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  • SharePoint Transferring pages to Production

    - by bmw0128
    I am building an internet site, using my local machine as the development box (I have MOSS 2007 installed). I have a custom master page, packaged in a WSP, so I may use STSADM on the production server to install it. I have made some pages via SPD (on my local machine) and put them in the "Pages" gallery. What is the recommended way to get these pages to production. Also, is the process the same when I make edits to current pages?

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  • GAE how to see production exceptions?

    - by bach
    Hi, I'm getting an error on the production env but not on the local one. Is there a way to see the exception that is probably being thrown from production? In tomcat - the user will be able the see the exception as the servlet returns its output

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  • App_GlobalResources not detected on production server

    - by Hugo Zapata
    I have a asp.net web application project, with some global resources. If i deploy to my dev machine, the resources are used correctly, however in the production server the text appears in the default language so the global resources are not being read. Any ideas? (i copied the App_GlobalResources directory to the production web dir root)

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  • Adding Client Validation To DataAnnotations DataType Attribute

    - by srkirkland
    The System.ComponentModel.DataAnnotations namespace contains a validation attribute called DataTypeAttribute, which takes an enum specifying what data type the given property conforms to.  Here are a few quick examples: public class DataTypeEntity { [DataType(DataType.Date)] public DateTime DateTime { get; set; }   [DataType(DataType.EmailAddress)] public string EmailAddress { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This attribute comes in handy when using ASP.NET MVC, because the type you specify will determine what “template” MVC uses.  Thus, for the DateTime property if you create a partial in Views/[loc]/EditorTemplates/Date.ascx (or cshtml for razor), that view will be used to render the property when using any of the Html.EditorFor() methods. One thing that the DataType() validation attribute does not do is any actual validation.  To see this, let’s take a look at the EmailAddress property above.  It turns out that regardless of the value you provide, the entity will be considered valid: //valid new DataTypeEntity {EmailAddress = "Foo"}; .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Hmmm.  Since DataType() doesn’t validate, that leaves us with two options: (1) Create our own attributes for each datatype to validate, like [Date], or (2) add validation into the DataType attribute directly.  In this post, I will show you how to hookup client-side validation to the existing DataType() attribute for a desired type.  From there adding server-side validation would be a breeze and even writing a custom validation attribute would be simple (more on that in future posts). Validation All The Way Down Our goal will be to leave our DataTypeEntity class (from above) untouched, requiring no reference to System.Web.Mvc.  Then we will make an ASP.NET MVC project that allows us to create a new DataTypeEntity and hookup automatic client-side date validation using the suggested “out-of-the-box” jquery.validate bits that are included with ASP.NET MVC 3.  For simplicity I’m going to focus on the only DateTime field, but the concept is generally the same for any other DataType. Building a DataTypeAttribute Adapter To start we will need to build a new validation adapter that we can register using ASP.NET MVC’s DataAnnotationsModelValidatorProvider.RegisterAdapter() method.  This method takes two Type parameters; The first is the attribute we are looking to validate with and the second is an adapter that should subclass System.Web.Mvc.ModelValidator. Since we are extending DataAnnotations we can use the subclass of ModelValidator called DataAnnotationsModelValidator<>.  This takes a generic argument of type DataAnnotations.ValidationAttribute, which lucky for us means the DataTypeAttribute will fit in nicely. So starting from there and implementing the required constructor, we get: public class DataTypeAttributeAdapter : DataAnnotationsModelValidator<DataTypeAttribute> { public DataTypeAttributeAdapter(ModelMetadata metadata, ControllerContext context, DataTypeAttribute attribute) : base(metadata, context, attribute) { } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now you have a full-fledged validation adapter, although it doesn’t do anything yet.  There are two methods you can override to add functionality, IEnumerable<ModelValidationResult> Validate(object container) and IEnumerable<ModelClientValidationRule> GetClientValidationRules().  Adding logic to the server-side Validate() method is pretty straightforward, and for this post I’m going to focus on GetClientValidationRules(). Adding a Client Validation Rule Adding client validation is now incredibly easy because jquery.validate is very powerful and already comes with a ton of validators (including date and regular expressions for our email example).  Teamed with the new unobtrusive validation javascript support we can make short work of our ModelClientValidationDateRule: public class ModelClientValidationDateRule : ModelClientValidationRule { public ModelClientValidationDateRule(string errorMessage) { ErrorMessage = errorMessage; ValidationType = "date"; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } If your validation has additional parameters you can the ValidationParameters IDictionary<string,object> to include them.  There is a little bit of conventions magic going on here, but the distilled version is that we are defining a “date” validation type, which will be included as html5 data-* attributes (specifically data-val-date).  Then jquery.validate.unobtrusive takes this attribute and basically passes it along to jquery.validate, which knows how to handle date validation. Finishing our DataTypeAttribute Adapter Now that we have a model client validation rule, we can return it in the GetClientValidationRules() method of our DataTypeAttributeAdapter created above.  Basically I want to say if DataType.Date was provided, then return the date rule with a given error message (using ValidationAttribute.FormatErrorMessage()).  The entire adapter is below: public class DataTypeAttributeAdapter : DataAnnotationsModelValidator<DataTypeAttribute> { public DataTypeAttributeAdapter(ModelMetadata metadata, ControllerContext context, DataTypeAttribute attribute) : base(metadata, context, attribute) { }   public override System.Collections.Generic.IEnumerable<ModelClientValidationRule> GetClientValidationRules() { if (Attribute.DataType == DataType.Date) { return new[] { new ModelClientValidationDateRule(Attribute.FormatErrorMessage(Metadata.GetDisplayName())) }; }   return base.GetClientValidationRules(); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Putting it all together Now that we have an adapter for the DataTypeAttribute, we just need to tell ASP.NET MVC to use it.  The easiest way to do this is to use the built in DataAnnotationsModelValidatorProvider by calling RegisterAdapter() in your global.asax startup method. DataAnnotationsModelValidatorProvider.RegisterAdapter(typeof(DataTypeAttribute), typeof(DataTypeAttributeAdapter)); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Show and Tell Let’s see this in action using a clean ASP.NET MVC 3 project.  First make sure to reference the jquery, jquery.vaidate and jquery.validate.unobtrusive scripts that you will need for client validation. Next, let’s make a model class (note we are using the same built-in DataType() attribute that comes with System.ComponentModel.DataAnnotations). public class DataTypeEntity { [DataType(DataType.Date, ErrorMessage = "Please enter a valid date (ex: 2/14/2011)")] public DateTime DateTime { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Then we make a create page with a strongly-typed DataTypeEntity model, the form section is shown below (notice we are just using EditorForModel): @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>Fields</legend>   @Html.EditorForModel()   <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The final step is to register the adapter in our global.asax file: DataAnnotationsModelValidatorProvider.RegisterAdapter(typeof(DataTypeAttribute), typeof(DataTypeAttributeAdapter)); Now we are ready to run the page: Looking at the datetime field’s html, we see that our adapter added some data-* validation attributes: <input type="text" value="1/1/0001" name="DateTime" id="DateTime" data-val-required="The DateTime field is required." data-val-date="Please enter a valid date (ex: 2/14/2011)" data-val="true" class="text-box single-line valid"> .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here data-val-required was added automatically because DateTime is non-nullable, and data-val-date was added by our validation adapter.  Now if we try to add an invalid date: Our custom error message is displayed via client-side validation as soon as we tab out of the box.  If we didn’t include a custom validation message, the default DataTypeAttribute “The field {0} is invalid” would have been shown (of course we can change the default as well).  Note we did not specify server-side validation, but in this case we don’t have to because an invalid date will cause a server-side error during model binding. Conclusion I really like how easy it is to register new data annotations model validators, whether they are your own or, as in this post, supplements to existing validation attributes.  I’m still debating about whether adding the validation directly in the DataType attribute is the correct place to put it versus creating a dedicated “Date” validation attribute, but it’s nice to know either option is available and, as we’ve seen, simple to implement. I’m also working through the nascent stages of an open source project that will create validation attribute extensions to the existing data annotations providers using similar techniques as seen above (examples: Email, Url, EqualTo, Min, Max, CreditCard, etc).  Keep an eye on this blog and subscribe to my twitter feed (@srkirkland) if you are interested for announcements.

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  • Is anyone using KVM in production?

    - by Andy Shellam
    I've been trying to set up a pair of servers utilising KVM on Ubuntu 9.10 to host 8 virtual machines between them and ended up with various issues from the VMs freezing, to not powering on. I had one virtual server set up and running and was setting up a second, when any operation involving OpenSSL would cause the VM to lock up in a weird way - all network traffic would cease, it wouldn't process logins on the console, but it wasn't taking any CPU time off the host. The first virtual server was identical and worked perfectly. Another VM I tried to setup had installed Ubuntu fine then refused to reboot, throwing kernel exceptions to do with XFS. I've now installed Citrix XenServer 5.5 on both hosts, and am now setting up my third VM with absolutely no issues. I also had the same experience when I tried VMware, but I preferred Xen as it appears to give more features on the free license. My question is am I just unlucky with KVM, or is KVM as unstable as it appears? Are you using, or planning on using, KVM in production, and how successful have you been?

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  • ubuntu - Best way of repartitioning a (running) production server

    - by egarcia
    I've got an (externally hosted) production server running Ubuntu LTS. It serves webpages (rails) and has an svn repository accesible through Apache, and a PostgreSQL db. I've got ssh access to the server and root privileges. Most of the "interesting" stuff is located in /var/ : svn repositories are inside /var/svn, web pages under /var/www, etc. Yesterday I was curious about how much disk space had it left, so I did the following: $ df -h Filesystem Size Used Avail Use% Mounted on /dev/md1 950M 402M 500M 45% / varrun 990M 64K 990M 1% /var/run varlock 990M 0 990M 0% /var/lock udev 990M 76K 989M 1% /dev devshm 990M 0 990M 0% /dev/shm /dev/md5 4.7G 668M 4.1G 15% /usr /dev/md6 4.7G 1.4G 3.4G 29% /var /dev/md7 221G 28M 221G 1% /home none 990M 4.0K 990M 1% /tmp My 'var' partition, which holds most of the interesting part, is only 4.7G big. The /home/ partition, on the other hand, is 221G, but it is mostly unused. I should have checked the disk layout before starting installing stuff. Ideally I would need /var/ and /home/ to be "switched" - /home/ should be the one with 4.7G, and /var/ the one with 221G. Is there a way to solve this without having to reinstall the whole thing?

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  • Real benefits of tcp TIME-WAIT and implications in production environment

    - by user64204
    SOME THEORY I've been doing some reading on tcp TIME-WAIT (here and there) and what I read is that it's a value set to 2 x MSL (maximum segment life) which keeps a connection in the "connection table" for a while to guarantee that, "before your allowed to create a connection with the same tuple, all the packets belonging to previous incarnations of that tuple will be dead". Since segments received (apart from SYN under specific circumstances) while a connection is either in TIME-WAIT or no longer existing would be discarded, why not close the connection right away? Q1: Is it because there is less processing involved in dealing with segments from old connections and less processing to create a new connection on the same tuple when in TIME-WAIT (i.e. are there performance benefits)? If the above explanation doesn't stand, the only reason I see the TIME-WAIT being useful would be if a client sends a SYN for a connection before it sends remaining segments for an old connection on the same tuple in which case the receiver would re-open the connection but then get bad segments and and would have to terminate it. Q2: Is this analysis correct? Q3: Are there other benefits to using TIME-WAIT? SOME PRACTICE I've been looking at the munin graphs on a production server that I administrate. Here is one: As you can see there are more connections in TIME-WAIT than ESTABLISHED, around twice as many most of the time, on some occasions four times as many. Q4: Does this have an impact on performance? Q5: If so, is it wise/recommended to reduce the TIME-WAIT value (and what to)? Q6: Is this ratio of TIME-WAIT / ESTABLISHED connections normal? Could this be related to malicious connection attempts?

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  • Production monitoring for EC2 instances

    - by Janine
    I'm setting up my first production instance on EC2 and want to make sure I have all necessary monitoring in place. There are three different types of things I want to monitor: Is the instance running? EC2 instances can be terminated without warning if the underlying hardware fails, and as far as I know they aren't automatically restarted. So if not, start it back up. Is UNIX running properly? This is the usual stuff about CPU load, disk space, etc. Is the website responding? If not, restart it. I initially set up Nagios on a physical server outside the cloud, but it is really only helpful for item 2. It can tell me if the instance is gone or if the website is not responding, but as far as I can tell it can't execute any commands to fix the situation. My Googling on this subject has yielded a plethora of options - Cacti, Monit, God, Ganglia, and probably more I'm forgetting now. I don't have time to research them all. I am aware of Amazon's Cloudwatch but it doesn't seem to do anything that my Nagios installation doesn't already do. If you already have something like this in place, can you please share what has worked well for you?

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • Fun with Aggregates

    - by Paul White
    There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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  • php frameworks - build your own vs pre-made

    - by christopher-mccann
    Hi, I am building an application currently in PHP and I am trying to decide on whether to use a pre-existing framework like codeigniter or build my own framework. The application needs to be really scalable and I want to be completely in control of it which makes me think I should build my own but at the same time I dont want to reinvent the wheel if I dont have to. Any advice greatly appreciated. Thanks

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  • Pre-drawing a UIView

    - by LK
    There is information that is only available after drawRect that I need to access when loading a UIView. Is there any way to do a "pre-draw" or offscreen in order to get this information earlier?

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  • How to pre-populate the Facebook status message through an URL similar to pre-populating a tweet?

    - by Crashalot
    This question has been asked before on SO, but most of those questions were asked a long time ago. Essentially, we want a simple way to pre-populate the Facebook status message through the URL much like you can with Twitter. We're aware of the Facebook APIs, but are wondering if there is a more lightweight approach. We don't need programmatically to post a message, but just provide some default text that the user can edit before sharing.

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  • post increment vs pre increment [closed]

    - by mousey
    Possible Duplicate: Difference between i++ and ++i in a loop? Hi, Can some one please help me when to use pre increment or post increment in a for loop. I am getting the same results for both the loops! I also would like to know when and where to choose one between the two. Thanks in advance.

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  • android - pre-allocate space for a file before downloading it

    - by android developer
    how can i create a pre-allocated file on android? something like a sparse file ? i need to make an applicaton that will download a large file , and i want to avoid having an out-of-space error while the download is commencing . my solution needs to support resuming and writing to both internal and external storage. i've tried what is written here: Create file with given size in Java but none of the solutions there didn't work for some reason.

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  • No pre-built ActionBar for Android pre-3.0?

    - by Ollie C
    I note the release a few days ago of the static library bringing fragments to Android versions prior to 3.0, but does this library include the ActionBar? I suspect not. I assume that for an app that will work on pre-3.0 versions, that it needs a hand-built ActionBar implementation for versions up to 2.3 and then to use the OS default ActionBar in v3.0? for some reason I assumed the library had ActionBar in it, but as I dig further I'm not finding any evidence of its presence.

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  • Git pre-receive hook to lunch PHP CodeSniffer

    - by Ralphz
    Hey. I'd like to check code committed to my remote git repository with PHP CodeSniffer and reject it if there are any problems code standards. Does anyone have an example how to use it on git remote repository or maybe example how to use it with pre-receive hook? Thanks.

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  • pdf <pre> equivalent

    - by ddowns
    I'm trying to put some code examples in a pdf, but copying them out messes up the formatting and rearranges the lines, there's a lot of manual cleanup needed after pasting. Is there a equivalent to html's pre for PDFs? For "this" block of text respect line breaks, spacing, and copy as plain text like its shown. The closest thing I can see is adding note annotations next to every code example.

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