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  • C read X bytes from a file, padding if needed

    - by Hunter McMillen
    I am trying to read in an input file 64 bits at a time, then do some calculations on those 64 bits, the problem is I need to convert the ascii text to hexadecimal characters. I have searched around but none of the answers posted seem to work for my situation. Here is what I have: int main(int argc, int * argv) { char buffer[9]; FILE *f; unsigned long long test; if(f = fopen("input2.txt", "r")) { while( fread(buffer, 8, 1, f) != 0) //while not EOF read 8 bytes at a time { buffer[8] = '\0'; test = strtoull(buffer, NULL, 16); //interpret as hex printf("%llu\n", test); printf("%s\n", buffer); } fclose(f); } } For an input like this: "testing string to hex conversion" I get results like this: 0 testing 0 string t 0 o hex co 0 nversion Where I would expect: 74 65 73 74 69 6e 67 20 <- "testing" in hex testing 73 74 72 69 6e 67 20 74 <- "string t" in hex string t 6f 20 68 65 78 20 63 6f <- "o hex co" in hex o hex co 6e 76 65 72 73 69 6f 6e <- "nversion" in hex nversion Can anyone see where I misstepped?

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  • C# 4: The Curious ConcurrentDictionary

    - by James Michael Hare
    In my previous post (here) I did a comparison of the new ConcurrentQueue versus the old standard of a System.Collections.Generic Queue with simple locking.  The results were exactly what I would have hoped, that the ConcurrentQueue was faster with multi-threading for most all situations.  In addition, concurrent collections have the added benefit that you can enumerate them even if they're being modified. So I set out to see what the improvements would be for the ConcurrentDictionary, would it have the same performance benefits as the ConcurrentQueue did?  Well, after running some tests and multiple tweaks and tunes, I have good and bad news. But first, let's look at the tests.  Obviously there's many things we can do with a dictionary.  One of the most notable uses, of course, in a multi-threaded environment is for a small, local in-memory cache.  So I set about to do a very simple simulation of a cache where I would create a test class that I'll just call an Accessor.  This accessor will attempt to look up a key in the dictionary, and if the key exists, it stops (i.e. a cache "hit").  However, if the lookup fails, it will then try to add the key and value to the dictionary (i.e. a cache "miss").  So here's the Accessor that will run the tests: 1: internal class Accessor 2: { 3: public int Hits { get; set; } 4: public int Misses { get; set; } 5: public Func<int, string> GetDelegate { get; set; } 6: public Action<int, string> AddDelegate { get; set; } 7: public int Iterations { get; set; } 8: public int MaxRange { get; set; } 9: public int Seed { get; set; } 10:  11: public void Access() 12: { 13: var randomGenerator = new Random(Seed); 14:  15: for (int i=0; i<Iterations; i++) 16: { 17: // give a wide spread so will have some duplicates and some unique 18: var target = randomGenerator.Next(1, MaxRange); 19:  20: // attempt to grab the item from the cache 21: var result = GetDelegate(target); 22:  23: // if the item doesn't exist, add it 24: if(result == null) 25: { 26: AddDelegate(target, target.ToString()); 27: Misses++; 28: } 29: else 30: { 31: Hits++; 32: } 33: } 34: } 35: } Note that so I could test different implementations, I defined a GetDelegate and AddDelegate that will call the appropriate dictionary methods to add or retrieve items in the cache using various techniques. So let's examine the three techniques I decided to test: Dictionary with mutex - Just your standard generic Dictionary with a simple lock construct on an internal object. Dictionary with ReaderWriterLockSlim - Same Dictionary, but now using a lock designed to let multiple readers access simultaneously and then locked when a writer needs access. ConcurrentDictionary - The new ConcurrentDictionary from System.Collections.Concurrent that is supposed to be optimized to allow multiple threads to access safely. So the approach to each of these is also fairly straight-forward.  Let's look at the GetDelegate and AddDelegate implementations for the Dictionary with mutex lock: 1: var addDelegate = (key,val) => 2: { 3: lock (_mutex) 4: { 5: _dictionary[key] = val; 6: } 7: }; 8: var getDelegate = (key) => 9: { 10: lock (_mutex) 11: { 12: string val; 13: return _dictionary.TryGetValue(key, out val) ? val : null; 14: } 15: }; Nothing new or fancy here, just your basic lock on a private object and then query/insert into the Dictionary. Now, for the Dictionary with ReadWriteLockSlim it's a little more complex: 1: var addDelegate = (key,val) => 2: { 3: _readerWriterLock.EnterWriteLock(); 4: _dictionary[key] = val; 5: _readerWriterLock.ExitWriteLock(); 6: }; 7: var getDelegate = (key) => 8: { 9: string val; 10: _readerWriterLock.EnterReadLock(); 11: if(!_dictionary.TryGetValue(key, out val)) 12: { 13: val = null; 14: } 15: _readerWriterLock.ExitReadLock(); 16: return val; 17: }; And finally, the ConcurrentDictionary, which since it does all it's own concurrency control, is remarkably elegant and simple: 1: var addDelegate = (key,val) => 2: { 3: _concurrentDictionary[key] = val; 4: }; 5: var getDelegate = (key) => 6: { 7: string s; 8: return _concurrentDictionary.TryGetValue(key, out s) ? s : null; 9: };                    Then, I set up a test harness that would simply ask the user for the number of concurrent Accessors to attempt to Access the cache (as specified in Accessor.Access() above) and then let them fly and see how long it took them all to complete.  Each of these tests was run with 10,000,000 cache accesses divided among the available Accessor instances.  All times are in milliseconds. 1: Dictionary with Mutex Locking 2: --------------------------------------------------- 3: Accessors Mostly Misses Mostly Hits 4: 1 7916 3285 5: 10 8293 3481 6: 100 8799 3532 7: 1000 8815 3584 8:  9:  10: Dictionary with ReaderWriterLockSlim Locking 11: --------------------------------------------------- 12: Accessors Mostly Misses Mostly Hits 13: 1 8445 3624 14: 10 11002 4119 15: 100 11076 3992 16: 1000 14794 4861 17:  18:  19: Concurrent Dictionary 20: --------------------------------------------------- 21: Accessors Mostly Misses Mostly Hits 22: 1 17443 3726 23: 10 14181 1897 24: 100 15141 1994 25: 1000 17209 2128 The first test I did across the board is the Mostly Misses category.  The mostly misses (more adds because data requested was not in the dictionary) shows an interesting trend.  In both cases the Dictionary with the simple mutex lock is much faster, and the ConcurrentDictionary is the slowest solution.  But this got me thinking, and a little research seemed to confirm it, maybe the ConcurrentDictionary is more optimized to concurrent "gets" than "adds".  So since the ratio of misses to hits were 2 to 1, I decided to reverse that and see the results. So I tweaked the data so that the number of keys were much smaller than the number of iterations to give me about a 2 to 1 ration of hits to misses (twice as likely to already find the item in the cache than to need to add it).  And yes, indeed here we see that the ConcurrentDictionary is indeed faster than the standard Dictionary here.  I have a strong feeling that as the ration of hits-to-misses gets higher and higher these number gets even better as well.  This makes sense since the ConcurrentDictionary is read-optimized. Also note that I tried the tests with capacity and concurrency hints on the ConcurrentDictionary but saw very little improvement, I think this is largely because on the 10,000,000 hit test it quickly ramped up to the correct capacity and concurrency and thus the impact was limited to the first few milliseconds of the run. So what does this tell us?  Well, as in all things, ConcurrentDictionary is not a panacea.  It won't solve all your woes and it shouldn't be the only Dictionary you ever use.  So when should we use each? Use System.Collections.Generic.Dictionary when: You need a single-threaded Dictionary (no locking needed). You need a multi-threaded Dictionary that is loaded only once at creation and never modified (no locking needed). You need a multi-threaded Dictionary to store items where writes are far more prevalent than reads (locking needed). And use System.Collections.Concurrent.ConcurrentDictionary when: You need a multi-threaded Dictionary where the writes are far more prevalent than reads. You need to be able to iterate over the collection without locking it even if its being modified. Both Dictionaries have their strong suits, I have a feeling this is just one where you need to know from design what you hope to use it for and make your decision based on that criteria.

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  • Structuring multi-threaded programs

    - by davidk01
    Are there any canonical sources for learning how to structure multi-threaded programs? Even with all the concurrency utility classes that Java provides I'm having a hard time properly structuring multi-threaded programs. Whenever threads are involved my code becomes very brittle, any little change can potentially break the program because the code that jumps back and forth between the threads tends to be very convoluted.

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  • Waterfall Model (SDLC) vs. Prototyping Model

    The characters in the fable of the Tortoise and the Hare can easily be used to demonstrate the similarities and differences between the Waterfall and Prototyping software development models. This children fable is about a race between a consistently slow moving but steadfast turtle and an extremely fast but unreliable rabbit. After closely comparing each character’s attributes in correlation with both software development models, a trend seems to appear in that the Waterfall closely resembles the Tortoise in that Waterfall Model is typically a slow moving process that is broken up in to multiple sequential steps that must be executed in a standard linear pattern. The Tortoise can be quoted several times in the story saying “Slow and steady wins the race.” This is the perfect mantra for the Waterfall Model in that this model is seen as a cumbersome and slow moving. Waterfall Model Phases Requirement Analysis & Definition This phase focuses on defining requirements for a project that is to be developed and determining if the project is even feasible. Requirements are collected by analyzing existing systems and functionality in correlation with the needs of the business and the desires of the end users. The desired output for this phase is a list of specific requirements from the business that are to be designed and implemented in the subsequent steps. In addition this phase is used to determine if any value will be gained by completing the project. System Design This phase focuses primarily on the actual architectural design of a system, and how it will interact within itself and with other existing applications. Projects at this level should be viewed at a high level so that actual implementation details are decided in the implementation phase. However major environmental decision like hardware and platform decision are typically decided in this phase. Furthermore the basic goal of this phase is to design an application at the system level in those classes, interfaces, and interactions are defined. Additionally decisions about scalability, distribution and reliability should also be considered for all decisions. The desired output for this phase is a functional  design document that states all of the architectural decisions that have been made in regards to the project as well as a diagrams like a sequence and class diagrams. Software Design This phase focuses primarily on the refining of the decisions found in the functional design document. Classes and interfaces are further broken down in to logical modules based on the interfaces and interactions previously indicated. The output of this phase is a formal design document. Implementation / Coding This phase focuses primarily on implementing the previously defined modules in to units of code. These units are developed independently are intergraded as the system is put together as part of a whole system. Software Integration & Verification This phase primarily focuses on testing each of the units of code developed as well as testing the system as a whole. There are basic types of testing at this phase and they include: Unit Test and Integration Test. Unit Test are built to test the functionality of a code unit to ensure that it preforms its desired task. Integration testing test the system as a whole because it focuses on results of combining specific units of code and validating it against expected results. The output of this phase is a test plan that includes test with expected results and actual results. System Verification This phase primarily focuses on testing the system as a whole in regards to the list of project requirements and desired operating environment. Operation & Maintenance his phase primarily focuses on handing off the competed project over to the customer so that they can verify that all of their requirements have been met based on their original requirements. This phase will also validate the correctness of their requirements and if any changed need to be made. In addition, any problems not resolved in the previous phase will be handled in this section. The Waterfall Model’s linear and sequential methodology does offer a project certain advantages and disadvantages. Advantages of the Waterfall Model Simplistic to implement and execute for projects and/or company wide Limited demand on resources Large emphasis on documentation Disadvantages of the Waterfall Model Completed phases cannot be revisited regardless if issues arise within a project Accurate requirement are never gather prior to the completion of the requirement phase due to the lack of clarification in regards to client’s desires. Small changes or errors that arise in applications may cause additional problems The client cannot change any requirements once the requirements phase has been completed leaving them no options for changes as they see their requirements changes as the customers desires change. Excess documentation Phases are cumbersome and slow moving Learn more about the Major Process in the Sofware Development Life Cycle and Waterfall Model. Conversely, the Hare shares similar traits with the prototyping software development model in that ideas are rapidly converted to basic working examples and subsequent changes are made to quickly align the project with customers desires as they are formulated and as software strays from the customers vision. The basic concept of prototyping is to eliminate the use of well-defined project requirements. Projects are allowed to grow as the customer needs and request grow. Projects are initially designed according to basic requirements and are refined as requirement become more refined. This process allows customer to feel their way around the application to ensure that they are developing exactly what they want in the application This model also works well for determining the feasibility of certain approaches in regards to an application. Prototypes allow for quickly developing examples of implementing specific functionality based on certain techniques. Advantages of Prototyping Active participation from users and customers Allows customers to change their mind in specifying requirements Customers get a better understanding of the system as it is developed Earlier bug/error detection Promotes communication with customers Prototype could be used as final production Reduced time needed to develop applications compared to the Waterfall method Disadvantages of Prototyping Promotes constantly redefining project requirements that cause major system rewrites Potential for increased complexity of a system as scope of the system expands Customer could believe the prototype as the working version. Implementation compromises could increase the complexity when applying updates and or application fixes When companies trying to decide between the Waterfall model and Prototype model they need to evaluate the benefits and disadvantages for both models. Typically smaller companies or projects that have major time constraints typically head for more of a Prototype model approach because it can reduce the time needed to complete the project because there is more of a focus on building a project and less on defining requirements and scope prior to the start of a project. On the other hand, Companies with well-defined requirements and time allowed to generate proper documentation should steer towards more of a waterfall model because they are in a position to obtain clarified requirements and have to design and optimal solution prior to the start of coding on a project.

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  • How would you rank these programming skills in order of learning them? [closed]

    - by mumtaz
    As a general purpose programmer, what should you learn first and what should you learn later on? Here are some skills I wonder about... SQL Regular Expressions Multi-threading / Concurrency Functional Programming Graphics The mastery of your mother programming language's syntax/semantics/featureset The mastery of your base class framework libraries Version Control System Unit Testing XML Do you know other important ones? Please specify them... On which skills should I focus first?

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  • What is the diffference between "data hiding" and "encapsulation"?

    - by john smith optional
    I'm reading "Java concurrency in practice" and there is said: "Fortunately, the same object-oriented techniques that help you write well-organized, maintainable classes - such as encapsulation and data hiding -can also help you crate thread-safe classes." The problem #1 - I never heard about data hiding and don't know what it is. The problem #2 - I always thought that encapsulation is using private vs public, and is actually the data hiding. Can you please explain what data hiding is and how it differs from encapsulation?

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  • Book Review: Brownfield Application Development in .NET

    - by DotNetBlues
    I recently finished reading the book Brownfield Application Development in .NET by Kyle Baley and Donald Belcham.  The book is available from Manning.  First off, let me say that I'm a huge fan of Manning as a publisher.  I've found their books to be top-quality, over all.  As a Kindle owner, I also appreciate getting an ebook copy along with the dead tree copy.  I find ebooks to be much more convenient to read, but hard-copies are easier to reference. The book covers, surprisingly enough, working with brownfield applications.  Which is well and good, if that term has meaning to you.  It didn't for me.  Without retreading a chunk of the first chapter, the authors break code bases into three broad categories: greenfield, brownfield, and legacy.  Greenfield is, essentially, new development that hasn't had time to rust and is (hopefully) being approached with some discipline.  Legacy applications are those that are more or less stable and functional, that do not expect to see a lot of work done to them, and are more likely to be replaced than reworked. Brownfield code is the gray (brown?) area between the two and the authors argue, quite effectively, that it is the most likely state for an application to be in.  Brownfield code has, in some way, been allowed to tarnish around the edges and can be difficult to work with.  Although I hadn't realized it, most of the code I've worked on has been brownfield.  Sometimes, there's talk of scrapping and starting over.  Sometimes, the team dismisses increased discipline as ivory tower nonsense.  And, sometimes, I've been the ignorant culprit vexing my future self. The book is broken into two major sections, plus an introduction chapter and an appendix.  The first section covers what the authors refer to as "The Ecosystem" which consists of version control, build and integration, testing, metrics, and defect management.  The second section is on actually writing code for brownfield applications and discusses object-oriented principles, architecture, external dependencies, and, of course, how to deal with these when coming into an existing code base. The ecosystem section is just shy of 140 pages long and brings some real meat to the matter.  The focus on "pain points" immediately sets the tone as problem-solution, rather than academic.  The authors also approach some of the topics from a different angle than some essays I've read on similar topics.  For example, the chapter on automated testing is on just that -- automated testing.  It's all well and good to criticize a project as conflating integration tests with unit tests, but it really doesn't make anyone's life better.  The discussion on testing is more focused on the "right" level of testing for existing projects.  Sometimes, an integration test is the best you can do without gutting a section of functional code.  Even if you can sell other developers and/or management on doing so, it doesn't actually provide benefit to your customers to rewrite code that works.  This isn't to say the authors encourage sloppy coding.  Far from it.  Just that they point out the wisdom of ignoring the sleeping bear until after you deal with the snarling wolf. The other sections take a similarly real-world, workable approach to the pain points they address.  As the section moves from technical solutions like version control and continuous integration (CI) to the softer, process issues of metrics and defect tracking, the authors begin to gently suggest moving toward a zero defect count.  While that really sounds like an unreasonable goal for a lot of ongoing projects, it's quite apparent that the authors have first-hand experience with taming some gruesome projects.  The suggestions are grounded and workable, and the difficulty of some situations is explicitly acknowledged. I have to admit that I started getting bored by the end of the ecosystem section.  No matter how valuable I think a good project manager or business analyst is to a successful ALM, at the end of the day, I'm a gear-head.  Also, while I agreed with a lot of the ecosystem ideas, in theory, I didn't necessarily feel that a lot of the single-developer projects that I'm often involved in really needed that level of rigor.  It's only after reading the sidebars and commentary in the coding section that I had the context for the arguments made in favor of a strong ecosystem supporting the development process.  That isn't to say that I didn't support good product management -- indeed, I've probably pushed too hard, on occasion, for a strong ALM outside of just development.  This book gave me deeper insight into why some corners shouldn't be cut and how damaging certain sins of omission can be. The code section, though, kept me engaged for its entirety.  Many technical books can be used as reference material from day one.  The authors were clear, however, that this book is not one of these.  The first chapter of the section (chapter seven, over all) addresses object oriented (OO) practices.  I've read any number of definitions, discussions, and treatises on OO.  None of the chapter was new to me, but it was a good review, and I'm of the opinion that it's good to review the foundations of what you do, from time to time, so I didn't mind. The remainder of the book is really just about how to apply OOP to existing code -- and, just because all your code exists in classes does not mean that it's object oriented.  That topic has the potential to be extremely condescending, but the authors miraculously managed to never once make me feel like a dolt or that they were wagging their finger at me for my prior sins.  Instead, they continue the "pain points" and problem-solution presentation to give concrete examples of how to apply some pretty academic-sounding ideas.  That's a point worth emphasizing, as my experience with most OO discussions is that they stay in the academic realm.  This book gives some very, very good explanations of why things like the Liskov Substitution Principle exist and why a corporate programmer should even care.  Even if you know, with absolute certainty, that you'll never have to work on an existing code-base, I would recommend this book just for the clarity it provides on OOP. This book goes beyond just theory, or even real-world application.  It presents some methods for fixing problems that any developer can, and probably will, encounter in the wild.  First, the authors address refactoring application layers and internal dependencies.  Then, they take you through those layers from the UI to the data access layer and external dependencies.  Finally, they come full circle to tie it all back to the overall process.  By the time the book is done, you're left with a lot of ideas, but also a reasonable plan to begin to improve an existing project structure. Throughout the book, it's apparent that the authors have their own preferred methodology (TDD and domain-driven design), as well as some preferred tools.  The "Our .NET Toolbox" is something of a neon sign pointing to that latter point.  They do not beat the reader over the head with anything resembling a "One True Way" mentality.  Even for the most emphatic points, the tone is quite congenial and helpful.  With some of the near-theological divides that exist within the tech community, I found this to be one of the more remarkable characteristics of the book.  Although the authors favor tools that might be considered Alt.NET, there is no reason the advice and techniques given couldn't be quite successful in a pure Microsoft shop with Team Foundation Server.  For that matter, even though the book specifically addresses .NET, it could be applied to a Java and Oracle shop, as well.

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  • Why people don't patch and upgrade?!?

    - by Mike Dietrich
    Discussing the topic "Why Upgrade" or "Why not Upgrade" is not always fun. Actually the arguments repeat from customer to customer. Typically we hear things such as: A PSU or Patch Set introduces new bugs A new PSU or Patch Set introduces new features which lead to risk and require application verification  Patching means risk Patching changes the execution plans Patching requires too much testing Patching is too much work for our DBAs Patching costs a lot of money and doesn't pay out And to be very honest sometimes it's hard for me to stay calm in such discussions. Let's discuss some of these points a bit more in detail. A PSU or Patch Set introduces new bugsWell, yes, that is true as no software containing more than some lines of code is bug free. This applies to Oracle's code as well as too any application or operating system code. But first of all, does that mean you never patch your OS because the patch may introduce new flaws? And second, what is the point of saying "it introduces new bugs"? Does that mean you will never get rid of the mean issues we know about and we fixed already? Scroll down from MOS Note:161818.1 to the patch release you are on, no matter if it's 10.2.0.4 or 11.2.0.3 and check for the Known Issues And Alerts.Will you take responsibility to know about all these issues and refuse to upgrade to 11.2.0.4? I won't. A new PSU or Patch Set introduces new featuresOk, we can discuss that. Offering new functionality within a database patch set is a dubious thing. It has advantages such as in 11.2.0.4 where we backported Database Redaction to. But this is something you will only use once you have an Advanced Security license. I interpret that statement I've heard quite often from customers in a different way: People don't want to get surprises such as new behaviour. This certainly gives everybody a hard time. And we've had many examples in the past (SESSION_CACHED_CURSROS in 10.2.0.4,  _DATAFILE_WRITE_ERRORS_CRASH_INSTANCE in 11.2.0.2 and others) where those things weren't documented, not even in the README. Thanks to many friends out there I learned about those as well. So new behaviour is the topic people consider as risky - not really new features. And just to point this out: A PSU never brings in new features or new behaviour by definition! Patching means riskDoes it really mean risk? Yes, there were issues in the past (and sometimes in the present as well) where a patch didn't get installed correctly. But personally I consider it way more risky to not patch. Keep that in mind: The day Oracle publishes an PSU (or CPU) containing security fixes all the great security experts out there go public with their findings as well. So from that day on even my grandma can find out about those issues and try to attack somebody. Now a lot of people say: "My database does not face the internet." And I will answer: "The enemy is sitting already behind your firewalls. And knows potentially about these things." My statement: Not patching introduces way more risk to your environment than patching. Seriously! Patching changes the execution plansDo they really? I agree - there's a very small risk for this happening with Patch Sets. But not with PSUs or CPUs as they contain no optimizer fixes changing behaviour (but they may contain fixes curing wrong-query-result-bugs). But what's the point of a changing execution plan? In Oracle Database 11g it is so simple to be prepared. SQL Plan Management is a free EE feature - so once that occurs you'll put the plan into the Plan Baseline. Basta! Yes, you wouldn't like to get such surprises? Than please use the SQL Performance Analyzer (SPA) from Real Application Testing and you'll detect that easily upfront in minutes. And not to forget this, a plan change can also be very positive!Yes, there's a little risk with a database patchset - and we have many possibilites to detect this before patching. Patching requires too much testingWell, does it really? I have seen in the past 12 years how people test. There are very different efforts and approaches on this. I have seen people spending a hell of money on licenses or on project team staffing. And I have seen people sailing blindly without any tests just going the John-Wayne-approach.Proper tools will allow you to test easily without too much efforts. See the paragraph above. We have used Real Application Testing in so many customer projects reducing the amount of work spend on testing by over 50%. But apart from that at some point you will have to stop testing. If you don't you'll get lost and you'll burn money. There's no 100% guaranty. You will have to deal with a little risk as reaching the final 5% of certainty will cost you the same as it did cost to reach 95%. And doing this will lead to abnormal long product cycles that you'll run behind forever. And this will cost even more money. Patching is too much work for our DBAsPatching is a lot of work. I agree. And it's no fun work. It's boring, annoying. You don't learn much from that. That's why you should try to automate this task. Use the Database's Lifecycle Management Pack. And don't cry about the fact that it costs money. Yes it does. But it will ease the process and you'll save a lot of costs as you don't waste your valuable time with patching. Or use Oracle Database 12c Oracle Multitenant and patch either by unplug/plug or patch an entire container database with all PDBs with one patch in one task. We have customer reference cases proofing it saved them 75% of time, effort and cost since they've used Lifecycle Management Pack. So why don't you use it? Patching costs a lot of money and doesn't pay outWell, see my statements in the paragraph above. And it pays out as flying with a database with 100 known critical flaws in it which are already fixed by Oracle (such as in the Oct 2013 PSU for Oracle Database 12c) will cost ways more in case of failure or even data loss. Bet with me? Let me finally ask you some questions. What cell phone are you using and which OS does it run? Do you have an iPhone 5 and did you upgrade already to iOS 7.0.3? I've just encountered on mine that the alarm (which I rely on when traveling) has gotten now a dependency on the physical switch "sound on/off". If it is switched to "off" physically the alarm rings "silently". What a wonderful example of a behaviour change coming in with a patch set. Will this push you to stay with iOS5 or iOS6? No, because those have security flaws which won't be fixed anymore. What browser are you surfing with? Do you use Mozilla 3.6? Well, congratulations to all the hackers. It will be easy for them to attack you and harm your system. I'd guess you have the auto updater on.  Same for Google Chrome, Safari, IE. Right? -Mike The T.htmtableborders, .htmtableborders td, .htmtableborders th {border : 1px dashed lightgrey ! important;} html, body { border: 0px; } body { background-color: #ffffff; } img, hr { cursor: default }

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  • Do you need to know Java before trying Scala

    - by gizgok
    I'm interested in learning Scala. I've been reading a lot about it, but a lot of people value it because it has an actor model which is better for concurrency, it handles xml in a much better way, solves the problem of first class functions. My question is do you need to know Java to understand/appreciate the way things work in Scala? Is it better to first take a stab at Java and then try Scala or you can start Scala with absolutely no java backround?

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  • parallel_for_each from amp.h – part 1

    - by Daniel Moth
    This posts assumes that you've read my other C++ AMP posts on index<N> and extent<N>, as well as about the restrict modifier. It also assumes you are familiar with C++ lambdas (if not, follow my links to C++ documentation). Basic structure and parameters Now we are ready for part 1 of the description of the new overload for the concurrency::parallel_for_each function. The basic new parallel_for_each method signature returns void and accepts two parameters: a grid<N> (think of it as an alias to extent) a restrict(direct3d) lambda, whose signature is such that it returns void and accepts an index of the same rank as the grid So it looks something like this (with generous returns for more palatable formatting) assuming we are dealing with a 2-dimensional space: // some_code_A parallel_for_each( g, // g is of type grid<2> [ ](index<2> idx) restrict(direct3d) { // kernel code } ); // some_code_B The parallel_for_each will execute the body of the lambda (which must have the restrict modifier), on the GPU. We also call the lambda body the "kernel". The kernel will be executed multiple times, once per scheduled GPU thread. The only difference in each execution is the value of the index object (aka as the GPU thread ID in this context) that gets passed to your kernel code. The number of GPU threads (and the values of each index) is determined by the grid object you pass, as described next. You know that grid is simply a wrapper on extent. In this context, one way to think about it is that the extent generates a number of index objects. So for the example above, if your grid was setup by some_code_A as follows: extent<2> e(2,3); grid<2> g(e); ...then given that: e.size()==6, e[0]==2, and e[1]=3 ...the six index<2> objects it generates (and hence the values that your lambda would receive) are:    (0,0) (1,0) (0,1) (1,1) (0,2) (1,2) So what the above means is that the lambda body with the algorithm that you wrote will get executed 6 times and the index<2> object you receive each time will have one of the values just listed above (of course, each one will only appear once, the order is indeterminate, and they are likely to call your code at the same exact time). Obviously, in real GPU programming, you'd typically be scheduling thousands if not millions of threads, not just 6. If you've been following along you should be thinking: "that is all fine and makes sense, but what can I do in the kernel since I passed nothing else meaningful to it, and it is not returning any values out to me?" Passing data in and out It is a good question, and in data parallel algorithms indeed you typically want to pass some data in, perform some operation, and then typically return some results out. The way you pass data into the kernel, is by capturing variables in the lambda (again, if you are not familiar with them, follow the links about C++ lambdas), and the way you use data after the kernel is done executing is simply by using those same variables. In the example above, the lambda was written in a fairly useless way with an empty capture list: [ ](index<2> idx) restrict(direct3d), where the empty square brackets means that no variables were captured. If instead I write it like this [&](index<2> idx) restrict(direct3d), then all variables in the some_code_A region are made available to the lambda by reference, but as soon as I try to use any of those variables in the lambda, I will receive a compiler error. This has to do with one of the direct3d restrictions, where only one type can be capture by reference: objects of the new concurrency::array class that I'll introduce in the next post (suffice for now to think of it as a container of data). If I write the lambda line like this [=](index<2> idx) restrict(direct3d), all variables in the some_code_A region are made available to the lambda by value. This works for some types (e.g. an integer), but not for all, as per the restrictions for direct3d. In particular, no useful data classes work except for one new type we introduce with C++ AMP: objects of the new concurrency::array_view class, that I'll introduce in the post after next. Also note that if you capture some variable by value, you could use it as input to your algorithm, but you wouldn’t be able to observe changes to it after the parallel_for_each call (e.g. in some_code_B region since it was passed by value) – the exception to this rule is the array_view since (as we'll see in a future post) it is a wrapper for data, not a container. Finally, for completeness, you can write your lambda, e.g. like this [av, &ar](index<2> idx) restrict(direct3d) where av is a variable of type array_view and ar is a variable of type array - the point being you can be very specific about what variables you capture and how. So it looks like from a large data perspective you can only capture array and array_view objects in the lambda (that is how you pass data to your kernel) and then use the many threads that call your code (each with a unique index) to perform some operation. You can also capture some limited types by value, as input only. When the last thread completes execution of your lambda, the data in the array_view or array are ready to be used in the some_code_B region. We'll talk more about all this in future posts… (a)synchronous Please note that the parallel_for_each executes as if synchronous to the calling code, but in reality, it is asynchronous. I.e. once the parallel_for_each call is made and the kernel has been passed to the runtime, the some_code_B region continues to execute immediately by the CPU thread, while in parallel the kernel is executed by the GPU threads. However, if you try to access the (array or array_view) data that you captured in the lambda in the some_code_B region, your code will block until the results become available. Hence the correct statement: the parallel_for_each is as-if synchronous in terms of visible side-effects, but asynchronous in reality.   That's all for now, we'll revisit the parallel_for_each description, once we introduce properly array and array_view – coming next. Comments about this post by Daniel Moth welcome at the original blog.

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  • Is there a canonical resource on multi-tenancy web applications using ruby + rails

    - by AlexC
    Is there a canonical resource on multi-tenancy web applications using ruby + rails. There are a number of ways to develop rails apps using cloud capabilities with real elastic properties but there seems to be a lack of clarity with how to achieve multitenancy, specifically at the model / data level. Is there a canonical resource on options to developing multitenancy rails applications with the required characteristics of data seperation, security, concurrency and contention required by an enterprise level cloud application.

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  • Parallel Computing Features Tour in VS2010

    Just realized that I have not linked from here to a screencast I recorded a couple weeks ago that shows the API, parallel debugger and concurrency visualizer in VS2010. Take a few minutes to watch the VS2010 Parallel Computing Features Tour. Comments about this post welcome at the original blog.

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  • Delight and Excite

    - by Applications User Experience
    Mick McGee, CEO & President, EchoUser Editor’s Note: EchoUser is a User Experience design firm in San Francisco and a member of the Oracle Usability Advisory Board. Mick and his staff regularly consult on Oracle Applications UX projects. Being part of a user experience design firm, we have the luxury of working with a lot of great people across many great companies. We get to help people solve their problems.  At least we used to. The basic design challenge is still the same; however, the goal is not necessarily to solve “problems” anymore; it is, “I want our products to delight and excite!” The question for us as UX professionals is how to design to those goals, and then how to assess them from a usability perspective. I’m not sure where I first heard “delight and excite” (A book? blog post? Facebook  status? Steve Jobs quote?), but now I hear these listed as user experience goals all the time. In particular, somewhat paradoxically, I routinely hear them in enterprise software conversations. And when asking these same enterprise companies what will make the project successful, we very often hear, “Make it like Apple.” In past days, it was “make it like Yahoo (or Amazon or Google“) but now Apple is the common benchmark. Steve Jobs and Apple were not secrets, but with Jobs’ passing and Apple becoming the world’s most valuable company in the last year, the impact of great design and experience is suddenly very widespread. In particular, users’ expectations have gone way up. Being an enterprise company is no shield to the general expectations that users now have, for all products. Designing a “Minimum Viable Product” The user experience challenge has historically been, to echo the words of Eric Ries (author of Lean Startup) , to create a “minimum viable product”: the proverbial, “make it good enough”. But, in our profession, the “minimum viable” part of that phrase has oftentimes, unfortunately, referred to the design and user experience. Technology typically dominated the focus of the biggest, most successful companies. Few have had the laser focus of Apple to also create and sell design and user experience alongside great technology. But now that Apple is the most valuable company in the world, copying their success is a common undertaking. Great design is now a premium offering that everyone wants, from the one-person startup to the largest companies, consumer and enterprise. This emerging business paradigm will have significant impact across the user experience design process and profession. One area that particularly interests me is, how are we going to evaluate these new emerging “delight and excite” experiences, which are further customized to each particular domain? How to Measure “Delight and Excite” Traditional usability measures of task completion rate, assists, time, and errors are still extremely useful in many situations; however, they are too blunt to offer much insight into emerging experiences “Satisfaction” is usually assessed in user testing, in roughly equivalent importance to the above objective metrics. Various surveys and scales have provided ways to measure satisfying UX, with whatever questions they include. However, to meet the demands of new business goals and keep users at the center of design and development processes, we have to explore new methods to better capture custom-experience goals and emotion-driven user responses. We have had success assessing custom experiences, including “delight and excite”, by employing a variety of user testing methods that tend to combine formative and summative techniques (formative being focused more on identifying usability issues and ways to improve design, and summative focused more on metrics). Our most successful tool has been one we’ve been using for a long time, Magnitude Estimation Technique (MET). But it’s not necessarily about MET as a measure, rather how it is created. Caption: For one client, EchoUser did two rounds of testing.  Each test was a mix of performing representative tasks and gathering qualitative impressions. Each user participated in an in-person moderated 1-on-1 session for 1 hour, using a testing set-up where they held the phone. The primary goal was to identify usability issues and recommend design improvements. MET is based on a definition of the desired experience, which users will then use to rate items of interest (usually tasks in a usability test). In other words, a custom experience definition needs to be created. This can then be used to measure satisfaction in accomplishing tasks; “delight and excite”; or anything else from strategic goals, user demands, or elsewhere. For reference, our standard MET definition in usability testing is: “User experience is your perception of how easy to use, well designed and productive an interface is to complete tasks.” Articulating the User Experience We’ve helped construct experience definitions for several clients to better match their business goals. One example is a modification of the above that was needed for a company that makes medical-related products: “User experience is your perception of how easy to use, well-designed, productive and safe an interface is for conducting tasks. ‘Safe’ is how free an environment (including devices, software, facilities, people, etc.) is from danger, risk, and injury.” Another example is from a company that is pushing hard to incorporate “delight” into their enterprise business line: “User experience is your perception of a product’s ease of use and learning, satisfaction and delight in design, and ability to accomplish objectives.” I find the last one particularly compelling in that there is little that identifies the experience as being for a highly technical enterprise application. That definition could easily be applied to any number of consumer products. We have gone further than the above, including “sexy” and “cool” where decision-makers insisted they were part of the desired experience. We also applied it to completely different experiences where the “interface” was, for example, riding public transit, the “tasks” were train rides, and we followed the participants through the train-riding journey and rated various aspects accordingly: “A good public transportation experience is a cost-effective way of reliably, conveniently, and safely getting me to my intended destination on time.” To construct these definitions, we’ve employed both bottom-up and top-down approaches, depending on circumstances. For bottom-up, user inputs help dictate the terms that best fit the desired experience (usually by way of cluster and factor analysis). Top-down depends on strategic, visionary goals expressed by upper management that we then attempt to integrate into product development (e.g., “delight and excite”). We like a combination of both approaches to push the innovation envelope, but still be mindful of current user concerns. Hopefully the idea of crafting your own custom experience, and a way to measure it, can provide you with some ideas how you can adapt your user experience needs to whatever company you are in. Whether product-development or service-oriented, nearly every company is ultimately providing a user experience. The Bottom Line Creating great experiences may have been popularized by Steve Jobs and Apple, but I’ll be honest, it’s a good feeling to be moving from “good enough” to “delight and excite,” despite the challenge that entails. In fact, it’s because of that challenge that we will expand what we do as UX professionals to help deliver and assess those experiences. I’m excited to see how we, Oracle, and the rest of the industry will live up to that challenge.

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  • What is the canonical resource on multi-tenancy web applications using ruby + rails

    - by AlexC
    What is the canonical resource on multi-tenancy web applications using ruby + rails. There are a number of ways to develop rails apps using cloud capabilities with real elastic properties but there seems to be a lack of clarity with how to achieve multitenancy, specifically at the model / data level. Is there a canonical resource on options to developing multitenancy rails applications with the required characteristics of data seperation, security, concurrency and contention required by an enterprise level cloud application.

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  • Are my actual worker threads exceeding the sp_configure 'max worker threads' value?

    Tom Stringer (@SQLife) was working on some HADR testing for a customer to simulate many availability groups and introduce significant load into the system to measure overhead and such. In his quest to do that he was seeing behavior that he couldn’t really explain and so worked with him to uncover what was happening under the covers. Understand Locking, Blocking & Row VersioningRead Kalen Delaney's eBook to understand SQL Server concurrency, and use SQL Monitor to pinpoint excessive blocking and deadlocking. Download free resources.

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  • NUMA-aware constructs for java.util.concurrent

    - by Dave
    The constructs in the java.util.concurrent JSR-166 "JUC" concurrency library are currently NUMA-oblivious. That's because we currently don't have the topology discovery infrastructure and underpinnings in place that would allow and enable NUMA-awareness. But some quick throw-away prototypes show that it's possible to write NUMA-aware library code. I happened to use JUC Exchanger as a research vehicle. Another interesting idea is to adapt fork-join work-stealing to favor stealing from queues associated with 'nearby' threads.

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  • What is the difference between "data hiding" and "encapsulation"?

    - by Software Engeneering Learner
    I'm reading "Java concurrency in practice" and there is said: "Fortunately, the same object-oriented techniques that help you write well-organized, maintainable classes - such as encapsulation and data hiding -can also help you create thread-safe classes." The problem #1 - I never heard about data hiding and don't know what it is. The problem #2 - I always thought that encapsulation is using private vs public, and is actually the data hiding. Can you please explain what data hiding is and how it differs from encapsulation?

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  • What is the actual problem with a prototype based design?

    - by WindScar
    I feel like anything that can be developed using OO/functional languages can be generally made 'better' using a prototype based language, because they appaer to have the best of them all: high order functions, flexibility to simulate any OO structure, productivity (low verbosity) and scalability because of concurrency. But it seems like they are avoided for the creation of executable applications and of bigger projects in general. Why that?

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  • Game engine development in C++ [closed]

    - by Chris Cochran
    I am arriving at completion on a multithreaded concurrency framework designed for high-performance computing. Though I am not a gamer, it has occurred to me that this stand-alone software core could be an ideal basis for a multiprocessor game engine (64-bit native C++, 5000+ entry points). Are there any websites I could visit to discuss this technology with programmers and developers who could really benefit from it?

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  • Parallel Computing Features Tour in VS2010

    Just realized that I have not linked from here to a screencast I recorded a couple weeks ago that shows the API, parallel debugger and concurrency visualizer in VS2010. Take a few minutes to watch the VS2010 Parallel Computing Features Tour. Comments about this post welcome at the original blog.

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  • Basics of SQL Server 2008 Locking

    Relational databases are designed for multiple simultaneous users, and Microsoft SQL Server is no different. However, supporting multiple users requires some form of concurrency control, which in SQL Server's case means transaction isolation and locking. Read on to learn how SQL Server 2008 implements locking.

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