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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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  • How-to call server side Java from JavaScript

    - by frank.nimphius
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} The af:serverListener tag in Oracle ADF Faces allows JavaScript to call into server side Java. The example shown below uses an af:clientListener tag to invoke client side JavaScript in response to a key stroke in an Input Text field. The script then call a defined af:serverListener by its name defined in the type attribute. The server listener can be defined anywhere on the page, though from a code readability perspective it sounds like a good idea to put it close to from where it is invoked. <af:inputText id="it1" label="...">   <af:clientListener method="handleKeyUp" type="keyUp"/>   <af:serverListener type="MyCustomServerEvent"                      method="#{mybean.handleServerEvent}"/> </af:inputText> The JavaScript function below reads the event source from the event object that gets passed into the called JavaScript function. The call to the server side Java method, which is defined on a managed bean, is issued by a JavaScript call to AdfCustomEvent. The arguments passed to the custom event are the event source, the name of the server listener, a message payload formatted as an array of key:value pairs, and true/false indicating whether or not to make the call immediate in the request lifecycle. <af:resource type="javascript">     function handleKeyUp (evt) {    var inputTextComponen = event.getSource();       AdfCustomEvent.queue(inputTextComponent,                         "MyCustomServerEvent ",                         {fvalue:component.getSubmittedValue()},                         false);    event.cancel();}   </af:resource> The server side managed bean method uses a single argument signature with the argument type being ClientEvent. The client event provides information about the event source object - as provided in the call to AdfCustomEvent, as well as the payload keys and values. The payload is accessible from a call to getParameters, which returns a HashMap to get the values by its key identifiers.  public void handleServerEvent(ClientEvent ce){    String message = (String) ce.getParameters().get("fvalue");   ...  } Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Find the tag library at: http://download.oracle.com/docs/cd/E15523_01/apirefs.1111/e12419/tagdoc/af_serverListener.html

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  • EPM 11.1.2 - In WebLogic Server, Enable Native IO Performance Pack

    - by Ahmed Awan
    Performance can be improved by enabling native IO in production mode. WebLogic Server benchmarks show major performance improvements when native performance packs are used on machines that host Oracle WebLogic Server instances. Important Note:  Always enable native I/O, if available, and check for errors at startup to make sure it is being initialed properly. Tip: The use of NATIVE performance packs are enabled by default in the configuration shipped with your distribution. You can use the Administration Console to verify that performance packs are enabled by clicking on each managed server and click on Tuning tab.

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  • ADF Desktop Integration Security Explained

    - by juan.ruiz
    ADFdi provides a secure access to spreadsheets within MS-Excel. Developers as well as administrators could wonder how the security features work in this mixed layout -having MS-Excel accessing JavaEE business services? and also what do system administrators should expect when deploying an ADF solution that offers ADFdi capabilities? Shaun Logan from the ADFdi team published an excellent article back in January where you can find in a great detail the ADF desktop integration security features and implementation. You can find the article here: http://www.oracle.com/technology/products/jdev/11/collateral/security%20whitepaper%20for%20adfdi%20r1%20final.pdf Enjoy!

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  • Mismanaged Session Cookie Issue Fixed for EBS in JRE 1.6.0_23

    - by Steven Chan
    At last:  some good news for those of you affected by the mismanaged session cookie issue in E-Business Suite environments.  This issue is resolved by the latest Sun Java Runtime Environment 1.6.0_23 (a.k.a. JRE 6u23, internal version 1.6.0_23-b05).See the 1.6.0_23 Update Release Notes for details about what has changed in this release.  This release is available for download from the usual Sun channels and through the 'Java Automatic Update' mechanism.This JRE release has been certified with both Oracle E-Business Suite Release 11i and 12.  We recommend this release for all E-Business Suite users.

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  • Concurrent Business Events

    - by Manoj Madhusoodanan
    This blog describes the various business events related to concurrent requests.In the concurrent program definition screen we can see the various business events which are attached to concurrent processing. Following are the actual definition of above business events. Each event will have following parameters. Create subscriptions to above business events.Before testing enable profile option 'Concurrent: Business Intelligence Integration Enable' to Yes. ExampleI have created a scenario.Whenever my concurrent request completes normally I want to send out file as attachment to my mail.So following components I have created.1) Host file deployed on $XXCUST_TOP/bin to send mail.It accepts mail ids,subject and output file.(Code here)2) Concurrent Program to send mail which points to above host file.3) Subscription package to oracle.apps.fnd.concurrent.request.completed.(Code here)Choose a concurrent program which you want to send the out file as attachment.Check Request Completed check box.Submit the program.If it completes normally the business event subscription program will send the out file as attachment to the specified mail id.

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  • Innovating with Customer Needs Management

    - by Anurag Batra
    We're pleased to announce the addition of Agile Customer Needs Management (CNM) to the portfolio of PLM offerings by Oracle. CNM allows manufacturing companies to capture the voice of the customer and market, and arm their product designers with the information that they need to better meet customer requirements. It's an Enterprise 2.0 product that focuses on the quick information capture, ease of organizing information and association of that information with the product record - some of the key aspects of early stage innovation. Read on to learn more about this revolutionary new product that redefines how information is used to drive innovation.

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  • Replication - between pools in the same system

    - by Steve Tunstall
    OK, I fully understand that's it's been a LONG time since I've blogged with any tips or tricks on the ZFSSA, and I'm way behind. Hey, I just wrote TWO BLOGS ON THE SAME DAY!!! Make sure you keep scrolling down to see the next one too, or you may have missed it. To celebrate, for the one or two of you out there who are still reading this, I got something for you. The first TWO people who make any comment below, with your real name and email so I can contact you, will get some cool Oracle SWAG that I have to give away. Don't get excited, it's not an iPad, but it pretty good stuff. Only the first two, so if you already see two below, then settle down. Now, let's talk about Replication and Migration.  I have talked before about Shadow Migration here: https://blogs.oracle.com/7000tips/entry/shadow_migrationShadow Migration lets one take a NFS or CIFS share in one pool on a system and migrate that data over to another pool in the same system. That's handy, but right now it's only for file systems like NFS and CIFS. It will not work for LUNs. LUN shadow migration is a roadmap item, however. So.... What if you have a ZFSSA cluster with multiple pools, and you have a LUN in one pool but later you decide it's best if it was in the other pool? No problem. Replication to the rescue. What's that? Replication is only for replicating data between two different systems? Who told you that? We've been able to replicate to the same system now for a few code updates back. These instructions below will also work just fine if you're setting up replication between two different systems. After replication is complete, you can easily break replication, change the new LUN into a primary LUN and then delete the source LUN. Bam. Step 1- setup a target system. In our case, the target system is ourself, but you still have to set it up like it's far away. Go to Configuration-->Services-->Remote Replication. Click the plus sign and setup the target, which is the ZFSSA you're on now. Step 2. Now you can go to the LUN you want to replicate. Take note which Pool and Project you're in. In my case, I have a LUN in Pool2 called LUNp2 that I wish to replicate to Pool1.  Step 3. In my case, I made a Project called "Luns" and it has LUNp2 inside of it. I am going to replicate the Project, which will automatically replicate all of the LUNs and/or Filesystems inside of it.  Now, you can also replicate from the Share level instead of the Project. That will only replicate the share, and not all the other shares of a project. If someone tells you that if you replicate a share, it always replicates all the other shares also in that Project, don't listen to them.Note below how I can choose not only the Target (which is myself), but I can also choose which Pool to replicate it to. So I choose Pool1.  Step 4. I did not choose a schedule or pick the "Continuous" button, which means my replication will be manual only. I can now push the Manual Replicate button on my Actions list and you will see it start. You will see both a barber pole animation and also an update in the status bar on the top of the screen that a replication event has begun. This also goes into the event log.  Step 5. The status bar will also log an event when it's done. Step 6. If you go back to Configuration-->Services-->Remote Replication, you will see your event. Step 7. Done. To see your new replica, go to the other Pool (Pool1 for me), and click the "Replica" area below the words "Filesystems | LUNs" Here, you will see any replicas that have come in from any of your sources. It's a simple matter from here to break the replication, which will change this to a "Local" LUN, and then delete the original LUN back in Pool2. Ok, that's all for now, but I promise to give out more tricks sometime in November !!! There's very exciting stuff coming down the pipe for the ZFSSA. Both new hardware and new software features that I'm just drooling over. That's all I can say, but contact your local sales SC to get a NDA roadmap talk if you want to hear more.   Happy Halloween,Steve 

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  • Matthias Weiss: Virtualisierung - auf allen Ebenen. Da ist Potential im Mittelstand.

    - by A&C Redaktion
    Vom Storage, über den Server bis hin zum Desktop. Virtualisierung ist nur eine Technologie, intelligent die Ressourcen zu managen. Es ist gerade das Potenzial der Kostenersparnis, so Matthias Weiss, Direktor Mittelstand Technologie, das gerade bei mittelständischen Unternehmen Virtualisierung so begehrt macht. Es gibt heute bereits Virtualisierungslösungen von Oracle, die neue Chancen zur Umsatzsteigerung für Beratungsleistungen ermöglichen.  Wie sich die langfristige IT-Strategie bei mittelständischen Unternehmen durch Partner positiv beeinflussen lässt? Eine Frage, auf die Matthias Weiss auch eine Antwort im folgenden Video bereit hält.

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  • Data Warehouse Best Practices

    - by jean-pierre.dijcks
    In our quest to share our endless wisdom (ahem…) one of the things we figured might be handy is recording some of the best practices for data warehousing. And so we did. And, we did some more… We now have recreated our websites on Oracle Technology Network and have a separate page for best practices, parallelism and other cool topics related to data warehousing. But the main topic of this post is the set of recorded best practices. Here is what is available (and it is a series that ties together but can be read independently), applicable for almost any database version: Partitioning 3NF schema design for a data warehouse Star schema design Data Loading Parallel Execution Optimizer and Stats management The best practices page has a lot of other useful information so have a look here.

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  • BIP Enterprise Patches

    - by Tim Dexter
    Got some input from the support team yesterday BIP patching. I dont have any control over the patches but have a voice to get information out there. Just to clarify for you all, the recent 'rollup' patch, 9546699 that I blogged a while back supersedes all other patches for the standalone release. If you have an issue and log an SR, please ensure you have applied 9546699 and re-checked the issue. There are so many fixes and enhancements in that patch that your issue may be fixed or addressed. If you are a new customer and download the latest release, 10.1.3.4.1 from oracle.com. Please get onto the support site and get 9546699 applied straight away. For more information check out Pieter's Note on BIP patching - 797057.1

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  • Auto DOP and Concurrency

    - by jean-pierre.dijcks
    After spending some time in the cloud, I figured it is time to come down to earth and start discussing some of the new Auto DOP features some more. As Database Machines (the v2 machine runs Oracle Database 11.2) are effectively selling like hotcakes, it makes some sense to talk about the new parallel features in more detail. For basic understanding make sure you have read the initial post. The focus there is on Auto DOP and queuing, which is to some extend the focus here. But now I want to discuss the concurrency a little and explain some of the relevant parameters and their impact, specifically in a situation with concurrency on the system. The goal of Auto DOP The idea behind calculating the Automatic Degree of Parallelism is to find the highest possible DOP (ideal DOP) that still scales. In other words, if we were to increase the DOP even more  above a certain DOP we would see a tailing off of the performance curve and the resource cost / performance would become less optimal. Therefore the ideal DOP is the best resource/performance point for that statement. The goal of Queuing On a normal production system we should see statements running concurrently. On a Database Machine we typically see high concurrency rates, so we need to find a way to deal with both high DOP’s and high concurrency. Queuing is intended to make sure we Don’t throttle down a DOP because other statements are running on the system Stay within the physical limits of a system’s processing power Instead of making statements go at a lower DOP we queue them to make sure they will get all the resources they want to run efficiently without trashing the system. The theory – and hopefully – practice is that by giving a statement the optimal DOP the sum of all statements runs faster with queuing than without queuing. Increasing the Number of Potential Parallel Statements To determine how many statements we will consider running in parallel a single parameter should be looked at. That parameter is called PARALLEL_MIN_TIME_THRESHOLD. The default value is set to 10 seconds. So far there is nothing new here…, but do realize that anything serial (e.g. that stays under the threshold) goes straight into processing as is not considered in the rest of this post. Now, if you have a system where you have two groups of queries, serial short running and potentially parallel long running ones, you may want to worry only about the long running ones with this parallel statement threshold. As an example, lets assume the short running stuff runs on average between 1 and 15 seconds in serial (and the business is quite happy with that). The long running stuff is in the realm of 1 – 5 minutes. It might be a good choice to set the threshold to somewhere north of 30 seconds. That way the short running queries all run serial as they do today (if it ain’t broken, don’t fix it) and allows the long running ones to be evaluated for (higher degrees of) parallelism. This makes sense because the longer running ones are (at least in theory) more interesting to unleash a parallel processing model on and the benefits of running these in parallel are much more significant (again, that is mostly the case). Setting a Maximum DOP for a Statement Now that you know how to control how many of your statements are considered to run in parallel, lets talk about the specific degree of any given statement that will be evaluated. As the initial post describes this is controlled by PARALLEL_DEGREE_LIMIT. This parameter controls the degree on the entire cluster and by default it is CPU (meaning it equals Default DOP). For the sake of an example, let’s say our Default DOP is 32. Looking at our 5 minute queries from the previous paragraph, the limit to 32 means that none of the statements that are evaluated for Auto DOP ever runs at more than DOP of 32. Concurrently Running a High DOP A basic assumption about running high DOP statements at high concurrency is that you at some point in time (and this is true on any parallel processing platform!) will run into a resource limitation. And yes, you can then buy more hardware (e.g. expand the Database Machine in Oracle’s case), but that is not the point of this post… The goal is to find a balance between the highest possible DOP for each statement and the number of statements running concurrently, but with an emphasis on running each statement at that highest efficiency DOP. The PARALLEL_SERVER_TARGET parameter is the all important concurrency slider here. Setting this parameter to a higher number means more statements get to run at their maximum parallel degree before queuing kicks in.  PARALLEL_SERVER_TARGET is set per instance (so needs to be set to the same value on all 8 nodes in a full rack Database Machine). Just as a side note, this parameter is set in processes, not in DOP, which equates to 4* Default DOP (2 processes for a DOP, default value is 2 * Default DOP, hence a default of 4 * Default DOP). Let’s say we have PARALLEL_SERVER_TARGET set to 128. With our limit set to 32 (the default) we are able to run 4 statements concurrently at the highest DOP possible on this system before we start queuing. If these 4 statements are running, any next statement will be queued. To run a system at high concurrency the PARALLEL_SERVER_TARGET should be raised from its default to be much closer (start with 60% or so) to PARALLEL_MAX_SERVERS. By using both PARALLEL_SERVER_TARGET and PARALLEL_DEGREE_LIMIT you can control easily how many statements run concurrently at good DOPs without excessive queuing. Because each workload is a little different, it makes sense to plan ahead and look at these parameters and set these based on your requirements.

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  • The Modern Marketer’s Guide to Connected Customer Journeys

    - by Richard Lefebvre
    By Amanda Batista on Thursday, August 14, 2014 in Marketing Efficiency Organizations are striving to deliver consistent experiences but very few feel they are there yet. It’s a simple consideration for marketers, really. Not only does industry data continue to support that customers demand personalized experiences when engaging with brands, but if you think about your own consumer driven shopping experiences, you, too, expect that stellar experience at every touch point. And when you don’t get it, that brand has potentially alienated the experience, as well as their shot at engaging with you in more meaningful ways. Oracle Marketing Cloud partnered with marketingfinder.co.uk to conduct a survey exploring how marketers are adapting to this new age of the customer and the challenges they face. Less than half (40%) of marketers in the study were able to track the customer journey across channels. These findings, as well as other data points showcasing marketers’ challenges, are explored in our latest eBook, “The Modern Marketer's Guide to Connected Customer Journeys.” Read the entire article and order your copy of the full report here

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  • security stuff's

    - by raghu.yadav
    http://fmwdocs.us.oracle.com/doclibs/fmw/E10285_01/appslib7/web.1111/b31974/adding_security.htm#BGBGJEAH At design time, JDeveloper saves all policy store and identity store changes in a single file for the entire application. In the development environment, this is the jazn-data.xml file. After you configure the jazn-data.xml file using the editors, you can run the application in Integrated WebLogic Server and the contents of the policy store will be added to the domain-level store, the system-jazn-data.xml file, while the test users will be migrated to the embedded LDAP server that Integrated WebLogic Server uses for its identity store. The domain-level store allows you to test the security implementation by logging on as test users that you have created. looks like above part did went well with me, apart from following all instruction provided in doc, I need to create users from adminconsole in security-realms-Users and Groups sections to successfully login to pages.

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  • The Work Order Printing Challenge

    - by celine.beck
    One of the biggest concerns we've heard from maintenance practitioners is the ability to print and batch print work order details along with its accompanying attachments. Indeed, maintenance workers traditionally rely on work order packets to complete their job. A standard work order packet can include a variety of information like equipment documentation, operating instructions, checklists, end-of-task feedback forms and the likes. Now, the problem is that most Asset Lifecycle Management applications do not provide a simple and efficient solution for process printing with document attachments. Work order forms can be easily printed but attachments are usually left out of the printing process. This sounds like a minor problem, but when you are processing high volume of work orders on a regular basis, this inconvenience can result in important inefficiencies. In order to print work order and its related attachments, maintenance personnel need to print the work order details and then go back to the work order and open each individual attachment using the proper authoring application to view and print each document. The printed output is collated into a work order packet. The AutoVue Document Print Service products that were just released in April 2010 aim at helping organizations address the work order printing challenge. Customers and partners can leverage the AutoVue Document Print Services to build a complete printing solution that complements their existing print server solution with AutoVue's document- and platform-agnostic document print services. The idea is to leverage AutoVue's printing services to invoke printing either programmatically or manually directly from within the work order management application, and efficiently process the printing of complete work order packets, including all types of attachments, from office files to more advanced engineering documents like 2D CAD drawings. Oracle partners like MIPRO Consulting, specialists in PeopleSoft implementations, have already expressed interest in the AutoVue Document Print Service products for their ability to offer print services to the PeopleSoft ALM suite, so that customers are able to print packages of documents for maintenance personnel. For more information on the subject, please consult MIPRO Consulting's article entitled Unsung Value: Primavera and AutoVue Integration into PeopleSoft posted on their blog. The blog post entitled Introducing AutoVue Document Print Service provides additional information on how the solution works. We would also love to hear what your thoughts are on the topic, so please do not hesitate to post your comments/feedback on our blog. Related Articles: Introducing AutoVue Document Print Service Print Any Document Type with AutoVue Document Print Services

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  • QotD: Peter Wayner on Programming trend No. 1

    - by $utils.escapeXML($entry.author)
    Programming trend No. 1: The JVM is not just for Java anymoreA long time ago, Sun created Java and shared the virtual machine with the world. By the time Microsoft created C#, people recognized that the VM didn't have to be limited to one language. Anything that could be transformed into the byte code could use it.Now, it seems that everyone is building their language to do just that. Leave the job of building a virtual machine to Sun/Oracle, and concentrate your efforts on the syntactic bells and structural whistles, goes the mantra today.Peter Wayner in an article on "11 programming trends to watch" at ITWorld.

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  • UNHCR and Stanyslas Matayo Receive Duke's Choice Award 2012

    - by Geertjan
    This year, NetBeans Platform applications winning Duke's Choice Awards were not only AgroSense, by Ordina in the Netherlands, and the air command and control system by NATO... but also Level One, the UNHCR registration and emergency management system. Unfortunately, Stanyslas Matayo, the architect and lead engineer of Level One, was unable to be at JavaOne to receive his award. It would have been really cool to meet him in person, of course, and he would have joined the NetBeans Party and NetBeans Day, as well as the NetBeans Platform panel discussions that happened at various stages throughout JavaOne. Instead, he received his award at Oracle Day 2012 Nairobi, some days ago, where he presented Level One and received the Duke's Choice Award: Level One is the UNHCR (UN refugee agency) application for capturing information on the first level details of refugees in an emergency context. In its recently released initial version, the application was used in Niger to register information about families in emergency contexts. Read more about it here and see the screenshot below. Congratulations, Stanyslas, and the rest of the development team working on this interesting and important project!

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  • Retrieve Performance Data from SOA Infrastructure Database

    - by fip
    My earlier blog posting shows how to enable, retrieve and interpret BPEL engine performance statistics to aid performance troubleshooting. The strength of BPEL engine statistics at EM is its break down per request. But there are some limitations with the BPEL performance statistics mentioned in that blog posting: The statistics were stored in memory instead of being persisted. To avoid memory overflow, the data are stored to a buffer with limited size. When the statistic entries exceed the limitation, old data will be flushed out to give ways to new statistics. Therefore it can only keep the last X number of entries of data. The statistics 5 hour ago may not be there anymore. The BPEL engine performance statistics only includes latencies. It does not provide throughputs. Fortunately, Oracle SOA Suite runs with the SOA Infrastructure database and a lot of performance data are naturally persisted there. It is at a more coarse grain than the in-memory BPEL Statistics, but it does have its own strengths as it is persisted. Here I would like offer examples of some basic SQL queries you can run against the infrastructure database of Oracle SOA Suite 11G to acquire the performance statistics for a given period of time. You can run it immediately after you modify the date range to match your actual system. 1. Asynchronous/one-way messages incoming rates The following query will show number of messages sent to one-way/async BPEL processes during a given time period, organized by process names and states select composite_name composite, state, count(*) Count from dlv_message where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; 2. Throughput of BPEL process instances The following query shows the number of synchronous and asynchronous process instances created during a given time period. It list instances of all states, including the unfinished and faulted ones. The results will include all composites cross all SOA partitions select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype order by count(*) desc; 3. Throughput and latencies of BPEL process instances This query is augmented on the previous one, providing more comprehensive information. It gives not only throughput but also the maximum, minimum and average elapse time BPEL process instances. select composite_name Composite, component_name Process, componenttype, state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime from cube_instance where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;   4. Combine all together Now let's combine all of these 3 queries together, and parameterize the start and end time stamps to make the script a bit more robust. The following script will prompt for the start and end time before querying against the database: accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)' accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)' Prompt "==== Rejected Messages ===="; REM 2012-10-24 21:00:00 REM 2012-10-24 21:59:59 select count(*), composite_dn from rejected_message where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS') and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_dn; Prompt " "; Prompt "==== Throughput of one-way/asynchronous messages ===="; select state, count(*) Count, composite_name composite from dlv_message where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, state order by Count; Prompt " "; Prompt "==== Throughput and latency of BPEL process instances ====" select state, count(*) Count, trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MaxTime, trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) MinTime, trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 + extract(hour from (modify_date-creation_date))*60*60 + extract(minute from (modify_date-creation_date))*60 + extract(second from (modify_date-creation_date))),4) AvgTime, composite_name Composite, component_name Process, componenttype from cube_instance where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS') and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS') group by composite_name, component_name, componenttype, state order by count(*) desc;  

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  • Two Free Training Webcasts Open for Registration

    - by KKline
    We've got two sessions that you need to sign up for right away. The upcoming webcast for Oracle-oriented folks has huge registration numbers. So get in while you still can before we hit the limit of what LiveMeeting can handle. Pain of the Week: SQL Server for the Oracle DBA Webcast: SQL Server for the Oracle DBA Date: Thursday, May 27, 2010 (Just a couple days hence!) Time: 8 a.m. Pacific / 11 a.m. Eastern / 4 p.m. United Kingdom / 5 p.m. Central Europe Duration: 45-60 minutes Cost: FREE In enterprise...(read more)

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  • Blicken Sie über den Tellerrand hinaus. Da sind neue Märkte.

    - by A&C Redaktion
    Über 60 Spezialisierungen wird es bis Ende des Jahres bei Oracle geben. Thomas Gartner, Senior Vertriebsleiter Business Partner, macht angesichts dieser Zahl eine einfache Rechnung auf. In den angestammten Märkten tummeln sich zahlreiche Partner, die miteinander im Wettbewerb stehen. Wer jetzt, so Gartner, über den Tellerrand hinaus schaue, neue Möglichkeiten für sein Unternehmen recherchiert, der gewinnt in mehrfacher Hinsicht: Erstens gibt es zahlreiche Märkte, die noch erhebliches Wachstumspotenzial bieten. Zweitens entstehen neue Nischen, die sich über gezielte Differenzierung und Spezialisierung lukrativ erschließen lassen. Drittens unterstützt das Team von Thomas Gartner engagierte Business Partner gerne mit einem individuellen Vertriebscoaching, damit es noch schneller geht, mit dem gewünschten Wachstum. Hier geht es zum Blick über den Tellerrand hinaus.

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  • Interactive Master Detail Report Just A Few Minutes Away!

    - by kanichiro.nishida
    Oracle BI Publisher 11G have not just made Master Detail report development much easier and quicker, but also made it more interactive and fun without any coding or scripting. I’ve just created a short video that shows how to create such Master Detail report within a few minutes, so please take a look if you’re interested in!     With 11G, now you can create such report only with your browser very quickly and your report audience will be not only able to interact with the report but also able to view it in a pixel-perfect way with many different formats such as PDF, Excel, Word, PPT, etc. Happy Master Detail Reports development and design! Please share any feedback you have with Interactive Viewer and Layout Editor with us!

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  • What Works in Data Integration?

    - by dain.hansen
    TDWI just recently put out this paper on "What Works in Data Integration". I invite you especially to take a look at the section on "Accelerating your Business with Real-time Data Integration" and the DIRECTV case study. The article discusses some of the technology considerations for BI/DW and how data integration plays a role to deliver timely, accessible, and high-quality data. It goes on to outline the three key requirements for how to deliver high performance, low impact, and reliability and how that can translate to faster results. The DIRECTV webinar is something you definitely want to take a look at, you'll hear how DIRECTV successfully transformed their data warehouse investments into a competitive advantage with Oracle GoldenGate.

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  • study materials for Mysql certification?

    - by Andre
    I'm preparing for Mysql certification, nowadays officially titled: Oracle Certified Professional, MySQL 5.0 Developer certification After looking through Mysql forum it looks like most people recommended this book: http://www.amazon.com/MySQL-5-0-Certification-Study-Guide/dp/0672328127/ref=sr_1_1?ie=UTF8&qid=1299972594&sr=8-1 Which as far as I learned - was the official preparation source at the time when Mysql was controlled by Mysql AB and Sun. Now, however - Oracle officially doesn't recommend this book. to be precise - I don't now what they recommend. I could only find this "value package":( http://education.oracle.com/pls/web_prod-plq-dad/db_pages.getpage?page_id=532 Can someone who got mysql certification confirm that this book is what they have used? Also -If there is any other moderately priced study materials out there - plz let me know. Thanks P.s. mods - feel free to kick this question into more suitable site.:)

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  • CMS DITA North America Conference / Agile Doc

    - by ultan o'broin
    I attended and presented, along with a colleague, at the Content Management Strategies DITA North America Conference 2010 in Santa Clara this week. It was touch and go whether I would make it across the Atlantic, but as usual the Irish always got through! Our presentation was about DITA and Writing Patterns, and there was three other presentations from Oracle folks too, all very well delivered and received. The interaction with other companies was superb, and the sparks of innovation that flew as a result left me with three use case ideas for UX investigation and implementation. My colleague had a similar experience. Well worth attending! One of the last sessions was about Authoring in an Agile environment, presented by Julio Vasquez. This was an excellent, common sense, and forthright no-nonsense delivery that made complete sense to me. I'd encourage you, if you are interested in the subject, to check out Julio's white paper on the subject too, available from the SDI website.

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  • Mi van a supportban, mit kapunk érte?

    - by peter.nagy
    Ez mostanában Glassfish vonatkozásban sokszor teszik fel nekem. Most picit leegyszerusítem a magam dolgát és belinkelek egy régebbi még Sun-os idokbol származó blogbejegyzést. A lényeg benne van. Nevezetesen, hogy mit kapunk, miért fizetünk, hogy muködik. Ja és a válasz egy másik surun feltett kérdésre is; vagyis miben különbözik forrásszinten a két verzió. (Lelövöm a poént: semmiben. Persze eltekintve a késobbi patch-ektol) Egyébként az Oracle változat lényegesen még annyiban is módosult, hogy más a licencelési metrika és a hozzáadott szoftverek jelentek meg a csomagban.

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