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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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  • Nullable types and ?? operator C# [en-US]

    - by ruimachado
    Nullable types vs Non-nullable types   While developing our C# projects its frequent the null comparison operation to avoid null exceptions. This simple operation is mainly coded using the "var x = null" code example inside an if clause. However not all types of variables are nullable, which means that setting a variable to null is not allowed in every cases, it depends on what kind of type are you defining. But what if there was an extension to your non-nullable type that would convert your variable types to nullable? This extension really exists. As I said before in C# you have nullable types which represent all the values of an underlying type, and an additional null value and can be declared easily using "T?", where T is the type of the variable and for example the normal int type cannot be null, so its a non-nullable type, however if you define a "int?" your variable can be null, what you do is convert a non-nullable type to a nullable type. Example: int x=null;     Not allowed     int? x=null;   Allowed     While using nullable types you can check if a variable is null the same way you do it with nullable types:     But what about setting a default value when a certain variable is null?   In this cases the c# .net framework let you set a default value when you try to assign a nullable type to a non-nullable type, using the ?? operator. If you don't use this operator you can still catch the InvalidOperationException which is throw in this cases. For example  without the ?? operator :     Using the ?? operator your code becomes cleaner and more easy to read and you get a bonus, you can set a default value for multiple variables using the ?? in a chain set.     That’s it,   Thanks, Rui Machado rpmachado.wordpress.com

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  • Nullable Enum nullable type question

    - by Michael Kniskern
    I get the following compilation error with the following source code: Compilation Error: Type of conditional expression cannot be determined because there is no implicit conversion between '' and 'MyEnum' Source Code public enum MyEnum { Value1, Value2, Value3 } public class MyClass { public MyClass() {} public MyEnum? MyClassEnum { get; set; } } public class Main() { object x = new object(); MyClass mc = new MyClass() { MyClassEnum = Convert.IsDBNull(x) : null ? (MyEnum) Enum.Parse(typeof(MyEnum), x.ToString(), true) }; } How can I resolve this error?

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  • C#/.NET Little Wonders: The Nullable static class

    - by James Michael Hare
    Once again, in this series of posts I look at the parts of the .NET Framework that may seem trivial, but can help improve your code by making it easier to write and maintain. The index of all my past little wonders posts can be found here. Today we’re going to look at an interesting Little Wonder that can be used to mitigate what could be considered a Little Pitfall.  The Little Wonder we’ll be examining is the System.Nullable static class.  No, not the System.Nullable<T> class, but a static helper class that has one useful method in particular that we will examine… but first, let’s look at the Little Pitfall that makes this wonder so useful. Little Pitfall: Comparing nullable value types using <, >, <=, >= Examine this piece of code, without examining it too deeply, what’s your gut reaction as to the result? 1: int? x = null; 2:  3: if (x < 100) 4: { 5: Console.WriteLine("True, {0} is less than 100.", 6: x.HasValue ? x.ToString() : "null"); 7: } 8: else 9: { 10: Console.WriteLine("False, {0} is NOT less than 100.", 11: x.HasValue ? x.ToString() : "null"); 12: } Your gut would be to say true right?  It would seem to make sense that a null integer is less than the integer constant 100.  But the result is actually false!  The null value is not less than 100 according to the less-than operator. It looks even more outrageous when you consider this also evaluates to false: 1: int? x = null; 2:  3: if (x < int.MaxValue) 4: { 5: // ... 6: } So, are we saying that null is less than every valid int value?  If that were true, null should be less than int.MinValue, right?  Well… no: 1: int? x = null; 2:  3: // um... hold on here, x is NOT less than min value? 4: if (x < int.MinValue) 5: { 6: // ... 7: } So what’s going on here?  If we use greater than instead of less than, we see the same little dilemma: 1: int? x = null; 2:  3: // once again, null is not greater than anything either... 4: if (x > int.MinValue) 5: { 6: // ... 7: } It turns out that four of the comparison operators (<, <=, >, >=) are designed to return false anytime at least one of the arguments is null when comparing System.Nullable wrapped types that expose the comparison operators (short, int, float, double, DateTime, TimeSpan, etc.).  What’s even odder is that even though the two equality operators (== and !=) work correctly, >= and <= have the same issue as < and > and return false if both System.Nullable wrapped operator comparable types are null! 1: DateTime? x = null; 2: DateTime? y = null; 3:  4: if (x <= y) 5: { 6: Console.WriteLine("You'd think this is true, since both are null, but it's not."); 7: } 8: else 9: { 10: Console.WriteLine("It's false because <=, <, >, >= don't work on null."); 11: } To make matters even more confusing, take for example your usual check to see if something is less than, greater to, or equal: 1: int? x = null; 2: int? y = 100; 3:  4: if (x < y) 5: { 6: Console.WriteLine("X is less than Y"); 7: } 8: else if (x > y) 9: { 10: Console.WriteLine("X is greater than Y"); 11: } 12: else 13: { 14: // We fall into the "equals" assumption, but clearly null != 100! 15: Console.WriteLine("X is equal to Y"); 16: } Yes, this code outputs “X is equal to Y” because both the less-than and greater-than operators return false when a Nullable wrapped operator comparable type is null.  This violates a lot of our assumptions because we assume is something is not less than something, and it’s not greater than something, it must be equal.  So keep in mind, that the only two comparison operators that work on Nullable wrapped types where at least one is null are the equals (==) and not equals (!=) operators: 1: int? x = null; 2: int? y = 100; 3:  4: if (x == y) 5: { 6: Console.WriteLine("False, x is null, y is not."); 7: } 8:  9: if (x != y) 10: { 11: Console.WriteLine("True, x is null, y is not."); 12: } Solution: The Nullable static class So we’ve seen that <, <=, >, and >= have some interesting and perhaps unexpected behaviors that can trip up a novice developer who isn’t expecting the kinks that System.Nullable<T> types with comparison operators can throw.  How can we easily mitigate this? Well, obviously, you could do null checks before each check, but that starts to get ugly: 1: if (x.HasValue) 2: { 3: if (y.HasValue) 4: { 5: if (x < y) 6: { 7: Console.WriteLine("x < y"); 8: } 9: else if (x > y) 10: { 11: Console.WriteLine("x > y"); 12: } 13: else 14: { 15: Console.WriteLine("x == y"); 16: } 17: } 18: else 19: { 20: Console.WriteLine("x > y because y is null and x isn't"); 21: } 22: } 23: else if (y.HasValue) 24: { 25: Console.WriteLine("x < y because x is null and y isn't"); 26: } 27: else 28: { 29: Console.WriteLine("x == y because both are null"); 30: } Yes, we could probably simplify this logic a bit, but it’s still horrendous!  So what do we do if we want to consider null less than everything and be able to properly compare Nullable<T> wrapped value types? The key is the System.Nullable static class.  This class is a companion class to the System.Nullable<T> class and allows you to use a few helper methods for Nullable<T> wrapped types, including a static Compare<T>() method of the. What’s so big about the static Compare<T>() method?  It implements an IComparer compatible comparison on Nullable<T> types.  Why do we care?  Well, if you look at the MSDN description for how IComparer works, you’ll read: Comparing null with any type is allowed and does not generate an exception when using IComparable. When sorting, null is considered to be less than any other object. This is what we probably want!  We want null to be less than everything!  So now we can change our logic to use the Nullable.Compare<T>() static method: 1: int? x = null; 2: int? y = 100; 3:  4: if (Nullable.Compare(x, y) < 0) 5: { 6: // Yes! x is null, y is not, so x is less than y according to Compare(). 7: Console.WriteLine("x < y"); 8: } 9: else if (Nullable.Compare(x, y) > 0) 10: { 11: Console.WriteLine("x > y"); 12: } 13: else 14: { 15: Console.WriteLine("x == y"); 16: } Summary So, when doing math comparisons between two numeric values where one of them may be a null Nullable<T>, consider using the System.Nullable.Compare<T>() method instead of the comparison operators.  It will treat null less than any value, and will avoid logic consistency problems when relying on < returning false to indicate >= is true and so on. Tweet   Technorati Tags: C#,C-Sharp,.NET,Little Wonders,Little Pitfalls,Nulalble

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  • Smart way to find the corresponding nullable type?

    - by Marc Wittke
    How could I avoid this dictionary (or create it dynamically)? Dictionary<Type,Type> CorrespondingNullableType = new Dictionary<Type, Type> { {typeof(bool), typeof(bool?)}, {typeof(byte), typeof(byte?)}, {typeof(sbyte), typeof(sbyte?)}, {typeof(char), typeof(char?)}, {typeof(decimal), typeof(decimal?)}, {typeof(double), typeof(double?)}, {typeof(float), typeof(float?)}, {typeof(int), typeof(int?)}, {typeof(uint), typeof(uint?)}, {typeof(long), typeof(long?)}, {typeof(ulong), typeof(ulong?)}, {typeof(short), typeof(short?)}, {typeof(ushort), typeof(ushort?)}, {typeof(Guid), typeof(Guid?)}, };

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  • Where in memory are stored nullable types?

    - by Ondrej Slinták
    This is maybe a follow up to question about nullable types. Where exactly are nullable value types (int?...) stored in memory? First I thought it's clear enough, as Nullable<T> is struct and those are value types. Then I found Jon Skeet's article "Memory in .NET", which says: Note that a value type variable can never have a value of null - it wouldn't make any sense, as null is a reference type concept, meaning "the value of this reference type variable isn't a reference to any object at all". I am little bit confused after reading this statement. So let's say I have int? a = null;. As int is normally a value type, is it stored somehow inside struct Nullable<T> in stack (I used "normally" because I don't know what happens with value type when it becomes nullable)? Or anything else happens here - perhaps in heap?

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  • FluentNHibernate: Not.Nullable() doesn't affect output schema

    - by alex
    Hello I'm using fluent nhibernate v. 1.0.0.595. There is a class: public class Weight { public virtual int Id { get; set; } public virtual double Value { get; set; } } I want to map it on the following table: create table [Weight] ( WeightId INT IDENTITY NOT NULL, Weight DOUBLE not null, primary key (WeightId) ) Here is the map: public class WeightMap : ClassMap<Weight> { public WeightMap() { Table("[Weight]"); Id(x => x.Id, "WeightId"); Map(x => x.Value, "Weight").Not.Nullable(); } } The problem is that this mapping produces table with nullable Weight column: Weight DOUBLE null Not-nullable column is generated only with default convention for column name (i.e. Map(x = x.Value).Not.Nullable() instead of Map(x = x.Value, "Weight").Not.Nullable()), but in this case there will be Value column instead of Weight: create table [Weight] ( WeightId INT IDENTITY NOT NULL, Value DOUBLE not null, primary key (WeightId) ) I found similiar problem here: http://code.google.com/p/fluent-nhibernate/issues/detail?id=121, but seems like mentioned workaround with SetAttributeOnColumnElement("not-null", "true") is outdated. Does anybody encountered with this problem? Is there a way to specify named column as not-nullable?

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  • SQL SERVER – Wait Stats – Wait Types – Wait Queues – Day 0 of 28

    - by pinaldave
    This blog post will have running account of the all the blog post I will be doing in this month related to SQL Server Wait Types and Wait Queues. SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28 SQL SERVER – Signal Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28 SQL SERVER – DMV – sys.dm_os_wait_stats Explanation – Wait Type – Day 3 of 28 SQL SERVER – DMV – sys.dm_os_waiting_tasks and sys.dm_exec_requests – Wait Type – Day 4 of 28 SQL SERVER – Capturing Wait Types and Wait Stats Information at Interval – Wait Type – Day 5 of 28 SQL SERVER – CXPACKET – Parallelism – Usual Solution – Wait Type – Day 6 of 28 SQL SERVER – CXPACKET – Parallelism – Advanced Solution – Wait Type – Day 7 of 28 SQL SERVER – SOS_SCHEDULER_YIELD – Wait Type – Day 8 of 28 SQL SERVER – PAGEIOLATCH_DT, PAGEIOLATCH_EX, PAGEIOLATCH_KP, PAGEIOLATCH_SH, PAGEIOLATCH_UP – Wait Type – Day 9 of 28 SQL SERVER – IO_COMPLETION – Wait Type – Day 10 of 28 SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28 SQL SERVER – PAGELATCH_DT, PAGELATCH_EX, PAGELATCH_KP, PAGELATCH_SH, PAGELATCH_UP – Wait Type – Day 12 of 28 SQL SERVER – FT_IFTS_SCHEDULER_IDLE_WAIT – Full Text – Wait Type – Day 13 of 28 SQL SERVER – BACKUPIO, BACKUPBUFFER – Wait Type – Day 14 of 28 SQL SERVER – LCK_M_XXX – Wait Type – Day 15 of 28 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – Wait Stats – Wait Types – Wait Queues – Day 0 of 28

    - by pinaldave
    This blog post will have running account of the all the blog post I will be doing in this month related to SQL Server Wait Types and Wait Queues. SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28 SQL SERVER – Single Wait Time Introduction with Simple Example – Wait Type – Day 2 of 28 Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • How to compare nullable types?

    - by David_001
    I have a few places where I need to compare 2 (nullable) values, to see if they're the same. I think there should be something in the framework to support this, but can't find anything, so instead have the following: public static bool IsDifferentTo(this bool? x, bool? y) { return (x.HasValue != y.HasValue) ? true : x.HasValue && x.Value != y.Value; } Then, within code I have if (x.IsDifferentTo(y)) ... I then have similar methods for nullable ints, nullable doubles etc. Is there not an easier way to see if two nullable types are the same? Update: Turns out that the reason this method existed was because the code has been converted from VB.Net, where Nothing = Nothing returns false (compare to C# where null == null returns true). The VB.Net code should have used .Equals... instead.

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  • SQL SERVER – Guest Post – Jacob Sebastian – Filestream – Wait Types – Wait Queues – Day 22 of 28

    - by pinaldave
    Jacob Sebastian is a SQL Server MVP, Author, Speaker and Trainer. Jacob is one of the top rated expert community. Jacob wrote the book The Art of XSD – SQL Server XML Schema Collections and wrote the XML Chapter in SQL Server 2008 Bible. See his Blog | Profile. He is currently researching on the subject of Filestream and have submitted this interesting article on the very subject. What is FILESTREAM? FILESTREAM is a new feature introduced in SQL Server 2008 which provides an efficient storage and management option for BLOB data. Many applications that deal with BLOB data today stores them in the file system and stores the path to the file in the relational tables. Storing BLOB data in the file system is more efficient that storing them in the database. However, this brings up a few disadvantages as well. When the BLOB data is stored in the file system, it is hard to ensure transactional consistency between the file system data and relational data. Some applications store the BLOB data within the database to overcome the limitations mentioned earlier. This approach ensures transactional consistency between the relational data and BLOB data, but is very bad in terms of performance. FILESTREAM combines the benefits of both approaches mentioned above without the disadvantages we examined. FILESTREAM stores the BLOB data in the file system (thus takes advantage of the IO Streaming capabilities of NTFS) and ensures transactional consistency between the BLOB data in the file system and the relational data in the database. For more information on the FILESTREAM feature, visit: http://beyondrelational.com/filestream/default.aspx FILESTREAM Wait Types Since this series is on the different SQL Server wait types, let us take a look at the various wait types that are related to the FILESTREAM feature. FS_FC_RWLOCK This wait type is generated by FILESTREAM Garbage Collector. This occurs when Garbage collection is disabled prior to a backup/restore operation or when a garbage collection cycle is being executed. FS_GARBAGE_COLLECTOR_SHUTDOWN This wait type occurs when during the cleanup process of a garbage collection cycle. It indicates that that garbage collector is waiting for the cleanup tasks to be completed. FS_HEADER_RWLOCK This wait type indicates that the process is waiting for obtaining access to the FILESTREAM header file for read or write operation. The FILESTREAM header is a disk file located in the FILESTREAM data container and is named “filestream.hdr”. FS_LOGTRUNC_RWLOCK This wait type indicates that the process is trying to perform a FILESTREAM log truncation related operation. It can be either a log truncate operation or to disable log truncation prior to a backup or restore operation. FSA_FORCE_OWN_XACT This wait type occurs when a FILESTREAM file I/O operation needs to bind to the associated transaction, but the transaction is currently owned by another session. FSAGENT This wait type occurs when a FILESTREAM file I/O operation is waiting for a FILESTREAM agent resource that is being used by another file I/O operation. FSTR_CONFIG_MUTEX This wait type occurs when there is a wait for another FILESTREAM feature reconfiguration to be completed. FSTR_CONFIG_RWLOCK This wait type occurs when there is a wait to serialize access to the FILESTREAM configuration parameters. Waits and Performance System waits has got a direct relationship with the overall performance. In most cases, when waits increase the performance degrades. SQL Server documentation does not say much about how we can reduce these waits. However, following the FILESTREAM best practices will help you to improve the overall performance and reduce the wait types to a good extend. Read all the post in the Wait Types and Queue series. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology Tagged: Filestream

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  • Why shouldn't I always use nullable types in C#.

    - by Matthew Vines
    I've been searching for some good guidance on this since the concept was introduced in .net 2.0. Why would I ever want to use non-nullable data types in c#? (A better question is why wouldn't I choose nullable types by default, and only use non-nullable types when that explicitly makes sense.) Is there a 'significant' performance hit to choosing a nullable data type over its non-nullable peer? I much prefer to check my values against null instead of Guid.empty, string.empty, DateTime.MinValue,<= 0, etc, and to work with nullable types in general. And the only reason I don't choose nullable types more often is the itchy feeling in the back of my head that makes me feel like it's more than backwards compatibility that forces that extra '?' character to explicitly allow a null value. Is there anybody out there that always (most always) chooses nullable types rather than non-nullable types? Thanks for your time,

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  • SQLAuthority News – Online Webcast How to Identify Resource Bottlenecks – Wait Types and Queues

    - by pinaldave
    As all of you know I have been working a recently on the subject SQL Server Wait Statistics, the reason is since I have published book on this subject SQL Wait Stats Joes 2 Pros: SQL Performance Tuning Techniques Using Wait Statistics, Types & Queues [Amazon] | [Flipkart] | [Kindle], lots of question and answers I am encountering. When I was writing the book, I kept version 1 of the book in front of me. I wanted to write something which one can use right away. I wanted to create an primer for everybody who have not explored wait stats method of performance tuning. Well, the books have been very well received and in fact we ran out of huge stock 2 times in India so far and once in USA during SQLPASS. I have received so many questions on this subject that I feel I can write one more book of the same size. I have been asked if I can create videos which can go along with this book. Personally I am working with SQL Server 2012 CTP3 and there are so many new wait types, I feel the subject of wait stats is going to be very very crucial in next version of SQL Server. If you have not started learning about this subject, I suggest you at least start exploring this right now. Learn how to begin on this subject atleast as when the next version comes in, you know how to read DMVs. I will be presenting on the same subject of performance tuning by wait stats in webcast embarcadero SQL Server Community Webinar. Here are few topics which we will be covering during the webinar. Beginning with SQL Wait Stats Understanding various aspect of SQL Wait Stats Understanding Query Life Cycle Identifying three TOP wait Stats Resolution of the common 3 wait types and queues Details of the webcast: How to Identify Resource Bottlenecks – Wait Types and Queues Date and Time: Wednesday, November 2, 11:00 AM PDT Registration Link I thank embarcadero for organizing opportunity for me to share my experience on subject of wait stats and connecting me with community to further take this subject to next level. One more interesting thing, I will ask one question at the end of the webinar and I will be giving away 5 copy of my SQL Wait Stats print book to first five correct answers. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: About Me, Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Nullable enum in html helper

    - by Fabien Piron
    I have a view model that contains enum with nullable type like this one : public StudyLevel? studyLevel { get; set; } I have made custom html helper to display a dropdownlist for rendering the enum into the view, the nullable case is displayed using <option value="null">No value</option> the problem is that when i submit the form modelstate give me the error : studylevel cannot be "null" . Could you suggest me any way to help me handle the nullable type in the view ?

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  • Nullable<> as TModel for ViewPage

    - by Alexander Prokofyev
    What are the possible reasons what Nullable<> types are disallowed to be passed as TModel parameter of System.Web.Mvc.ViewPage<TModel> generic? This could be handy sometimes. In ASP.NET MVC source defined what TModel should be a class: public class ViewPage<TModel> : ViewPage where TModel : class but Nullable types are value types. Maybe definition could be less restrictive...

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  • Having an issue with Nullable MySQL columns in SubSonic 3.0 templates

    - by omegawkd
    Looking at this line in the Settings.ttinclude string CheckNullable(Column col){ string result=""; if(col.IsNullable && col.SysType !="byte[]" && col.SysType !="string") result="?"; return result; } It describes how it determines if the column is nullable based on requirements and returns either "" or "?" to the generated code. Now I'm not too familiar with the ? nullable type operator but from what I can see a cast is required. For instance, if I have a nullable integer MySQL column and I generate the code using the default template files it returns a line similar to this: int? _User_ID; When trying to compile the project I get the error: Cannot implicitly convert type 'int?' to 'int'. An explicit conversion exists (are you missing a cast?) I checked teh Settings files for the other database types and they all seems to have the same routine. So my question is, is this behaviour expected or is this a bug? I need to solve it one way or the other before I can procede. Thanks for your help.

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  • SQL SERVER – Introduction to Wait Stats and Wait Types – Wait Type – Day 1 of 28

    - by pinaldave
    I have been working a lot on Wait Stats and Wait Types recently. Last Year, I requested blog readers to send me their respective server’s wait stats. I appreciate their kind response as I have received  Wait stats from my readers. I took each of the results and carefully analyzed them. I provided necessary feedback to the person who sent me his wait stats and wait types. Based on the feedbacks I got, many of the readers have tuned their server. After a while I got further feedbacks on my recommendations and again, I collected wait stats. I recorded the wait stats and my recommendations and did further research. At some point at time, there were more than 10 different round trips of the recommendations and suggestions. Finally, after six month of working my hands on performance tuning, I have collected some real world wisdom because of this. Now I plan to share my findings with all of you over here. Before anything else, please note that all of these are based on my personal observations and opinions. They may or may not match the theory available at other places. Some of the suggestions may not match your situation. Remember, every server is different and consequently, there is more than one solution to a particular problem. However, this series is written with kept wait stats in mind. While I was working on various performance tuning consultations, I did many more things than just tuning wait stats. Today we will discuss how to capture the wait stats. I use the script diagnostic script created by my friend and SQL Server Expert Glenn Berry to collect wait stats. Here is the script to collect the wait stats: -- Isolate top waits for server instance since last restart or statistics clear WITH Waits AS (SELECT wait_type, wait_time_ms / 1000. AS wait_time_s, 100. * wait_time_ms / SUM(wait_time_ms) OVER() AS pct, ROW_NUMBER() OVER(ORDER BY wait_time_ms DESC) AS rn FROM sys.dm_os_wait_stats WHERE wait_type NOT IN ('CLR_SEMAPHORE','LAZYWRITER_SLEEP','RESOURCE_QUEUE','SLEEP_TASK' ,'SLEEP_SYSTEMTASK','SQLTRACE_BUFFER_FLUSH','WAITFOR', 'LOGMGR_QUEUE','CHECKPOINT_QUEUE' ,'REQUEST_FOR_DEADLOCK_SEARCH','XE_TIMER_EVENT','BROKER_TO_FLUSH','BROKER_TASK_STOP','CLR_MANUAL_EVENT' ,'CLR_AUTO_EVENT','DISPATCHER_QUEUE_SEMAPHORE', 'FT_IFTS_SCHEDULER_IDLE_WAIT' ,'XE_DISPATCHER_WAIT', 'XE_DISPATCHER_JOIN', 'SQLTRACE_INCREMENTAL_FLUSH_SLEEP')) SELECT W1.wait_type, CAST(W1.wait_time_s AS DECIMAL(12, 2)) AS wait_time_s, CAST(W1.pct AS DECIMAL(12, 2)) AS pct, CAST(SUM(W2.pct) AS DECIMAL(12, 2)) AS running_pct FROM Waits AS W1 INNER JOIN Waits AS W2 ON W2.rn <= W1.rn GROUP BY W1.rn, W1.wait_type, W1.wait_time_s, W1.pct HAVING SUM(W2.pct) - W1.pct < 99 OPTION (RECOMPILE); -- percentage threshold GO This script uses Dynamic Management View sys.dm_os_wait_stats to collect the wait stats. It omits the system-related wait stats which are not useful to diagnose performance-related bottleneck. Additionally, not OPTION (RECOMPILE) at the end of the DMV will ensure that every time the query runs, it retrieves new data and not the cached data. This dynamic management view collects all the information since the time when the SQL Server services have been restarted. You can also manually clear the wait stats using the following command: DBCC SQLPERF('sys.dm_os_wait_stats', CLEAR); Once the wait stats are collected, we can start analysis them and try to see what is causing any particular wait stats to achieve higher percentages than the others. Many waits stats are related to one another. When the CPU pressure is high, all the CPU-related wait stats show up on top. But when that is fixed, all the wait stats related to the CPU start showing reasonable percentages. It is difficult to have a sure solution, but there are good indications and good suggestions on how to solve this. I will keep this blog post updated as I will post more details about wait stats and how I reduce them. The reference to Book On Line is over here. Of course, I have selected February to run this Wait Stats series. I am already cheating by having the smallest month to run this series. :) Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: DMV, Pinal Dave, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • Order by nullable property simultaneously with ordering by not nullable property in HQL

    - by Episodex
    Hi, I have a table called Users in my database. Let's assume that User has only 3 properties int ID; string? Name; string Login; If user doesn't specify his name then Login is displayed. Otherwise Name is displayed. I wan't to get list of all users sorted by what is displayed. So if user specified Name, it is taken into consideration while sorting, otherwise his position on the list should be determined by Login. Eventually whole list should be ordered alphabetically. I hope I made myself clear... Is that possible to do in HQL?

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  • How to parse a string into a nullable int in C# (.NET 3.5)

    - by Glenn Slaven
    I'm wanting to parse a string into a nullable int in C#. ie. I want to get back either the int value of the string or null if it can't be parsed. I was kind of hoping that this would work int? val = stringVal as int?; But that won't work, so the way I'm doing it now is I've written this extension method public static int? ParseNullableInt(this string value) { if (value == null || value.Trim() == string.Empty) { return null; } else { try { return int.Parse(value); } catch { return null; } } } Is there a better way of doing this? EDIT: Thanks for the TryParse suggestions, I did know about that, but it worked out about the same. I'm more interested in knowing if there is a built-in framework method that will parse directly into a nullable int?

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