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  • why IEEE floating point number calculate exponent using a biased form?

    - by lenatis
    let's say, for the float type in c, according to the IEEE floating point specification, there are 8-bit used for the fraction filed, and it is calculated as first taken these 8-bit and translated it into an unsigned number, and then minus the BIASE, which is 2^7 - 1 = 127, and the result is an exponent ranges from -127 to 128, inclusive. But why can't we just treat these 8-bit pattern as a signed number, since the resulting range is [-128,127], which is almost the same as the previous one.

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  • Why doesn't Java allow for the creaton of generic arrays?

    - by byte
    There are plenty of questions on stackoverflow from people who have attempted to create an array of generics like so: ArrayList<Foo>[] poo = new ArrayList<Foo>[5]; And the answer of course is that the Java specification doesn't allow you to declare an array of generics. My question however is why ? What is the technical reason underlying this restriction in the java language or java vm? It's a technical curiosity I've always wondered about.

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  • nonatomic property in model class when using NSOperationQueue (iPhone)?

    - by Andrew B.
    I have a custom model class with an NSMutableData ivar that will be accessed by custom NSOperation subclasses (using an NSOperationQueue). I think I can guarantee thread-safe access to the ivar from multiple NSOperations by using dependencies, and I can guarantee that I don't access the ivar from other code (say my main app thread) by waiting until the Q has finished all operations. Should I use a nonatomic property specification, or leave it atomic? Is there a significant impact on performance?

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  • Can the .NET MethodInfo cache be cleared or disabled?

    - by Anton
    Per MSDN, calling Type.GetMethods() stores reflected method information in a MemberInfo cache so the expensive operation doesn't have to be performed again. I have an application that scans assemblies/types, looking for methods that match a given specification. The problem is that memory consumption increases significantly (especially with large numbers of referenced assemblies) since .NET hangs onto the method metadata. Is there any way to clear or disable this MemberInfo cache?

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  • how to generate JMX MXBean compatible java class model from JAXB?

    - by yucubby
    From the JMX MXBean specification, a java class type J must satisfy Either if J has at least one public constructor with a ConstructorProperties annotation, Or if J has a public no-arg constructor, and for every getter in J with type T and name N there is a corresponding setter with the same name and type So how can I use JAXB to generate JAVA class model which satisfy the MXBean constrain? Thanks YU

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  • Are there any lightweight analogues to CORBA/RPC for embedded programs?

    - by Mtr
    I am writing embedded applications for different hardware (avr, arm7, tms55xx…) and different rtoses (freeRTOS, rtx, dsp/bios). And every second of them needs to communicate with PC or another digital device. Sometimes interactions logic is very advanced. So I'm interesting in common methodology (like state-machine programming style), protocol specification or library, that could simplify developing such things.

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  • Problem with index server talking to remote server names with dashes or dots in them

    - by Aim Kai
    Hi I am having a problem, accessing a remote index server catalog. The name of the server has - in it, so i put the index catalog name as: i.e num.num.num.num\name of catalog or an-example-server I get the following error when using an ole data connection to pull results from the index: "Format of the initialization string does not conform to specification starting at index 39" I tried putting single quotes and &qoute; with no luck - anyone have idea? PS. This Microsoft Index Server Question!

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  • How to remove compiler flag when building Boost

    - by mlo
    I need to build Boost with a non-standard set of flags (due to a conflict between Boost threading and C++/CLI). I'm adding the required flag (/clr) using CXXFLAGS, but this flag conflicts with the Boost default /EHs flag (/clr implies /EHa which is incompatible with /EHs), so that needs to be suppressed. Is there a mechanism like CXXFLAGS to suppress a default Boost flag or must I edit all of the compiler specification files by hand?

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  • Is PKCS#1 V2.0 implemented for Java?

    - by Tom Brito
    I need encrypt data using exactly the PKCS#1 V2.0 encryption method (defined in item 7.2.1 of the PKCS#1V2 specification). Is it already implemented for Java? I'm thinking in something like just pass a parameter to javax.crypto.Cipher specifying "PKCS#1V2", I wonder if there is something like this?

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  • C++ class is not recognizing string data type

    - by reallythecrash
    I'm working on a program from my C++ textbook, and this this the first time I've really run into trouble. I just can't seem to see what is wrong here. Visual Studio is telling me Error: identifier "string" is undefined. I separated the program into three files. A header file for the class specification, a .cpp file for the class implementation and the main program file. These are the instructions from my book: Write a class named Car that has the following member variables: year. An int that holds the car's model year. make. A string that holds the make of the car. speed. An int that holds the car's current speed. In addition, the class should have the following member functions. Constructor. The constructor should accept the car's year and make as arguments and assign these values to the object's year and make member variables. The constructor should initialize the speed member variable to 0. Accessors. Appropriate accessor functions should be created to allow values to be retrieved from an object's year, make and speed member variables. There are more instructions, but they are not necessary to get this part to work. Here is my source code: // File Car.h -- Car class specification file #ifndef CAR_H #define CAR_H class Car { private: int year; string make; int speed; public: Car(int, string); int getYear(); string getMake(); int getSpeed(); }; #endif // File Car.cpp -- Car class function implementation file #include "Car.h" // Default Constructor Car::Car(int inputYear, string inputMake) { year = inputYear; make = inputMake; speed = 0; } // Accessors int Car::getYear() { return year; } string Car::getMake() { return make; } int Car::getSpeed() { return speed; } // Main program #include <iostream> #include <string> #include "Car.h" using namespace std; int main() { } I haven't written anything in the main program yet, because I can't get the class to compile. I've only linked the header file to the main program. Thanks in advance to all who take the time to investigate this problem for me.

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  • this parameter modifier in C#?

    - by Ivan
    I'm curious about this code snippet: public static class XNAExtensions { /// <summary> /// Write a Point /// </summary> public static void Write(this NetOutgoingMessage message, Point value) { message.Write(value.X); message.Write(value.Y); } // ... }; What does the this keyword mean next to the parameter type? I can't seem to find any information about it anywhere, even in the C# specification.

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  • embedded image data

    - by kurkevan
    I've noticed that in recent versions of Firefox, some images are displayed even when I disable images (e.g., Google News). Apparently this is due to images being embedded in the code using the "data:image" specification. Does anyone know of a way to disable these images from being displayed? Thanks.

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  • ODI 12c - Parallel Table Load

    - by David Allan
    In this post we will look at the ODI 12c capability of parallel table load from the aspect of the mapping developer and the knowledge module developer - two quite different viewpoints. This is about parallel table loading which isn't to be confused with loading multiple targets per se. It supports the ability for ODI mappings to be executed concurrently especially if there is an overlap of the datastores that they access, so any temporary resources created may be uniquely constructed by ODI. Temporary objects can be anything basically - common examples are staging tables, indexes, views, directories - anything in the ETL to help the data integration flow do its job. In ODI 11g users found a few workarounds (such as changing the technology prefixes - see here) to build unique temporary names but it was more of a challenge in error cases. ODI 12c mappings by default operate exactly as they did in ODI 11g with respect to these temporary names (this is also true for upgraded interfaces and scenarios) but can be configured to support the uniqueness capabilities. We will look at this feature from two aspects; that of a mapping developer and that of a developer (of procedures or KMs). 1. Firstly as a Mapping Developer..... 1.1 Control when uniqueness is enabled A new property is available to set unique name generation on/off. When unique names have been enabled for a mapping, all temporary names used by the collection and integration objects will be generated using unique names. This property is presented as a check-box in the Property Inspector for a deployment specification. 1.2 Handle cleanup after successful execution Provided that all temporary objects that are created have a corresponding drop statement then all of the temporary objects should be removed during a successful execution. This should be the case with the KMs developed by Oracle. 1.3 Handle cleanup after unsuccessful execution If an execution failed in ODI 11g then temporary tables would have been left around and cleaned up in the subsequent run. In ODI 12c, KM tasks can now have a cleanup-type task which is executed even after a failure in the main tasks. These cleanup tasks will be executed even on failure if the property 'Remove Temporary Objects on Error' is set. If the agent was to crash and not be able to execute this task, then there is an ODI tool (OdiRemoveTemporaryObjects here) you can invoke to cleanup the tables - it supports date ranges and the like. That's all there is to it from the aspect of the mapping developer it's much, much simpler and straightforward. You can now execute the same mapping concurrently or execute many mappings using the same resource concurrently without worrying about conflict.  2. Secondly as a Procedure or KM Developer..... In the ODI Operator the executed code shows the actual name that is generated - you can also see the runtime code prior to execution (introduced in 11.1.1.7), for example below in the code type I selected 'Pre-executed Code' this lets you see the code about to be processed and you can also see the executed code (which is the default view). References to the collection (C$) and integration (I$) names will be automatically made unique by using the odiRef APIs - these objects will have unique names whenever concurrency has been enabled for a particular mapping deployment specification. It's also possible to use name uniqueness functions in procedures and your own KMs. 2.1 New uniqueness tags  You can also make your own temporary objects have unique names by explicitly including either %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG in the name passed to calls to the odiRef APIs. Such names would always include the unique tag regardless of the concurrency setting. To illustrate, let's look at the getObjectName() method. At <% expansion time, this API will append %UNIQUE_STEP_TAG to the object name for collection and integration tables. The name parameter passed to this API may contain  %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. This API always generates to the <? version of getObjectName() At execution time this API will replace the unique tag macros with a string that is unique to the current execution scope. The returned name will conform to the name-length restriction for the target technology, and its pattern for the unique tag. Any necessary truncation will be performed against the initial name for the object and any other fixed text that may have been specified. Examples are:- <?=odiRef.getObjectName("L", "%COL_PRFEMP%UNIQUE_STEP_TAG", "D")?> SCOTT.C$_EABH7QI1BR1EQI3M76PG9SIMBQQ <?=odiRef.getObjectName("L", "EMP%UNIQUE_STEP_TAG_AE", "D")?> SCOTT.EMPAO96Q2JEKO0FTHQP77TMSAIOSR_ Methods which have this kind of support include getFrom, getTableName, getTable, getObjectShortName and getTemporaryIndex. There are APIs for retrieving this tag info also, the getInfo API has been extended with the following properties (the UNIQUE* properties can also be used in ODI procedures); UNIQUE_STEP_TAG - Returns the unique value for the current step scope, e.g. 5rvmd8hOIy7OU2o1FhsF61 Note that this will be a different value for each loop-iteration when the step is in a loop. UNIQUE_SESSION_TAG - Returns the unique value for the current session scope, e.g. 6N38vXLrgjwUwT5MseHHY9 IS_CONCURRENT - Returns info about the current mapping, will return 0 or 1 (only in % phase) GUID_SRC_SET - Returns the UUID for the current source set/execution unit (only in % phase) The getPop API has been extended with the IS_CONCURRENT property which returns info about an mapping, will return 0 or 1.  2.2 Additional APIs Some new APIs are provided including getFormattedName which will allow KM developers to construct a name from fixed-text or ODI symbols that can be optionally truncate to a max length and use a specific encoding for the unique tag. It has syntax getFormattedName(String pName[, String pTechnologyCode]) This API is available at both the % and the ? phase.  The format string can contain the ODI prefixes that are available for getObjectName(), e.g. %INT_PRF, %COL_PRF, %ERR_PRF, %IDX_PRF alongwith %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. The latter tags will be expanded into a unique string according to the specified technology. Calls to this API within the same execution context are guaranteed to return the same unique name provided that the same parameters are passed to the call. e.g. <%=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")%> <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")?> C$_MY_TAB7wDiBe80vBog1auacS1xB_AE <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG.log", "FILE")?> C2_MY_TAB7wDiBe80vBog1auacS1xB.log 2.3 Name length generation  As part of name generation, the length of the generated name will be compared with the maximum length for the target technology and truncation may need to be applied. When a unique tag is included in the generated string it is important that uniqueness is not compromised by truncation of the unique tag. When a unique tag is NOT part of the generated name, the name will be truncated by removing characters from the end - this is the existing 11g algorithm. When a unique tag is included, the algorithm will first truncate the <postfix> and if necessary  the <prefix>. It is recommended that users will ensure there is sufficient uniqueness in the <prefix> section to ensure uniqueness of the final resultant name. SUMMARY To summarize, ODI 12c make it much simpler to utilize mappings in concurrent cases and provides APIs for helping developing any procedures or custom knowledge modules in such a way they can be used in highly concurrent, parallel scenarios. 

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • Silverlight for Everyone!!

    - by subodhnpushpak
    Someone asked me to compare Silverlight / HTML development. I realized that the question can be answered in many ways: Below is the high level comparison between a HTML /JavaScript client and Silverlight client and why silverlight was chosen over HTML / JavaScript client (based on type of users and major functionalities provided): 1. For end users Browser compatibility Silverlight is a plug-in and requires installation first. However, it does provides consistent look and feel across all browsers. For HTML / DHTML, there is a need to tweak JavaScript for each of the browser supported. In fact, tags like <span> and <div> works differently on different browser / version. So, HTML works on most of the systems but also requires lot of efforts coding-wise to adhere to all standards/ browsers / versions. Out of browser support No support in HTML. Third party tools like  Google gears offers some functionalities but there are lots of issues around platform and accessibility. Out of box support for out-of-browser support. provides features like drag and drop onto application surface. Cut and copy paste in HTML HTML is displayed in browser; which, in turn provides facilities for cut copy and paste. Silverlight (specially 4) provides rich features for cut-copy-paste along with full control over what can be cut copy pasted by end users and .advanced features like visual tree printing. Rich user experience HTML can provide some rich experience by use of some JavaScript libraries like JQuery. However, extensive use of JavaScript combined with various versions of browsers and the supported JavaScript makes the solution cumbersome. Silverlight is meant for RIA experience. User data storage on client end In HTML only small amount of data can be stored that too in cookies. In Silverlight large data may be stored, that too in secure way. This increases the response time. Post back In HTML / JavaScript the post back can be stopped by use of AJAX. Extensive use of AJAX can be a bottleneck as browser stack is used for the calls. Both look and feel and data travel over network.                           In Silverlight everything run the client side. Calls are made to server ONLY for data; which also reduces network traffic in long run. 2. For Developers Coding effort HTML / JavaScript can take considerable amount to code if features (requirements) are rich. For AJAX like interfaces; knowledge of third party kits like DOJO / Yahoo UI / JQuery is required which has steep learning curve. ASP .Net coding world revolves mostly along <table> tags for alignments whereas most popular tools provide <div> tags; which requires lots of tweaking. AJAX calls can be a bottlenecks for performance, if the calls are many. In Silverlight; coding is in C#, which is managed code. XAML is also very intuitive and Blend can be used to provide look and feel. Event handling is much clean than in JavaScript. Provides for many clean patterns like MVVM and composable application. Each call to server is asynchronous in silverlight. AJAX is in built into silverlight. Threading can be done at the client side itself to provide for better responsiveness; etc. Debugging Debugging in HTML / JavaScript is difficult. As JavaScript is interpreted; there is NO compile time error handling. Debugging in Silverlight is very helpful. As it is compiled; it provides rich features for both compile time and run time error handling. Multi -targeting browsers HTML / JavaScript have different rendering behaviours in different browsers / and their versions. JavaScript have to be written to sublime the differences in browser behaviours. Silverlight works exactly the same in all browsers and works on almost all popular browser. Multi-targeting desktop No support in HTML / JavaScript Silverlight is very close to WPF. Bot the platform may be easily targeted while maintaining the same source code. Rich toolkit HTML /JavaScript have limited toolkit as controls Silverlight provides a rich set of controls including graphs, audio, video, layout, etc. 3. For Architects Design Patterns Silverlight provides for patterns like MVVM (MVC) and rich (fat)  client architecture. This segregates the "separation of concern" very clearly. Client (silverlight) does what it is expected to do and server does what it is expected of. In HTML / JavaScript world most of the processing is done on the server side. Extensibility Silverlight provides great deal of extensibility as custom controls may be made. Extensibility is NOT restricted by browser but by the plug-in silverlight runs in. HTML / JavaScript works in a certain way and extensibility is generally done on the server side rather than client end. Client side is restricted by the limitations of the browser. Performance Silverlight provides localized storage which may be used for cached data storage. this reduces the response time. As processing can be done on client side itself; there is no need for server round trips. this decreases the round about time. Look and feel of the application is downloaded ONLY initially, afterwards ONLY data is fetched form the server. Security Silverlight is compiled code downloaded as .XAP; As compared to HTML / JavaScript, it provides more secure sandboxed approach. Cross - scripting is inherently prohibited in silverlight by default. If proper guidelines are followed silverlight provides much robust security mechanism as against HTML / JavaScript world. For example; knowing server Address in obfuscated JavaScript is easier than a compressed compiled obfuscated silverlight .XAP file. Some of these like (offline and Canvas support) will be available in HTML5. However, the timelines are not encouraging at all. According to Ian Hickson, editor of the HTML5 specification, the specification to reach the W3C Candidate Recommendation stage during 2012, and W3C Recommendation in the year 2022 or later. see http://en.wikipedia.org/wiki/HTML5 for details. The above is MY opinion. I will love to hear yours; do let me know via comments. Technorati Tags: Silverlight

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  • WebSocket Applications using Java: JSR 356 Early Draft Now Available (TOTD #183)

    - by arungupta
    WebSocket provide a full-duplex and bi-directional communication protocol over a single TCP connection. JSR 356 is defining a standard API for creating WebSocket applications in the Java EE 7 Platform. This Tip Of The Day (TOTD) will provide an introduction to WebSocket and how the JSR is evolving to support the programming model. First, a little primer on WebSocket! WebSocket is a combination of IETF RFC 6455 Protocol and W3C JavaScript API (still a Candidate Recommendation). The protocol defines an opening handshake and data transfer. The API enables Web pages to use the WebSocket protocol for two-way communication with the remote host. Unlike HTTP, there is no need to create a new TCP connection and send a chock-full of headers for every message exchange between client and server. The WebSocket protocol defines basic message framing, layered over TCP. Once the initial handshake happens using HTTP Upgrade, the client and server can send messages to each other, independent from the other. There are no pre-defined message exchange patterns of request/response or one-way between client and and server. These need to be explicitly defined over the basic protocol. The communication between client and server is pretty symmetric but there are two differences: A client initiates a connection to a server that is listening for a WebSocket request. A client connects to one server using a URI. A server may listen to requests from multiple clients on the same URI. Other than these two difference, the client and server behave symmetrically after the opening handshake. In that sense, they are considered as "peers". After a successful handshake, clients and servers transfer data back and forth in conceptual units referred as "messages". On the wire, a message is composed of one or more frames. Application frames carry payload intended for the application and can be text or binary data. Control frames carry data intended for protocol-level signaling. Now lets talk about the JSR! The Java API for WebSocket is worked upon as JSR 356 in the Java Community Process. This will define a standard API for building WebSocket applications. This JSR will provide support for: Creating WebSocket Java components to handle bi-directional WebSocket conversations Initiating and intercepting WebSocket events Creation and consumption of WebSocket text and binary messages The ability to define WebSocket protocols and content models for an application Configuration and management of WebSocket sessions, like timeouts, retries, cookies, connection pooling Specification of how WebSocket application will work within the Java EE security model Tyrus is the Reference Implementation for JSR 356 and is already integrated in GlassFish 4.0 Promoted Builds. And finally some code! The API allows to create WebSocket endpoints using annotations and interface. This TOTD will show a simple sample using annotations. A subsequent blog will show more advanced samples. A POJO can be converted to a WebSocket endpoint by specifying @WebSocketEndpoint and @WebSocketMessage. @WebSocketEndpoint(path="/hello")public class HelloBean {     @WebSocketMessage    public String sayHello(String name) {         return "Hello " + name + "!";     }} @WebSocketEndpoint marks this class as a WebSocket endpoint listening at URI defined by the path attribute. The @WebSocketMessage identifies the method that will receive the incoming WebSocket message. This first method parameter is injected with payload of the incoming message. In this case it is assumed that the payload is text-based. It can also be of the type byte[] in case the payload is binary. A custom object may be specified if decoders attribute is specified in the @WebSocketEndpoint. This attribute will provide a list of classes that define how a custom object can be decoded. This method can also take an optional Session parameter. This is injected by the runtime and capture a conversation between two endpoints. The return type of the method can be String, byte[] or a custom object. The encoders attribute on @WebSocketEndpoint need to define how a custom object can be encoded. The client side is an index.jsp with embedded JavaScript. The JSP body looks like: <div style="text-align: center;"> <form action="">     <input onclick="say_hello()" value="Say Hello" type="button">         <input id="nameField" name="name" value="WebSocket" type="text"><br>    </form> </div> <div id="output"></div> The code is relatively straight forward. It has an HTML form with a button that invokes say_hello() method and a text field named nameField. A div placeholder is available for displaying the output. Now, lets take a look at some JavaScript code: <script language="javascript" type="text/javascript"> var wsUri = "ws://localhost:8080/HelloWebSocket/hello";     var websocket = new WebSocket(wsUri);     websocket.onopen = function(evt) { onOpen(evt) };     websocket.onmessage = function(evt) { onMessage(evt) };     websocket.onerror = function(evt) { onError(evt) };     function init() {         output = document.getElementById("output");     }     function say_hello() {      websocket.send(nameField.value);         writeToScreen("SENT: " + nameField.value);     } This application is deployed as "HelloWebSocket.war" (download here) on GlassFish 4.0 promoted build 57. So the WebSocket endpoint is listening at "ws://localhost:8080/HelloWebSocket/hello". A new WebSocket connection is initiated by specifying the URI to connect to. The JavaScript API defines callback methods that are invoked when the connection is opened (onOpen), closed (onClose), error received (onError), or a message from the endpoint is received (onMessage). The client API has several send methods that transmit data over the connection. This particular script sends text data in the say_hello method using nameField's value from the HTML shown earlier. Each click on the button sends the textbox content to the endpoint over a WebSocket connection and receives a response based upon implementation in the sayHello method shown above. How to test this out ? Download the entire source project here or just the WAR file. Download GlassFish4.0 build 57 or later and unzip. Start GlassFish as "asadmin start-domain". Deploy the WAR file as "asadmin deploy HelloWebSocket.war". Access the application at http://localhost:8080/HelloWebSocket/index.jsp. After clicking on "Say Hello" button, the output would look like: Here are some references for you: WebSocket - Protocol and JavaScript API JSR 356: Java API for WebSocket - Specification (Early Draft) and Implementation (already integrated in GlassFish 4 promoted builds) Subsequent blogs will discuss the following topics (not necessary in that order) ... Binary data as payload Custom payloads using encoder/decoder Error handling Interface-driven WebSocket endpoint Java client API Client and Server configuration Security Subprotocols Extensions Other topics from the API Capturing WebSocket on-the-wire messages

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  • Oracle Database 12 c New Partition Maintenance Features by Gwen Lazenby

    - by hamsun
    One of my favourite new features in Oracle Database 12c is the ability to perform partition maintenance operations on multiple partitions. This means we can now add, drop, truncate and merge multiple partitions in one operation, and can split a single partition into more than two partitions also in just one command. This would certainly have made my life slightly easier had it been available when I administered a data warehouse at Oracle 9i. To demonstrate this new functionality and syntax, I am going to create two tables, ORDERS and ORDERS_ITEMS which have a parent-child relationship. ORDERS is to be partitioned using range partitioning on the ORDER_DATE column, and ORDER_ITEMS is going to partitioned using reference partitioning and its foreign key relationship with the ORDERS table. This form of partitioning was a new feature in 11g and means that any partition maintenance operations performed on the ORDERS table will also take place on the ORDER_ITEMS table as well. First create the ORDERS table - SQL CREATE TABLE orders ( order_id NUMBER(12), order_date TIMESTAMP, order_mode VARCHAR2(8), customer_id NUMBER(6), order_status NUMBER(2), order_total NUMBER(8,2), sales_rep_id NUMBER(6), promotion_id NUMBER(6), CONSTRAINT orders_pk PRIMARY KEY(order_id) ) PARTITION BY RANGE(order_date) (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007 VALUES LESS THAN (TO_DATE('01-JAN-2008','DD-MON-YYYY')) ); Table created. Now the ORDER_ITEMS table SQL CREATE TABLE order_items ( order_id NUMBER(12) NOT NULL, line_item_id NUMBER(3) NOT NULL, product_id NUMBER(6) NOT NULL, unit_price NUMBER(8,2), quantity NUMBER(8), CONSTRAINT order_items_fk FOREIGN KEY(order_id) REFERENCES orders(order_id) on delete cascade) PARTITION BY REFERENCE(order_items_fk) tablespace example; Table created. Now look at DBA_TAB_PARTITIONS to get details of what partitions we have in the two tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 Just as an aside it is also now possible in 12c to use interval partitioning on reference partitioned tables. In 11g it was not possible to combine these two new partitioning features. For our first example of the new 12cfunctionality, let us add all the partitions necessary for 2008 to the tables using one command. Notice that the partition specification part of the add command is identical in format to the partition specification part of the create command as shown above - SQL alter table orders add PARTITION Q1_2008 VALUES LESS THAN (TO_DATE('01-APR-2008','DD-MON-YYYY')), PARTITION Q2_2008 VALUES LESS THAN (TO_DATE('01-JUL-2008','DD-MON-YYYY')), PARTITION Q3_2008 VALUES LESS THAN (TO_DATE('01-OCT-2008','DD-MON-YYYY')), PARTITION Q4_2008 VALUES LESS THAN (TO_DATE('01-JAN-2009','DD-MON-YYYY')); Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 4 new partitions have been added to both tables – SQL select table_name,partition_name, partition_position position, high_value from dba_tab_partitions where table_owner='SH' and table_name like 'ORDER_%' order by partition_position, table_name; TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q1_2008 5 TIMESTAMP' 2008-04-01 00:00:00' ORDER_ITEMS Q1_2008 5 ORDERS Q2_2008 6 TIMESTAMP' 2008-07-01 00:00:00' ORDER_ITEM Q2_2008 6 ORDERS Q3_2008 7 TIMESTAMP' 2008-10-01 00:00:00' ORDER_ITEMS Q3_2008 7 ORDERS Q4_2008 8 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 8 Next, we can drop or truncate multiple partitions by giving a comma separated list in the alter table command. Note the use of the plural ‘partitions’ in the command as opposed to the singular ‘partition’ prior to 12c– SQL alter table orders drop partitions Q3_2008,Q2_2008,Q1_2008; Table altered. Now look at DBA_TAB_PARTITIONS and we can see that the 3 partitions have been dropped in both the two tables – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Now let us merge all the 2007 partitions together to form one single partition – SQL alter table orders merge partitions Q1_2005, Q2_2005, Q3_2005, Q4_2005 into partition Y_2007; Table altered. TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Y_2007 1 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Y_2007 1 ORDERS Q4_2008 2 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 2 Splitting partitions is a slightly more involved. In the case of range partitioning one of the new partitions must have no high value defined, and in list partitioning one of the new partitions must have no list of values defined. I call these partitions the ‘everything else’ partitions, and will contain any rows contained in the original partition that are not contained in the any of the other new partitions. For example, let us split the Y_2007 partition back into 4 quarterly partitions – SQL alter table orders split partition Y_2007 into (PARTITION Q1_2007 VALUES LESS THAN (TO_DATE('01-APR-2007','DD-MON-YYYY')), PARTITION Q2_2007 VALUES LESS THAN (TO_DATE('01-JUL-2007','DD-MON-YYYY')), PARTITION Q3_2007 VALUES LESS THAN (TO_DATE('01-OCT-2007','DD-MON-YYYY')), PARTITION Q4_2007); Now look at DBA_TAB_PARTITIONS to get details of the new partitions – TABLE_NAME PARTITION_NAME POSITION HIGH_VALUE -------------- --------------- -------- ------------------------- ORDERS Q1_2007 1 TIMESTAMP' 2007-04-01 00:00:00' ORDER_ITEMS Q1_2007 1 ORDERS Q2_2007 2 TIMESTAMP' 2007-07-01 00:00:00' ORDER_ITEMS Q2_2007 2 ORDERS Q3_2007 3 TIMESTAMP' 2007-10-01 00:00:00' ORDER_ITEMS Q3_2007 3 ORDERS Q4_2007 4 TIMESTAMP' 2008-01-01 00:00:00' ORDER_ITEMS Q4_2007 4 ORDERS Q4_2008 5 TIMESTAMP' 2009-01-01 00:00:00' ORDER_ITEMS Q4_2008 5 Partition Q4_2007 has a high value equal to the high value of the original Y_2007 partition, and so has inherited its upper boundary from the partition that was split. As for a list partitioning example let look at the following another table, SALES_PAR_LIST, which has 2 partitions, Americas and Europe and a partitioning key of country_name. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE -------------- --------------- ----------------------------- SALES_PAR_LIST AMERICAS 'Argentina', 'Canada', 'Peru', 'USA', 'Honduras', 'Brazil', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' Now split the Americas partition into 3 partitions – SQL alter table sales_par_list split partition americas into (partition south_america values ('Argentina','Peru','Brazil'), partition north_america values('Canada','USA'), partition central_america); Table altered. Note that no list of values was given for the ‘Central America’ partition. However it should have inherited any values in the original ‘Americas’ partition that were not assigned to either the ‘North America’ or ‘South America’ partitions. We can confirm this by looking at the DBA_TAB_PARTITIONS view. SQL select table_name,partition_name, high_value from dba_tab_partitions where table_owner='SH' and table_name = 'SALES_PAR_LIST'; TABLE_NAME PARTITION_NAME HIGH_VALUE --------------- --------------- -------------------------------- SALES_PAR_LIST SOUTH_AMERICA 'Argentina', 'Peru', 'Brazil' SALES_PAR_LIST NORTH_AMERICA 'Canada', 'USA' SALES_PAR_LIST CENTRAL_AMERICA 'Honduras', 'Nicaragua' SALES_PAR_LIST EUROPE 'France', 'Spain', 'Ireland', 'Germany', 'Belgium', 'Portugal', 'Denmark' In conclusion, I hope that DBA’s whose work involves maintaining partitions will find the operations a bit more straight forward to carry out once they have upgraded to Oracle Database 12c. Gwen Lazenby is a Principal Training Consultant at Oracle. She is part of Oracle University's Core Technology delivery team based in the UK, teaching Database Administration and Linux courses. Her specialist topics include using Oracle Partitioning and Parallelism in Data Warehouse environments, as well as Oracle Spatial and RMAN.

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  • Evaluating Solutions to Manage Product Compliance? Don't Wait Much Longer

    - by Kerrie Foy
    Depending on severity, product compliance issues can cause all sorts of problems from run-away budgets to business closures. But effective policies and safeguards can create a strong foundation for innovation, productivity, market penetration and competitive advantage. If you’ve been putting off a systematic approach to product compliance, it is time to reconsider that decision, or indecision. Why now?  No matter what industry, companies face a litany of worldwide and regional regulations that require proof of product compliance and environmental friendliness for market access.  For example, Restriction of Hazardous Substances (RoHS) is a regulation that restricts the use of six dangerous materials used in the manufacture of electronic and electrical equipment.  ROHS was originally adopted by the European Union in 2003 for implementation in 2006, and it has evolved over time through various regional versions for North America, China, Japan, Korea, Norway and Turkey.  In addition, the RoHS directive allowed for material exemptions used in Medical Devices, but that exemption ends in 2014.   Additional regulations worth watching are the Battery Directive, Waste Electrical and Electronic Equipment (WEEE), and Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) directives.  Additional evolving regulations are coming from governing bodies like the Food and Drug Administration (FDA) and the International Organization for Standardization (ISO). Corporate sustainability initiatives are also gaining urgency and influencing product design. In a survey of 405 corporations in the Global 500 by Carbon Disclosure Project, co-written by PwC (CDP Global 500 Climate Change Report 2012 entitled Business Resilience in an Uncertain, Resource-Constrained World), 48% of the respondents indicated they saw potential to create new products and business services as a response to climate change. Just 21% reported a dedicated budget for the research. However, the report goes on to explain that those few companies are winning over new customers and driving additional profits by exploiting their abilities to adapt to environmental needs. The article cites Dell as an example – Dell has invested in research to develop new products designed to reduce its customers’ emissions by more than 10 million metric tons of CO2e per year. This reduction in emissions should save Dell’s customers over $1billion per year as a result! Over time we expect to see many additional companies prove that eco-design provides marketplace benefits through differentiation and direct customer value. How do you meet compliance requirements and also successfully invest in eco-friendly designs? No doubt companies struggle to answer this question. After all, the journey to get there may involve transforming business models, go-to-market strategies, supply networks, quality assurance policies and compliance processes per the rapidly evolving global and regional directives. There may be limited executive focus on the initiative, inability to quantify noncompliance, or not enough resources to justify investment. To make things even more difficult to address, compliance responsibility can be a passionate topic within an organization, making the prospect of change on an enterprise scale problematic and time-consuming. Without a single source of truth for product data and without proper processes in place, ensuring product compliance burgeons into a crushing task that is cost-prohibitive and overwhelming to an organization. With all the overhead, certain markets or demographics become simply inaccessible. Therefore, the risk to consumer goodwill and satisfaction, revenue, business continuity, and market potential is too great not to solve the compliance challenge. Companies are beginning to adapt and even thrive in today’s highly regulated and transparent environment by implementing systematic approaches to product compliance that are more than functional bandages but revenue-generating engines. Consider partnering with Oracle to help you address your compliance needs. Many of the world’s most innovative leaders and pioneers are leveraging Oracle’s Agile Product Lifecycle Management (PLM) portfolio of enterprise applications to manage the product value chain, centralize product data, automate processes, and launch more eco-friendly products to market faster.   Particularly, the Agile Product Governance & Compliance (PG&C) solution provides out-of-the-box functionality to integrate actionable regulatory information into the enterprise product record from the ideation to the disposal/recycling phase. Agile PG&C makes it possible to efficiently manage compliance per corporate green initiatives as well as regional and global directives. Options are critical, but so is ease-of-use. Anyone who’s grappled with compliance policy knows legal interpretation plays a major role in determining how an organization responds to regulation. Agile PG&C gives you the freedom to configure product compliance per your needs, while maintaining rigorous control over the product record in an easy-to-use interface that facilitates adoption efforts. It allows you to assign regulations as specifications for a part or BOM roll-up. Each specification has a threshold value that alerts you to a non-compliance issue if the threshold value is exceeded. Set however many regulations as specifications you need to make sure a product can be sold in your target countries. Another option is to implement like one of our leading consumer electronics customers and define your own “catch-all” specification to ensure compliance in all markets. You can give your suppliers secure access to enter their component data or integrate a third party’s data. With Agile PG&C you are able to design compliance earlier into your products to reduce cost and improve quality downstream when stakes are higher. Agile PG&C is a comprehensive solution that makes product compliance more reliable and efficient. Throughout product lifecycles, use the solution to support full material disclosures, efficiently manage declarations with your suppliers, feed compliance data into a corrective action if a product must be changed, and swiftly satisfy audits by showing all due diligence tracked in one solution. Given the compounding regulation and consumer focus on urgent environmental issues, now is the time to act. Implementing an enterprise, systematic approach to product compliance is a competitive investment. From the start, Agile Product Governance & Compliance enables companies to confidently design for compliance and sustainability, reduce the cost of compliance, minimize the risk of business interruption, deliver responsible products, and inspire new innovation.  Don’t wait any longer! To find out more about Agile Product Governance & Compliance download the data sheet, contact your sales representative, or call Oracle at 1-800-633-0738. Many thanks to Shane Goodwin, Senior Manager, Oracle Agile PLM Product Management, for contributions to this article. 

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • Testing Workflows &ndash; Test-First

    - by Timothy Klenke
    Originally posted on: http://geekswithblogs.net/TimothyK/archive/2014/05/30/testing-workflows-ndash-test-first.aspxThis is the second of two posts on some common strategies for approaching the job of writing tests.  The previous post covered test-after workflows where as this will focus on test-first.  Each workflow presented is a method of attack for adding tests to a project.  The more tools in your tool belt the better.  So here is a partial list of some test-first methodologies. Ping Pong Ping Pong is a methodology commonly used in pair programing.  One developer will write a new failing test.  Then they hand the keyboard to their partner.  The partner writes the production code to get the test passing.  The partner then writes the next test before passing the keyboard back to the original developer. The reasoning behind this testing methodology is to facilitate pair programming.  That is to say that this testing methodology shares all the benefits of pair programming, including ensuring multiple team members are familiar with the code base (i.e. low bus number). Test Blazer Test Blazing, in some respects, is also a pairing strategy.  The developers don’t work side by side on the same task at the same time.  Instead one developer is dedicated to writing tests at their own desk.  They write failing test after failing test, never touching the production code.  With these tests they are defining the specification for the system.  The developer most familiar with the specifications would be assigned this task. The next day or later in the same day another developer fetches the latest test suite.  Their job is to write the production code to get those tests passing.  Once all the tests pass they fetch from source control the latest version of the test project to get the newer tests. This methodology has some of the benefits of pair programming, namely lowering the bus number.  This can be good way adding an extra developer to a project without slowing it down too much.  The production coder isn’t slowed down writing tests.  The tests are in another project from the production code, so there shouldn’t be any merge conflicts despite two developers working on the same solution. This methodology is also a good test for the tests.  Can another developer figure out what system should do just by reading the tests?  This question will be answered as the production coder works there way through the test blazer’s tests. Test Driven Development (TDD) TDD is a highly disciplined practice that calls for a new test and an new production code to be written every few minutes.  There are strict rules for when you should be writing test or production code.  You start by writing a failing (red) test, then write the simplest production code possible to get the code working (green), then you clean up the code (refactor).  This is known as the red-green-refactor cycle. The goal of TDD isn’t the creation of a suite of tests, however that is an advantageous side effect.  The real goal of TDD is to follow a practice that yields a better design.  The practice is meant to push the design toward small, decoupled, modularized components.  This is generally considered a better design that large, highly coupled ball of mud. TDD accomplishes this through the refactoring cycle.  Refactoring is only possible to do safely when tests are in place.  In order to use TDD developers must be trained in how to look for and repair code smells in the system.  Through repairing these sections of smelly code (i.e. a refactoring) the design of the system emerges. For further information on TDD, I highly recommend the series “Is TDD Dead?”.  It discusses its pros and cons and when it is best used. Acceptance Test Driven Development (ATDD) Whereas TDD focuses on small unit tests that concentrate on a small piece of the system, Acceptance Tests focuses on the larger integrated environment.  Acceptance Tests usually correspond to user stories, which come directly from the customer. The unit tests focus on the inputs and outputs of smaller parts of the system, which are too low level to be of interest to the customer. ATDD generally uses the same tools as TDD.  However, ATDD uses fewer mocks and test doubles than TDD. ATDD often complements TDD; they aren’t competing methods.  A full test suite will usually consist of a large number of unit (created via TDD) tests and a smaller number of acceptance tests. Behaviour Driven Development (BDD) BDD is more about audience than workflow.  BDD pushes the testing realm out towards the client.  Developers, managers and the client all work together to define the tests. Typically different tooling is used for BDD than acceptance and unit testing.  This is done because the audience is not just developers.  Tools using the Gherkin family of languages allow for test scenarios to be described in an English format.  Other tools such as MSpec or FitNesse also strive for highly readable behaviour driven test suites. Because these tests are public facing (viewable by people outside the development team), the terminology usually changes.  You can’t get away with the same technobabble you can with unit tests written in a programming language that only developers understand.  For starters, they usually aren’t called tests.  Usually they’re called “examples”, “behaviours”, “scenarios”, or “specifications”. This may seem like a very subtle difference, but I’ve seen this small terminology change have a huge impact on the acceptance of the process.  Many people have a bias that testing is something that comes at the end of a project.  When you say we need to define the tests at the start of the project many people will immediately give that a lower priority on the project schedule.  But if you say we need to define the specification or behaviour of the system before we can start, you’ll get more cooperation.   Keep these test-first and test-after workflows in your tool belt.  With them you’ll be able to find new opportunities to apply them.

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