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  • is it possible to load a shared library on a shared memory?

    - by quimm2003
    I have a server and a client written in C. I try to load a shared library in the server and then pass library function pointers to the client. This way I can change the library without have to compile the client. Because of every process has its own separate memory space, I wonder if it is possible to load a shared library on a shared memory, pass the function pointers and map the shared memory on the client and then make the client execute the code of the library loaded by the server.

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  • How to switch data pins on/off on parallel port?

    - by Matt
    I want to simply switch certain data pins on and off, so that they can control a set of relays. I'm not asking about the hardware bit (should be easy), but I don't know where to begin writing the software. I don't want a high level library that can send bytes to a device - I literally want to switch on/off certain pins. I'm running Linux and I want to do this in Java, so would I just need a library? It would be nice if the library has good documentation and is easy to use, but if not then a short example code will help me get started.

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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • SQL University: Parallelism Week - Part 3, Settings and Options

    - by Adam Machanic
    Congratulations! You've made it back for the the third and final installment of Parallelism Week here at SQL University . So far we've covered the fundamentals of multitasking vs. parallel processing and delved into how parallel query plans actually work . Today we'll take a look at the settings and options that influence intra-query parallelism and discuss how best to set things up in various situations. Instance-Level Configuration Your database server probably has more than one logical processor....(read more)

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  • BizTalk 2009 - Creating a Custom Functoid Library

    - by StuartBrierley
    If you find that you have a need to created multiple Custom Functoids you may also choose to create a Custom Functoid Library - a single project containing many custom functoids.  As previsouly discussed, the Custom Functoid Wizard can be used to create a project with a new custom functoid inside.  But what if you want to extend this project to include more custom functoids and create your Custom Functoid Library?  First create a Custom Functoid Library project and your first Custom Functoid using the Custom Functoid Wizard. When you open your Custom Functoid Library project in Visual Studio you will see that it contains your custom functoid class file along with its resource file.  One of the items this resource file contains is the ID of the the custom functoid.  Each custom functoid needs a unique ID that is over 6000.  When creating a Custom Functoid Library I would first suggest that you delete the ID from this resource file and instead create a _FunctoidIDs class containing constants for each of your custom functoids.  In this way you can easily see which custom functoid IDs are assigned to which custom functoid and which ID is next in the sequence of availability: namespace MyCompany.BizTalk.Functoids.TestFunctoids {     class _FunctoidIDs     {         public const int TestFunctoid                       = 6001;     } } You will then need to update the base() function in your existing functoid class to reference these constant values rather than the current resource file. From:    int functoidID;    // This has to be a number greater than 6000    functoidID = System.Convert.ToInt32(resmgr.GetString("FunctoidId"));    this.ID = functoidID; To: this.ID = _FunctoidIDs.TestFunctoid; To create a new custom functoid you can copy the existing custom functoid, renaming the resultant class file as appropriate.  Once it is renamed you will need to change the Class name, ResourceName reference and Base function name in the class code to those of your new custom functoid.  You will also need to create a new constant value in the _FunctoidIDs class and update the ID reference in your code to match this.  Assuming that you need some different functionalty from your new  customfunctoid you will need to check or amend the following in your functoid class file: Min and Max connections Functoid Category Input and Output connection types The parameters and functionality of the Execute function To change the appearance of you new custom functoid you will need to check or amend the following in the functoid resource file: Name Description Tooltip Exception Icon You can change the String values by double clicking the resource file and amending the value fields in the string table. To amend the functoid icon you will need to create a 16x16 bitmap image.  Once you have saved this you are then ready to import it into the functoid resource file.  In Visual Studio change the resource view to images, right click the icon and choose import from file. You have now completed your new custom functoid and created a Custom Functoid Library.  You can test your new library of functoids by building the project, copying the resultant DLL to C:\Program Files\Microsoft BizTalk Server 2009\Developer Tools\Mapper Extensions and then resetting the toolbox in Visual Studio.

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  • Why do weekly tasks created via PowerShell using a different user fail with error 0x41306

    - by Danny Tuppeny
    We have some scripts that create scheduled jobs using PowerShell as part of our application. When testing them recently, I noticed that some of them always failed immediately, and no output is ever produced (they don't even appear in the Get-Job list). After many days of tweaking, we've managed to isolate it to any jobs that are set to run weekly. Below is a script that creates two jobs that do exactly the same thing. When we run this on our domain, and provide credentials of a domain user, then force both jobs to run in the Task Scheduler GUI (right-click - Run), the daily one runs fine (0x0 result) and the weekly one fails (0x41306). Note: If I don't provide the -Credential param, both jobs work fine. The jobs only fail if the task is both weekly, and running as this domain user. I can't find information on why this is happening, nor think of any reason it would behave differently for weekly jobs. The "History£ tab in the Task Scheduler has almost no useful information, just "Task stopping due to user request" and "Task terminated", both of which have no useful info: Task Scheduler terminated "{eabba479-f8fc-4f0e-bf5e-053dfbfe9f62}" instance of the "\Microsoft\Windows\PowerShell\ScheduledJobs\Test1" task. Task Scheduler stopped instance "{eabba479-f8fc-4f0e-bf5e-053dfbfe9f62}" of task "\Microsoft\Windows\PowerShell\ScheduledJobs\Test1" as request by user "MyDomain\SomeUser" . What's up with this? Why do weekly tasks run differently, and how can I diganose this issue? This is PowerShell v3 on Windows Server 2008 R2. I've been unable to reproduce this locally, but I don't have a user set up in the same way as the one in our production domain (I'm working on this, but I wanted to post this ASAP in the hope someone knows what's happening!). Import-Module PSScheduledJob $Action = { "Executing job!" } $cred = Get-Credential "MyDomain\SomeUser" # Remove previous versions (to allow re-running this script) Get-ScheduledJob Test1 | Unregister-ScheduledJob Get-ScheduledJob Test2 | Unregister-ScheduledJob # Create two identical jobs, with different triggers Register-ScheduledJob "Test1" -ScriptBlock $Action -Credential $cred -Trigger (New-JobTrigger -Weekly -At 1:25am -DaysOfWeek Sunday) Register-ScheduledJob "Test2" -ScriptBlock $Action -Credential $cred -Trigger (New-JobTrigger -Daily -At 1:25am)

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  • Troubleshooting High-CPU Utilization for SQL Server

    - by Susantha Bathige
    The objective of this FAQ is to outline the basic steps in troubleshooting high CPU utilization on  a server hosting a SQL Server instance. The first and the most common step if you suspect high CPU utilization (or are alerted for it) is to login to the physical server and check the Windows Task Manager. The Performance tab will show the high utilization as shown below: Next, we need to determine which process is responsible for the high CPU consumption. The Processes tab of the Task Manager will show this information: Note that to see all processes you should select Show processes from all user. In this case, SQL Server (sqlserver.exe) is consuming 99% of the CPU (a normal benchmark for max CPU utilization is about 50-60%). Next we examine the scheduler data. Scheduler is a component of SQLOS which evenly distributes load amongst CPUs. The query below returns the important columns for CPU troubleshooting. Note – if your server is under severe stress and you are unable to login to SSMS, you can use another machine’s SSMS to login to the server through DAC – Dedicated Administrator Connection (see http://msdn.microsoft.com/en-us/library/ms189595.aspx for details on using DAC) SELECT scheduler_id ,cpu_id ,status ,runnable_tasks_count ,active_workers_count ,load_factor ,yield_count FROM sys.dm_os_schedulers WHERE scheduler_id See below for the BOL definitions for the above columns. scheduler_id – ID of the scheduler. All schedulers that are used to run regular queries have ID numbers less than 1048576. Those schedulers that have IDs greater than or equal to 1048576 are used internally by SQL Server, such as the dedicated administrator connection scheduler. cpu_id – ID of the CPU with which this scheduler is associated. status – Indicates the status of the scheduler. runnable_tasks_count – Number of workers, with tasks assigned to them that are waiting to be scheduled on the runnable queue. active_workers_count – Number of workers that are active. An active worker is never preemptive, must have an associated task, and is either running, runnable, or suspended. current_tasks_count - Number of current tasks that are associated with this scheduler. load_factor – Internal value that indicates the perceived load on this scheduler. yield_count – Internal value that is used to indicate progress on this scheduler.                                                                 Now to interpret the above data. There are four schedulers and each assigned to a different CPU. All the CPUs are ready to accept user queries as they all are ONLINE. There are 294 active tasks in the output as per the current_tasks_count column. This count indicates how many activities currently associated with the schedulers. When a  task is complete, this number is decremented. The 294 is quite a high figure and indicates all four schedulers are extremely busy. When a task is enqueued, the load_factor  value is incremented. This value is used to determine whether a new task should be put on this scheduler or another scheduler. The new task will be allocated to less loaded scheduler by SQLOS. The very high value of this column indicates all the schedulers have a high load. There are 268 runnable tasks which mean all these tasks are assigned a worker and waiting to be scheduled on the runnable queue.   The next step is  to identify which queries are demanding a lot of CPU time. The below query is useful for this purpose (note, in its current form,  it only shows the top 10 records). SELECT TOP 10 st.text  ,st.dbid  ,st.objectid  ,qs.total_worker_time  ,qs.last_worker_time  ,qp.query_plan FROM sys.dm_exec_query_stats qs CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) st CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp ORDER BY qs.total_worker_time DESC This query as total_worker_time as the measure of CPU load and is in descending order of the  total_worker_time to show the most expensive queries and their plans at the top:      Note the BOL definitions for the important columns: total_worker_time - Total amount of CPU time, in microseconds, that was consumed by executions of this plan since it was compiled. last_worker_time - CPU time, in microseconds, that was consumed the last time the plan was executed.   I re-ran the same query again after few seconds and was returned the below output. After few seconds the SP dbo.TestProc1 is shown in fourth place and once again the last_worker_time is the highest. This means the procedure TestProc1 consumes a CPU time continuously each time it executes.      In this case, the primary cause for high CPU utilization was a stored procedure. You can view the execution plan by clicking on query_plan column to investigate why this is causing a high CPU load. I have used SQL Server 2008 (SP1) to test all the queries used in this article.

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  • How to perform a Depth First Search iteratively using async/parallel processing?

    - by Prabhu
    Here is a method that does a DFS search and returns a list of all items given a top level item id. How could I modify this to take advantage of parallel processing? Currently, the call to get the sub items is made one by one for each item in the stack. It would be nice if I could get the sub items for multiple items in the stack at the same time, and populate my return list faster. How could I do this (either using async/await or TPL, or anything else) in a thread safe manner? private async Task<IList<Item>> GetItemsAsync(string topItemId) { var items = new List<Item>(); var topItem = await GetItemAsync(topItemId); Stack<Item> stack = new Stack<Item>(); stack.Push(topItem); while (stack.Count > 0) { var item = stack.Pop(); items.Add(item); var subItems = await GetSubItemsAsync(item.SubId); foreach (var subItem in subItems) { stack.Push(subItem); } } return items; } EDIT: I was thinking of something along these lines, but it's not coming together: var tasks = stack.Select(async item => { items.Add(item); var subItems = await GetSubItemsAsync(item.SubId); foreach (var subItem in subItems) { stack.Push(subItem); } }).ToList(); if (tasks.Any()) await Task.WhenAll(tasks); UPDATE: If I wanted to chunk the tasks, would something like this work? foreach (var batch in items.BatchesOf(100)) { var tasks = batch.Select(async item => { await DoSomething(item); }).ToList(); if (tasks.Any()) { await Task.WhenAll(tasks); } } The language I'm using is C#.

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  • Executing multiple DbCommands in an open connection with Enterprise Library

    - by Lieven Cardoen
    How can you execute multiple DbCommands with one connection? Example: var db = DatabaseFactory.CreateDatabase(); var dbCommand = db.GetSqlStringCommand(InsertCommandText); ... db.ExecuteNonQuery(dbCommand); Now, I want to be able to Execute multiple dbCommands. For instance in pseudo kind of code: var db = DatabaseFactory.CreateDatabase(); var dbCommand1 = db.GetSqlStringCommand(InsertCommandText); ... var dbCommand1 = db.GetSqlStringCommand(InsertCommandText); ... Adding both commands to db Executing them

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  • Java: how to use 3rd-party library?

    - by HH
    $ cat MultiTest.java import com.*; // CODE $ javac Code.java MultiTest.java:1: package com does not exist import com.*; ^ Google Collections Com-dir in the dir where the MultiTest.java -file is located.

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  • A good file path builder library for C#?

    - by Igor Brejc
    System.IO.Path in .NET is notoriously clumsy to work with. In my various projects I keep encountering the same usage scenarios which require repetitive, verbose and thus error-prone code snippets that use Path.Combine, Path.GetFileName, Path.GetDirectoryName, String.Format, etc. Scenarios like: changing the extension for a given file name changing the directory path for a given file name building a file path using string formatting (like "Package{0}.zip") building a path without resorting to using hard-coded directory delimiters like \ (since they don't work on Linux on Mono) etc etc Before starting to write my own PathBuilder class or something similar: is there a good (and proven) open-source implementation of such a thing in C#?

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  • Dynamic DateTimeRangeValidator using Enterprise Library 4.1?

    - by Toran Billups
    I'm trying to add a range of - 365 days and + 365 days but it appears that using this attribute in EL 4.1 only accepts a special ISO formatted string ... thus I can't simply add a normal string to this validation routine. <DateTimeRangeValidator(DateTime.Now.AddDays(2), DateTime.Now.AddDays(4))> _ I wanted to do something similar to the above - fyi Does anyone know how you can force this attribute to accept this ISO formatted string w/out hand coding this value?

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  • 640 enterprise library caching threads - how?

    - by JohnW
    We have an application that is undergoing performance testing. Today, I decided to take a dump of w3wp & load it in windbg to see what is going on underneath the covers. Imagine my surprise when I ran !threads and saw that there are 640 background threads, almost all of which seem to say the following: OS Thread Id: 0x1c38 (651) Child-SP RetAddr Call Site 0000000023a9d290 000007ff002320e2 Microsoft.Practices.EnterpriseLibrary.Caching.ProducerConsumerQueue.WaitUntilInterrupted() 0000000023a9d2d0 000007ff00231f7e Microsoft.Practices.EnterpriseLibrary.Caching.ProducerConsumerQueue.Dequeue() 0000000023a9d330 000007fef727c978 Microsoft.Practices.EnterpriseLibrary.Caching.BackgroundScheduler.QueueReader() 0000000023a9d380 000007fef9001552 System.Threading.ExecutionContext.runTryCode(System.Object) 0000000023a9dc30 000007fef72f95fd System.Threading.ExecutionContext.Run(System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0000000023a9dc80 000007fef9001552 System.Threading.ThreadHelper.ThreadStart() If i had to give a guess, I'm thinkign that one of these threads are getting spawned for each run of our app - we have 2 app servers, 20 concurrent users, and ran the test approximately 30 times...it's in the neighborhood. Is this 'expected behavior', or perhaps have we implemented something improperly? The test ran hours ago, so i would have expected any timeouts to have occurred already.

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  • Multiple Configuration Sources for Enterprise Library 4.1?

    - by Martijn B
    Hi All, We use the caching and logging application blocks from entlib 4.1. We want to keep the configuration of those two in seperate files. How can we achieve this? It looks like entlib is always using the selectedSource as it configuration. I tried the following: <?xml version="1.0" encoding="utf-8" ?> <configuration> <configSections> <section name="enterpriseLibrary.ConfigurationSource" type="Microsoft.Practices.EnterpriseLibrary.Common.Configuration.ConfigurationSourceSection, Microsoft.Practices.EnterpriseLibrary.Common, Version=4.1.0.0, Culture=neutral, PublicKeyToken=9057346a2b2dcfc8" /> </configSections> <enterpriseLibrary.ConfigurationSource selectedSource="messagesCache"> <sources> <add name="messagesCache" filePath="Configuration\\messagesCache.config" type="Microsoft.Practices.EnterpriseLibrary.Common.Configuration.FileConfigurationSource, Microsoft.Practices.EnterpriseLibrary.Common, Version=4.1.0.0, Culture=neutral, PublicKeyToken=9057346a2b2dcfc8" /> <add name="logging" filePath="Configuration\\logging.config" type="Microsoft.Practices.EnterpriseLibrary.Common.Configuration.FileConfigurationSource, Microsoft.Practices.EnterpriseLibrary.Common, Version=4.1.0.0, Culture=neutral, PublicKeyToken=9057346a2b2dcfc8" /> </sources> </enterpriseLibrary.ConfigurationSource> </configuration> But this doesn't work because the application blocks always use the selectedSource attribute value. Any suggestions woulde be welcome! Gr Martijn

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  • Log to rolling CSV file with Enterprise Library

    - by Tinminator
    Need logging to: Rolling file, to avoid 1 big log file. CSV format for easier look up. I can see EntLib (5.0) have Microsoft.Practices.EnterpriseLibrary.Logging.TraceListeners.RollingFlatFileTraceListener to log to rolling log file. To make the log entries look like a CSV row, I can change the Logging.Formatters.TextFormatter.Template to put double quote around the values. Also change the Listener's Footer and Header to nothing, so they won't be output. Under normal circumstance, this would give me a well formed CSV file. However if a token value in the Template contain double quote, this would not be escaped, hence the log file become an invalid CSV file. Is there any way to resolve this? Is there any alternative solutions to this problem?

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