Search Results

Search found 7967 results on 319 pages for 'dizzy bryan high'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 67/319 | < Previous Page | 63 64 65 66 67 68 69 70 71 72 73 74  | Next Page >

  • VB6 Serial port programming

    - by commodus86
    I'm not much experienced in VB6 serial port programming. I need to control another circuit through serial port. (I have heard that pin 4 and pin 7 are used for that purpose. If these pins are incorrect please tell me what pins are used for such purposes) My requirement is to set those pins to high or low and read their levels(high or low) through VB6 program. How to do the task?? Thanx,

    Read the article

  • mono in production websites?

    - by sirmak
    Hi, I'm investigating the use of mono in real world high traffic web applications. There are some references on the mono site (companies using mono), but I couldn't find a high traffic website sample other than Deki powered ones. And I've read some mailings about mod_mono stability problems because of inexistence of compacting GC. Please reference your app and give some info, if is there anyone using mono in production. ...or do I have to look at Java ? Regards, sirmak

    Read the article

  • how to display fixed dropdown in django admin?

    - by FurtiveFelon
    Hi all, I would like to display priority information in a drop down. Currently i am using a integer field to store the priority, but i would like to display high/medium/low instead of letting user type in a priority. A way to approximate this is to use a Priority database which stores 3 elements, 1:high, 2:medium, 3:low, but it seems like an overkill. Any easier way would be much appreciated! Jason

    Read the article

  • Keeping DB Table sorted using multi-field formula (Microsoft SQL Server)

    - by user298167
    I have a JOB table, with two interesting columns: Creation Date Importance (high - 3, medium 2, low - 1). A JOB record's priority calculated like this: Priority = Importance * (time passed since creation) The problem is, every time I would like to pick 200 jobs with highest priority, and I don't want to resort the table. Is there a way to keep rows sorted? I was also thinking about having three tables one for High, Medium and Low and then sort those by Creation Date.

    Read the article

  • How can i implement the NULL Object Design Pattern in a generic form?

    - by Colour Blend
    Is there a way to implement the null object design pattern in a generic form so that i don't need to implement it for every buisness object. For me, there are two high level classes you'll need for every business class. One for a single record and another for a list. So i think there should be a way to implement the NULL Object design pattern at a high level and not have to implement it for every class. Is there a way and how please?

    Read the article

  • Algorithm to suggest a list of tags to users

    - by Itay Moav
    Given a free text, I need to analyse this this text and suggest a list of tags from a pre existing list. What algorithms are out there in the market? Can they handle a case where, for example, the text have a word like high cholesterol and I would like it so suggest heart disease although "high cholesterol" might not exists (initially) in the pre defined list.

    Read the article

  • How to Embed thumbnails in images

    - by user198222
    I have a bunch of 200X120 thumbnails and a bunch of high quality images, Apparently when I just resize the thumbnails with img size the quality suffers, so we are loading two images from the server the image thumbnail and the actual, I am just trying to optimize my site for speed a little saw someone mention embedding thumbs in the image, does anyone know a program that does that? Is this the best way to achieve my goal of high quality thumbnails?

    Read the article

  • A way of doing real-world test-driven development (and some thoughts about it)

    - by Thomas Weller
    Lately, I exchanged some arguments with Derick Bailey about some details of the red-green-refactor cycle of the Test-driven development process. In short, the issue revolved around the fact that it’s not enough to have a test red or green, but it’s also important to have it red or green for the right reasons. While for me, it’s sufficient to initially have a NotImplementedException in place, Derick argues that this is not totally correct (see these two posts: Red/Green/Refactor, For The Right Reasons and Red For The Right Reason: Fail By Assertion, Not By Anything Else). And he’s right. But on the other hand, I had no idea how his insights could have any practical consequence for my own individual interpretation of the red-green-refactor cycle (which is not really red-green-refactor, at least not in its pure sense, see the rest of this article). This made me think deeply for some days now. In the end I found out that the ‘right reason’ changes in my understanding depending on what development phase I’m in. To make this clear (at least I hope it becomes clear…) I started to describe my way of working in some detail, and then something strange happened: The scope of the article slightly shifted from focusing ‘only’ on the ‘right reason’ issue to something more general, which you might describe as something like  'Doing real-world TDD in .NET , with massive use of third-party add-ins’. This is because I feel that there is a more general statement about Test-driven development to make:  It’s high time to speak about the ‘How’ of TDD, not always only the ‘Why’. Much has been said about this, and me myself also contributed to that (see here: TDD is not about testing, it's about how we develop software). But always justifying what you do is very unsatisfying in the long run, it is inherently defensive, and it costs time and effort that could be used for better and more important things. And frankly: I’m somewhat sick and tired of repeating time and again that the test-driven way of software development is highly preferable for many reasons - I don’t want to spent my time exclusively on stating the obvious… So, again, let’s say it clearly: TDD is programming, and programming is TDD. Other ways of programming (code-first, sometimes called cowboy-coding) are exceptional and need justification. – I know that there are many people out there who will disagree with this radical statement, and I also know that it’s not a description of the real world but more of a mission statement or something. But nevertheless I’m absolutely sure that in some years this statement will be nothing but a platitude. Side note: Some parts of this post read as if I were paid by Jetbrains (the manufacturer of the ReSharper add-in – R#), but I swear I’m not. Rather I think that Visual Studio is just not production-complete without it, and I wouldn’t even consider to do professional work without having this add-in installed... The three parts of a software component Before I go into some details, I first should describe my understanding of what belongs to a software component (assembly, type, or method) during the production process (i.e. the coding phase). Roughly, I come up with the three parts shown below:   First, we need to have some initial sort of requirement. This can be a multi-page formal document, a vague idea in some programmer’s brain of what might be needed, or anything in between. In either way, there has to be some sort of requirement, be it explicit or not. – At the C# micro-level, the best way that I found to formulate that is to define interfaces for just about everything, even for internal classes, and to provide them with exhaustive xml comments. The next step then is to re-formulate these requirements in an executable form. This is specific to the respective programming language. - For C#/.NET, the Gallio framework (which includes MbUnit) in conjunction with the ReSharper add-in for Visual Studio is my toolset of choice. The third part then finally is the production code itself. It’s development is entirely driven by the requirements and their executable formulation. This is the delivery, the two other parts are ‘only’ there to make its production possible, to give it a decent quality and reliability, and to significantly reduce related costs down the maintenance timeline. So while the first two parts are not really relevant for the customer, they are very important for the developer. The customer (or in Scrum terms: the Product Owner) is not interested at all in how  the product is developed, he is only interested in the fact that it is developed as cost-effective as possible, and that it meets his functional and non-functional requirements. The rest is solely a matter of the developer’s craftsmanship, and this is what I want to talk about during the remainder of this article… An example To demonstrate my way of doing real-world TDD, I decided to show the development of a (very) simple Calculator component. The example is deliberately trivial and silly, as examples always are. I am totally aware of the fact that real life is never that simple, but I only want to show some development principles here… The requirement As already said above, I start with writing down some words on the initial requirement, and I normally use interfaces for that, even for internal classes - the typical question “intf or not” doesn’t even come to mind. I need them for my usual workflow and using them automatically produces high componentized and testable code anyway. To think about their usage in every single situation would slow down the production process unnecessarily. So this is what I begin with: namespace Calculator {     /// <summary>     /// Defines a very simple calculator component for demo purposes.     /// </summary>     public interface ICalculator     {         /// <summary>         /// Gets the result of the last successful operation.         /// </summary>         /// <value>The last result.</value>         /// <remarks>         /// Will be <see langword="null" /> before the first successful operation.         /// </remarks>         double? LastResult { get; }       } // interface ICalculator   } // namespace Calculator So, I’m not beginning with a test, but with a sort of code declaration - and still I insist on being 100% test-driven. There are three important things here: Starting this way gives me a method signature, which allows to use IntelliSense and AutoCompletion and thus eliminates the danger of typos - one of the most regular, annoying, time-consuming, and therefore expensive sources of error in the development process. In my understanding, the interface definition as a whole is more of a readable requirement document and technical documentation than anything else. So this is at least as much about documentation than about coding. The documentation must completely describe the behavior of the documented element. I normally use an IoC container or some sort of self-written provider-like model in my architecture. In either case, I need my components defined via service interfaces anyway. - I will use the LinFu IoC framework here, for no other reason as that is is very simple to use. The ‘Red’ (pt. 1)   First I create a folder for the project’s third-party libraries and put the LinFu.Core dll there. Then I set up a test project (via a Gallio project template), and add references to the Calculator project and the LinFu dll. Finally I’m ready to write the first test, which will look like the following: namespace Calculator.Test {     [TestFixture]     public class CalculatorTest     {         private readonly ServiceContainer container = new ServiceContainer();           [Test]         public void CalculatorLastResultIsInitiallyNull()         {             ICalculator calculator = container.GetService<ICalculator>();               Assert.IsNull(calculator.LastResult);         }       } // class CalculatorTest   } // namespace Calculator.Test       This is basically the executable formulation of what the interface definition states (part of). Side note: There’s one principle of TDD that is just plain wrong in my eyes: I’m talking about the Red is 'does not compile' thing. How could a compiler error ever be interpreted as a valid test outcome? I never understood that, it just makes no sense to me. (Or, in Derick’s terms: this reason is as wrong as a reason ever could be…) A compiler error tells me: Your code is incorrect, but nothing more.  Instead, the ‘Red’ part of the red-green-refactor cycle has a clearly defined meaning to me: It means that the test works as intended and fails only if its assumptions are not met for some reason. Back to our Calculator. When I execute the above test with R#, the Gallio plugin will give me this output: So this tells me that the test is red for the wrong reason: There’s no implementation that the IoC-container could load, of course. So let’s fix that. With R#, this is very easy: First, create an ICalculator - derived type:        Next, implement the interface members: And finally, move the new class to its own file: So far my ‘work’ was six mouse clicks long, the only thing that’s left to do manually here, is to add the Ioc-specific wiring-declaration and also to make the respective class non-public, which I regularly do to force my components to communicate exclusively via interfaces: This is what my Calculator class looks like as of now: using System; using LinFu.IoC.Configuration;   namespace Calculator {     [Implements(typeof(ICalculator))]     internal class Calculator : ICalculator     {         public double? LastResult         {             get             {                 throw new NotImplementedException();             }         }     } } Back to the test fixture, we have to put our IoC container to work: [TestFixture] public class CalculatorTest {     #region Fields       private readonly ServiceContainer container = new ServiceContainer();       #endregion // Fields       #region Setup/TearDown       [FixtureSetUp]     public void FixtureSetUp()     {        container.LoadFrom(AppDomain.CurrentDomain.BaseDirectory, "Calculator.dll");     }       ... Because I have a R# live template defined for the setup/teardown method skeleton as well, the only manual coding here again is the IoC-specific stuff: two lines, not more… The ‘Red’ (pt. 2) Now, the execution of the above test gives the following result: This time, the test outcome tells me that the method under test is called. And this is the point, where Derick and I seem to have somewhat different views on the subject: Of course, the test still is worthless regarding the red/green outcome (or: it’s still red for the wrong reasons, in that it gives a false negative). But as far as I am concerned, I’m not really interested in the test outcome at this point of the red-green-refactor cycle. Rather, I only want to assert that my test actually calls the right method. If that’s the case, I will happily go on to the ‘Green’ part… The ‘Green’ Making the test green is quite trivial. Just make LastResult an automatic property:     [Implements(typeof(ICalculator))]     internal class Calculator : ICalculator     {         public double? LastResult { get; private set; }     }         One more round… Now on to something slightly more demanding (cough…). Let’s state that our Calculator exposes an Add() method:         ...   /// <summary>         /// Adds the specified operands.         /// </summary>         /// <param name="operand1">The operand1.</param>         /// <param name="operand2">The operand2.</param>         /// <returns>The result of the additon.</returns>         /// <exception cref="ArgumentException">         /// Argument <paramref name="operand1"/> is &lt; 0.<br/>         /// -- or --<br/>         /// Argument <paramref name="operand2"/> is &lt; 0.         /// </exception>         double Add(double operand1, double operand2);       } // interface ICalculator A remark: I sometimes hear the complaint that xml comment stuff like the above is hard to read. That’s certainly true, but irrelevant to me, because I read xml code comments with the CR_Documentor tool window. And using that, it looks like this:   Apart from that, I’m heavily using xml code comments (see e.g. here for a detailed guide) because there is the possibility of automating help generation with nightly CI builds (using MS Sandcastle and the Sandcastle Help File Builder), and then publishing the results to some intranet location.  This way, a team always has first class, up-to-date technical documentation at hand about the current codebase. (And, also very important for speeding up things and avoiding typos: You have IntelliSense/AutoCompletion and R# support, and the comments are subject to compiler checking…).     Back to our Calculator again: Two more R# – clicks implement the Add() skeleton:         ...           public double Add(double operand1, double operand2)         {             throw new NotImplementedException();         }       } // class Calculator As we have stated in the interface definition (which actually serves as our requirement document!), the operands are not allowed to be negative. So let’s start implementing that. Here’s the test: [Test] [Row(-0.5, 2)] public void AddThrowsOnNegativeOperands(double operand1, double operand2) {     ICalculator calculator = container.GetService<ICalculator>();       Assert.Throws<ArgumentException>(() => calculator.Add(operand1, operand2)); } As you can see, I’m using a data-driven unit test method here, mainly for these two reasons: Because I know that I will have to do the same test for the second operand in a few seconds, I save myself from implementing another test method for this purpose. Rather, I only will have to add another Row attribute to the existing one. From the test report below, you can see that the argument values are explicitly printed out. This can be a valuable documentation feature even when everything is green: One can quickly review what values were tested exactly - the complete Gallio HTML-report (as it will be produced by the Continuous Integration runs) shows these values in a quite clear format (see below for an example). Back to our Calculator development again, this is what the test result tells us at the moment: So we’re red again, because there is not yet an implementation… Next we go on and implement the necessary parameter verification to become green again, and then we do the same thing for the second operand. To make a long story short, here’s the test and the method implementation at the end of the second cycle: // in CalculatorTest:   [Test] [Row(-0.5, 2)] [Row(295, -123)] public void AddThrowsOnNegativeOperands(double operand1, double operand2) {     ICalculator calculator = container.GetService<ICalculator>();       Assert.Throws<ArgumentException>(() => calculator.Add(operand1, operand2)); }   // in Calculator: public double Add(double operand1, double operand2) {     if (operand1 < 0.0)     {         throw new ArgumentException("Value must not be negative.", "operand1");     }     if (operand2 < 0.0)     {         throw new ArgumentException("Value must not be negative.", "operand2");     }     throw new NotImplementedException(); } So far, we have sheltered our method from unwanted input, and now we can safely operate on the parameters without further caring about their validity (this is my interpretation of the Fail Fast principle, which is regarded here in more detail). Now we can think about the method’s successful outcomes. First let’s write another test for that: [Test] [Row(1, 1, 2)] public void TestAdd(double operand1, double operand2, double expectedResult) {     ICalculator calculator = container.GetService<ICalculator>();       double result = calculator.Add(operand1, operand2);       Assert.AreEqual(expectedResult, result); } Again, I’m regularly using row based test methods for these kinds of unit tests. The above shown pattern proved to be extremely helpful for my development work, I call it the Defined-Input/Expected-Output test idiom: You define your input arguments together with the expected method result. There are two major benefits from that way of testing: In the course of refining a method, it’s very likely to come up with additional test cases. In our case, we might add tests for some edge cases like ‘one of the operands is zero’ or ‘the sum of the two operands causes an overflow’, or maybe there’s an external test protocol that has to be fulfilled (e.g. an ISO norm for medical software), and this results in the need of testing against additional values. In all these scenarios we only have to add another Row attribute to the test. Remember that the argument values are written to the test report, so as a side-effect this produces valuable documentation. (This can become especially important if the fulfillment of some sort of external requirements has to be proven). So your test method might look something like that in the end: [Test, Description("Arguments: operand1, operand2, expectedResult")] [Row(1, 1, 2)] [Row(0, 999999999, 999999999)] [Row(0, 0, 0)] [Row(0, double.MaxValue, double.MaxValue)] [Row(4, double.MaxValue - 2.5, double.MaxValue)] public void TestAdd(double operand1, double operand2, double expectedResult) {     ICalculator calculator = container.GetService<ICalculator>();       double result = calculator.Add(operand1, operand2);       Assert.AreEqual(expectedResult, result); } And this will produce the following HTML report (with Gallio):   Not bad for the amount of work we invested in it, huh? - There might be scenarios where reports like that can be useful for demonstration purposes during a Scrum sprint review… The last requirement to fulfill is that the LastResult property is expected to store the result of the last operation. I don’t show this here, it’s trivial enough and brings nothing new… And finally: Refactor (for the right reasons) To demonstrate my way of going through the refactoring portion of the red-green-refactor cycle, I added another method to our Calculator component, namely Subtract(). Here’s the code (tests and production): // CalculatorTest.cs:   [Test, Description("Arguments: operand1, operand2, expectedResult")] [Row(1, 1, 0)] [Row(0, 999999999, -999999999)] [Row(0, 0, 0)] [Row(0, double.MaxValue, -double.MaxValue)] [Row(4, double.MaxValue - 2.5, -double.MaxValue)] public void TestSubtract(double operand1, double operand2, double expectedResult) {     ICalculator calculator = container.GetService<ICalculator>();       double result = calculator.Subtract(operand1, operand2);       Assert.AreEqual(expectedResult, result); }   [Test, Description("Arguments: operand1, operand2, expectedResult")] [Row(1, 1, 0)] [Row(0, 999999999, -999999999)] [Row(0, 0, 0)] [Row(0, double.MaxValue, -double.MaxValue)] [Row(4, double.MaxValue - 2.5, -double.MaxValue)] public void TestSubtractGivesExpectedLastResult(double operand1, double operand2, double expectedResult) {     ICalculator calculator = container.GetService<ICalculator>();       calculator.Subtract(operand1, operand2);       Assert.AreEqual(expectedResult, calculator.LastResult); }   ...   // ICalculator.cs: /// <summary> /// Subtracts the specified operands. /// </summary> /// <param name="operand1">The operand1.</param> /// <param name="operand2">The operand2.</param> /// <returns>The result of the subtraction.</returns> /// <exception cref="ArgumentException"> /// Argument <paramref name="operand1"/> is &lt; 0.<br/> /// -- or --<br/> /// Argument <paramref name="operand2"/> is &lt; 0. /// </exception> double Subtract(double operand1, double operand2);   ...   // Calculator.cs:   public double Subtract(double operand1, double operand2) {     if (operand1 < 0.0)     {         throw new ArgumentException("Value must not be negative.", "operand1");     }       if (operand2 < 0.0)     {         throw new ArgumentException("Value must not be negative.", "operand2");     }       return (this.LastResult = operand1 - operand2).Value; }   Obviously, the argument validation stuff that was produced during the red-green part of our cycle duplicates the code from the previous Add() method. So, to avoid code duplication and minimize the number of code lines of the production code, we do an Extract Method refactoring. One more time, this is only a matter of a few mouse clicks (and giving the new method a name) with R#: Having done that, our production code finally looks like that: using System; using LinFu.IoC.Configuration;   namespace Calculator {     [Implements(typeof(ICalculator))]     internal class Calculator : ICalculator     {         #region ICalculator           public double? LastResult { get; private set; }           public double Add(double operand1, double operand2)         {             ThrowIfOneOperandIsInvalid(operand1, operand2);               return (this.LastResult = operand1 + operand2).Value;         }           public double Subtract(double operand1, double operand2)         {             ThrowIfOneOperandIsInvalid(operand1, operand2);               return (this.LastResult = operand1 - operand2).Value;         }           #endregion // ICalculator           #region Implementation (Helper)           private static void ThrowIfOneOperandIsInvalid(double operand1, double operand2)         {             if (operand1 < 0.0)             {                 throw new ArgumentException("Value must not be negative.", "operand1");             }               if (operand2 < 0.0)             {                 throw new ArgumentException("Value must not be negative.", "operand2");             }         }           #endregion // Implementation (Helper)       } // class Calculator   } // namespace Calculator But is the above worth the effort at all? It’s obviously trivial and not very impressive. All our tests were green (for the right reasons), and refactoring the code did not change anything. It’s not immediately clear how this refactoring work adds value to the project. Derick puts it like this: STOP! Hold on a second… before you go any further and before you even think about refactoring what you just wrote to make your test pass, you need to understand something: if your done with your requirements after making the test green, you are not required to refactor the code. I know… I’m speaking heresy, here. Toss me to the wolves, I’ve gone over to the dark side! Seriously, though… if your test is passing for the right reasons, and you do not need to write any test or any more code for you class at this point, what value does refactoring add? Derick immediately answers his own question: So why should you follow the refactor portion of red/green/refactor? When you have added code that makes the system less readable, less understandable, less expressive of the domain or concern’s intentions, less architecturally sound, less DRY, etc, then you should refactor it. I couldn’t state it more precise. From my personal perspective, I’d add the following: You have to keep in mind that real-world software systems are usually quite large and there are dozens or even hundreds of occasions where micro-refactorings like the above can be applied. It’s the sum of them all that counts. And to have a good overall quality of the system (e.g. in terms of the Code Duplication Percentage metric) you have to be pedantic on the individual, seemingly trivial cases. My job regularly requires the reading and understanding of ‘foreign’ code. So code quality/readability really makes a HUGE difference for me – sometimes it can be even the difference between project success and failure… Conclusions The above described development process emerged over the years, and there were mainly two things that guided its evolution (you might call it eternal principles, personal beliefs, or anything in between): Test-driven development is the normal, natural way of writing software, code-first is exceptional. So ‘doing TDD or not’ is not a question. And good, stable code can only reliably be produced by doing TDD (yes, I know: many will strongly disagree here again, but I’ve never seen high-quality code – and high-quality code is code that stood the test of time and causes low maintenance costs – that was produced code-first…) It’s the production code that pays our bills in the end. (Though I have seen customers these days who demand an acceptance test battery as part of the final delivery. Things seem to go into the right direction…). The test code serves ‘only’ to make the production code work. But it’s the number of delivered features which solely counts at the end of the day - no matter how much test code you wrote or how good it is. With these two things in mind, I tried to optimize my coding process for coding speed – or, in business terms: productivity - without sacrificing the principles of TDD (more than I’d do either way…).  As a result, I consider a ratio of about 3-5/1 for test code vs. production code as normal and desirable. In other words: roughly 60-80% of my code is test code (This might sound heavy, but that is mainly due to the fact that software development standards only begin to evolve. The entire software development profession is very young, historically seen; only at the very beginning, and there are no viable standards yet. If you think about software development as a kind of casting process, where the test code is the mold and the resulting production code is the final product, then the above ratio sounds no longer extraordinary…) Although the above might look like very much unnecessary work at first sight, it’s not. With the aid of the mentioned add-ins, doing all the above is a matter of minutes, sometimes seconds (while writing this post took hours and days…). The most important thing is to have the right tools at hand. Slow developer machines or the lack of a tool or something like that - for ‘saving’ a few 100 bucks -  is just not acceptable and a very bad decision in business terms (though I quite some times have seen and heard that…). Production of high-quality products needs the usage of high-quality tools. This is a platitude that every craftsman knows… The here described round-trip will take me about five to ten minutes in my real-world development practice. I guess it’s about 30% more time compared to developing the ‘traditional’ (code-first) way. But the so manufactured ‘product’ is of much higher quality and massively reduces maintenance costs, which is by far the single biggest cost factor, as I showed in this previous post: It's the maintenance, stupid! (or: Something is rotten in developerland.). In the end, this is a highly cost-effective way of software development… But on the other hand, there clearly is a trade-off here: coding speed vs. code quality/later maintenance costs. The here described development method might be a perfect fit for the overwhelming majority of software projects, but there certainly are some scenarios where it’s not - e.g. if time-to-market is crucial for a software project. So this is a business decision in the end. It’s just that you have to know what you’re doing and what consequences this might have… Some last words First, I’d like to thank Derick Bailey again. His two aforementioned posts (which I strongly recommend for reading) inspired me to think deeply about my own personal way of doing TDD and to clarify my thoughts about it. I wouldn’t have done that without this inspiration. I really enjoy that kind of discussions… I agree with him in all respects. But I don’t know (yet?) how to bring his insights into the described production process without slowing things down. The above described method proved to be very “good enough” in my practical experience. But of course, I’m open to suggestions here… My rationale for now is: If the test is initially red during the red-green-refactor cycle, the ‘right reason’ is: it actually calls the right method, but this method is not yet operational. Later on, when the cycle is finished and the tests become part of the regular, automated Continuous Integration process, ‘red’ certainly must occur for the ‘right reason’: in this phase, ‘red’ MUST mean nothing but an unfulfilled assertion - Fail By Assertion, Not By Anything Else!

    Read the article

  • SQL Server 2012 - AlwaysOn

    - by Claus Jandausch
    Ich war nicht nur irritiert, ich war sogar regelrecht schockiert - und für einen kurzen Moment sprachlos (was nur selten der Fall ist). Gerade eben hatte mich jemand gefragt "Wann Oracle denn etwas Vergleichbares wie AlwaysOn bieten würde - und ob überhaupt?" War ich hier im falschen Film gelandet? Ich konnte nicht anders, als meinen Unmut kundzutun und zu erklären, dass die Fragestellung normalerweise anders herum läuft. Zugegeben - es mag vielleicht strittige Punkte geben im Vergleich zwischen Oracle und SQL Server - bei denen nicht unbedingt immer Oracle die Nase vorn haben muss - aber das Thema Clustering für Hochverfügbarkeit (HA), Disaster Recovery (DR) und Skalierbarkeit gehört mit Sicherheit nicht dazu. Dieses Erlebnis hakte ich am Nachgang als Einzelfall ab, der so nie wieder vorkommen würde. Bis ich kurz darauf eines Besseren belehrt wurde und genau die selbe Frage erneut zu hören bekam. Diesmal sogar im Exadata-Umfeld und einem Oracle Stretch Cluster. Einmal ist keinmal, doch zweimal ist einmal zu viel... Getreu diesem alten Motto war mir klar, dass man das so nicht länger stehen lassen konnte. Ich habe keine Ahnung, wie die Microsoft Marketing Abteilung es geschafft hat, unter dem AlwaysOn Brading eine innovative Technologie vermuten zu lassen - aber sie hat ihren Job scheinbar gut gemacht. Doch abgesehen von einem guten Marketing, stellt sich natürlich die Frage, was wirklich dahinter steckt und wie sich das Ganze mit Oracle vergleichen lässt - und ob überhaupt? Damit wären wir wieder bei der ursprünglichen Frage angelangt.  So viel zum Hintergrund dieses Blogbeitrags - von meiner Antwort handelt der restliche Blog. "Windows was the God ..." Um den wahren Unterschied zwischen Oracle und Microsoft verstehen zu können, muss man zunächst das bedeutendste Microsoft Dogma kennen. Es lässt sich schlicht und einfach auf den Punkt bringen: "Alles muss auf Windows basieren." Die Überschrift dieses Absatzes ist kein von mir erfundener Ausspruch, sondern ein Zitat. Konkret stammt es aus einem längeren Artikel von Kurt Eichenwald in der Vanity Fair aus dem August 2012. Er lautet Microsoft's Lost Decade und sei jedem ans Herz gelegt, der die "Microsoft-Maschinerie" unter Steve Ballmer und einige ihrer Kuriositäten besser verstehen möchte. "YOU TALKING TO ME?" Microsoft C.E.O. Steve Ballmer bei seiner Keynote auf der 2012 International Consumer Electronics Show in Las Vegas am 9. Januar   Manche Dinge in diesem Artikel mögen überspitzt dargestellt erscheinen - sind sie aber nicht. Vieles davon kannte ich bereits aus eigener Erfahrung und kann es nur bestätigen. Anderes hat sich mir erst so richtig erschlossen. Insbesondere die folgenden Passagen führten zum Aha-Erlebnis: “Windows was the god—everything had to work with Windows,” said Stone... “Every little thing you want to write has to build off of Windows (or other existing roducts),” one software engineer said. “It can be very confusing, …” Ich habe immer schon darauf hingewiesen, dass in einem SQL Server Failover Cluster die Microsoft Datenbank eigentlich nichts Nenneswertes zum Geschehen beiträgt, sondern sich voll und ganz auf das Windows Betriebssystem verlässt. Deshalb muss man auch die Windows Server Enterprise Edition installieren, soll ein Failover Cluster für den SQL Server eingerichtet werden. Denn hier werden die Cluster Services geliefert - nicht mit dem SQL Server. Er ist nur lediglich ein weiteres Server Produkt, für das Windows in Ausfallszenarien genutzt werden kann - so wie Microsoft Exchange beispielsweise, oder Microsoft SharePoint, oder irgendein anderes Server Produkt das auf Windows gehostet wird. Auch Oracle kann damit genutzt werden. Das Stichwort lautet hier: Oracle Failsafe. Nur - warum sollte man das tun, wenn gleichzeitig eine überlegene Technologie wie die Oracle Real Application Clusters (RAC) zur Verfügung steht, die dann auch keine Windows Enterprise Edition voraussetzen, da Oracle die eigene Clusterware liefert. Welche darüber hinaus für kürzere Failover-Zeiten sorgt, da diese Cluster-Technologie Datenbank-integriert ist und sich nicht auf "Dritte" verlässt. Wenn man sich also schon keine technischen Vorteile mit einem SQL Server Failover Cluster erkauft, sondern zusätzlich noch versteckte Lizenzkosten durch die Lizenzierung der Windows Server Enterprise Edition einhandelt, warum hat Microsoft dann in den vergangenen Jahren seit SQL Server 2000 nicht ebenfalls an einer neuen und innovativen Lösung gearbeitet, die mit Oracle RAC mithalten kann? Entwickler hat Microsoft genügend? Am Geld kann es auch nicht liegen? Lesen Sie einfach noch einmal die beiden obenstehenden Zitate und sie werden den Grund verstehen. Anders lässt es sich ja auch gar nicht mehr erklären, dass AlwaysOn aus zwei unterschiedlichen Technologien besteht, die beide jedoch wiederum auf dem Windows Server Failover Clustering (WSFC) basieren. Denn daraus ergeben sich klare Nachteile - aber dazu später mehr. Um AlwaysOn zu verstehen, sollte man sich zunächst kurz in Erinnerung rufen, was Microsoft bisher an HA/DR (High Availability/Desaster Recovery) Lösungen für SQL Server zur Verfügung gestellt hat. Replikation Basiert auf logischer Replikation und Pubisher/Subscriber Architektur Transactional Replication Merge Replication Snapshot Replication Microsoft's Replikation ist vergleichbar mit Oracle GoldenGate. Oracle GoldenGate stellt jedoch die umfassendere Technologie dar und bietet High Performance. Log Shipping Microsoft's Log Shipping stellt eine einfache Technologie dar, die vergleichbar ist mit Oracle Managed Recovery in Oracle Version 7. Das Log Shipping besitzt folgende Merkmale: Transaction Log Backups werden von Primary nach Secondary/ies geschickt Einarbeitung (z.B. Restore) auf jedem Secondary individuell Optionale dritte Server Instanz (Monitor Server) für Überwachung und Alarm Log Restore Unterbrechung möglich für Read-Only Modus (Secondary) Keine Unterstützung von Automatic Failover Database Mirroring Microsoft's Database Mirroring wurde verfügbar mit SQL Server 2005, sah aus wie Oracle Data Guard in Oracle 9i, war funktional jedoch nicht so umfassend. Für ein HA/DR Paar besteht eine 1:1 Beziehung, um die produktive Datenbank (Principle DB) abzusichern. Auf der Standby Datenbank (Mirrored DB) werden alle Insert-, Update- und Delete-Operationen nachgezogen. Modi Synchron (High-Safety Modus) Asynchron (High-Performance Modus) Automatic Failover Unterstützt im High-Safety Modus (synchron) Witness Server vorausgesetzt     Zur Frage der Kontinuität Es stellt sich die Frage, wie es um diesen Technologien nun im Zusammenhang mit SQL Server 2012 bestellt ist. Unter Fanfaren seinerzeit eingeführt, war Database Mirroring das erklärte Mittel der Wahl. Ich bin kein Produkt Manager bei Microsoft und kann hierzu nur meine Meinung äußern, aber zieht man den SQL AlwaysOn Team Blog heran, so sieht es nicht gut aus für das Database Mirroring - zumindest nicht langfristig. "Does AlwaysOn Availability Group replace Database Mirroring going forward?” “The short answer is we recommend that you migrate from the mirroring configuration or even mirroring and log shipping configuration to using Availability Group. Database Mirroring will still be available in the Denali release but will be phased out over subsequent releases. Log Shipping will continue to be available in future releases.” Damit wären wir endlich beim eigentlichen Thema angelangt. Was ist eine sogenannte Availability Group und was genau hat es mit der vielversprechend klingenden Bezeichnung AlwaysOn auf sich?   SQL Server 2012 - AlwaysOn Zwei HA-Features verstekcne sich hinter dem “AlwaysOn”-Branding. Einmal das AlwaysOn Failover Clustering aka SQL Server Failover Cluster Instances (FCI) - zum Anderen die AlwaysOn Availability Groups. Failover Cluster Instances (FCI) Entspricht ungefähr dem Stretch Cluster Konzept von Oracle Setzt auf Windows Server Failover Clustering (WSFC) auf Bietet HA auf Instanz-Ebene AlwaysOn Availability Groups (Verfügbarkeitsgruppen) Ähnlich der Idee von Consistency Groups, wie in Storage-Level Replikations-Software von z.B. EMC SRDF Abhängigkeiten zu Windows Server Failover Clustering (WSFC) Bietet HA auf Datenbank-Ebene   Hinweis: Verwechseln Sie nicht eine SQL Server Datenbank mit einer Oracle Datenbank. Und auch nicht eine Oracle Instanz mit einer SQL Server Instanz. Die gleichen Begriffe haben hier eine andere Bedeutung - nicht selten ein Grund, weshalb Oracle- und Microsoft DBAs schnell aneinander vorbei reden. Denken Sie bei einer SQL Server Datenbank eher an ein Oracle Schema, das kommt der Sache näher. So etwas wie die SQL Server Northwind Datenbank ist vergleichbar mit dem Oracle Scott Schema. Wenn Sie die genauen Unterschiede kennen möchten, finden Sie eine detaillierte Beschreibung in meinem Buch "Oracle10g Release 2 für Windows und .NET", erhältich bei Lehmanns, Amazon, etc.   Windows Server Failover Clustering (WSFC) Wie man sieht, basieren beide AlwaysOn Technologien wiederum auf dem Windows Server Failover Clustering (WSFC), um einerseits Hochverfügbarkeit auf Ebene der Instanz zu gewährleisten und andererseits auf der Datenbank-Ebene. Deshalb nun eine kurze Beschreibung der WSFC. Die WSFC sind ein mit dem Windows Betriebssystem geliefertes Infrastruktur-Feature, um HA für Server Anwendungen, wie Microsoft Exchange, SharePoint, SQL Server, etc. zu bieten. So wie jeder andere Cluster, besteht ein WSFC Cluster aus einer Gruppe unabhängiger Server, die zusammenarbeiten, um die Verfügbarkeit einer Applikation oder eines Service zu erhöhen. Falls ein Cluster-Knoten oder -Service ausfällt, kann der auf diesem Knoten bisher gehostete Service automatisch oder manuell auf einen anderen im Cluster verfügbaren Knoten transferriert werden - was allgemein als Failover bekannt ist. Unter SQL Server 2012 verwenden sowohl die AlwaysOn Avalability Groups, als auch die AlwaysOn Failover Cluster Instances die WSFC als Plattformtechnologie, um Komponenten als WSFC Cluster-Ressourcen zu registrieren. Verwandte Ressourcen werden in eine Ressource Group zusammengefasst, die in Abhängigkeit zu anderen WSFC Cluster-Ressourcen gebracht werden kann. Der WSFC Cluster Service kann jetzt die Notwendigkeit zum Neustart der SQL Server Instanz erfassen oder einen automatischen Failover zu einem anderen Server-Knoten im WSFC Cluster auslösen.   Failover Cluster Instances (FCI) Eine SQL Server Failover Cluster Instanz (FCI) ist eine einzelne SQL Server Instanz, die in einem Failover Cluster betrieben wird, der aus mehreren Windows Server Failover Clustering (WSFC) Knoten besteht und so HA (High Availability) auf Ebene der Instanz bietet. Unter Verwendung von Multi-Subnet FCI kann auch Remote DR (Disaster Recovery) unterstützt werden. Eine weitere Option für Remote DR besteht darin, eine unter FCI gehostete Datenbank in einer Availability Group zu betreiben. Hierzu später mehr. FCI und WSFC Basis FCI, das für lokale Hochverfügbarkeit der Instanzen genutzt wird, ähnelt der veralteten Architektur eines kalten Cluster (Aktiv-Passiv). Unter SQL Server 2008 wurde diese Technologie SQL Server 2008 Failover Clustering genannt. Sie nutzte den Windows Server Failover Cluster. In SQL Server 2012 hat Microsoft diese Basistechnologie unter der Bezeichnung AlwaysOn zusammengefasst. Es handelt sich aber nach wie vor um die klassische Aktiv-Passiv-Konfiguration. Der Ablauf im Failover-Fall ist wie folgt: Solange kein Hardware-oder System-Fehler auftritt, werden alle Dirty Pages im Buffer Cache auf Platte geschrieben Alle entsprechenden SQL Server Services (Dienste) in der Ressource Gruppe werden auf dem aktiven Knoten gestoppt Die Ownership der Ressource Gruppe wird auf einen anderen Knoten der FCI transferriert Der neue Owner (Besitzer) der Ressource Gruppe startet seine SQL Server Services (Dienste) Die Connection-Anforderungen einer Client-Applikation werden automatisch auf den neuen aktiven Knoten mit dem selben Virtuellen Network Namen (VNN) umgeleitet Abhängig vom Zeitpunkt des letzten Checkpoints, kann die Anzahl der Dirty Pages im Buffer Cache, die noch auf Platte geschrieben werden müssen, zu unvorhersehbar langen Failover-Zeiten führen. Um diese Anzahl zu drosseln, besitzt der SQL Server 2012 eine neue Fähigkeit, die Indirect Checkpoints genannt wird. Indirect Checkpoints ähnelt dem Fast-Start MTTR Target Feature der Oracle Datenbank, das bereits mit Oracle9i verfügbar war.   SQL Server Multi-Subnet Clustering Ein SQL Server Multi-Subnet Failover Cluster entspricht vom Konzept her einem Oracle RAC Stretch Cluster. Doch dies ist nur auf den ersten Blick der Fall. Im Gegensatz zu RAC ist in einem lokalen SQL Server Failover Cluster jeweils nur ein Knoten aktiv für eine Datenbank. Für die Datenreplikation zwischen geografisch entfernten Sites verlässt sich Microsoft auf 3rd Party Lösungen für das Storage Mirroring.     Die Verbesserung dieses Szenario mit einer SQL Server 2012 Implementierung besteht schlicht darin, dass eine VLAN-Konfiguration (Virtual Local Area Network) nun nicht mehr benötigt wird, so wie dies bisher der Fall war. Das folgende Diagramm stellt dar, wie der Ablauf mit SQL Server 2012 gehandhabt wird. In Site A und Site B wird HA jeweils durch einen lokalen Aktiv-Passiv-Cluster sichergestellt.     Besondere Aufmerksamkeit muss hier der Konfiguration und dem Tuning geschenkt werden, da ansonsten völlig inakzeptable Failover-Zeiten resultieren. Dies liegt darin begründet, weil die Downtime auf Client-Seite nun nicht mehr nur von der reinen Failover-Zeit abhängt, sondern zusätzlich von der Dauer der DNS Replikation zwischen den DNS Servern. (Rufen Sie sich in Erinnerung, dass wir gerade von Multi-Subnet Clustering sprechen). Außerdem ist zu berücksichtigen, wie schnell die Clients die aktualisierten DNS Informationen abfragen. Spezielle Konfigurationen für Node Heartbeat, HostRecordTTL (Host Record Time-to-Live) und Intersite Replication Frequeny für Active Directory Sites und Services werden notwendig. Default TTL für Windows Server 2008 R2: 20 Minuten Empfohlene Einstellung: 1 Minute DNS Update Replication Frequency in Windows Umgebung: 180 Minuten Empfohlene Einstellung: 15 Minuten (minimaler Wert)   Betrachtet man diese Werte, muss man feststellen, dass selbst eine optimale Konfiguration die rigiden SLAs (Service Level Agreements) heutiger geschäftskritischer Anwendungen für HA und DR nicht erfüllen kann. Denn dies impliziert eine auf der Client-Seite erlebte Failover-Zeit von insgesamt 16 Minuten. Hierzu ein Auszug aus der SQL Server 2012 Online Dokumentation: Cons: If a cross-subnet failover occurs, the client recovery time could be 15 minutes or longer, depending on your HostRecordTTL setting and the setting of your cross-site DNS/AD replication schedule.    Wir sind hier an einem Punkt unserer Überlegungen angelangt, an dem sich erklärt, weshalb ich zuvor das "Windows was the God ..." Zitat verwendet habe. Die unbedingte Abhängigkeit zu Windows wird zunehmend zum Problem, da sie die Komplexität einer Microsoft-basierenden Lösung erhöht, anstelle sie zu reduzieren. Und Komplexität ist das Letzte, was sich CIOs heutzutage wünschen.  Zur Ehrenrettung des SQL Server 2012 und AlwaysOn muss man sagen, dass derart lange Failover-Zeiten kein unbedingtes "Muss" darstellen, sondern ein "Kann". Doch auch ein "Kann" kann im unpassenden Moment unvorhersehbare und kostspielige Folgen haben. Die Unabsehbarkeit ist wiederum Ursache vieler an der Implementierung beteiligten Komponenten und deren Abhängigkeiten, wie beispielsweise drei Cluster-Lösungen (zwei von Microsoft, eine 3rd Party Lösung). Wie man die Sache auch dreht und wendet, kommt man an diesem Fakt also nicht vorbei - ganz unabhängig von der Dauer einer Downtime oder Failover-Zeiten. Im Gegensatz zu AlwaysOn und der hier vorgestellten Version eines Stretch-Clusters, vermeidet eine entsprechende Oracle Implementierung eine derartige Komplexität, hervorgerufen duch multiple Abhängigkeiten. Den Unterschied machen Datenbank-integrierte Mechanismen, wie Fast Application Notification (FAN) und Fast Connection Failover (FCF). Für Oracle MAA Konfigurationen (Maximum Availability Architecture) sind Inter-Site Failover-Zeiten im Bereich von Sekunden keine Seltenheit. Wenn Sie dem Link zur Oracle MAA folgen, finden Sie außerdem eine Reihe an Customer Case Studies. Auch dies ist ein wichtiges Unterscheidungsmerkmal zu AlwaysOn, denn die Oracle Technologie hat sich bereits zigfach in höchst kritischen Umgebungen bewährt.   Availability Groups (Verfügbarkeitsgruppen) Die sogenannten Availability Groups (Verfügbarkeitsgruppen) sind - neben FCI - der weitere Baustein von AlwaysOn.   Hinweis: Bevor wir uns näher damit beschäftigen, sollten Sie sich noch einmal ins Gedächtnis rufen, dass eine SQL Server Datenbank nicht die gleiche Bedeutung besitzt, wie eine Oracle Datenbank, sondern eher einem Oracle Schema entspricht. So etwas wie die SQL Server Northwind Datenbank ist vergleichbar mit dem Oracle Scott Schema.   Eine Verfügbarkeitsgruppe setzt sich zusammen aus einem Set mehrerer Benutzer-Datenbanken, die im Falle eines Failover gemeinsam als Gruppe behandelt werden. Eine Verfügbarkeitsgruppe unterstützt ein Set an primären Datenbanken (primäres Replikat) und einem bis vier Sets von entsprechenden sekundären Datenbanken (sekundäre Replikate).       Es können jedoch nicht alle SQL Server Datenbanken einer AlwaysOn Verfügbarkeitsgruppe zugeordnet werden. Der SQL Server Spezialist Michael Otey zählt in seinem SQL Server Pro Artikel folgende Anforderungen auf: Verfügbarkeitsgruppen müssen mit Benutzer-Datenbanken erstellt werden. System-Datenbanken können nicht verwendet werden Die Datenbanken müssen sich im Read-Write Modus befinden. Read-Only Datenbanken werden nicht unterstützt Die Datenbanken in einer Verfügbarkeitsgruppe müssen Multiuser Datenbanken sein Sie dürfen nicht das AUTO_CLOSE Feature verwenden Sie müssen das Full Recovery Modell nutzen und es muss ein vollständiges Backup vorhanden sein Eine gegebene Datenbank kann sich nur in einer einzigen Verfügbarkeitsgruppe befinden und diese Datenbank düerfen nicht für Database Mirroring konfiguriert sein Microsoft empfiehl außerdem, dass der Verzeichnispfad einer Datenbank auf dem primären und sekundären Server identisch sein sollte Wie man sieht, eignen sich Verfügbarkeitsgruppen nicht, um HA und DR vollständig abzubilden. Die Unterscheidung zwischen der Instanzen-Ebene (FCI) und Datenbank-Ebene (Availability Groups) ist von hoher Bedeutung. Vor kurzem wurde mir gesagt, dass man mit den Verfügbarkeitsgruppen auf Shared Storage verzichten könne und dadurch Kosten spart. So weit so gut ... Man kann natürlich eine Installation rein mit Verfügbarkeitsgruppen und ohne FCI durchführen - aber man sollte sich dann darüber bewusst sein, was man dadurch alles nicht abgesichert hat - und dies wiederum für Desaster Recovery (DR) und SLAs (Service Level Agreements) bedeutet. Kurzum, um die Kombination aus beiden AlwaysOn Produkten und der damit verbundene Komplexität kommt man wohl in der Praxis nicht herum.    Availability Groups und WSFC AlwaysOn hängt von Windows Server Failover Clustering (WSFC) ab, um die aktuellen Rollen der Verfügbarkeitsreplikate einer Verfügbarkeitsgruppe zu überwachen und zu verwalten, und darüber zu entscheiden, wie ein Failover-Ereignis die Verfügbarkeitsreplikate betrifft. Das folgende Diagramm zeigt de Beziehung zwischen Verfügbarkeitsgruppen und WSFC:   Der Verfügbarkeitsmodus ist eine Eigenschaft jedes Verfügbarkeitsreplikats. Synychron und Asynchron können also gemischt werden: Availability Modus (Verfügbarkeitsmodus) Asynchroner Commit-Modus Primäres replikat schließt Transaktionen ohne Warten auf Sekundäres Synchroner Commit-Modus Primäres Replikat wartet auf Commit von sekundärem Replikat Failover Typen Automatic Manual Forced (mit möglichem Datenverlust) Synchroner Commit-Modus Geplanter, manueller Failover ohne Datenverlust Automatischer Failover ohne Datenverlust Asynchroner Commit-Modus Nur Forced, manueller Failover mit möglichem Datenverlust   Der SQL Server kennt keinen separaten Switchover Begriff wie in Oracle Data Guard. Für SQL Server werden alle Role Transitions als Failover bezeichnet. Tatsächlich unterstützt der SQL Server keinen Switchover für asynchrone Verbindungen. Es gibt nur die Form des Forced Failover mit möglichem Datenverlust. Eine ähnliche Fähigkeit wie der Switchover unter Oracle Data Guard ist so nicht gegeben.   SQL Sever FCI mit Availability Groups (Verfügbarkeitsgruppen) Neben den Verfügbarkeitsgruppen kann eine zweite Failover-Ebene eingerichtet werden, indem SQL Server FCI (auf Shared Storage) mit WSFC implementiert wird. Ein Verfügbarkeitesreplikat kann dann auf einer Standalone Instanz gehostet werden, oder einer FCI Instanz. Zum Verständnis: Die Verfügbarkeitsgruppen selbst benötigen kein Shared Storage. Diese Kombination kann verwendet werden für lokale HA auf Ebene der Instanz und DR auf Datenbank-Ebene durch Verfügbarkeitsgruppen. Das folgende Diagramm zeigt dieses Szenario:   Achtung! Hier handelt es sich nicht um ein Pendant zu Oracle RAC plus Data Guard, auch wenn das Bild diesen Eindruck vielleicht vermitteln mag - denn alle sekundären Knoten im FCI sind rein passiv. Es existiert außerdem eine weitere und ernsthafte Einschränkung: SQL Server Failover Cluster Instanzen (FCI) unterstützen nicht das automatische AlwaysOn Failover für Verfügbarkeitsgruppen. Jedes unter FCI gehostete Verfügbarkeitsreplikat kann nur für manuelles Failover konfiguriert werden.   Lesbare Sekundäre Replikate Ein oder mehrere Verfügbarkeitsreplikate in einer Verfügbarkeitsgruppe können für den lesenden Zugriff konfiguriert werden, wenn sie als sekundäres Replikat laufen. Dies ähnelt Oracle Active Data Guard, jedoch gibt es Einschränkungen. Alle Abfragen gegen die sekundäre Datenbank werden automatisch auf das Snapshot Isolation Level abgebildet. Es handelt sich dabei um eine Versionierung der Rows. Microsoft versuchte hiermit die Oracle MVRC (Multi Version Read Consistency) nachzustellen. Tatsächlich muss man die SQL Server Snapshot Isolation eher mit Oracle Flashback vergleichen. Bei der Implementierung des Snapshot Isolation Levels handelt sich um ein nachträglich aufgesetztes Feature und nicht um einen inhärenten Teil des Datenbank-Kernels, wie im Falle Oracle. (Ich werde hierzu in Kürze einen weiteren Blogbeitrag verfassen, wenn ich mich mit der neuen SQL Server 2012 Core Lizenzierung beschäftige.) Für die Praxis entstehen aus der Abbildung auf das Snapshot Isolation Level ernsthafte Restriktionen, derer man sich für den Betrieb in der Praxis bereits vorab bewusst sein sollte: Sollte auf der primären Datenbank eine aktive Transaktion zu dem Zeitpunkt existieren, wenn ein lesbares sekundäres Replikat in die Verfügbarkeitsgruppe aufgenommen wird, werden die Row-Versionen auf der korrespondierenden sekundären Datenbank nicht sofort vollständig verfügbar sein. Eine aktive Transaktion auf dem primären Replikat muss zuerst abgeschlossen (Commit oder Rollback) und dieser Transaktions-Record auf dem sekundären Replikat verarbeitet werden. Bis dahin ist das Isolation Level Mapping auf der sekundären Datenbank unvollständig und Abfragen sind temporär geblockt. Microsoft sagt dazu: "This is needed to guarantee that row versions are available on the secondary replica before executing the query under snapshot isolation as all isolation levels are implicitly mapped to snapshot isolation." (SQL Storage Engine Blog: AlwaysOn: I just enabled Readable Secondary but my query is blocked?)  Grundlegend bedeutet dies, dass ein aktives lesbares Replikat nicht in die Verfügbarkeitsgruppe aufgenommen werden kann, ohne das primäre Replikat vorübergehend stillzulegen. Da Leseoperationen auf das Snapshot Isolation Transaction Level abgebildet werden, kann die Bereinigung von Ghost Records auf dem primären Replikat durch Transaktionen auf einem oder mehreren sekundären Replikaten geblockt werden - z.B. durch eine lang laufende Abfrage auf dem sekundären Replikat. Diese Bereinigung wird auch blockiert, wenn die Verbindung zum sekundären Replikat abbricht oder der Datenaustausch unterbrochen wird. Auch die Log Truncation wird in diesem Zustant verhindert. Wenn dieser Zustand längere Zeit anhält, empfiehlt Microsoft das sekundäre Replikat aus der Verfügbarkeitsgruppe herauszunehmen - was ein ernsthaftes Downtime-Problem darstellt. Die Read-Only Workload auf den sekundären Replikaten kann eingehende DDL Änderungen blockieren. Obwohl die Leseoperationen aufgrund der Row-Versionierung keine Shared Locks halten, führen diese Operatioen zu Sch-S Locks (Schemastabilitätssperren). DDL-Änderungen durch Redo-Operationen können dadurch blockiert werden. Falls DDL aufgrund konkurrierender Lese-Workload blockiert wird und der Schwellenwert für 'Recovery Interval' (eine SQL Server Konfigurationsoption) überschritten wird, generiert der SQL Server das Ereignis sqlserver.lock_redo_blocked, welches Microsoft zum Kill der blockierenden Leser empfiehlt. Auf die Verfügbarkeit der Anwendung wird hierbei keinerlei Rücksicht genommen.   Keine dieser Einschränkungen existiert mit Oracle Active Data Guard.   Backups auf sekundären Replikaten  Über die sekundären Replikate können Backups (BACKUP DATABASE via Transact-SQL) nur als copy-only Backups einer vollständigen Datenbank, Dateien und Dateigruppen erstellt werden. Das Erstellen inkrementeller Backups ist nicht unterstützt, was ein ernsthafter Rückstand ist gegenüber der Backup-Unterstützung physikalischer Standbys unter Oracle Data Guard. Hinweis: Ein möglicher Workaround via Snapshots, bleibt ein Workaround. Eine weitere Einschränkung dieses Features gegenüber Oracle Data Guard besteht darin, dass das Backup eines sekundären Replikats nicht ausgeführt werden kann, wenn es nicht mit dem primären Replikat kommunizieren kann. Darüber hinaus muss das sekundäre Replikat synchronisiert sein oder sich in der Synchronisation befinden, um das Beackup auf dem sekundären Replikat erstellen zu können.   Vergleich von Microsoft AlwaysOn mit der Oracle MAA Ich komme wieder zurück auf die Eingangs erwähnte, mehrfach an mich gestellte Frage "Wann denn - und ob überhaupt - Oracle etwas Vergleichbares wie AlwaysOn bieten würde?" und meine damit verbundene (kurze) Irritation. Wenn Sie diesen Blogbeitrag bis hierher gelesen haben, dann kennen Sie jetzt meine darauf gegebene Antwort. Der eine oder andere Punkt traf dabei nicht immer auf Jeden zu, was auch nicht der tiefere Sinn und Zweck meiner Antwort war. Wenn beispielsweise kein Multi-Subnet mit im Spiel ist, sind alle diesbezüglichen Kritikpunkte zunächst obsolet. Was aber nicht bedeutet, dass sie nicht bereits morgen schon wieder zum Thema werden könnten (Sag niemals "Nie"). In manch anderes Fettnäpfchen tritt man wiederum nicht unbedingt in einer Testumgebung, sondern erst im laufenden Betrieb. Erst recht nicht dann, wenn man sich potenzieller Probleme nicht bewusst ist und keine dedizierten Tests startet. Und wer AlwaysOn erfolgreich positionieren möchte, wird auch gar kein Interesse daran haben, auf mögliche Schwachstellen und den besagten Teufel im Detail aufmerksam zu machen. Das ist keine Unterstellung - es ist nur menschlich. Außerdem ist es verständlich, dass man sich in erster Linie darauf konzentriert "was geht" und "was gut läuft", anstelle auf das "was zu Problemen führen kann" oder "nicht funktioniert". Wer will schon der Miesepeter sein? Für mich selbst gesprochen, kann ich nur sagen, dass ich lieber vorab von allen möglichen Einschränkungen wissen möchte, anstelle sie dann nach einer kurzen Zeit der heilen Welt schmerzhaft am eigenen Leib erfahren zu müssen. Ich bin davon überzeugt, dass es Ihnen nicht anders geht. Nachfolgend deshalb eine Zusammenfassung all jener Punkte, die ich im Vergleich zur Oracle MAA (Maximum Availability Architecture) als unbedingt Erwähnenswert betrachte, falls man eine Evaluierung von Microsoft AlwaysOn in Betracht zieht. 1. AlwaysOn ist eine komplexe Technologie Der SQL Server AlwaysOn Stack ist zusammengesetzt aus drei verschiedenen Technlogien: Windows Server Failover Clustering (WSFC) SQL Server Failover Cluster Instances (FCI) SQL Server Availability Groups (Verfügbarkeitsgruppen) Man kann eine derartige Lösung nicht als nahtlos bezeichnen, wofür auch die vielen von Microsoft dargestellten Einschränkungen sprechen. Während sich frühere SQL Server Versionen in Richtung eigener HA/DR Technologien entwickelten (wie Database Mirroring), empfiehlt Microsoft nun die Migration. Doch weshalb dieser Schwenk? Er führt nicht zu einem konsisten und robusten Angebot an HA/DR Technologie für geschäftskritische Umgebungen.  Liegt die Antwort in meiner These begründet, nach der "Windows was the God ..." noch immer gilt und man die Nachteile der allzu engen Kopplung mit Windows nicht sehen möchte? Entscheiden Sie selbst ... 2. Failover Cluster Instanzen - Kein RAC-Pendant Die SQL Server und Windows Server Clustering Technologie basiert noch immer auf dem veralteten Aktiv-Passiv Modell und führt zu einer Verschwendung von Systemressourcen. In einer Betrachtung von lediglich zwei Knoten erschließt sich auf Anhieb noch nicht der volle Mehrwert eines Aktiv-Aktiv Clusters (wie den Real Application Clusters), wie er von Oracle bereits vor zehn Jahren entwickelt wurde. Doch kennt man die Vorzüge der Skalierbarkeit durch einfaches Hinzufügen weiterer Cluster-Knoten, die dann alle gemeinsam als ein einziges logisches System zusammenarbeiten, versteht man was hinter dem Motto "Pay-as-you-Grow" steckt. In einem Aktiv-Aktiv Cluster geht es zwar auch um Hochverfügbarkeit - und ein Failover erfolgt zudem schneller, als in einem Aktiv-Passiv Modell - aber es geht eben nicht nur darum. An dieser Stelle sei darauf hingewiesen, dass die Oracle 11g Standard Edition bereits die Nutzung von Oracle RAC bis zu vier Sockets kostenfrei beinhaltet. Möchten Sie dazu Windows nutzen, benötigen Sie keine Windows Server Enterprise Edition, da Oracle 11g die eigene Clusterware liefert. Sie kommen in den Genuss von Hochverfügbarkeit und Skalierbarkeit und können dazu die günstigere Windows Server Standard Edition nutzen. 3. SQL Server Multi-Subnet Clustering - Abhängigkeit zu 3rd Party Storage Mirroring  Die SQL Server Multi-Subnet Clustering Architektur unterstützt den Aufbau eines Stretch Clusters, basiert dabei aber auf dem Aktiv-Passiv Modell. Das eigentlich Problematische ist jedoch, dass man sich zur Absicherung der Datenbank auf 3rd Party Storage Mirroring Technologie verlässt, ohne Integration zwischen dem Windows Server Failover Clustering (WSFC) und der darunterliegenden Mirroring Technologie. Wenn nun im Cluster ein Failover auf Instanzen-Ebene erfolgt, existiert keine Koordination mit einem möglichen Failover auf Ebene des Storage-Array. 4. Availability Groups (Verfügbarkeitsgruppen) - Vier, oder doch nur Zwei? Ein primäres Replikat erlaubt bis zu vier sekundäre Replikate innerhalb einer Verfügbarkeitsgruppe, jedoch nur zwei im Synchronen Commit Modus. Während dies zwar einen Vorteil gegenüber dem stringenten 1:1 Modell unter Database Mirroring darstellt, fällt der SQL Server 2012 damit immer noch weiter zurück hinter Oracle Data Guard mit bis zu 30 direkten Stanbdy Zielen - und vielen weiteren durch kaskadierende Ziele möglichen. Damit eignet sich Oracle Active Data Guard auch für die Bereitstellung einer Reader-Farm Skalierbarkeit für Internet-basierende Unternehmen. Mit AwaysOn Verfügbarkeitsgruppen ist dies nicht möglich. 5. Availability Groups (Verfügbarkeitsgruppen) - kein asynchrones Switchover  Die Technologie der Verfügbarkeitsgruppen wird auch als geeignetes Mittel für administrative Aufgaben positioniert - wie Upgrades oder Wartungsarbeiten. Man muss sich jedoch einem gravierendem Defizit bewusst sein: Im asynchronen Verfügbarkeitsmodus besteht die einzige Möglichkeit für Role Transition im Forced Failover mit Datenverlust! Um den Verlust von Daten durch geplante Wartungsarbeiten zu vermeiden, muss man den synchronen Verfügbarkeitsmodus konfigurieren, was jedoch ernstzunehmende Auswirkungen auf WAN Deployments nach sich zieht. Spinnt man diesen Gedanken zu Ende, kommt man zu dem Schluss, dass die Technologie der Verfügbarkeitsgruppen für geplante Wartungsarbeiten in einem derartigen Umfeld nicht effektiv genutzt werden kann. 6. Automatisches Failover - Nicht immer möglich Sowohl die SQL Server FCI, als auch Verfügbarkeitsgruppen unterstützen automatisches Failover. Möchte man diese jedoch kombinieren, wird das Ergebnis kein automatisches Failover sein. Denn ihr Zusammentreffen im Failover-Fall führt zu Race Conditions (Wettlaufsituationen), weshalb diese Konfiguration nicht länger das automatische Failover zu einem Replikat in einer Verfügbarkeitsgruppe erlaubt. Auch hier bestätigt sich wieder die tiefere Problematik von AlwaysOn, mit einer Zusammensetzung aus unterschiedlichen Technologien und der Abhängigkeit zu Windows. 7. Problematische RTO (Recovery Time Objective) Microsoft postioniert die SQL Server Multi-Subnet Clustering Architektur als brauchbare HA/DR Architektur. Bedenkt man jedoch die Problematik im Zusammenhang mit DNS Replikation und den möglichen langen Wartezeiten auf Client-Seite von bis zu 16 Minuten, sind strenge RTO Anforderungen (Recovery Time Objectives) nicht erfüllbar. Im Gegensatz zu Oracle besitzt der SQL Server keine Datenbank-integrierten Technologien, wie Oracle Fast Application Notification (FAN) oder Oracle Fast Connection Failover (FCF). 8. Problematische RPO (Recovery Point Objective) SQL Server ermöglicht Forced Failover (erzwungenes Failover), bietet jedoch keine Möglichkeit zur automatischen Übertragung der letzten Datenbits von einem alten zu einem neuen primären Replikat, wenn der Verfügbarkeitsmodus asynchron war. Oracle Data Guard hingegen bietet diese Unterstützung durch das Flush Redo Feature. Dies sichert "Zero Data Loss" und beste RPO auch in erzwungenen Failover-Situationen. 9. Lesbare Sekundäre Replikate mit Einschränkungen Aufgrund des Snapshot Isolation Transaction Level für lesbare sekundäre Replikate, besitzen diese Einschränkungen mit Auswirkung auf die primäre Datenbank. Die Bereinigung von Ghost Records auf der primären Datenbank, wird beeinflusst von lang laufenden Abfragen auf der lesabaren sekundären Datenbank. Die lesbare sekundäre Datenbank kann nicht in die Verfügbarkeitsgruppe aufgenommen werden, wenn es aktive Transaktionen auf der primären Datenbank gibt. Zusätzlich können DLL Änderungen auf der primären Datenbank durch Abfragen auf der sekundären blockiert werden. Und imkrementelle Backups werden hier nicht unterstützt.   Keine dieser Restriktionen existiert unter Oracle Data Guard.

    Read the article

  • Memcached Lagging

    - by Brad Dwyer
    Let me preface this by saying that this is a followup question to this topic. That was "solved" by switching from Solaris (SmartOS) to Ubuntu for the memcached server. Now we've multiplied load by about 5x and are running into problems again. We are running a site that is doing about 1000 requests/minute, each request hits Memcached with approximately 3 reads and 1 write. So load is approximately 65 requests per second. Total data in the cache is about 37M, and each key contains a very small amount of data (a JSON-encoded array of integers amounting to less than 1K). We have setup a benchmarking script on these pages and fed the data into StatsD for logging. The problem is that there are spikes where Memcached takes a very long time to respond. These do not appear to correlate with spikes in traffic. What could be causing these spikes? Why would memcached take over a second to reply? We just booted up a second server to put in the pool and it didn't make any noticeable difference in the frequency or severity of the spikes. This is the output of getStats() on the servers: Array ( [-----------] => Array ( [pid] => 1364 [uptime] => 3715684 [threads] => 4 [time] => 1336596719 [pointer_size] => 64 [rusage_user_seconds] => 7924 [rusage_user_microseconds] => 170000 [rusage_system_seconds] => 187214 [rusage_system_microseconds] => 190000 [curr_items] => 12578 [total_items] => 53516300 [limit_maxbytes] => 943718400 [curr_connections] => 14 [total_connections] => 72550117 [connection_structures] => 165 [bytes] => 2616068 [cmd_get] => 450388258 [cmd_set] => 53493365 [get_hits] => 450388258 [get_misses] => 2244297 [evictions] => 0 [bytes_read] => 2138744916 [bytes_written] => 745275216 [version] => 1.4.2 ) [-----------:11211] => Array ( [pid] => 8099 [uptime] => 4687 [threads] => 4 [time] => 1336596719 [pointer_size] => 64 [rusage_user_seconds] => 7 [rusage_user_microseconds] => 170000 [rusage_system_seconds] => 290 [rusage_system_microseconds] => 990000 [curr_items] => 2384 [total_items] => 225964 [limit_maxbytes] => 943718400 [curr_connections] => 7 [total_connections] => 588097 [connection_structures] => 91 [bytes] => 562641 [cmd_get] => 1012562 [cmd_set] => 225778 [get_hits] => 1012562 [get_misses] => 125161 [evictions] => 0 [bytes_read] => 91270698 [bytes_written] => 350071516 [version] => 1.4.2 ) ) Edit: Here is the result of a set and retrieve of 10,000 values. Normal: Stored 10000 values in 5.6118 seconds. Average: 0.0006 High: 0.1958 Low: 0.0003 Fetched 10000 values in 5.1215 seconds. Average: 0.0005 High: 0.0141 Low: 0.0003 When Spiking: Stored 10000 values in 16.5074 seconds. Average: 0.0017 High: 0.9288 Low: 0.0003 Fetched 10000 values in 19.8771 seconds. Average: 0.0020 High: 0.9478 Low: 0.0003

    Read the article

  • CLSF & CLK 2013 Trip Report by Jeff Liu

    - by jamesmorris
    This is a contributed post from Jeff Liu, lead XFS developer for the Oracle mainline Linux kernel team. Recently, I attended both the China Linux Storage and Filesystem workshop (CLSF), and the China Linux Kernel conference (CLK), which were held in Shanghai. Here are the highlights for both events. CLSF - 17th October XFS update (led by Jeff Liu) XFS keeps rapid progress with a lot of changes, especially focused on the infrastructure/performance improvements as well as  new feature development.  This can be reflected with a sample statistics among XFS/Ext4+JBD2/Btrfs via: # git diff --stat --minimal -C -M v3.7..v3.12-rc4 -- fs/xfs|fs/ext4+fs/jbd2|fs/btrfs XFS: 141 files changed, 27598 insertions(+), 19113 deletions(-) Ext4+JBD2: 39 files changed, 10487 insertions(+), 5454 deletions(-) Btrfs: 70 files changed, 19875 insertions(+), 8130 deletions(-) What made up those changes in XFS? Self-describing metadata(CRC32c). This is a new feature and it contributed about 70% code changes, it can be enabled via `mkfs.xfs -m crc=1 /dev/xxx` for v5 superblock. Transaction log space reservation improvements. With this change, we can calculate the log space reservation at mount time rather than runtime to reduce the the CPU overhead. User namespace support. So both XFS and USERNS can be enabled on kernel configuration begin from Linux 3.10. Thanks Dwight Engen's efforts for this thing. Split project/group quota inodes. Originally, project quota can not be enabled with group quota at the same time because they were share the same quota file inode, now it works but only for v5 super block. i.e, CRC enabled. CONFIG_XFS_WARN, an new lightweight runtime debugger which can be deployed in production environment. Readahead log object recovery, this change can speed up the log replay progress significantly. Speculative preallocation inode tracking, clearing and throttling. The main purpose is to deal with inodes with post-EOF space due to speculative preallocation, support improved quota management to free up a significant amount of unwritten space when at or near EDQUOT. It support backgroup scanning which occurs on a longish interval(5 mins by default, tunable), and on-demand scanning/trimming via ioctl(2). Bitter arguments ensued from this session, especially for the comparison between Ext4 and Btrfs in different areas, I have to spent a whole morning of the 1st day answering those questions. We basically agreed on XFS is the best choice in Linux nowadays because: Stable, XFS has a good record in stability in the past 10 years. Fengguang Wu who lead the 0-day kernel test project also said that he has observed less error than other filesystems in the past 1+ years, I own it to the XFS upstream code reviewer, they always performing serious code review as well as testing. Good performance for large/small files, XFS does not works very well for small files has already been an old story for years. Best choice (maybe) for distributed PB filesystems. e.g, Ceph recommends delopy OSD daemon on XFS because Ext4 has limited xattr size. Best choice for large storage (>16TB). Ext4 does not support a single file more than around 15.95TB. Scalability, any objection to XFS is best in this point? :) XFS is better to deal with transaction concurrency than Ext4, why? The maximum size of the log in XFS is 2038MB compare to 128MB in Ext4. Misc. Ext4 is widely used and it has been proved fast/stable in various loads and scenarios, XFS just need more customers, and Btrfs is still on the road to be a manhood. Ceph Introduction (Led by Li Wang) This a hot topic.  Li gave us a nice introduction about the design as well as their current works. Actually, Ceph client has been included in Linux kernel since 2.6.34 and supported by Openstack since Folsom but it seems that it has not yet been widely deployment in production environment. Their major work is focus on the inline data support to separate the metadata and data storage, reduce the file access time, i.e, a file access need communication twice, fetch the metadata from MDS and then get data from OSD, and also, the small file access is limited by the network latency. The solution is, for the small files they would like to store the data at metadata so that when accessing a small file, the metadata server can push both metadata and data to the client at the same time. In this way, they can reduce the overhead of calculating the data offset and save the communication to OSD. For this feature, they have only run some small scale testing but really saw noticeable improvements. Test environment: Intel 2 CPU 12 Core, 64GB RAM, Ubuntu 12.04, Ceph 0.56.6 with 200GB SATA disk, 15 OSD, 1 MDS, 1 MON. The sequence read performance for 1K size files improved about 50%. I have asked Li and Zheng Yan (the core developer of Ceph, who also worked on Btrfs) whether Ceph is really stable and can be deployed at production environment for large scale PB level storage, but they can not give a positive answer, looks Ceph even does not spread over Dreamhost (subject to confirmation). From Li, they only deployed Ceph for a small scale storage(32 nodes) although they'd like to try 6000 nodes in the future. Improve Linux swap for Flash storage (led by Shaohua Li) Because of high density, low power and low price, flash storage (SSD) is a good candidate to partially replace DRAM. A quick answer for this is using SSD as swap. But Linux swap is designed for slow hard disk storage, so there are a lot of challenges to efficiently use SSD for swap. SWAPOUT swap_map scan swap_map is the in-memory data structure to track swap disk usage, but it is a slow linear scan. It will become a bottleneck while finding many adjacent pages in the use of SSD. Shaohua Li have changed it to a cluster(128K) list, resulting in O(1) algorithm. However, this apporoach needs restrictive cluster alignment and only enabled for SSD. IO pattern In most cases, the swap io is in interleaved pattern because of mutiple reclaimers or a free cluster is shared by all reclaimers. Even though block layer can merge interleaved IO to some extent, but we cannot count on it completely. Hence the per-cpu cluster is added base on the previous change, it can help reclaimer do sequential IO and the block layer will be easier to merge IO. TLB flush: If we're reclaiming one active page, we should first move the page from active lru list to inactive lru list, and then reclaim the page from inactive lru to swap it out. During the process, we need to clear PTE twice: first is 'A'(ACCESS) bit, second is 'P'(PRESENT) bit. Processors need to send lots of ipi which make the TLB flush really expensive. Some works have been done to improve this, including rework smp_call_functiom_many() or remove the first TLB flush in x86, but there still have some arguments here and only parts of works have been pushed to mainline. SWAPIN: Page fault does iodepth=1 sync io, but it's a little waste if only issue a page size's IO. The obvious solution is doing swap readahead. But the current in-kernel swap readahead is arbitary(always 8 pages), and it always doesn't perform well for both random and sequential access workload. Shaohua introduced a new flag for madvise(MADV_WILLNEED) to do swap prefetch, so the changes happen in userspace API and leave the in-kernel readahead unchanged(but I think some improvement can also be done here). SWAP discard As we know, discard is important for SSD write throughout, but the current swap discard implementation is synchronous. He changed it to async discard which allow discard and write run in the same time. Meanwhile, the unit of discard is also optimized to cluster. Misc: lock contention For many concurrent swapout and swapin , the lock contention such as anon_vma or swap_lock is high, so he changed the swap_lock to a per-swap lock. But there still have some lock contention in very high speed SSD because of swapcache address_space lock. Zproject (led by Bob Liu) Bob gave us a very nice introduction about the current memory compression status. Now there are 3 projects(zswap/zram/zcache) which all aim at smooth swap IO storm and promote performance, but they all have their own pros and cons. ZSWAP It is implemented based on frontswap API and it uses a dynamic allocater named Zbud to allocate free pages. Zbud means pairs of zpages are "buddied" and it can only store at most two compressed pages in one page frame, so the max compress ratio is 50%. Each page frame is lru-linked and can do shink in memory pressure. If the compressed memory pool reach its limitation, shink or reclaim happens. It decompress the page frame into two new allocated pages and then write them to real swap device, but it can fail when allocating the two pages. ZRAM Acts as a compressed ramdisk and used as swap device, and it use zsmalloc as its allocator which has high density but may have fragmentation issues. Besides, page reclaim is hard since it will need more pages to uncompress and free just one page. ZRAM is preferred by embedded system which may not have any real swap device. Now both ZRAM and ZSWAP are in driver/staging tree, and in the mm community there are some disscussions of merging ZRAM into ZSWAP or viceversa, but no agreement yet. ZCACHE Handles file page compression but it is removed out of staging recently. From industry (led by Tang Jie, LSI) An LSI engineer introduced several new produces to us. The first is raid5/6 cards that it use full stripe writes to improve performance. The 2nd one he introduced is SandForce flash controller, who can understand data file types (data entropy) to reduce write amplification (WA) for nearly all writes. It's called DuraWrite and typical WA is 0.5. What's more, if enable its Dynamic Logical Capacity function module, the controller can do data compression which is transparent to upper layer. LSI testing shows that with this virtual capacity enables 1x TB drive can support up to 2x TB capacity, but the application must monitor free flash space to maintain optimal performance and to guard against free flash space exhaustion. He said the most useful application is for datebase. Another thing I think it's worth to mention is that a NV-DRAM memory in NMR/Raptor which is directly exposed to host system. Applications can directly access the NV-DRAM via a memory address - using standard system call mmap(). He said that it is very useful for database logging now. This kind of NVM produces are beginning to appear in recent years, and it is said that Samsung is building a research center in China for related produces. IMHO, NVM will bring an effect to current os layer especially on file system, e.g. its journaling may need to redesign to fully utilize these nonvolatile memory. OCFS2 (led by Canquan Shen) Without a doubt, HuaWei is the biggest contributor to OCFS2 in the past two years. They have posted 46 upstream patches and 39 patches have been merged. Their current project is based on 32/64 nodes cluster, but they also tried 128 nodes at the experimental stage. The major work they are working is to support ATS (atomic test and set), it can be works with DLM at the same time. Looks this idea is inspired by the vmware VMFS locking, i.e, http://blogs.vmware.com/vsphere/2012/05/vmfs-locking-uncovered.html CLK - 18th October 2013 Improving Linux Development with Better Tools (Andi Kleen) This talk focused on how to find/solve bugs along with the Linux complexity growing. Generally, we can do this with the following kind of tools: Static code checkers tools. e.g, sparse, smatch, coccinelle, clang checker, checkpatch, gcc -W/LTO, stanse. This can help check a lot of things, simple mistakes, complex problems, but the challenges are: some are very slow, false positives, may need a concentrated effort to get false positives down. Especially, no static checker I found can follow indirect calls (“OO in C”, common in kernel): struct foo_ops { int (*do_foo)(struct foo *obj); } foo->do_foo(foo); Dynamic runtime checkers, e.g, thread checkers, kmemcheck, lockdep. Ideally all kernel code would come with a test suite, then someone could run all the dynamic checkers. Fuzzers/test suites. e.g, Trinity is a great tool, it finds many bugs, but needs manual model for each syscall. Modern fuzzers around using automatic feedback, but notfor kernel yet: http://taviso.decsystem.org/making_software_dumber.pdf Debuggers/Tracers to understand code, e.g, ftrace, can dump on events/oops/custom triggers, but still too much overhead in many cases to run always during debug. Tools to read/understand source, e.g, grep/cscope work great for many cases, but do not understand indirect pointers (OO in C model used in kernel), give us all “do_foo” instances: struct foo_ops { int (*do_foo)(struct foo *obj); } = { .do_foo = my_foo }; foo>do_foo(foo); That would be great to have a cscope like tool that understands this based on types/initializers XFS: The High Performance Enterprise File System (Jeff Liu) [slides] I gave a talk for introducing the disk layout, unique features, as well as the recent changes.   The slides include some charts to reflect the performances between XFS/Btrfs/Ext4 for small files. About a dozen users raised their hands when I asking who has experienced with XFS. I remembered that when I asked the same question in LinuxCon/Japan, only 3 people raised their hands, but they are Chris Mason, Ric Wheeler, and another attendee. The attendee questions were mainly focused on stability, and comparison with other file systems. Linux Containers (Feng Gao) The speaker introduced us that the purpose for those kind of namespaces, include mount/UTS/IPC/Network/Pid/User, as well as the system API/ABI. For the userspace tools, He mainly focus on the Libvirt LXC rather than us(LXC). Libvirt LXC is another userspace container management tool, implemented as one type of libvirt driver, it can manage containers, create namespace, create private filesystem layout for container, Create devices for container and setup resources controller via cgroup. In this talk, Feng also mentioned another two possible new namespaces in the future, the 1st is the audit, but not sure if it should be assigned to user namespace or not. Another is about syslog, but the question is do we really need it? In-memory Compression (Bob Liu) Same as CLSF, a nice introduction that I have already mentioned above. Misc There were some other talks related to ACPI based memory hotplug, smart wake-affinity in scheduler etc., but my head is not big enough to record all those things. -- Jeff Liu

    Read the article

  • Metro: Understanding CSS Media Queries

    - by Stephen.Walther
    If you are building a Metro style application then your application needs to look great when used on a wide variety of devices. Your application needs to work on tiny little phones, slates, desktop monitors, and the super high resolution displays of the future. Your application also must support portable devices used with different orientations. If someone tilts their phone from portrait to landscape mode then your application must still be usable. Finally, your Metro style application must look great in different states. For example, your Metro application can be in a “snapped state” when it is shrunk so it can share screen real estate with another application. In this blog post, you learn how to use Cascading Style Sheet media queries to support different devices, different device orientations, and different application states. First, you are provided with an overview of the W3C Media Query recommendation and you learn how to detect standard media features. Next, you learn about the Microsoft extensions to media queries which are supported in Metro style applications. For example, you learn how to use the –ms-view-state feature to detect whether an application is in a “snapped state” or “fill state”. Finally, you learn how to programmatically detect the features of a device and the state of an application. You learn how to use the msMatchMedia() method to execute a media query with JavaScript. Using CSS Media Queries Media queries enable you to apply different styles depending on the features of a device. Media queries are not only supported by Metro style applications, most modern web browsers now support media queries including Google Chrome 4+, Mozilla Firefox 3.5+, Apple Safari 4+, and Microsoft Internet Explorer 9+. Loading Different Style Sheets with Media Queries Imagine, for example, that you want to display different content depending on the horizontal resolution of a device. In that case, you can load different style sheets optimized for different sized devices. Consider the following HTML page: <!DOCTYPE html> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <title>U.S. Robotics and Mechanical Men</title> <link href="main.css" rel="stylesheet" type="text/css" /> <!-- Less than 1100px --> <link href="medium.css" rel="stylesheet" type="text/css" media="(max-width:1100px)" /> <!-- Less than 800px --> <link href="small.css" rel="stylesheet" type="text/css" media="(max-width:800px)" /> </head> <body> <div id="header"> <h1>U.S. Robotics and Mechanical Men</h1> </div> <!-- Advertisement Column --> <div id="leftColumn"> <img src="advertisement1.gif" alt="advertisement" /> <img src="advertisement2.jpg" alt="advertisement" /> </div> <!-- Product Search Form --> <div id="mainContentColumn"> <label>Search Products</label> <input id="search" /><button>Search</button> </div> <!-- Deal of the Day Column --> <div id="rightColumn"> <h1>Deal of the Day!</h1> <p> Buy two cameras and get a third camera for free! Offer is good for today only. </p> </div> </body> </html> The HTML page above contains three columns: a leftColumn, mainContentColumn, and rightColumn. When the page is displayed on a low resolution device, such as a phone, only the mainContentColumn appears: When the page is displayed in a medium resolution device, such as a slate, both the leftColumn and the mainContentColumns are displayed: Finally, when the page is displayed in a high-resolution device, such as a computer monitor, all three columns are displayed: Different content is displayed with the help of media queries. The page above contains three style sheet links. Two of the style links include a media attribute: <link href="main.css" rel="stylesheet" type="text/css" /> <!-- Less than 1100px --> <link href="medium.css" rel="stylesheet" type="text/css" media="(max-width:1100px)" /> <!-- Less than 800px --> <link href="small.css" rel="stylesheet" type="text/css" media="(max-width:800px)" /> The main.css style sheet contains default styles for the elements in the page. The medium.css style sheet is applied when the page width is less than 1100px. This style sheet hides the rightColumn and changes the page background color to lime: html { background-color: lime; } #rightColumn { display:none; } Finally, the small.css style sheet is loaded when the page width is less than 800px. This style sheet hides the leftColumn and changes the page background color to red: html { background-color: red; } #leftColumn { display:none; } The different style sheets are applied as you stretch and contract your browser window. You don’t need to refresh the page after changing the size of the page for a media query to be applied: Using the @media Rule You don’t need to divide your styles into separate files to take advantage of media queries. You can group styles by using the @media rule. For example, the following HTML page contains one set of styles which are applied when a device’s orientation is portrait and another set of styles when a device’s orientation is landscape: <!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <title>Application1</title> <style type="text/css"> html { font-family:'Segoe UI Semilight'; font-size: xx-large; } @media screen and (orientation:landscape) { html { background-color: lime; } p.content { width: 50%; margin: auto; } } @media screen and (orientation:portrait) { html { background-color: red; } p.content { width: 90%; margin: auto; } } </style> </head> <body> <p class="content"> Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. Fusce posuere, magna sed pulvinar ultricies, purus lectus malesuada libero, sit amet commodo magna eros quis urna. </p> </body> </html> When a device has a landscape orientation then the background color is set to the color lime and the text only takes up 50% of the available horizontal space: When the device has a portrait orientation then the background color is red and the text takes up 90% of the available horizontal space: Using Standard CSS Media Features The official list of standard media features is contained in the W3C CSS Media Query recommendation located here: http://www.w3.org/TR/css3-mediaqueries/ Here is the official list of the 13 media features described in the standard: · width – The current width of the viewport · height – The current height of the viewport · device-width – The width of the device · device-height – The height of the device · orientation – The value portrait or landscape · aspect-ratio – The ratio of width to height · device-aspect-ratio – The ratio of device width to device height · color – The number of bits per color supported by the device · color-index – The number of colors in the color lookup table of the device · monochrome – The number of bits in the monochrome frame buffer · resolution – The density of the pixels supported by the device · scan – The values progressive or interlace (used for TVs) · grid – The values 0 or 1 which indicate whether the device supports a grid or a bitmap Many of the media features in the list above support the min- and max- prefix. For example, you can test for the min-width using a query like this: (min-width:800px) You can use the logical and operator with media queries when you need to check whether a device supports more than one feature. For example, the following query returns true only when the width of the device is between 800 and 1,200 pixels: (min-width:800px) and (max-width:1200px) Finally, you can use the different media types – all, braille, embossed, handheld, print, projection, screen, speech, tty, tv — with a media query. For example, the following media query only applies to a page when a page is being printed in color: print and (color) If you don’t specify a media type then media type all is assumed. Using Metro Style Media Features Microsoft has extended the standard list of media features which you can include in a media query with two custom media features: · -ms-high-contrast – The values any, black-white, white-black · -ms-view-state – The values full-screen, fill, snapped, device-portrait You can take advantage of the –ms-high-contrast media feature to make your web application more accessible to individuals with disabilities. In high contrast mode, you should make your application easier to use for individuals with vision disabilities. The –ms-view-state media feature enables you to detect the state of an application. For example, when an application is snapped, the application only occupies part of the available screen real estate. The snapped application appears on the left or right side of the screen and the rest of the screen real estate is dominated by the fill application (Metro style applications can only be snapped on devices with a horizontal resolution of greater than 1,366 pixels). Here is a page which contains style rules for an application in both a snap and fill application state: <!DOCTYPE html> <html> <head> <meta charset="utf-8" /> <title>MyWinWebApp</title> <style type="text/css"> html { font-family:'Segoe UI Semilight'; font-size: xx-large; } @media screen and (-ms-view-state:snapped) { html { background-color: lime; } } @media screen and (-ms-view-state:fill) { html { background-color: red; } } </style> </head> <body> <p class="content"> Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas porttitor congue massa. Fusce posuere, magna sed pulvinar ultricies, purus lectus malesuada libero, sit amet commodo magna eros quis urna. </p> </body> </html> When the application is snapped, the application appears with a lime background color: When the application state is fill then the background color changes to red: When the application takes up the entire screen real estate – it is not in snapped or fill state – then no special style rules apply and the application appears with a white background color. Querying Media Features with JavaScript You can perform media queries using JavaScript by taking advantage of the window.msMatchMedia() method. This method returns a MSMediaQueryList which has a matches method that represents success or failure. For example, the following code checks whether the current device is in portrait mode: if (window.msMatchMedia("(orientation:portrait)").matches) { console.log("portrait"); } else { console.log("landscape"); } If the matches property returns true, then the device is in portrait mode and the message “portrait” is written to the Visual Studio JavaScript Console window. Otherwise, the message “landscape” is written to the JavaScript Console window. You can create an event listener which triggers code whenever the results of a media query changes. For example, the following code writes a message to the JavaScript Console whenever the current device is switched into or out of Portrait mode: window.msMatchMedia("(orientation:portrait)").addListener(function (mql) { if (mql.matches) { console.log("Switched to portrait"); } }); Be aware that the event listener is triggered whenever the result of the media query changes. So the event listener is triggered both when you switch from landscape to portrait and when you switch from portrait to landscape. For this reason, you need to verify that the matches property has the value true before writing the message. Summary The goal of this blog entry was to explain how CSS media queries work in the context of a Metro style application written with JavaScript. First, you were provided with an overview of the W3C CSS Media Query recommendation. You learned about the standard media features which you can query such as width and orientation. Next, we focused on the Microsoft extensions to media queries. You learned how to use –ms-view-state to detect whether a Metro style application is in “snapped” or “fill” state. You also learned how to use the msMatchMedia() method to perform a media query from JavaScript.

    Read the article

  • problems mounting an external IDE drive via USB in ubuntu

    - by Roy Rico
    I am having a problem connecting a specific IDE drive to my linux box. It's an old drive which I just want to get about 3 GB of files off of. INFO I am trying to connect a 200GB IDE Maxtor Drive, internally and externally... externally: I am using an self powered USB IDE external drive enclosure which I have used to connect various drives, under ubuntu and windows, in the past. The other posts stated it coudl be a problem I think i may have formatted the /dev/sdc partition instead of /dev/sdc1 partition when i originally formatted the drive. internally: I only have one machine left that has an internal IDE interface, and it's got XP on it. I plugged this drive internally into this machine with windows XP and used the ext2/ext3 drivers to mount this drive, but some files have question marks (?) in the file names which is messing up my copy process in windows. I can't delete the files under windows. Ubuntu Linux will not install on my only remaining machine that has IDE controller. I have tried the suggestions in the questions below http://superuser.com/questions/88182/mount-an-external-drive-in-ubuntu http://superuser.com/questions/23210/ubuntu-fails-to-mount-usb-drive it looks like i can see the drive in /proc/partitions $ cat /proc/partitions major minor #blocks name 8 0 78125000 sda 8 1 74894998 sda1 8 2 1 sda2 8 5 3229033 sda5 8 16 199148544 sdb <-- could be my drive? but it's not listed under fdisk -l $ fdisk -l Disk /dev/sda: 80.0 GB, 80000000000 bytes 255 heads, 63 sectors/track, 9726 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Disk identifier: 0xd0f4738c Device Boot Start End Blocks Id System /dev/sda1 * 1 9324 74894998+ 83 Linux /dev/sda2 9325 9726 3229065 5 Extended /dev/sda5 9325 9726 3229033+ 82 Linux swap / Solaris and here is my log of /var/log/messages. with a bunch of weird output, can someone let me know what that weird output is? Mar 3 19:49:40 mala kernel: [687455.112029] usb 1-7: new high speed USB device using ehci_hcd and address 3 Mar 3 19:49:41 mala kernel: [687455.248576] usb 1-7: configuration #1 chosen from 1 choice Mar 3 19:49:41 mala kernel: [687455.267450] Initializing USB Mass Storage driver... Mar 3 19:49:41 mala kernel: [687455.269180] scsi4 : SCSI emulation for USB Mass Storage devices Mar 3 19:49:41 mala kernel: [687455.269410] usbcore: registered new interface driver usb-storage Mar 3 19:49:41 mala kernel: [687455.269416] USB Mass Storage support registered. Mar 3 19:49:46 mala kernel: [687460.270917] scsi 4:0:0:0: Direct-Access Maxtor 6 Y200P0 YAR4 PQ: 0 ANSI: 2 Mar 3 19:49:46 mala kernel: [687460.271485] sd 4:0:0:0: Attached scsi generic sg2 type 0 Mar 3 19:49:46 mala kernel: [687460.278858] sd 4:0:0:0: [sdb] 398297088 512-byte logical blocks: (203 GB/189 GiB) Mar 3 19:49:46 mala kernel: [687460.280866] sd 4:0:0:0: [sdb] Write Protect is off Mar 3 19:50:16 mala kernel: [687460.283784] sdb: Mar 3 19:50:16 mala kernel: [687491.112020] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:50:47 mala kernel: [687522.120030] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:51:18 mala kernel: [687553.112034] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:51:49 mala kernel: [687584.116025] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:52:02 mala kernel: [687596.170632] type=1505 audit(1267671122.035:31): operation="profile_replace" pid=8426 name=/usr/lib/cups/backend/cups-pdf Mar 3 19:52:02 mala kernel: [687596.171551] type=1505 audit(1267671122.035:32): operation="profile_replace" pid=8426 name=/usr/sbin/cupsd Mar 3 19:52:06 mala kernel: [687600.908056] async/0 D c08145c0 0 7655 2 0x00000000 Mar 3 19:52:06 mala kernel: [687600.908062] e5601d38 00000046 e5774000 c08145c0 e4c2a848 c08145c0 d203973a 0002713d Mar 3 19:52:06 mala kernel: [687600.908072] c08145c0 c08145c0 e4c2a848 c08145c0 00000000 0002713d c08145c0 f0a98c00 Mar 3 19:52:06 mala kernel: [687600.908079] e4c2a5b0 c20125c0 00000002 e5601d80 e5601d44 c056f3be e5601d78 e5601d4c Mar 3 19:52:06 mala kernel: [687600.908087] Call Trace: Mar 3 19:52:06 mala kernel: [687600.908099] [<c056f3be>] io_schedule+0x1e/0x30 Mar 3 19:52:06 mala kernel: [687600.908107] [<c01b2cf5>] sync_page+0x35/0x40 Mar 3 19:52:06 mala kernel: [687600.908111] [<c056f8f7>] __wait_on_bit_lock+0x47/0x90 Mar 3 19:52:06 mala kernel: [687600.908115] [<c01b2cc0>] ? sync_page+0x0/0x40 Mar 3 19:52:06 mala kernel: [687600.908121] [<c020f390>] ? blkdev_readpage+0x0/0x20 Mar 3 19:52:06 mala kernel: [687600.908125] [<c01b2ca9>] __lock_page+0x79/0x80 Mar 3 19:52:06 mala kernel: [687600.908130] [<c015c130>] ? wake_bit_function+0x0/0x50 Mar 3 19:52:06 mala kernel: [687600.908135] [<c01b459f>] read_cache_page_async+0xbf/0xd0 Mar 3 19:52:06 mala kernel: [687600.908139] [<c01b45c2>] read_cache_page+0x12/0x60 Mar 3 19:52:06 mala kernel: [687600.908144] [<c0232dca>] read_dev_sector+0x3a/0x80 Mar 3 19:52:06 mala kernel: [687600.908148] [<c0233d3e>] adfspart_check_ICS+0x1e/0x160 Mar 3 19:52:06 mala kernel: [687600.908152] [<c023339f>] ? disk_name+0xaf/0xc0 Mar 3 19:52:06 mala kernel: [687600.908157] [<c0233d20>] ? adfspart_check_ICS+0x0/0x160 Mar 3 19:52:06 mala kernel: [687600.908161] [<c02334de>] check_partition+0x10e/0x180 Mar 3 19:52:06 mala kernel: [687600.908165] [<c02335f6>] rescan_partitions+0xa6/0x330 Mar 3 19:52:06 mala kernel: [687600.908171] [<c0312472>] ? kobject_get+0x12/0x20 Mar 3 19:52:06 mala kernel: [687600.908175] [<c0312472>] ? kobject_get+0x12/0x20 Mar 3 19:52:06 mala kernel: [687600.908180] [<c039fc43>] ? get_device+0x13/0x20 Mar 3 19:52:06 mala kernel: [687600.908185] [<c03c263f>] ? sd_open+0x5f/0x1b0 Mar 3 19:52:06 mala kernel: [687600.908189] [<c020fda0>] __blkdev_get+0x140/0x310 Mar 3 19:52:06 mala kernel: [687600.908194] [<c020f0ac>] ? bdget+0xec/0x100 Mar 3 19:52:06 mala kernel: [687600.908198] [<c020ff7a>] blkdev_get+0xa/0x10 Mar 3 19:52:06 mala kernel: [687600.908202] [<c0232f30>] register_disk+0x120/0x140 Mar 3 19:52:06 mala kernel: [687600.908207] [<c0308b4d>] ? blk_register_region+0x2d/0x40 Mar 3 19:52:06 mala kernel: [687600.908211] [<c03084f0>] ? exact_match+0x0/0x10 Mar 3 19:52:06 mala kernel: [687600.908216] [<c0308cf0>] add_disk+0x80/0x140 Mar 3 19:52:06 mala kernel: [687600.908221] [<c03084f0>] ? exact_match+0x0/0x10 Mar 3 19:52:06 mala kernel: [687600.908225] [<c0308860>] ? exact_lock+0x0/0x20 Mar 3 19:52:06 mala kernel: [687600.908230] [<c03c53df>] sd_probe_async+0xff/0x1c0

    Read the article

  • problems mounting an external IDE drive via USB in ubuntu

    - by Roy Rico
    I am having a problem connecting a specific IDE drive to my linux box. It's an old drive which I just want to get about 3 GB of files off of. INFO I am trying to connect a 200GB IDE Maxtor Drive, internally and externally... externally: I am using an self powered USB IDE external drive enclosure which I have used to connect various drives, under ubuntu and windows, in the past. The other posts stated it coudl be a problem I think i may have formatted the /dev/sdc partition instead of /dev/sdc1 partition when i originally formatted the drive. internally: I only have one machine left that has an internal IDE interface, and it's got XP on it. I plugged this drive internally into this machine with windows XP and used the ext2/ext3 drivers to mount this drive, but some files have question marks (?) in the file names which is messing up my copy process in windows. I can't delete the files under windows. Ubuntu Linux will not install on my only remaining machine that has IDE controller. I have tried the suggestions in the questions below http://superuser.com/questions/88182/mount-an-external-drive-in-ubuntu http://superuser.com/questions/23210/ubuntu-fails-to-mount-usb-drive it looks like i can see the drive in /proc/partitions $ cat /proc/partitions major minor #blocks name 8 0 78125000 sda 8 1 74894998 sda1 8 2 1 sda2 8 5 3229033 sda5 8 16 199148544 sdb <-- could be my drive? but it's not listed under fdisk -l $ fdisk -l Disk /dev/sda: 80.0 GB, 80000000000 bytes 255 heads, 63 sectors/track, 9726 cylinders Units = cylinders of 16065 * 512 = 8225280 bytes Disk identifier: 0xd0f4738c Device Boot Start End Blocks Id System /dev/sda1 * 1 9324 74894998+ 83 Linux /dev/sda2 9325 9726 3229065 5 Extended /dev/sda5 9325 9726 3229033+ 82 Linux swap / Solaris and here is my log of /var/log/messages. with a bunch of weird output, can someone let me know what that weird output is? Mar 3 19:49:40 mala kernel: [687455.112029] usb 1-7: new high speed USB device using ehci_hcd and address 3 Mar 3 19:49:41 mala kernel: [687455.248576] usb 1-7: configuration #1 chosen from 1 choice Mar 3 19:49:41 mala kernel: [687455.267450] Initializing USB Mass Storage driver... Mar 3 19:49:41 mala kernel: [687455.269180] scsi4 : SCSI emulation for USB Mass Storage devices Mar 3 19:49:41 mala kernel: [687455.269410] usbcore: registered new interface driver usb-storage Mar 3 19:49:41 mala kernel: [687455.269416] USB Mass Storage support registered. Mar 3 19:49:46 mala kernel: [687460.270917] scsi 4:0:0:0: Direct-Access Maxtor 6 Y200P0 YAR4 PQ: 0 ANSI: 2 Mar 3 19:49:46 mala kernel: [687460.271485] sd 4:0:0:0: Attached scsi generic sg2 type 0 Mar 3 19:49:46 mala kernel: [687460.278858] sd 4:0:0:0: [sdb] 398297088 512-byte logical blocks: (203 GB/189 GiB) Mar 3 19:49:46 mala kernel: [687460.280866] sd 4:0:0:0: [sdb] Write Protect is off Mar 3 19:50:16 mala kernel: [687460.283784] sdb: Mar 3 19:50:16 mala kernel: [687491.112020] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:50:47 mala kernel: [687522.120030] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:51:18 mala kernel: [687553.112034] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:51:49 mala kernel: [687584.116025] usb 1-7: reset high speed USB device using ehci_hcd and address 3 Mar 3 19:52:02 mala kernel: [687596.170632] type=1505 audit(1267671122.035:31): operation="profile_replace" pid=8426 name=/usr/lib/cups/backend/cups-pdf Mar 3 19:52:02 mala kernel: [687596.171551] type=1505 audit(1267671122.035:32): operation="profile_replace" pid=8426 name=/usr/sbin/cupsd Mar 3 19:52:06 mala kernel: [687600.908056] async/0 D c08145c0 0 7655 2 0x00000000 Mar 3 19:52:06 mala kernel: [687600.908062] e5601d38 00000046 e5774000 c08145c0 e4c2a848 c08145c0 d203973a 0002713d Mar 3 19:52:06 mala kernel: [687600.908072] c08145c0 c08145c0 e4c2a848 c08145c0 00000000 0002713d c08145c0 f0a98c00 Mar 3 19:52:06 mala kernel: [687600.908079] e4c2a5b0 c20125c0 00000002 e5601d80 e5601d44 c056f3be e5601d78 e5601d4c Mar 3 19:52:06 mala kernel: [687600.908087] Call Trace: Mar 3 19:52:06 mala kernel: [687600.908099] [<c056f3be>] io_schedule+0x1e/0x30 Mar 3 19:52:06 mala kernel: [687600.908107] [<c01b2cf5>] sync_page+0x35/0x40 Mar 3 19:52:06 mala kernel: [687600.908111] [<c056f8f7>] __wait_on_bit_lock+0x47/0x90 Mar 3 19:52:06 mala kernel: [687600.908115] [<c01b2cc0>] ? sync_page+0x0/0x40 Mar 3 19:52:06 mala kernel: [687600.908121] [<c020f390>] ? blkdev_readpage+0x0/0x20 Mar 3 19:52:06 mala kernel: [687600.908125] [<c01b2ca9>] __lock_page+0x79/0x80 Mar 3 19:52:06 mala kernel: [687600.908130] [<c015c130>] ? wake_bit_function+0x0/0x50 Mar 3 19:52:06 mala kernel: [687600.908135] [<c01b459f>] read_cache_page_async+0xbf/0xd0 Mar 3 19:52:06 mala kernel: [687600.908139] [<c01b45c2>] read_cache_page+0x12/0x60 Mar 3 19:52:06 mala kernel: [687600.908144] [<c0232dca>] read_dev_sector+0x3a/0x80 Mar 3 19:52:06 mala kernel: [687600.908148] [<c0233d3e>] adfspart_check_ICS+0x1e/0x160 Mar 3 19:52:06 mala kernel: [687600.908152] [<c023339f>] ? disk_name+0xaf/0xc0 Mar 3 19:52:06 mala kernel: [687600.908157] [<c0233d20>] ? adfspart_check_ICS+0x0/0x160 Mar 3 19:52:06 mala kernel: [687600.908161] [<c02334de>] check_partition+0x10e/0x180 Mar 3 19:52:06 mala kernel: [687600.908165] [<c02335f6>] rescan_partitions+0xa6/0x330 Mar 3 19:52:06 mala kernel: [687600.908171] [<c0312472>] ? kobject_get+0x12/0x20 Mar 3 19:52:06 mala kernel: [687600.908175] [<c0312472>] ? kobject_get+0x12/0x20 Mar 3 19:52:06 mala kernel: [687600.908180] [<c039fc43>] ? get_device+0x13/0x20 Mar 3 19:52:06 mala kernel: [687600.908185] [<c03c263f>] ? sd_open+0x5f/0x1b0 Mar 3 19:52:06 mala kernel: [687600.908189] [<c020fda0>] __blkdev_get+0x140/0x310 Mar 3 19:52:06 mala kernel: [687600.908194] [<c020f0ac>] ? bdget+0xec/0x100 Mar 3 19:52:06 mala kernel: [687600.908198] [<c020ff7a>] blkdev_get+0xa/0x10 Mar 3 19:52:06 mala kernel: [687600.908202] [<c0232f30>] register_disk+0x120/0x140 Mar 3 19:52:06 mala kernel: [687600.908207] [<c0308b4d>] ? blk_register_region+0x2d/0x40 Mar 3 19:52:06 mala kernel: [687600.908211] [<c03084f0>] ? exact_match+0x0/0x10 Mar 3 19:52:06 mala kernel: [687600.908216] [<c0308cf0>] add_disk+0x80/0x140 Mar 3 19:52:06 mala kernel: [687600.908221] [<c03084f0>] ? exact_match+0x0/0x10 Mar 3 19:52:06 mala kernel: [687600.908225] [<c0308860>] ? exact_lock+0x0/0x20 Mar 3 19:52:06 mala kernel: [687600.908230] [<c03c53df>] sd_probe_async+0xff/0x1c0

    Read the article

  • SQL SERVER – Shrinking Database is Bad – Increases Fragmentation – Reduces Performance

    - by pinaldave
    Earlier, I had written two articles related to Shrinking Database. I wrote about why Shrinking Database is not good. SQL SERVER – SHRINKDATABASE For Every Database in the SQL Server SQL SERVER – What the Business Says Is Not What the Business Wants I received many comments on Why Database Shrinking is bad. Today we will go over a very interesting example that I have created for the same. Here are the quick steps of the example. Create a test database Create two tables and populate with data Check the size of both the tables Size of database is very low Check the Fragmentation of one table Fragmentation will be very low Truncate another table Check the size of the table Check the fragmentation of the one table Fragmentation will be very low SHRINK Database Check the size of the table Check the fragmentation of the one table Fragmentation will be very HIGH REBUILD index on one table Check the size of the table Size of database is very HIGH Check the fragmentation of the one table Fragmentation will be very low Here is the script for the same. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO Let us check the table size and fragmentation. Now let us TRUNCATE the table and check the size and Fragmentation. USE MASTER GO CREATE DATABASE ShrinkIsBed GO USE ShrinkIsBed GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Create FirstTable CREATE TABLE FirstTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_FirstTable_ID] ON FirstTable ( [ID] ASC ) ON [PRIMARY] GO -- Create SecondTable CREATE TABLE SecondTable (ID INT, FirstName VARCHAR(100), LastName VARCHAR(100), City VARCHAR(100)) GO -- Create Clustered Index on ID CREATE CLUSTERED INDEX [IX_SecondTable_ID] ON SecondTable ( [ID] ASC ) ON [PRIMARY] GO -- Insert One Hundred Thousand Records INSERT INTO FirstTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Insert One Hundred Thousand Records INSERT INTO SecondTable (ID,FirstName,LastName,City) SELECT TOP 100000 ROW_NUMBER() OVER (ORDER BY a.name) RowID, 'Bob', CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%2 = 1 THEN 'Smith' ELSE 'Brown' END, CASE WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 1 THEN 'New York' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 5 THEN 'San Marino' WHEN ROW_NUMBER() OVER (ORDER BY a.name)%10 = 3 THEN 'Los Angeles' ELSE 'Houston' END FROM sys.all_objects a CROSS JOIN sys.all_objects b GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can clearly see that after TRUNCATE, the size of the database is not reduced and it is still the same as before TRUNCATE operation. After the Shrinking database operation, we were able to reduce the size of the database. If you notice the fragmentation, it is considerably high. The major problem with the Shrink operation is that it increases fragmentation of the database to very high value. Higher fragmentation reduces the performance of the database as reading from that particular table becomes very expensive. One of the ways to reduce the fragmentation is to rebuild index on the database. Let us rebuild the index and observe fragmentation and database size. -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REBUILD GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can notice that after rebuilding, Fragmentation reduces to a very low value (almost same to original value); however the database size increases way higher than the original. Before rebuilding, the size of the database was 5 MB, and after rebuilding, it is around 20 MB. Regular rebuilding the index is rebuild in the same user database where the index is placed. This usually increases the size of the database. Look at irony of the Shrinking database. One person shrinks the database to gain space (thinking it will help performance), which leads to increase in fragmentation (reducing performance). To reduce the fragmentation, one rebuilds index, which leads to size of the database to increase way more than the original size of the database (before shrinking). Well, by Shrinking, one did not gain what he was looking for usually. Rebuild indexing is not the best suggestion as that will create database grow again. I have always remembered the excellent post from Paul Randal regarding Shrinking the database is bad. I suggest every one to read that for accuracy and interesting conversation. Let us run following script where we Shrink the database and REORGANIZE. -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Shrink the Database DBCC SHRINKDATABASE (ShrinkIsBed); GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO -- Rebuild Index on FirstTable ALTER INDEX IX_SecondTable_ID ON SecondTable REORGANIZE GO -- Name of the Database and Size SELECT name, (size*8) Size_KB FROM sys.database_files GO -- Check Fragmentations in the database SELECT avg_fragmentation_in_percent, fragment_count FROM sys.dm_db_index_physical_stats (DB_ID(), OBJECT_ID('SecondTable'), NULL, NULL, 'LIMITED') GO You can see that REORGANIZE does not increase the size of the database or remove the fragmentation. Again, I no way suggest that REORGANIZE is the solution over here. This is purely observation using demo. Read the blog post of Paul Randal. Following script will clean up the database -- Clean up USE MASTER GO ALTER DATABASE ShrinkIsBed SET SINGLE_USER WITH ROLLBACK IMMEDIATE GO DROP DATABASE ShrinkIsBed GO There are few valid cases of the Shrinking database as well, but that is not covered in this blog post. We will cover that area some other time in future. Additionally, one can rebuild index in the tempdb as well, and we will also talk about the same in future. Brent has written a good summary blog post as well. Are you Shrinking your database? Well, when are you going to stop Shrinking it? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Index, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQLServer, T SQL, Technology

    Read the article

  • MySQL – Scalability on Amazon RDS: Scale out to multiple RDS instances

    - by Pinal Dave
    Today, I’d like to discuss getting better MySQL scalability on Amazon RDS. The question of the day: “What can you do when a MySQL database needs to scale write-intensive workloads beyond the capabilities of the largest available machine on Amazon RDS?” Let’s take a look. In a typical EC2/RDS set-up, users connect to app servers from their mobile devices and tablets, computers, browsers, etc.  Then app servers connect to an RDS instance (web/cloud services) and in some cases they might leverage some read-only replicas.   Figure 1. A typical RDS instance is a single-instance database, with read replicas.  This is not very good at handling high write-based throughput. As your application becomes more popular you can expect an increasing number of users, more transactions, and more accumulated data.  User interactions can become more challenging as the application adds more sophisticated capabilities. The result of all this positive activity: your MySQL database will inevitably begin to experience scalability pressures. What can you do? Broadly speaking, there are four options available to improve MySQL scalability on RDS. 1. Larger RDS Instances – If you’re not already using the maximum available RDS instance, you can always scale up – to larger hardware.  Bigger CPUs, more compute power, more memory et cetera. But the largest available RDS instance is still limited.  And they get expensive. “High-Memory Quadruple Extra Large DB Instance”: 68 GB of memory 26 ECUs (8 virtual cores with 3.25 ECUs each) 64-bit platform High I/O Capacity Provisioned IOPS Optimized: 1000Mbps 2. Provisioned IOPs – You can get provisioned IOPs and higher throughput on the I/O level. However, there is a hard limit with a maximum instance size and maximum number of provisioned IOPs you can buy from Amazon and you simply cannot scale beyond these hardware specifications. 3. Leverage Read Replicas – If your application permits, you can leverage read replicas to offload some reads from the master databases. But there are a limited number of replicas you can utilize and Amazon generally requires some modifications to your existing application. And read-replicas don’t help with write-intensive applications. 4. Multiple Database Instances – Amazon offers a fourth option: “You can implement partitioning,thereby spreading your data across multiple database Instances” (Link) However, Amazon does not offer any guidance or facilities to help you with this. “Multiple database instances” is not an RDS feature.  And Amazon doesn’t explain how to implement this idea. In fact, when asked, this is the response on an Amazon forum: Q: Is there any documents that describe the partition DB across multiple RDS? I need to use DB with more 1TB but exist a limitation during the create process, but I read in the any FAQ that you need to partition database, but I don’t find any documents that describe it. A: “DB partitioning/sharding is not an official feature of Amazon RDS or MySQL, but a technique to scale out database by using multiple database instances. The appropriate way to split data depends on the characteristics of the application or data set. Therefore, there is no concrete and specific guidance.” So now what? The answer is to scale out with ScaleBase. Amazon RDS with ScaleBase: What you get – MySQL Scalability! ScaleBase is specifically designed to scale out a single MySQL RDS instance into multiple MySQL instances. Critically, this is accomplished with no changes to your application code.  Your application continues to “see” one database.   ScaleBase does all the work of managing and enforcing an optimized data distribution policy to create multiple MySQL instances. With ScaleBase, data distribution, transactions, concurrency control, and two-phase commit are all 100% transparent and 100% ACID-compliant, so applications, services and tooling continue to interact with your distributed RDS as if it were a single MySQL instance. The result: now you can cost-effectively leverage multiple MySQL RDS instance to scale out write-intensive workloads to an unlimited number of users, transactions, and data. Amazon RDS with ScaleBase: What you keep – Everything! And how does this change your Amazon environment? 1. Keep your application, unchanged – There is no change your application development life-cycle at all.  You still use your existing development tools, frameworks and libraries.  Application quality assurance and testing cycles stay the same. And, critically, you stay with an ACID-compliant MySQL environment. 2. Keep your RDS value-added services – The value-added services that you rely on are all still available. Amazon will continue to handle database maintenance and updates for you. You can still leverage High Availability via Multi A-Z.  And, if it benefits youra application throughput, you can still use read replicas. 3. Keep your RDS administration – Finally the RDS monitoring and provisioning tools you rely on still work as they did before. With your one large MySQL instance, now split into multiple instances, you can actually use less expensive, smallersmaller available RDS hardware and continue to see better database performance. Conclusion Amazon RDS is a tremendous service, but it doesn’t offer solutions to scale beyond a single MySQL instance. Larger RDS instances get more expensive.  And when you max-out on the available hardware, you’re stuck.  Amazon recommends scaling out your single instance into multiple instances for transaction-intensive apps, but offers no services or guidance to help you. This is where ScaleBase comes in to save the day. It gives you a simple and effective way to create multiple MySQL RDS instances, while removing all the complexities typically caused by “DIY” sharding andwith no changes to your applications . With ScaleBase you continue to leverage the AWS/RDS ecosystem: commodity hardware and value added services like read replicas, multi A-Z, maintenance/updates and administration with monitoring tools and provisioning. SCALEBASE ON AMAZON If you’re curious to try ScaleBase on Amazon, it can be found here – Download NOW. Reference: Pinal Dave (http://blog.sqlauthority.com)Filed under: MySQL, PostADay, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

    Read the article

  • General monitoring for SQL Server Analysis Services using Performance Monitor

    - by Testas
    A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs.     File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor,  a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used……  Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total  The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above  25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file 

    Read the article

  • How to Achieve OC4J RMI Load Balancing

    - by fip
    This is an old, Oracle SOA and OC4J 10G topic. In fact this is not even a SOA topic per se. Questions of RMI load balancing arise when you developed custom web applications accessing human tasks running off a remote SOA 10G cluster. Having returned from a customer who faced challenges with OC4J RMI load balancing, I felt there is still some confusions in the field how OC4J RMI load balancing work. Hence I decide to dust off an old tech note that I wrote a few years back and share it with the general public. Here is the tech note: Overview A typical use case in Oracle SOA is that you are building web based, custom human tasks UI that will interact with the task services housed in a remote BPEL 10G cluster. Or, in a more generic way, you are just building a web based application in Java that needs to interact with the EJBs in a remote OC4J cluster. In either case, you are talking to an OC4J cluster as RMI client. Then immediately you must ask yourself the following questions: 1. How do I make sure that the web application, as an RMI client, even distribute its load against all the nodes in the remote OC4J cluster? 2. How do I make sure that the web application, as an RMI client, is resilient to the node failures in the remote OC4J cluster, so that in the unlikely case when one of the remote OC4J nodes fail, my web application will continue to function? That is the topic of how to achieve load balancing with OC4J RMI client. Solutions You need to configure and code RMI load balancing in two places: 1. Provider URL can be specified with a comma separated list of URLs, so that the initial lookup will land to one of the available URLs. 2. Choose a proper value for the oracle.j2ee.rmi.loadBalance property, which, along side with the PROVIDER_URL property, is one of the JNDI properties passed to the JNDI lookup.(http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI) More details below: About the PROVIDER_URL The JNDI property java.name.provider.url's job is, when the client looks up for a new context at the very first time in the client session, to provide a list of RMI context The value of the JNDI property java.name.provider.url goes by the format of a single URL, or a comma separate list of URLs. A single URL. For example: opmn:ormi://host1:6003:oc4j_instance1/appName1 A comma separated list of multiple URLs. For examples:  opmn:ormi://host1:6003:oc4j_instanc1/appName, opmn:ormi://host2:6003:oc4j_instance1/appName, opmn:ormi://host3:6003:oc4j_instance1/appName When the client looks up for a new Context the very first time in the client session, it sends a query against the OPMN referenced by the provider URL. The OPMN host and port specifies the destination of such query, and the OC4J instance name and appName are actually the “where clause” of the query. When the PROVIDER URL reference a single OPMN server Let's consider the case when the provider url only reference a single OPMN server of the destination cluster. In this case, that single OPMN server receives the query and returns a list of the qualified Contexts from all OC4Js within the cluster, even though there is a single OPMN server in the provider URL. A context represent a particular starting point at a particular server for subsequent object lookup. For example, if the URL is opmn:ormi://host1:6003:oc4j_instance1/appName, then, OPMN will return the following contexts: appName on oc4j_instance1 on host1 appName on oc4j_instance1 on host2, appName on oc4j_instance1 on host3,  (provided that host1, host2, host3 are all in the same cluster) Please note that One OPMN will be sufficient to find the list of all contexts from the entire cluster that satisfy the JNDI lookup query. You can do an experiment by shutting down appName on host1, and observe that OPMN on host1 will still be able to return you appname on host2 and appName on host3. When the PROVIDER URL reference a comma separated list of multiple OPMN servers When the JNDI propery java.naming.provider.url references a comma separated list of multiple URLs, the lookup will return the exact same things as with the single OPMN server: a list of qualified Contexts from the cluster. The purpose of having multiple OPMN servers is to provide high availability in the initial context creation, such that if OPMN at host1 is unavailable, client will try the lookup via OPMN on host2, and so on. After the initial lookup returns and cache a list of contexts, the JNDI URL(s) are no longer used in the same client session. That explains why removing the 3rd URL from the list of JNDI URLs will not stop the client from getting the EJB on the 3rd server. About the oracle.j2ee.rmi.loadBalance Property After the client acquires the list of contexts, it will cache it at the client side as “list of available RMI contexts”.  This list includes all the servers in the destination cluster. This list will stay in the cache until the client session (JVM) ends. The RMI load balancing against the destination cluster is happening at the client side, as the client is switching between the members of the list. Whether and how often the client will fresh the Context from the list of Context is based on the value of the  oracle.j2ee.rmi.loadBalance. The documentation at http://docs.oracle.com/cd/B31017_01/web.1013/b28958/rmi.htm#BABDGFBI list all the available values for the oracle.j2ee.rmi.loadBalance. Value Description client If specified, the client interacts with the OC4J process that was initially chosen at the first lookup for the entire conversation. context Used for a Web client (servlet or JSP) that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be returned each time InitialContext() is invoked. lookup Used for a standalone client that will access EJBs in a clustered OC4J environment. If specified, a new Context object for a randomly-selected OC4J instance will be created each time the client calls Context.lookup(). Please note the regardless of the setting of oracle.j2ee.rmi.loadBalance property, the “refresh” only occurs at the client. The client can only choose from the "list of available context" that was returned and cached from the very first lookup. That is, the client will merely get a new Context object from the “list of available RMI contexts” from the cache at the client side. The client will NOT go to the OPMN server again to get the list. That also implies that if you are adding a node to the server cluster AFTER the client’s initial lookup, the client would not know it because neither the server nor the client will initiate a refresh of the “list of available servers” to reflect the new node. About High Availability (i.e. Resilience Against Node Failure of Remote OC4J Cluster) What we have discussed above is about load balancing. Let's also discuss high availability. This is how the High Availability works in RMI: when the client use the context but get an exception such as socket is closed, it knows that the server referenced by that Context is problematic and will try to get another unused Context from the “list of available contexts”. Again, this list is the list that was returned and cached at the very first lookup in the entire client session.

    Read the article

  • People, Process & Engagement: WebCenter Partner Keste

    - by Michael Snow
    v\:* {behavior:url(#default#VML);} o\:* {behavior:url(#default#VML);} w\:* {behavior:url(#default#VML);} .shape {behavior:url(#default#VML);} Within the WebCenter group here at Oracle, discussions about people, process and engagement cross over many vertical industries and products. Amidst our growing partner ecosystem, the community provides us insight into great customer use cases every day. Such is the case with our partner, Keste, who provides us a guest post on our blog today with an overview of their innovative solution for a customer in the transportation industry. Keste is an Oracle software solutions and development company headquartered in Dallas, Texas. As a Platinum member of the Oracle® PartnerNetwork, Keste designs, develops and deploys custom solutions that automate complex business processes. Seamless Customer Self-Service Experience in the Trucking Industry with Oracle WebCenter Portal  Keste, Oracle Platinum Partner Customer Overview Omnitracs, Inc., a Qualcomm company provides mobility solutions for trucking fleets to companies in the transportation industry. Omnitracs’ mobility services include basic communications such as text as well as advanced monitoring services such as GPS tracking, temperature tracking of perishable goods, load tracking and weighting distribution, and many others. Customer Business Needs Already the leading provider of mobility solutions for large trucking fleets, they chose to target smaller trucking fleets as new customers. However their existing high-touch customer support method would not be a cost effective or scalable method to manage and service these smaller customers. Omnitracs needed to provide several self-service features to make customer support more scalable while keeping customer satisfaction levels high and the costs manageable. The solution also had to be very intuitive and easy to use. The systems that Omnitracs sells to these trucking customers require professional installation and smaller customers need to track and schedule the installation. Information captured in Oracle eBusiness Suite needed to be readily available for new customers to track these purchases and delivery details. Omnitracs wanted a high impact User Interface to significantly improve customer experience with the ability to integrate with EBS, provisioning systems as well as CRM systems that were already implemented. Omnitracs also wanted to build an architecture platform that could potentially be extended to other Portals. Omnitracs’ stated goal was to deliver an “eBay-like” or “Amazon-like” experience for all of their customers so that they could reach a much broader market beyond their large company customer base. Solution Overview In order to manage the increased complexity, the growing support needs of global customers and improve overall product time-to-market in a cost-effective manner, IT began to deliver a self-service model. This self service model not only transformed numerous business processes but is also allowing the business to keep up with the growing demands of the (internal and external) customers. This solution was a customer service Portal that provided self service capabilities for large and small customers alike for Activation of mobility products, managing add-on applications for the devices (much like the Apple App Store), transferring services when trucks are sold to other companies as well as deactivation all without the involvement of a call service agent or sending multiple emails to different Omnitracs contacts. This is a conceptual view of the Customer Portal showing the details of the components that make up the solution. 12.00 The portal application for transactions was entirely built using ADF 11g R2. Omnitracs’ business had a pressing requirement to have a portal available 24/7 for its customers. Since there were interactions with EBS in the back-end, the downtimes on the EBS would negate this availability. Omnitracs devised a decoupling strategy at the database side for the EBS data. The decoupling of the database was done using Oracle Data Guard and completely insulated the solution from any eBusiness Suite down time. The customer has no knowledge whether eBS is running or not. Here are two sample screenshots of the portal application built in Oracle ADF. Customer Benefits The Customer Portal not only provided the scalability to grow the business but also provided the seamless integration with other disparate applications. Some of the key benefits are: Improved Customer Experience: With a modern look and feel and a Portal that has the aspects of an App Store, the customer experience was significantly improved. Page response times went from several seconds to sub-second for all of the pages. Enabled new product launches: After successfully dominating the large fleet market, Omnitracs now has a scalable solution to sell and manage smaller fleet customers giving them a huge advantage over their nearest competitors. Dozens of new customers have been acquired via this portal through an onboarding process that now takes minutes Seamless Integrations Improves Customer Support: ADF 11gR2 allowed Omnitracs to bring a diverse list of applications into one integrated solution. This provided a seamless experience for customers to route them from Marketing focused application to a customer-oriented portal. Internally, it also allowed Sales Representatives to have an integrated flow for taking a prospect through the various steps to onboard them as a customer. Key integrations included: Unity Core Salesforce.com Merchant e-Solution for credit card Custom Omnitracs Applications like CUPS and AUTO Security utilizing OID and OVD Back end integration with EBS (Data Guard) and iQ Database Business Impact Significant business impacts were realized through the launch of customer portal. It not only allows the business to push through in underserved segments, but also reduces the time it needs to spend on customer support—allowing the business to focus more on sales and identifying the market for new products. Some of the Immediate Benefits are The entire onboarding process is now completely automated and now completes in minutes. This represents an 85% productivity improvement over their previous processes. And it was 160 times faster! With the success of this self-service solution, the business is now targeting about 3X customer growth in the next five years. This represents a tripling of their overall customer base and significant downstream revenue for the ongoing services. 90%+ improvement of customer onboarding and management process by utilizing, single sign on integration using OID/OAM solution, performance improvements and new self-service functionality Unified login for all Customers, Partners and Internal Users enables login to a common portal and seamless access to all other integrated applications targeted at the respective audience Significantly improved customer experience with a better look and feel with a more user experience focused Portal screens. Helped sales of the new product by having an easy way of ordering and activating the product. Data Guard helped increase availability of the Portal to 99%+ and make it independent of EBS downtime. This gave customers the feel of high availability of the portal application. Some of the anticipated longer term Benefits are: Platform that can be leveraged to launch any new product introduction and enable all product teams to reach new customers and new markets Easy integration with content management to allow business owners more control of the product catalog Overall reduced TCO with standardization of the Oracle platform Managed IT support cost savings through optimization of technology skills needed to support and modify this solution ------------------------------------------------------------ 12.00 Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 -"/ /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Times New Roman","serif";}

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 63 64 65 66 67 68 69 70 71 72 73 74  | Next Page >