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  • Stored procedure optimization

    - by George Zacharia
    Hi, i have a stored procedure which takes lot of time to execure .Can any one suggest a better approch so that the same result set is achived. ALTER PROCEDURE [dbo].[spFavoriteRecipesGET] @USERID INT, @PAGENUMBER INT, @PAGESIZE INT, @SORTDIRECTION VARCHAR(4), @SORTORDER VARCHAR(4),@FILTERBY INT AS BEGIN DECLARE @ROW_START INT DECLARE @ROW_END INT SET @ROW_START = (@PageNumber-1)* @PageSize+1 SET @ROW_END = @PageNumber*@PageSize DECLARE @RecipeCount INT DECLARE @RESULT_SET_TABLE TABLE ( Id INT NOT NULL IDENTITY(1,1), FavoriteRecipeId INT, RecipeId INT, DateAdded DATETIME, Title NVARCHAR(255), UrlFriendlyTitle NVARCHAR(250), [Description] NVARCHAR(MAX), AverageRatingId FLOAT, SubmittedById INT, SubmittedBy VARCHAR(250), RecipeStateId INT, RecipeRatingId INT, ReviewCount INT, TweaksCount INT, PhotoCount INT, ImageName NVARCHAR(50) ) INSERT INTO @RESULT_SET_TABLE SELECT FavoriteRecipes.FavoriteRecipeId, Recipes.RecipeId, FavoriteRecipes.DateAdded, Recipes.Title, Recipes.UrlFriendlyTitle, Recipes.[Description], Recipes.AverageRatingId, Recipes.SubmittedById, COALESCE(users.DisplayName,users.UserName,Recipes.SubmittedBy) As SubmittedBy, Recipes.RecipeStateId, RecipeReviews.RecipeRatingId, COUNT(RecipeReviews.Review), COUNT(RecipeTweaks.Tweak), COUNT(Photos.PhotoId), dbo.udfGetRecipePhoto(Recipes.RecipeId) AS ImageName FROM FavoriteRecipes INNER JOIN Recipes ON FavoriteRecipes.RecipeId=Recipes.RecipeId AND Recipes.RecipeStateId <> 3 LEFT OUTER JOIN RecipeReviews ON RecipeReviews.RecipeId=Recipes.RecipeId AND RecipeReviews.ReviewedById=@UserId AND RecipeReviews.RecipeRatingId= ( SELECT MAX(RecipeReviews.RecipeRatingId) FROM RecipeReviews WHERE RecipeReviews.ReviewedById=@UserId AND RecipeReviews.RecipeId=FavoriteRecipes.RecipeId ) OR RecipeReviews.RecipeRatingId IS NULL LEFT OUTER JOIN RecipeTweaks ON RecipeTweaks.RecipeId = Recipes.RecipeId AND RecipeTweaks.TweakedById= @UserId LEFT OUTER JOIN Photos ON Photos.RecipeId = Recipes.RecipeId AND Photos.UploadedById = @UserId AND Photos.RecipeId = FavoriteRecipes.RecipeId AND Photos.PhotoTypeId = 1 LEFT OUTER JOIN users ON Recipes.SubmittedById = users.UserId WHERE FavoriteRecipes.UserId=@UserId GROUP BY FavoriteRecipes.FavoriteRecipeId, Recipes.RecipeId, FavoriteRecipes.DateAdded, Recipes.Title, Recipes.UrlFriendlyTitle, Recipes.[Description], Recipes.AverageRatingId, Recipes.SubmittedById, Recipes.SubmittedBy, Recipes.RecipeStateId, RecipeReviews.RecipeRatingId, users.DisplayName, users.UserName, Recipes.SubmittedBy; WITH SortResults AS ( SELECT ROW_NUMBER() OVER ( ORDER BY CASE WHEN @SORTDIRECTION = 't' AND @SORTORDER='a' THEN TITLE END ASC, CASE WHEN @SORTDIRECTION = 't' AND @SORTORDER='d' THEN TITLE END DESC, CASE WHEN @SORTDIRECTION = 'r' AND @SORTORDER='a' THEN AverageRatingId END ASC, CASE WHEN @SORTDIRECTION = 'r' AND @SORTORDER='d' THEN AverageRatingId END DESC, CASE WHEN @SORTDIRECTION = 'mr' AND @SORTORDER='a' THEN RecipeRatingId END ASC, CASE WHEN @SORTDIRECTION = 'mr' AND @SORTORDER='d' THEN RecipeRatingId END DESC, CASE WHEN @SORTDIRECTION = 'd' AND @SORTORDER='a' THEN DateAdded END ASC, CASE WHEN @SORTDIRECTION = 'd' AND @SORTORDER='d' THEN DateAdded END DESC ) RowNumber, FavoriteRecipeId, RecipeId, DateAdded, Title, UrlFriendlyTitle, [Description], AverageRatingId, SubmittedById, SubmittedBy, RecipeStateId, RecipeRatingId, ReviewCount, TweaksCount, PhotoCount, ImageName FROM @RESULT_SET_TABLE WHERE ((@FILTERBY = 1 AND SubmittedById= @USERID) OR ( @FILTERBY = 2 AND (SubmittedById <> @USERID OR SubmittedById IS NULL)) OR ( @FILTERBY <> 1 AND @FILTERBY <> 2)) ) SELECT RowNumber, FavoriteRecipeId, RecipeId, DateAdded, Title, UrlFriendlyTitle, [Description], AverageRatingId, SubmittedById, SubmittedBy, RecipeStateId, RecipeRatingId, ReviewCount, TweaksCount, PhotoCount, ImageName FROM SortResults WHERE RowNumber BETWEEN @ROW_START AND @ROW_END print @ROW_START print @ROW_END SELECT @RecipeCount=dbo.udfGetFavRecipesCount(@UserId) SELECT @RecipeCount AS RecipeCount SELECT COUNT(Id) as FilterCount FROM @RESULT_SET_TABLE WHERE ((@FILTERBY = 1 AND SubmittedById= @USERID) OR (@FILTERBY = 2 AND (SubmittedById <> @USERID OR SubmittedById IS NULL)) OR (@FILTERBY <> 1 AND @FILTERBY <> 2)) END

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  • python django automated data addition

    - by zubin71
    I have a script which reads data from a csv file. I need to store the data into a database which has already been created as $ python manage.py syncdb so, that automated data entry is possible in an easier manner, as available in the django shell.

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  • Efficient way to delete a line from a text file (C#)

    - by Valentin Vasilyev
    Hello. I need to delete a certain line from a text file. What is the most efficient way of doing this? File can be potentially large(over million records). Thank you. UPDATE: below is the code I'm currently using, but I'm not sure if it is good. internal void DeleteMarkedEntries() { string tempPath=Path.GetTempFileName(); using (var reader = new StreamReader(logPath)) { using (var writer = new StreamWriter(File.OpenWrite(tempPath))) { int counter = 0; while (!reader.EndOfStream) { if (!_deletedLines.Contains(counter)) { writer.WriteLine(reader.ReadLine()); } ++counter; } } } if (File.Exists(tempPath)) { File.Delete(logPath); File.Move(tempPath, logPath); } }

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  • Where is Win7's jump list data stored?

    - by DigiMarco
    As per Jumplist Extender, I'm trying to prevent other apps from refreshing their jump lists (it's assumed that the user WANTS to do this, seeing as this is a JL editor.) One idea is to look for file or registry changes, where the data may be stored, and prevent the data from being written to. The question is, where is the jump list data stored? It has to be somewhere! I know there's a folder location for pinned items, but I forgot what it is. It'd be great if I can get the "task" data, as well. Here's the original report.

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  • Measuring debug vs release of ASP.NET applications

    - by Alex Angas
    A question at work came up about building ASP.NET applications in release vs debug mode. When researching further (particularly on SO), general advice is that setting <compilation debug="true"> in web.config has a much bigger impact. Has anyone done any testing to get some actual numbers about this? Here's the sort of information I'm looking for (which may give away my experience with testing such things): Execution time | Debug build | Release build -------------------+---------------+--------------- Debug web.config | average 1 | average 2 Retail web.config | average 3 | average 4 Max memory usage | Debug build | Release build -------------------+---------------+--------------- Debug web.config | average 1 | average 2 Retail web.config | average 3 | average 4 Output file size | Debug build | Release build -------------------+---------------+--------------- | size 1 | size 2

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  • socket programming: How do I handle out of band data

    - by soulmerge
    I just looked into wikipedia's entry on out-of-band data and as far as I understand, OOB data is somehow flagged more important and treated as ordinary data, but transmitted in a seperate stream, which profoundly confuses me. The actual question would be (besides "Could someone explain what OOB data is?"): I'm writing a unix application that uses sockets and need to make use of select() and was wondering what to do with the exceptfds parameter? Do I need to put all my sockets into this parameter and react to such events? Or do I just ignore them?

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  • Reasonably faster way to traverse a directory tree in Python?

    - by Sridhar Ratnakumar
    Assuming that the given directory tree is of reasonable size: say an open source project like Twisted or Python, what is the fastest way to traverse and iterate over the absolute path of all files/directories inside that directory? I want to do this from within Python (subprocess is allowed). os.path.walk is slow. So I tried ls -lR and tree -fi. For a project with about 8337 files (including tmp, pyc, test, .svn files): $ time tree -fi > /dev/null real 0m0.170s user 0m0.044s sys 0m0.123s $ time ls -lR > /dev/null real 0m0.292s user 0m0.138s sys 0m0.152s $ time find . > /dev/null real 0m0.074s user 0m0.017s sys 0m0.056s $ tree appears to be faster than ls -lR (though ls -R is faster than tree, but it does not give full paths). find is the fastest. Can anyone think of a faster and/or better approach? On Windows, I may simply ship a 32-bit binary tree.exe or ls.exe if necessary. Update 1: Added find

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  • How can I load .obj files in the Soya3D engine?

    - by John Riselvato
    I recently just found soya3d. I want to import .obj files, but it seems to only accept .data files. How can I import .obj files? Importing a .obj file named "house" produces this error: Traceback (most recent call last): File "introduction.py", line 7, in <module> model = soya.Model.get("house") File "/usr/lib/pymodules/python2.6/soya/__init__.py", line 259, in get return klass._alls.get(filename) or klass._alls.setdefault(filename, klass.load(filename)) File "/usr/lib/pymodules/python2.6/soya/__init__.py", line 268, in load dirname = klass._get_directory_for_loading_and_check_export(filename) File "/usr/lib/pymodules/python2.6/soya/__init__.py", line 194, in _get_directory_for_loading_and_check_export dirname = klass._get_directory_for_loading(filename, ext) File "/usr/lib/pymodules/python2.6/soya/__init__.py", line 171, in _get_directory_for_loading raise ValueError("Cannot find a %s named %s!" % (klass, filename)) ValueError: Cannot find a <class 'soya.Model'> named house! * Soya3D * Quit...

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  • What's an acceptable "Avg. Page Load Time"?

    - by hawbsl
    Is there any industry rule of thumb for what's considered an unacceptable load time v. an OK one v. a blistering fast one? We're just reviewing some Google Analytics data and getting 0.74 Avg. Page Load Time reported. I guess that's OK. However it would be good if some meatier comparison data were available, or a blog post, or somewhere where there's some analysis of what speeds are generally being achieved by various kinds of sites. Any useful links to help someone interpret these speeds? If you Google it you just get a lot of results dealing with how to improve your speed. We're not at that stage yet.

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  • Sql Server 2000 Stored Procedure Prevent Parallelism or something?

    - by user187305
    I have a huge disgusting stored procedure that wasn't slow a couple months ago, but now is. I barely know what this thing does and I am in no way interested in rewriting it. I do know that if I take the body of the stored procedure and then declare/set the values of the parameters and run it in query analyzer that it runs more than 20x faster. From the internet, I've read that this is probably due to a bad cached query plan. So, I've tried running the sp with "WITH RECOMPILE" after the EXEC and I've also tried putting the "WITH RECOMPLE" inside the sp, but neither of those helped even a little bit. When I look at the execution plan of the sp vs the query, the biggest difference is that the sp has "Parallelism" operations all over the place and the query doesn't have any. Can this be the cause of the difference in speeds? Thank you, any ideas would be great... I'm stuck.

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  • C#/WPF FileSystemWatcher on every extension on every path

    - by BlueMan
    I need FileSystemWatcher, that can observing same specific paths, and specific extensions. But the paths could by dozens, hundreds or maybe thousand (hope not :P), the same with extensions. The paths and ext are added by user. Creating hundreds of FileSystemWatcher it's not good idea, isn't it? So - how to do it? Is it possible to watch/observing every device (HDDs, SD flash, pendrives, etc.)? Will it be efficient? I don't think so... . Every changing Windows log file, scanning file by antyvirus program - it could realy slow down my program with SystemWatcher :(

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  • Storing header and data sections in a CSV file

    - by morpheous
    This should be relatively easy to do, but after several hours straight programming my mind seems a bit frazzled and could do with some help. I have a C++ class which I am currently using to store read/write data to file. I was initially using binary data, but have decided to store the data as CSV in order to let programs written in other languages be able to load the data. The C++ class looks a bit like this: class BinaryData { public: BinaryData(); void serialize(std::ostream& output) const; void deserialize(std::istream& input); private: Header m_hdr; std::vector<Row> m_rows; }; I am simply rewriting the serialize/deserialize methods to write to a CSV file. I am not sure on the "best" way to store a header section and a "data" section in a "flat" CSV file though - any suggestions on the most sensible way to do this?

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  • Jmeter- HTTP Cache Manager, Unable to cache everything what it is being cached by Browser

    - by chinmay brahma
    I used HTTP Chache Manager to Cache files which are being cached in browser. I am successful of doing it for some of the pages. Number of files being cached in Jmeter is equal to Number of files being cached by browser. But in some cases : I found number files being cached is lesser than the files being cached by browser. Using Jmeter I found only 5 files are being cached but in real browser 12 files are getting cached. Thanks in advance

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  • Core data storage is repeated...

    - by Kamlesh
    Hi all, I am trying to use Core Data in my application and I have been succesful in storing data into the entity.The data storage is done in the applicationDidFinishLaunchingWithOptions() method.But when I run the app again,it again gets saved.So how do I check if the data is already present or not?? Here is the code(Saving):-`NSManagedObjectContext *context = [self managedObjectContext]; NSManagedObject *failedBankInfo = [NSEntityDescription insertNewObjectForEntityForName:@"FailedBankInfo" inManagedObjectContext:context]; [failedBankInfo setValue:@"Test Bank" forKey:@"name"]; [failedBankInfo setValue:@"Testville" forKey:@"city"]; [failedBankInfo setValue:@"Testland" forKey:@"state"]; NSError *error; if (![context save:&error]) { NSLog(@"Whoops, couldn't save: %@", [error localizedDescription]); } (Retrieving):- NSFetchRequest *fetchRequest = [[NSFetchRequest alloc] init]; NSEntityDescription *entity = [NSEntityDescription entityForName:@"FailedBankInfo" inManagedObjectContext:context]; [fetchRequest setEntity:entity]; NSArray *fetchedObjects = [context executeFetchRequest:fetchRequest error:&error]; for (NSManagedObject *info in fetchedObjects) { NSLog(@"Name: %@", [info valueForKey:@"name"]); } `

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  • Can this loop be sped up in pure Python?

    - by Noctis Skytower
    I was trying out an experiment with Python, trying to find out how many times it could add one to an integer in one minute's time. Assuming two computers are the same except for the speed of the CPUs, this should give an estimate of how fast some CPU operations may take for the computer in question. The code below is an example of a test designed to fulfill the requirements given above. This version is about 20% faster than the first attempt and 150% faster than the third attempt. Can anyone make any suggestions as to how to get the most additions in a minute's time span? Higher numbers are desireable. EDIT: This experiment is being written in Python 3.1 and is 15% faster than the fourth speed-up attempt. def start(seconds): import time, _thread def stop(seconds, signal): time.sleep(seconds) signal.pop() total, signal = 0, [None] _thread.start_new_thread(stop, (seconds, signal)) while signal: total += 1 return total if __name__ == '__main__': print('Testing the CPU speed ...') print('Relative speed:', start(60))

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  • Why are difference lists more efficient than regular concatenation?

    - by Craig Innes
    I am currently working my way through the Learn you a haskell book online, and have come to a chapter where the author is explaining that some list concatenations can be ineffiecient: For example ((((a ++ b) ++ c) ++ d) ++ e) ++ f Is supposedly inefficient. The solution the author comes up with is to use 'difference lists' defined as newtype DiffList a = DiffList {getDiffList :: [a] -> [a] } instance Monoid (DiffList a) where mempty = DiffList (\xs -> [] ++ xs) (DiffList f) `mappend` (DiffList g) = DiffList (\xs -> f (g xs)) I am struggling to understand why DiffList is more computationally efficient than a simple concatenation in some cases. Could someone explain to me in simple terms why the above example is so inefficient, and in what way the DiffList solves this problem?

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  • Windows Workflow runs very slowlyh on my DEV machine

    - by Joon
    I am developing an app using WF hosted in IIS as WCF services as a business layer. This runs quickly on any machine running Windows Server 2008 R2, but very slowly on our dev machines, running Windows XP SP3. Yesterday, the workflows were as fast on my dev machine as they are on the server for the whole day. Today, they are back to running slowly again (I rebooted overnight) Has anyone else experienced this problem with workflows running slowly on IIS in XP? What did you do to fix it?

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  • JSON.Stringify data including boolean values

    - by ancdev
    What I'm trying to do is to pass JSON object to a WebAPI ajax call and mapped to a strongly typed object on the server side. String values are being posted perfectly however when it comes to boolean values, they are not being passed at all. Below is my code: var gsGasolineField = $('.gsGasoline').val(); blData = { Gasoline: gsGasolineField }; var json = JSON.stringify(blData); $.ajax({ type: "POST", url: url, data: json, contentType: "application/json", dataType: "json", statusCode: { 201 /*Created"*/: function (data) { $("#BusinessLayerDialog").dialog("close"); ClearForm("#BusinessLayerForm"); }, 400: /*Bad request - validation error*/ function (data) { $("#BusinessLayerForm").validate().form(); }, 500: function (data) { alert('err'); } }, beforeSend: setHeader }); Gasoline property is of type boolean on the server side.

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  • Quickest way to compare a bunch of array or list of values.

    - by zapping
    Can you please let me know on the quickest and efficient way to compare a large set of values. Its like there are a list of parent codes(string) and each code has a series of child values(string). The child lists have to be compared with each other and find out duplicates and count how many times they repeat. code1(code1_value1, code1_value2, code3_value3, ..., code1_valueN); code2(code2_value1, code1_value2, code2_value3, ..., code2_valueN); code3(code2_value1, code3_value2, code3_value3, ..., code3_valueN); . . . codeN(codeN_value1, codeN_value2, codeN_value3, ..., codeN_valueN); The lists are huge say like there are 100 parent codes and each has about 250 values in them. There will not be duplicates within a code list. Doing it in java and the solution i could figure out is. Store the values of first set of code in as codeMap.put(codeValue, duplicateCount). The count initialized to 0. Then compare the rest of the values with this. If its in the map then increment the count otherwise append it to the map. The downfall of this is to get the duplicates. Another iteration needs to be performed on a very large list. An alternative is to maintain another hashmap for duplicates like duplicateCodeMap.put(codeValue, duplicateCount) and change the initial hashmap to codeMap.put(codeValue, codeValue). Speed is what is requirement. Hope one of you can help me with it.

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  • Slow query with unexpected scan

    - by zerkms
    Hello I have this query: SELECT * FROM SAMPLE SAMPLE INNER JOIN TEST TEST ON SAMPLE.SAMPLE_NUMBER = TEST.SAMPLE_NUMBER INNER JOIN RESULT RESULT ON TEST.TEST_NUMBER = RESULT . TEST_NUMBER WHERE SAMPLED_DATE BETWEEN '2010-03-17 09:00' AND '2010-03-17 12:00' the biggest table here is RESULT, contains 11.1M records. The left 2 tables about 1M. this query works slowly (more than 10 minutes) and returns about 800 records. executing plan shows clustered index scan over all 11M records. RESULT.TEST_NUMBER is a clustered primary key. if I change 2010-03-17 09:00 to 2010-03-17 10:00 - i get about 40 records. it executes for 300ms. and plan shows clustered index seek if i replace * in SELECT clause to RESULT.TEST_NUMBER (covered with index) - then all become fast in first case too. this points to hdd io issues, but doesn't clarifies changing plan. so, any ideas?

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  • Are there any tools to optimize the number of consumer and producer threads on a JMS queue?

    - by lindelof
    I'm working on an application that is distributed over two JBoss instances and that produces/consumes JMS messages on several JMS queues. When we configured the application we had to determine which threading model we would use, in particular the number of producing and consuming threads per queue. We have done this in a rather ad-hoc fashion but after reading the most recent columns by Herb Sutter in Dr Dobbs (in particular this one) I would like to size our threads in a more rigorous manner. Are there any methods/tools to measure the throughput of JMS queues (in particular JBoss Messaging queues) as a function of the number of producing/consuming threads?

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