for the sample applications. Add tuple-like bytecodes (with or without generic type reification) to a major revision of the JVM, and teach the Java compiler to switch in the new bytecodes without code changes. That’s enough musing me for now.  Back to work!

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• SQL SERVER – Retrieve and Explore Database Backup without Restoring Database – Idera virtual databas

  - by pinaldave

  I recently downloaded Idera’s SQL virtual database, and tested it. There are a few things about this tool which caught my attention. My Scenario It is quite common in real life that sometimes observing or retrieving older data is necessary; however, it had changed as time passed by. The full database backup was 40 GB in size, and, to restore it on our production server, it usually takes around 16 to 22 minutes, depending on the load server that is usually present. This range in time varies from one server to another as per the configuration of the computer. Some other issues we used to have are the following: When we try to restore a large 40-GB database, we needed at least that much space on our production server. Once in a while, we even had to make changes in the restored database, and use the said changed and restored database for our purpose, making it more time-consuming. My Solution I have heard a lot about the Idera’s SQL virtual database tool.. Well, right after we started to test this tool, we found out that it really delivers what it promises. Using this software was very easy and we were able to restore our database from backup in less than 2 minutes, sparing us from the usual longer time of 16–22 minutes. The needful was finished in a total of 10 minutes. Another interesting observation is that there is no need to have an additional space for restoring the database. For complete database restoration, the single additional MB on the drive is not required anymore. We can use the database in the same way as our regular database, and there is no need for any additional configuration and setup. Let us look at the most relevant points of this product based on my initial experience: Quick restoration of the database backup No additional space required for database restoration virtual database has no physical .MDF or .LDF The database which is restored is, in fact, the backup file converted in the virtual database. DDL and DML queries can be executed against this virtually restored database. Regular backup operation can be implemented against virtual database, creating a physical .bak file that can be used for future use. There was no observed degradation in performance on the original database as well the restored virtual database. Additional T-SQL queries can be let off on the virtual database. Well, this summarizes my quick review. And, as I was saying, I am very impressed with the product and I plan to explore it more. There are many features that I have noticed in this tool, which I think can be very useful if properly understood. I had taken a few screenshots using my demo database afterwards. Let us see what other things this tool can do besides the mentioned activities. I am surprised with its performance so I want to know how exactly this feature works, specifically in the matter of why it does not create any additional files and yet, it still allows update on the virtually restored database. I guess I will have to send an e-mail to the developers of Idera and try to figure this out from them. I think this tool is very useful, and it delivers a high level of performance way more than what I expected. Soon, I will write a review for additional uses of SQL virtual database.. If you are using SQL virtual database in your production environment, I am eager to learn more about it and your experience while using it. The ‘Virtual’ Part of virtual database When I set out to test this software, I thought virtual database had something to do with Hyper-V or visualization. In fact, the virtual database is a kind of database which shows up in your SQL Server Management Studio without actually restoring or even creating it. This tool creates a database in SSMS from the backup of the same database. The backup, however, works virtually the same way as original database. Potential Usage of virtual database: As soon as I described this tool to my teammate, I think his very first reaction was, “hey, if we have this then there is no need for log shipping.” I find his comment very interesting as log shipping is something where logs are moved to another server. In fact, there are no updates on the database from log; I would rather compare it with Snapshot Replication. In fact, whatever we use, snapshot replicated database can be similarly used and configured with virtual database. I totally believe that we can use it for reporting purpose. In fact, after this database was configured, I think the uses of this tool are unlimited. I will have to spend some more time studying it and will get back to you. Click on images to see larger images. virtual database Console Harddrive Space before virtual database Setup Attach Full Backup Screen Backup on Harddrive Attach Full Backup Screen with Settings virtual database Setup – less than 60 sec virtual database Setup – Online Harddrive Space after virtual database Setup Point in Time Recovery Option – Timeline View virtual database Summary No Performance Difference between Regular DB vs Virtual DB Please note that all SQL Server MVP gets free license of this software. Reference: Pinal Dave (http://blog.SQLAuthority.com), Idera (virtual database) Filed under: Database, Pinal Dave, SQL, SQL Add-On, SQL Authority, SQL Backup and Restore, SQL Data Storage, SQL Query, SQL Server, SQL Tips and Tricks, SQL Utility, SQLAuthority News, T SQL, Technology Tagged: Idera

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• MVC Portable Areas Enhancement &ndash; Embedded Resource Controller

  - by Steve Michelotti

  MvcContrib contains a feature called Portable Areas which I’ve recently blogged about. In short, portable areas provide a way to distribute MVC binary components as simple .NET assemblies where the aspx/ascx files are actually compiled into the assembly as embedded resources. This is an extremely cool feature but once you start building robust portable areas, you’ll also want to be able to access other external files like css and javascript.  After my recent post suggesting portable areas be expanded to include other embedded resources, Eric Hexter asked me if I’d like to contribute the code to MvcContrib (which of course I did!). Embedded resources are stored in a case-sensitive way in .NET assemblies and the existing embedded view engine inside MvcContrib already took this into account. Obviously, we’d want the same case sensitivity handling to be taken into account for any embedded resource so my job consisted of 1) adding the Embedded Resource Controller, and 2) a little refactor to extract the logic that deals with embedded resources so that the embedded view engine and the embedded resource controller could both leverage it and, therefore, keep the code DRY. The embedded resource controller targets these scenarios: External image files that are referenced in an <img> tag External files referenced like css or JavaScript files Image files referenced inside css files Embedded Resources Walkthrough This post will describe a walkthrough of using the embedded resource controller in your portable areas to include the scenarios outlined above. I will build a trivial “Quick Links” widget to illustrate the concepts. The portable area registration is the starting point for all portable areas. The MvcContrib.PortableAreas.EmbeddedResourceController is optional functionality – you must opt-in if you want to use it.  To do this, you simply “register” it by providing a route in your area registration that uses it like this: 1: context.MapRoute("ResourceRoute", "quicklinks/resource/{resourceName}", 2: new { controller = "EmbeddedResource", action = "Index" }, 3: new string[] { "MvcContrib.PortableAreas" }); First, notice that I can specify any route I want (e.g., “quicklinks/resources/…”).  Second, notice that I need to include the “MvcContrib.PortableAreas” namespace as the fourth parameter so that the framework is able to find the EmbeddedResourceController at runtime. The handling of embedded views and embedded resources have now been merged.  Therefore, the call to: 1: RegisterTheViewsInTheEmmeddedViewEngine(GetType()); has now been removed (breaking change).  It has been replaced with: 1: RegisterAreaEmbeddedResources(); Other than that, the portable area registration remains unchanged. The solution structure for the static files in my portable area looks like this: I’ve got a css file in a folder called “Content” as well as a couple of image files in a folder called “images”. To reference these in my aspx/ascx code, all of have to do is this: 1: <link href="<%= Url.Resource("Content.QuickLinks.css") %>" rel="stylesheet" type="text/css" /> 2: <img src="<%= Url.Resource("images.globe.png") %>" /> This results in the following HTML mark up: 1: <link href="/quicklinks/resource/Content.QuickLinks.css" rel="stylesheet" type="text/css" /> 2: <img src="/quicklinks/resource/images.globe.png" /> The Url.Resource() method is now included in MvcContrib as well. Make sure you import the “MvcContrib” namespace in your views. Next, I have to following html to render the quick links: 1: <ul class="links"> 2: <li><a href="http://www.google.com">Google</a></li> 3: <li><a href="http://www.bing.com">Bing</a></li> 4: <li><a href="http://www.yahoo.com">Yahoo</a></li> 5: </ul> Notice the <ul> tag has a class called “links”. This is defined inside my QuickLinks.css file and looks like this: 1: ul.links li 2: { 3: background: url(/quicklinks/resource/images.navigation.png) left 4px no-repeat; 4: padding-left: 20px; 5: margin-bottom: 4px; 6: } On line 3 we’re able to refer to the url for the background property. As a final note, although we already have complete control over the location of the embedded resources inside the assembly, what if we also want control over the physical URL routes as well. This point was raised by John Nelson in this post. This has been taken into account as well. For example, suppose you want your physical url to look like this: 1: <img src="/quicklinks/images/globe.png" /> instead of the same corresponding URL shown above (i.e., “/quicklinks/resources/images.globe.png”). You can do this easily by specifying another route for it which includes a “resourcePath” parameter that is pre-pended. Here is the complete code for the area registration with the custom route for the images shown on lines 9-11: 1: public class QuickLinksRegistration : PortableAreaRegistration 2: { 3: public override void RegisterArea(System.Web.Mvc.AreaRegistrationContext context, IApplicationBus bus) 4: { 5: context.MapRoute("ResourceRoute", "quicklinks/resource/{resourceName}", 6: new { controller = "EmbeddedResource", action = "Index" }, 7: new string[] { "MvcContrib.PortableAreas" }); 8:   9: context.MapRoute("ResourceImageRoute", "quicklinks/images/{resourceName}", 10: new { controller = "EmbeddedResource", action = "Index", resourcePath = "images" }, 11: new string[] { "MvcContrib.PortableAreas" }); 12:   13: context.MapRoute("quicklink", "quicklinks/{controller}/{action}", 14: new {controller = "links", action = "index"}); 15:   16: this.RegisterAreaEmbeddedResources(); 17: } 18:   19: public override string AreaName 20: { 21: get 22: { 23: return "QuickLinks"; 24: } 25: } 26: } The Quick Links portable area results in the following requests (including custom route formats): The complete code for this post is now included in the Portable Areas sample solution in the latest MvcContrib source code. You can get the latest code now.  Portable Areas open up exciting new possibilities for MVC development!

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• Another "Windows 7 entry missing from Grub2" Question

  - by 4x10

  Like many before me had the following problem that after installing Ubuntu (with windows 7 already installed), the grub boot loader wouldnt show windows 7 as a boot option, though i can boot fine if I use the "Choose Boot Device" options on the x220. The difference is that I try using UEFI only so many answers didn't really fit my problem, though i tried several stuffs: after running boot repair it destroyed the ubuntu boot loader custom entry in /etc/grub.d/40_custom for windows which doesnt show up many update-grub and reboots trying windows repair recovery thing while being there i also did bootrec.exe /FixBoot and update-grub and reboot again and finaly because it was so much fun, i installed linux all over again, while formatting and deleting everything linux related before that. Now that i think of it, Ubuntu also didn't notice Windows being there during the Setup and it still doesnt according to the Boot Info from Boot Repair. Boot Info Script 0.61-git-patched [23 April 2012] ============================= Boot Info Summary: =============================== => No boot loader is installed in the MBR of /dev/sda. sda1: __________________________________________________________________________ File system: vfat Boot sector type: Windows 7: FAT32 Boot sector info: No errors found in the Boot Parameter Block. Operating System: Boot files: /efi/Boot/bootx64.efi /efi/ubuntu/grubx64.efi sda2: __________________________________________________________________________ File system: Boot sector type: - Boot sector info: Mounting failed: mount: unknown filesystem type '' sda3: __________________________________________________________________________ File system: ntfs Boot sector type: Windows Vista/7: NTFS Boot sector info: No errors found in the Boot Parameter Block. Operating System: Windows 7 Boot files: /Windows/System32/winload.exe sda4: __________________________________________________________________________ File system: ext4 Boot sector type: - Boot sector info: Operating System: Ubuntu precise (development branch) Boot files: /boot/grub/grub.cfg /etc/fstab sda5: __________________________________________________________________________ File system: ext4 Boot sector type: - Boot sector info: Operating System: Boot files: sda6: __________________________________________________________________________ File system: swap Boot sector type: - Boot sector info: ============================ Drive/Partition Info: ============================= Drive: sda _____________________________________________________________________ Disk /dev/sda: 320.1 GB, 320072933376 bytes 255 heads, 63 sectors/track, 38913 cylinders, total 625142448 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes Partition Boot Start Sector End Sector # of Sectors Id System /dev/sda1 1 625,142,447 625,142,447 ee GPT GUID Partition Table detected. Partition Start Sector End Sector # of Sectors System /dev/sda1 2,048 206,847 204,800 EFI System partition /dev/sda2 206,848 468,991 262,144 Microsoft Reserved Partition (Windows) /dev/sda3 468,992 170,338,303 169,869,312 Data partition (Windows/Linux) /dev/sda4 170,338,304 330,338,304 160,000,001 Data partition (Windows/Linux) /dev/sda5 330,338,305 617,141,039 286,802,735 Data partition (Windows/Linux) /dev/sda6 617,141,040 625,141,040 8,000,001 Swap partition (Linux) "blkid" output: ________________________________________________________________ Device UUID TYPE LABEL /dev/sda1 885C-ED1B vfat /dev/sda3 EE06CC0506CBCCB1 ntfs /dev/sda4 604dd3b2-64ca-4200-b8fb-820e8d0ca899 ext4 /dev/sda5 d62515fd-8120-4a74-b17b-0bdf244124a3 ext4 /dev/sda6 7078b649-fb2a-4c59-bd03-fd31ef440d37 swap ================================ Mount points: ================================= Device Mount_Point Type Options /dev/sda1 /boot/efi vfat (rw) /dev/sda4 / ext4 (rw,errors=remount-ro) /dev/sda5 /home ext4 (rw) =========================== sda4/boot/grub/grub.cfg: =========================== -------------------------------------------------------------------------------- # # DO NOT EDIT THIS FILE # # It is automatically generated by grub-mkconfig using templates # from /etc/grub.d and settings from /etc/default/grub # ### BEGIN /etc/grub.d/00_header ### if [ -s $prefix/grubenv ]; then set have_grubenv=true load_env fi set default="0" if [ "${prev_saved_entry}" ]; then set saved_entry="${prev_saved_entry}" save_env saved_entry set prev_saved_entry= save_env prev_saved_entry set boot_once=true fi function savedefault { if [ -z "${boot_once}" ]; then saved_entry="${chosen}" save_env saved_entry fi } function recordfail { set recordfail=1 if [ -n "${have_grubenv}" ]; then if [ -z "${boot_once}" ]; then save_env recordfail; fi; fi } function load_video { insmod efi_gop insmod efi_uga insmod video_bochs insmod video_cirrus } insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 if loadfont /usr/share/grub/unicode.pf2 ; then set gfxmode=auto load_video insmod gfxterm insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 set locale_dir=($root)/boot/grub/locale set lang=en_US insmod gettext fi terminal_output gfxterm if [ "${recordfail}" = 1 ]; then set timeout=-1 else set timeout=10 fi ### END /etc/grub.d/00_header ### ### BEGIN /etc/grub.d/05_debian_theme ### set menu_color_normal=white/black set menu_color_highlight=black/light-gray if background_color 44,0,30; then clear fi ### END /etc/grub.d/05_debian_theme ### ### BEGIN /etc/grub.d/10_linux ### function gfxmode { set gfxpayload="$1" if [ "$1" = "keep" ]; then set vt_handoff=vt.handoff=7 else set vt_handoff= fi } if [ ${recordfail} != 1 ]; then if [ -e ${prefix}/gfxblacklist.txt ]; then if hwmatch ${prefix}/gfxblacklist.txt 3; then if [ ${match} = 0 ]; then set linux_gfx_mode=keep else set linux_gfx_mode=text fi else set linux_gfx_mode=text fi else set linux_gfx_mode=keep fi else set linux_gfx_mode=text fi export linux_gfx_mode if [ "$linux_gfx_mode" != "text" ]; then load_video; fi menuentry 'Ubuntu, with Linux 3.2.0-20-generic' --class ubuntu --class gnu-linux --class gnu --class os { recordfail gfxmode $linux_gfx_mode insmod gzio insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 linux /boot/vmlinuz-3.2.0-20-generic root=UUID=604dd3b2-64ca-4200-b8fb-820e8d0ca899 ro quiet splash $vt_handoff initrd /boot/initrd.img-3.2.0-20-generic } menuentry 'Ubuntu, with Linux 3.2.0-20-generic (recovery mode)' --class ubuntu --class gnu-linux --class gnu --class os { recordfail insmod gzio insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 echo 'Loading Linux 3.2.0-20-generic ...' linux /boot/vmlinuz-3.2.0-20-generic root=UUID=604dd3b2-64ca-4200-b8fb-820e8d0ca899 ro recovery nomodeset echo 'Loading initial ramdisk ...' initrd /boot/initrd.img-3.2.0-20-generic } ### END /etc/grub.d/10_linux ### ### BEGIN /etc/grub.d/20_linux_xen ### ### END /etc/grub.d/20_linux_xen ### ### BEGIN /etc/grub.d/20_memtest86+ ### menuentry "Memory test (memtest86+)" { insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 linux16 /boot/memtest86+.bin } menuentry "Memory test (memtest86+, serial console 115200)" { insmod part_gpt insmod ext2 set root='(hd0,gpt4)' search --no-floppy --fs-uuid --set=root 604dd3b2-64ca-4200-b8fb-820e8d0ca899 linux16 /boot/memtest86+.bin console=ttyS0,115200n8 } ### END /etc/grub.d/20_memtest86+ ### ### BEGIN /etc/grub.d/30_os-prober ### ### END /etc/grub.d/30_os-prober ### ### BEGIN /etc/grub.d/40_custom ### # This file provides an easy way to add custom menu entries. Simply type the # menu entries you want to add after this comment. Be careful not to change # the 'exec tail' line above. ### END /etc/grub.d/40_custom ### ### BEGIN /etc/grub.d/41_custom ### if [ -f $prefix/custom.cfg ]; then source $prefix/custom.cfg; fi ### END /etc/grub.d/41_custom ### -------------------------------------------------------------------------------- =============================== sda4/etc/fstab: ================================ -------------------------------------------------------------------------------- # /etc/fstab: static file system information. # # Use 'blkid' to print the universally unique identifier for a # device; this may be used with UUID= as a more robust way to name devices # that works even if disks are added and removed. See fstab(5). # # <file system> <mount point> <type> <options> <dump> <pass> proc /proc proc nodev,noexec,nosuid 0 0 # / was on /dev/sda4 during installation UUID=604dd3b2-64ca-4200-b8fb-820e8d0ca899 / ext4 errors=remount-ro 0 1 # /boot/efi was on /dev/sda1 during installation UUID=885C-ED1B /boot/efi vfat defaults 0 1 # /home was on /dev/sda5 during installation UUID=d62515fd-8120-4a74-b17b-0bdf244124a3 /home ext4 defaults 0 2 # swap was on /dev/sda6 during installation UUID=7078b649-fb2a-4c59-bd03-fd31ef440d37 none swap sw 0 0 -------------------------------------------------------------------------------- =================== sda4: Location of files loaded by Grub: ==================== GiB - GB File Fragment(s) 129.422874451 = 138.966753280 boot/grub/grub.cfg 1 83.059570312 = 89.184534528 boot/initrd.img-3.2.0-20-generic 2 101.393131256 = 108.870045696 boot/vmlinuz-3.2.0-20-generic 1 83.059570312 = 89.184534528 initrd.img 2 101.393131256 = 108.870045696 vmlinuz 1 ADDITIONAL INFORMATION : =================== log of boot-repair 2012-04-25__23h40 =================== boot-repair version : 3.18-0ppa3~precise boot-sav version : 3.18-0ppa4~precise glade2script version : 0.3.2.1-0ppa7~precise internet: connected python-software-properties version : 0.82.7 0 upgraded, 0 newly installed, 1 reinstalled, 0 to remove and 591 not upgraded. dpkg-preconfigure: unable to re-open stdin: No such file or directory boot-repair is executed in installed-session (Ubuntu precise (development branch) , precise , Ubuntu , x86_64) WARNING: GPT (GUID Partition Table) detected on '/dev/sda'! The util fdisk doesn't support GPT. Use GNU Parted. =================== OSPROBER: /dev/sda4:The OS now in use - Ubuntu precise (development branch) CurrentSession:linux =================== BLKID: /dev/sda3: UUID="EE06CC0506CBCCB1" TYPE="ntfs" /dev/sda1: UUID="885C-ED1B" TYPE="vfat" /dev/sda4: UUID="604dd3b2-64ca-4200-b8fb-820e8d0ca899" TYPE="ext4" /dev/sda5: UUID="d62515fd-8120-4a74-b17b-0bdf244124a3" TYPE="ext4" /dev/sda6: UUID="7078b649-fb2a-4c59-bd03-fd31ef440d37" TYPE="swap" 1 disks with OS, 1 OS : 1 Linux, 0 MacOS, 0 Windows, 0 unknown type OS. WARNING: GPT (GUID Partition Table) detected on '/dev/sda'! The util sfdisk doesn't support GPT. Use GNU Parted. =================== /etc/default/grub : # If you change this file, run 'update-grub' afterwards to update # /boot/grub/grub.cfg. # For full documentation of the options in this file, see: # info -f grub -n 'Simple configuration' GRUB_DEFAULT=0 #GRUB_HIDDEN_TIMEOUT=0 #GRUB_HIDDEN_TIMEOUT_QUIET=true GRUB_TIMEOUT=10 GRUB_DISTRIBUTOR=`lsb_release -i -s 2> /dev/null || echo Debian` GRUB_CMDLINE_LINUX_DEFAULT="quiet splash" GRUB_CMDLINE_LINUX="" # Uncomment to enable BadRAM filtering, modify to suit your needs # This works with Linux (no patch required) and with any kernel that obtains # the memory map information from GRUB (GNU Mach, kernel of FreeBSD ...) #GRUB_BADRAM="0x01234567,0xfefefefe,0x89abcdef,0xefefefef" # Uncomment to disable graphical terminal (grub-pc only) #GRUB_TERMINAL=console # The resolution used on graphical terminal # note that you can use only modes which your graphic card supports via VBE # you can see them in real GRUB with the command `vbeinfo' #GRUB_GFXMODE=640x480 # Uncomment if you don't want GRUB to pass "root=UUID=xxx" parameter to Linux #GRUB_DISABLE_LINUX_UUID=true # Uncomment to disable generation of recovery mode menu entries #GRUB_DISABLE_RECOVERY="true" # Uncomment to get a beep at grub start #GRUB_INIT_TUNE="480 440 1" EFI_OF_PART[1] (, ) =================== dmesg | grep EFI : [ 0.000000] EFI v2.00 by Lenovo [ 0.000000] Kernel-defined memdesc doesn't match the one from EFI! [ 0.000000] EFI: mem00: type=3, attr=0xf, range=[0x0000000000000000-0x0000000000001000) (0MB) [ 0.000000] EFI: mem01: type=7, attr=0xf, range=[0x0000000000001000-0x000000000004e000) (0MB) [ 0.000000] EFI: mem02: type=3, attr=0xf, range=[0x000000000004e000-0x0000000000058000) (0MB) [ 0.000000] EFI: mem03: type=10, attr=0xf, range=[0x0000000000058000-0x0000000000059000) (0MB) [ 0.000000] EFI: mem04: type=7, attr=0xf, range=[0x0000000000059000-0x000000000005e000) (0MB) [ 0.000000] EFI: mem05: type=4, attr=0xf, range=[0x000000000005e000-0x000000000005f000) (0MB) [ 0.000000] EFI: mem06: type=3, attr=0xf, range=[0x000000000005f000-0x00000000000a0000) (0MB) [ 0.000000] EFI: mem07: type=2, attr=0xf, range=[0x0000000000100000-0x00000000005b9000) (4MB) [ 0.000000] EFI: mem08: type=7, attr=0xf, range=[0x00000000005b9000-0x0000000020000000) (506MB) [ 0.000000] EFI: mem09: type=0, attr=0xf, range=[0x0000000020000000-0x0000000020200000) (2MB) [ 0.000000] EFI: mem10: type=7, attr=0xf, range=[0x0000000020200000-0x00000000364e4000) (354MB) [ 0.000000] EFI: mem11: type=2, attr=0xf, range=[0x00000000364e4000-0x000000003726a000) (13MB) [ 0.000000] EFI: mem12: type=7, attr=0xf, range=[0x000000003726a000-0x0000000040000000) (141MB) [ 0.000000] EFI: mem13: type=0, attr=0xf, range=[0x0000000040000000-0x0000000040200000) (2MB) [ 0.000000] EFI: mem14: type=7, attr=0xf, range=[0x0000000040200000-0x000000009df35000) (1501MB) [ 0.000000] EFI: mem15: type=2, attr=0xf, range=[0x000000009df35000-0x00000000d39a0000) (858MB) [ 0.000000] EFI: mem16: type=4, attr=0xf, range=[0x00000000d39a0000-0x00000000d39c0000) (0MB) [ 0.000000] EFI: mem17: type=7, attr=0xf, range=[0x00000000d39c0000-0x00000000d5df5000) (36MB) [ 0.000000] EFI: mem18: type=4, attr=0xf, range=[0x00000000d5df5000-0x00000000d6990000) (11MB) [ 0.000000] EFI: mem19: type=7, attr=0xf, range=[0x00000000d6990000-0x00000000d6b82000) (1MB) [ 0.000000] EFI: mem20: type=1, attr=0xf, range=[0x00000000d6b82000-0x00000000d6b9f000) (0MB) [ 0.000000] EFI: mem21: type=7, attr=0xf, range=[0x00000000d6b9f000-0x00000000d77b0000) (12MB) [ 0.000000] EFI: mem22: type=4, attr=0xf, range=[0x00000000d77b0000-0x00000000d780a000) (0MB) [ 0.000000] EFI: mem23: type=7, attr=0xf, range=[0x00000000d780a000-0x00000000d7826000) (0MB) [ 0.000000] EFI: mem24: type=4, attr=0xf, range=[0x00000000d7826000-0x00000000d7868000) (0MB) [ 0.000000] EFI: mem25: type=7, attr=0xf, range=[0x00000000d7868000-0x00000000d7869000) (0MB) [ 0.000000] EFI: mem26: type=4, attr=0xf, range=[0x00000000d7869000-0x00000000d786a000) (0MB) [ 0.000000] EFI: mem27: type=7, attr=0xf, range=[0x00000000d786a000-0x00000000d786b000) (0MB) [ 0.000000] EFI: mem28: type=4, attr=0xf, range=[0x00000000d786b000-0x00000000d786c000) (0MB) [ 0.000000] EFI: mem29: type=7, attr=0xf, range=[0x00000000d786c000-0x00000000d786d000) (0MB) [ 0.000000] EFI: mem30: type=4, attr=0xf, range=[0x00000000d786d000-0x00000000d825f000) (9MB) [ 0.000000] EFI: mem31: type=7, attr=0xf, range=[0x00000000d825f000-0x00000000d8261000) (0MB) [ 0.000000] EFI: mem32: type=4, attr=0xf, range=[0x00000000d8261000-0x00000000d82f7000) (0MB) [ 0.000000] EFI: mem33: type=7, attr=0xf, range=[0x00000000d82f7000-0x00000000d82f8000) (0MB) [ 0.000000] EFI: mem34: type=4, attr=0xf, range=[0x00000000d82f8000-0x00000000d8705000) (4MB) [ 0.000000] EFI: mem35: type=7, attr=0xf, range=[0x00000000d8705000-0x00000000d8706000) (0MB) [ 0.000000] EFI: mem36: type=4, attr=0xf, range=[0x00000000d8706000-0x00000000d8761000) (0MB) [ 0.000000] EFI: mem37: type=7, attr=0xf, range=[0x00000000d8761000-0x00000000d8768000) (0MB) [ 0.000000] EFI: mem38: type=4, attr=0xf, range=[0x00000000d8768000-0x00000000d9b9f000) (20MB) [ 0.000000] EFI: mem39: type=7, attr=0xf, range=[0x00000000d9b9f000-0x00000000d9e4c000) (2MB) [ 0.000000] EFI: mem40: type=2, attr=0xf, range=[0x00000000d9e4c000-0x00000000d9e52000) (0MB) [ 0.000000] EFI: mem41: type=3, attr=0xf, range=[0x00000000d9e52000-0x00000000da59f000) (7MB) [ 0.000000] EFI: mem42: type=5, attr=0x800000000000000f, range=[0x00000000da59f000-0x00000000da6c3000) (1MB) [ 0.000000] EFI: mem43: type=5, attr=0x800000000000000f, range=[0x00000000da6c3000-0x00000000da79f000) (0MB) [ 0.000000] EFI: mem44: type=6, attr=0x800000000000000f, range=[0x00000000da79f000-0x00000000da8b1000) (1MB) [ 0.000000] EFI: mem45: type=6, attr=0x800000000000000f, range=[0x00000000da8b1000-0x00000000da99f000) (0MB) [ 0.000000] EFI: mem46: type=0, attr=0xf, range=[0x00000000da99f000-0x00000000daa22000) (0MB) [ 0.000000] EFI: mem47: type=0, attr=0xf, range=[0x00000000daa22000-0x00000000daa9b000) (0MB) [ 0.000000] EFI: mem48: type=0, attr=0xf, range=[0x00000000daa9b000-0x00000000daa9c000) (0MB) [ 0.000000] EFI: mem49: type=0, attr=0xf, range=[0x00000000daa9c000-0x00000000daa9f000) (0MB) [ 0.000000] EFI: mem50: type=10, attr=0xf, range=[0x00000000daa9f000-0x00000000daadd000) (0MB) [ 0.000000] EFI: mem51: type=10, attr=0xf, range=[0x00000000daadd000-0x00000000dab9f000) (0MB) [ 0.000000] EFI: mem52: type=9, attr=0xf, range=[0x00000000dab9f000-0x00000000dabdc000) (0MB) [ 0.000000] EFI: mem53: type=9, attr=0xf, range=[0x00000000dabdc000-0x00000000dabff000) (0MB) [ 0.000000] EFI: mem54: type=4, attr=0xf, range=[0x00000000dabff000-0x00000000dac00000) (0MB) [ 0.000000] EFI: mem55: type=7, attr=0xf, range=[0x0000000100000000-0x000000021e600000) (4582MB) [ 0.000000] EFI: mem56: type=11, attr=0x8000000000000001, range=[0x00000000f80f8000-0x00000000f80f9000) (0MB) [ 0.000000] EFI: mem57: type=11, attr=0x8000000000000001, range=[0x00000000fed1c000-0x00000000fed20000) (0MB) [ 0.000000] ACPI: UEFI 00000000dabde000 0003E (v01 LENOVO TP-8D 00001280 PTL 00000002) [ 0.000000] ACPI: UEFI 00000000dabdd000 00042 (v01 PTL COMBUF 00000001 PTL 00000001) [ 0.000000] ACPI: UEFI 00000000dabdc000 00292 (v01 LENOVO TP-8D 00001280 PTL 00000002) [ 0.795807] fb0: EFI VGA frame buffer device [ 1.057243] EFI Variables Facility v0.08 2004-May-17 [ 9.122104] fb: conflicting fb hw usage inteldrmfb vs EFI VGA - removing generic driver ReadEFI: /dev/sda , N 128 , 0 , , PRStart 1024 , PRSize 128 WARNING: GPT (GUID Partition Table) detected on '/dev/sda'! The util fdisk doesn't support GPT. Use GNU Parted. =================== PARTITIONS & DISKS: sda4 : sda, not-sepboot, grubenv-ok grub2, grub-efi, update-grub, 64, with-boot, is-os, gpt-but-not-EFI, fstab-has-bad-efi, no-nt, no-winload, no-recov-nor-hid, no-bmgr, no-grldr, no-b-bcd, apt-get, grub-install, . sda3 : sda, maybesepboot, no-grubenv nogrub, no-docgrub, no-update-grub, 32, no-boot, no-os, gpt-but-not-EFI, part-has-no-fstab, no-nt, haswinload, no-recov-nor-hid, no-bmgr, no-grldr, no-b-bcd, nopakmgr, nogrubinstall, /mnt/boot-sav/sda3. sda1 : sda, maybesepboot, no-grubenv nogrub, no-docgrub, no-update-grub, 32, no-boot, no-os, is-correct-EFI, part-has-no-fstab, no-nt, no-winload, no-recov-nor-hid, no-bmgr, no-grldr, no-b-bcd, nopakmgr, nogrubinstall, /boot/efi. sda5 : sda, maybesepboot, no-grubenv nogrub, no-docgrub, no-update-grub, 32, no-boot, no-os, gpt-but-not-EFI, part-has-no-fstab, no-nt, no-winload, no-recov-nor-hid, no-bmgr, no-grldr, no-b-bcd, nopakmgr, nogrubinstall, /home. sda : GPT-BIS, GPT, no-BIOS_boot, has-correctEFI, 2048 sectors * 512 bytes =================== PARTED: Model: ATA HITACHI HTS72323 (scsi) Disk /dev/sda: 320GB Sector size (logical/physical): 512B/512B Partition Table: gpt Number Start End Size File system Name Flags 1 1049kB 106MB 105MB fat32 EFI system partition boot 2 106MB 240MB 134MB Microsoft reserved partition msftres 3 240MB 87.2GB 87.0GB ntfs Basic data partition 4 87.2GB 169GB 81.9GB ext4 5 169GB 316GB 147GB ext4 6 316GB 320GB 4096MB linux-swap(v1) =================== MOUNT: /dev/sda4 on / type ext4 (rw,errors=remount-ro) proc on /proc type proc (rw,noexec,nosuid,nodev) sysfs on /sys type sysfs (rw,noexec,nosuid,nodev) none on /sys/fs/fuse/connections type fusectl (rw) none on /sys/kernel/debug type debugfs (rw) none on /sys/kernel/security type securityfs (rw) udev on /dev type devtmpfs (rw,mode=0755) devpts on /dev/pts type devpts (rw,noexec,nosuid,gid=5,mode=0620) tmpfs on /run type tmpfs (rw,noexec,nosuid,size=10%,mode=0755) none on /run/lock type tmpfs (rw,noexec,nosuid,nodev,size=5242880) none on /run/shm type tmpfs (rw,nosuid,nodev) /dev/sda1 on /boot/efi type vfat (rw) /dev/sda5 on /home type ext4 (rw) gvfs-fuse-daemon on /home/vierlex/.gvfs type fuse.gvfs-fuse-daemon (rw,nosuid,nodev,user=vierlex) /dev/sda3 on /mnt/boot-sav/sda3 type fuseblk (rw,nosuid,nodev,allow_other,blksize=4096) /sys/block/sda: alignment_offset bdi capability dev device discard_alignment events events_async events_poll_msecs ext_range holders inflight power queue range removable ro sda1 sda2 sda3 sda4 sda5 sda6 size slaves stat subsystem trace uevent /dev: agpgart autofs block bsg btrfs-control bus char console core cpu cpu_dma_latency disk dri ecryptfs fb0 fd full fuse hpet input kmsg log mapper mcelog mei mem net network_latency network_throughput null oldmem port ppp psaux ptmx pts random rfkill rtc rtc0 sda sda1 sda2 sda3 sda4 sda5 sda6 sg0 shm snapshot snd stderr stdin stdout tpm0 uinput urandom usbmon0 usbmon1 usbmon2 v4l vga_arbiter video0 watchdog zero /dev/mapper: control /boot/efi: EFI /boot/efi/EFI: Boot Microsoft ubuntu /boot/efi/efi: Boot Microsoft ubuntu /boot/efi/efi/Boot: bootx64.efi /boot/efi/efi/ubuntu: grubx64.efi WARNING: GPT (GUID Partition Table) detected on '/dev/sda'! The util fdisk doesn't support GPT. Use GNU Parted. =================== DF: Filesystem Type Size Used Avail Use% Mounted on /dev/sda4 ext4 77G 4.1G 69G 6% / udev devtmpfs 3.9G 12K 3.9G 1% /dev tmpfs tmpfs 1.6G 864K 1.6G 1% /run none tmpfs 5.0M 0 5.0M 0% /run/lock none tmpfs 3.9G 152K 3.9G 1% /run/shm /dev/sda1 vfat 96M 18M 79M 19% /boot/efi /dev/sda5 ext4 137G 2.2G 128G 2% /home /dev/sda3 fuseblk 81G 30G 52G 37% /mnt/boot-sav/sda3 =================== FDISK: Disk /dev/sda: 320.1 GB, 320072933376 bytes 255 heads, 63 sectors/track, 38913 cylinders, total 625142448 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0xf34fe538 Device Boot Start End Blocks Id System /dev/sda1 1 625142447 312571223+ ee GPT =================== Before mainwindow FSCK no PASTEBIN yes WUBI no WINBOOT yes recommendedrepair, purge, QTY_OF_PART_FOR_REINSTAL 1 no-kernel-purge UNHIDEBOOT_ACTION yes (10s), noflag () PART_TO_REINSTALL_GRUB sda4, FORCE_GRUB no (sda) REMOVABLEDISK no USE_SEPARATEBOOTPART no (sda3) grub2 () UNCOMMENT_GFXMODE no ATA ADD_KERNEL_OPTION no (acpi=off) MBR_TO_RESTORE ( ) EFI detected. Please check the options. =================== Actions FSCK no PASTEBIN yes WUBI no WINBOOT no bootinfo, nombraction, QTY_OF_PART_FOR_REINSTAL 1 no-kernel-purge UNHIDEBOOT_ACTION no (10s), noflag () PART_TO_REINSTALL_GRUB sda4, FORCE_GRUB no (sda) REMOVABLEDISK no USE_SEPARATEBOOTPART no (sda3) grub2 () UNCOMMENT_GFXMODE no ATA ADD_KERNEL_OPTION no (acpi=off) MBR_TO_RESTORE ( ) No change has been performed on your computer. See you soon! internet: connected Thanks for your time and attention. EDIT: additional Info Request =No boot loader is installed in the MBR of /dev/sda. But maybe this is how it is supposed to work? yea this is ok. boot stuff seems to be on a seperate partition, in my case sda1. I'm very new to this UEFI thing too. missing files like bootmgr i don't really have a clue :D but yea, maybe thats how it suppose to be? Instead and whats not shown in the log for some reason: There is additional microsoft bootfiles on sda1 under /efi/microsoft/ [much stuff] I remember also doing some kind of hack to make a UEFI windows 7 usb stick. http://jake.io/b/2011/installing-windows-7-with-uefi-boot-on-an-x220-from-usb/ In short: creating and placing bootx64.efi on the stick so it can be booted in UEFI mode. boot order i decide that in my BIOS. i read somwhere that the thinkpad x220 (essential part of the serial number: 4921 http://www.lenovo.com/shop/americas/content/user_guides/x220_x220i_x220tablet_x220itablet_ug_en.pdf) doesnt really have UEFI interface or something, still, these 2 options are listed with all the other usual devices you can give a boot priority to. Right now it looks like this: Boot Priority Order 1. ubuntu 2. Windows Boot Manager 3. USB FDD 4. USB HDD 5. ATA HDD0 HITACHI [random string]

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• Improving Partitioned Table Join Performance

  - by Paul White

  The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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• What are best practices when switching between projects/coming back to projects frequently?

  - by dj444

  The nature of my job is that I have to switch back and forth between projects every few weeks. I find that one of the biggest impediments to my productivity is the ramp-up time to getting all the relevant pieces of code "back in my head" again after not seeing it for a period. This happens to a smaller and larger extent for briefer breaks / longer breaks. Obviously, good design, documentation, commenting, and physical structure all help with this (not to mention switching between projects as infrequently as possible). But I'm wondering if there are practices/tools that I may be missing out on. What are your specific practices for improving on this?

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• Tweaking a few URL validation settings on ASP.NET v4.0

  - by Carlyle Dacosta

  ASP.NET has a few default settings for URLs out of the box. These can be configured quite easily in the web.config file within the  <system.web>/<httpRuntime> configuration section. Some of these are: <httpRuntime maxUrlLength=”<number here>”. This number should be an integer value (defaults to 260 characters). The value must be greater than or equal to zero, though obviously small values will lead to an un-useable website. This attribute gates the length of the Url without query string. <httpRuntime maxQueryStringLength=”<number here>”. This number should be an integer value (defaults to 2048 characters). The value must be greater than or equal to zero, though obviously small values will lead to an un-useable website. <httpRuntime requestPathInvalidCharacters=”List of characters you need included in ASP.NETs validation checks”. By default the characters are “<,>,*,%,&,:,\,?”. However once can easily change this by setting by modifying web.config. Remember, these characters can be specified in a variety of formats. For example, I want the character ‘!’ to be included in ASP.NETs URL validation logic. So I set the following: <httpRuntime requestPathInvalidCharacters=”<,>,*,%,&,:,\,?,!”. A character could also be specified in its xml encoded form. ‘&lt;;’ would mean the ‘<’ sign). I could specify the ‘!’ in its xml encoded unicode format such as requestPathInvalidCharacters=”<,>,*,%,&,:,\,?,$#x0021;” or I could specify it in its unicode encoded form or in the “<,>,*,%,&,:,\,?,%u0021” format. The following settings can be applied at Root Web.Config level, App Web.config level, Folder level or within a location tag: <location path="some path here"> <system.web> <httpRuntime maxUrlLength="" maxQueryStringLength="" requestPathInvalidChars="" .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } If any of the above settings fail request validation, an Http 400 “Bad Request” HttpException is thrown. These can be easily handled on the Application_Error handler on Global.asax.   Also, a new attribute in <httpRuntime /> called “relaxedUrlToFileSystemMapping” has been added with a default of false. <httpRuntime … relaxedUrlToFileSystemMapping="true|false" /> When the relaxedUrlToFileSystemMapping attribute is set to false inbound Urls still need to be valid NTFS file paths. For example Urls (sans query string) need to be less than 260 characters; no path segment within a Url can use old-style DOS device names (LPT1, COM1, etc…); Urls must be valid Windows file paths. A url like “http://digg.com/http://cnn.com” should work with this attribute set to true (of course a few characters will need to be unblocked by removing them from requestPathInvalidCharacters="" above). Managed configuration for non-NTFS-compliant Urls is determined from the first valid configuration path found when walking up the path segments of the Url. For example, if the request Url is "/foo/bar/baz/<blah>data</blah>", and there is a web.config in the "/foo/bar" directory, then the managed configuration for the request comes from merging the configuration hierarchy to include the web.config from "/foo/bar". The value of the public property HttpRequest.PhysicalPath is set to [physical file path of the application root] + "REQUEST_URL_IS_NOT_A_VALID_FILESYSTEM_PATH". For example, given a request Url like "/foo/bar/baz/<blah>data</blah>", where the application root is "/foo/bar" and the physical file path for that root is "c:\inetpub\wwwroot\foo\bar", then PhysicalPath would be "c:\inetpub\wwwroot\foo\bar\ REQUEST_URL_IS_NOT_A_VALID_FILESYSTEM_PATH". Carl Dacosta ASP.NET QA Team

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• BizTalk Server 2009 - Architecture Options

  - by StuartBrierley

  I recently needed to put forward a proposal for a BizTalk 2009 implementation and as a part of this needed to describe some of the basic architecture options available for consideration.  While I already had an idea of the type of environment that I would be looking to recommend, I felt that presenting a range of options while trying to explain some of the strengths and weaknesses of those options was a good place to start.  These outline architecture options should be equally valid for any version of BizTalk Server from 2004, through 2006 and R2, up to 2009.   The following diagram shows a crude representation of the common implementation options to consider when designing a BizTalk environment.         Each of these options provides differing levels of resilience in the case of failure or disaster, with the later options also providing more scope for performance tuning and scalability.   Some of the options presented above make use of clustering. Clustering may best be described as a technology that automatically allows one physical server to take over the tasks and responsibilities of another physical server that has failed. Given that all computer hardware and software will eventually fail, the goal of clustering is to ensure that mission-critical applications will have little or no downtime when such a failure occurs. Clustering can also be configured to provide load balancing, which should generally lead to performance gains and increased capacity and throughput.   (A) Single Servers   This option is the most basic BizTalk implementation that should be considered. It involves the deployment of a single BizTalk server in conjunction with a single SQL server. This configuration does not provide for any resilience in the case of the failure of either server. It is however the cheapest and easiest to implement option of those available.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (B) Single BizTalk Server with Clustered SQL Servers   This option uses a single BizTalk server with a cluster of SQL servers. By utilising clustered SQL servers we can ensure that there is some resilience to the implementation in respect of the databases that BizTalk relies on to operate. The clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition. While this option offers improved resilience over option (A) it does still present a potential single point of failure at the BizTalk server.   Using a single BizTalk server does not provide for the level of performance tuning that is otherwise available when using more than one BizTalk server in a cluster.   The common edition of BizTalk used in single server implementations is the standard edition. It should be noted however that if future demand requires increased capacity for a solution, this BizTalk edition is limited to scaling up the implementation and not scaling out the number of servers in use. You are also unable to take advantage of multiple message boxes, which would allow us to balance the SQL load in the event of any bottlenecks in this area of the implementation. Any need to scale out the solution would require an upgrade to the enterprise edition of BizTalk.   (C) Clustered BizTalk Servers with Clustered SQL Servers   This option makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in the case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    The use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning any implemented solutions. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling out the solution as future demand requires.   This might be seen as the middle cost option, providing a good level of protection in the case of failure, a decent level of future proofing, but at a higher cost than the single BizTalk server implementations.   (D) Clustered BizTalk Servers with Clustered SQL Servers – with disaster recovery/service continuity   This option is similar to that offered by (C) and makes use of a cluster of BizTalk servers with a cluster of SQL servers to offer high availability and resilience in case of failure of either of the server types involved. Clustering of BizTalk is only available with the enterprise edition of the product. Clustering of two SQL servers is possible with the standard edition but to go beyond this would require the enterprise level edition.    As with (C) the use of a BizTalk cluster also provides for the ability to balance load across the servers and gives more scope for performance tuning the implemented solution. It is also possible to add more BizTalk servers to an existing cluster, giving scope for scaling the solution out as future demand requires.   In this scenario however, we would be including some form of disaster recovery or service continuity. An example of this would be making use of multiple sites, with the BizTalk server cluster operating across sites to offer resilience in case of the loss of one or more sites. In this scenario there are options available for the SQL implementation depending on the network implementation; making use of either one cluster per site or a single SQL cluster across the network. A multi-site SQL implementation would require some form of data replication across the sites involved.   This is obviously an expensive and complex option, but does provide an extraordinary amount of protection in the case of failure.

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• Connecting to Microsoft Excel using Oracle Data Integrator

  - by julien.testut

  The posts in this series assume that you have some level of familiarity with ODI. The concepts of Topology, Data Server, Physical and Logical Architecture are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for more details. In this post I will describe how a Microsoft Excel spreadsheet can be used in Oracle Data Integrator. Microsoft Excel is one of the many different technologies you can leverage in ODI as a source or as a target. Prepare your Excel spreadsheet Prior to using a Microsoft Excel spreadsheet in ODI we need to specify a name for the different cell tables we want to use. You can have multiple names in the same spreadsheet. First open up a Microsoft Excel spreadsheet, we will need to define a named range.

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• Problems with graphics of Sony Vaio Z

  - by dpcat237

  Hello, I have problem with my Sony Vaio Z VPCZ1. It has physical selector of GPUs which Linux kernel not detect. So after GRUB I see black display (I tried different distributions of Ubuntu and other Linux OS). I read in Ubuntu 10.10 was solve same problem with hybrid graphics but not in my case ^^ I found solutions (not easy at do) for oldest models. But I'm not expert in Linux and before I prefer ask people with more experience. Somebody can help me? Someone installed Ubuntu in same laptop? PS. for more information I found different webs: http://goo.gl/ktvq Thanks Regards

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• Tweaking a few URL validation settings on ASP.NET v4.0

  - by Carlyle Dacosta

  ASP.NET has a few default settings for URLs out of the box. These can be configured quite easily in the web.config file within the  <system.web>/<httpRuntime> configuration section. Some of these are: <httpRuntime maxUrlLength=”<number here>” This number should be an integer value (defaults to 260 characters). The value must be greater than or equal to zero, though obviously small values will lead to an un-useable website. This attribute gates the length of the Url without query string. <httpRuntime maxQueryStringLength=”<number here>”. This number should be an integer value (defaults to 2048 characters). The value must be greater than or equal to zero, though obviously small values will lead to an un-useable website. <httpRuntime requestPathInvalidCharacters=”List of characters you need included in ASP.NETs validation checks” /> By default the characters are “<,>,*,%,&,:,\,?”. However once can easily change this by setting by modifying web.config. Remember, these characters can be specified in a variety of formats. For example, I want the character ‘!’ to be included in ASP.NETs URL validation logic. So I set the following: <httpRuntime requestPathInvalidCharacters=”<,>,*,%,&,:,\,?,!”. A character could also be specified in its xml encoded form. ‘&lt;;’ would mean the ‘<’ sign). I could specify the ‘!’ in its xml encoded unicode format such as requestPathInvalidCharacters=”<,>,*,%,&,:,\,?,$#x0021;” or I could specify it in its unicode encoded form or in the “<,>,*,%,&,:,\,?,%u0021” format. The following settings can be applied at Root Web.Config level, App Web.config level, Folder level or within a location tag: <location path="some path here"> <system.web> <httpRuntime maxUrlLength="" maxQueryStringLength="" requestPathInvalidChars="" /> .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } If any of the above settings fail request validation, an Http 400 “Bad Request” HttpException is thrown. These can be easily handled on the Application_Error handler on Global.asax.   Also, a new attribute in <httpRuntime /> called “relaxedUrlToFileSystemMapping” has been added with a default of false. <httpRuntime … relaxedUrlToFileSystemMapping="true|false" /> When the relaxedUrlToFileSystemMapping attribute is set to false inbound Urls still need to be valid NTFS file paths. For example Urls (sans query string) need to be less than 260 characters; no path segment within a Url can use old-style DOS device names (LPT1, COM1, etc…); Urls must be valid Windows file paths. A url like “http://digg.com/http://cnn.com” should work with this attribute set to true (of course a few characters will need to be unblocked by removing them from requestPathInvalidCharacters="" above). Managed configuration for non-NTFS-compliant Urls is determined from the first valid configuration path found when walking up the path segments of the Url. For example, if the request Url is "/foo/bar/baz/<blah>data</blah>", and there is a web.config in the "/foo/bar" directory, then the managed configuration for the request comes from merging the configuration hierarchy to include the web.config from "/foo/bar". The value of the public property HttpRequest.PhysicalPath is set to [physical file path of the application root] + "REQUEST_URL_IS_NOT_A_VALID_FILESYSTEM_PATH". For example, given a request Url like "/foo/bar/baz/<blah>data</blah>", where the application root is "/foo/bar" and the physical file path for that root is "c:\inetpub\wwwroot\foo\bar", then PhysicalPath would be "c:\inetpub\wwwroot\foo\bar\ REQUEST_URL_IS_NOT_A_VALID_FILESYSTEM_PATH".

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• 202 blog articles

  - by mprove

  All my blog articles under blogs.oracle.com since August 2005: 202 blog articles Apr 2012 blogs.oracle.com design patch Mar 2012 Interaction 12 - Critique Mar 2012 Typing. Clicking. Dancing. Feb 2012 Desktop Mobility in Hospitals with Oracle VDI /video Feb 2012 Interaction 12 in Dublin - Highlights of Day 3 Feb 2012 Interaction 12 in Dublin - Highlights of Day 2 Feb 2012 Interaction 12 in Dublin - Highlights of Day 1 Feb 2012 Shit Interaction Designers Say Feb 2012 Tips'n'Tricks for WebCenter #3: How to display custom page titles in Spaces Jan 2012 Tips'n'Tricks for WebCenter #2: How to create an Admin menu in Spaces and save a lot of time Jan 2012 Tips'n'Tricks for WebCenter #1: How to apply custom resources in Spaces Jan 2012 Merry XMas and a Happy 2012! Dec 2011 One Year Oracle SocialChat - The Movie Nov 2011 Frank Ludolph's Last Working Day Nov 2011 Hans Rosling at TED Oct 2011 200 Countries x 200 Years Oct 2011 Blog Aggregation for Desktop Virtualization Oct 2011 Oracle VDI at OOW 2011 Sep 2011 Design for Conversations & Conversations for Design Sep 2011 All Oracle UX Blogs Aug 2011 Farewell Loriot Aug 2011 Oracle VDI 3.3 Overview Aug 2011 Sutherland's Closing Remarks at HyperKult Aug 2011 Surface and Subface Aug 2011 Back to Childhood in UI Design Jul 2011 The Art of Engineering and The Engineering of Art Jul 2011 Oracle VDI Seminar - June-30 Jun 2011 SGD White Paper May 2011 TEDxHamburg Live Feed May 2011 Oracle VDI in 3 Minutes May 2011 Space Ship Earth 2011 May 2011 blog moving times Apr 2011 Frozen tag cloud Apr 2011 Oracle: Hardware Software Complete in 1953 Apr 2011 Interaction Design with Wireframes Apr 2011 A guide to closing down a project Feb 2011 Oracle VDI 3.2.2 Jan 2011 free VDI charts Jan 2011 Sun Founders Panel 2006 Dec 2010 Sutherland on Leadership Dec 2010 SocialChat: Efficiency of E20 Dec 2010 ALWAYS ON Desktop Virtualization Nov 2010 12,000 Desktops at JavaOne Nov 2010 SocialChat on Sharing Best Practices Oct 2010 Globe of Visitors Oct 2010 SocialChat about the Next Big Thing Oct 2010 Oracle VDI UX Story - Wireframes Oct 2010 What's a PC anyway? Oct 2010 SocialChat on Getting Things Done Oct 2010 SocialChat on Infoglut Oct 2010 IT Twenty Twenty Oct 2010 Desktop Virtualization Webcasts from OOW Oct 2010 Oracle VDI 3.2 Overview Sep 2010 Blog Usability Top 7 Sep 2010 100 and counting Aug 2010 Oracle'izing the VDI Blogs Aug 2010 SocialChat on Apple Aug 2010 SocialChat on Video Conferencing Aug 2010 Oracle VDI 3.2 - Features and Screenshots Aug 2010 SocialChat: Don't stop making waves Aug 2010 SocialChat: Giving Back to the Community Aug 2010 SocialChat on Learning in Meetings Aug 2010 iPAD's Natural User Interface Jul 2010 Last day for Sun Microsystems GmbH Jun 2010 SirValUse Celebration Snippets Jun 2010 10 years SirValUse - Happy Birthday! Jun 2010 Wim on Virtualization May 2010 New Home for Oracle VDI Apr 2010 Renaissance Slide Sorter Comments Apr 2010 Unboxing Sun Ray 3 Plus Apr 2010 Desktop Virtualisierung mit Sun VDI 3.1 Apr 2010 Blog Relaunch Mar 2010 Social Messaging Slides from CeBIT Mar 2010 Social Messaging Talk at CeBIT Feb 2010 Welcome Oracle Jan 2010 My last presentation at Sun Jan 2010 Ivan Sutherland on Leadership Jan 2010 Learning French with Sun VDI Jan 2010 Learning Danish with Sun Ray Jan 2010 VDI workshop in Nieuwegein Jan 2010 Happy New Year 2010 Jan 2010 On Creating Slides Dec 2009 Best VDI Ever Nov 2009 How to store the Big Bang Nov 2009 Social Enterprise Tools. Beipiel Sun. Nov 2009 Nov-19 Nov 2009 PDF and ODF links on your blog Nov 2009 Q&A on VDI and MySQL Cluster Nov 2009 Zürich next week: Swiss Intranet Summit 09 Nov 2009 Designing for a Sustainable World - World Usabiltiy Day, Nov-12 Nov 2009 How to export a desktop from VDI 3 Nov 2009 Virtualisation Roadshow in the UK Nov 2009 Project Wonderland at EDUCAUSE 09 Nov 2009 VDI Roadshow in Dublin, Nov-26, 2009 Nov 2009 Sun VDI at EDUCAUSE 09 Nov 2009 Sun VDI 3.1 Architecture and New Features Oct 2009 VDI 3.1 is Early-Access Sep 2009 Virtualization for MySQL on VMware Sep 2009 Silpion & 13. Stock Sommerparty Sep 2009 Sun Ray and VMware View 3.1.1 2009-08-31 New Set of Sun Ray Status Icons 2009-08-25 Virtualizing the VDI Core? 2009-08-23 World Usability Day Hamburg 2009 - CfP 2009-07-16 Rising Sun 2009-07-15 featuring twittermeme 2009-06-19 ISC09 Student Party on June-20 /Hamburg 2009-06-18 Before and behind the curtain of JavaOne 2009-06-09 20k desktops at JavaOne 2009-06-01 sweet microblogging 2009-05-25 VDI 3 - Why you need 3 VDI hosts and what you can do about that? 2009-05-21 IA Konferenz 2009 2009-05-20 Sun VDI 3 UX Story - Power of the Web 2009-05-06 Planet of Sun and Oracle User Experience Design 2009-04-22 Sun VDI 3 UX Story - User Research 2009-04-08 Sun VDI 3 UX Story - Concept Workshops 2009-04-06 Localized documentation for Sun Ray Connector for VMware View Manager 1.1 2009-04-03 Sun VDI 3 Press Release 2009-03-25 Sun VDI 3 launches today! 2009-03-25 Sun Ray Connector for VMware View Manager 1.1 Update 2009-03-11 desktop virtualization wiki relaunch 2009-03-06 VDI 3 at CeBIT hall 6, booth E36 2009-03-02 Keyboard layout problems with Sun Ray Connector for VMware VDM 2009-02-23 wikis.sun.com tips & tricks 2009-02-23 Sun VDI 3 is in Early Access 2009-02-09 VirtualCenter unable to decrypt passwords 2009-02-02 Sun & VMware Desktop Training 2009-01-30 VDI at next09? 2009-01-16 Sun VDI: How to use virtual machines with multiple network adapters 2009-01-07 Sun Ray and VMware View 2009-01-07 Hamburg World Usability Day 2008 - Webcasts 2009-01-06 Sun Ray Connector for VMware VDM slides 2008-12-15 mother of all demos 2008-12-08 Build your own Thumper 2008-12-03 Troubleshooting Sun Ray Connector for VMware VDM 2008-12-02 My Roller Tag Cloud 2008-11-28 Sun Ray Connector: SSL connection to VDM 2008-11-25 Setting up SSL and Sun Ray Connector for VMware VDM 2008-11-13 Inspiration for Today and Tomorrow 2008-10-23 Sun Ray Connector for VMware VDM released 2008-10-14 From Sketchpad to ILoveSketch 2008-10-09 Desktop Virtualization on Xing 2008-10-06 User Experience Forum on Xing 2008-10-06 Sun Ray Connector for VMware VDM certified 2008-09-17 Virtual Clouds over Las Vegas 2008-09-14 Bill Verplank sketches metaphors 2008-09-04 End of Early Access - Sun Ray Connector for VMware 2008-08-27 Early Access: Sun Ray Connector for VMware Virtual Desktop Manager 2008-08-12 Sun Virtual Desktop Connector - Insides on Recycling Part 2 2008-07-20 Sun Virtual Desktop Connector - Insides on Recycling Part 3 2008-07-20 Sun Virtual Desktop Connector - Insides on Recycling 2008-07-20 lost in wiki space 2008-07-07 Evolution of the Desktop 2008-06-17 Virtual Desktop Webcast 2008-06-16 Woodstock 2008-06-16 What's a Desktop PC anyway? 2008-06-09 Virtual-T-Box 2008-06-05 Virtualization Glossary 2008-05-06 Five User Experience Principles 2008-04-25 Virtualization News Feed 2008-04-21 Acetylcholinesterase - Second Season 2008-04-18 Acetylcholinesterase - End of Signal 2007-12-31 Produkt-Management ist... 2007-10-22 Usability Verbände, Verteiler und Netzwerke. 2007-10-02 The Meaning is the Message 2007-09-28 Visualization Methods 2007-09-10 Inhouse und Open Source Projekte – Usability verankern und Synergien nutzen 2007-09-03 Der Schwabe Darth Vader entdeckt das Virale Marketing 2007-08-29 Dick Hardt 3.0 on Identity 2.0 2007-08-27 quality of written text depends on the tool 2007-07-27 podcasts for reboot9 2007-06-04 It is the user's itch that need to be scratched 2007-05-25 A duel at reboot9 2007-05-14 Taxonomien und Folksonomien - Tagging als neues HCI-Element 2007-05-10 Dueling Interaction Models of Personal-Computing and Web-Computing 2007-03-01 22.März: Weizenbaum. Rebel at Work. /Filmpremiere Hamburg 2007-02-25 Bruce Sterling at UbiComp 2006 /webcast 2006-11-12 FSOSS 2006 /webcasts 2006-11-10 Highway 101 2006-11-09 User Experience Roundtable Hamburg: EuroGEL 2006 2006-11-08 Douglas Adams' Hyperland (BBC 1990) 2006-10-08 Taxonomien und Folksonomien – Tagging als neues HCI-Element 2006-09-13 Usability im Unternehmen 2006-09-13 Doug does HyperScope 2006-08-26 TED Talks and TechTalks 2006-08-21 Kai Krause über seine Freundschaft zu Douglas Adams 2006-07-20 Rebel At Work: Film Portrait on Weizenbaum 2006-07-04 Gabriele Fischer, mp3 2006-06-07 Dick Hardt at ETech 06 2006-06-05 Weinberger: From Control to Conversation 2006-04-16 Eye Tracking at User Experience Roundtable Hamburg 2006-04-14 dropping knowledge 2006-04-09 GEL 2005 2006-03-13 slide photos of reboot7 2006-03-04 Dick Hardt on Identity 2.0 2006-02-28 User Experience Newsletter #13: Versioning 2006-02-03 Ester Dyson on Choice and Happyness 2006-02-02 Requirements-Engineering im Spannungsfeld von Individual- und Produktsoftware 2006-01-15 User Experience Newsletter #12: Intuition Quiz 2005-11-30 User Experience und Requirements-Engineering für Software-Projekte 2005-10-31 Ivan Sutherland on "Research and Fun" 2005-10-18 Ars Electronica / Mensch und Computer 2005 2005-09-14 60 Jahre nach Memex: Über die Unvereinbarkeit von Desktop- und Web-Paradigma 2005-08-31 reboot 7 2005-06-30

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• Testing Mobile Websites with Adobe Shadow

  - by dwahlin

  It’s no surprise that mobile development is all the rage these days. With all of the new mobile devices being released nearly every day the ability for developers to deliver mobile solutions is more important than ever. Nearly every developer or company I’ve talked to recently about mobile development in training classes, at conferences, and on consulting projects says that they need to find a solution to get existing websites into the mobile space. Although there are several different frameworks out there that can be used such as jQuery Mobile, Sencha Touch, jQTouch, and others, how do you test how your site renders on iOS, Android, Blackberry, Windows Phone, and the variety of mobile form factors out there? Although there are different virtual solutions that can be used including Electric Plum for iOS, emulators, browser plugins for resizing the laptop/desktop browser, and more, at some point you need to test on as many physical devices as possible. This can be extremely challenging and quite time consuming though especially when you consider that you have to manually enter URLs into devices and click links on each one to drill-down into sites. Adobe Labs just released a product called Adobe Shadow (thanks to Kurt Sprinzl for letting me know about it) that significantly simplifies testing sites on physical devices, debugging problems you find, and even making live modifications to HTML and CSS content while viewing a site on the device to see how rendering changes. You can view a page in your laptop/desktop browser and have it automatically pushed to all of your devices without actually touching the device (a huge time saver). See a problem with a device? Locate it using the free Chrome extension, pull up inspection tools (based on the Chrome Developer tools) and make live changes through Chrome that appear on the respective device so that it’s easy to identify how problems can be resolved. I’ve been using Adobe Shadow and am very impressed with the amount of time saved and the different features that it offers. In the rest of the post I’ll walk through how to get it installed, get it started, and use it to view and debug pages.   Getting Adobe Shadow Installed The following steps can be used to get Adobe Shadow installed: 1. Download and install Adobe Shadow on your laptop/desktop 2. Install the Adobe Shadow extension for Chrome 3. Install the Adobe Shadow app on all of your devices (you can find it in various app stores) 4. Connect your devices to Wifi. Make sure they’re on the same network that your laptop/desktop machine is on   Getting Adobe Shadow Started Once Adobe Shadow is installed, you’ll need to get it running on your laptop/desktop and on all your mobile devices. The following steps walk through that process: 1. Start the Adobe Shadow application on your laptop/desktop 2. Start the Adobe Shadow app on each of your mobile devices 3. Locate the laptop/desktop name in the list that’s shown on each mobile device: 4. Select the laptop/desktop name and a passcode will be shown: 5. Open the Adobe Shadow Chrome extension on the laptop/desktop and enter the passcode for the given device: Using Adobe Shadow to View and Modify Pages Once Adobe Shadow is up and running on your laptop/desktop and on all of your mobile devices you can navigate to a page in Chrome on the laptop/desktop and it will automatically be pushed out to all connected mobile devices. If you have 5 mobile devices setup they’ll all navigate to the page displayed in Chrome (pretty awesome!). This makes it super easy to see how a given page looks on your iPad, Android device, etc. without having to touch the device itself. If you find a problem with a page on a device you can select the device in the Chrome Adobe Shadow extension on your laptop/desktop and select the remote inspector icon (it’s the < > icon): This will pull up the Adobe Shadow remote debugging window which contains the standard Chrome Developer tool tabs such as Elements, Resources, Network, etc. Click on the Elements tab to see the HTML rendered for the target device and then drill into the respective HTML content, CSS styles, etc. As HTML elements are selected in the Adobe Shadow debugging tool they’ll be highlighted on the device itself just like they would if you were debugging a page directly in Chrome with the developer tools. Here’s an example from my Android device that shows how the page looks on the device as I select different HTML elements on the laptop/desktop: Conclusion I’m really impressed with what I’ve to this point from Adobe Shadow. Controlling pages that display on devices directly from my laptop/desktop is a big time saver and the ability to remotely see changes made through the Chrome Developer Tools (on my laptop/desktop) really pushes the tool over the top. If you’re developing mobile applications it’s definitely something to check out. It’s currently free to download and use. For additional details check out the video below:  

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• MacBook Pro Compatibility, Multitouch, and General Experience

  - by jondavidjohn

  I am a Ex-Ubuntu user and decided to go to OSX mainly because I was going to be working in an OSX shop and felt like I needed a more mainstream OS to run Production level software packages like Adobe. 6 months in, and I am more than happy with my MacBook Pro purchase. Just the physical build quality alone warrants the premium price tag, but I am now looking at my day to day demands and realize that I really do not use any software that prevents me from turning back to Ubuntu. My question now is, in terms of 2010 MacBook Pro, How is the hardware compatibility? Does the trackpad multitouch gesture work with 10.10? is it oversensitive? And for anyone that has a relatively new macbook pro that is running Ubuntu, How is the general experience coming from an OSX environment?

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• Mike Neuenschwander on the Identity Platform

  - by Naresh Persaud

  If you are in London on March 22nd, check out the Identity Platform Event. Mike is deeply passionate about the platform. I caught up with Mike recently for an interview to discuss his perspective on the Oracle Identity Platform. Identity Management is not a department level initiative. To unlock the business potential of Identity Management, we have to think organizationally and holistically. To learn more about how to take a strategic approach to Identity Management, visit one of our physical events globally.  Here are some of the listings and registrations world wide: North America, Asia Pacific, Europe .

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• Why isn't software as reliable as a car?

  - by Alex Angas

  I had a user ask me this question. We know that cars break down, but that's because of something physical (unless software is involved!). I tried to answer that software is a much younger industry, but the user countered with "didn't the automobile industry become much more stable than and reliable with less people?". I also tried to answer that software is more complex, but the user countered that there are many thousands of parts that make up a car. People that design and build cars generally just know their component(s) very well, but they still all end up working together as an end result. So, why isn't software as reliable as a car?

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• I made a 2D ENGINE for Android, looking for cooperation.

  - by Roger Travis

  My name is Robert, I am an Android programmer and wanted to show off my latest project - a 2d game engine. You can see it in action here - https://play.google.com/store/apps/details?id=engineDemo.com My engine's main advantage is its ease of use. To have your level up and running, you'll need only 3 lines of code. ABoxView aboxView = new ABoxView(this); setContentView(aboxView); aboxView.loadLevel("level/level02"); Level are created in a special level constructor and object physical properties are stored in a corresponding XML file. I am looking to cooperate with those, who might be interesting in using my engine in their games. You can email me at [email protected] or post here. Thanks, Robert

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• Kinect Turns DaVinci Physics Application Super Cool

  - by Gopinath

  Guys at RazorFish who are well known for their Microsoft Surface impressive stuff has ported their Da Vinci Application over to Kinect device. The end result is a super cool gesture based application. Check out the embedded video demonstration below If you wondering what is Da Vince Application is all about, here are few lines from RazorFish DaVinci is a Microsoft Surface application that blurs the lines between the physical and virtual world by combining object recognition, real-world physics simulation and gestural interface design. Related:Kinect + Windows 7 = Control PC With Hand Gestures This article titled,Kinect Turns DaVinci Physics Application Super Cool, was originally published at Tech Dreams. Grab our rss feed or fan us on Facebook to get updates from us.

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• Dissecting ASP.NET Routing

  The ASP.NET Routing framework allows developers to decouple the URL of a resource from the physical file on the web server. Specifically, the developer defines <i>routing rules</i>, which map URL patterns to a class or ASP.NET page that generates the content. For instance, you could create a URL pattern of the form <code>Categories/<i>CategoryName</i></code> and map it to the ASP.NET page <code>ShowCategoryDetails.aspx</code>; the <code>ShowCategoryDetails.aspx</code> page would display details about the category <i>CategoryName</i>. With such a mapping, users could view category about the Beverages category by visiting <code>www.yoursite.com/Categories/Beverages</code>. In short, ASP.NET Routing allows

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• Game mechanics patterns database?

  - by Klaim

  Do you know http://tvtropes.org ? It's a kind of wiki/database with scenaristic tropes, patterns that you can find in tones of stories, in tv shows, games, books, etc. Each trope/pattern have a (funny) name and there are references to where it appears, and the other way arround : each book/game/etc. have a list of tropes that it contains. I'm looking for an equivalent but for game mechanics patterns, something like "Death is definitive", "Perfect physical control (no inertia)", "Excell table gameplay", etc. I think it would be really useful. I can't find an equivalent for game mechanics (tvtrope is oriented to scenario, not game mechanics). Do you know any?

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• Tae Kwon Do in Overland Park

  - by [C.B.W]

  If you are in the Overland Park area and are in need of some physical recreation (and who isn’t) I have to recommend Master’s Tae Kwon Do in Overland Park KS . Master Tom is an 8th Dan teaching Tae Kwon Do and Hapkido. Yah, he teaches almost all of the classes himself. I used to take ishin ryu but stopped some 12 years ago (seems like yesterday. God I am getting old.)    I had wanted to get back into some type of Martial Arts training and I wanted to get my son involved as well – Master’s Tae Kwon Do has the best schedule.   My son and I can go to any of the classes together. Tae Kwon Do is a pretty good work out, lots of kicks so gets the blood pumping. Work out and learn how to defend yourself all at one time. Great for those of us short on time.

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• How do I set up a virtual network interface with its own IP address?

  - by Stefano Palazzo

  I vaguely remember that it's possible to set up virtual network interfaces with their own IP addresses, using only one physical network connection. I can find a few guides on the internet that recommend setting these up in /etc/network/interfaces, but Ubuntu doesn't use this file. Therefore my question: What's the correct way of setting these up in recent versions of Ubuntu? As this is a laptop, and I need it to connect to all kinds of different networks, I want to keep the network manager and all its configuration. To be more clear: at the end of this, I want to have a new network interface (e.g. "eth42") with its own IP address, but using whatever is connected in network manager to send the actual packets. In NM, it should appear as if I just had a second ethernet adapter installed in my system.

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• gpgpu vs. physX for physics simulation

  - by notabene

  Hello First theoretical question. What is better (faster)? Develop your own gpgpu techniques for physics simulation (cloth, fluids, colisions...) or to use PhysX? (If i say develop i mean implement existing algorithms like navier-strokes...) I don't care about what will take more time to develop. What will be faster for end user? As i understand that physx are accelerated through PPU units in gpu, does it mean that physical simulation can run in paralel with rastarization? Are PPUs different units than unified shader units used as vertex/geometry/pixel/gpgpu shader units? And little non-theoretical question: Is physx able to do sofisticated simulation equal to lets say Autodesk's Maya fluid solver? Are there any c++ gpu accelerated physics frameworks to try? (I am interested in both physx and gpgpu, commercial engines are ok too).

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• Fix overlapping partitions

  - by Alex

  I have problem with overlapping partitions. GParted shows me all my disk as unallocated area, output of fdisk below: alex@alex-ThinkPad-SL510:~$ sudo fdisk -l /dev/sda Disk /dev/sda: 320.1 GB, 320072933376 bytes 255 heads, 63 sectors/track, 38913 cylinders, total 625142448 sectors Units = sectors of 1 * 512 = 512 bytes Sector size (logical/physical): 512 bytes / 512 bytes I/O size (minimum/optimal): 512 bytes / 512 bytes Disk identifier: 0xfb4b9b90 Device Boot Start End Blocks Id System /dev/sda1 * 2048 2457599 1227776 7 HPFS/NTFS/exFAT /dev/sda2 2457600 571351724 284447062+ 7 HPFS/NTFS/exFAT /dev/sda3 571342846 604661759 16659457 5 Extended /dev/sda4 604661760 625137663 10237952 7 HPFS/NTFS/exFAT /dev/sda5 598650880 604661759 3005440 82 Linux swap / Solaris /dev/sda6 571342848 598650879 13654016 83 Linux Partition table entries are not in disk order Do I understand correctly that overlapping partitions are sda2 and sda3 (sda2 and sda6 overlaps too, because sda6 is the first chunk of sda3, sda3 has type "extended")? Are sda2 and sda3 the cause of problem? How can i fix it without deleting partitions? My OS is Ubuntu 12.04, 64 bit. Thanks in advance.

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