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  • .NET --- Textbox control - wait till user is done typing

    - by Cj Anderson
    Greetings all, Is there a built in way to know when a user is done typing into a textbox? (Before hitting tab, Or moving the mouse) I have a database query that occurs on the textchanged event and everything works perfectly. However, I noticed that there is a bit of lag of course because if a user is quickly typing into the textbox the program is busy doing a query for each character. So what I was hoping for was a way to see if the user has finished typing. So if they type "a" and stop then an event fires. However, if they type "all the way" the event fires after the y keyup. I have some ideas floating around my head but I'm sure they aren't the most efficient. Like measuring the time since the last textchange event and if it was than a certain value then it would proceed to run the rest of my procedures. let me know what you think. Language: VB.NET Framework: .Net 2.0 --Edited to clarify "done typing"

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  • Python File Search Line And Return Specific Number of Lines after Match

    - by Simos Anderson
    I have a text file that has lines representing some data sets. The file itself is fairly long but it contains certain sections of the following format: Series_Name INFO Number of teams : n1 | Team | # | wins | | TeamName1 | x | y | . . . | TeamNamen1 | numn | numn | Some Irrelevant lines Series_Name2 INFO Number of teams : n1 | Team | # | wins | | TeamName1 | num1 | num2 | . where each section has a header that begins with the Series_Name. Each Series_Name is different. The line with the header also includes the number of teams in that series, n1. Following the header line is a set of lines that represents a table of data. For each series there are n1+1 rows in the table, where each row shows an individual team name and associated stats. I have been trying to implement a function that will allow the user to search for a Team name and then print out the line in the table associated with that team. However, certain team names show up under multiple series. To resolve this, I am currently trying to write my code so that the user can search for the header line with series name first and then print out just the following n1+1 lines that represent the data associated with the series. Here's what I have come up with so far: import re print fname = raw_input("Enter filename: ") seriesname = raw_input("Enter series: ") def findcounter(fname, seriesname): logfile = open(fname, "r") pat = 'INFO Number of teams :' for line in logfile: if seriesname in line: if pat in line: s=line pattern = re.compile(r"""(?P<name>.*?) #starting name \s*INFO #whitespace and success \s*Number\s*of\s*teams #whitespace and strings \s*\:\s*(?P<n1>.*)""",re.VERBOSE) match = pattern.match(s) name = match.group("name") n1 = int(match.group("n1")) print name + " has " + str(n1) + " teams" lcount = 0 for line in logfile: if line.startswith(name): if pat in line: while lcount <= n1: s.append(line) lcount += 1 return result The first part of my code works; it matches the header line that the person searches for, parses the line, and then prints out how many teams are in that series. Since the header line basically tells me how many lines are in the table, I thought that I could use that information to construct a loop that would continue printing each line until a set counter reached n1. But I've tried running it, and I realize that the way I've set it up so far isn't correct. So here's my question: How do you return a number of lines after a matched line when given the number of desired lines that follow the match? I'm new to programming, and I apologize if this question seems silly. I have been working on this quite diligently with no luck and would appreciate any help.

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  • Python urllib.urlopen() call doesn't work with a URL that a browser accepts

    - by Charles Anderson
    If I point Firefox at http://bitbucket.org/tortoisehg/stable/wiki/Home/ReleaseNotes, I get a page of HTML. But if I try this in Python: import urllib site = 'http://bitbucket.org/tortoisehg/stable/wiki/Home/ReleaseNotes' req = urllib.urlopen(site) text = req.read() I get the following: 500 Internal Server Error The server encountered an internal error or misconfiguration and was unable to complete your request. What am I doing wrong?

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  • Fixing parent controller's elements after screen orientation

    - by Jonas Anderson
    I have a tab bar application with mixed orientation support for only some views. One of the child view controller shown from one of the tab's navigation controller is displayed only in Landscape mode. In order to accomplish this, I've done the view transformation for the child view as suggested here: Is there a documented way to set the iPhone orientation? The only problem I'm seeing is that after I've performed the orientation adjustment for the child controller and then readjusted orientation back to normal on its dismissal, the contents of the (parent) navigation controller is still shown with Landscape mode dimensions despite the navigation controller reporting the correct value for the interfaceOrientation. How do I ensure that view's size is reset to match the orientation without hardcoding screen dimensions? I have the following in the root navigation controller's viewWillAppear (invoked after the child controller is dismissed): - (void)viewWillAppear:(BOOL)animated { NSLog(@"viewFrame: (%2f, %2f), width: %2f, height: %2f\n", self.view.frame.origin.x, self.view.frame.origin.y, self.view.frame.size.width, self.view.frame.size.height); // Frame values are (0, 0) for (x,y) width: 320, height: 367 before I // displayed child controller. // Frame values are (0,0) width: 480, height: 219 after returning from child // controller -- still has the landscape dimensions NSLog(@"orientation: %d", self.interfaceOrientation); // reports portrait as expected } I've tried to invoke 'layoutIfNeeded' as well as 'setNeedsDisplay' on the view but neither of them bring the view contents into the correct display. Any suggestions would be greatly appreciated.

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  • Trying to not need two separate solutions for x86 and x64 program.

    - by Sean Anderson
    Hi all, I have a program which needs to function in both an x86 and an x64 environment. It is using Oracle's ODBC drivers. I have a reference to Oracle.DataAccess.DLL. This DLL is different depending on whether the system is x64 or x86, though. Currently, I have two separate solutions and I am maintaining the code on both. This is atrocious. I was wondering what the proper solution is? I have my platform set to "Any CPU." and it is my understanding that VS should compile the DLL to an intermediary language such that it should not matter if I use the x86 or x64 version. Yet, if I attempt to use the x64 DLL I receive the error "Could not load file or assembly 'Oracle.DataAccess, Version=2.102.3.2, Culture=neutral, PublicKeyToken=89b483f429c47342' or one of its dependencies. An attempt was made to load a program with an incorrect format." I am running on a 32 bit machine, so the error message makes sense, but it leaves me wondering how I am supposed to efficiently develop this program when it needs to work on x64. Thanks.

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  • Server -> Desktop Push API

    - by Rich Anderson
    Hi I am looking for solution which can push automatically a certain event (let's say RSS message) realtime to a desktop user. A toolbar app or a desktop (growl like) will be super for this push. I have looked at few options but cannot find much info on these kind of apps. I have looked at conduit - it sucks as there is lot of other fancy options which I am not interested in offering to users. Please let me know. Thanks.

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  • jQuery('body').text() gives different answers in different browsers

    - by Charles Anderson
    My HTML looks like this: <html> <head> <title>Test</title> <script type="text/javascript" src="jQuery.js"></script> <script type="text/javascript"> function init() { var text = jQuery('body').text(); alert('length = ' + text.length); } </script> </head> <body onload="init()">0123456789</body> </html> When I load this in Firefox, the length is reported as 10. However, in Chrome it's 11 because it thinks there's a linefeed after the '9'. In IE it's also 11, but the last character is an escape. Meanwhile, Opera thinks there are 12 characters, with the last two being CR LF. If I change the body element to include a span: <body onload="init()"><span>0123456789</span></body> and the jQuery call to: var text = jQuery('body span').text(); then all the browsers agree that the length is 10. Clearly it's the body element that's causing the issue, but can anyone explain exactly why this is happening? I'm particularly surprised because the excellent jQuery is normally browser-independent.

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  • How can I prevent a ToggleButton from being Toggled without setting IsEnabled=False

    - by Bryan Anderson
    I have a list of ToggleButtons being used as the ItemTemplate in a ListBox similar to this answer using the MultiSelect mode of the Listbox. However I need to make sure at least one item is always selected. I can get the proper behavior from the ListBox by just adding an item back into the ListBox's SelectedItems collection on the ListBox.SelectionChanged event but my ToggleButton still moves out of its toggled state so I think I need to stop it earlier in the process. I would like to do it without setting IsEnabled="False" on the last button Selected because I'd prefer to stay with the Enabled visual style without having to redo my button templates. Any ideas?

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  • Require Integer Value not Memory Address whilst avoiding Invalid receiver type compiler warning

    - by Dave Anderson
    I have the following code; int days = [[SettingsUtils daysToRetainHistory] intValue]; [retainHistory setText:[NSString stringWithFormat:@"Days to retain History: %d", days]]; [daysToRetainHistory setValue:days animated:NO]; where [SettingsUtils daysToRetainHistory] is as follows; + (int) daysToRetainHistory { return (int)[[NSUserDefaults standardUserDefaults] objectForKey:@"CaseBaseDaysToRetainHistory"]; } I get the compiler warning Invalid receiver type 'int' because I call intValue on an int but unless I do this I can't seem to get the integer value out and always end up with the memory address i.e. 98765432 instead of 9 which ruins the UILabel display [retainHistory] and the UISlider [daysToRetainHistory] value. How do I avoid the compiler warning and still get my integer value in the label and the necessary float value for setting the UISlider value?

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  • JavaScript regular expression literal persists between function calls

    - by Charles Anderson
    I have this piece of code: function func1(text) { var pattern = /([\s\S]*?)(\<\?(?:attrib |if |else-if |else|end-if|search |for |end-for)[\s\S]*?\?\>)/g; var result; while (result = pattern.exec(text)) { if (some condition) { throw new Error('failed'); } ... } } This works, unless the throw statement is executed. In that case, the next time I call the function, the exec() call starts where it left off, even though I am supplying it with a new value of 'text'. I can fix it by writing var pattern = new RegExp('.....'); instead, but I don't understand why the first version is failing. How is the regular expression persisting between function calls? (This is happening in the latest versions of Firefox and Chrome.) Edit Complete test case: <!DOCTYPE HTML> <html> <head> <meta http-equiv="Content-type" content="text/html;charset=UTF-8"> <title>Test Page</title> <style type='text/css'> body { font-family: sans-serif; } #log p { margin: 0; padding: 0; } </style> <script type='text/javascript'> function func1(text, count) { var pattern = /(one|two|three|four|five|six|seven|eight)/g; log("func1"); var result; while (result = pattern.exec(text)) { log("result[0] = " + result[0] + ", pattern.index = " + pattern.index); if (--count <= 0) { throw "Error"; } } } function go() { try { func1("one two three four five six seven eight", 3); } catch (e) { } try { func1("one two three four five six seven eight", 2); } catch (e) { } try { func1("one two three four five six seven eight", 99); } catch (e) { } try { func1("one two three four five six seven eight", 2); } catch (e) { } } function log(msg) { var log = document.getElementById('log'); var p = document.createElement('p'); p.innerHTML = msg; log.appendChild(p); } </script> </head> <body><div> <input type='button' id='btnGo' value='Go' onclick='go();'> <hr> <div id='log'></div> </div></body> </html> The regular expression continues with 'four' as of the second call on FF and Chrome, not on IE7 or Opera.

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  • SQL join from multiple tables

    - by Kenny Anderson
    Hi all We've got a system (MS SQL 2008 R2-based) that has a number of "input" database and a one "output" database. I'd like to write a query that will read from the output DB, and JOIN it to data in one of the source DB. However, the source table may be one or more individual tables :( The name of the source DB is included in the output DB; ideally, I'd like to do something like the following (pseudo-SQL ahoy) SELECT output.UID, output.description, input.data from output.dbo.description LEFT JOIN (SELECT input.UID, input.data FROM [output.sourcedb].dbo.datatable ) AS input ON input.UID=output.UID Is there any way to do something like the above - "dynamically" specify the database and table to be joined on for each row in the query?

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  • Linq Query ignore empty parameters

    - by Cj Anderson
    How do I get Linq to ignore any parameters that are empty? So Lastname, Firstname, etc? If I have data in all parameters it works fine. refinedresult = From x In theresult _ Where x.<thelastname>.Value.TestPhoneElement(LastName) Or _ x.<thefirstname>.Value.TestPhoneElement(FirstName) Or _ x.<id>.Value.TestPhoneElement(Id) Or _ x.<number>.Value.TestPhoneElement(Telephone) Or _ x.<location>.Value.TestPhoneElement(Location) Or _ x.<building>.Value.TestPhoneElement(building) Or _ x.<department>.Value.TestPhoneElement(Department) _ Select x Public Function TestPhoneElement(ByVal parent As String, ByVal value2compare As String) As Boolean 'find out if a value is null, if not then compare the passed value to see if it starts with Dim ret As Boolean = False If String.IsNullOrEmpty(parent) Then Return False End If If String.IsNullOrEmpty(value2compare) Then Return ret Else ret = parent.ToLower.StartsWith(value2compare.ToLower.Trim) End If Return ret End Function

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  • How can I break if gdb is attached, but continue if it is not?

    - by Michael Anderson
    I have some debugging code that if executed while running with GBD attached should break the execution of the application, but if GDB is not running it should continue. The code I'm working with looks something like this in structure: try { if( some_complex_expression ) { gdb_should_berak_here(); do_some_stuff(); throw MyException(); } } catch( const MyException & e ) { handle_exception_and_continue(); } What should gdb_should_break_here be?

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  • How can I track down these Firefox warning messages?

    - by Charles Anderson
    Since I upgraded to jQuery 1.4.4 I've been getting several new warning messages when I run my unit tests in Firefox 3.6.13. Here's a typical one: Warning: Unexpected token in attribute selector: '!'. Source File: http://localhost/unitTests/devunitTests.html Line: 0 Or the even more useful: Warning: Selector expected. Source File: http://localhost/unitTests/ui/editors/iframe2.html?test=15 Line: 0 The web page renders nicely, and all my JavaScript code seems to be running okay too, so I'm reluctant to spend a potentially large amount of time chopping away at my code to track these messages down. However, can anyone suggest what's provoking the warnings?

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  • d3 tree - parents having same children

    - by Larry Anderson
    I've been transitioning my code from JIT to D3, and working with the tree layout. I've replicated http://mbostock.github.com/d3/talk/20111018/tree.html with my tree data, but I wanted to do a little more. In my case I will need to create child nodes that merge back to form a parent at a lower level, which I realize is more of a directed graph structure, but would like the tree to accomodate (i.e. notice that common id's between child nodes should merge). So basically a tree that divides like normal on the way from parents to children, but then also has the ability to bring those children nodes together to be parents (sort of an incestual relationship or something :)). Asks something similar - How to layout a non-tree hierarchy with D3 It sounds like I might be able to use hierarchical edge bundling in conjunction with the tree hierarchy layout, but I haven't seen that done. I might be a little off with that though.

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  • Android - Looping Activity to Repeat MediaPlayer

    - by Austin Anderson
    I'm trying to create a soundboard for longer audio files and can't figure out how to stop an audio file and start it again without closing the activity. Let's say each audio file is one minute long. If I play the first audio file for 20 seconds and start the next audio file, the first stops playing and the second starts playing. However, if I click the first audio file again, the second stops playing and the first does not. I need help. This is driving me insane. bAudio1 = (ImageButton) findViewById(R.id.bAudio1); bAudio2 = (ImageButton) findViewById(R.id.bAudio2); mpAudio1 = MediaPlayer.create(this, R.raw.audio1); mpAudio2 = MediaPlayer.create(this, R.raw.audio2); bAudio1.setOnClickListener(new View.OnClickListener() { public void onClick(View v) { if(mpAudio1.isPlaying()) { mpAudio1.stop(); } else { if(mpAudio2.isPlaying()) { mpAudio2.stop(); } mpAudio1.start(); } } }); bAudio2.setOnClickListener(new View.OnClickListener() { public void onClick(View v) { if(mpAudio2.isPlaying()) { mpAudio2.stop(); } else { if(mpAudio1.isPlaying()) { mpAudio1.stop(); } mpAudio2.start(); } } }); Thanks.

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  • Problem prompting user for extended permissions using showPermissionDialog in FB page tab

    - by snipe
    I have an FBML app that will use the tab as a promo tab before the full app goes live. The purpose of the promo tab is to allow users to opt in to email notifications (using the FB API sendNotifications call), so I need to prompt them to allow the app and grant extended permissions on that promo tab. The tab code is: <?php require_once 'config.php'; ?> <form id="form1"> <h1> <a href="#" clickrewriteform="form1" clickrewriteurl="http://www.mydomain.com/fanpageajax/result.php" clickrewriteid="allowapp">Step 1. Allow the Application</a> </h1> <div id="allowapp"></div> </form> <h1><a onclick="Facebook.showPermissionDialog('email');return false;"> Step 2. Grant extended permissions (intab)</a></h1> The result.php page just tags the API to ensure the allow prompt will show up. The problem is with the Step 2. Once the user has allowed the app, and they click on the Step 2, nothing happens. If they click on it twice, THEN the extended permissions dialog box popups up, but it asks them to grant extended permissions TWICE. OR.... If the user clicks on Step 1, and allows the app, and then reloads the fan page tab, they only have to click on the Step 2 link once, and the permissions show up. Anyone have any ideas? I have been beating myself in the head over this for hours.

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  • Detecting Handlers of a jQuery "Live" Event

    - by Anderson De Andrade
    Using: $('#foo').data('events').click We are able to access an iterative object of click handlers added to the element '#foo' but only when they were added with .bind() Is there a way to get the handlers for an event added with .live()? Is there any other way to know if an element has a click handler assigned?

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  • Push alerts to notification tray app in Java

    - by Rich Anderson
    Hi - how do I push server alerts to tray apps in java without using xmpp or other heavy protocols? Do you recommend a way to accomplish this? I was planning to write an app which uses URLConnection on a server equipped with Comet but I doubt if that would work as the client requires a JS to be invoked and URLConnection is not a browser.. What is the best way to push instead of using a proprietary client-server approach?

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  • Improving Partitioned Table Join Performance

    - by Paul White
    The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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  • Problem with icacls on Windows 2003: "Acl length is incorrect"

    - by Andrew J. Brehm
    I am confused by the output of icacls on Windows 2003. Everything appears to work on Windows 2008. I am trying to change permissions on a directory: icacls . /grant mydomain\someuser:(OI)(CI)(F) This results in the following error: .: Acl length is incorrect. .: An internal error occurred. Successfully processed 0 files; Failed processing 1 files The same command used on a file named "file" works: icacls file /grant mydomain\someuser:(OI)(CI)(F) Result is: processed file: file Successfully processed 1 files; Failed processing 0 files What's going on?

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  • How do I create statistics to make ‘small’ objects appear ‘large’ to the Optmizer?

    - by Maria Colgan
    I recently spoke with a customer who has a development environment that is a tiny fraction of the size of their production environment. His team has been tasked with identifying problem SQL statements in this development environment before new code is released into production. The problem is the objects in the development environment are so small, the execution plans selected in the development environment rarely reflects what actually happens in production. To ensure the development environment accurately reflects production, in the eyes of the Optimizer, the statistics used in the development environment must be the same as the statistics used in production. This can be achieved by exporting the statistics from production and import them into the development environment. Even though the underlying objects are a fraction of the size of production, the Optimizer will see them as the same size and treat them the same way as it would in production. Below are the necessary steps to achieve this in their environment. I am using the SH sample schema as the application schema who's statistics we want to move from production to development. Step 1. Create a staging table, in the production environment, where the statistics can be stored Step 2. Export the statistics for the application schema, from the data dictionary in production, into the staging table Step 3. Create an Oracle directory on the production system where the export of the staging table will reside and grant the SH user the necessary privileges on it. Step 4. Export the staging table from production using data pump export Step 5. Copy the dump file containing the stating table from production to development Step 6. Create an Oracle directory on the development system where the export of the staging table resides and grant the SH user the necessary privileges on it.  Step 7. Import the staging table into the development environment using data pump import Step 8. Import the statistics from the staging table into the dictionary in the development environment. You can get a copy of the script I used to generate this post here. +Maria Colgan

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