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  • 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.  

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  • 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.

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  • MongoDB in Go (golang) with mgo: How do I update a record, find out if update was successful and get the data in a single atomic operation?

    - by Sebastián Grignoli
    I am using mgo driver for MongoDB under Go. My application asks for a task (with just a record select in Mongo from a collection called "jobs") and then registers itself as an asignee to complete that task (an update to that same "job" record, setting itself as assignee). The program will be running on several machines, all talking to the same Mongo. When my program lists the available tasks and then picks one, other instances might have already obtained that assignment, and the current assignment would have failed. How can I get sure that the record I read and then update does or does not have a certain value (in this case, an assignee) at the time of being updated? I am trying to get one assignment, no matter wich one, so I think I should first select a pending task and try to assign it, keeping it just in the case the updating was successful. So, my query should be something like: "From all records on collection 'jobs', update just one that has asignee=null, setting my ID as the assignee. Then, give me that record so I could run the job." How could I express that with mgo driver for Go?

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  • Maintaining shared service in ASP.NET MVC Application

    - by kazimanzurrashid
    Depending on the application sometimes we have to maintain some shared service throughout our application. Let’s say you are developing a multi-blog supported blog engine where both the controller and view must know the currently visiting blog, it’s setting , user information and url generation service. In this post, I will show you how you can handle this kind of case in most convenient way. First, let see the most basic way, we can create our PostController in the following way: public class PostController : Controller { public PostController(dependencies...) { } public ActionResult Index(string blogName, int? page) { BlogInfo blog = blogSerivce.FindByName(blogName); if (blog == null) { return new NotFoundResult(); } IEnumerable<PostInfo> posts = postService.FindPublished(blog.Id, PagingCalculator.StartIndex(page, blog.PostPerPage), blog.PostPerPage); int count = postService.GetPublishedCount(blog.Id); UserInfo user = null; if (HttpContext.User.Identity.IsAuthenticated) { user = userService.FindByName(HttpContext.User.Identity.Name); } return View(new IndexViewModel(urlResolver, user, blog, posts, count, page)); } public ActionResult Archive(string blogName, int? page, ArchiveDate archiveDate) { BlogInfo blog = blogSerivce.FindByName(blogName); if (blog == null) { return new NotFoundResult(); } IEnumerable<PostInfo> posts = postService.FindArchived(blog.Id, archiveDate, PagingCalculator.StartIndex(page, blog.PostPerPage), blog.PostPerPage); int count = postService.GetArchivedCount(blog.Id, archiveDate); UserInfo user = null; if (HttpContext.User.Identity.IsAuthenticated) { user = userService.FindByName(HttpContext.User.Identity.Name); } return View(new ArchiveViewModel(urlResolver, user, blog, posts, count, page, achiveDate)); } public ActionResult Tag(string blogName, string tagSlug, int? page) { BlogInfo blog = blogSerivce.FindByName(blogName); if (blog == null) { return new NotFoundResult(); } TagInfo tag = tagService.FindBySlug(blog.Id, tagSlug); if (tag == null) { return new NotFoundResult(); } IEnumerable<PostInfo> posts = postService.FindPublishedByTag(blog.Id, tag.Id, PagingCalculator.StartIndex(page, blog.PostPerPage), blog.PostPerPage); int count = postService.GetPublishedCountByTag(tag.Id); UserInfo user = null; if (HttpContext.User.Identity.IsAuthenticated) { user = userService.FindByName(HttpContext.User.Identity.Name); } return View(new TagViewModel(urlResolver, user, blog, posts, count, page, tag)); } } As you can see the above code heavily depends upon the current blog and the blog retrieval code is duplicated in all of the action methods, once the blog is retrieved the same blog is passed in the view model. Other than the blog the view also needs the current user and url resolver to render it properly. One way to remove the duplicate blog retrieval code is to create a custom model binder which converts the blog from a blog name and use the blog a parameter in the action methods instead of the string blog name, but it only helps the first half in the above scenario, the action methods still have to pass the blog, user and url resolver etc in the view model. Now lets try to improve the the above code, first lets create a new class which would contain the shared services, lets name it as BlogContext: public class BlogContext { public BlogInfo Blog { get; set; } public UserInfo User { get; set; } public IUrlResolver UrlResolver { get; set; } } Next, we will create an interface, IContextAwareService: public interface IContextAwareService { BlogContext Context { get; set; } } The idea is, whoever needs these shared services needs to implement this interface, in our case both the controller and the view model, now we will create an action filter which will be responsible for populating the context: public class PopulateBlogContextAttribute : FilterAttribute, IActionFilter { private static string blogNameRouteParameter = "blogName"; private readonly IBlogService blogService; private readonly IUserService userService; private readonly BlogContext context; public PopulateBlogContextAttribute(IBlogService blogService, IUserService userService, IUrlResolver urlResolver) { Invariant.IsNotNull(blogService, "blogService"); Invariant.IsNotNull(userService, "userService"); Invariant.IsNotNull(urlResolver, "urlResolver"); this.blogService = blogService; this.userService = userService; context = new BlogContext { UrlResolver = urlResolver }; } public static string BlogNameRouteParameter { [DebuggerStepThrough] get { return blogNameRouteParameter; } [DebuggerStepThrough] set { blogNameRouteParameter = value; } } public void OnActionExecuting(ActionExecutingContext filterContext) { string blogName = (string) filterContext.Controller.ValueProvider.GetValue(BlogNameRouteParameter).ConvertTo(typeof(string), Culture.Current); if (!string.IsNullOrWhiteSpace(blogName)) { context.Blog = blogService.FindByName(blogName); } if (context.Blog == null) { filterContext.Result = new NotFoundResult(); return; } if (filterContext.HttpContext.User.Identity.IsAuthenticated) { context.User = userService.FindByName(filterContext.HttpContext.User.Identity.Name); } IContextAwareService controller = filterContext.Controller as IContextAwareService; if (controller != null) { controller.Context = context; } } public void OnActionExecuted(ActionExecutedContext filterContext) { Invariant.IsNotNull(filterContext, "filterContext"); if ((filterContext.Exception == null) || filterContext.ExceptionHandled) { IContextAwareService model = filterContext.Controller.ViewData.Model as IContextAwareService; if (model != null) { model.Context = context; } } } } As you can see we are populating the context in the OnActionExecuting, which executes just before the controllers action methods executes, so by the time our action methods executes the context is already populated, next we are are assigning the same context in the view model in OnActionExecuted method which executes just after we set the  model and return the view in our action methods. Now, lets change the view models so that it implements this interface: public class IndexViewModel : IContextAwareService { // More Codes } public class ArchiveViewModel : IContextAwareService { // More Codes } public class TagViewModel : IContextAwareService { // More Codes } and the controller: public class PostController : Controller, IContextAwareService { public PostController(dependencies...) { } public BlogContext Context { get; set; } public ActionResult Index(int? page) { IEnumerable<PostInfo> posts = postService.FindPublished(Context.Blog.Id, PagingCalculator.StartIndex(page, Context.Blog.PostPerPage), Context.Blog.PostPerPage); int count = postService.GetPublishedCount(Context.Blog.Id); return View(new IndexViewModel(posts, count, page)); } public ActionResult Archive(int? page, ArchiveDate archiveDate) { IEnumerable<PostInfo> posts = postService.FindArchived(Context.Blog.Id, archiveDate, PagingCalculator.StartIndex(page, Context.Blog.PostPerPage), Context.Blog.PostPerPage); int count = postService.GetArchivedCount(Context.Blog.Id, archiveDate); return View(new ArchiveViewModel(posts, count, page, achiveDate)); } public ActionResult Tag(string blogName, string tagSlug, int? page) { TagInfo tag = tagService.FindBySlug(Context.Blog.Id, tagSlug); if (tag == null) { return new NotFoundResult(); } IEnumerable<PostInfo> posts = postService.FindPublishedByTag(Context.Blog.Id, tag.Id, PagingCalculator.StartIndex(page, Context.Blog.PostPerPage), Context.Blog.PostPerPage); int count = postService.GetPublishedCountByTag(tag.Id); return View(new TagViewModel(posts, count, page, tag)); } } Now, the last thing where we have to glue everything, I will be using the AspNetMvcExtensibility to register the action filter (as there is no better way to inject the dependencies in action filters). public class RegisterFilters : RegisterFiltersBase { private static readonly Type controllerType = typeof(Controller); private static readonly Type contextAwareType = typeof(IContextAwareService); protected override void Register(IFilterRegistry registry) { TypeCatalog controllers = new TypeCatalogBuilder() .Add(GetType().Assembly) .Include(type => controllerType.IsAssignableFrom(type) && contextAwareType.IsAssignableFrom(type)); registry.Register<PopulateBlogContextAttribute>(controllers); } } Thoughts and Comments?

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  • xml file save/read error (making a highscore system for XNA game)

    - by Eddy
    i get an error after i write player name to the file for second or third time (An unhandled exception of type 'System.InvalidOperationException' occurred in System.Xml.dll Additional information: There is an error in XML document (18, 17).) (in highscores load method In data = (HighScoreData)serializer.Deserialize(stream); it stops) the problem is that some how it adds additional "" at the end of my .dat file could anyone tell me how to fix this? the file before save looks: <?xml version="1.0"?> <HighScoreData xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <PlayerName> <string>neil</string> <string>shawn</string> <string>mark</string> <string>cindy</string> <string>sam</string> </PlayerName> <Score> <int>200</int> <int>180</int> <int>150</int> <int>100</int> <int>50</int> </Score> <Count>5</Count> </HighScoreData> the file after save looks: <?xml version="1.0"?> <HighScoreData xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"> <PlayerName> <string>Nick</string> <string>Nick</string> <string>neil</string> <string>shawn</string> <string>mark</string> </PlayerName> <Score> <int>210</int> <int>210</int> <int>200</int> <int>180</int> <int>150</int> </Score> <Count>5</Count> </HighScoreData>> the part of my code that does all of save load to xml is: DECLARATIONS PART [Serializable] public struct HighScoreData { public string[] PlayerName; public int[] Score; public int Count; public HighScoreData(int count) { PlayerName = new string[count]; Score = new int[count]; Count = count; } } IAsyncResult result = null; bool inputName; HighScoreData data; int Score = 0; public string NAME; public string HighScoresFilename = "highscores.dat"; Game1 constructor public Game1() { graphics = new GraphicsDeviceManager(this); Content.RootDirectory = "Content"; Width = graphics.PreferredBackBufferWidth = 960; Height = graphics.PreferredBackBufferHeight =640; GamerServicesComponent GSC = new GamerServicesComponent(this); Components.Add(GSC); } Inicialize function (end of it) protected override void Initialize() { //other game code base.Initialize(); string fullpath =Path.Combine(HighScoresFilename); if (!File.Exists(fullpath)) { //If the file doesn't exist, make a fake one... // Create the data to save data = new HighScoreData(5); data.PlayerName[0] = "neil"; data.Score[0] = 200; data.PlayerName[1] = "shawn"; data.Score[1] = 180; data.PlayerName[2] = "mark"; data.Score[2] = 150; data.PlayerName[3] = "cindy"; data.Score[3] = 100; data.PlayerName[4] = "sam"; data.Score[4] = 50; SaveHighScores(data, HighScoresFilename); } } all methods for loading saving and output public static void SaveHighScores(HighScoreData data, string filename) { // Get the path of the save game string fullpath = Path.Combine("highscores.dat"); // Open the file, creating it if necessary FileStream stream = File.Open(fullpath, FileMode.OpenOrCreate); try { // Convert the object to XML data and put it in the stream XmlSerializer serializer = new XmlSerializer(typeof(HighScoreData)); serializer.Serialize(stream, data); } finally { // Close the file stream.Close(); } } /* Load highscores */ public static HighScoreData LoadHighScores(string filename) { HighScoreData data; // Get the path of the save game string fullpath = Path.Combine("highscores.dat"); // Open the file FileStream stream = File.Open(fullpath, FileMode.OpenOrCreate, FileAccess.Read); try { // Read the data from the file XmlSerializer serializer = new XmlSerializer(typeof(HighScoreData)); data = (HighScoreData)serializer.Deserialize(stream);//this is the line // where program gives an error } finally { // Close the file stream.Close(); } return (data); } /* Save player highscore when game ends */ private void SaveHighScore() { // Create the data to saved HighScoreData data = LoadHighScores(HighScoresFilename); int scoreIndex = -1; for (int i = 0; i < data.Count ; i++) { if (Score > data.Score[i]) { scoreIndex = i; break; } } if (scoreIndex > -1) { //New high score found ... do swaps for (int i = data.Count - 1; i > scoreIndex; i--) { data.PlayerName[i] = data.PlayerName[i - 1]; data.Score[i] = data.Score[i - 1]; } data.PlayerName[scoreIndex] = NAME; //Retrieve User Name Here data.Score[scoreIndex] = Score; // Retrieve score here SaveHighScores(data, HighScoresFilename); } } /* Iterate through data if highscore is called and make the string to be saved*/ public string makeHighScoreString() { // Create the data to save HighScoreData data2 = LoadHighScores(HighScoresFilename); // Create scoreBoardString string scoreBoardString = "Highscores:\n\n"; for (int i = 0; i<5;i++) { scoreBoardString = scoreBoardString + data2.PlayerName[i] + "-" + data2.Score[i] + "\n"; } return scoreBoardString; } when ill make this work i will start this code when i call game over (now i start it when i press some buttons, so i could test it faster) public void InputYourName() { if (result == null && !Guide.IsVisible) { string title = "Name"; string description = "Write your name in order to save your Score"; string defaultText = "Nick"; PlayerIndex playerIndex = new PlayerIndex(); result= Guide.BeginShowKeyboardInput(playerIndex, title, description, defaultText, null, null); // NAME = result.ToString(); } if (result != null && result.IsCompleted) { NAME = Guide.EndShowKeyboardInput(result); result = null; inputName = false; SaveHighScore(); } } this where i call output to the screen (ill call this in highscores meniu section when i am done with debugging) spriteBatch.DrawString(Font1, "" + makeHighScoreString(),new Vector2(500,200), Color.White); }

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  • how to get this row count for jquery grid..

    - by kumar
    I used this code to get the count of records in the jquery grid var numRows = jQuery("#mygrid").jqGrid ('getGridParam', 'records'); when i place anywhere in my view after or before grid?? am allways geting 0 result.. bec its allways taking before grid loading.. i need to place this code where i need to check after grid loading.. if i put something like this. alert("hello"); var numRows = jQuery("#mygrid").jqGrid ('getGridParam', 'records'); alert(numRows); first if i keep any alert message and then if i count i am getting the number of records.. but if i give directly this code var numRows = jQuery("#mygrid").jqGrid ('getGridParam', 'records'); alert(numRows); i am getting out put as 0.. i dont know why its behaving like this.. if we keep first alert box anywhere for second alert box i am getting rowcounts.. can anybody help me out .. thanks

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  • Is the salt contained in a phpass hash or do you need to salt its input?

    - by Exception e
    phpass is a widely used hashing 'framework'. Is it good practice to salt the plain password before giving it to PasswordHash (v0.2), like so?: $dynamicSalt = $record['salt']; $staticSalt = 'i5ininsfj5lt4hbfduk54fjbhoxc80sdf'; $plainPassword = $_POST['password']; $password = $plainPassword . $dynamicSalt . $staticSalt; $passwordHash = new PasswordHash(8, false); $storedPassword = $passwordHash->HashPassword($password); For reference the phpsalt class: # Portable PHP password hashing framework. # # Version 0.2 / genuine. # # Written by Solar Designer <solar at openwall.com> in 2004-2006 and placed in # the public domain. # # # class PasswordHash { var $itoa64; var $iteration_count_log2; var $portable_hashes; var $random_state; function PasswordHash($iteration_count_log2, $portable_hashes) { $this->itoa64 = './0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'; if ($iteration_count_log2 < 4 || $iteration_count_log2 > 31) $iteration_count_log2 = 8; $this->iteration_count_log2 = $iteration_count_log2; $this->portable_hashes = $portable_hashes; $this->random_state = microtime() . getmypid(); } function get_random_bytes($count) { $output = ''; if (is_readable('/dev/urandom') && ($fh = @fopen('/dev/urandom', 'rb'))) { $output = fread($fh, $count); fclose($fh); } if (strlen($output) < $count) { $output = ''; for ($i = 0; $i < $count; $i += 16) { $this->random_state = md5(microtime() . $this->random_state); $output .= pack('H*', md5($this->random_state)); } $output = substr($output, 0, $count); } return $output; } function encode64($input, $count) { $output = ''; $i = 0; do { $value = ord($input[$i++]); $output .= $this->itoa64[$value & 0x3f]; if ($i < $count) $value |= ord($input[$i]) << 8; $output .= $this->itoa64[($value >> 6) & 0x3f]; if ($i++ >= $count) break; if ($i < $count) $value |= ord($input[$i]) << 16; $output .= $this->itoa64[($value >> 12) & 0x3f]; if ($i++ >= $count) break; $output .= $this->itoa64[($value >> 18) & 0x3f]; } while ($i < $count); return $output; } function gensalt_private($input) { $output = '$P$'; $output .= $this->itoa64[min($this->iteration_count_log2 + ((PHP_VERSION >= '5') ? 5 : 3), 30)]; $output .= $this->encode64($input, 6); return $output; } function crypt_private($password, $setting) { $output = '*0'; if (substr($setting, 0, 2) == $output) $output = '*1'; if (substr($setting, 0, 3) != '$P$') return $output; $count_log2 = strpos($this->itoa64, $setting[3]); if ($count_log2 < 7 || $count_log2 > 30) return $output; $count = 1 << $count_log2; $salt = substr($setting, 4, 8); if (strlen($salt) != 8) return $output; # We're kind of forced to use MD5 here since it's the only # cryptographic primitive available in all versions of PHP # currently in use. To implement our own low-level crypto # in PHP would result in much worse performance and # consequently in lower iteration counts and hashes that are # quicker to crack (by non-PHP code). if (PHP_VERSION >= '5') { $hash = md5($salt . $password, TRUE); do { $hash = md5($hash . $password, TRUE); } while (--$count); } else { $hash = pack('H*', md5($salt . $password)); do { $hash = pack('H*', md5($hash . $password)); } while (--$count); } $output = substr($setting, 0, 12); $output .= $this->encode64($hash, 16); return $output; } function gensalt_extended($input) { $count_log2 = min($this->iteration_count_log2 + 8, 24); # This should be odd to not reveal weak DES keys, and the # maximum valid value is (2**24 - 1) which is odd anyway. $count = (1 << $count_log2) - 1; $output = '_'; $output .= $this->itoa64[$count & 0x3f]; $output .= $this->itoa64[($count >> 6) & 0x3f]; $output .= $this->itoa64[($count >> 12) & 0x3f]; $output .= $this->itoa64[($count >> 18) & 0x3f]; $output .= $this->encode64($input, 3); return $output; } function gensalt_blowfish($input) { # This one needs to use a different order of characters and a # different encoding scheme from the one in encode64() above. # We care because the last character in our encoded string will # only represent 2 bits. While two known implementations of # bcrypt will happily accept and correct a salt string which # has the 4 unused bits set to non-zero, we do not want to take # chances and we also do not want to waste an additional byte # of entropy. $itoa64 = './ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'; $output = '$2a$'; $output .= chr(ord('0') + $this->iteration_count_log2 / 10); $output .= chr(ord('0') + $this->iteration_count_log2 % 10); $output .= '$'; $i = 0; do { $c1 = ord($input[$i++]); $output .= $itoa64[$c1 >> 2]; $c1 = ($c1 & 0x03) << 4; if ($i >= 16) { $output .= $itoa64[$c1]; break; } $c2 = ord($input[$i++]); $c1 |= $c2 >> 4; $output .= $itoa64[$c1]; $c1 = ($c2 & 0x0f) << 2; $c2 = ord($input[$i++]); $c1 |= $c2 >> 6; $output .= $itoa64[$c1]; $output .= $itoa64[$c2 & 0x3f]; } while (1); return $output; } function HashPassword($password) { $random = ''; if (CRYPT_BLOWFISH == 1 && !$this->portable_hashes) { $random = $this->get_random_bytes(16); $hash = crypt($password, $this->gensalt_blowfish($random)); if (strlen($hash) == 60) return $hash; } if (CRYPT_EXT_DES == 1 && !$this->portable_hashes) { if (strlen($random) < 3) $random = $this->get_random_bytes(3); $hash = crypt($password, $this->gensalt_extended($random)); if (strlen($hash) == 20) return $hash; } if (strlen($random) < 6) $random = $this->get_random_bytes(6); $hash = $this->crypt_private($password, $this->gensalt_private($random)); if (strlen($hash) == 34) return $hash; # Returning '*' on error is safe here, but would _not_ be safe # in a crypt(3)-like function used _both_ for generating new # hashes and for validating passwords against existing hashes. return '*'; } function CheckPassword($password, $stored_hash) { $hash = $this->crypt_private($password, $stored_hash); if ($hash[0] == '*') $hash = crypt($password, $stored_hash); return $hash == $stored_hash; } }

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  • How can * be a safe hashed password?

    - by Exception e
    phpass is a widely used hashing 'framework'. While evaluating phpass' HashPassword I came across this odd method fragment. function HashPassword($password) { // <snip> trying to generate a hash… # Returning '*' on error is safe here, but would _not_ be safe # in a crypt(3)-like function used _both_ for generating new # hashes and for validating passwords against existing hashes. return '*'; } This is the complete phpsalt class: # Portable PHP password hashing framework. # # Version 0.2 / genuine. # # Written by Solar Designer <solar at openwall.com> in 2004-2006 and placed in # the public domain. # # # class PasswordHash { var $itoa64; var $iteration_count_log2; var $portable_hashes; var $random_state; function PasswordHash($iteration_count_log2, $portable_hashes) { $this->itoa64 = './0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'; if ($iteration_count_log2 < 4 || $iteration_count_log2 > 31) $iteration_count_log2 = 8; $this->iteration_count_log2 = $iteration_count_log2; $this->portable_hashes = $portable_hashes; $this->random_state = microtime() . getmypid(); } function get_random_bytes($count) { $output = ''; if (is_readable('/dev/urandom') && ($fh = @fopen('/dev/urandom', 'rb'))) { $output = fread($fh, $count); fclose($fh); } if (strlen($output) < $count) { $output = ''; for ($i = 0; $i < $count; $i += 16) { $this->random_state = md5(microtime() . $this->random_state); $output .= pack('H*', md5($this->random_state)); } $output = substr($output, 0, $count); } return $output; } function encode64($input, $count) { $output = ''; $i = 0; do { $value = ord($input[$i++]); $output .= $this->itoa64[$value & 0x3f]; if ($i < $count) $value |= ord($input[$i]) << 8; $output .= $this->itoa64[($value >> 6) & 0x3f]; if ($i++ >= $count) break; if ($i < $count) $value |= ord($input[$i]) << 16; $output .= $this->itoa64[($value >> 12) & 0x3f]; if ($i++ >= $count) break; $output .= $this->itoa64[($value >> 18) & 0x3f]; } while ($i < $count); return $output; } function gensalt_private($input) { $output = '$P$'; $output .= $this->itoa64[min($this->iteration_count_log2 + ((PHP_VERSION >= '5') ? 5 : 3), 30)]; $output .= $this->encode64($input, 6); return $output; } function crypt_private($password, $setting) { $output = '*0'; if (substr($setting, 0, 2) == $output) $output = '*1'; if (substr($setting, 0, 3) != '$P$') return $output; $count_log2 = strpos($this->itoa64, $setting[3]); if ($count_log2 < 7 || $count_log2 > 30) return $output; $count = 1 << $count_log2; $salt = substr($setting, 4, 8); if (strlen($salt) != 8) return $output; # We're kind of forced to use MD5 here since it's the only # cryptographic primitive available in all versions of PHP # currently in use. To implement our own low-level crypto # in PHP would result in much worse performance and # consequently in lower iteration counts and hashes that are # quicker to crack (by non-PHP code). if (PHP_VERSION >= '5') { $hash = md5($salt . $password, TRUE); do { $hash = md5($hash . $password, TRUE); } while (--$count); } else { $hash = pack('H*', md5($salt . $password)); do { $hash = pack('H*', md5($hash . $password)); } while (--$count); } $output = substr($setting, 0, 12); $output .= $this->encode64($hash, 16); return $output; } function gensalt_extended($input) { $count_log2 = min($this->iteration_count_log2 + 8, 24); # This should be odd to not reveal weak DES keys, and the # maximum valid value is (2**24 - 1) which is odd anyway. $count = (1 << $count_log2) - 1; $output = '_'; $output .= $this->itoa64[$count & 0x3f]; $output .= $this->itoa64[($count >> 6) & 0x3f]; $output .= $this->itoa64[($count >> 12) & 0x3f]; $output .= $this->itoa64[($count >> 18) & 0x3f]; $output .= $this->encode64($input, 3); return $output; } function gensalt_blowfish($input) { # This one needs to use a different order of characters and a # different encoding scheme from the one in encode64() above. # We care because the last character in our encoded string will # only represent 2 bits. While two known implementations of # bcrypt will happily accept and correct a salt string which # has the 4 unused bits set to non-zero, we do not want to take # chances and we also do not want to waste an additional byte # of entropy. $itoa64 = './ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'; $output = '$2a$'; $output .= chr(ord('0') + $this->iteration_count_log2 / 10); $output .= chr(ord('0') + $this->iteration_count_log2 % 10); $output .= '$'; $i = 0; do { $c1 = ord($input[$i++]); $output .= $itoa64[$c1 >> 2]; $c1 = ($c1 & 0x03) << 4; if ($i >= 16) { $output .= $itoa64[$c1]; break; } $c2 = ord($input[$i++]); $c1 |= $c2 >> 4; $output .= $itoa64[$c1]; $c1 = ($c2 & 0x0f) << 2; $c2 = ord($input[$i++]); $c1 |= $c2 >> 6; $output .= $itoa64[$c1]; $output .= $itoa64[$c2 & 0x3f]; } while (1); return $output; } function HashPassword($password) { $random = ''; if (CRYPT_BLOWFISH == 1 && !$this->portable_hashes) { $random = $this->get_random_bytes(16); $hash = crypt($password, $this->gensalt_blowfish($random)); if (strlen($hash) == 60) return $hash; } if (CRYPT_EXT_DES == 1 && !$this->portable_hashes) { if (strlen($random) < 3) $random = $this->get_random_bytes(3); $hash = crypt($password, $this->gensalt_extended($random)); if (strlen($hash) == 20) return $hash; } if (strlen($random) < 6) $random = $this->get_random_bytes(6); $hash = $this->crypt_private($password, $this->gensalt_private($random)); if (strlen($hash) == 34) return $hash; # Returning '*' on error is safe here, but would _not_ be safe # in a crypt(3)-like function used _both_ for generating new # hashes and for validating passwords against existing hashes. return '*'; } function CheckPassword($password, $stored_hash) { $hash = $this->crypt_private($password, $stored_hash); if ($hash[0] == '*') $hash = crypt($password, $stored_hash); return $hash == $stored_hash; } }

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  • How do I find, count, and display unique elements of an array using Perl?

    - by Luke
    I am a novice Perl programmer and would like some help. I have an array list that I am trying to split each element based on the pipe into two scalar elements. From there I would like to spike out only the lines that read ‘PJ RER Apts to Share’ as the first element. Then I want to print out the second element only once while counting each time the element appears. I wrote the piece of code below but can’t figure out where I am going wrong. It might be something small that I am just overlooking. Any help would be greatly appreciated. ## CODE ## my @data = ('PJ RER Apts to Share|PROVIDENCE', 'PJ RER Apts to Share|JOHNSTON', 'PJ RER Apts to Share|JOHNSTON', 'PJ RER Apts to Share|JOHNSTON', 'PJ RER Condo|WEST WARWICK', 'PJ RER Condo|WARWICK'); foreach my $line (@data) { $count = @data; chomp($line); @fields = split(/\|/,$line); if (($fields[0] =~ /PJ RER Apts to Share/g)){ @array2 = $fields[1]; my %seen; my @uniq = grep { ! $seen{$_}++ } @array2; my $count2 = scalar(@uniq); print "$array2[0] ($count2)","\n" } } print "$count","\n"; ## OUTPUT ## PROVIDENCE (1) JOHNSTON (1) JOHNSTON (1) JOHNSTON (1) 6

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  • Criteria API - How to get records based on collection count?

    - by Cosmo
    Hello Guys! I have a Question class in ActiveRecord with following fields: [ActiveRecord("`Question`")] public class Question : ObcykaniDb<Question> { private long id; private IList<Question> relatedQuestions; [PrimaryKey("`Id`")] private long Id { get { return this.id; } set { this.id = value; } } [HasAndBelongsToMany(typeof(Question), ColumnRef = "ChildId", ColumnKey = "ParentId", Table = "RelatedQuestion")] private IList<Question> RelatedQuestions { get { return this.relatedQuestions; } set { this.relatedQuestions = value; } } } How do I write a DetachedCriteria query to get all Questions that have at least 5 related questions (count) in the RelatedQuestions collection? For now this gives me strange results: DetachedCriteria dCriteria = DetachedCriteria.For<Question>() .CreateCriteria("RelatedQuestions") .SetProjection(Projections.Count("Id")) .Add(Restrictions.EqProperty(Projections.Id(), "alias.Id")); DetachedCriteria dc = DetachedCriteria.For<Question>("alias").Add(Subqueries.Le(5, dCriteria)); IList<Question> results = Question.FindAll(dc); Any ideas what I'm doing wrong?

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  • In Excel 2010, how can I show a count of occurrences on a specific date within multiple time ranges?

    - by Justin
    Here's what I'm trying to do. I have three columns of data. ID, Date(MM/DD/YY), Time(00:00). I need to create a chart or table that shows the number of occurrences on, say, 12/10/2010 between 00:00 and 00:59, 1:00 and 1:59, etc, for each hour of the day. I can do countif and get results for the date, but I cannot figure out how to show a summary of the count of occurrences per hour for the 24 hour period. I have months of data and many times each day. Example of data set is below. Any help is greatly ID Date Time 221 12/10/2010 00:01 223 12/10/2010 00:45 227 12/10/2010 01:13 334 12/11/2010 14:45 I would like the results to read: Date Time Count 12/10/2010 00:00AM - 00:59AM 2 12/10/2010 01:00AM - 01:59AM 1 12/10/2010 02:00AM - 02:59AM 0 ......(continues for every hour of the day) 12/11/2010 00:00AM - 00:59AM 0 ......... 12/11/2010 14:00PM - 14:59PM 1 And so on. Sorry for the length but I wanted to be clear. EDIT Here is a sample spreadsheet. Very little data, but I couldn't figure out a better way without having a huge file. Tested in notepad for formatting and worked ok on import as csv. PID,Date,Time 2888759,12/10/2010,0:10 2888760,12/10/2010,0:10 2888761,12/10/2010,0:10 2888762,12/10/2010,0:11 2889078,12/10/2010,15:45 2889079,12/10/2010,15:57 2889080,12/10/2010,15:57 2889081,12/10/2010,15:58 2889082,12/10/2010,16:10 2889083,12/10/2010,16:11 2889084,12/10/2010,16:11 2889085,12/10/2010,16:12 2889086,12/10/2010,16:12 2889087,12/10/2010,16:12 2889088,12/10/2010,16:13 2891529,12/14/2010,16:21

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  • SSIS how to split a single record in to two different records?

    - by Dr. Zim
    I have a Product record that has multiple "zoned" prices, one for each store that sells the product. ProductID int Name string PriceA money PriceB money PriceC money In SQL Server Integration Services, I need to split this in to multiple records: ProductID int Version string // A, B, or C Price money // PriceA if A, PriceB if B, etc. This would be within a Data Flow, I presume as a Transformation between Excel source and OLE DB destination. (Assuming OLE DB is a good destination for MS SQL server).

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  • how to update a selectecd record in a dataset an update a onother datatable in another Adoconecion

    - by ml
    I have 2 adoconections and 2 datatables in each conecion (Local Table1_master Table1_Detail) (Network Table1_master Table1_Detail) i show thwm in a DBgrid and now i wouth like to update the (Local Table1_master Table1_Detail) from the tables in (Network Table1_master Table1_Detail) how can i upddate the selected record´s .!!! i try many ways but normaly it insert more recordes and don´t update record i use a .MDB database

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  • Why can't I insert record with foregion key in a single server request?

    - by Eran Betzalel
    I'm tryring to do a simple insert with foregion key, but it seems that I need to use db.SaveChanges() for every record insert. How can I manage to use only one db.SaveChanges() at the end of this program? foreach (var file in files) { db.AddToFileSet(file); db.SaveChanges(); db.AddToDirectorySet( new GlxCustomerPhone { SimIdentifier = file.Name + "Dir", CreationDate = DateTime.UtcNow, file_relation = file }); db.SaveChanges(); }

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  • What libraries are available to record a user browsing your website for usability testing?

    - by John
    I remember seeing a JavaScript library a long time ago that offered the ability to record where users clicked and moved their mouse on your website, in order to do usability testing. I can't seem to find it anymore. Are there any libraries out there that do something like this? What I'm looking for is something like http://clixpy.com/, where you can include some javascript on a page and get videos of what users do.

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  • Crystal Reports API - chart: "for all records" or "for each record"?

    - by Epaga
    Is there any way to determine whether a chart in Crystal Reports 2008 (using either the RAS SDK or the older RDC API) is set to display values "for each record" or "for all records"? I can get access to a CrystalDecisions.ReportAppServer.ReportDefModel.ChartObject but can't find any API there to access which type of chart it is - "for each" or "for all".

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  • MS Sql Get Top xx Blog Record From Umbraco by parameter...

    - by ccppjava
    Following SQL get what I need: SELECT TOP (50) [nodeId] FROM [dbo].[cmsContentXml] WHERE [xml] like '%creatorID="29"%' AND [xml] like '%nodeType="1086"%' ORDER BY [nodeId] DESC I need to pass in the numbers as parameters, so I have follows: exec sp_executesql N'SELECT TOP (@max) [nodeId] FROM [dbo].[cmsContentXml] WHERE [xml] like ''%creatorID="@creatorID"%'' AND [xml] like ''%nodeType="@nodeType"%'' ORDER BY [nodeId] DESC',N'@max int,@creatorID int,@nodeType int',@max=50,@creatorID=29,@nodeType=1086 which however, returns no record, any idea?

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  • how to update a selected record in a dataset and update another datatable in another Adoconnection?

    - by ml
    I have 2 adoconnections and 2 datatables in each connection (Local Table1_master Table1_Detail) (Network Table1_master Table1_Detail). I show them in a DBgrid and now I would like to update the (Local Table1_master Table1_Detail) from the tables in (Network Table1_master Table1_Detail). How can I update the selected records? I have tried many ways but normally it inserts more records and doesn´t update the record. I use a .MDB database.

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