Search Results

Search found 7571 results on 303 pages for 'msbuild target'.

Page 132/303 | < Previous Page | 128 129 130 131 132 133 134 135 136 137 138 139  | Next Page >

  • Connecting to Microsoft Excel using Oracle Data Integrator

    - by julien.testut
    The posts in this series assume that you have some level of familiarity with ODI. The concepts of Topology, Data Server, Physical and Logical Architecture are used here assuming that you understand them in the context of ODI. If you need more details on these elements, please refer to the ODI Tutorial for a quick introduction, or to the complete ODI documentation for more details. In this post I will describe how a Microsoft Excel spreadsheet can be used in Oracle Data Integrator. Microsoft Excel is one of the many different technologies you can leverage in ODI as a source or as a target. Prepare your Excel spreadsheet Prior to using a Microsoft Excel spreadsheet in ODI we need to specify a name for the different cell tables we want to use. You can have multiple names in the same spreadsheet. First open up a Microsoft Excel spreadsheet, we will need to define a named range.

    Read the article

  • How to find and fix performance problems in ORM powered applications

    - by FransBouma
    Once in a while we get requests about how to fix performance problems with our framework. As it comes down to following the same steps and looking into the same things every single time, I decided to write a blogpost about it instead, so more people can learn from this and solve performance problems in their O/R mapper powered applications. In some parts it's focused on LLBLGen Pro but it's also usable for other O/R mapping frameworks, as the vast majority of performance problems in O/R mapper powered applications are not specific for a certain O/R mapper framework. Too often, the developer looks at the wrong part of the application, trying to fix what isn't a problem in that part, and getting frustrated that 'things are so slow with <insert your favorite framework X here>'. I'm in the O/R mapper business for a long time now (almost 10 years, full time) and as it's a small world, we O/R mapper developers know almost all tricks to pull off by now: we all know what to do to make task ABC faster and what compromises (because there are almost always compromises) to deal with if we decide to make ABC faster that way. Some O/R mapper frameworks are faster in X, others in Y, but you can be sure the difference is mainly a result of a compromise some developers are willing to deal with and others aren't. That's why the O/R mapper frameworks on the market today are different in many ways, even though they all fetch and save entities from and to a database. I'm not suggesting there's no room for improvement in today's O/R mapper frameworks, there always is, but it's not a matter of 'the slowness of the application is caused by the O/R mapper' anymore. Perhaps query generation can be optimized a bit here, row materialization can be optimized a bit there, but it's mainly coming down to milliseconds. Still worth it if you're a framework developer, but it's not much compared to the time spend inside databases and in user code: if a complete fetch takes 40ms or 50ms (from call to entity object collection), it won't make a difference for your application as that 10ms difference won't be noticed. That's why it's very important to find the real locations of the problems so developers can fix them properly and don't get frustrated because their quest to get a fast, performing application failed. Performance tuning basics and rules Finding and fixing performance problems in any application is a strict procedure with four prescribed steps: isolate, analyze, interpret and fix, in that order. It's key that you don't skip a step nor make assumptions: these steps help you find the reason of a problem which seems to be there, and how to fix it or leave it as-is. Skipping a step, or when you assume things will be bad/slow without doing analysis will lead to the path of premature optimization and won't actually solve your problems, only create new ones. The most important rule of finding and fixing performance problems in software is that you have to understand what 'performance problem' actually means. Most developers will say "when a piece of software / code is slow, you have a performance problem". But is that actually the case? If I write a Linq query which will aggregate, group and sort 5 million rows from several tables to produce a resultset of 10 rows, it might take more than a couple of milliseconds before that resultset is ready to be consumed by other logic. If I solely look at the Linq query, the code consuming the resultset of the 10 rows and then look at the time it takes to complete the whole procedure, it will appear to me to be slow: all that time taken to produce and consume 10 rows? But if you look closer, if you analyze and interpret the situation, you'll see it does a tremendous amount of work, and in that light it might even be extremely fast. With every performance problem you encounter, always do realize that what you're trying to solve is perhaps not a technical problem at all, but a perception problem. The second most important rule you have to understand is based on the old saying "Penny wise, Pound Foolish": the part which takes e.g. 5% of the total time T for a given task isn't worth optimizing if you have another part which takes a much larger part of the total time T for that same given task. Optimizing parts which are relatively insignificant for the total time taken is not going to bring you better results overall, even if you totally optimize that part away. This is the core reason why analysis of the complete set of application parts which participate in a given task is key to being successful in solving performance problems: No analysis -> no problem -> no solution. One warning up front: hunting for performance will always include making compromises. Fast software can be made maintainable, but if you want to squeeze as much performance out of your software, you will inevitably be faced with the dilemma of compromising one or more from the group {readability, maintainability, features} for the extra performance you think you'll gain. It's then up to you to decide whether it's worth it. In almost all cases it's not. The reason for this is simple: the vast majority of performance problems can be solved by implementing the proper algorithms, the ones with proven Big O-characteristics so you know the performance you'll get plus you know the algorithm will work. The time taken by the algorithm implementing code is inevitable: you already implemented the best algorithm. You might find some optimizations on the technical level but in general these are minor. Let's look at the four steps to see how they guide us through the quest to find and fix performance problems. Isolate The first thing you need to do is to isolate the areas in your application which are assumed to be slow. For example, if your application is a web application and a given page is taking several seconds or even minutes to load, it's a good candidate to check out. It's important to start with the isolate step because it allows you to focus on a single code path per area with a clear begin and end and ignore the rest. The rest of the steps are taken per identified problematic area. Keep in mind that isolation focuses on tasks in an application, not code snippets. A task is something that's started in your application by either another task or the user, or another program, and has a beginning and an end. You can see a task as a piece of functionality offered by your application.  Analyze Once you've determined the problem areas, you have to perform analysis on the code paths of each area, to see where the performance problems occur and which areas are not the problem. This is a multi-layered effort: an application which uses an O/R mapper typically consists of multiple parts: there's likely some kind of interface (web, webservice, windows etc.), a part which controls the interface and business logic, the O/R mapper part and the RDBMS, all connected with either a network or inter-process connections provided by the OS or other means. Each of these parts, including the connectivity plumbing, eat up a part of the total time it takes to complete a task, e.g. load a webpage with all orders of a given customer X. To understand which parts participate in the task / area we're investigating and how much they contribute to the total time taken to complete the task, analysis of each participating task is essential. Start with the code you wrote which starts the task, analyze the code and track the path it follows through your application. What does the code do along the way, verify whether it's correct or not. Analyze whether you have implemented the right algorithms in your code for this particular area. Remember we're looking at one area at a time, which means we're ignoring all other code paths, just the code path of the current problematic area, from begin to end and back. Don't dig in and start optimizing at the code level just yet. We're just analyzing. If your analysis reveals big architectural stupidity, it's perhaps a good idea to rethink the architecture at this point. For the rest, we're analyzing which means we collect data about what could be wrong, for each participating part of the complete application. Reviewing the code you wrote is a good tool to get deeper understanding of what is going on for a given task but ultimately it lacks precision and overview what really happens: humans aren't good code interpreters, computers are. We therefore need to utilize tools to get deeper understanding about which parts contribute how much time to the total task, triggered by which other parts and for example how many times are they called. There are two different kind of tools which are necessary: .NET profilers and O/R mapper / RDBMS profilers. .NET profiling .NET profilers (e.g. dotTrace by JetBrains or Ants by Red Gate software) show exactly which pieces of code are called, how many times they're called, and the time it took to run that piece of code, at the method level and sometimes even at the line level. The .NET profilers are essential tools for understanding whether the time taken to complete a given task / area in your application is consumed by .NET code, where exactly in your code, the path to that code, how many times that code was called by other code and thus reveals where hotspots are located: the areas where a solution can be found. Importantly, they also reveal which areas can be left alone: remember our penny wise pound foolish saying: if a profiler reveals that a group of methods are fast, or don't contribute much to the total time taken for a given task, ignore them. Even if the code in them is perhaps complex and looks like a candidate for optimization: you can work all day on that, it won't matter.  As we're focusing on a single area of the application, it's best to start profiling right before you actually activate the task/area. Most .NET profilers support this by starting the application without starting the profiling procedure just yet. You navigate to the particular part which is slow, start profiling in the profiler, in your application you perform the actions which are considered slow, and afterwards you get a snapshot in the profiler. The snapshot contains the data collected by the profiler during the slow action, so most data is produced by code in the area to investigate. This is important, because it allows you to stay focused on a single area. O/R mapper and RDBMS profiling .NET profilers give you a good insight in the .NET side of things, but not in the RDBMS side of the application. As this article is about O/R mapper powered applications, we're also looking at databases, and the software making it possible to consume the database in your application: the O/R mapper. To understand which parts of the O/R mapper and database participate how much to the total time taken for task T, we need different tools. There are two kind of tools focusing on O/R mappers and database performance profiling: O/R mapper profilers and RDBMS profilers. For O/R mapper profilers, you can look at LLBLGen Prof by hibernating rhinos or the Linq to Sql/LLBLGen Pro profiler by Huagati. Hibernating rhinos also have profilers for other O/R mappers like NHibernate (NHProf) and Entity Framework (EFProf) and work the same as LLBLGen Prof. For RDBMS profilers, you have to look whether the RDBMS vendor has a profiler. For example for SQL Server, the profiler is shipped with SQL Server, for Oracle it's build into the RDBMS, however there are also 3rd party tools. Which tool you're using isn't really important, what's important is that you get insight in which queries are executed during the task / area we're currently focused on and how long they took. Here, the O/R mapper profilers have an advantage as they collect the time it took to execute the query from the application's perspective so they also collect the time it took to transport data across the network. This is important because a query which returns a massive resultset or a resultset with large blob/clob/ntext/image fields takes more time to get transported across the network than a small resultset and a database profiler doesn't take this into account most of the time. Another tool to use in this case, which is more low level and not all O/R mappers support it (though LLBLGen Pro and NHibernate as well do) is tracing: most O/R mappers offer some form of tracing or logging system which you can use to collect the SQL generated and executed and often also other activity behind the scenes. While tracing can produce a tremendous amount of data in some cases, it also gives insight in what's going on. Interpret After we've completed the analysis step it's time to look at the data we've collected. We've done code reviews to see whether we've done anything stupid and which parts actually take place and if the proper algorithms have been implemented. We've done .NET profiling to see which parts are choke points and how much time they contribute to the total time taken to complete the task we're investigating. We've performed O/R mapper profiling and RDBMS profiling to see which queries were executed during the task, how many queries were generated and executed and how long they took to complete, including network transportation. All this data reveals two things: which parts are big contributors to the total time taken and which parts are irrelevant. Both aspects are very important. The parts which are irrelevant (i.e. don't contribute significantly to the total time taken) can be ignored from now on, we won't look at them. The parts which contribute a lot to the total time taken are important to look at. We now have to first look at the .NET profiler results, to see whether the time taken is consumed in our own code, in .NET framework code, in the O/R mapper itself or somewhere else. For example if most of the time is consumed by DbCommand.ExecuteReader, the time it took to complete the task is depending on the time the data is fetched from the database. If there was just 1 query executed, according to tracing or O/R mapper profilers / RDBMS profilers, check whether that query is optimal, uses indexes or has to deal with a lot of data. Interpret means that you follow the path from begin to end through the data collected and determine where, along the path, the most time is contributed. It also means that you have to check whether this was expected or is totally unexpected. My previous example of the 10 row resultset of a query which groups millions of rows will likely reveal that a long time is spend inside the database and almost no time is spend in the .NET code, meaning the RDBMS part contributes the most to the total time taken, the rest is compared to that time, irrelevant. Considering the vastness of the source data set, it's expected this will take some time. However, does it need tweaking? Perhaps all possible tweaks are already in place. In the interpret step you then have to decide that further action in this area is necessary or not, based on what the analysis results show: if the analysis results were unexpected and in the area where the most time is contributed to the total time taken is room for improvement, action should be taken. If not, you can only accept the situation and move on. In all cases, document your decision together with the analysis you've done. If you decide that the perceived performance problem is actually expected due to the nature of the task performed, it's essential that in the future when someone else looks at the application and starts asking questions you can answer them properly and new analysis is only necessary if situations changed. Fix After interpreting the analysis results you've concluded that some areas need adjustment. This is the fix step: you're actively correcting the performance problem with proper action targeted at the real cause. In many cases related to O/R mapper powered applications it means you'll use different features of the O/R mapper to achieve the same goal, or apply optimizations at the RDBMS level. It could also mean you apply caching inside your application (compromise memory consumption over performance) to avoid unnecessary re-querying data and re-consuming the results. After applying a change, it's key you re-do the analysis and interpretation steps: compare the results and expectations with what you had before, to see whether your actions had any effect or whether it moved the problem to a different part of the application. Don't fall into the trap to do partly analysis: do the full analysis again: .NET profiling and O/R mapper / RDBMS profiling. It might very well be that the changes you've made make one part faster but another part significantly slower, in such a way that the overall problem hasn't changed at all. Performance tuning is dealing with compromises and making choices: to use one feature over the other, to accept a higher memory footprint, to go away from the strict-OO path and execute queries directly onto the RDBMS, these are choices and compromises which will cross your path if you want to fix performance problems with respect to O/R mappers or data-access and databases in general. In most cases it's not a big issue: alternatives are often good choices too and the compromises aren't that hard to deal with. What is important is that you document why you made a choice, a compromise: which analysis data, which interpretation led you to the choice made. This is key for good maintainability in the years to come. Most common performance problems with O/R mappers Below is an incomplete list of common performance problems related to data-access / O/R mappers / RDBMS code. It will help you with fixing the hotspots you found in the interpretation step. SELECT N+1: (Lazy-loading specific). Lazy loading triggered performance bottlenecks. Consider a list of Orders bound to a grid. You have a Field mapped onto a related field in Order, Customer.CompanyName. Showing this column in the grid will make the grid fetch (indirectly) for each row the Customer row. This means you'll get for the single list not 1 query (for the orders) but 1+(the number of orders shown) queries. To solve this: use eager loading using a prefetch path to fetch the customers with the orders. SELECT N+1 is easy to spot with an O/R mapper profiler or RDBMS profiler: if you see a lot of identical queries executed at once, you have this problem. Prefetch paths using many path nodes or sorting, or limiting. Eager loading problem. Prefetch paths can help with performance, but as 1 query is fetched per node, it can be the number of data fetched in a child node is bigger than you think. Also consider that data in every node is merged on the client within the parent. This is fast, but it also can take some time if you fetch massive amounts of entities. If you keep fetches small, you can use tuning parameters like the ParameterizedPrefetchPathThreshold setting to get more optimal queries. Deep inheritance hierarchies of type Target Per Entity/Type. If you use inheritance of type Target per Entity / Type (each type in the inheritance hierarchy is mapped onto its own table/view), fetches will join subtype- and supertype tables in many cases, which can lead to a lot of performance problems if the hierarchy has many types. With this problem, keep inheritance to a minimum if possible, or switch to a hierarchy of type Target Per Hierarchy, which means all entities in the inheritance hierarchy are mapped onto the same table/view. Of course this has its own set of drawbacks, but it's a compromise you might want to take. Fetching massive amounts of data by fetching large lists of entities. LLBLGen Pro supports paging (and limiting the # of rows returned), which is often key to process through large sets of data. Use paging on the RDBMS if possible (so a query is executed which returns only the rows in the page requested). When using paging in a web application, be sure that you switch server-side paging on on the datasourcecontrol used. In this case, paging on the grid alone is not enough: this can lead to fetching a lot of data which is then loaded into the grid and paged there. Keep note that analyzing queries for paging could lead to the false assumption that paging doesn't occur, e.g. when the query contains a field of type ntext/image/clob/blob and DISTINCT can't be applied while it should have (e.g. due to a join): the datareader will do DISTINCT filtering on the client. this is a little slower but it does perform paging functionality on the data-reader so it won't fetch all rows even if the query suggests it does. Fetch massive amounts of data because blob/clob/ntext/image fields aren't excluded. LLBLGen Pro supports field exclusion for queries. You can exclude fields (also in prefetch paths) per query to avoid fetching all fields of an entity, e.g. when you don't need them for the logic consuming the resultset. Excluding fields can greatly reduce the amount of time spend on data-transport across the network. Use this optimization if you see that there's a big difference between query execution time on the RDBMS and the time reported by the .NET profiler for the ExecuteReader method call. Doing client-side aggregates/scalar calculations by consuming a lot of data. If possible, try to formulate a scalar query or group by query using the projection system or GetScalar functionality of LLBLGen Pro to do data consumption on the RDBMS server. It's far more efficient to process data on the RDBMS server than to first load it all in memory, then traverse the data in-memory to calculate a value. Using .ToList() constructs inside linq queries. It might be you use .ToList() somewhere in a Linq query which makes the query be run partially in-memory. Example: var q = from c in metaData.Customers.ToList() where c.Country=="Norway" select c; This will actually fetch all customers in-memory and do an in-memory filtering, as the linq query is defined on an IEnumerable<T>, and not on the IQueryable<T>. Linq is nice, but it can often be a bit unclear where some parts of a Linq query might run. Fetching all entities to delete into memory first. To delete a set of entities it's rather inefficient to first fetch them all into memory and then delete them one by one. It's more efficient to execute a DELETE FROM ... WHERE query on the database directly to delete the entities in one go. LLBLGen Pro supports this feature, and so do some other O/R mappers. It's not always possible to do this operation in the context of an O/R mapper however: if an O/R mapper relies on a cache, these kind of operations are likely not supported because they make it impossible to track whether an entity is actually removed from the DB and thus can be removed from the cache. Fetching all entities to update with an expression into memory first. Similar to the previous point: it is more efficient to update a set of entities directly with a single UPDATE query using an expression instead of fetching the entities into memory first and then updating the entities in a loop, and afterwards saving them. It might however be a compromise you don't want to take as it is working around the idea of having an object graph in memory which is manipulated and instead makes the code fully aware there's a RDBMS somewhere. Conclusion Performance tuning is almost always about compromises and making choices. It's also about knowing where to look and how the systems in play behave and should behave. The four steps I provided should help you stay focused on the real problem and lead you towards the solution. Knowing how to optimally use the systems participating in your own code (.NET framework, O/R mapper, RDBMS, network/services) is key for success as well as knowing what's going on inside the application you built. I hope you'll find this guide useful in tracking down performance problems and dealing with them in a useful way.  

    Read the article

  • LLBLGen Pro v3.5 has been released!

    - by FransBouma
    Last weekend we released LLBLGen Pro v3.5! Below the list of what's new in this release. Of course, not everything is on this list, like the large amount of work we put in refactoring the runtime framework. The refactoring was necessary because our framework has two paradigms which are added to the framework at a different time, and from a design perspective in the wrong order (the paradigm we added first, SelfServicing, should have been built on top of Adapter, the other paradigm, which was added more than a year after the first released version). The refactoring made sure the framework re-uses more code across the two paradigms (they already shared a lot of code) and is better prepared for the future. We're not done yet, but refactoring a massive framework like ours without breaking interfaces and existing applications is ... a bit of a challenge ;) To celebrate the release of v3.5, we give every customer a 30% discount! Use the coupon code NR1ORM with your order :) The full list of what's new: Designer Rule based .NET Attribute definitions. It's now possible to specify a rule using fine-grained expressions with an attribute definition to define which elements of a given type will receive the attribute definition. Rules can be assigned to attribute definitions on the project level, to make it even easier to define attribute definitions in bulk for many elements in the project. More information... Revamped Project Settings dialog. Multiple project related properties and settings dialogs have been merged into a single dialog called Project Settings, which makes it easier to configure the various settings related to project elements. It also makes it easier to find features previously not used  by many (e.g. type conversions) More information... Home tab with Quick Start Guides. To make new users feel right at home, we added a home tab with quick start guides which guide you through four main use cases of the designer. System Type Converters. Many common conversions have been implemented by default in system type converters so users don't have to develop their own type converters anymore for these type conversions. Bulk Element Setting Manipulator. To change setting values for multiple project elements, it was a little cumbersome to do that without a lot of clicking and opening various editors. This dialog makes changing settings for multiple elements very easy. EDMX Importer. It's now possible to import entity model data information from an existing Entity Framework EDMX file. Other changes and fixes See for the full list of changes and fixes the online documentation. LLBLGen Pro Runtime Framework WCF Data Services (OData) support has been added. It's now possible to use your LLBLGen Pro runtime framework powered domain layer in a WCF Data Services application using the VS.NET tools for WCF Data Services. WCF Data Services is a Microsoft technology for .NET 4 to expose your domain model using OData. More information... New query specification and execution API: QuerySpec. QuerySpec is our new query specification and execution API as an alternative to Linq and our more low-level API. It's build, like our Linq provider, on top of our lower-level API. More information... SQL Server 2012 support. The SQL Server DQE allows paging using the new SQL Server 2012 style. More information... System Type converters. For a common set of types the LLBLGen Pro runtime framework contains built-in type conversions so you don't need to write your own type converters anymore. Public/NonPublic property support. It's now possible to mark a field / navigator as non-public which is reflected in the runtime framework as an internal/friend property instead of a public property. This way you can hide properties from the public interface of a generated class and still access it through code added to the generated code base. FULL JOIN support. It's now possible to perform FULL JOIN joins using the native query api and QuerySpec. It's left to the developer to check whether the used target database supports FULL (OUTER) JOINs. Using a FULL JOIN with entity fetches is not recommended, and should only be used when both participants in the join aren't the target of the fetch. Dependency Injection Tracing. It's now possible to enable tracing on dependency injection. Enable tracing at level '4' on the traceswitch 'ORMGeneral'. This will emit trace information about which instance of which type got an instance of type T injected into property P. Entity Instances in projections in Linq. It's now possible to return an entity instance in a custom Linq projection. It's now also possible to pass this instance to a method inside the query projection. Inheritance fully supported in this construct. Entity Framework support The Entity Framework has been updated in the recent year with code-first support and a new simpler context api: DbContext (with DbSet). The amount of code to generate is smaller and the context simpler. LLBLGen Pro v3.5 comes with support for DbContext and DbSet and generates code which utilizes these new classes. NHibernate support NHibernate v3.2+ built-in proxy factory factory support. By default the built-in ProxyFactoryFactory is selected. FluentNHibernate Session Manager uses 1.2 syntax. Fluent NHibernate mappings generate a SessionManager which uses the v1.2 syntax for the ProxyFactoryFactory location Optionally emit schema / catalog name in mappings Two settings have been added which allow the user to control whether the catalog name and/or schema name as known in the project in the designer is emitted into the mappings.

    Read the article

  • Algorithm to make groups of units

    - by M28
    In Age of Mythology and some other strategy games, when you select multiple units and order them to move to some place, they make a "group" when they reach the desired location: I have a Vector with several sprites, which are the selected units, the variables tarX and tarY are the target x and y. I just want an example, so you can just set the x and y position and I can adapt it to my code. Also, I would like to ask that the algorithm calls "isWalkable" for the x and y position, to determine if it's a valid position for each unit.

    Read the article

  • 2d movement solution

    - by Phil
    Hi! I'm making a simple top-down tank game on the ipad where the user controls the movement of the tank with the left "joystick" and the rotation of the turret with the right one. I've spent several hours just trying to get it to work decently but now I turn to the pros :) I have two referencial objects, one for the movement and one for the rotation. The referencial objects always stay max two units away from the tank and I use them to tell the tank in what direction to move. I chose this approach to decouple movement and rotational behaviour from the raw input of the joysticks, I believe this will make it simpler to implement whatever behaviour I want for the tank. My problem is 1; the turret rotates the long way to the target. With this I mean that the target can be -5 degrees away in rotation and still it rotates 355 degrees instead of -5 degrees. I can't figure out why. The other problem is with the movement. It just doesn't feel right to have the tank turn while moving. I'd like to have a solution that would work as well for the AI as for the player. A blackbox function for the movement where the player only specifies in what direction it should move and it moves there under the constraints that are imposed on it. I am using the standard joystick class found in the Unity iPhone package. This is the code I'm using for the movement: public class TankFollow : MonoBehaviour { //Check angle difference and turn accordingly public GameObject followPoint; public float speed; public float turningSpeed; void Update() { transform.position = Vector3.Slerp(transform.position, followPoint.transform.position, speed * Time.deltaTime); //Calculate angle var forwardA = transform.forward; var forwardB = (followPoint.transform.position - transform.position); var angleA = Mathf.Atan2(forwardA.x, forwardA.z) * Mathf.Rad2Deg; var angleB = Mathf.Atan2(forwardB.x, forwardB.z) * Mathf.Rad2Deg; var angleDiff = Mathf.DeltaAngle(angleA, angleB); //print(angleDiff.ToString()); if (angleDiff > 5) { //Rotate to transform.Rotate(new Vector3(0, (-turningSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else if (angleDiff < 5) { transform.Rotate(new Vector3(0, (turningSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else { } transform.position = new Vector3(transform.position.x, 0, transform.position.z); } } And this is the code I'm using to rotate the turret: void LookAt() { var forwardA = -transform.right; var forwardB = (toLookAt.transform.position - transform.position); var angleA = Mathf.Atan2(forwardA.x, forwardA.z) * Mathf.Rad2Deg; var angleB = Mathf.Atan2(forwardB.x, forwardB.z) * Mathf.Rad2Deg; var angleDiff = Mathf.DeltaAngle(angleA, angleB); //print(angleDiff.ToString()); if (angleDiff - 180 > 1) { //Rotate to transform.Rotate(new Vector3(0, (turretSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else if (angleDiff - 180 < -1) { transform.Rotate(new Vector3(0, (-turretSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); print((angleDiff - 180).ToString()); } else { } } Since I want the turret reference point to turn in relation to the tank (when you rotate the body, the turret should follow and not stay locked on since it makes it impossible to control when you've got two thumbs to work with), I've made the TurretFollowPoint a child of the Turret object, which in turn is a child of the body. I'm thinking that I'm making it too difficult for myself with the reference points but I'm imagining that it's a good idea. Please be honest about this point. So I'll be grateful for any help I can get! I'm using Unity3d iPhone. Thanks!

    Read the article

  • Integrate Microsoft Translator into your ASP.Net application

    - by sreejukg
    In this article I am going to explain how easily you can integrate the Microsoft translator API to your ASP.Net application. Why we need a translation API? Once you published a website, you are opening a channel to the global audience. So making the web content available only in one language doesn’t cover all your audience. Especially when you are offering products/services it is important to provide contents in multiple languages. Users will be more comfortable when they see the content in their native language. How to achieve this, hiring translators and translate the content to all your user’s languages will cost you lot of money, and it is not a one time job, you need to translate the contents on the go. What is the alternative, we need to look for machine translation. Thankfully there are some translator engines available that gives you API level access, so that automatically you can translate the content and display to the user. Microsoft Translator API is an excellent set of web service APIs that allows developers to use the machine translation technology in their own applications. The Microsoft Translator API is offered through Windows Azure market place. In order to access the data services published in Windows Azure market place, you need to have an account. The registration process is simple, and it is common for all the services offered through the market place. Last year I had written an article about Bing Search API, where I covered the registration process. You can refer the article here. http://weblogs.asp.net/sreejukg/archive/2012/07/04/integrate-bing-search-api-to-asp-net-application.aspx Once you registered with Windows market place, you will get your APP ID. Now you can visit the Microsoft Translator page and click on the sign up button. http://datamarket.azure.com/dataset/bing/microsofttranslator As you can see, there are several options available for you to subscribe. There is a free version available, great. Click on the sign up button under the package that suits you. Clicking on the sign up button will bring the sign up form, where you need to agree on the terms and conditions and go ahead. You need to have a windows live account in order to sign up for any service available in Windows Azure market place. Once you signed up successfully, you will receive the thank you page. You can download the C# class library from here so that the integration can be made without writing much code. The C# file name is TranslatorContainer.cs. At any point of time, you can visit https://datamarket.azure.com/account/datasets to see the applications you are subscribed to. Click on the Use link next to each service will give you the details of the application. You need to not the primary account key and URL of the service to use in your application. Now let us start our ASP.Net project. I have created an empty ASP.Net web application using Visual Studio 2010 and named it Translator Sample, any name could work. By default, the web application in solution explorer looks as follows. Now right click the project and select Add -> Existing Item and then browse to the TranslatorContainer.cs. Now let us create a page where user enter some data and perform the translation. I have added a new web form to the project with name Translate.aspx. I have placed one textbox control for user to type the text to translate, the dropdown list to select the target language, a label to display the translated text and a button to perform the translation. For the dropdown list I have selected some languages supported by Microsoft translator. You can get all the supported languages with their codes from the below link. http://msdn.microsoft.com/en-us/library/hh456380.aspx The form looks as below in the design surface of Visual Studio. All the class libraries in the windows market place requires reference to System.Data.Services.Client, let us add the reference. You can find the documentation of how to use the downloaded class library from the below link. http://msdn.microsoft.com/en-us/library/gg312154.aspx Let us evaluate the translatorContainer.cs file. You can refer the code and it is self-explanatory. Note the namespace name used (Microsoft), you need to add the namespace reference to your page. I have added the following event for the translate button. The code is self-explanatory. You are creating an object of TranslatorContainer class by passing the translation service URL. Now you need to set credentials for your Translator container object, which will be your account key. The TranslatorContainer support a method that accept a text input, source language and destination language and returns DataServiceQuery<Translation>. Let us see this working, I just ran the application and entered Good Morning in the Textbox. Selected target language and see the output as follows. It is easy to build great translator applications using Microsoft translator API, and there is a reasonable amount of translation you can perform in your application for free. For enterprises, you can subscribe to the appropriate package and make your application multi-lingual.

    Read the article

  • Webcenter 11g Patch Set 3 (PS3) - Formação para Parceiros - 19/Jan/11

    - by Claudia Costa
    O Oracle WebCenter Suite consiste num conjunto integrado de soluções  baseadas em standards de mercado, com o objectivo de criar aplicações de negócio, portais corporativos, comunidades de interesse, grupos colaborativos, redes sociais, consolidar sistemas aplicacionais web e potenciar as funcionalidades web 2.0 dentro e fora de uma organização. Toda a solução está em conformidade com os principais Standards de mercado e baseia-se numa arquitectura totalmente orientada ao serviço (SOA). Venha conhecer nesta sessão as novas funcionalidades do Webcenter Suite 11g PS3. Agenda 09.30h Boas Vindas e Introdução 09.40h Oracle & Enterprise 2.0 - João Borrego 10.00h Introduction to Oracle WebCenter Suite as a User Experience Platform (UXP) - Monte Kluemper 10.40h Coffee Break 11.00h Building Rich UIs with Oracle WebCenter Portal - Monte Kluemper 12.00h Interactive Q&A Session - João Borrego e Monte Kluemper 12.30h Almoço 14.00h Deep-dive into WebCenter Technical Architecture - Monte Kluemper 15.30h Final da Sessão Target: - Equipas de Venda e Comerciais (manhã)- Equipas Técnicas e de pré-venda (tarde) Data e Local:19 de JaneiroLagoas Park Hotel Para este Workshop os lugares estão limitados. Por favor aguarde um email de confirmação de sua inscrição.Inscrições : Email Para mais informações, por favor contacte: Claudia Costa / 21 423 50 27

    Read the article

  • The first Oracle Solaris 11 book is now available

    - by user12608550
    The first Oracle Solaris 11 book is now available: Oracle Solaris 11 System Administration - The Complete Reference by Michael Jang, Harry Foxwell, Christine Tran, and Alan Formy-Duval The book covers the Oracle Solaris 11 11/11 release; although the next OS release will be available soon, the book covers major topics and features that are not expected to change significantly. The target audience is broad, and includes Solaris admins, Linux admins and developers, and even those somewhat unfamiliar with UNIX. The coauthors include practitioners and developers from outside of Oracle, emphasizing their field experience using Solaris 11. The book complements the extensive Oracle Solaris 11 Information Library, and covers the main system administration topics of installation, configuration, and management. More Oracle Solaris 11 info here

    Read the article

  • jQuery Selector Tester and Cheat Sheet

    - by SGWellens
    I've always appreciated these tools: Expresso and XPath Builder. They make designing regular expressions and XPath selectors almost fun! Did I say fun? I meant less painful. Being able to paste/load text and then interactively play with the search criteria is infinitely better than the code/compile/run/test cycle. It's faster and you get a much better feel for how the expressions work. So, I decided to make my own interactive tool to test jQuery selectors:  jQuery Selector Tester.   Here's a sneak peek: Note: There are some existing tools you may like better: http://www.woods.iki.fi/interactive-jquery-tester.html http://www.w3schools.com/jquery/trysel.asp?filename=trysel_basic&jqsel=p.intro,%23choose My tool is different: It is one page. You can save it and run it locally without a Web Server. It shows the results as a list of iterated objects instead of highlighted html. A cheat sheet is on the same page as the tester which is handy. I couldn't upload an .htm or .html file to this site so I hosted it on my personal site here: jQuery Selector Tester. Design Highlights: To make the interactive search work, I added a hidden div to the page: <!--Hidden div holds DOM elements for jQuery to search--><div id="HiddenDiv" style="display: none"></div> When ready to search, the searchable html text is copied into the hidden div…this renders the DOM tree in the hidden div: // get the html to search, insert it to the hidden divvar Html = $("#TextAreaHTML").val();$("#HiddenDiv").html(Html); When doing a search, I modify the search pattern to look only in the HiddenDiv. To do that, I put a space between the patterns.  The space is the Ancestor operator (see the Cheat Sheet): // modify search string to only search in our// hidden div and do the searchvar SearchString = "#HiddenDiv " + SearchPattern;try{    var $FoundItems = $(SearchString);}   Big Fat Stinking Faux Pas: I was about to publish this article when I made a big mistake: I tested the tool with Mozilla FireFox. It blowed up…it blowed up real good. In the past I’ve only had to target IE so this was quite a revelation. When I started to learn JavaScript, I was disgusted to see all the browser dependent code. Who wants to spend their time testing against different browsers and versions of browsers? Adding a bunch of ‘if-else’ code is a tedious and thankless task. I avoided client code as much as I could. Then jQuery came along and all was good. It was browser independent and freed us from the tedium of worrying about version N of the Acme browser. Right? Wrong! I had used outerHTML to display the selected elements. The problem is Mozilla FireFox doesn’t implement outerHTML. I replaced this: // encode the html markupvar OuterHtml = $('<div/>').text(this.outerHTML).html(); With this: // encode the html markupvar Html = $('<div>').append(this).html();var OuterHtml = $('<div/>').text(Html).html(); Another problem was that Mozilla FireFox doesn’t implement srcElement. I replaced this: var Row = e.srcElement.parentNode;  With this: var Row = e.target.parentNode; Another problem was the indexing. The browsers have different ways of indexing. I replaced this: // this cell has the search pattern  var Cell = Row.childNodes[1];   // put the pattern in the search box and search                    $("#TextSearchPattern").val(Cell.innerText);  With this: // get the correct cell and the text in the cell// place the text in the seach box and serachvar Cell = $(Row).find("TD:nth-child(2)");var CellText = Cell.text();$("#TextSearchPattern").val(CellText);   So much for the myth of browser independence. Was I overly optimistic and gullible? I don’t think so. And when I get my millions from the deposed Nigerian prince I sent money to, you’ll see that having faith is not futile. Notes: My goal was to have a single standalone file. I tried to keep the features and CSS to a minimum–adding only enough to make it useful and visually pleasing. When testing, I often thought there was a problem with the jQuery selector. Invariable it was invalid html code. If your results aren't what you expect, don't assume it's the jQuery selector pattern: The html may be invalid. To help in development and testing, I added a double-click handler to the rows in the Cheat Sheet table. If you double-click a row, the search pattern is put in the search box, a search is performed and the page is scrolled so you can see the results. I left the test html and code in the page. If you are using a CDN (non-local) version of the jQuery libraray, the designer in Visual Studio becomes extremely slow.  That's why there are two version of the library in the header and one is commented out. For reference, here is the jQuery documentation on selectors: http://api.jquery.com/category/selectors/ Here is a much more comprehensive list of CSS selectors (which jQuery uses): http://www.w3.org/TR/CSS2/selector.html I hope someone finds this useful. Steve WellensCodeProject

    Read the article

  • Understanding G1 GC Logs

    - by poonam
    The purpose of this post is to explain the meaning of GC logs generated with some tracing and diagnostic options for G1 GC. We will take a look at the output generated with PrintGCDetails which is a product flag and provides the most detailed level of information. Along with that, we will also look at the output of two diagnostic flags that get enabled with -XX:+UnlockDiagnosticVMOptions option - G1PrintRegionLivenessInfo that prints the occupancy and the amount of space used by live objects in each region at the end of the marking cycle and G1PrintHeapRegions that provides detailed information on the heap regions being allocated and reclaimed. We will be looking at the logs generated with JDK 1.7.0_04 using these options. Option -XX:+PrintGCDetails Here's a sample log of G1 collection generated with PrintGCDetails. 0.522: [GC pause (young), 0.15877971 secs] [Parallel Time: 157.1 ms] [GC Worker Start (ms): 522.1 522.2 522.2 522.2 Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] [Processed Buffers : 2 2 3 2 Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] [GC Worker Other (ms): 0.3 0.3 0.3 0.3 Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] [Clear CT: 0.1 ms] [Other: 1.5 ms] [Choose CSet: 0.0 ms] [Ref Proc: 0.3 ms] [Ref Enq: 0.0 ms] [Free CSet: 0.3 ms] [Eden: 12M(12M)->0B(10M) Survivors: 0B->2048K Heap: 13M(64M)->9739K(64M)] [Times: user=0.59 sys=0.02, real=0.16 secs] This is the typical log of an Evacuation Pause (G1 collection) in which live objects are copied from one set of regions (young OR young+old) to another set. It is a stop-the-world activity and all the application threads are stopped at a safepoint during this time. This pause is made up of several sub-tasks indicated by the indentation in the log entries. Here's is the top most line that gets printed for the Evacuation Pause. 0.522: [GC pause (young), 0.15877971 secs] This is the highest level information telling us that it is an Evacuation Pause that started at 0.522 secs from the start of the process, in which all the regions being evacuated are Young i.e. Eden and Survivor regions. This collection took 0.15877971 secs to finish. Evacuation Pauses can be mixed as well. In which case the set of regions selected include all of the young regions as well as some old regions. 1.730: [GC pause (mixed), 0.32714353 secs] Let's take a look at all the sub-tasks performed in this Evacuation Pause. [Parallel Time: 157.1 ms] Parallel Time is the total elapsed time spent by all the parallel GC worker threads. The following lines correspond to the parallel tasks performed by these worker threads in this total parallel time, which in this case is 157.1 ms. [GC Worker Start (ms): 522.1 522.2 522.2 522.2Avg: 522.2, Min: 522.1, Max: 522.2, Diff: 0.1] The first line tells us the start time of each of the worker thread in milliseconds. The start times are ordered with respect to the worker thread ids – thread 0 started at 522.1ms and thread 1 started at 522.2ms from the start of the process. The second line tells the Avg, Min, Max and Diff of the start times of all of the worker threads. [Ext Root Scanning (ms): 1.6 1.5 1.6 1.9 Avg: 1.7, Min: 1.5, Max: 1.9, Diff: 0.4] This gives us the time spent by each worker thread scanning the roots (globals, registers, thread stacks and VM data structures). Here, thread 0 took 1.6ms to perform the root scanning task and thread 1 took 1.5 ms. The second line clearly shows the Avg, Min, Max and Diff of the times spent by all the worker threads. [Update RS (ms): 38.7 38.8 50.6 37.3 Avg: 41.3, Min: 37.3, Max: 50.6, Diff: 13.3] Update RS gives us the time each thread spent in updating the Remembered Sets. Remembered Sets are the data structures that keep track of the references that point into a heap region. Mutator threads keep changing the object graph and thus the references that point into a particular region. We keep track of these changes in buffers called Update Buffers. The Update RS sub-task processes the update buffers that were not able to be processed concurrently, and updates the corresponding remembered sets of all regions. [Processed Buffers : 2 2 3 2Sum: 9, Avg: 2, Min: 2, Max: 3, Diff: 1] This tells us the number of Update Buffers (mentioned above) processed by each worker thread. [Scan RS (ms): 9.9 9.7 0.0 9.7 Avg: 7.3, Min: 0.0, Max: 9.9, Diff: 9.9] These are the times each worker thread had spent in scanning the Remembered Sets. Remembered Set of a region contains cards that correspond to the references pointing into that region. This phase scans those cards looking for the references pointing into all the regions of the collection set. [Object Copy (ms): 106.7 106.8 104.6 107.9 Avg: 106.5, Min: 104.6, Max: 107.9, Diff: 3.3] These are the times spent by each worker thread copying live objects from the regions in the Collection Set to the other regions. [Termination (ms): 0.0 0.0 0.0 0.0 Avg: 0.0, Min: 0.0, Max: 0.0, Diff: 0.0] Termination time is the time spent by the worker thread offering to terminate. But before terminating, it checks the work queues of other threads and if there are still object references in other work queues, it tries to steal object references, and if it succeeds in stealing a reference, it processes that and offers to terminate again. [Termination Attempts : 1 4 4 6 Sum: 15, Avg: 3, Min: 1, Max: 6, Diff: 5] This gives the number of times each thread has offered to terminate. [GC Worker End (ms): 679.1 679.1 679.1 679.1 Avg: 679.1, Min: 679.1, Max: 679.1, Diff: 0.1] These are the times in milliseconds at which each worker thread stopped. [GC Worker (ms): 156.9 157.0 156.9 156.9 Avg: 156.9, Min: 156.9, Max: 157.0, Diff: 0.1] These are the total lifetimes of each worker thread. [GC Worker Other (ms): 0.3 0.3 0.3 0.3Avg: 0.3, Min: 0.3, Max: 0.3, Diff: 0.0] These are the times that each worker thread spent in performing some other tasks that we have not accounted above for the total Parallel Time. [Clear CT: 0.1 ms] This is the time spent in clearing the Card Table. This task is performed in serial mode. [Other: 1.5 ms] Time spent in the some other tasks listed below. The following sub-tasks (which individually may be parallelized) are performed serially. [Choose CSet: 0.0 ms] Time spent in selecting the regions for the Collection Set. [Ref Proc: 0.3 ms] Total time spent in processing Reference objects. [Ref Enq: 0.0 ms] Time spent in enqueuing references to the ReferenceQueues. [Free CSet: 0.3 ms] Time spent in freeing the collection set data structure. [Eden: 12M(12M)->0B(13M) Survivors: 0B->2048K Heap: 14M(64M)->9739K(64M)] This line gives the details on the heap size changes with the Evacuation Pause. This shows that Eden had the occupancy of 12M and its capacity was also 12M before the collection. After the collection, its occupancy got reduced to 0 since everything is evacuated/promoted from Eden during a collection, and its target size grew to 13M. The new Eden capacity of 13M is not reserved at this point. This value is the target size of the Eden. Regions are added to Eden as the demand is made and when the added regions reach to the target size, we start the next collection. Similarly, Survivors had the occupancy of 0 bytes and it grew to 2048K after the collection. The total heap occupancy and capacity was 14M and 64M receptively before the collection and it became 9739K and 64M after the collection. Apart from the evacuation pauses, G1 also performs concurrent-marking to build the live data information of regions. 1.416: [GC pause (young) (initial-mark), 0.62417980 secs] ….... 2.042: [GC concurrent-root-region-scan-start] 2.067: [GC concurrent-root-region-scan-end, 0.0251507] 2.068: [GC concurrent-mark-start] 3.198: [GC concurrent-mark-reset-for-overflow] 4.053: [GC concurrent-mark-end, 1.9849672 sec] 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] 4.090: [GC concurrent-cleanup-start] 4.091: [GC concurrent-cleanup-end, 0.0002721] The first phase of a marking cycle is Initial Marking where all the objects directly reachable from the roots are marked and this phase is piggy-backed on a fully young Evacuation Pause. 2.042: [GC concurrent-root-region-scan-start] This marks the start of a concurrent phase that scans the set of root-regions which are directly reachable from the survivors of the initial marking phase. 2.067: [GC concurrent-root-region-scan-end, 0.0251507] End of the concurrent root region scan phase and it lasted for 0.0251507 seconds. 2.068: [GC concurrent-mark-start] Start of the concurrent marking at 2.068 secs from the start of the process. 3.198: [GC concurrent-mark-reset-for-overflow] This indicates that the global marking stack had became full and there was an overflow of the stack. Concurrent marking detected this overflow and had to reset the data structures to start the marking again. 4.053: [GC concurrent-mark-end, 1.9849672 sec] End of the concurrent marking phase and it lasted for 1.9849672 seconds. 4.055: [GC remark 4.055: [GC ref-proc, 0.0000254 secs], 0.0030184 secs] This corresponds to the remark phase which is a stop-the-world phase. It completes the left over marking work (SATB buffers processing) from the previous phase. In this case, this phase took 0.0030184 secs and out of which 0.0000254 secs were spent on Reference processing. 4.088: [GC cleanup 117M->106M(138M), 0.0015198 secs] Cleanup phase which is again a stop-the-world phase. It goes through the marking information of all the regions, computes the live data information of each region, resets the marking data structures and sorts the regions according to their gc-efficiency. In this example, the total heap size is 138M and after the live data counting it was found that the total live data size dropped down from 117M to 106M. 4.090: [GC concurrent-cleanup-start] This concurrent cleanup phase frees up the regions that were found to be empty (didn't contain any live data) during the previous stop-the-world phase. 4.091: [GC concurrent-cleanup-end, 0.0002721] Concurrent cleanup phase took 0.0002721 secs to free up the empty regions. Option -XX:G1PrintRegionLivenessInfo Now, let's look at the output generated with the flag G1PrintRegionLivenessInfo. This is a diagnostic option and gets enabled with -XX:+UnlockDiagnosticVMOptions. G1PrintRegionLivenessInfo prints the live data information of each region during the Cleanup phase of the concurrent-marking cycle. 26.896: [GC cleanup ### PHASE Post-Marking @ 26.896### HEAP committed: 0x02e00000-0x0fe00000 reserved: 0x02e00000-0x12e00000 region-size: 1048576 Cleanup phase of the concurrent-marking cycle started at 26.896 secs from the start of the process and this live data information is being printed after the marking phase. Committed G1 heap ranges from 0x02e00000 to 0x0fe00000 and the total G1 heap reserved by JVM is from 0x02e00000 to 0x12e00000. Each region in the G1 heap is of size 1048576 bytes. ### type address-range used prev-live next-live gc-eff### (bytes) (bytes) (bytes) (bytes/ms) This is the header of the output that tells us about the type of the region, address-range of the region, used space in the region, live bytes in the region with respect to the previous marking cycle, live bytes in the region with respect to the current marking cycle and the GC efficiency of that region. ### FREE 0x02e00000-0x02f00000 0 0 0 0.0 This is a Free region. ### OLD 0x02f00000-0x03000000 1048576 1038592 1038592 0.0 Old region with address-range from 0x02f00000 to 0x03000000. Total used space in the region is 1048576 bytes, live bytes as per the previous marking cycle are 1038592 and live bytes with respect to the current marking cycle are also 1038592. The GC efficiency has been computed as 0. ### EDEN 0x03400000-0x03500000 20992 20992 20992 0.0 This is an Eden region. ### HUMS 0x0ae00000-0x0af00000 1048576 1048576 1048576 0.0### HUMC 0x0af00000-0x0b000000 1048576 1048576 1048576 0.0### HUMC 0x0b000000-0x0b100000 1048576 1048576 1048576 0.0### HUMC 0x0b100000-0x0b200000 1048576 1048576 1048576 0.0### HUMC 0x0b200000-0x0b300000 1048576 1048576 1048576 0.0### HUMC 0x0b300000-0x0b400000 1048576 1048576 1048576 0.0### HUMC 0x0b400000-0x0b500000 1001480 1001480 1001480 0.0 These are the continuous set of regions called Humongous regions for storing a large object. HUMS (Humongous starts) marks the start of the set of humongous regions and HUMC (Humongous continues) tags the subsequent regions of the humongous regions set. ### SURV 0x09300000-0x09400000 16384 16384 16384 0.0 This is a Survivor region. ### SUMMARY capacity: 208.00 MB used: 150.16 MB / 72.19 % prev-live: 149.78 MB / 72.01 % next-live: 142.82 MB / 68.66 % At the end, a summary is printed listing the capacity, the used space and the change in the liveness after the completion of concurrent marking. In this case, G1 heap capacity is 208MB, total used space is 150.16MB which is 72.19% of the total heap size, live data in the previous marking was 149.78MB which was 72.01% of the total heap size and the live data as per the current marking is 142.82MB which is 68.66% of the total heap size. Option -XX:+G1PrintHeapRegions G1PrintHeapRegions option logs the regions related events when regions are committed, allocated into or are reclaimed. COMMIT/UNCOMMIT events G1HR COMMIT [0x6e900000,0x6ea00000]G1HR COMMIT [0x6ea00000,0x6eb00000] Here, the heap is being initialized or expanded and the region (with bottom: 0x6eb00000 and end: 0x6ec00000) is being freshly committed. COMMIT events are always generated in order i.e. the next COMMIT event will always be for the uncommitted region with the lowest address. G1HR UNCOMMIT [0x72700000,0x72800000]G1HR UNCOMMIT [0x72600000,0x72700000] Opposite to COMMIT. The heap got shrunk at the end of a Full GC and the regions are being uncommitted. Like COMMIT, UNCOMMIT events are also generated in order i.e. the next UNCOMMIT event will always be for the committed region with the highest address. GC Cycle events G1HR #StartGC 7G1HR CSET 0x6e900000G1HR REUSE 0x70500000G1HR ALLOC(Old) 0x6f800000G1HR RETIRE 0x6f800000 0x6f821b20G1HR #EndGC 7 This shows start and end of an Evacuation pause. This event is followed by a GC counter tracking both evacuation pauses and Full GCs. Here, this is the 7th GC since the start of the process. G1HR #StartFullGC 17G1HR UNCOMMIT [0x6ed00000,0x6ee00000]G1HR POST-COMPACTION(Old) 0x6e800000 0x6e854f58G1HR #EndFullGC 17 Shows start and end of a Full GC. This event is also followed by the same GC counter as above. This is the 17th GC since the start of the process. ALLOC events G1HR ALLOC(Eden) 0x6e800000 The region with bottom 0x6e800000 just started being used for allocation. In this case it is an Eden region and allocated into by a mutator thread. G1HR ALLOC(StartsH) 0x6ec00000 0x6ed00000G1HR ALLOC(ContinuesH) 0x6ed00000 0x6e000000 Regions being used for the allocation of Humongous object. The object spans over two regions. G1HR ALLOC(SingleH) 0x6f900000 0x6f9eb010 Single region being used for the allocation of Humongous object. G1HR COMMIT [0x6ee00000,0x6ef00000]G1HR COMMIT [0x6ef00000,0x6f000000]G1HR COMMIT [0x6f000000,0x6f100000]G1HR COMMIT [0x6f100000,0x6f200000]G1HR ALLOC(StartsH) 0x6ee00000 0x6ef00000G1HR ALLOC(ContinuesH) 0x6ef00000 0x6f000000G1HR ALLOC(ContinuesH) 0x6f000000 0x6f100000G1HR ALLOC(ContinuesH) 0x6f100000 0x6f102010 Here, Humongous object allocation request could not be satisfied by the free committed regions that existed in the heap, so the heap needed to be expanded. Thus new regions are committed and then allocated into for the Humongous object. G1HR ALLOC(Old) 0x6f800000 Old region started being used for allocation during GC. G1HR ALLOC(Survivor) 0x6fa00000 Region being used for copying old objects into during a GC. Note that Eden and Humongous ALLOC events are generated outside the GC boundaries and Old and Survivor ALLOC events are generated inside the GC boundaries. Other Events G1HR RETIRE 0x6e800000 0x6e87bd98 Retire and stop using the region having bottom 0x6e800000 and top 0x6e87bd98 for allocation. Note that most regions are full when they are retired and we omit those events to reduce the output volume. A region is retired when another region of the same type is allocated or we reach the start or end of a GC(depending on the region). So for Eden regions: For example: 1. ALLOC(Eden) Foo2. ALLOC(Eden) Bar3. StartGC At point 2, Foo has just been retired and it was full. At point 3, Bar was retired and it was full. If they were not full when they were retired, we will have a RETIRE event: 1. ALLOC(Eden) Foo2. RETIRE Foo top3. ALLOC(Eden) Bar4. StartGC G1HR CSET 0x6e900000 Region (bottom: 0x6e900000) is selected for the Collection Set. The region might have been selected for the collection set earlier (i.e. when it was allocated). However, we generate the CSET events for all regions in the CSet at the start of a GC to make sure there's no confusion about which regions are part of the CSet. G1HR POST-COMPACTION(Old) 0x6e800000 0x6e839858 POST-COMPACTION event is generated for each non-empty region in the heap after a full compaction. A full compaction moves objects around, so we don't know what the resulting shape of the heap is (which regions were written to, which were emptied, etc.). To deal with this, we generate a POST-COMPACTION event for each non-empty region with its type (old/humongous) and the heap boundaries. At this point we should only have Old and Humongous regions, as we have collapsed the young generation, so we should not have eden and survivors. POST-COMPACTION events are generated within the Full GC boundary. G1HR CLEANUP 0x6f400000G1HR CLEANUP 0x6f300000G1HR CLEANUP 0x6f200000 These regions were found empty after remark phase of Concurrent Marking and are reclaimed shortly afterwards. G1HR #StartGC 5G1HR CSET 0x6f400000G1HR CSET 0x6e900000G1HR REUSE 0x6f800000 At the end of a GC we retire the old region we are allocating into. Given that its not full, we will carry on allocating into it during the next GC. This is what REUSE means. In the above case 0x6f800000 should have been the last region with an ALLOC(Old) event during the previous GC and should have been retired before the end of the previous GC. G1HR ALLOC-FORCE(Eden) 0x6f800000 A specialization of ALLOC which indicates that we have reached the max desired number of the particular region type (in this case: Eden), but we decided to allocate one more. Currently it's only used for Eden regions when we extend the young generation because we cannot do a GC as the GC-Locker is active. G1HR EVAC-FAILURE 0x6f800000 During a GC, we have failed to evacuate an object from the given region as the heap is full and there is no space left to copy the object. This event is generated within GC boundaries and exactly once for each region from which we failed to evacuate objects. When Heap Regions are reclaimed ? It is also worth mentioning when the heap regions in the G1 heap are reclaimed. All regions that are in the CSet (the ones that appear in CSET events) are reclaimed at the end of a GC. The exception to that are regions with EVAC-FAILURE events. All regions with CLEANUP events are reclaimed. After a Full GC some regions get reclaimed (the ones from which we moved the objects out). But that is not shown explicitly, instead the non-empty regions that are left in the heap are printed out with the POST-COMPACTION events.

    Read the article

  • What's the best way of marketing to programmers?

    - by Stuart
    Disclaimer up front - I'm definitely not going to include any links in here - this question isn't part of my marketing! I've had a few projects recently where the end product is something that developers will use. In the past I've been on the receiving end of all sorts of marketing - as a developer I've gotten no end of junk - 1000s of pens, tee-shirts and mouse pads; enough CDs to keep my desk tea-free; some very useful USB keys with some logos I no longer recognise; a small forest's worth of leaflets; a bulging spam folder full of ignored emails, etc... So that's my question - What are good ways to market to developers? And as an aside - are developers the wrong people to target? - since we so often don't have a purchasing budget anyways!

    Read the article

  • 2d tank movement and turret solution

    - by Phil
    Hi! I'm making a simple top-down tank game on the ipad where the user controls the movement of the tank with the left "joystick" and the rotation of the turret with the right one. I've spent several hours just trying to get it to work decently but now I turn to the pros :) I have two referencial objects, one for the movement and one for the rotation. The referencial objects always stay max two units away from the tank and I use them to tell the tank in what direction to move. I chose this approach to decouple movement and rotational behaviour from the raw input of the joysticks, I believe this will make it simpler to implement whatever behaviour I want for the tank. My problem is 1; the turret rotates the long way to the target. With this I mean that the target can be -5 degrees away in rotation and still it rotates 355 degrees instead of -5 degrees. I can't figure out why. The other problem is with the movement. It just doesn't feel right to have the tank turn while moving. I'd like to have a solution that would work as well for the AI as for the player. A blackbox function for the movement where the player only specifies in what direction it should move and it moves there under the constraints that are imposed on it. I am using the standard joystick class found in the Unity iPhone package. This is the code I'm using for the movement: public class TankFollow : MonoBehaviour { //Check angle difference and turn accordingly public GameObject followPoint; public float speed; public float turningSpeed; void Update() { transform.position = Vector3.Slerp(transform.position, followPoint.transform.position, speed * Time.deltaTime); //Calculate angle var forwardA = transform.forward; var forwardB = (followPoint.transform.position - transform.position); var angleA = Mathf.Atan2(forwardA.x, forwardA.z) * Mathf.Rad2Deg; var angleB = Mathf.Atan2(forwardB.x, forwardB.z) * Mathf.Rad2Deg; var angleDiff = Mathf.DeltaAngle(angleA, angleB); //print(angleDiff.ToString()); if (angleDiff > 5) { //Rotate to transform.Rotate(new Vector3(0, (-turningSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else if (angleDiff < 5) { transform.Rotate(new Vector3(0, (turningSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else { } transform.position = new Vector3(transform.position.x, 0, transform.position.z); } } And this is the code I'm using to rotate the turret: void LookAt() { var forwardA = -transform.right; var forwardB = (toLookAt.transform.position - transform.position); var angleA = Mathf.Atan2(forwardA.x, forwardA.z) * Mathf.Rad2Deg; var angleB = Mathf.Atan2(forwardB.x, forwardB.z) * Mathf.Rad2Deg; var angleDiff = Mathf.DeltaAngle(angleA, angleB); //print(angleDiff.ToString()); if (angleDiff - 180 > 1) { //Rotate to transform.Rotate(new Vector3(0, (turretSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); } else if (angleDiff - 180 < -1) { transform.Rotate(new Vector3(0, (-turretSpeed * Time.deltaTime),0)); //transform.rotation = new Quaternion(transform.rotation.x, transform.rotation.y + adjustment, transform.rotation.z, transform.rotation.w); print((angleDiff - 180).ToString()); } else { } } Since I want the turret reference point to turn in relation to the tank (when you rotate the body, the turret should follow and not stay locked on since it makes it impossible to control when you've got two thumbs to work with), I've made the TurretFollowPoint a child of the Turret object, which in turn is a child of the body. I'm thinking that I'm making it too difficult for myself with the reference points but I'm imagining that it's a good idea. Please be honest about this point. So I'll be grateful for any help I can get! I'm using Unity3d iPhone. Thanks!

    Read the article

  • Dash is slow and shows irrelevant results

    - by Alexey Frishman
    I currently have the latest Ubuntu 12.10 installed on my laptop. Usually I use Launchy application to have a quick access to any app/config/file etc. Now I'm trying to get used to Dash, which is supposed to be default way to do such things in recent Ubuntu versions. The difference between the usage of Launchy and Dash is following: Launchy: Alt+Space - Launchy shell shown instantly - type your request - open the target Dash: SuperKey - PERIOD - Dash is shown - type your request - PERIOD - navigate with arrow buttons between the results - open the desired result Another problem. When I type the term "ryth" (which is incorrectly spelled part of "Rhythmbox") what is shown in these 2 shells: Launchy: 1 result, which is Rhythmbox. The letters 'r', 'y', 't' and 'h' are highlighted. Dash: 2 results, which are MP3s from Amazon and are completely irrelevant to my request So is there any way to tweak the Dash to allow me to use it as I use Launchy with the same performance and results?

    Read the article

  • Url Navigation

    - by russ.bishop
    One of the new features is URL-based navigation which is useful for creating intranet links or auto-generating email links (such as from workflow systems, etc). For IIS 6 and earlier, the format is as follows: http://machine/drm-client/Logon.aspx? app=<appname>&action=go&ver=<version name>&hier=<hier name>&node=<node name> Just replace the fields with their appropriate values (URL-encoded of course). <node name> is optional. If provided it will open the hierarchy and expand directly to the target node. Otherwise the hierarchy is opened to the top node. Note that if the specified version is not loaded it will be loaded automatically.

    Read the article

  • Multi-Device Development in Visual Studio

    - by Daniel Moth
    You've read on Soma's blog post that Microsoft is broadening Visual Studio's reach to other platforms (including for example Android)…  specifically this is what Soma wrote: "With bring-your-own-device trends in the enterprise, and heterogeneity in the consumer mobile device market, developers are increasingly focused on building apps that can target a variety of devices. We are committed to enabling developers to build apps for this heterogeneous, mobile-first world with Visual Studio for the technology of your choice - whether .NET, C++ or JavaScript." If you live in Washington state in the USA (or are willing to relocate here) I am looking for a Program Manager to help with this effort – read the rest of the job description here which is also where you can apply for the position (or email me). Comments about this post by Daniel Moth welcome at the original blog.

    Read the article

  • New Horizons now less than 6 Au from Pluto

    - by TATWORTH
    Originally posted on: http://geekswithblogs.net/TATWORTH/archive/2013/06/22/new-horizons-now-less-than-6-au-from-pluto.aspxThe New Horizons space craft as of 13:00 hrs UTC yesterday is now within 6 AU of  its next target - Pluto. While this is still a long way yet from Pluto, it is the closest spacecraft to Pluto. Closest approach is now some 752 days away on 14 July 2015.There are very interesting articles on the investigation work the New Horizons team has done to plan the path of New Horizons through the Plutonian system:http://pluto.jhuapl.edu/overview/piPerspective.phphttp://pluto.jhuapl.edu/news_center/news/20130614.phpWell done New Horizons team!

    Read the article

  • Microsoft Codename Houston

    - by kaleidoscope
    On one of the final talks about SQL Azure in Day 3 of PDC09, David Robinson, Senior PM on the Azure team, announced a project codenamed ‘Houston’ which is basically a Silverlight equivalent of SQL Server Management Studio. The concept comes from the SQL Azure being within the cloud, and if the only way to interact with it is by installing SSMS locally then it does not feel like a consistent story. From the limited preview, it only contains the basics but it clearly lets you create tables, stored procedures and views, edit them, even add data to tables in a grid view reminiscent of Microsoft Access. The UI was based around the standard ribbon bar, object window on the left and working pane on the right. As of now this tool is still pre-alpha and it seems like a basic tool that will facilitate rapid database development on cloud. When asked about general availability, no dates were given but calendar 2010 was indicated as the target. More information can be found at:      http://sqlfascination.com/2009/11/20/pdc-09-day-3-sql-azure-and-codename-houston-announcement/   Tinu, O

    Read the article

  • concurrency::accelerator

    - by Daniel Moth
    Overview An accelerator represents a "target" on which C++ AMP code can execute and where data can reside. Typically (but not necessarily) an accelerator is a GPU device. Accelerators are represented in C++ AMP as objects of the accelerator class. For many scenarios, you do not need to obtain an accelerator object, since the runtime has a notion of a default accelerator, which is what it thinks is the best one in the system. Examples where you need to deal with accelerator objects are if you need to pick your own accelerator (based on your specific criteria), or if you need to use more than one accelerators from your app. Construction and operator usage You can query and obtain a std::vector of all the accelerators on your system, which the runtime discovers on startup. Beyond enumerating accelerators, you can also create one directly by passing to the constructor a system-wide unique path to a device if you know it (i.e. the “Device Instance Path” property for the device in Device Manager), e.g. accelerator acc(L"PCI\\VEN_1002&DEV_6898&SUBSYS_0B001002etc"); There are some predefined strings (for predefined accelerators) that you can pass to the accelerator constructor (and there are corresponding constants for those on the accelerator class itself, so you don’t have to hardcode them every time). Examples are the following: accelerator::default_accelerator represents the default accelerator that the C++ AMP runtime picks for you if you don’t pick one (the heuristics of how it picks one will be covered in a future post). Example: accelerator acc; accelerator::direct3d_ref represents the reference rasterizer emulator that simulates a direct3d device on the CPU (in a very slow manner). This emulator is available on systems with Visual Studio installed and is useful for debugging. More on debugging in general in future posts. Example: accelerator acc(accelerator::direct3d_ref); accelerator::direct3d_warp represents a target that I will cover in future blog posts. Example: accelerator acc(accelerator::direct3d_warp); accelerator::cpu_accelerator represents the CPU. In this first release the only use of this accelerator is for using the staging arrays technique that I'll cover separately. Example: accelerator acc(accelerator::cpu_accelerator); You can also create an accelerator by shallow copying another accelerator instance (via the corresponding constructor) or simply assigning it to another accelerator instance (via the operator overloading of =). Speaking of operator overloading, you can also compare (for equality and inequality) two accelerator objects between them to determine if they refer to the same underlying device. Querying accelerator characteristics Given an accelerator object, you can access its description, version, device path, size of dedicated memory in KB, whether it is some kind of emulator, whether it has a display attached, whether it supports double precision, and whether it was created with the debugging layer enabled for extensive error reporting. Below is example code that accesses some of the properties; in your real code you'd probably be checking one or more of them in order to pick an accelerator (or check that the default one is good enough for your specific workload): void inspect_accelerator(concurrency::accelerator acc) { std::wcout << "New accelerator: " << acc.description << std::endl; std::wcout << "is_debug = " << acc.is_debug << std::endl; std::wcout << "is_emulated = " << acc.is_emulated << std::endl; std::wcout << "dedicated_memory = " << acc.dedicated_memory << std::endl; std::wcout << "device_path = " << acc.device_path << std::endl; std::wcout << "has_display = " << acc.has_display << std::endl; std::wcout << "version = " << (acc.version >> 16) << '.' << (acc.version & 0xFFFF) << std::endl; } accelerator_view In my next blog post I'll cover a related class: accelerator_view. Suffice to say here that each accelerator may have from 1..n related accelerator_view objects. You can get the accelerator_view from an accelerator via the default_view property, or create new ones by invoking the create_view method that creates an accelerator_view object for you (by also accepting a queuing_mode enum value of deferred or immediate that we'll also explore in the next blog post). Comments about this post by Daniel Moth welcome at the original blog.

    Read the article

  • Expression Blend 4 available and training resources

    - by pluginbaby
    As you may know Expression Blend 4 has shipped! It is still part of Expression Studio, which now comes in 2 “flavors”: Expression Studio 4 Ultimate Expression Blend SketchFlow Expression Web + SuperPreview Expression Encoder Expression Design Expression Studio 4 Web Professional Expression Web + SuperPreview Expression Encoder Expression Design So the version you want for Silverlight is Expression Studio 4 Ultimate (because you can’t buy Expression Blend alone). Expression Blend is an awesome tool but might be difficult to approach at first, specially for people coming from Visual Studio… this tool target designers so it can takes time for a developer to get comfortable enough. Good news is the availability of a free “Blend Fundamentals Training” which contains plenty of resources to help you master Expression Blend in 5 days: http://www.microsoft.com/expression/resources/BlendTraining/   Also don’t forget the .toolbox: http://www.microsoft.com/design/toolbox/ This Microsoft website contains courses and tutorials to help you learn UI Design for Silverlight with Expression Blend.

    Read the article

  • Doing your first mock with JustMock

    In this post, i will start with a  more traditional mocking example that  includes a fund transfer scenario between two different currency account using JustMock.Our target interface that we will be mocking looks similar to: public interface ICurrencyService { float GetConversionRate(string fromCurrency, string toCurrency); } Moving forward the SUT or class that will be consuming the  service and will be invoked by user [provided that the ICurrencyService will be passed...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

    Read the article

  • Dynamic Memory Allocation and Memory Management

    - by Bunkai.Satori
    In an average game, there are hundreds or maybe thousands of objects in the scene. Is it completely correct to allocate memory for all objects, including gun shots (bullets), dynamically via default new()? Should I create any memory pool for dynamic allocation, or is there no need to bother with this? What if the target platform are mobile devices? Is there a need for a memory manager in a mobile game, please? Thank you. Language Used: C++; Currently developed under Windows, but planned to be ported later.

    Read the article

  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

    Read the article

  • Exporting SWF With Transparent Background For Scaleform/UDK

    - by Alex Shepard
    After looking all over in the UDN and forums I have yet to find a solution for this: I am currently using Flash CS3 and Actionscript 2.0 to build my scaleform menus and I can use them in the UDK. For various reasons I can't use the handy plugin Autodesk supplies to enable this export so I publish my flash documents to swf the old fassioned way and manually use the gfxexport.exe tool to get my .gfx file. I can then import into the UDK the normal way. My problem is that the flash movies that I import will not alpha blend even if the material is set to blend in the alpha channel of the target render texture. My project images are set up to export properly. My classpath for Actionscript 2.0 is set to the correct location. My HTML publish settings have window mode set to Transparent Windowless. Is it possible to export without the scaleform flash extension and still get the desired effects and if so how might I do so? Am I merely missing something from my project setup?

    Read the article

  • Can I share a TV card?

    - by Boris
    My environment: 2 PCs, a desktop and a laptop, both on Oneiric they are connected together by ethernet wire nfs-common is installed and configured: the desktop is the server a TV tuner card is installed on the desktop, I can watch TV with the software Me-TV It works fine, TV on desktop, and my network too: I share folders thanks to NFS. But I would like more: How can I share my TV tuner card from the desktop and be able to watch TV on the laptop too? If possible I would like a solution that allows me to keep using the software Me-TV, on both PCs. I bet that there is a solution to create a fake TV card on the 2nd PC with xNBD. I'm trying xnbd-server --target /dev/dvb/adapter0/demux0 but I cant make it work. Trying to understand some examples of xNBD command lines, it seems to be meant only for sharing disk player. If someone as ever used xNBD, he's welcome.

    Read the article

  • OS Analytics with Oracle Enterprise Manager (by Eran Steiner)

    - by Zeynep Koch
    Oracle Enterprise Manager Ops Center provides a feature called "OS Analytics". This feature allows you to get a better understanding of how the Operating System is being utilized. You can research the historical usage as well as real time data. This post will show how you can benefit from OS Analytics and how it works behind the scenes. The recording of our call to discuss this blog is available here: https://oracleconferencing.webex.com/oracleconferencing/ldr.php?AT=pb&SP=MC&rID=71517797&rKey=4ec9d4a3508564b3Download the presentation here See also: Blog about Alert Monitoring and Problem Notification Blog about Using Operational Profiles to Install Packages and other content Here is quick summary of what you can do with OS Analytics in Ops Center: View historical charts and real time value of CPU, memory, network and disk utilization Find the top CPU and Memory processes in real time or at a certain historical day Determine proper monitoring thresholds based on historical data Drill down into a process details Where to start To start with OS Analytics, choose the OS asset in the tree and click the Analytics tab. You can see the CPU utilization, Memory utilization and Network utilization, along with the current real time top 5 processes in each category (click the image to see a larger version):  In the above screen, you can click each of the top 5 processes to see a more detailed view of that process. Here is an example of one of the processes: One of the cool things is that you can see the process tree for this process along with some port binding and open file descriptors. Next, click the "Processes" tab to see real time information of all the processes on the machine: An interesting column is the "Target" column. If you configured Ops Center to work with Enterprise Manager Cloud Control, then the two products will talk to each other and Ops Center will display the correlated target from Cloud Control in this table. If you are only using Ops Center - this column will remain empty. The "Threshold" tab is particularly helpful - you can view historical trends of different monitored values and based on the graph - determine what the monitoring values should be: You can ask Ops Center to suggest monitoring levels based on the historical values or you can set your own. The different colors in the graph represent the current set levels: Red for critical, Yellow for warning and Blue for Information, allowing you to quickly see how they're positioned against real data. It's important to note that when looking at longer periods, Ops Center smooths out the data and uses averages. So when looking at values such as CPU Usage, try shorter time frames which are more detailed, such as one hour or one day. Applying new monitoring values When first applying new values to monitored attributes - a popup will come up asking if it's OK to get you out of the current Monitoring Policy. This is OK if you want to either have custom monitoring for a specific machine, or if you want to use this current machine as a "Gold image" and extract a Monitoring Policy from it. You can later apply the new Monitoring Policy to other machines and also set it as a default Monitoring Profile. Once you're done with applying the different monitoring values, you can review and change them in the "Monitoring" tab. You can also click the "Extract a Monitoring Policy" in the actions pane on the right to save all the new values to a new Monitoring Policy, which can then be found under "Plan Management" -> "Monitoring Policies". Visiting the past Under the "History" tab you can "go back in time". This is very helpful when you know that a machine was busy a few hours ago (perhaps in the middle of the night?), but you were not around to take a look at it in real time. Here's a view into yesterday's data on one of the machines: You can see an interesting CPU spike happening at around 3:30 am along with some memory use. In the bottom table you can see the top 5 CPU and Memory consumers at the requested time. Very quickly you can see that this spike is related to the Solaris 11 IPS repository synchronization process using the "pkgrecv" command. The "time machine" doesn't stop here - you can also view historical data to determine which of the zones was the busiest at a given time: Under the hood The data collected is stored on each of the agents under /var/opt/sun/xvm/analytics/historical/ An "os.zip" file exists for the main OS. Inside you will find many small text files, named after the Epoch time stamp in which they were taken If you have any zones, there will be a file called "guests.zip" containing the same small files for all the zones, as well as a folder with the name of the zone along with "os.zip" in it If this is the Enterprise Controller or the Proxy Controller, you will have folders called "proxy" and "sat" in which you will find the "os.zip" for that controller The actual script collecting the data can be viewed for debugging purposes as well: On Linux, the location is: /opt/sun/xvmoc/private/os_analytics/collect If you would like to redirect all the standard error into a file for debugging, touch the following file and the output will go into it: # touch /tmp/.collect.stderr   The temporary data is collected under /var/opt/sun/xvm/analytics/.collectdb until it is zipped. If you would like to review the properties for the Analytics, you can view those per each agent in /opt/sun/n1gc/lib/XVM.properties. Find the section "Analytics configurable properties for OS and VSC" to view the Analytics specific values. I hope you find this helpful! Please post questions in the comments below. Eran Steiner

    Read the article

< Previous Page | 128 129 130 131 132 133 134 135 136 137 138 139  | Next Page >