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  • Legal Issue: Remove/Hide links on Google Login page

    - by Rowell
    For the background: I'm developing a device application which offers connection to Google Drive. My end-users will need to login to their Google Account and authorize my application to access their Google Drive. I'm using OAuth 2.0 to do this. But my concern is that I don't want users to navigate away from my application using the links on the Google Login page. Basically, I don't want them to use my application to browse the internet. Question: Will I violate any terms of service/usage if I hide or change the href the links using GreaseMonkey or TamperMonkey? The changes will only be on the client side and I won't alter any processing at all. I already checked https://developers.google.com/terms/ but I found no item related to modifying the pages on client side. Thanks in advance.

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  • combining two png files in android

    - by John
    I have two png image files that I would like my android app to combine problematically into one png image file and am wondering if it is possible to do so? if so, what I would like to do is just overlay them on each other to create one file. the idea behind this is that I have a handful of png files, some with a portion of the image on the left with the rest transparent and the others with an image on the right and the rest transparent. and based on user input it will combine the two to make one file to display. (and i cant just display the two images side by side, they need to be one file) is this possible and how so?

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  • jQuery.remove() - Is there a way to get the object back after you remove it?

    - by Jack Marchetti
    I basically have the same problem in this questions: Flash Video still playing in hidden div I've used the .remove jquery call and this works. However, I have previous/next buttons when a user scrolls through hidden/non-hidden divs. What I need to know is, once I remove the flash object, is there a way to get it back other than refreshing the page? Basically, can this be handled client side or am I going to need to implement some server side handling. detach() won't work because the flash video continues to play. I can't just hide it because the video continues to play as well.

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  • Regex for removing an item from a comma-separated string?

    - by ingredient_15939
    Say I have a string like this: "1,2,3,11" A simple regex like this will find a number in the string: (?<=^|,)1(?=,|$) - this will correctly find the "1" (ie. not the first "1" in "11"). However, to remove a number from the string, leaving the string properly formatted with commas in between each number, I need to include one adjacent comma only. For example, matching 1,, 2, or ,11. So the trick is to match one comma, on either side of the number, but to ignore the comma on the opposite side (if there is one). Can someone help me with this?

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  • jQuery & elastic Problem

    - by Fincha
    Hello eveyone, i using elastic in my script. I have also jQuery Tabs (every will be get over AJAX and content a textarea) and a timer for Saving content all 3 minutes. So some JS code... I have 2 parts on my site, left and right. On the right side i have 2 tabs (jQuery not AJAX) with each one textarea. And Left side between 5-10 Textareas each in Tab but they gonna be loaded only if Tab is activ (AJAX). my Problem is: If i paste a lot of text in a Textarea (1000 characters) the writing get slowed, not fluid, jerky. It ist 100% the elastic problem, without elastic there no Problem while writing. Have some one an idea for the solution of this Porblem? Is it Overload?

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  • Hover multiple elements affecting only 1 item

    - by lilsizzo
    hi guys i was wondering if there is a way to hover a few elements with the same class name which is placed side by side and actions would be trigger upon leaving the area of the elements. For example : <div class="hoverme"></div> <div class="hoverme"></div> <div class="hoverme"></div> <div class="hoverme"></div> <div class="hoverme"></div> the javascript of "unhover" below should only be called when they leave the whole area of "hoverme" class. $('.hoverme').live('mouseover mouseout', function(event) { if (event.type == 'mouseover') { if(!$("#stage1 td").hasClass("hover")) { $("#stage1 td").addClass("hover",200) } } else { //$("#stage1 td").removeClass("hover",200) } }); Is there a way for this action??

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  • Certificate Trusts Lists in IIS7

    - by BrettRobi
    I am trying to enable mutual authentication for my WebService hosted in IIS7. I have the server side cert setup and working but cannot figure out how to get a Certificate Trust List created and setup in IIS7 so that I can require and validate client side certificates. All of my client side certs are signed by my own root cert so I need to create a CTL that contains just my root cert and then have IIS validate client provided certs against the CTL. Can anyone shed some light on how to do this? IIS6 had a UI for assigning a CTL, but I can find nothing similar in IIS7. Update: I have now successfully used MakeCTL in wizard mode to create a CTL with a Friendly Name. However I don't have adsutil support on my IIS7 box so via other posts elsewhere I am trying to use the 'netsh http add sslcert' command to assign the CTL to my site. Before I could use this command I had to remove the existing SSL cert that was assigned to my site for server authentication. Then in my netsh command I specify the thumbprint of that very same SSL cert I removed, plus a made up appid, plus 'sslctlidentifier=MyCTL sslctlstorename=CA'. The resulting command is: netsh http add sslcert ipport=10.10.10.10:443 certhash=adfdffa988bb50736b8e58a54c1eac26ed005050 appid={ffc3e181-e14b-4a21-b022-59fc669b09ff} sslctlidentifier=MyCTL sslctlstorename=CA (the IP addr is munged), but I am getting this error: SSL Certificate add failed, Error: 1312 A specified logon session does not exist. It may already have been terminated. I am sure the error is related to the CTL options because if I remove them it works (though no CTL is assigned of course). Can anyone help me take this last step and make this work? UPDATE 01-07-2010: I never resolved this with IIS 7.0 and have since migrated our app to IIS 7.5 and am giving this another try. Per the response from Taras Chuhay I installed IIS6 Compatibility on my test server and tried the steps he documented using adsutil.vbs (which can also be found here). I immediately ran into this error: ErrNumber: -2147023584 Error trying to SET the Property: SslCtlIdentifier when running this command: adsutil.vbs set w3svc/1/SslCtlIdentifier MyFriendlyName I then went on to try the next adsutil.vbs command documented and it failed with the same error. I have verified that the CTL I created has a Friendly Name of MyFriendlyName and that it exists in the 'Intermediate Certification Authorities\Certificate Trust List' store of LocalComputer. So once again I am at a dead standstill. I don't know what else to try. Has anyone ever gotten CTL's to work with IIS7 or 7.5? Ever? Am I beating a DEAD horse. Google turns up nothing but my own posts and other similar stories. Update 2/23/10 - I've confirmed with Microsoft that this is a bug with IIS 7.5, but it does work with IIS 7. Check out this link for details: http://viisual.net/configuration/IIS7-CTLs.htm Update 6/08/10 - I can now confirm that KB981506 resolves this issue. There is a patch associated with this KB that must be applied to Server 2008 R2 machines to enable this functionality. Once that is installed all works flawlessly for me.

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  • synchronization of file locations between two machines

    - by intuited
    Although similar threads have been asked on this site and its siblings before, I've not managed to glean the answer to this persistent question. Any help is much appreciated. The situation: I've got two laptops; both contain a ton of music. Sometimes I move these music files to different locations, or change the metadata in them, or convert them to a different format. I might do any of these things on either machine. I rarely do all of them at once — ie it's unlikely that I'll convert a file's format and move it to a different location all in one go. I'd like to be able to synchronize these changes without having to sift through everything that was renamed or moved. I'm familiar with rsync but I find it inadequate, because although it can compute checksums, it doesn't have any way to store them. So if a file differs, it can't figure out which side it changed on. This also means that it can't attempt to match a missing file to a new one with the same checksum (ie a move) if the filesize and date are the same, it , so it takes an epoch to do a sync on a large repository. I would like to only check the checksum if the files even if you turn on checksumming, it still doesn't use it intelligently: ie it checksums files even if the sizes differ. IIRC. it's not able to use file metadata as a means of file comparison. this is sort of a wishlist item but it seems doable. I've also looked into rsnapshot, but its requirement to create a full backup is impractical in this situation. I don't need a backup, I just need a record of what file with each hash was where when. Unison seems like it might be able to do something vaguely along these lines, but I'm loathe to spend hours wading through its details only to discover that it's sadly lacking. Plus, it's fun asking questions on here. What I'd like is a tool that does something along these lines: keeps track of file checksums or of actual renames, possibly using inotify to greatly reduce resource consumption/latency stores a database containing this info, along with other pertinencies like the file format and metadata, the actual inode, the filename history, etc. uses this info to provide more-intelligent synchronization with a counterpart on the other side. So for example: if a file has been converted from flac to ogg, but kept the same base filename, or the same metadata, it should be able to send the new version over, and the other side should delete the original. Probably it should actually sequester it somewhere in case they or you screwed up, but that's a detail. And then when the transaction is done, the state is logged so that the next time the two interact they can work out their differences. Maybe all this metadata stuff is a fancy pipe dream. I would actually be pretty happy if there was something out there that could just use checksums in an intelligent way. This would be sort of like having the intelligence of something like git, minus the need to duplicate data in an index/backup/etc (and branching, and checkouts, and all the other great stuff that RCSs do. basically just fast forward commit pushes are all I want, with maybe the option to roll back.) So is there something out there that can do this? If not, can someone suggest a good way to start making it?

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  • Apache config that uses two document roots based on whether the requested resource exists in the first

    - by mattalexx
    Background I have a client site that consists of a CakePHP installation and a Magento installation: /web/example.com/ /web/example.com/app/ <== CakePHP /web/example.com/app/webroot/ <== DocumentRoot /web/example.com/app/webroot/store/ <== Magento /web/example.com/config/ <== Site-wide config /web/example.com/vendors/ <== Site-wide libraries The server runs Apache 2.2.3. The problem The whole company has FTP access and got used to clogging up the /web/example.com/, /web/example.com/app/webroot/, and /web/example.com/app/webroot/store/ directories with their own files. Sometimes these files need HTTP access and sometimes they don't. In any case, this mess makes my job harder when it comes to maintaining the site. Code merges, tarring the live code, etc, is very complicated and usually requires a bunch of filters. Abandoned solution At first, I thought I would set up a new subdomain on the same server, move all of their files there, and change their FTP chroot. But that wouldn't work for these reasons: Firstly, I have no idea (and neither do they remember) what marketing materials they've sent out that contain URLs to certain resources they've uploaded to the server, using the main domain, and also using abstract subdomains that use the main virtual host because it has ServerAlias *.example.com. So suddenly having them only use static.example.com isn't feasible. Secondly, The PHP scripts in their projects are potentially very non-portable. I want their files to stay in as similar an environment as they were built as I can. Also, I do not want to debug their code to make it portable. Half-baked solution After some thought, I decided to find a way to section off the actual website files into another directory that they would not touch. The company's uploaded files would stay where they were. This would ensure that I didn't break any of their projects that needed HTTP access. It would look something like this: /web/example.com/ <== A bunch of their files are in here /web/example.com/app/webroot/ <== 1st DocumentRoot; A bunch of their files are in here /web/example.com/app/webroot/store/ <== Some more are in here /web/example.com/site/ <== New dir; Contains only site files /web/example.com/site/app/ <== CakePHP /web/example.com/site/app/webroot/ <== 2nd DocumentRoot /web/example.com/site/app/webroot/store/ <== Magento /web/example.com/site/config/ <== Site-wide config /web/example.com/site/vendors/ <== Site-wide libraries After I made this change, I would not need to pay attention to anything except for the stuff within /web/example.com/site/ and my job would be a lot easier. I would be the only one changing stuff in there. So here's where the Apache magic would happen: I need an HTTP request to http://www.example.com/ to first use /web/example.com/app/webroot/ as the document root. If nothing is found (no miscellaneous uploaded company projects are found), try finding something within /web/example.com/site/app/webroot/. Another thing to keep in mind is, the site might have some problems if the $_SERVER['DOCUMENT_ROOT'] variable reads /web/example.com/app/webroot/ but the actual files are within /web/example.com/site/app/webroot/. It would be better if the DOCUMENT_ROOT environment variable could be /web/example.com/site/app/webroot/ for anything within the /web/example.com/site/app/webroot/ directory. Conclusion Is my half-baked solution possible with Apache 2.2.3? Is there a better way to solve this problem?

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  • ASP.NET MVC 2 Released

    - by ScottGu
    I’m happy to announce that the final release of ASP.NET MVC 2 is now available for VS 2008/Visual Web Developer 2008 Express with ASP.NET 3.5.  You can download and install it from the following locations: Download ASP.NET MVC 2 using the Microsoft Web Platform Installer Download ASP.NET MVC 2 from the Download Center The final release of VS 2010 and Visual Web Developer 2010 will have ASP.NET MVC 2 built-in – so you won’t need an additional install in order to use ASP.NET MVC 2 with them.  ASP.NET MVC 2 We shipped ASP.NET MVC 1 a little less than a year ago.  Since then, almost 1 million developers have downloaded and used the final release, and its popularity has steadily grown month over month. ASP.NET MVC 2 is the next significant update of ASP.NET MVC. It is a compatible update to ASP.NET MVC 1 – so all the knowledge, skills, code, and extensions you already have with ASP.NET MVC continue to work and apply going forward. Like the first release, we are also shipping the source code for ASP.NET MVC 2 under an OSI-compliant open-source license. ASP.NET MVC 2 can be installed side-by-side with ASP.NET MVC 1 (meaning you can have some apps built with V1 and others built with V2 on the same machine).  We have instructions on how to update your existing ASP.NET MVC 1 apps to use ASP.NET MVC 2 using VS 2008 here.  Note that VS 2010 has an automated upgrade wizard that can automatically migrate your existing ASP.NET MVC 1 applications to ASP.NET MVC 2 for you. ASP.NET MVC 2 Features ASP.NET MVC 2 adds a bunch of new capabilities and features.  I’ve started a blog series about some of the new features, and will be covering them in more depth in the weeks ahead.  Some of the new features and capabilities include: New Strongly Typed HTML Helpers Enhanced Model Validation support across both server and client Auto-Scaffold UI Helpers with Template Customization Support for splitting up large applications into “Areas” Asynchronous Controllers support that enables long running tasks in parallel Support for rendering sub-sections of a page/site using Html.RenderAction Lots of new helper functions, utilities, and API enhancements Improved Visual Studio tooling support You can learn more about these features in the “What’s New in ASP.NET MVC 2” document on the www.asp.net/mvc web-site.  We are going to be posting a lot of new tutorials and videos shortly on www.asp.net/mvc that cover all the features in ASP.NET MVC 2 release.  We will also post an updated end-to-end tutorial built entirely with ASP.NET MVC 2 (much like the NerdDinner tutorial that I wrote that covers ASP.NET MVC 1).  Summary The ASP.NET MVC team delivered regular V2 preview releases over the last year to get feedback on the feature set.  I’d like to say a big thank you to everyone who tried out the previews and sent us suggestions/feedback/bug reports.  We hope you like the final release! Scott

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  • SQL SERVER – Remove Debug Button in SSMS – SQL in Sixty Seconds #020 – Video

    - by pinaldave
    SQL in Sixty Seconds is indeed tremendous fun to do. Every week, we try to come up with some new learning which we can share in Sixty Seconds. In this busy world, we all have sixty seconds to learn something new – no matter how much busy we are. In this episode of the series, we talk about another interesting feature of SQL Server Management Studio. In SQL Server Management Studio (SSMS) we have two button side by side. 1) Execute (!) and 2) Debug (>). It is quite confusing to a few developers. The debug button which looks like a play button encourages developers to click on the same thinking it will execute the code. Also developer with a Visual Studio background often click it because of their habit. However, Debug button is not the same as Execute button. In most of the cases developers want to click on Execute to run the query but by mistake they click on Debug and it wastes their valuable time. It is very easy to fix this. If developers are not frequently using a debug feature in SQL Server they should hide it from the toolbar itself. This will reduce the chances to incorrectly click on the debug button greatly as well save lots of time for developer as invoking debug processes and turning it off takes a few extra moments. In this Sixty second video we will discuss how one can hide the debug button and avoid confusion regarding execution button. I personally use function key F5 to execute the T-SQL code so I do not face this problem that often. More on Removing Debug Button in SSMS: SQL SERVER – Read Only Files and SQL Server Management Studio (SSMS) SQL SERVER – Standard Reports from SQL Server Management Studio – SQL in Sixty Seconds #016 – Video SQL SERVER – Discard Results After Query Execution – SSMS SQL SERVER – Tricks to Comment T-SQL in SSMS – SQL in Sixty Seconds #019 – Video SQL SERVER – Right Aligning Numerics in SQL Server Management Studio (SSMS) I encourage you to submit your ideas for SQL in Sixty Seconds. We will try to accommodate as many as we can. If we like your idea we promise to share with you educational material. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, Pinal Dave, PostADay, SQL, SQL Authority, SQL in Sixty Seconds, SQL Query, SQL Scripts, SQL Server, SQL Server Management Studio, SQL Tips and Tricks, T SQL, Technology, Video

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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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  • More information on the Patch Tuesday updates for SQL Server

    - by AaronBertrand
    Last week, Microsoft released a series of patches for all supported versions of SQL Server (from SQL Server 2005 SP3 all the way to SQL Server 2008 R2). The reason for the patch against SQL Server installations is largely a client-side issue with the XML viewer application, and for SQL Server specifically, the exploit is limited to potential information disclosure. A very easy way to avoid exposure to this exploit is simply to never open a file with the .disco extension (these files are likely already...(read more)

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  • Back from Teched US

    - by gsusx
    It's been a few weeks since I last blogged and, trust me, I am not happy about it :( I have been crazily busy with some of our projects at Tellago which you are going to hear more about in the upcoming weeks :) I was so busy that I didn't even have time to blog about my sessions at Teched US last week. This year I ended up presenting three sessions on three different tracks: BIE403 | Real-Time Business Intelligence with Microsoft SQL Server 2008 R2 Session Type: Breakout Session Real-time business...(read more)

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  • Scrum in 5 Minutes

    - by Stephen.Walther
    The goal of this blog entry is to explain the basic concepts of Scrum in less than five minutes. You learn how Scrum can help a team of developers to successfully complete a complex software project. Product Backlog and the Product Owner Imagine that you are part of a team which needs to create a new website – for example, an e-commerce website. You have an overwhelming amount of work to do. You need to build (or possibly buy) a shopping cart, install an SSL certificate, create a product catalog, create a Facebook page, and at least a hundred other things that you have not thought of yet. According to Scrum, the first thing you should do is create a list. Place the highest priority items at the top of the list and the lower priority items lower in the list. For example, creating the shopping cart and buying the domain name might be high priority items and creating a Facebook page might be a lower priority item. In Scrum, this list is called the Product Backlog. How do you prioritize the items in the Product Backlog? Different stakeholders in the project might have different priorities. Gary, your division VP, thinks that it is crucial that the e-commerce site has a mobile app. Sally, your direct manager, thinks taking advantage of new HTML5 features is much more important. Multiple people are pulling you in different directions. According to Scrum, it is important that you always designate one person, and only one person, as the Product Owner. The Product Owner is the person who decides what items should be added to the Product Backlog and the priority of the items in the Product Backlog. The Product Owner could be the customer who is paying the bills, the project manager who is responsible for delivering the project, or a customer representative. The critical point is that the Product Owner must always be a single person and that single person has absolute authority over the Product Backlog. Sprints and the Sprint Backlog So now the developer team has a prioritized list of items and they can start work. The team starts implementing the first item in the Backlog — the shopping cart — and the team is making good progress. Unfortunately, however, half-way through the work of implementing the shopping cart, the Product Owner changes his mind. The Product Owner decides that it is much more important to create the product catalog before the shopping cart. With some frustration, the team switches their developmental efforts to focus on implementing the product catalog. However, part way through completing this work, once again the Product Owner changes his mind about the highest priority item. Getting work done when priorities are constantly shifting is frustrating for the developer team and it results in lower productivity. At the same time, however, the Product Owner needs to have absolute authority over the priority of the items which need to get done. Scrum solves this conflict with the concept of Sprints. In Scrum, a developer team works in Sprints. At the beginning of a Sprint the developers and the Product Owner agree on the items from the backlog which they will complete during the Sprint. This subset of items from the Product Backlog becomes the Sprint Backlog. During the Sprint, the Product Owner is not allowed to change the items in the Sprint Backlog. In other words, the Product Owner cannot shift priorities on the developer team during the Sprint. Different teams use Sprints of different lengths such as one month Sprints, two-week Sprints, and one week Sprints. For high-stress, time critical projects, teams typically choose shorter sprints such as one week sprints. For more mature projects, longer one month sprints might be more appropriate. A team can pick whatever Sprint length makes sense for them just as long as the team is consistent. You should pick a Sprint length and stick with it. Daily Scrum During a Sprint, the developer team needs to have meetings to coordinate their work on completing the items in the Sprint Backlog. For example, the team needs to discuss who is working on what and whether any blocking issues have been discovered. Developers hate meetings (well, sane developers hate meetings). Meetings take developers away from their work of actually implementing stuff as opposed to talking about implementing stuff. However, a developer team which never has meetings and never coordinates their work also has problems. For example, Fred might get stuck on a programming problem for days and never reach out for help even though Tom (who sits in the cubicle next to him) has already solved the very same problem. Or, both Ted and Fred might have started working on the same item from the Sprint Backlog at the same time. In Scrum, these conflicting needs – limiting meetings but enabling team coordination – are resolved with the idea of the Daily Scrum. The Daily Scrum is a meeting for coordinating the work of the developer team which happens once a day. To keep the meeting short, each developer answers only the following three questions: 1. What have you done since yesterday? 2. What do you plan to do today? 3. Any impediments in your way? During the Daily Scrum, developers are not allowed to talk about issues with their cat, do demos of their latest work, or tell heroic stories of programming problems overcome. The meeting must be kept short — typically about 15 minutes. Issues which come up during the Daily Scrum should be discussed in separate meetings which do not involve the whole developer team. Stories and Tasks Items in the Product or Sprint Backlog – such as building a shopping cart or creating a Facebook page – are often referred to as User Stories or Stories. The Stories are created by the Product Owner and should represent some business need. Unlike the Product Owner, the developer team needs to think about how a Story should be implemented. At the beginning of a Sprint, the developer team takes the Stories from the Sprint Backlog and breaks the stories into tasks. For example, the developer team might take the Create a Shopping Cart story and break it into the following tasks: · Enable users to add and remote items from shopping cart · Persist the shopping cart to database between visits · Redirect user to checkout page when Checkout button is clicked During the Daily Scrum, members of the developer team volunteer to complete the tasks required to implement the next Story in the Sprint Backlog. When a developer talks about what he did yesterday or plans to do tomorrow then the developer should be referring to a task. Stories are owned by the Product Owner and a story is all about business value. In contrast, the tasks are owned by the developer team and a task is all about implementation details. A story might take several days or weeks to complete. A task is something which a developer can complete in less than a day. Some teams get lazy about breaking stories into tasks. Neglecting to break stories into tasks can lead to “Never Ending Stories” If you don’t break a story into tasks, then you can’t know how much of a story has actually been completed because you don’t have a clear idea about the implementation steps required to complete the story. Scrumboard During the Daily Scrum, the developer team uses a Scrumboard to coordinate their work. A Scrumboard contains a list of the stories for the current Sprint, the tasks associated with each Story, and the state of each task. The developer team uses the Scrumboard so everyone on the team can see, at a glance, what everyone is working on. As a developer works on a task, the task moves from state to state and the state of the task is updated on the Scrumboard. Common task states are ToDo, In Progress, and Done. Some teams include additional task states such as Needs Review or Needs Testing. Some teams use a physical Scrumboard. In that case, you use index cards to represent the stories and the tasks and you tack the index cards onto a physical board. Using a physical Scrumboard has several disadvantages. A physical Scrumboard does not work well with a distributed team – for example, it is hard to share the same physical Scrumboard between Boston and Seattle. Also, generating reports from a physical Scrumboard is more difficult than generating reports from an online Scrumboard. Estimating Stories and Tasks Stakeholders in a project, the people investing in a project, need to have an idea of how a project is progressing and when the project will be completed. For example, if you are investing in creating an e-commerce site, you need to know when the site can be launched. It is not enough to just say that “the project will be done when it is done” because the stakeholders almost certainly have a limited budget to devote to the project. The people investing in the project cannot determine the business value of the project unless they can have an estimate of how long it will take to complete the project. Developers hate to give estimates. The reason that developers hate to give estimates is that the estimates are almost always completely made up. For example, you really don’t know how long it takes to build a shopping cart until you finish building a shopping cart, and at that point, the estimate is no longer useful. The problem is that writing code is much more like Finding a Cure for Cancer than Building a Brick Wall. Building a brick wall is very straightforward. After you learn how to add one brick to a wall, you understand everything that is involved in adding a brick to a wall. There is no additional research required and no surprises. If, on the other hand, I assembled a team of scientists and asked them to find a cure for cancer, and estimate exactly how long it will take, they would have no idea. The problem is that there are too many unknowns. I don’t know how to cure cancer, I need to do a lot of research here, so I cannot even begin to estimate how long it will take. So developers hate to provide estimates, but the Product Owner and other product stakeholders, have a legitimate need for estimates. Scrum resolves this conflict by using the idea of Story Points. Different teams use different units to represent Story Points. For example, some teams use shirt sizes such as Small, Medium, Large, and X-Large. Some teams prefer to use Coffee Cup sizes such as Tall, Short, and Grande. Finally, some teams like to use numbers from the Fibonacci series. These alternative units are converted into a Story Point value. Regardless of the type of unit which you use to represent Story Points, the goal is the same. Instead of attempting to estimate a Story in hours (which is doomed to failure), you use a much less fine-grained measure of work. A developer team is much more likely to be able to estimate that a Story is Small or X-Large than the exact number of hours required to complete the story. So you can think of Story Points as a compromise between the needs of the Product Owner and the developer team. When a Sprint starts, the developer team devotes more time to thinking about the Stories in a Sprint and the developer team breaks the Stories into Tasks. In Scrum, you estimate the work required to complete a Story by using Story Points and you estimate the work required to complete a task by using hours. The difference between Stories and Tasks is that you don’t create a task until you are just about ready to start working on a task. A task is something that you should be able to create within a day, so you have a much better chance of providing an accurate estimate of the work required to complete a task than a story. Burndown Charts In Scrum, you use Burndown charts to represent the remaining work on a project. You use Release Burndown charts to represent the overall remaining work for a project and you use Sprint Burndown charts to represent the overall remaining work for a particular Sprint. You create a Release Burndown chart by calculating the remaining number of uncompleted Story Points for the entire Product Backlog every day. The vertical axis represents Story Points and the horizontal axis represents time. A Sprint Burndown chart is similar to a Release Burndown chart, but it focuses on the remaining work for a particular Sprint. There are two different types of Sprint Burndown charts. You can either represent the remaining work in a Sprint with Story Points or with task hours (the following image, taken from Wikipedia, uses hours). When each Product Backlog Story is completed, the Release Burndown chart slopes down. When each Story or task is completed, the Sprint Burndown chart slopes down. Burndown charts typically do not always slope down over time. As new work is added to the Product Backlog, the Release Burndown chart slopes up. If new tasks are discovered during a Sprint, the Sprint Burndown chart will also slope up. The purpose of a Burndown chart is to give you a way to track team progress over time. If, halfway through a Sprint, the Sprint Burndown chart is still climbing a hill then you know that you are in trouble. Team Velocity Stakeholders in a project always want more work done faster. For example, the Product Owner for the e-commerce site wants the website to launch before tomorrow. Developers tend to be overly optimistic. Rarely do developers acknowledge the physical limitations of reality. So Project stakeholders and the developer team often collude to delude themselves about how much work can be done and how quickly. Too many software projects begin in a state of optimism and end in frustration as deadlines zoom by. In Scrum, this problem is overcome by calculating a number called the Team Velocity. The Team Velocity is a measure of the average number of Story Points which a team has completed in previous Sprints. Knowing the Team Velocity is important during the Sprint Planning meeting when the Product Owner and the developer team work together to determine the number of stories which can be completed in the next Sprint. If you know the Team Velocity then you can avoid committing to do more work than the team has been able to accomplish in the past, and your team is much more likely to complete all of the work required for the next Sprint. Scrum Master There are three roles in Scrum: the Product Owner, the developer team, and the Scrum Master. I’v e already discussed the Product Owner. The Product Owner is the one and only person who maintains the Product Backlog and prioritizes the stories. I’ve also described the role of the developer team. The members of the developer team do the work of implementing the stories by breaking the stories into tasks. The final role, which I have not discussed, is the role of the Scrum Master. The Scrum Master is responsible for ensuring that the team is following the Scrum process. For example, the Scrum Master is responsible for making sure that there is a Daily Scrum meeting and that everyone answers the standard three questions. The Scrum Master is also responsible for removing (non-technical) impediments which the team might encounter. For example, if the team cannot start work until everyone installs the latest version of Microsoft Visual Studio then the Scrum Master has the responsibility of working with management to get the latest version of Visual Studio as quickly as possible. The Scrum Master can be a member of the developer team. Furthermore, different people can take on the role of the Scrum Master over time. The Scrum Master, however, cannot be the same person as the Product Owner. Using SonicAgile SonicAgile (SonicAgile.com) is an online tool which you can use to manage your projects using Scrum. You can use the SonicAgile Product Backlog to create a prioritized list of stories. You can estimate the size of the Stories using different Story Point units such as Shirt Sizes and Coffee Cup sizes. You can use SonicAgile during the Sprint Planning meeting to select the Stories that you want to complete during a particular Sprint. You can configure Sprints to be any length of time. SonicAgile calculates Team Velocity automatically and displays a warning when you add too many stories to a Sprint. In other words, it warns you when it thinks you are overcommitting in a Sprint. SonicAgile also includes a Scrumboard which displays the list of Stories selected for a Sprint and the tasks associated with each story. You can drag tasks from one task state to another. Finally, SonicAgile enables you to generate Release Burndown and Sprint Burndown charts. You can use these charts to view the progress of your team. To learn more about SonicAgile, visit SonicAgile.com. Summary In this post, I described many of the basic concepts of Scrum. You learned how a Product Owner uses a Product Backlog to create a prioritized list of tasks. I explained why work is completed in Sprints so the developer team can be more productive. I also explained how a developer team uses the daily scrum to coordinate their work. You learned how the developer team uses a Scrumboard to see, at a glance, who is working on what and the state of each task. I also discussed Burndown charts. You learned how you can use both Release and Sprint Burndown charts to track team progress in completing a project. Finally, I described the crucial role of the Scrum Master – the person who is responsible for ensuring that the rules of Scrum are being followed. My goal was not to describe all of the concepts of Scrum. This post was intended to be an introductory overview. For a comprehensive explanation of Scrum, I recommend reading Ken Schwaber’s book Agile Project Management with Scrum: http://www.amazon.com/Agile-Project-Management-Microsoft-Professional/dp/073561993X/ref=la_B001H6ODMC_1_1?ie=UTF8&qid=1345224000&sr=1-1

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  • Add a Scrollable Multi-Row Bookmarks Toolbar to Firefox

    - by Asian Angel
    If you keep a lot of bookmarks available in your Bookmarks Toolbar then you know that accessing some of them is not as easy as you would like. Now you can simplify the access process with the Multirow Bookmarks Toolbar for Firefox. Before As you can see it has not taken long to fill up our “Bookmarks Toolbar” and use of the drop-down list is required. If you do not keep too many bookmarks in the “Bookmarks Toolbar” then that may not be a bad thing but what if you have a very large number of bookmarks there? Multirow Bookmarks Toolbar in Action As soon as you have installed the extension and restarted Firefox you will see the default three rows display. If you are not worried about UI space then you are good to go. Those of you who like keeping the UI space to a minimum will want to have a look at this next part… You are not locked into a “three rows setup” with this extension. If you are ok with two rows then you can select for that in the “Options” and and enjoy a mini scrollbar on the right side. For our example we still had easy access to all three rows. Two rows still too much? Not a problem. Set the number of rows for one only in the “Options” and still enjoy that scrolling goodness. If you do select for one row only do not panic when you do not see a scrollbar…it is still there. Hold your mouse over where the scrollbar is shown in the image above and use your middle mouse button to scroll through the multiple rows. You can see the transition between the second and third rows on our browser here… Nice, huh? Options The “Options” are extremely easy to work with…just enable/disable the extension here and set the number of rows that you want visible. Conclusion While the Multirow Bookmarks Toolbar extension may not seem like much at first glance it does provide some nice flexibility for your “Bookmarks Toolbar”. You can save space and access your bookmarks easily without those drop-down lists. If you are looking for another great way to make the best use of the space available in your “Bookmarks Toolbar” then be sure to read our article on the Smart Bookmarks Bar extension for Firefox here. Links Download the Multirow Bookmarks Toolbar extension (Mozilla Add-ons) Similar Articles Productive Geek Tips Reduce Your Bookmarks Toolbar to a Toolbar ButtonConserve Space in Firefox by Combining ToolbarsAdd the Bookmarks Menu to Your Bookmarks Toolbar with Bookmarks UI ConsolidatorAdd a Vertical Bookmarks Toolbar to FirefoxCondense the Bookmarks in the Firefox Bookmarks Toolbar TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Dark Side of the Moon (8-bit) Norwegian Life If Web Browsers Were Modes of Transportation Google Translate (for animals) Out of 100 Tweeters Roadkill’s Scan Port scans for open ports

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  • Daily tech links for .net and related technologies - Apr 26-28, 2010

    - by SanjeevAgarwal
    Daily tech links for .net and related technologies - Apr 26-28, 2010 Web Development MVC: Unit Testing Action Filters - Donn ASP.NET MVC 2: Ninja Black Belt Tips - Scott Hanselman Turn on Compile-time View Checking for ASP.NET MVC Projects in TFS Build 2010 - Jim Lamb Web Design List of 25+ New tags introduced in HTML 5 - techfreakstuff 15 CSS Habits to Develop for Frustration-Free Coding - noupe Silverlight, WPF & RIA Essential Silverlight and WPF Skills: The UI Thread, Dispatchers, Background...(read more)

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  • Friday Fun: Play Tetris in Google Chrome

    - by Asian Angel
    Do you prefer playing classic games rather than the newer ones? Then get ready for some classic goodness with the JC-Tetris extension for Google Chrome. JC-Tetris in Action When you click on your new “JC-Tetris Toolbar Button” a new mini-Chrome window will open with the game displayed inside. This could be very convenient for those who would like or need to pause the game, minimize the window, and finish the game later. All that is needed to play are the four “Arrow Keys & the Space Bar”. Note: The text was small when the window first opened during our test so we used the “Ctrl +” keyboard shortcut twice to enlarge it. You may or may not experience similar text size results. Like any Tetris game things start out “quietly enough” but this one speeds up quickly, so be prepared! Notice that you do get a warning of what is waiting to drop onto the game board on the left side. Whenever you complete a game you will see this small window asking if you would like to enter a name for the score…you can easily ignore/bypass the window by clicking “Cancel”. Another game and a much better result. Do not be surprised if you feel that little burst of “rushed panic” at the end! Conclusion JC-Tetris is an enjoyable way to relax when you need a break. The ability to pause the game and minimize it for later makes it even better. Have fun! Links Download the JC-Tetris extension (Google Chrome Extensions) Similar Articles Productive Geek Tips Friday Fun: Get Your Mario OnFriday Fun: First Person TetrisFriday Fun: Play MineSweeper in Google ChromeFriday Fun: Play 3D Rally Racing in Google ChromeHow to Make Google Chrome Your Default Browser TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows PC Tools Internet Security Suite 2010 Dark Side of the Moon (8-bit) Norwegian Life If Web Browsers Were Modes of Transportation Google Translate (for animals) Out of 100 Tweeters Roadkill’s Scan Port scans for open ports

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  • c++ ide & tools with clang integration

    - by lurscher
    recently i read this blog about google integrating clang parser into their code analysis tools This is something in which c++ is at least a decade behind other languages like java, but now that llvm-clang is almost c++ iso-ready, i think its possible for c++ code analysis tools to begin using the c++ parser effectively, since it has been designed from the ground up precisely for this so i'm wondering if there are existing open source or known commercial projects taking this path, integrating with clang to provide higher-level analysis tools?

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