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• Understanding and Controlling Parallel Query Processing in SQL Server

  Data warehousing and general reporting applications tend to be CPU intensive because they need to read and process a large number of rows. To facilitate quick data processing for queries that touch a large amount of data, Microsoft SQL Server exploits the power of multiple logical processors to provide parallel query processing operations such as parallel scans. Through extensive testing, we have learned that, for most large queries that are executed in a parallel fashion, SQL Server can deliver linear or nearly linear response time speedup as the number of logical processors increases. However, some queries in high parallelism scenarios perform suboptimally. There are also some parallelism issues that can occur in a multi-user parallel query workload. This white paper describes parallel performance problems you might encounter when you run such queries and workloads, and it explains why these issues occur. In addition, it presents how data warehouse developers can detect these issues, and how they can work around them or mitigate them.

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• Factors to consider when building an algorithm for gun recoil

  - by Nate Bross

  What would be a good algorithm for calculating the recoil of a shooting guns cross-hairs? What I've got now, is something like this: Define min/max recoil based on weapon size Generate random number of "delta" movement Apply random value to X, Y, or both of cross-hairs (only "up" on the Y axis) Multiply new delta based on time from the previous shot (more recoil for full-auto) What I'm worried about is that this feels rather predicable, what other factors should one take into account when building recoil? While I'd like it to be somewhat predictable, I'd also like to keep players on their toes. I'm thinking about increasing the min/max recoil values by a large amount (relatively) and adding a weighting, so large recoils will be more rare -- it seems like a lot of effort to go into something I felt would be simple. Maybe this is just something that needs to be fine-tuned with additional playtesting, and more playtesters? I think that it's important to note, that the recoil will be a large part of the game, and is a key factor in the game being fun/challenging or not.

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• How to promote/market an event that needs many people?

  - by stjowa

  My team is about to launch a new web application, http://wethepixels.com, that requires a lot of people to be on the site at the same time for the concept to be successful. Our team is preparing to promote/market an event for a specific date and time, in order to try to grab a large group of people to the site at once. For those who have gone through a similar web launch, we would love to hear ideas on the best way to market for a large group in a relatively short period of time. We have created a Facebook page and a Facebook event, but it has yet to grab much attention (surprisingly to us). Is there a better way to attract a large number of users in a short period of time? Thanks

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• Too early to apply for post-graduation jobs?

  - by Rob Lourens

  I graduate in May 2012. I'm on an internship with one company right now that will probably make me an offer in August, but I will only have a couple weeks to take or leave it. I'm not sure whether I'll want to accept it- it will depend on the specifics. So I plan to apply for other jobs to see if I can get another offer, but would it be too early to be applying over the next few weeks when I wouldn't start until next May at least? I hate to turn down an offer having nothing else lined up. I'm a software engineer at one large software company and I would apply for jobs at other large software companies. I assume a smaller company would work on a much shorter hiring schedule, but maybe large companies wouldn't mind hiring 8-9 months in advance? I also hate to start applying any earlier than I have to- I know I'll only have more experience and be more employable with time.

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• Using Git in Enterprise environment

  - by sarat

  Git is an excellent version control. If we exclude the fact that, it doesn't have an excellent GUI support, it's really good and fast. But the source controls like Clearcase has large support for enterprise customers. Companies investing huge amount for source control servers and licesense. Of late most of the large companies like Google adopting Git over the other version controls. But the company is having strong open source group which consistently provide development and support for the tool (Even they might be having a custom version of Git of their own). At the same time, large companies are not really bothered about adopting open source projects and make it relevant for them. Is Git really a reliable tool for enterprise environment, especially for Windows Platform? The support is a question for Git as it's an open source version control. Any companies are there to provide solutions and support? How the server costs comparing to other version controls like Clear-case?

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• Is it correct to add booleans in order to count the number of true values in a vector?

  - by gerrit

  Is it conceptually correct to sum a vector of booleans? From a mathematical point of view, I would argue it's not: True + True != 2. But it's quite practical to do so still! Example using the vectorised Python library numpy: In [1]: X = rand(10) In [2]: large = X>0.6 In [3]: large.dtype Out[3]: dtype('bool') In [4]: large.sum() Out[4]: 7 I don't like it, but it's very practical. Is this a good practice? Update: the aim is to count the number of true values in a vector.

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• Guide to reduce TFS database growth using the Test Attachment Cleaner

  - by terje

  Recently there has been several reports on TFS databases growing too fast and growing too big.  Notable this has been observed when one has started to use more features of the Testing system.  Also, the TFS 2010 handles test results differently from TFS 2008, and this leads to more data stored in the TFS databases. As a consequence of this there has been released some tools to remove unneeded data in the database, and also some fixes to correct for bugs which has been found and corrected during this process.  Further some preventive practices and maintenance rules should be adopted. A lot of people have blogged about this, among these are: Anu’s very important blog post here describes both the problem and solutions to handle it.  She describes both the Test Attachment Cleaner tool, and also some QFE/CU releases to fix some underlying bugs which prevented the tool from being fully effective. Brian Harry’s blog post here describes the problem too This forum thread describes the problem with some solution hints. Ravi Shanker’s blog post here describes best practices on solving this (TBP) Grant Holidays blogpost here describes strategies to use the Test Attachment Cleaner both to detect space problems and how to rectify them.   The problem can be divided into the following areas: Publishing of test results from builds Publishing of manual test results and their attachments in particular Publishing of deployment binaries for use during a test run Bugs in SQL server preventing total cleanup of data (All the published data above is published into the TFS database as attachments.) The test results will include all data being collected during the run.  Some of this data can grow rather large, like IntelliTrace logs and video recordings.   Also the pushing of binaries which happen for automated test runs, including tests run during a build using code coverage which will include all the files in the deployment folder, contributes a lot to the size of the attached data.   In order to handle this systematically, I have set up a 3-stage process: Find out if you have a database space issue Set up your TFS server to minimize potential database issues If you have the “problem”, clean up the database and otherwise keep it clean   Analyze the data Are your database( s) growing ?  Are unused test results growing out of proportion ? To find out about this you need to query your TFS database for some of the information, and use the Test Attachment Cleaner (TAC) to obtain some  more detailed information. If you don’t have too many databases you can use the SQL Server reports from within the Management Studio to analyze the database and table sizes. Or, you can use a set of queries . I find queries often faster to use because I can tweak them the way I want them.  But be aware that these queries are non-documented and non-supported and may change when the product team wants to change them. If you have multiple Project Collections, find out which might have problems: (Disclaimer: The queries below work on TFS 2010. They will not work on Dev-11, since the table structure have been changed.  I will try to update them for Dev-11 when it is released.) Open a SQL Management Studio session onto the SQL Server where you have your TFS Databases. Use the query below to find the Project Collection databases and their sizes, in descending size order.  use master select DB_NAME(database_id) AS DBName, (size/128) SizeInMB FROM sys.master_files where type=0 and substring(db_name(database_id),1,4)='Tfs_' and DB_NAME(database_id)<>'Tfs_Configuration' order by size desc Doing this on one of our SQL servers gives the following results: It is pretty easy to see on which collection to start the work   Find out which tables are possibly too large Keep a special watch out for the Tfs_Attachment table. Use the script at the bottom of Grant’s blog to find the table sizes in descending size order. In our case we got this result: From Grant’s blog we learnt that the tbl_Content is in the Version Control category, so the major only big issue we have here is the tbl_AttachmentContent.   Find out which team projects have possibly too large attachments In order to use the TAC to find and eventually delete attachment data we need to find out which team projects have these attachments. The team project is a required parameter to the TAC. Use the following query to find this, replace the collection database name with whatever applies in your case:   use Tfs_DefaultCollection select p.projectname, sum(a.compressedlength)/1024/1024 as sizeInMB from dbo.tbl_Attachment as a inner join tbl_testrun as tr on a.testrunid=tr.testrunid inner join tbl_project as p on p.projectid=tr.projectid group by p.projectname order by sum(a.compressedlength) desc In our case we got this result (had to remove some names), out of more than 100 team projects accumulated over quite some years: As can be seen here it is pretty obvious the “Byggtjeneste – Projects” are the main team project to take care of, with the ones on lines 2-4 as the next ones.  Check which attachment types takes up the most space It can be nice to know which attachment types takes up the space, so run the following query: use Tfs_DefaultCollection select a.attachmenttype, sum(a.compressedlength)/1024/1024 as sizeInMB from dbo.tbl_Attachment as a inner join tbl_testrun as tr on a.testrunid=tr.testrunid inner join tbl_project as p on p.projectid=tr.projectid group by a.attachmenttype order by sum(a.compressedlength) desc We then got this result: From this it is pretty obvious that the problem here is the binary files, as also mentioned in Anu’s blog. Check which file types, by their extension, takes up the most space Run the following query use Tfs_DefaultCollection select SUBSTRING(filename,len(filename)-CHARINDEX('.',REVERSE(filename))+2,999)as Extension, sum(compressedlength)/1024 as SizeInKB from tbl_Attachment group by SUBSTRING(filename,len(filename)-CHARINDEX('.',REVERSE(filename))+2,999) order by sum(compressedlength) desc This gives a result like this:   Now you should have collected enough information to tell you what to do – if you got to do something, and some of the information you need in order to set up your TAC settings file, both for a cleanup and for scheduled maintenance later.    Get your TFS server and environment properly set up Even if you have got the problem or if have yet not got the problem, you should ensure the TFS server is set up so that the risk of getting into this problem is minimized.  To ensure this you should install the following set of updates and components. The assumption is that your TFS Server is at SP1 level. Install the QFE for KB2608743 – which also contains detailed instructions on its use, download from here. The QFE changes the default settings to not upload deployed binaries, which are used in automated test runs. Binaries will still be uploaded if: Code coverage is enabled in the test settings. You change the UploadDeploymentItem to true in the testsettings file. Be aware that this might be reset back to false by another user which haven't installed this QFE. The hotfix should be installed to The build servers (the build agents) The machine hosting the Test Controller Local development computers (Visual Studio) Local test computers (MTM) It is not required to install it to the TFS Server, test agents or the build controller – it has no effect on these programs. If you use the SQL Server 2008 R2 you should also install the CU 10 (or later).  This CU fixes a potential problem of hanging “ghost” files.  This seems to happen only in certain trigger situations, but to ensure it doesn’t bite you, it is better to make sure this CU is installed. There is no such CU for SQL Server 2008 pre-R2 Work around:  If you suspect hanging ghost files, they can be – with some mental effort, deduced from the ghost counters using the following SQL query: use master SELECT DB_NAME(database_id) as 'database',OBJECT_NAME(object_id) as 'objectname', index_type_desc,ghost_record_count,version_ghost_record_count,record_count,avg_record_size_in_bytes FROM sys.dm_db_index_physical_stats (DB_ID(N'<DatabaseName>'), OBJECT_ID(N'<TableName>'), NULL, NULL , 'DETAILED') The problem is a stalled ghost cleanup process.  Restarting the SQL server after having stopped all components that depends on it, like the TFS Server and SPS services – that is all applications that connect to the SQL server. Then restart the SQL server, and finally start up all dependent processes again.  (I would guess a complete server reboot would do the trick too.) After this the ghost cleanup process will run properly again. The fix will come in the next CU cycle for SQL Server R2 SP1.  The R2 pre-SP1 and R2 SP1 have separate maintenance cycles, and are maintained individually. Each have its own set of CU’s. When it comes I will add the link here to that CU. The "hanging ghost file” issue came up after one have run the TAC, and deleted enourmes amount of data.  The SQL Server can get into this hanging state (without the QFE) in certain cases due to this. And of course, install and set up the Test Attachment Cleaner command line power tool.  This should be done following some guidelines from Ravi Shanker: “When you run TAC, ensure that you are deleting small chunks of data at regular intervals (say run TAC every night at 3AM to delete data that is between age 730 to 731 days) – this will ensure that small amounts of data are being deleted and SQL ghosted record cleanup can catch up with the number of deletes performed. “ This rule minimizes the risk of the ghosted hang problem to occur, and further makes it easier for the SQL server ghosting process to work smoothly. “Run DBCC SHRINKDB post the ghosted records are cleaned up to physically reclaim the space on the file system” This is the last step in a 3 step process of removing SQL server data. First they are logically deleted. Then they are cleaned out by the ghosting process, and finally removed using the shrinkdb command. Cleaning out the attachments The TAC is run from the command line using a set of parameters and controlled by a settingsfile.  The parameters point out a server uri including the team project collection and also point at a specific team project. So in order to run this for multiple team projects regularly one has to set up a script to run the TAC multiple times, once for each team project.  When you install the TAC there is a very useful readme file in the same directory. When the deployment binaries are published to the TFS server, ALL items are published up from the deployment folder. That often means much more files than you would assume are necessary. This is a brute force technique. It works, but you need to take care when cleaning up. Grant has shown how their settings file looks in his blog post, removing all attachments older than 180 days , as long as there are no active workitems connected to them. This setting can be useful to clean out all items, both in a clean-up once operation, and in a general There are two scenarios we need to consider: Cleaning up an existing overgrown database Maintaining a server to avoid an overgrown database using scheduled TAC   1. Cleaning up a database which has grown too big due to these attachments. This job is a “Once” job.  We do this once and then move on to make sure it won’t happen again, by taking the actions in 2) below.  In this scenario you should only consider the large files. Your goal should be to simply reduce the size, and don’t bother about  the smaller stuff. That can be left a scheduled TAC cleanup ( 2 below). Here you can use a very general settings file, and just remove the large attachments, or you can choose to remove any old items.  Grant’s settings file is an example of the last one.  A settings file to remove only large attachments could look like this: <!-- Scenario : Remove large files --> <DeletionCriteria> <TestRun /> <Attachment> <SizeInMB GreaterThan="10" /> </Attachment> </DeletionCriteria> Or like this: If you want only to remove dll’s and pdb’s about that size, add an Extensions-section.  Without that section, all extensions will be deleted. <!-- Scenario : Remove large files of type dll's and pdb's --> <DeletionCriteria> <TestRun /> <Attachment> <SizeInMB GreaterThan="10" /> <Extensions> <Include value="dll" /> <Include value="pdb" /> </Extensions> </Attachment> </DeletionCriteria> Before you start up your scheduled maintenance, you should clear out all older items. 2. Scheduled maintenance using the TAC If you run a schedule every night, and remove old items, and also remove them in small batches.  It is important to run this often, like every night, in order to keep the number of deleted items low. That way the SQL ghost process works better. One approach could be to delete all items older than some number of days, let’s say 180 days. This could be combined with restricting it to keep attachments with active or resolved bugs.  Doing this every night ensures that only small amounts of data is deleted. <!-- Scenario : Remove old items except if they have active or resolved bugs --> <DeletionCriteria> <TestRun> <AgeInDays OlderThan="180" /> </TestRun> <Attachment /> <LinkedBugs> <Exclude state="Active" /> <Exclude state="Resolved"/> </LinkedBugs> </DeletionCriteria> In my experience there are projects which are left with active or resolved workitems, akthough no further work is done.  It can be wise to have a cleanup process with no restrictions on linked bugs at all. Note that you then have to remove the whole LinkedBugs section. A approach which could work better here is to do a two step approach, use the schedule above to with no LinkedBugs as a sweeper cleaning task taking away all data older than you could care about.  Then have another scheduled TAC task to take out more specifically attachments that you are not likely to use. This task could be much more specific, and based on your analysis clean out what you know is troublesome data. <!-- Scenario : Remove specific files early --> <DeletionCriteria> <TestRun > <AgeInDays OlderThan="30" /> </TestRun> <Attachment> <SizeInMB GreaterThan="10" /> <Extensions> <Include value="iTrace"/> <Include value="dll"/> <Include value="pdb"/> <Include value="wmv"/> </Extensions> </Attachment> <LinkedBugs> <Exclude state="Active" /> <Exclude state="Resolved" /> </LinkedBugs> </DeletionCriteria> The readme document for the TAC says that it recognizes “internal” extensions, but it does recognize any extension. To run the tool do the following command: tcmpt attachmentcleanup /collection:your_tfs_collection_url /teamproject:your_team_project /settingsfile:path_to_settingsfile /outputfile:%temp%/teamproject.tcmpt.log /mode:delete   Shrinking the database You could run a shrink database command after the TAC has run in cases where there are a lot of data being deleted.  In this case you SHOULD do it, to free up all that space.  But, after the shrink operation you should do a rebuild indexes, since the shrink operation will leave the database in a very fragmented state, which will reduce performance. Note that you need to rebuild indexes, reorganizing is not enough. For smaller amounts of data you should NOT shrink the database, since the data will be reused by the SQL server when it need to add more records.  In fact, it is regarded as a bad practice to shrink the database regularly.  So on a daily maintenance schedule you should NOT shrink the database. To shrink the database you do a DBCC SHRINKDATABASE command, and then follow up with a DBCC INDEXDEFRAG afterwards.  I find the easiest way to do this is to create a SQL Maintenance plan including the Shrink Database Task and the Rebuild Index Task and just execute it when you need to do this.

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• Twitter Bootstrap styling conflicts with plug-ins like jqGrid and other third part libraries

  - by Renso

  Issues:The concern is that the Twitter Bootstrap framework is that some of their css selectors are simply too generic and have incompatibility issues and conflicts with most third party plug-ins and css libraries, like jQuery-UI and jqGrid.My most pressing concern is only with the generic selector for the styling of "INPUT" controls.Some concerns:So basically anyone using BS (Bootstrap) will have to override styling 100% of the time on all input controls on all their web pages for all the plug-ins they use that render their own styling for input controls. This seems to chisel away any reason for using Bootstrap. Overriding Bootstrap css in this case seems illogical at best as it implies the BS styling is not correct or as granular as it is supposed to be. It also suggests you realize there is an issue here. Any person who has written a fair amount of css will realize that it is a mammoth task to to take an existing app, converting it to BS and then having to find all non-BS input controls and styling them all. The worst part is that there is no generic styling for this as each input control has a different source/context, some are regular tags and some belong to plug-ins, each with their own flavor of styling. For new web apps the challenge is not that different, each time you add a new plug-in you will have to test all facets of it, and I mean all of it, pop-ups, etc, that contain any kind of input control to make sure it is styled correctly. I am having a hard time seeing the benefits of BS in this context. So until the BS team addresses the issue, or not, you may be wondering what is the easiest solution.Help the community to drive this issue home by creating a new issue on github, see my entry here: https://github.com/twitter/bootstrap/issues/4008. As you can see I got some good and some negative feedback, but we all agree it is an issue. I do believe my solution below should be reverse compatible if the proper class declarations were followed as recommended by Bootstrap.The solution:Add a higher-level qualifier to the input selector, which may not break anything.  Add "control-group" and "controls" classes as higher-level selectors, as they have to be declared inside those classes anyway as far as I understand the design approach of BS. So in my example below can modify the css without possible breaking anything, see the css at the bottom. I tested this briefly and seems to render just as expected. May not be complete as I only spent a few minutes on the css. Your feedback will be greatly appreciated. <div class="control-group">    <label title="" for="Contact_FirstName" class="control-label">First Name</label>    <div class="controls">        <input type="text" value="" name="Contact.FirstName" id="Contact_FirstName" data-val-required="The Reader Contact&amp;#39;s First Name is required" data-val-length-min="2" data-val-length-max="250" data-val-length="The maximum length allowed for the Reader Contact&amp;#39;s First Name is 250 characters and must be two or more characters long" data-val="true" class="input-medium">        <span data-valmsg-replace="true" data-valmsg-for="Contact.FirstName" class="field-validation-valid"></span>    </div></div>Here are the SCSS (SASS) updates. In stead of just including the updates I decided to include the entire bootstrap SCSS file so you can just copy-and-paste it in stead of trying to figure out what selectors have changed./*! * Bootstrap v2.0.4 * Enhacement by Renso Hollhumer * Copyright 2012 Twitter, Inc * Licensed under the Apache License v2.0 * http://www.apache.org/licenses/LICENSE-2.0 * * Designed and built with all the love in the world @twitter by @mdo and @fat. * Enhancement by Renso Hollhumer: To isolate styling of INPUT tags to the Bootstrap context only */.clearfix {  *zoom: 1;}.clearfix:before,.clearfix:after {  display: table;  content: "";}.clearfix:after {  clear: both;}.hide-text {  font: 0/0 a;  color: transparent;  text-shadow: none;  background-color: transparent;  border: 0;}.input-block-level {  display: block;  width: 100%;  min-height: 28px;  -webkit-box-sizing: border-box;  -moz-box-sizing: border-box;  -ms-box-sizing: border-box;  box-sizing: border-box;}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section {  display: block;}audio,canvas,video {  display: inline-block;  *display: inline;  *zoom: 1;}audio:not([controls]) {  display: none;}html {  font-size: 100%;  -webkit-text-size-adjust: 100%;  -ms-text-size-adjust: 100%;}a:focus {  outline: thin dotted #333;  outline: 5px auto -webkit-focus-ring-color;  outline-offset: -2px;}a:hover,a:active {  outline: 0;}sub,sup {  position: relative;  font-size: 75%;  line-height: 0;  vertical-align: baseline;}sup {  top: -0.5em;}sub {  bottom: -0.25em;}img {  max-width: 100%;  vertical-align: middle;  border: 0;  -ms-interpolation-mode: bicubic;}#map_canvas img {  max-width: none;}button,input,select,textarea {  margin: 0;  font-size: 100%;  vertical-align: middle;}button,input {  *overflow: visible;  line-height: normal;}button::-moz-focus-inner,input::-moz-focus-inner {  padding: 0;  border: 0;}button,input[type="button"],input[type="reset"],input[type="submit"] {  cursor: pointer;  -webkit-appearance: button;}input[type="search"] {  -webkit-box-sizing: content-box;  -moz-box-sizing: content-box;  box-sizing: content-box;  -webkit-appearance: textfield;}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button {  -webkit-appearance: none;}textarea {  overflow: auto;  vertical-align: top;}body {  margin: 0;  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;  font-size: 13px;  line-height: 18px;  color: #333333;  background-color: #ffffff;}a {  color: #0088cc;  text-decoration: none;}a:hover {  color: #005580;  text-decoration: underline;}.row {  margin-left: -20px;  *zoom: 1;}.row:before,.row:after {  display: table;  content: "";}.row:after {  clear: both;}[class*="span"] {  float: left;  margin-left: 20px;}.container,.navbar-fixed-top .container,.navbar-fixed-bottom .container {  width: 940px;}.span12 {  width: 940px;}.span11 {  width: 860px;}.span10 {  width: 780px;}.span9 {  width: 700px;}.span8 {  width: 620px;}.span7 {  width: 540px;}.span6 {  width: 460px;}.span5 {  width: 380px;}.span4 {  width: 300px;}.span3 {  width: 220px;}.span2 {  width: 140px;}.span1 {  width: 60px;}.offset12 {  margin-left: 980px;}.offset11 {  margin-left: 900px;}.offset10 {  margin-left: 820px;}.offset9 {  margin-left: 740px;}.offset8 {  margin-left: 660px;}.offset7 {  margin-left: 580px;}.offset6 {  margin-left: 500px;}.offset5 {  margin-left: 420px;}.offset4 {  margin-left: 340px;}.offset3 {  margin-left: 260px;}.offset2 {  margin-left: 180px;}.offset1 {  margin-left: 100px;}.row-fluid {  width: 100%;  *zoom: 1;}.row-fluid:before,.row-fluid:after {  display: table;  content: "";}.row-fluid:after {  clear: both;}.row-fluid [class*="span"] {  display: block;  width: 100%;  min-height: 28px;  -webkit-box-sizing: border-box;  -moz-box-sizing: border-box;  -ms-box-sizing: border-box;  box-sizing: border-box;  float: left;  margin-left: 2.127659574%;  *margin-left: 2.0744680846382977%;}.row-fluid [class*="span"]:first-child {  margin-left: 0;}.row-fluid .span12 {  width: 99.99999998999999%;  *width: 99.94680850063828%;}.row-fluid .span11 {  width: 91.489361693%;  *width: 91.4361702036383%;}.row-fluid .span10 {  width: 82.97872339599999%;  *width: 82.92553190663828%;}.row-fluid .span9 {  width: 74.468085099%;  *width: 74.4148936096383%;}.row-fluid .span8 {  width: 65.95744680199999%;  *width: 65.90425531263828%;}.row-fluid .span7 {  width: 57.446808505%;  *width: 57.3936170156383%;}.row-fluid .span6 {  width: 48.93617020799999%;  *width: 48.88297871863829%;}.row-fluid .span5 {  width: 40.425531911%;  *width: 40.3723404216383%;}.row-fluid .span4 {  width: 31.914893614%;  *width: 31.8617021246383%;}.row-fluid .span3 {  width: 23.404255317%;  *width: 23.3510638276383%;}.row-fluid .span2 {  width: 14.89361702%;  *width: 14.8404255306383%;}.row-fluid .span1 {  width: 6.382978723%;  *width: 6.329787233638298%;}.container {  margin-right: auto;  margin-left: auto;  *zoom: 1;}.container:before,.container:after {  display: table;  content: "";}.container:after {  clear: both;}.container-fluid {  padding-right: 20px;  padding-left: 20px;  *zoom: 1;}.container-fluid:before,.container-fluid:after {  display: table;  content: "";}.container-fluid:after {  clear: both;}p {  margin: 0 0 9px;}p small {  font-size: 11px;  color: #999999;}.lead {  margin-bottom: 18px;  font-size: 20px;  font-weight: 200;  line-height: 27px;}h1,h2,h3,h4,h5,h6 {  margin: 0;  font-family: inherit;  font-weight: bold;  color: inherit;  text-rendering: optimizelegibility;}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small {  font-weight: normal;  color: #999999;}h1 {  font-size: 30px;  line-height: 36px;}h1 small {  font-size: 18px;}h2 {  font-size: 24px;  line-height: 36px;}h2 small {  font-size: 18px;}h3 {  font-size: 18px;  line-height: 27px;}h3 small {  font-size: 14px;}h4,h5,h6 {  line-height: 18px;}h4 {  font-size: 14px;}h4 small {  font-size: 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input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input {  -webkit-border-radius: 0;  -moz-border-radius: 0;  border-radius: 0;}.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child {  margin-right: -1px;  -webkit-border-radius: 3px 0 0 3px;  -moz-border-radius: 3px 0 0 3px;  border-radius: 3px 0 0 3px;}.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child {  margin-left: -1px;  -webkit-border-radius: 0 3px 3px 0;  -moz-border-radius: 0 3px 3px 0;  border-radius: 0 3px 3px 0;}.search-query {  padding-right: 14px;  padding-right: 4px \9;  padding-left: 14px;  padding-left: 4px \9;  /* IE7-8 doesn't have border-radius, so don't indent the padding */  margin-bottom: 0;  -webkit-border-radius: 14px;  -moz-border-radius: 14px;  border-radius: 14px;}.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append {  display: inline-block;  *display: inline;  /* IE7 inline-block hack */  *zoom: 1;  margin-bottom: 0;}.form-search .hide,.form-inline .hide,.form-horizontal .hide {  display: none;}.form-search label,.form-inline label {  display: inline-block;}.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend {  margin-bottom: 0;}.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox {  padding-left: 0;  margin-bottom: 0;  vertical-align: middle;}.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"] {  float: left;  margin-right: 3px;  margin-left: 0;}.control-group {  margin-bottom: 9px;}legend + .control-group {  margin-top: 18px;  -webkit-margin-top-collapse: separate;}.form-horizontal .control-group {  margin-bottom: 18px;  *zoom: 1;}.form-horizontal .control-group:before,.form-horizontal .control-group:after {  display: table;  content: "";}.form-horizontal .control-group:after {  clear: both;}.form-horizontal .control-label {  float: left;  width: 140px;  padding-top: 5px;  text-align: right;}.form-horizontal .controls {  *display: inline-block;  *padding-left: 20px;  margin-left: 160px;  *margin-left: 0;}.form-horizontal .controls:first-child {  *padding-left: 160px;}.form-horizontal .help-block {  margin-top: 9px;  margin-bottom: 0;}.form-horizontal .form-actions {  padding-left: 160px;}.btn {  display: inline-block;  *display: inline;  /* IE7 inline-block hack */  *zoom: 1;  padding: 4px 10px 4px;  margin-bottom: 0;  font-size: 13px;  line-height: 18px;  *line-height: 20px;  color: #333333;  text-align: center;  text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75);  vertical-align: middle;  cursor: pointer;  background-color: #f5f5f5;  background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -ms-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6));  background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6);  background-image: -o-linear-gradient(top, #ffffff, #e6e6e6);  background-image: linear-gradient(top, #ffffff, #e6e6e6);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffff', endColorstr='#e6e6e6', GradientType=0);  border-color: #e6e6e6 #e6e6e6 #bfbfbf;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #e6e6e6;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);  border: 1px solid #cccccc;  *border: 0;  border-bottom-color: #b3b3b3;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;  *margin-left: .3em;  -webkit-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);}.btn:hover,.btn:active,.btn.active,.btn.disabled,.btn[disabled] {  background-color: #e6e6e6;  *background-color: #d9d9d9;}.btn:active,.btn.active {  background-color: #cccccc \9;}.btn:first-child {  *margin-left: 0;}.btn:hover {  color: #333333;  text-decoration: none;  background-color: #e6e6e6;  *background-color: #d9d9d9;  /* Buttons in IE7 don't get borders, so darken on hover */  background-position: 0 -15px;  -webkit-transition: background-position 0.1s linear;  -moz-transition: background-position 0.1s linear;  -ms-transition: background-position 0.1s linear;  -o-transition: background-position 0.1s linear;  transition: background-position 0.1s linear;}.btn:focus {  outline: thin dotted #333;  outline: 5px auto -webkit-focus-ring-color;  outline-offset: -2px;}.btn.active,.btn:active {  background-color: #e6e6e6;  background-color: #d9d9d9 \9;  background-image: none;  outline: 0;  -webkit-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);}.btn.disabled,.btn[disabled] {  cursor: default;  background-color: #e6e6e6;  background-image: none;  opacity: 0.65;  filter: alpha(opacity=65);  -webkit-box-shadow: none;  -moz-box-shadow: none;  box-shadow: none;}.btn-large {  padding: 9px 14px;  font-size: 15px;  line-height: normal;  -webkit-border-radius: 5px;  -moz-border-radius: 5px;  border-radius: 5px;}.btn-large [class^="icon-"] {  margin-top: 1px;}.btn-small {  padding: 5px 9px;  font-size: 11px;  line-height: 16px;}.btn-small [class^="icon-"] {  margin-top: -1px;}.btn-mini {  padding: 2px 6px;  font-size: 11px;  line-height: 14px;}.btn-primary,.btn-primary:hover,.btn-warning,.btn-warning:hover,.btn-danger,.btn-danger:hover,.btn-success,.btn-success:hover,.btn-info,.btn-info:hover,.btn-inverse,.btn-inverse:hover {  color: #ffffff;  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);}.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active {  color: rgba(255, 255, 255, 0.75);}.btn {  border-color: #ccc;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);}.btn-primary {  background-color: #0074cc;  background-image: -moz-linear-gradient(top, #0088cc, #0055cc);  background-image: -ms-linear-gradient(top, #0088cc, #0055cc);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0055cc));  background-image: -webkit-linear-gradient(top, #0088cc, #0055cc);  background-image: -o-linear-gradient(top, #0088cc, #0055cc);  background-image: linear-gradient(top, #0088cc, #0055cc);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#0088cc', endColorstr='#0055cc', GradientType=0);  border-color: #0055cc #0055cc #003580;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #0055cc;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-primary:hover,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled] {  background-color: #0055cc;  *background-color: #004ab3;}.btn-primary:active,.btn-primary.active {  background-color: #004099 \9;}.btn-warning {  background-color: #faa732;  background-image: -moz-linear-gradient(top, #fbb450, #f89406);  background-image: -ms-linear-gradient(top, #fbb450, #f89406);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);  background-image: -o-linear-gradient(top, #fbb450, #f89406);  background-image: linear-gradient(top, #fbb450, #f89406);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0);  border-color: #f89406 #f89406 #ad6704;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #f89406;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-warning:hover,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled] {  background-color: #f89406;  *background-color: #df8505;}.btn-warning:active,.btn-warning.active {  background-color: #c67605 \9;}.btn-danger {  background-color: #da4f49;  background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -ms-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));  background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f);  background-image: -o-linear-gradient(top, #ee5f5b, #bd362f);  background-image: linear-gradient(top, #ee5f5b, #bd362f);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#bd362f', GradientType=0);  border-color: #bd362f #bd362f #802420;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #bd362f;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-danger:hover,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled] {  background-color: #bd362f;  *background-color: #a9302a;}.btn-danger:active,.btn-danger.active {  background-color: #942a25 \9;}.btn-success {  background-color: #5bb75b;  background-image: -moz-linear-gradient(top, #62c462, #51a351);  background-image: -ms-linear-gradient(top, #62c462, #51a351);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));  background-image: -webkit-linear-gradient(top, #62c462, #51a351);  background-image: -o-linear-gradient(top, #62c462, #51a351);  background-image: linear-gradient(top, #62c462, #51a351);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#62c462', endColorstr='#51a351', GradientType=0);  border-color: #51a351 #51a351 #387038;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #51a351;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-success:hover,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled] {  background-color: #51a351;  *background-color: #499249;}.btn-success:active,.btn-success.active {  background-color: #408140 \9;}.btn-info {  background-color: #49afcd;  background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -ms-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));  background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4);  background-image: -o-linear-gradient(top, #5bc0de, #2f96b4);  background-image: linear-gradient(top, #5bc0de, #2f96b4);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#5bc0de', endColorstr='#2f96b4', GradientType=0);  border-color: #2f96b4 #2f96b4 #1f6377;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #2f96b4;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-info:hover,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled] {  background-color: #2f96b4;  *background-color: #2a85a0;}.btn-info:active,.btn-info.active {  background-color: #24748c \9;}.btn-inverse {  background-color: #414141;  background-image: -moz-linear-gradient(top, #555555, #222222);  background-image: -ms-linear-gradient(top, #555555, #222222);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#555555), to(#222222));  background-image: -webkit-linear-gradient(top, #555555, #222222);  background-image: -o-linear-gradient(top, #555555, #222222);  background-image: linear-gradient(top, #555555, #222222);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#555555', endColorstr='#222222', GradientType=0);  border-color: #222222 #222222 #000000;  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);  *background-color: #222222;  /* Darken IE7 buttons by default so they stand out more given they won't have borders */  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-inverse:hover,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled] {  background-color: #222222;  *background-color: #151515;}.btn-inverse:active,.btn-inverse.active {  background-color: #080808 \9;}button.btn,input[type="submit"].btn {  *padding-top: 2px;  *padding-bottom: 2px;}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner {  padding: 0;  border: 0;}button.btn.btn-large,input[type="submit"].btn.btn-large {  *padding-top: 7px;  *padding-bottom: 7px;}button.btn.btn-small,input[type="submit"].btn.btn-small {  *padding-top: 3px;  *padding-bottom: 3px;}button.btn.btn-mini,input[type="submit"].btn.btn-mini {  *padding-top: 1px;  *padding-bottom: 1px;}.btn-group {  position: relative;  *zoom: 1;  *margin-left: .3em;}.btn-group:before,.btn-group:after {  display: table;  content: "";}.btn-group:after {  clear: both;}.btn-group:first-child {  *margin-left: 0;}.btn-group + .btn-group {  margin-left: 5px;}.btn-toolbar {  margin-top: 9px;  margin-bottom: 9px;}.btn-toolbar .btn-group {  display: inline-block;  *display: inline;  /* IE7 inline-block hack */  *zoom: 1;}.btn-group > .btn {  position: relative;  float: left;  margin-left: -1px;  -webkit-border-radius: 0;  -moz-border-radius: 0;  border-radius: 0;}.btn-group > .btn:first-child {  margin-left: 0;  -webkit-border-top-left-radius: 4px;  -moz-border-radius-topleft: 4px;  border-top-left-radius: 4px;  -webkit-border-bottom-left-radius: 4px;  -moz-border-radius-bottomleft: 4px;  border-bottom-left-radius: 4px;}.btn-group > .btn:last-child,.btn-group > .dropdown-toggle {  -webkit-border-top-right-radius: 4px;  -moz-border-radius-topright: 4px;  border-top-right-radius: 4px;  -webkit-border-bottom-right-radius: 4px;  -moz-border-radius-bottomright: 4px;  border-bottom-right-radius: 4px;}.btn-group > .btn.large:first-child {  margin-left: 0;  -webkit-border-top-left-radius: 6px;  -moz-border-radius-topleft: 6px;  border-top-left-radius: 6px;  -webkit-border-bottom-left-radius: 6px;  -moz-border-radius-bottomleft: 6px;  border-bottom-left-radius: 6px;}.btn-group > .btn.large:last-child,.btn-group > .large.dropdown-toggle {  -webkit-border-top-right-radius: 6px;  -moz-border-radius-topright: 6px;  border-top-right-radius: 6px;  -webkit-border-bottom-right-radius: 6px;  -moz-border-radius-bottomright: 6px;  border-bottom-right-radius: 6px;}.btn-group > .btn:hover,.btn-group > .btn:focus,.btn-group > .btn:active,.btn-group > .btn.active {  z-index: 2;}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle {  outline: 0;}.btn-group > .dropdown-toggle {  padding-left: 8px;  padding-right: 8px;  -webkit-box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);  *padding-top: 4px;  *padding-bottom: 4px;}.btn-group > .btn-mini.dropdown-toggle {  padding-left: 5px;  padding-right: 5px;}.btn-group > .btn-small.dropdown-toggle {  *padding-top: 4px;  *padding-bottom: 4px;}.btn-group > .btn-large.dropdown-toggle {  padding-left: 12px;  padding-right: 12px;}.btn-group.open .dropdown-toggle {  background-image: none;  -webkit-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  -moz-box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);  box-shadow: inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);}.btn-group.open .btn.dropdown-toggle {  background-color: #e6e6e6;}.btn-group.open .btn-primary.dropdown-toggle {  background-color: #0055cc;}.btn-group.open .btn-warning.dropdown-toggle {  background-color: #f89406;}.btn-group.open .btn-danger.dropdown-toggle {  background-color: #bd362f;}.btn-group.open .btn-success.dropdown-toggle {  background-color: #51a351;}.btn-group.open .btn-info.dropdown-toggle {  background-color: #2f96b4;}.btn-group.open .btn-inverse.dropdown-toggle {  background-color: #222222;}.btn .caret {  margin-top: 7px;  margin-left: 0;}.btn:hover .caret,.open.btn-group .caret {  opacity: 1;  filter: alpha(opacity=100);}.btn-mini .caret {  margin-top: 5px;}.btn-small .caret {  margin-top: 6px;}.btn-large .caret {  margin-top: 6px;  border-left-width: 5px;  border-right-width: 5px;  border-top-width: 5px;}.dropup .btn-large .caret {  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background-position: 0 0;  }}@-o-keyframes progress-bar-stripes {  from {    background-position: 0 0;  }  to {    background-position: 40px 0;  }}@keyframes progress-bar-stripes {  from {    background-position: 40px 0;  }  to {    background-position: 0 0;  }}.progress {  overflow: hidden;  height: 18px;  margin-bottom: 18px;  background-color: #f7f7f7;  background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -ms-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));  background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9);  background-image: linear-gradient(top, #f5f5f5, #f9f9f9);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#f5f5f5', endColorstr='#f9f9f9', GradientType=0);  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.progress .bar {  width: 0%;  height: 18px;  color: #ffffff;  font-size: 12px;  text-align: center;  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);  background-color: #0e90d2;  background-image: -moz-linear-gradient(top, #149bdf, #0480be);  background-image: -ms-linear-gradient(top, #149bdf, #0480be);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));  background-image: -webkit-linear-gradient(top, #149bdf, #0480be);  background-image: -o-linear-gradient(top, #149bdf, #0480be);  background-image: linear-gradient(top, #149bdf, #0480be);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#149bdf', endColorstr='#0480be', GradientType=0);  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);  -webkit-box-sizing: border-box;  -moz-box-sizing: border-box;  -ms-box-sizing: border-box;  box-sizing: border-box;  -webkit-transition: width 0.6s ease;  -moz-transition: width 0.6s ease;  -ms-transition: width 0.6s ease;  -o-transition: width 0.6s ease;  transition: width 0.6s ease;}.progress-striped .bar {  background-color: #149bdf;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  -webkit-background-size: 40px 40px;  -moz-background-size: 40px 40px;  -o-background-size: 40px 40px;  background-size: 40px 40px;}.progress.active .bar {  -webkit-animation: progress-bar-stripes 2s linear infinite;  -moz-animation: progress-bar-stripes 2s linear infinite;  -ms-animation: progress-bar-stripes 2s linear infinite;  -o-animation: progress-bar-stripes 2s linear infinite;  animation: progress-bar-stripes 2s linear infinite;}.progress-danger .bar {  background-color: #dd514c;  background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -ms-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));  background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35);  background-image: -o-linear-gradient(top, #ee5f5b, #c43c35);  background-image: linear-gradient(top, #ee5f5b, #c43c35);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ee5f5b', endColorstr='#c43c35', GradientType=0);}.progress-danger.progress-striped .bar {  background-color: #ee5f5b;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-success .bar {  background-color: #5eb95e;  background-image: -moz-linear-gradient(top, #62c462, #57a957);  background-image: -ms-linear-gradient(top, #62c462, #57a957);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));  background-image: -webkit-linear-gradient(top, #62c462, #57a957);  background-image: -o-linear-gradient(top, #62c462, #57a957);  background-image: linear-gradient(top, #62c462, #57a957);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#62c462', endColorstr='#57a957', GradientType=0);}.progress-success.progress-striped .bar {  background-color: #62c462;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-info .bar {  background-color: #4bb1cf;  background-image: -moz-linear-gradient(top, #5bc0de, #339bb9);  background-image: -ms-linear-gradient(top, #5bc0de, #339bb9);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));  background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9);  background-image: -o-linear-gradient(top, #5bc0de, #339bb9);  background-image: linear-gradient(top, #5bc0de, #339bb9);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#5bc0de', endColorstr='#339bb9', GradientType=0);}.progress-info.progress-striped .bar {  background-color: #5bc0de;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.progress-warning .bar {  background-color: #faa732;  background-image: -moz-linear-gradient(top, #fbb450, #f89406);  background-image: -ms-linear-gradient(top, #fbb450, #f89406);  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);  background-image: -o-linear-gradient(top, #fbb450, #f89406);  background-image: linear-gradient(top, #fbb450, #f89406);  background-repeat: repeat-x;  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fbb450', endColorstr='#f89406', GradientType=0);}.progress-warning.progress-striped .bar {  background-color: #fbb450;  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));  background-image: -webkit-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -moz-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -ms-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: -o-linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);  background-image: linear-gradient(-45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}.hero-unit {  padding: 60px;  margin-bottom: 30px;  background-color: #eeeeee;  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;}.hero-unit h1 {  margin-bottom: 0;  font-size: 60px;  line-height: 1;  color: inherit;  letter-spacing: -1px;}.hero-unit p {  font-size: 18px;  font-weight: 200;  line-height: 27px;  color: inherit;}.tooltip {  position: absolute;  z-index: 1020;  display: block;  visibility: visible;  padding: 5px;  font-size: 11px;  opacity: 0;  filter: alpha(opacity=0);}.tooltip.in {  opacity: 0.8;  filter: alpha(opacity=80);}.tooltip.top {  margin-top: -2px;}.tooltip.right {  margin-left: 2px;}.tooltip.bottom {  margin-top: 2px;}.tooltip.left {  margin-left: -2px;}.tooltip.top .tooltip-arrow {  bottom: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-top: 5px solid #000000;}.tooltip.left .tooltip-arrow {  top: 50%;  right: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-left: 5px solid #000000;}.tooltip.bottom .tooltip-arrow {  top: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-bottom: 5px solid #000000;}.tooltip.right .tooltip-arrow {  top: 50%;  left: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-right: 5px solid #000000;}.tooltip-inner {  max-width: 200px;  padding: 3px 8px;  color: #ffffff;  text-align: center;  text-decoration: none;  background-color: #000000;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.tooltip-arrow {  position: absolute;  width: 0;  height: 0;}.popover {  position: absolute;  top: 0;  left: 0;  z-index: 1010;  display: none;  padding: 5px;}.popover.top {  margin-top: -5px;}.popover.right {  margin-left: 5px;}.popover.bottom {  margin-top: 5px;}.popover.left {  margin-left: -5px;}.popover.top .arrow {  bottom: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-top: 5px solid #000000;}.popover.right .arrow {  top: 50%;  left: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-right: 5px solid #000000;}.popover.bottom .arrow {  top: 0;  left: 50%;  margin-left: -5px;  border-left: 5px solid transparent;  border-right: 5px solid transparent;  border-bottom: 5px solid #000000;}.popover.left .arrow {  top: 50%;  right: 0;  margin-top: -5px;  border-top: 5px solid transparent;  border-bottom: 5px solid transparent;  border-left: 5px solid #000000;}.popover .arrow {  position: absolute;  width: 0;  height: 0;}.popover-inner {  padding: 3px;  width: 280px;  overflow: hidden;  background: #000000;  background: rgba(0, 0, 0, 0.8);  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;  -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);}.popover-title {  padding: 9px 15px;  line-height: 1;  background-color: #f5f5f5;  border-bottom: 1px solid #eee;  -webkit-border-radius: 3px 3px 0 0;  -moz-border-radius: 3px 3px 0 0;  border-radius: 3px 3px 0 0;}.popover-content {  padding: 14px;  background-color: #ffffff;  -webkit-border-radius: 0 0 3px 3px;  -moz-border-radius: 0 0 3px 3px;  border-radius: 0 0 3px 3px;  -webkit-background-clip: padding-box;  -moz-background-clip: padding-box;  background-clip: padding-box;}.popover-content p,.popover-content ul,.popover-content ol {  margin-bottom: 0;}.modal-open .dropdown-menu {  z-index: 2050;}.modal-open .dropdown.open {  *z-index: 2050;}.modal-open .popover {  z-index: 2060;}.modal-open .tooltip {  z-index: 2070;}.modal-backdrop {  position: fixed;  top: 0;  right: 0;  bottom: 0;  left: 0;  z-index: 1040;  background-color: #000000;}.modal-backdrop.fade {  opacity: 0;}.modal-backdrop,.modal-backdrop.fade.in {  opacity: 0.8;  filter: alpha(opacity=80);}.modal {  position: fixed;  top: 50%;  left: 50%;  z-index: 1050;  overflow: auto;  width: 560px;  margin: -250px 0 0 -280px;  background-color: #ffffff;  border: 1px solid #999;  border: 1px solid rgba(0, 0, 0, 0.3);  *border: 1px solid #999;  /* IE6-7 */  -webkit-border-radius: 6px;  -moz-border-radius: 6px;  border-radius: 6px;  -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);  -webkit-background-clip: padding-box;  -moz-background-clip: padding-box;  background-clip: padding-box;}.modal.fade {  -webkit-transition: opacity .3s linear, top .3s ease-out;  -moz-transition: opacity .3s linear, top .3s ease-out;  -ms-transition: opacity .3s linear, top .3s ease-out;  -o-transition: opacity .3s linear, top .3s ease-out;  transition: opacity .3s linear, top .3s ease-out;  top: -25%;}.modal.fade.in {  top: 50%;}.modal-header {  padding: 9px 15px;  border-bottom: 1px solid #eee;}.modal-header .close {  margin-top: 2px;}.modal-body {  overflow-y: auto;  max-height: 400px;  padding: 15px;}.modal-form {  margin-bottom: 0;}.modal-footer {  padding: 14px 15px 15px;  margin-bottom: 0;  text-align: right;  background-color: #f5f5f5;  border-top: 1px solid #ddd;  -webkit-border-radius: 0 0 6px 6px;  -moz-border-radius: 0 0 6px 6px;  border-radius: 0 0 6px 6px;  -webkit-box-shadow: inset 0 1px 0 #ffffff;  -moz-box-shadow: inset 0 1px 0 #ffffff;  box-shadow: inset 0 1px 0 #ffffff;  *zoom: 1;}.modal-footer:before,.modal-footer:after {  display: table;  content: "";}.modal-footer:after {  clear: both;}.modal-footer .btn + .btn {  margin-left: 5px;  margin-bottom: 0;}.modal-footer .btn-group .btn + .btn {  margin-left: -1px;}.dropup,.dropdown {  position: relative;}.dropdown-toggle {  *margin-bottom: -3px;}.dropdown-toggle:active,.open .dropdown-toggle {  outline: 0;}.caret {  display: inline-block;  width: 0;  height: 0;  vertical-align: top;  border-top: 4px solid #000000;  border-right: 4px solid transparent;  border-left: 4px solid transparent;  content: "";  opacity: 0.3;  filter: alpha(opacity=30);}.dropdown .caret {  margin-top: 8px;  margin-left: 2px;}.dropdown:hover .caret,.open .caret {  opacity: 1;  filter: alpha(opacity=100);}.dropdown-menu {  position: absolute;  top: 100%;  left: 0;  z-index: 1000;  display: none;  float: left;  min-width: 160px;  padding: 4px 0;  margin: 1px 0 0;  list-style: none;  background-color: #ffffff;  border: 1px solid #ccc;  border: 1px solid rgba(0, 0, 0, 0.2);  *border-right-width: 2px;  *border-bottom-width: 2px;  -webkit-border-radius: 5px;  -moz-border-radius: 5px;  border-radius: 5px;  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);  -webkit-background-clip: padding-box;  -moz-background-clip: padding;  background-clip: padding-box;}.dropdown-menu.pull-right {  right: 0;  left: auto;}.dropdown-menu .divider {  *width: 100%;  height: 1px;  margin: 8px 1px;  *margin: -5px 0 5px;  overflow: hidden;  background-color: #e5e5e5;  border-bottom: 1px solid #ffffff;}.dropdown-menu a {  display: block;  padding: 3px 15px;  clear: both;  font-weight: normal;  line-height: 18px;  color: #333333;  white-space: nowrap;}.dropdown-menu li > a:hover,.dropdown-menu .active > a,.dropdown-menu .active > a:hover {  color: #ffffff;  text-decoration: none;  background-color: #0088cc;}.open {  *z-index: 1000;}.open  > .dropdown-menu {  display: block;}.pull-right > .dropdown-menu {  right: 0;  left: auto;}.dropup .caret,.navbar-fixed-bottom .dropdown .caret {  border-top: 0;  border-bottom: 4px solid #000000;  content: "\2191";}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu {  top: auto;  bottom: 100%;  margin-bottom: 1px;}.typeahead {  margin-top: 2px;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.accordion {  margin-bottom: 18px;}.accordion-group {  margin-bottom: 2px;  border: 1px solid #e5e5e5;  -webkit-border-radius: 4px;  -moz-border-radius: 4px;  border-radius: 4px;}.accordion-heading {  border-bottom: 0;}.accordion-heading .accordion-toggle {  display: block;  padding: 8px 15px;}.accordion-toggle {  cursor: pointer;}.accordion-inner {  padding: 9px 15px;  border-top: 1px solid #e5e5e5;}.carousel {  position: relative;  margin-bottom: 18px;  line-height: 1;}.carousel-inner {  overflow: hidden;  width: 100%;  position: relative;}.carousel .item {  display: none;  position: relative;  -webkit-transition: 0.6s ease-in-out left;  -moz-transition: 0.6s ease-in-out left;  -ms-transition: 0.6s ease-in-out left;  -o-transition: 0.6s ease-in-out left;  transition: 0.6s ease-in-out left;}.carousel .item > img {  display: block;  line-height: 1;}.carousel .active,.carousel .next,.carousel .prev {  display: block;}.carousel .active {  left: 0;}.carousel .next,.carousel .prev {  position: absolute;  top: 0;  width: 100%;}.carousel .next {  left: 100%;}.carousel .prev {  left: -100%;}.carousel .next.left,.carousel .prev.right {  left: 0;}.carousel .active.left {  left: -100%;}.carousel .active.right {  left: 100%;}.carousel-control {  position: absolute;  top: 40%;  left: 15px;  width: 40px;  height: 40px;  margin-top: -20px;  font-size: 60px;  font-weight: 100;  line-height: 30px;  color: #ffffff;  text-align: center;  background: #222222;  border: 3px solid #ffffff;  -webkit-border-radius: 23px;  -moz-border-radius: 23px;  border-radius: 23px;  opacity: 0.5;  filter: alpha(opacity=50);}.carousel-control.right {  left: auto;  right: 15px;}.carousel-control:hover {  color: #ffffff;  text-decoration: none;  opacity: 0.9;  filter: alpha(opacity=90);}.carousel-caption {  position: absolute;  left: 0;  right: 0;  bottom: 0;  padding: 10px 15px 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!important;}.hidden-desktop {  display: none !important;}@media (max-width: 767px) {  .visible-phone {    display: inherit !important;  }  .hidden-phone {    display: none !important;  }  .hidden-desktop {    display: inherit !important;  }  .visible-desktop {    display: none !important;  }}@media (min-width: 768px) and (max-width: 979px) {  .visible-tablet {    display: inherit !important;  }  .hidden-tablet {    display: none !important;  }  .hidden-desktop {    display: inherit !important;  }  .visible-desktop {    display: none !important ;  }}@media (max-width: 480px) {  .nav-collapse {    -webkit-transform: translate3d(0, 0, 0);  }  .page-header h1 small {    display: block;    line-height: 18px;  }  input[type="checkbox"],  input[type="radio"] {    border: 1px solid #ccc;  }  .form-horizontal .control-group > label {    float: none;    width: auto;    padding-top: 0;    text-align: left;  }  .form-horizontal .controls {    margin-left: 0;  }  .form-horizontal .control-list {    padding-top: 0;  }  .form-horizontal .form-actions {    padding-left: 10px;    padding-right: 10px;  }  .modal {    position: absolute;    top: 10px;    left: 10px;    right: 10px;    width: auto;    margin: 0;  }  .modal.fade.in {    top: auto;  }  .modal-header .close {    padding: 10px;    margin: -10px;  }  .carousel-caption {    position: static;  }}@media (max-width: 767px) {  body {    padding-left: 20px;    padding-right: 20px;  }  .navbar-fixed-top,  .navbar-fixed-bottom {    margin-left: -20px;    margin-right: -20px;  }  .container-fluid {    padding: 0;  }  .dl-horizontal dt {    float: none;    clear: none;    width: auto;    text-align: left;  }  .dl-horizontal dd {    margin-left: 0;  }  .container {    width: auto;  }  .row-fluid {    width: 100%;  }  .row,  .thumbnails {    margin-left: 0;  }  [class*="span"],  .row-fluid [class*="span"] {    float: none;    display: block;    width: auto;    margin-left: 0;  }  .input-large,  .input-xlarge,  .input-xxlarge,  input[class*="span"],  select[class*="span"],  textarea[class*="span"],  .uneditable-input {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;  }  .input-prepend input,  .input-append input,  .input-prepend input[class*="span"],  .input-append input[class*="span"] {    display: inline-block;    width: auto;  }}@media (min-width: 768px) and (max-width: 979px) {  .row {    margin-left: -20px;    *zoom: 1;  }  .row:before,  .row:after {    display: table;    content: "";  }  .row:after {    clear: both;  }  [class*="span"] {    float: left;    margin-left: 20px;  }  .container,  .navbar-fixed-top .container,  .navbar-fixed-bottom .container {    width: 724px;  }  .span12 {    width: 724px;  }  .span11 {    width: 662px;  }  .span10 {    width: 600px;  }  .span9 {    width: 538px;  }  .span8 {    width: 476px;  }  .span7 {    width: 414px;  }  .span6 {    width: 352px;  }  .span5 {    width: 290px;  }  .span4 {    width: 228px;  }  .span3 {    width: 166px;  }  .span2 {    width: 104px;  }  .span1 {    width: 42px;  }  .offset12 {    margin-left: 764px;  }  .offset11 {    margin-left: 702px;  }  .offset10 {    margin-left: 640px;  }  .offset9 {    margin-left: 578px;  }  .offset8 {    margin-left: 516px;  }  .offset7 {    margin-left: 454px;  }  .offset6 {    margin-left: 392px;  }  .offset5 {    margin-left: 330px;  }  .offset4 {    margin-left: 268px;  }  .offset3 {    margin-left: 206px;  }  .offset2 {    margin-left: 144px;  }  .offset1 {    margin-left: 82px;  }  .row-fluid {    width: 100%;    *zoom: 1;  }  .row-fluid:before,  .row-fluid:after {    display: table;    content: "";  }  .row-fluid:after {    clear: both;  }  .row-fluid [class*="span"] {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;    float: left;    margin-left: 2.762430939%;    *margin-left: 2.709239449638298%;  }  .row-fluid [class*="span"]:first-child {    margin-left: 0;  }  .row-fluid .span12 {    width: 99.999999993%;    *width: 99.9468085036383%;  }  .row-fluid .span11 {    width: 91.436464082%;    *width: 91.38327259263829%;  }  .row-fluid .span10 {    width: 82.87292817100001%;    *width: 82.8197366816383%;  }  .row-fluid .span9 {    width: 74.30939226%;    *width: 74.25620077063829%;  }  .row-fluid .span8 {    width: 65.74585634900001%;    *width: 65.6926648596383%;  }  .row-fluid .span7 {    width: 57.182320438000005%;    *width: 57.129128948638304%;  }  .row-fluid .span6 {    width: 48.618784527%;    *width: 48.5655930376383%;  }  .row-fluid .span5 {    width: 40.055248616%;    *width: 40.0020571266383%;  }  .row-fluid .span4 {    width: 31.491712705%;    *width: 31.4385212156383%;  }  .row-fluid .span3 {    width: 22.928176794%;    *width: 22.874985304638297%;  }  .row-fluid .span2 {    width: 14.364640883%;    *width: 14.311449393638298%;  }  .row-fluid .span1 {    width: 5.801104972%;    *width: 5.747913482638298%;  }  input,  textarea,  .uneditable-input {    margin-left: 0;  }  input.span12, textarea.span12, .uneditable-input.span12 {    width: 714px;  }  input.span11, textarea.span11, .uneditable-input.span11 {    width: 652px;  }  input.span10, textarea.span10, .uneditable-input.span10 {    width: 590px;  }  input.span9, textarea.span9, .uneditable-input.span9 {    width: 528px;  }  input.span8, textarea.span8, .uneditable-input.span8 {    width: 466px;  }  input.span7, textarea.span7, .uneditable-input.span7 {    width: 404px;  }  input.span6, textarea.span6, .uneditable-input.span6 {    width: 342px;  }  input.span5, textarea.span5, .uneditable-input.span5 {    width: 280px;  }  input.span4, textarea.span4, .uneditable-input.span4 {    width: 218px;  }  input.span3, textarea.span3, .uneditable-input.span3 {    width: 156px;  }  input.span2, textarea.span2, .uneditable-input.span2 {    width: 94px;  }  input.span1, textarea.span1, .uneditable-input.span1 {    width: 32px;  }}@media (min-width: 1200px) {  .row {    margin-left: -30px;    *zoom: 1;  }  .row:before,  .row:after {    display: table;    content: "";  }  .row:after {    clear: both;  }  [class*="span"] {    float: left;    margin-left: 30px;  }  .container,  .navbar-fixed-top .container,  .navbar-fixed-bottom .container {    width: 1170px;  }  .span12 {    width: 1170px;  }  .span11 {    width: 1070px;  }  .span10 {    width: 970px;  }  .span9 {    width: 870px;  }  .span8 {    width: 770px;  }  .span7 {    width: 670px;  }  .span6 {    width: 570px;  }  .span5 {    width: 470px;  }  .span4 {    width: 370px;  }  .span3 {    width: 270px;  }  .span2 {    width: 170px;  }  .span1 {    width: 70px;  }  .offset12 {    margin-left: 1230px;  }  .offset11 {    margin-left: 1130px;  }  .offset10 {    margin-left: 1030px;  }  .offset9 {    margin-left: 930px;  }  .offset8 {    margin-left: 830px;  }  .offset7 {    margin-left: 730px;  }  .offset6 {    margin-left: 630px;  }  .offset5 {    margin-left: 530px;  }  .offset4 {    margin-left: 430px;  }  .offset3 {    margin-left: 330px;  }  .offset2 {    margin-left: 230px;  }  .offset1 {    margin-left: 130px;  }  .row-fluid {    width: 100%;    *zoom: 1;  }  .row-fluid:before,  .row-fluid:after {    display: table;    content: "";  }  .row-fluid:after {    clear: both;  }  .row-fluid [class*="span"] {    display: block;    width: 100%;    min-height: 28px;    -webkit-box-sizing: border-box;    -moz-box-sizing: border-box;    -ms-box-sizing: border-box;    box-sizing: border-box;    float: left;    margin-left: 2.564102564%;    *margin-left: 2.510911074638298%;  }  .row-fluid [class*="span"]:first-child {    margin-left: 0;  }  .row-fluid .span12 {    width: 100%;    *width: 99.94680851063829%;  }  .row-fluid .span11 {    width: 91.45299145300001%;    *width: 91.3997999636383%;  }  .row-fluid .span10 {    width: 82.905982906%;    *width: 82.8527914166383%;  }  .row-fluid .span9 {    width: 74.358974359%;    *width: 74.30578286963829%;  }  .row-fluid .span8 {    width: 65.81196581200001%;    *width: 65.7587743226383%;  }  .row-fluid .span7 {    width: 57.264957265%;    *width: 57.2117657756383%;  }  .row-fluid .span6 {    width: 48.717948718%;    *width: 48.6647572286383%;  }  .row-fluid .span5 {    width: 40.170940171000005%;    *width: 40.117748681638304%;  }  .row-fluid .span4 {    width: 31.623931624%;    *width: 31.5707401346383%;  }  .row-fluid .span3 {    width: 23.076923077%;    *width: 23.0237315876383%;  }  .row-fluid .span2 {    width: 14.529914530000001%;    *width: 14.4767230406383%;  }  .row-fluid .span1 {    width: 5.982905983%;    *width: 5.929714493638298%;  }  input,  textarea,  .uneditable-input {    margin-left: 0;  }  input.span12, textarea.span12, .uneditable-input.span12 {    width: 1160px;  }  input.span11, textarea.span11, .uneditable-input.span11 {    width: 1060px;  }  input.span10, textarea.span10, .uneditable-input.span10 {    width: 960px;  }  input.span9, textarea.span9, .uneditable-input.span9 {    width: 860px;  }  input.span8, textarea.span8, .uneditable-input.span8 {    width: 760px;  }  input.span7, textarea.span7, .uneditable-input.span7 {    width: 660px;  }  input.span6, textarea.span6, .uneditable-input.span6 {    width: 560px;  }  input.span5, textarea.span5, .uneditable-input.span5 {    width: 460px;  }  input.span4, textarea.span4, .uneditable-input.span4 {    width: 360px;  }  input.span3, textarea.span3, .uneditable-input.span3 {    width: 260px;  }  input.span2, textarea.span2, .uneditable-input.span2 {    width: 160px;  }  input.span1, textarea.span1, .uneditable-input.span1 {    width: 60px;  }  .thumbnails {    margin-left: -30px;  }  .thumbnails > li {    margin-left: 30px;  }  .row-fluid .thumbnails {    margin-left: 0;  }}@media (max-width: 979px) {  body {    padding-top: 0;  }  .navbar-fixed-top,  .navbar-fixed-bottom {    position: static;  }  .navbar-fixed-top {    margin-bottom: 18px;  }  .navbar-fixed-bottom {    margin-top: 18px;  }  .navbar-fixed-top .navbar-inner,  .navbar-fixed-bottom .navbar-inner {    padding: 5px;  }  .navbar .container {    width: auto;    padding: 0;  }  .navbar .brand {    padding-left: 10px;    padding-right: 10px;    margin: 0 0 0 -5px;  }  .nav-collapse {    clear: both;  }  .nav-collapse .nav {    float: none;    margin: 0 0 9px;  }  .nav-collapse .nav > li {    float: none;  }  .nav-collapse .nav > li > a {    margin-bottom: 2px;  }  .nav-collapse .nav > .divider-vertical {    display: none;  }  .nav-collapse .nav .nav-header {    color: #999999;    text-shadow: none;  }  .nav-collapse .nav > li > a,  .nav-collapse .dropdown-menu a {    padding: 6px 15px;    font-weight: bold;    color: #999999;    -webkit-border-radius: 3px;    -moz-border-radius: 3px;    border-radius: 3px;  }  .nav-collapse .btn {    padding: 4px 10px 4px;    font-weight: normal;    -webkit-border-radius: 4px;    -moz-border-radius: 4px;    border-radius: 4px;  }  .nav-collapse .dropdown-menu li + li a {    margin-bottom: 2px;  }  .nav-collapse .nav > li > a:hover,  .nav-collapse .dropdown-menu a:hover {    background-color: #222222;  }  .nav-collapse.in .btn-group {    margin-top: 5px;    padding: 0;  }  .nav-collapse .dropdown-menu {    position: static;    top: auto;    left: auto;    float: none;    display: block;    max-width: none;    margin: 0 15px;    padding: 0;    background-color: transparent;    border: none;    -webkit-border-radius: 0;    -moz-border-radius: 0;    border-radius: 0;    -webkit-box-shadow: none;    -moz-box-shadow: none;    box-shadow: none;  }  .nav-collapse .dropdown-menu:before,  .nav-collapse .dropdown-menu:after {    display: none;  }  .nav-collapse .dropdown-menu .divider {    display: none;  }  .nav-collapse .navbar-form,  .nav-collapse .navbar-search {    float: none;    padding: 9px 15px;    margin: 9px 0;    border-top: 1px solid #222222;    border-bottom: 1px solid #222222;    -webkit-box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);    -moz-box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);    box-shadow: inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);  }  .navbar .nav-collapse .nav.pull-right {    float: none;    margin-left: 0;  }  .nav-collapse,  .nav-collapse.collapse {    overflow: hidden;    height: 0;  }  .navbar .btn-navbar {    display: block;  }  .navbar-static .navbar-inner {    padding-left: 10px;    padding-right: 10px;  }}@media (min-width: 980px) {  .nav-collapse.collapse {    height: auto !important;    overflow: visible !important;  }}

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• Worse is better. Is there an example?

  - by J.F. Sebastian

  Is there a widely-used algorithm that has time complexity worse than that of another known algorithm but it is a better choice in all practical situations (worse complexity but better otherwise)? An acceptable answer might be in a form: There are algorithms A and B that have O(N**2) and O(N) time complexity correspondingly, but B has such a big constant that it has no advantages over A for inputs less then a number of atoms in the Universe. Examples highlights from the answers: Simplex algorithm -- worst-case is exponential time -- vs. known polynomial-time algorithms for convex optimization problems. A naive median of medians algorithm -- worst-case O(N**2) vs. known O(N) algorithm. Backtracking regex engines -- worst-case exponential vs. O(N) Thompson NFA -based engines. All these examples exploit worst-case vs. average scenarios. Are there examples that do not rely on the difference between the worst case vs. average case scenario? Related: The Rise of ``Worse is Better''. (For the purpose of this question the "Worse is Better" phrase is used in a narrower (namely -- algorithmic time-complexity) sense than in the article) Python's Design Philosophy: The ABC group strived for perfection. For example, they used tree-based data structure algorithms that were proven to be optimal for asymptotically large collections (but were not so great for small collections). This example would be the answer if there were no computers capable of storing these large collections (in other words large is not large enough in this case). Coppersmith–Winograd algorithm for square matrix multiplication is a good example (it is the fastest (2008) but it is inferior to worse algorithms). Any others? From the wikipedia article: "It is not used in practice because it only provides an advantage for matrices so large that they cannot be processed by modern hardware (Robinson 2005)."

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• javasript function

  - by user289346

  var curtext="View large image"; function changeSrc() { if(curtext == "View large image"){ document.getElementById("boldStuff").innerHTML ="View small image"; curtext="View small image"; } else{ document.getElementById("boldStuff").innerHTML="View large image"; curtext="View large image"; } } var curimage="cottage_small.jpg"; function changeSrc(){ if(curimage == "cottage_small.jpg"){ document.getElementById("myImage").src="cottage_large.jpg"; curimage="cottage_large.jpg"; } else{ document.getElementById("myImage").src="cottage_small.jpg"; curimage="cottage_small.jpg"; } } </script> </head> <body> <!-- Your page here --> <h1> Pink Knoll Properties</h1> <h2> Single Family Homes</h2> <p> Cottage:<strong>$149,000</strong><br/> 2 bed, 1 bath, 1,189 square feet, 1.11 acres <br/><br/> <a href="#" onclick="changeSrc()"><b id="boldStuff" />View large image</a></p> <p><img id="myImage" src="cottage_small.jpg" alt="Photo of a cottage" /></p> </body> I need help, how to put as one function with two arguments. That means when you click, the image and text both will be change. Thank you! Bianca

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• Latex renewcommand not working properly

  - by Nazgulled

  Why is this not working: \documentclass[a4paper,10pt]{article} \usepackage{a4wide} \usepackage[T1]{fontenc} \usepackage[portuguese]{babel} \usepackage[latin1]{inputenc} \usepackage{indentfirst} \usepackage{listings} \usepackage{fancyhdr} \usepackage{url} \usepackage[compat2,a4paper,left=25mm,right=25mm,bottom=15mm,top=20mm]{geometry} \usepackage{color} \usepackage[colorlinks]{hyperref} \usepackage[pdftex]{graphicx} \renewcommand{\headrulewidth}{0.4pt} \renewcommand{\footrulewidth}{0.4pt} \pagestyle{fancy} \fancyhead[L]{\small Laboratórios de Informática III} \fancyhead[R]{\small Projecto 1 (Linguagem \textsf{C})} \lstset{ basicstyle=\ttfamily\footnotesize, showstringspaces=false, frame=single, tabsize=4, breaklines=true, } \definecolor{Section1}{rgb}{0.09,0.21,0.36} \definecolor{Section2}{rgb}{0.21,0.37,0.56} \definecolor{Section3}{rgb}{0.30,0.50,0.74} \hypersetup{ bookmarks=false, linkcolor=red, urlcolor=cyan, } \renewcommand{\section}[1]{\texorpdfstring{\color{green}#1}{#1}} \parskip=6pt \begin{document} \begin{titlepage} \begin{center} \includegraphics[width=5cm]{./logo.jpg}\\[1cm] \textsc{\LARGE Universidade do Minho}\\[1cm] \textsc{\large Licenciatura em Engenharia Informática\\Laboratórios de Informática III}\\[1.5cm] \rule{\linewidth}{0.5mm}\\[0.4cm] \huge{\textbf{\textsc{Relatório do Projecto 1 (Linguagem C)}}} \rule{\linewidth}{0.5mm} \vfill \begin{tabular}{c c} \includegraphics[width=3.5cm]{./nuno.jpg} & \includegraphics[width=3.5cm]{./ricardo.jpg} \\ \textsc{\large{Nuno Mendes (51161)}} & \textsc{\large{Ricardo Amaral (48404)}} \\ \end{tabular} \vfill \large{\today} \end{center} \end{titlepage} \tableofcontents \newpage \section{Introdução} Lorem ipsum... \newpage \appendix \section{\color{Section1}Diagrama das Estruturas de Dados} \begin{center} \includegraphics[width=16cm]{./Diagrama.pdf} \end{center} \end{document} ! LaTeX Error: Something's wrong--perhaps a missing \item. See the LaTeX manual or LaTeX Companion for explanation. Type H for immediate help. ... l.2 ...rline {1}\color {green}Teste}{3}{section.1} How can I make it work properly?

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• change image use javascript DOM

  - by user289346

  <html> <head> <script type="text/javascript"> var curimage = "cottage_small.jpg"; var curtext = "View large image"; function changeSrc() { if (curtext == "View large image"||curimage == "cottage_small.jpg") { document.getElementById("boldStuff").innerHTML = "View small image"; curtext="View small image"; document.getElementById("myImage")= "cottage_large.jpg"; curimage = "cottage_large.jpg"; } else { document.getElementById("boldStuff").innerHTML = "View large image"; curtext = "View large image"; document.getElementById("myImage")= "cottage_small.jpg"; curimage = "cottage_small.jpg"; } } </script> </head> <body> <!-- Your page here --> <h1> Pink Knoll Properties</h1> <h2> Single Family Homes</h2> <p> Cottage:<strong>$149,000</strong><br/> 2 bed, 1 bath, 1,189 square feet, 1.11 acres <br/><br/> <a href="#" onclick="changeSrc()"><b id="boldStuff" />View large image</a> </p> <p><img id="myImage" src="cottage_small.jpg" alt="Photo of a cottage" /></p> </body> </html> This is my coding I need to change the image and text the same time when I click it. I use LTS, it shows the line document.getElementById("myImage")= "cottage_large.jpg"; is a wrong number of arquments or invalid property assigment. Dose someone can help? Bianca

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• How to combine these two JavaScript functions?

  - by user289346

  var curtext = "View large image"; function changeSrc() { if (curtext == "View large image") { document.getElementById("boldStuff").innerHTML = "View small image"; curtext="View small image"; } else { document.getElementById("boldStuff").innerHTML = "View large image"; curtext = "View large image"; } } var curimage = "cottage_small.jpg"; function changeSrc() { if (curimage == "cottage_small.jpg") { document.getElementById("myImage").src = "cottage_large.jpg"; curimage = "cottage_large.jpg"; } else { document.getElementById("myImage").src = "cottage_small.jpg"; curimage = "cottage_small.jpg"; } } </script> </head> <body> <!-- Your page here --> <h1> Pink Knoll Properties</h1> <h2> Single Family Homes</h2> <p> Cottage:<strong>$149,000</strong><br/> 2 bed, 1 bath, 1,189 square feet, 1.11 acres <br/><br/> <a href="#" onclick="changeSrc()"><b id="boldStuff" />View large image</a></p> <p><img id="myImage" src="cottage_small.jpg" alt="Photo of a cottage" /></p> </body> I need help, how to put as one function with two arguments? That means when you click, the image and text both will be change. Thank you! Bianca

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• Performance in SQL Mobile with one big column that's not being selected

  - by Anthony Mastrean

  I have a SQL Mobile database with one table. It has several columns with useful, often queried data and one column that stores a relatively large string per record (1000+ characters) that is not queried often. Imagine this fake schema, the "lifeStory" field is the large one. table1 String firstName String lastName String address String lifeStory A representative query would be SELECT firstName, lastName, address FROM table1 WHERE firstName = :p1 Does anyone know of any performance concerns leaving that large, infrequently queried column in this table?

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• Google App Engine Python: get image upload size server-side

  - by goggin13

  I am building a Google App Engine App that lets users upload images; I have everything working fine, but I am struggling to find a way to ensure that the user does not upload an image too large (because I am resizing the images, so this crashes my python script). When a user uploads a large image, I get this error RequestTooLargeError: The request to API call images.Transform() was too large. I know that there is a size limitation on what GAE allows for it's image API, I am just trying to find a way to deal with this server side; something along the lines of if (image is too large): inform user else: proceed I haven't had any luck finding the right python code to do this; can anyone help me out?

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• "How to: Multiple jQuery Image Swap Galleries on the Same Page?" / ASAP

  - by Robert

  I have multiple image galleries that use the following code (simply view the demo at the following link). http://www.sohtanaka.com/web-design/fancy-thumbnail-hover-effect-w-jquery/ However, when one clicks on the thumbnails of one gallery to change the large image preview - all the galleries change automatically to that large preview as well. In short, the question is: "How do I seperate each gallery from each other so when the user clicks on the thumbnails of one specific gallery the large image preview of that gallery changes and not any other?"

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• Strange behavior: Dynamic expressions with RegExp Object in RegExp.match (Javascript)

  - by NeDark

  I have detect a strange behavior in regexps created with the RegExp object: With this code: var exp1 = /./; var exp2 = new RegExp('.'); ? var test1 = exp1.test('large\n\ntext..etc.'); var test2 = exp2.test('large\n\ntext..etc.'); ? var match1 = 'large\n\ntext..etc.'.match(exp1); var match2 = 'large\n\ntext..etc.'.match(exp2); ...the result is: test1 = true test2 = true ? match1 = 'l' (first match) match2 = null With the regexp maked with the regexp object from a string it finds nothing... Why does this happends? Thanks!!

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• Which is faster??

  - by kaki

  is opening a large file once reading it completely once to list faster (or) opening smaller files whose total sum of size is equal to large file and loading smaller file into list manupalating one by one faster? which is faster?? is the difference is time large enough to impact my program?? total time difference of lesser then of 30 sec is negligible for me

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• how to load a file into a blob without loading into RAM first?

  - by Ace Grace

  Hi, I am using C# and I am having trouble loading large files into a binary field use byte arrays. Basically, if I load a file that is too large, I get memory problems. Is there a way to load a file into a binary field without using large amounts of ram, i.e. avoid loading the file into memory first? If it helps, I am using Advantage Database Server and this is using a winforms app not a web app. Regards

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• Handling multiple media queries in Sass with Twitter Bootstrap

  - by Keith

  I have a Sass mixin for my media queries based on Twitter Bootstrap's responsive media queries: @mixin respond-to($media) { @if $media == handhelds { /* Landscape phones and down */ @media (max-width: 480px) { @content; } } @else if $media == small { /* Landscape phone to portrait tablet */ @media (max-width: 767px) {@content; } } @else if $media == medium { /* Portrait tablet to landscape and desktop */ @media (min-width: 768px) and (max-width: 979px) { @content; } } @else if $media == large { /* Large desktop */ @media (min-width: 1200px) { @content; } } @else { @media only screen and (max-width: #{$media}px) { @content; } } } And I call them throughout my SCSS file like so: .link { color:blue; @include respond-to(medium) { color: red; } } However, sometimes I want to style multiple queries with the same styles. Right now I'm doing them like this: .link { color:blue; /* this is fine for handheld and small sizes*/ /*now I want to change the styles that are cascading to medium and large*/ @include respond-to(medium) { color: red; } @include respond-to(large) { color: red; } } but I'm repeating code so I'm wondering if there is a more concise way to write it so I can target multiple queries. Something like this so I don't need to repeat my code (I know this doesn't work): @include respond-to(medium, large) { color: red; } Any suggestions on the best way to handle this?

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• Which Android hardware devices should I test on? [closed]

  - by Tchami

  Possible Duplicate: What hardware devices do you test your Android apps on? I'm trying to compile a list of Android hardware devices that it would make sense to buy and test against if you want to target an as broad audience as possible, while still not buying every single Android device out there. I know there's a lot of information regarding screen sizes and Android versions available elsewhere, but: when developing for Android it's not terribly useful to know if the screen size of a device is 480x800 or 320x240, unless you feel like doing the math to convert that into Android "units" (i.e. small, normal, large or xlarge screens, and ldpi, mdpi, hdpi or xhdpi densities). Even knowing the dimensions of a device, you cannot be sure of the actual Android units as there's some overlap, see Range of screens supported in the Android documentation Taking into account the distribution of Platform versions and Screen Sizes and Densities, below is my current list based on information from the Wikipedia article on Comparison of Android devices. I'm fairly sure the information in this list is correct, but I'd welcome any suggestions/changes. Phones | Model | Android Version | Screen Size | Density | | HTC Wildfire | 2.1/2.2 | Normal | mdpi | | HTC Tattoo | 1.6 | Normal | mdpi | | HTC Hero | 2.1 | Normal | mdpi | | HTC Legend | 2.1 | Normal | mdpi | | Sony Ericsson Xperia X8 | 1.6/2.1 | Normal | mdpi | | Motorola Droid | 2.0-2.2 | Normal | hdpi | | Samsung Galaxy S II | 2.3 | Normal | hdpi | | Samsung Galaxy Nexus | 4.0 | Normal | xhdpi | | Samsung Galaxy S III | 4.0 | Normal | xhdpi | **Tablets** | Model | Android Version | Screen Size | Density | | Samsung Galaxy Tab 7" | 2.2 | Large | hdpi | | Samsung Galaxy Tab 10" | 3.0 | X-Large | mdpi | | Asus Transformer Prime | 4.0 | X-Large | mdpi | | Motorola Xoom | 3.1/4.0 | X-Large | mdpi | N.B.: I have seen (and read) other posts on SO on this subject, e.g. Which Android devices should I test against? and What hardware devices do you test your Android apps on? but they don't seem very canonical. Maybe this should be marked community wiki?

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• I'm maintaining a java class that's 40K lines long.. problem?

  - by Billworth Vandory

  This may be a subjective question leading to deletion but I would really like some feedback. Recently, I moved to another very large enterprise project where I work as a C++ developer. I was aghast to find most classes in the project are anywhere from 8K to 50K lines long with methods that are 1K to 8K lines long. It's mostly business logic dealing with DB tables and data management, full of conditional statements to handle the use cases. Are classes this large common in large enterprise systems? I realize without looking at the code it's hard to make a determination, but have you ever worked on a system with classes this large?

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• SQL Server and Hyper-V Dynamic Memory Part 2

  - by SQLOS Team

  Part 1 of this series was an introduction and overview of Hyper-V Dynamic Memory. This part looks at SQL Server memory management and how the SQL engine responds to changing OS memory conditions.   Part 2: SQL Server Memory Management As with any Windows process, sqlserver.exe has a virtual address space (VAS) of 4GB on 32-bit and 8TB in 64-bit editions. Pages in its VAS are mapped to pages in physical memory when the memory is committed and referenced for the first time. The collection of VAS pages that have been recently referenced is known as the Working Set. How and when SQL Server allocates virtual memory and grows its working set depends on the memory model it uses. SQL Server supports three basic memory models:   1. Conventional Memory Model   The Conventional model is the default SQL Server memory model and has the following properties: - Dynamic - can grow or shrink its working set in response to load and external (operating system) memory conditions. - OS uses 4K pages – (not to be confused with SQL Server “pages” which are 8K regions of committed memory).- Pageable - Can be paged out to disk by the operating system.   2. Locked Page Model The locked page memory model is set when SQL Server is started with "Lock Pages in Memory" privilege*. It has the following characteristics: - Dynamic - can grow or shrink its working set in the same way as the Conventional model.- OS uses 4K pages - Non-Pageable – When memory is committed it is locked in memory, meaning that it will remain backed by physical memory and will not be paged out by the operating system. A common misconception is to interpret "locked" as non-dynamic. A SQL Server instance using the locked page memory model will grow and shrink (allocate memory and release memory) in response to changing workload and OS memory conditions in the same way as it does with the conventional model.   This is an important consideration when we look at Hyper-V Dynamic Memory – “locked” memory works perfectly well with “dynamic” memory.   * Note in “Denali” (Standard Edition and above), and in SQL 2008 R2 64-bit (Enterprise and above editions) the Lock Pages in Memory privilege is all that is required to set this model. In 2008 R2 64-Bit standard edition it also requires trace flag 845 to be set, in 2008 R2 32-bit editions it requires sp_configure 'awe enabled' 1.   3. Large Page Model The Large page model is set using trace flag 834 and potentially offers a small performance boost for systems that are configured with large pages. It is characterized by: - Static - memory is allocated at startup and does not change. - OS uses large (>2MB) pages - Non-Pageable The large page model is supported with Hyper-V Dynamic Memory (and Hyper-V also supports large pages), but you get no benefit from using Dynamic Memory with this model since SQL Server memory does not grow or shrink. The rest of this article will focus on the locked and conventional SQL Server memory models.   When does SQL Server grow? For “dynamic” configurations (Conventional and Locked memory models), the sqlservr.exe process grows – allocates and commits memory from the OS – in response to a workload. As much memory is allocated as is required to optimally run the query and buffer data for future queries, subject to limitations imposed by:   - SQL Server max server memory setting. If this configuration option is set, the buffer pool is not allowed to grow to more than this value. In SQL Server 2008 this value represents single page allocations, and in “Denali” it represents any size page allocations and also managed CLR procedure allocations.   - Memory signals from OS. The operating system sets a signal on memory resource notification objects to indicate whether it has memory available or whether it is low on available memory. If there is only 32MB free for every 4GB of memory a low memory signal is set, which continues until 64MB/4GB is free. If there is 96MB/4GB free the operating system sets a high memory signal. SQL Server only allocates memory when the high memory signal is set.   To summarize, for SQL Server to grow you need three conditions: a workload, max server memory setting higher than the current allocation, high memory signals from the OS.    When does SQL Server shrink caches? SQL Server as a rule does not like to return memory to the OS, but it will shrink its caches in response to memory pressure. Memory pressure can be divided into “internal” and “external”.   - External memory pressure occurs when the operating system is running low on memory and low memory signals are set. The SQL Server Resource Monitor checks for low memory signals approximately every 5 seconds and it will attempt to free memory until the signals stop.   To free memory SQL Server does the following: ·         Frees unused memory. ·         Notifies Memory Manager Clients to release memory o   Caches – Free unreferenced cache objects. o   Buffer pool - Based on oldest access times.   The freed memory is released back to the operating system. This process continues until the low memory resource notifications stop.    - Internal memory pressure occurs when the size of different caches and allocations increase but the SQL Server process needs to keep its total memory within a target value. For example if max server memory is set and certain caches are growing large, it will cause SQL to free memory for re-use internally, but not to release memory back to the OS. If you lower the value of max server memory you will generate internal memory pressure that will cause SQL to release memory back to the OS.    Memory pressure handling has not changed much since SQL 2005 and it was described in detail in a blog post by Slava Oks.   Note that SQL Server Express is an exception to the above behavior. Unlike other editions it does not assume it is the most important process running on the system but tries to be more “desktop” friendly. It will empty its working set after a period of inactivity.   How does SQL Server respond to changing OS memory?    In SQL Server 2005 support for Hot-Add memory was introduced. This feature, available in Enterprise and above editions, allows the server to make use of any extra physical memory that was added after SQL Server started. Being able to add physical memory when the system is running is limited to specialized hardware, but with the Hyper-V Dynamic Memory feature, when new memory is allocated to a guest virtual machine, it looks like hot-add physical memory to the guest. What this means is that thanks to the hot-add memory feature, SQL Server 2005 and higher can dynamically grow if more “physical” memory is granted to a guest VM by Hyper-V dynamic memory.   SQL Server checks OS memory every second and dynamically adjusts its “target” (based on available OS memory and max server memory) accordingly.   In “Denali” Standard Edition will also have sqlserver.exe support for hot-add memory when running virtualized (i.e. detecting and acting on Hyper-V Dynamic Memory allocations).   How does a SQL Server workload in a guest VM impact Hyper-V dynamic memory scheduling?   When a SQL workload causes the sqlserver.exe process to grow its working set, the Hyper-V memory scheduler will detect memory pressure in the guest VM and add memory to it. SQL Server will then detect the extra memory and grow according to workload demand. In our tests we have seen this feedback process cause a guest VM to grow quickly in response to SQL workload - we are still working on characterizing this ramp-up.    How does SQL Server respond when Hyper-V removes memory from a guest VM through ballooning?   If pressure from other VM's cause Hyper-V Dynamic Memory to take memory away from a VM through ballooning (allocating memory with a virtual device driver and returning it to the host OS), Windows Memory Manager will page out unlocked portions of memory and signal low resource notification events. When SQL Server detects these events it will shrink memory until the low memory notifications stop (see cache shrinking description above).    This raises another question. Can we make SQL Server release memory more readily and hence behave more "dynamically" without compromising performance? In certain circumstances where the application workload is predictable it may be possible to have a job which varies "max server memory" according to need, lowering it when the engine is inactive and raising it before a period of activity. This would have limited applicaability but it is something we're looking into.   What Memory Management changes are there in SQL Server “Denali”?   In SQL Server “Denali” (aka SQL11) the Memory Manager has been re-written to be more efficient. The main changes are summarized in this post. An important change with respect to Hyper-V Dynamic Memory support is that now the max server memory setting includes any size page allocations and managed CLR procedure allocations it now represents a closer approximation to total sqlserver.exe memory usage. This makes it easier to calculate a value for max server memory, which becomes important when configuring virtual machines to work well with Hyper-V Dynamic Memory Startup and Maximum RAM settings.   Another important change is no more AWE or hot-add support for 32-bit edition. This means if you're running a 32-bit edition of Denali you're limited to a 4GB address space and will not be able to take advantage of dynamically added OS memory that wasn't present when SQL Server started (though Hyper-V Dynamic Memory is still a supported configuration).   In part 3 we’ll develop some best practices for configuring and using SQL Server with Dynamic Memory. Originally posted at http://blogs.msdn.com/b/sqlosteam/

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• Upload File to Windows Azure Blob in Chunks through ASP.NET MVC, JavaScript and HTML5

  - by Shaun

  Originally posted on: http://geekswithblogs.net/shaunxu/archive/2013/07/01/upload-file-to-windows-azure-blob-in-chunks-through-asp.net.aspxMany people are using Windows Azure Blob Storage to store their data in the cloud. Blob storage provides 99.9% availability with easy-to-use API through .NET SDK and HTTP REST. For example, we can store JavaScript files, images, documents in blob storage when we are building an ASP.NET web application on a Web Role in Windows Azure. Or we can store our VHD files in blob and mount it as a hard drive in our cloud service. If you are familiar with Windows Azure, you should know that there are two kinds of blob: page blob and block blob. The page blob is optimized for random read and write, which is very useful when you need to store VHD files. The block blob is optimized for sequential/chunk read and write, which has more common usage. Since we can upload block blob in blocks through BlockBlob.PutBlock, and them commit them as a whole blob with invoking the BlockBlob.PutBlockList, it is very powerful to upload large files, as we can upload blocks in parallel, and provide pause-resume feature. There are many documents, articles and blog posts described on how to upload a block blob. Most of them are focus on the server side, which means when you had received a big file, stream or binaries, how to upload them into blob storage in blocks through .NET SDK.  But the problem is, how can we upload these large files from client side, for example, a browser. This questioned to me when I was working with a Chinese customer to help them build a network disk production on top of azure. The end users upload their files from the web portal, and then the files will be stored in blob storage from the Web Role. My goal is to find the best way to transform the file from client (end user’s machine) to the server (Web Role) through browser. In this post I will demonstrate and describe what I had done, to upload large file in chunks with high speed, and save them as blocks into Windows Azure Blob Storage.   Traditional Upload, Works with Limitation The simplest way to implement this requirement is to create a web page with a form that contains a file input element and a submit button. 1: @using (Html.BeginForm("About", "Index", FormMethod.Post, new { enctype = "multipart/form-data" })) 2: { 3: <input type="file" name="file" /> 4: <input type="submit" value="upload" /> 5: } And then in the backend controller, we retrieve the whole content of this file and upload it in to the blob storage through .NET SDK. We can split the file in blocks and upload them in parallel and commit. The code had been well blogged in the community. 1: [HttpPost] 2: public ActionResult About(HttpPostedFileBase file) 3: { 4: var container = _client.GetContainerReference("test"); 5: container.CreateIfNotExists(); 6: var blob = container.GetBlockBlobReference(file.FileName); 7: var blockDataList = new Dictionary<string, byte[]>(); 8: using (var stream = file.InputStream) 9: { 10: var blockSizeInKB = 1024; 11: var offset = 0; 12: var index = 0; 13: while (offset < stream.Length) 14: { 15: var readLength = Math.Min(1024 * blockSizeInKB, (int)stream.Length - offset); 16: var blockData = new byte[readLength]; 17: offset += stream.Read(blockData, 0, readLength); 18: blockDataList.Add(Convert.ToBase64String(BitConverter.GetBytes(index)), blockData); 19:  20: index++; 21: } 22: } 23:  24: Parallel.ForEach(blockDataList, (bi) => 25: { 26: blob.PutBlock(bi.Key, new MemoryStream(bi.Value), null); 27: }); 28: blob.PutBlockList(blockDataList.Select(b => b.Key).ToArray()); 29:  30: return RedirectToAction("About"); 31: } This works perfect if we selected an image, a music or a small video to upload. But if I selected a large file, let’s say a 6GB HD-movie, after upload for about few minutes the page will be shown as below and the upload will be terminated. In ASP.NET there is a limitation of request length and the maximized request length is defined in the web.config file. It’s a number which less than about 4GB. So if we want to upload a really big file, we cannot simply implement in this way. Also, in Windows Azure, a cloud service network load balancer will terminate the connection if exceed the timeout period. From my test the timeout looks like 2 - 3 minutes. Hence, when we need to upload a large file we cannot just use the basic HTML elements. Besides the limitation mentioned above, the simple HTML file upload cannot provide rich upload experience such as chunk upload, pause and pause-resume. So we need to find a better way to upload large file from the client to the server.   Upload in Chunks through HTML5 and JavaScript In order to break those limitation mentioned above we will try to upload the large file in chunks. This takes some benefit to us such as - No request size limitation: Since we upload in chunks, we can define the request size for each chunks regardless how big the entire file is. - No timeout problem: The size of chunks are controlled by us, which means we should be able to make sure request for each chunk upload will not exceed the timeout period of both ASP.NET and Windows Azure load balancer. It was a big challenge to upload big file in chunks until we have HTML5. There are some new features and improvements introduced in HTML5 and we will use them to implement our solution.   In HTML5, the File interface had been improved with a new method called “slice”. It can be used to read part of the file by specifying the start byte index and the end byte index. For example if the entire file was 1024 bytes, file.slice(512, 768) will read the part of this file from the 512nd byte to 768th byte, and return a new object of interface called "Blob”, which you can treat as an array of bytes. In fact,  a Blob object represents a file-like object of immutable, raw data. The File interface is based on Blob, inheriting blob functionality and expanding it to support files on the user's system. For more information about the Blob please refer here. File and Blob is very useful to implement the chunk upload. We will use File interface to represent the file the user selected from the browser and then use File.slice to read the file in chunks in the size we wanted. For example, if we wanted to upload a 10MB file with 512KB chunks, then we can read it in 512KB blobs by using File.slice in a loop.   Assuming we have a web page as below. User can select a file, an input box to specify the block size in KB and a button to start upload. 1: <div> 2: <input type="file" id="upload_files" name="files[]" /><br /> 3: Block Size: <input type="number" id="block_size" value="512" name="block_size" />KB<br /> 4: <input type="button" id="upload_button_blob" name="upload" value="upload (blob)" /> 5: </div> Then we can have the JavaScript function to upload the file in chunks when user clicked the button. 1: <script type="text/javascript"> 1: 2: $(function () { 3: $("#upload_button_blob").click(function () { 4: }); 5: });</script> Firstly we need to ensure the client browser supports the interfaces we are going to use. Just try to invoke the File, Blob and FormData from the “window” object. If any of them is “undefined” the condition result will be “false” which means your browser doesn’t support these premium feature and it’s time for you to get your browser updated. FormData is another new feature we are going to use in the future. It could generate a temporary form for us. We will use this interface to create a form with chunk and associated metadata when invoked the service through ajax. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: if (window.File && window.Blob && window.FormData) { 4: alert("Your brwoser is awesome, let's rock!"); 5: } 6: else { 7: alert("Oh man plz update to a modern browser before try is cool stuff out."); 8: return; 9: } 10: }); Each browser supports these interfaces by their own implementation and currently the Blob, File and File.slice are supported by Chrome 21, FireFox 13, IE 10, Opera 12 and Safari 5.1 or higher. After that we worked on the files the user selected one by one since in HTML5, user can select multiple files in one file input box. 1: var files = $("#upload_files")[0].files; 2: for (var i = 0; i < files.length; i++) { 3: var file = files[i]; 4: var fileSize = file.size; 5: var fileName = file.name; 6: } Next, we calculated the start index and end index for each chunks based on the size the user specified from the browser. We put them into an array with the file name and the index, which will be used when we upload chunks into Windows Azure Blob Storage as blocks since we need to specify the target blob name and the block index. At the same time we will store the list of all indexes into another variant which will be used to commit blocks into blob in Azure Storage once all chunks had been uploaded successfully. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10:  11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: var blockSizeInKB = $("#block_size").val(); 14: var blockSize = blockSizeInKB * 1024; 15: var blocks = []; 16: var offset = 0; 17: var index = 0; 18: var list = ""; 19: while (offset < fileSize) { 20: var start = offset; 21: var end = Math.min(offset + blockSize, fileSize); 22:  23: blocks.push({ 24: name: fileName, 25: index: index, 26: start: start, 27: end: end 28: }); 29: list += index + ","; 30:  31: offset = end; 32: index++; 33: } 34: } 35: }); Now we have all chunks’ information ready. The next step should be upload them one by one to the server side, and at the server side when received a chunk it will upload as a block into Blob Storage, and finally commit them with the index list through BlockBlobClient.PutBlockList. But since all these invokes are ajax calling, which means not synchronized call. So we need to introduce a new JavaScript library to help us coordinate the asynchronize operation, which named “async.js”. You can download this JavaScript library here, and you can find the document here. I will not explain this library too much in this post. We will put all procedures we want to execute as a function array, and pass into the proper function defined in async.js to let it help us to control the execution sequence, in series or in parallel. Hence we will define an array and put the function for chunk upload into this array. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4:  5: // start to upload each files in chunks 6: var files = $("#upload_files")[0].files; 7: for (var i = 0; i < files.length; i++) { 8: var file = files[i]; 9: var fileSize = file.size; 10: var fileName = file.name; 11: // calculate the start and end byte index for each blocks(chunks) 12: // with the index, file name and index list for future using 13: ... ... 14:  15: // define the function array and push all chunk upload operation into this array 16: blocks.forEach(function (block) { 17: putBlocks.push(function (callback) { 18: }); 19: }); 20: } 21: }); 22: }); As you can see, I used File.slice method to read each chunks based on the start and end byte index we calculated previously, and constructed a temporary HTML form with the file name, chunk index and chunk data through another new feature in HTML5 named FormData. Then post this form to the backend server through jQuery.ajax. This is the key part of our solution. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: blocks.forEach(function (block) { 15: putBlocks.push(function (callback) { 16: // load blob based on the start and end index for each chunks 17: var blob = file.slice(block.start, block.end); 18: // put the file name, index and blob into a temporary from 19: var fd = new FormData(); 20: fd.append("name", block.name); 21: fd.append("index", block.index); 22: fd.append("file", blob); 23: // post the form to backend service (asp.net mvc controller action) 24: $.ajax({ 25: url: "/Home/UploadInFormData", 26: data: fd, 27: processData: false, 28: contentType: "multipart/form-data", 29: type: "POST", 30: success: function (result) { 31: if (!result.success) { 32: alert(result.error); 33: } 34: callback(null, block.index); 35: } 36: }); 37: }); 38: }); 39: } 40: }); Then we will invoke these functions one by one by using the async.js. And once all functions had been executed successfully I invoked another ajax call to the backend service to commit all these chunks (blocks) as the blob in Windows Azure Storage. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.series(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); That’s all in the client side. The outline of our logic would be - Calculate the start and end byte index for each chunks based on the block size. - Defined the functions of reading the chunk form file and upload the content to the backend service through ajax. - Execute the functions defined in previous step with “async.js”. - Commit the chunks by invoking the backend service in Windows Azure Storage finally.   Save Chunks as Blocks into Blob Storage In above we finished the client size JavaScript code. It uploaded the file in chunks to the backend service which we are going to implement in this step. We will use ASP.NET MVC as our backend service, and it will receive the chunks, upload into Windows Azure Bob Storage in blocks, then finally commit as one blob. As in the client side we uploaded chunks by invoking the ajax call to the URL "/Home/UploadInFormData", I created a new action under the Index controller and it only accepts HTTP POST request. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: } 8: catch (Exception e) 9: { 10: error = e.ToString(); 11: } 12:  13: return new JsonResult() 14: { 15: Data = new 16: { 17: success = string.IsNullOrWhiteSpace(error), 18: error = error 19: } 20: }; 21: } Then I retrieved the file name, index and the chunk content from the Request.Form object, which was passed from our client side. And then, used the Windows Azure SDK to create a blob container (in this case we will use the container named “test”.) and create a blob reference with the blob name (same as the file name). Then uploaded the chunk as a block of this blob with the index, since in Blob Storage each block must have an index (ID) associated with so that finally we can put all blocks as one blob by specifying their block ID list. 1: [HttpPost] 2: public JsonResult UploadInFormData() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var index = int.Parse(Request.Form["index"]); 9: var file = Request.Files[0]; 10: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 11:  12: var container = _client.GetContainerReference("test"); 13: container.CreateIfNotExists(); 14: var blob = container.GetBlockBlobReference(name); 15: blob.PutBlock(id, file.InputStream, null); 16: } 17: catch (Exception e) 18: { 19: error = e.ToString(); 20: } 21:  22: return new JsonResult() 23: { 24: Data = new 25: { 26: success = string.IsNullOrWhiteSpace(error), 27: error = error 28: } 29: }; 30: } Next, I created another action to commit the blocks into blob once all chunks had been uploaded. Similarly, I retrieved the blob name from the Request.Form. I also retrieved the chunks ID list, which is the block ID list from the Request.Form in a string format, split them as a list, then invoked the BlockBlob.PutBlockList method. After that our blob will be shown in the container and ready to be download. 1: [HttpPost] 2: public JsonResult Commit() 3: { 4: var error = string.Empty; 5: try 6: { 7: var name = Request.Form["name"]; 8: var list = Request.Form["list"]; 9: var ids = list 10: .Split(',') 11: .Where(id => !string.IsNullOrWhiteSpace(id)) 12: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 13: .ToArray(); 14:  15: var container = _client.GetContainerReference("test"); 16: container.CreateIfNotExists(); 17: var blob = container.GetBlockBlobReference(name); 18: blob.PutBlockList(ids); 19: } 20: catch (Exception e) 21: { 22: error = e.ToString(); 23: } 24:  25: return new JsonResult() 26: { 27: Data = new 28: { 29: success = string.IsNullOrWhiteSpace(error), 30: error = error 31: } 32: }; 33: } Now we finished all code we need. The whole process of uploading would be like this below. Below is the full client side JavaScript code. 1: <script type="text/javascript" src="~/Scripts/async.js"></script> 2: <script type="text/javascript"> 3: $(function () { 4: $("#upload_button_blob").click(function () { 5: // assert the browser support html5 6: if (window.File && window.Blob && window.FormData) { 7: alert("Your brwoser is awesome, let's rock!"); 8: } 9: else { 10: alert("Oh man plz update to a modern browser before try is cool stuff out."); 11: return; 12: } 13:  14: // start to upload each files in chunks 15: var files = $("#upload_files")[0].files; 16: for (var i = 0; i < files.length; i++) { 17: var file = files[i]; 18: var fileSize = file.size; 19: var fileName = file.name; 20:  21: // calculate the start and end byte index for each blocks(chunks) 22: // with the index, file name and index list for future using 23: var blockSizeInKB = $("#block_size").val(); 24: var blockSize = blockSizeInKB * 1024; 25: var blocks = []; 26: var offset = 0; 27: var index = 0; 28: var list = ""; 29: while (offset < fileSize) { 30: var start = offset; 31: var end = Math.min(offset + blockSize, fileSize); 32:  33: blocks.push({ 34: name: fileName, 35: index: index, 36: start: start, 37: end: end 38: }); 39: list += index + ","; 40:  41: offset = end; 42: index++; 43: } 44:  45: // define the function array and push all chunk upload operation into this array 46: var putBlocks = []; 47: blocks.forEach(function (block) { 48: putBlocks.push(function (callback) { 49: // load blob based on the start and end index for each chunks 50: var blob = file.slice(block.start, block.end); 51: // put the file name, index and blob into a temporary from 52: var fd = new FormData(); 53: fd.append("name", block.name); 54: fd.append("index", block.index); 55: fd.append("file", blob); 56: // post the form to backend service (asp.net mvc controller action) 57: $.ajax({ 58: url: "/Home/UploadInFormData", 59: data: fd, 60: processData: false, 61: contentType: "multipart/form-data", 62: type: "POST", 63: success: function (result) { 64: if (!result.success) { 65: alert(result.error); 66: } 67: callback(null, block.index); 68: } 69: }); 70: }); 71: }); 72:  73: // invoke the functions one by one 74: // then invoke the commit ajax call to put blocks into blob in azure storage 75: async.series(putBlocks, function (error, result) { 76: var data = { 77: name: fileName, 78: list: list 79: }; 80: $.post("/Home/Commit", data, function (result) { 81: if (!result.success) { 82: alert(result.error); 83: } 84: else { 85: alert("done!"); 86: } 87: }); 88: }); 89: } 90: }); 91: }); 92: </script> And below is the full ASP.NET MVC controller code. 1: public class HomeController : Controller 2: { 3: private CloudStorageAccount _account; 4: private CloudBlobClient _client; 5:  6: public HomeController() 7: : base() 8: { 9: _account = CloudStorageAccount.Parse(CloudConfigurationManager.GetSetting("DataConnectionString")); 10: _client = _account.CreateCloudBlobClient(); 11: } 12:  13: public ActionResult Index() 14: { 15: ViewBag.Message = "Modify this template to jump-start your ASP.NET MVC application."; 16:  17: return View(); 18: } 19:  20: [HttpPost] 21: public JsonResult UploadInFormData() 22: { 23: var error = string.Empty; 24: try 25: { 26: var name = Request.Form["name"]; 27: var index = int.Parse(Request.Form["index"]); 28: var file = Request.Files[0]; 29: var id = Convert.ToBase64String(BitConverter.GetBytes(index)); 30:  31: var container = _client.GetContainerReference("test"); 32: container.CreateIfNotExists(); 33: var blob = container.GetBlockBlobReference(name); 34: blob.PutBlock(id, file.InputStream, null); 35: } 36: catch (Exception e) 37: { 38: error = e.ToString(); 39: } 40:  41: return new JsonResult() 42: { 43: Data = new 44: { 45: success = string.IsNullOrWhiteSpace(error), 46: error = error 47: } 48: }; 49: } 50:  51: [HttpPost] 52: public JsonResult Commit() 53: { 54: var error = string.Empty; 55: try 56: { 57: var name = Request.Form["name"]; 58: var list = Request.Form["list"]; 59: var ids = list 60: .Split(',') 61: .Where(id => !string.IsNullOrWhiteSpace(id)) 62: .Select(id => Convert.ToBase64String(BitConverter.GetBytes(int.Parse(id)))) 63: .ToArray(); 64:  65: var container = _client.GetContainerReference("test"); 66: container.CreateIfNotExists(); 67: var blob = container.GetBlockBlobReference(name); 68: blob.PutBlockList(ids); 69: } 70: catch (Exception e) 71: { 72: error = e.ToString(); 73: } 74:  75: return new JsonResult() 76: { 77: Data = new 78: { 79: success = string.IsNullOrWhiteSpace(error), 80: error = error 81: } 82: }; 83: } 84: } And if we selected a file from the browser we will see our application will upload chunks in the size we specified to the server through ajax call in background, and then commit all chunks in one blob. Then we can find the blob in our Windows Azure Blob Storage.   Optimized by Parallel Upload In previous example we just uploaded our file in chunks. This solved the problem that ASP.NET MVC request content size limitation as well as the Windows Azure load balancer timeout. But it might introduce the performance problem since we uploaded chunks in sequence. In order to improve the upload performance we could modify our client side code a bit to make the upload operation invoked in parallel. The good news is that, “async.js” library provides the parallel execution function. If you remembered the code we invoke the service to upload chunks, it utilized “async.series” which means all functions will be executed in sequence. Now we will change this code to “async.parallel”. This will invoke all functions in parallel. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallel(putBlocks, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: }); In this way all chunks will be uploaded to the server side at the same time to maximize the bandwidth usage. This should work if the file was not very large and the chunk size was not very small. But for large file this might introduce another problem that too many ajax calls are sent to the server at the same time. So the best solution should be, upload the chunks in parallel with maximum concurrency limitation. The code below specified the concurrency limitation to 4, which means at the most only 4 ajax calls could be invoked at the same time. 1: $("#upload_button_blob").click(function () { 2: // assert the browser support html5 3: ... ... 4: // start to upload each files in chunks 5: var files = $("#upload_files")[0].files; 6: for (var i = 0; i < files.length; i++) { 7: var file = files[i]; 8: var fileSize = file.size; 9: var fileName = file.name; 10: // calculate the start and end byte index for each blocks(chunks) 11: // with the index, file name and index list for future using 12: ... ... 13: // define the function array and push all chunk upload operation into this array 14: ... ... 15: // invoke the functions one by one 16: // then invoke the commit ajax call to put blocks into blob in azure storage 17: async.parallelLimit(putBlocks, 4, function (error, result) { 18: var data = { 19: name: fileName, 20: list: list 21: }; 22: $.post("/Home/Commit", data, function (result) { 23: if (!result.success) { 24: alert(result.error); 25: } 26: else { 27: alert("done!"); 28: } 29: }); 30: }); 31: } 32: });   Summary In this post we discussed how to upload files in chunks to the backend service and then upload them into Windows Azure Blob Storage in blocks. We focused on the frontend side and leverage three new feature introduced in HTML 5 which are - File.slice: Read part of the file by specifying the start and end byte index. - Blob: File-like interface which contains the part of the file content. - FormData: Temporary form element that we can pass the chunk alone with some metadata to the backend service. Then we discussed the performance consideration of chunk uploading. Sequence upload cannot provide maximized upload speed, but the unlimited parallel upload might crash the browser and server if too many chunks. So we finally came up with the solution to upload chunks in parallel with the concurrency limitation. We also demonstrated how to utilize “async.js” JavaScript library to help us control the asynchronize call and the parallel limitation.   Regarding the chunk size and the parallel limitation value there is no “best” value. You need to test vary composition and find out the best one for your particular scenario. It depends on the local bandwidth, client machine cores and the server side (Windows Azure Cloud Service Virtual Machine) cores, memory and bandwidth. Below is one of my performance test result. The client machine was Windows 8 IE 10 with 4 cores. I was using Microsoft Cooperation Network. The web site was hosted on Windows Azure China North data center (in Beijing) with one small web role (1.7GB 1 core CPU, 1.75GB memory with 100Mbps bandwidth). The test cases were - Chunk size: 512KB, 1MB, 2MB, 4MB. - Upload Mode: Sequence, parallel (unlimited), parallel with limit (4 threads, 8 threads). - Chunk Format: base64 string, binaries. - Target file: 100MB. - Each case was tested 3 times. Below is the test result chart. Some thoughts, but not guidance or best practice: - Parallel gets better performance than series. - No significant performance improvement between parallel 4 threads and 8 threads. - Transform with binaries provides better performance than base64. - In all cases, chunk size in 1MB - 2MB gets better performance.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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• Advanced TSQL Tuning: Why Internals Knowledge Matters

  - by Paul White

  There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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