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  • How does the dataset determine the return type of a scalar query?

    - by Tobias Funke
    I am attempting to add a scalar query to a dataset. The query is pretty straight forward, it's just adding up some decimal values in a few columns and returning them. I am 100% confident that only one row and one column is returned, and that it is of decimal type (SQL money type). The problem is that for some reason, the generated method (in the .designer.cs code file) is returning a value of type object, when it should be decimal. What's strange is that there's another scalar query that has the exact same SQL but is returning decimal like it should. How does the dataset designer determine the data type, and how can I tell it to return decimal?

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  • How does the dataset designer determine the return type of a scalar query?

    - by Tobias Funke
    I am attempting to add a scalar query to a dataset. The query is pretty straight forward, it's just adding up some decimal values in a few columns and returning them. I am 100% confident that only one row and one column is returned, and that it is of decimal type (SQL money type). The problem is that for some reason, the generated method (in the .designer.cs code file) is returning a value of type object, when it should be decimal. What's strange is that there's another scalar query that has the exact same SQL but is returning decimal like it should. How does the dataset designer determine the data type, and how can I tell it to return decimal?

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  • Is there a way to test if a scalar has been stringified or not?

    - by Yobert
    I am writing a thing to output something similar to JSON, from a perl structure. I want the quoting to behave like this: "string" outputs "string" "05" outputs "05" "5" outputs "5" 5 outputs 5 05 outputs 5, or 05 would be acceptable JSON::XS handles this by testing if a scalar has been "stringified" or not, which I think is very cool. But I can't find a way to do this test myself without writing XS, which I'd rather avoid. Is this possible? I can't find this anywhere on CPAN without finding vast pedantry about Scalar::Util::looks_like_number, etc which completely isn't what I want. The only stopgap I can find is Devel::Peek, which feels evil. And also, just like JSON::XS, I'm fine with this secenario: my $a = 5; print $a."\n"; # now $a outputs "5" instead of 5)

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  • Perl : In splice() type of arg1 must be array, cannot be scalar dereference. How to fix?

    - by Michael Mao
    I am trying to comprehend the reference/dereference system in Perl. What I am trying to do is to remove an element by using reference: my $ref= \@{$collection{$_[0]}}; # delete($$ref[$i]); # delete works, I've tested that already splice($$ref, $i, 1); # this wouldn't do. I first tried the delete() subroutine, it works; however, it doesn't shift the index after the removed elements forward by 1, so I cannot continue working on other stuff. I then Googled and found the splice() subroutine which does delete and shift in one go. But the error feedback tells me that "Type of arg 1 to splice must be array (not scalar dereference)..." I then tried something like this: splice(@{$$ref}, $i, 1); That resulted in another error like this: "Not a SCALAR reference at...(pointing at that line)" So I am a bit puzzled, how could I handle this issue? I prefer not using any CPAN or additional library for the solution, if possible.

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  • What to set the scalar type to contain a byte []. Entity in MVC2

    - by Brad8118
    I'm trying out the EF 4.0 and using the Model first approach. I'd like to store images into the database and I'm not sure of the best type for the scalar in the entity. I currently have it(the image scalar type) setup as a binary. From what I have been reading the best way to store the image in the db is a byte[]. So I'm assuming that binary is the way to go. If there is a better way I'd switch. In my controller I have: //file from client to store in the db HttpPostedFileBase file = Request.Files[inputTagName]; if (file.ContentLength > 0) { keyToAdd.Image = new byte[file.ContentLength]; file.InputStream.Write(keyToAdd.Image, 0, file.ContentLength); } This builds fine but when I run it I get an exception writing the stream to keyToAdd.Image. The exception is something like: Method does not exist. Any ideas? Note that when using a EF 4.0 model first approach I only have int16, int32, double, string, decimal, binary, byte, DateTime, Double, Single, and SByte as available types. Thanks

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  • When is a SQL function not a function?

    - by Rob Farley
    Should SQL Server even have functions? (Oh yeah – this is a T-SQL Tuesday post, hosted this month by Brad Schulz) Functions serve an important part of programming, in almost any language. A function is a piece of code that is designed to return something, as opposed to a piece of code which isn’t designed to return anything (which is known as a procedure). SQL Server is no different. You can call stored procedures, even from within other stored procedures, and you can call functions and use these in other queries. Stored procedures might query something, and therefore ‘return data’, but a function in SQL is considered to have the type of the thing returned, and can be used accordingly in queries. Consider the internal GETDATE() function. SELECT GETDATE(), SomeDatetimeColumn FROM dbo.SomeTable; There’s no logical difference between the field that is being returned by the function and the field that’s being returned by the table column. Both are the datetime field – if you didn’t have inside knowledge, you wouldn’t necessarily be able to tell which was which. And so as developers, we find ourselves wanting to create functions that return all kinds of things – functions which look up values based on codes, functions which do string manipulation, and so on. But it’s rubbish. Ok, it’s not all rubbish, but it mostly is. And this isn’t even considering the SARGability impact. It’s far more significant than that. (When I say the SARGability aspect, I mean “because you’re unlikely to have an index on the result of some function that’s applied to a column, so try to invert the function and query the column in an unchanged manner”) I’m going to consider the three main types of user-defined functions in SQL Server: Scalar Inline Table-Valued Multi-statement Table-Valued I could also look at user-defined CLR functions, including aggregate functions, but not today. I figure that most people don’t tend to get around to doing CLR functions, and I’m going to focus on the T-SQL-based user-defined functions. Most people split these types of function up into two types. So do I. Except that most people pick them based on ‘scalar or table-valued’. I’d rather go with ‘inline or not’. If it’s not inline, it’s rubbish. It really is. Let’s start by considering the two kinds of table-valued function, and compare them. These functions are going to return the sales for a particular salesperson in a particular year, from the AdventureWorks database. CREATE FUNCTION dbo.FetchSales_inline(@salespersonid int, @orderyear int) RETURNS TABLE AS  RETURN (     SELECT e.LoginID as EmployeeLogin, o.OrderDate, o.SalesOrderID     FROM Sales.SalesOrderHeader AS o     LEFT JOIN HumanResources.Employee AS e     ON e.EmployeeID = o.SalesPersonID     WHERE o.SalesPersonID = @salespersonid     AND o.OrderDate >= DATEADD(year,@orderyear-2000,'20000101')     AND o.OrderDate < DATEADD(year,@orderyear-2000+1,'20000101') ) ; GO CREATE FUNCTION dbo.FetchSales_multi(@salespersonid int, @orderyear int) RETURNS @results TABLE (     EmployeeLogin nvarchar(512),     OrderDate datetime,     SalesOrderID int     ) AS BEGIN     INSERT @results (EmployeeLogin, OrderDate, SalesOrderID)     SELECT e.LoginID, o.OrderDate, o.SalesOrderID     FROM Sales.SalesOrderHeader AS o     LEFT JOIN HumanResources.Employee AS e     ON e.EmployeeID = o.SalesPersonID     WHERE o.SalesPersonID = @salespersonid     AND o.OrderDate >= DATEADD(year,@orderyear-2000,'20000101')     AND o.OrderDate < DATEADD(year,@orderyear-2000+1,'20000101')     ;     RETURN END ; GO You’ll notice that I’m being nice and responsible with the use of the DATEADD function, so that I have SARGability on the OrderDate filter. Regular readers will be hoping I’ll show what’s going on in the execution plans here. Here I’ve run two SELECT * queries with the “Show Actual Execution Plan” option turned on. Notice that the ‘Query cost’ of the multi-statement version is just 2% of the ‘Batch cost’. But also notice there’s trickery going on. And it’s nothing to do with that extra index that I have on the OrderDate column. Trickery. Look at it – clearly, the first plan is showing us what’s going on inside the function, but the second one isn’t. The second one is blindly running the function, and then scanning the results. There’s a Sequence operator which is calling the TVF operator, and then calling a Table Scan to get the results of that function for the SELECT operator. But surely it still has to do all the work that the first one is doing... To see what’s actually going on, let’s look at the Estimated plan. Now, we see the same plans (almost) that we saw in the Actuals, but we have an extra one – the one that was used for the TVF. Here’s where we see the inner workings of it. You’ll probably recognise the right-hand side of the TVF’s plan as looking very similar to the first plan – but it’s now being called by a stack of other operators, including an INSERT statement to be able to populate the table variable that the multi-statement TVF requires. And the cost of the TVF is 57% of the batch! But it gets worse. Let’s consider what happens if we don’t need all the columns. We’ll leave out the EmployeeLogin column. Here, we see that the inline function call has been simplified down. It doesn’t need the Employee table. The join is redundant and has been eliminated from the plan, making it even cheaper. But the multi-statement plan runs the whole thing as before, only removing the extra column when the Table Scan is performed. A multi-statement function is a lot more powerful than an inline one. An inline function can only be the result of a single sub-query. It’s essentially the same as a parameterised view, because views demonstrate this same behaviour of extracting the definition of the view and using it in the outer query. A multi-statement function is clearly more powerful because it can contain far more complex logic. But a multi-statement function isn’t really a function at all. It’s a stored procedure. It’s wrapped up like a function, but behaves like a stored procedure. It would be completely unreasonable to expect that a stored procedure could be simplified down to recognise that not all the columns might be needed, but yet this is part of the pain associated with this procedural function situation. The biggest clue that a multi-statement function is more like a stored procedure than a function is the “BEGIN” and “END” statements that surround the code. If you try to create a multi-statement function without these statements, you’ll get an error – they are very much required. When I used to present on this kind of thing, I even used to call it “The Dangers of BEGIN and END”, and yes, I’ve written about this type of thing before in a similarly-named post over at my old blog. Now how about scalar functions... Suppose we wanted a scalar function to return the count of these. CREATE FUNCTION dbo.FetchSales_scalar(@salespersonid int, @orderyear int) RETURNS int AS BEGIN     RETURN (         SELECT COUNT(*)         FROM Sales.SalesOrderHeader AS o         LEFT JOIN HumanResources.Employee AS e         ON e.EmployeeID = o.SalesPersonID         WHERE o.SalesPersonID = @salespersonid         AND o.OrderDate >= DATEADD(year,@orderyear-2000,'20000101')         AND o.OrderDate < DATEADD(year,@orderyear-2000+1,'20000101')     ); END ; GO Notice the evil words? They’re required. Try to remove them, you just get an error. That’s right – any scalar function is procedural, despite the fact that you wrap up a sub-query inside that RETURN statement. It’s as ugly as anything. Hopefully this will change in future versions. Let’s have a look at how this is reflected in an execution plan. Here’s a query, its Actual plan, and its Estimated plan: SELECT e.LoginID, y.year, dbo.FetchSales_scalar(p.SalesPersonID, y.year) AS NumSales FROM (VALUES (2001),(2002),(2003),(2004)) AS y (year) CROSS JOIN Sales.SalesPerson AS p LEFT JOIN HumanResources.Employee AS e ON e.EmployeeID = p.SalesPersonID; We see here that the cost of the scalar function is about twice that of the outer query. Nicely, the query optimizer has worked out that it doesn’t need the Employee table, but that’s a bit of a red herring here. There’s actually something way more significant going on. If I look at the properties of that UDF operator, it tells me that the Estimated Subtree Cost is 0.337999. If I just run the query SELECT dbo.FetchSales_scalar(281,2003); we see that the UDF cost is still unchanged. You see, this 0.0337999 is the cost of running the scalar function ONCE. But when we ran that query with the CROSS JOIN in it, we returned quite a few rows. 68 in fact. Could’ve been a lot more, if we’d had more salespeople or more years. And so we come to the biggest problem. This procedure (I don’t want to call it a function) is getting called 68 times – each one between twice as expensive as the outer query. And because it’s calling it in a separate context, there is even more overhead that I haven’t considered here. The cheek of it, to say that the Compute Scalar operator here costs 0%! I know a number of IT projects that could’ve used that kind of costing method, but that’s another story that I’m not going to go into here. Let’s look at a better way. Suppose our scalar function had been implemented as an inline one. Then it could have been expanded out like a sub-query. It could’ve run something like this: SELECT e.LoginID, y.year, (SELECT COUNT(*)     FROM Sales.SalesOrderHeader AS o     LEFT JOIN HumanResources.Employee AS e     ON e.EmployeeID = o.SalesPersonID     WHERE o.SalesPersonID = p.SalesPersonID     AND o.OrderDate >= DATEADD(year,y.year-2000,'20000101')     AND o.OrderDate < DATEADD(year,y.year-2000+1,'20000101')     ) AS NumSales FROM (VALUES (2001),(2002),(2003),(2004)) AS y (year) CROSS JOIN Sales.SalesPerson AS p LEFT JOIN HumanResources.Employee AS e ON e.EmployeeID = p.SalesPersonID; Don’t worry too much about the Scan of the SalesOrderHeader underneath a Nested Loop. If you remember from plenty of other posts on the matter, execution plans don’t push the data through. That Scan only runs once. The Index Spool sucks the data out of it and populates a structure that is used to feed the Stream Aggregate. The Index Spool operator gets called 68 times, but the Scan only once (the Number of Executions property demonstrates this). Here, the Query Optimizer has a full picture of what’s being asked, and can make the appropriate decision about how it accesses the data. It can simplify it down properly. To get this kind of behaviour from a function, we need it to be inline. But without inline scalar functions, we need to make our function be table-valued. Luckily, that’s ok. CREATE FUNCTION dbo.FetchSales_inline2(@salespersonid int, @orderyear int) RETURNS table AS RETURN (SELECT COUNT(*) as NumSales     FROM Sales.SalesOrderHeader AS o     LEFT JOIN HumanResources.Employee AS e     ON e.EmployeeID = o.SalesPersonID     WHERE o.SalesPersonID = @salespersonid     AND o.OrderDate >= DATEADD(year,@orderyear-2000,'20000101')     AND o.OrderDate < DATEADD(year,@orderyear-2000+1,'20000101') ); GO But we can’t use this as a scalar. Instead, we need to use it with the APPLY operator. SELECT e.LoginID, y.year, n.NumSales FROM (VALUES (2001),(2002),(2003),(2004)) AS y (year) CROSS JOIN Sales.SalesPerson AS p LEFT JOIN HumanResources.Employee AS e ON e.EmployeeID = p.SalesPersonID OUTER APPLY dbo.FetchSales_inline2(p.SalesPersonID, y.year) AS n; And now, we get the plan that we want for this query. All we’ve done is tell the function that it’s returning a table instead of a single value, and removed the BEGIN and END statements. We’ve had to name the column being returned, but what we’ve gained is an actual inline simplifiable function. And if we wanted it to return multiple columns, it could do that too. I really consider this function to be superior to the scalar function in every way. It does need to be handled differently in the outer query, but in many ways it’s a more elegant method there too. The function calls can be put amongst the FROM clause, where they can then be used in the WHERE or GROUP BY clauses without fear of calling the function multiple times (another horrible side effect of functions). So please. If you see BEGIN and END in a function, remember it’s not really a function, it’s a procedure. And then fix it. @rob_farley

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  • SQL Server 2008 table variable error: Must declare the scalar variable "@RESULT".

    - by Trindaz
    I'm using table values for the first time as a parameter to a function in SQL Server 2008. The code below produces this error: Must declare the scalar variable "@RESULT". Why?! I'm declaring it on the first line of the function! ALTER FUNCTION f_Get_Total_Amount_Due( @CUSTOMER_LIST [tpCSFM_CUSTOMER_SET_FOR_MONEY] READONLY ) RETURNS [tpCSFM_CUSTOMER_SET_FOR_MONEY] AS BEGIN --Prepare the return value, start with initial customer list DECLARE @RESULT AS [tpCSFM_CUSTOMER_SET_FOR_MONEY] INSERT INTO @RESULT SELECT * FROM @CUSTOMER_LIST --Todo: populate with real values UPDATE @RESULT SET tpCSAM_MONEY_VALUE = 100 --return total amounts as currency RETURN @RESULT END

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  • Cannot call scalar-valued CLR UDF from select ... from table statement

    - by Henrik B
    I have created a scalar-valued CLR UDF (user defined function). It takes a timezone id and a datetime and returns the datetime converted to that timezone. I can call it from a simple select without problems: "select dbo.udfConvert('Romance Standard Time', @datetime)" (@datetime is of course a valid datetime variable) But if I call it passing in a datetime from a table it fails: "select dbo.udfConvert('Romance Standard Time', StartTime) from sometable" (column StartTime is of course a column of type datetime) The error message is: "Cannot find either column "dbo" or the user-defined function or aggregate "dbo.udfConvert", or the name is ambiguous." This message is really for beginners that has misspelled something, but as it works in one case and not in the other, I don't think I have done any misspellings. Any ideas?

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  • Why does Perl complain "Can't modify constant item in scalar assignment"?

    - by joe
    I have this Perl subroutine that is causing a problem: sub new { my $class = shift; my $ldap_obj = Net::LDAP->new( 'test.company.com' ) or die "$@"; my $self = { _ldap = $ldap_obj, _dn ='dc=users,dc=ldap,dc=company,dc=com', _dn_login = 'dc=login,dc=ldap,dc=company,dc=com', _description ='company', }; # Print all the values just for clarification. bless $self, $class; return $self; } what is wrong on this code : i got this error Can't modify constant item in scalar assignment at Core.pm line 12, near "$ldap_obj,"

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  • Cleaner way to store to replace a scalar hash value with an array ref?

    - by user275455
    I am building a hash where the keys, associated with scalars, are not necessarily unique. I want the desired behavior to be that if the key is unique, the value is the scalar. If the key is not unique, I want the value to be an array reference of the scalars associated witht the key. Since the hash is built up iteratively, I don't know if the key is unique ahead of time. Right now, I am doing something like this: if(!defined($hash{$key})){ $hash{$key} = $val; } elseif(ref($hash{$key}) ne 'ARRAY'){ my @a; push(@a, $hash{$key}); push(@, $val); $hash{$key} = \@a; } else{ push(@{$hash{$key}}, $val); } Is there a simpler way to do this?

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  • T-SQL User-Defined Functions: the good, the bad, and the ugly (part 2)

    - by Hugo Kornelis
    In a previous blog post , I demonstrated just how much you can hurt your performance by encapsulating expressions and computations in a user-defined function (UDF). I focused on scalar functions that didn’t include any data access. In this post, I will complete the discussion on scalar UDFs by covering the effect of data access in a scalar UDF. Note that, like the previous post, this all applies to T-SQL user-defined functions only. SQL Server also supports CLR user-defined functions (written in...(read more)

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  • T-SQL User-Defined Functions: the good, the bad, and the ugly (part 3)

    - by Hugo Kornelis
    I showed why T-SQL scalar user-defined functions are bad for performance in two previous posts. In this post, I will show that CLR scalar user-defined functions are bad as well (though not always quite as bad as T-SQL scalar user-defined functions). I will admit that I had not really planned to cover CLR in this series. But shortly after publishing the first part , I received an email from Adam Machanic , which basically said that I should make clear that the information in that post does not apply...(read more)

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  • T-SQL User-Defined Functions: the good, the bad, and the ugly (part 2)

    - by Hugo Kornelis
    In a previous blog post , I demonstrated just how much you can hurt your performance by encapsulating expressions and computations in a user-defined function (UDF). I focused on scalar functions that didn’t include any data access. In this post, I will complete the discussion on scalar UDFs by covering the effect of data access in a scalar UDF. Note that, like the previous post, this all applies to T-SQL user-defined functions only. SQL Server also supports CLR user-defined functions (written in...(read more)

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  • T-SQL User-Defined Functions: the good, the bad, and the ugly (part 3)

    - by Hugo Kornelis
    I showed why T-SQL scalar user-defined functions are bad for performance in two previous posts. In this post, I will show that CLR scalar user-defined functions are bad as well (though not always quite as bad as T-SQL scalar user-defined functions). I will admit that I had not really planned to cover CLR in this series. But shortly after publishing the first part , I received an email from Adam Machanic , which basically said that I should make clear that the information in that post does not apply...(read more)

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  • How to call stored procedure by hibernate?

    - by user367097
    Hi I have an oracle stored procedure GET_VENDOR_STATUS_COUNT(DOCUMENT_ID IN NUMBER , NOT_INVITED OUT NUMBER,INVITE_WITHDRAWN OUT NUMBER,... rest all parameters are OUT parameters. In hbm file I have written - <sql-query name="getVendorStatus" callable="true"> <return-scalar column="NOT_INVITED" type="string"/> <return-scalar column="INVITE_WITHDRAWN" type="string"/> <return-scalar column="INVITED" type="string"/> <return-scalar column="DISQUALIFIED" type="string"/> <return-scalar column="RESPONSE_AWAITED" type="string"/> <return-scalar column="RESPONSE_IN_PROGRESS" type="string"/> <return-scalar column="RESPONSE_RECEIVED" type="string"/> { call GET_VENDOR_STATUS_COUNT(:DOCUMENT_ID , :NOT_INVITED ,:INVITE_WITHDRAWN ,:INVITED ,:DISQUALIFIED ,:RESPONSE_AWAITED ,:RESPONSE_IN_PROGRESS ,:RESPONSE_RECEIVED ) } </sql-query> In java I have written - session.getNamedQuery("getVendorStatus").setParameter("DOCUMENT_ID", "DOCUMENT_ID").setParameter("NOT_INVITED", "NOT_INVITED") ... continue till all the parametes . I am getting the sql exception 18:29:33,056 WARN [JDBCExceptionReporter] SQL Error: 1006, SQLState: 72000 18:29:33,056 ERROR [JDBCExceptionReporter] ORA-01006: bind variable does not exist Please let me know what is the exact process of calling a stored procedure from hibernate. I do not want to use JDBC callable statement.

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  • How to update non-scalar entity properties in EF 4.0?

    - by Mike
    At first I was using this as an extension method to update my detached entities... Public Sub AttachUpdated(ByVal obj As ObjectContext, ByVal objectDetached As EntityObject) If objectDetached.EntityState = EntityState.Detached Then Dim original As Object = Nothing If obj.TryGetObjectByKey(objectDetached.EntityKey, original) Then obj.ApplyCurrentValues(objectDetached.EntityKey.EntitySetName, objectDetached) Else Throw New ObjectNotFoundException() End If End If End Sub Everything has been working great until I had to update non-scalar properties. Correct me if I am wrong but that is because "ApplyCurrentValues" only supports scalars. To get around this I was just saving the FK_ID field instead of the entity object relation. Now I am faced with a many to many relationship so its not that simple. I would like to do something like this... Dim Resource = RelatedResource.GetByID(item.Value) Condition.RelatedResources.Add(Resource) But when I call SaveChanges the added Resources aren't saved. I started to play around with self-tracking entities (not sure if they will help solve my prob) but it seems they cannot be serialized to ViewState and this is a requirement for me. I guess one solution would be to add the xRef table as an entity and add the fks myself but I would rather it just work how I expect it too. I am open to any suggestions on how to either save my many to many relationships or serialize self-tracking entities (if self-trackingwould even solve my problem). Thanks!

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  • How do I check to see if a scalar has a compiled regex in it with Perl?

    - by Robert P
    Let's say I have a subroutine/method that a user can call to test some data that (as an example) might look like this: sub test_output { my ($self, $test) = @_; my $output = $self->long_process_to_get_data(); if ($output =~ /\Q$test/) { $self->assert_something(); } else { $self->do_something_else(); } } Normally, $test is a string, which we're looking for anywhere in the output. This was an interface put together to make calling it very easy. However, we've found that sometimes, a straight string is problematic - for example, a large, possibly varying number of spaces...a pattern, if you will. Thus, I'd like to let them pass in a regex as an option. I could just do: $output =~ $test if I could assume that it's always a regex, but ah, but the backwards compatibility! If they pass in a string, it still needs to test it like a raw string. So in that case, I'll need to test to see if $test is a regex. Is there any good facility for detecting whether or not a scalar has a compiled regex in it?

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  • How do I check if a scalar has a compiled regex in it with Perl?

    - by Robert P
    Let's say I have a subroutine/method that a user can call to test some data that (as an example) might look like this: sub test_output { my ($self, $test) = @_; my $output = $self->long_process_to_get_data(); if ($output =~ /\Q$test/) { $self->assert_something(); } else { $self->do_something_else(); } } Normally, $test is a string, which we're looking for anywhere in the output. This was an interface put together to make calling it very easy. However, we've found that sometimes, a straight string is problematic - for example, a large, possibly varying number of spaces...a pattern, if you will. Thus, I'd like to let them pass in a regex as an option. I could just do: $output =~ $test if I could assume that it's always a regex, but ah, but the backwards compatibility! If they pass in a string, it still needs to test it like a raw string. So in that case, I'll need to test to see if $test is a regex. Is there any good facility for detecting whether or not a scalar has a compiled regex in it?

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  • Why can't I assign a scalar value to a class using shorthand, but instead declare it first, then set

    - by ~delan-azabani
    I am writing a UTF-8 library for C++ as an exercise as this is my first real-world C++ code. So far, I've implemented concatenation, character indexing, parsing and encoding UTF-8 in a class called "ustring". It looks like it's working, but two (seemingly equivalent) ways of declaring a new ustring behave differently. The first way: ustring a; a = "test"; works, and the overloaded "=" operator parses the string into the class (which stores the Unicode strings as an dynamically allocated int pointer). However, the following does not work: ustring a = "test"; because I get the following error: test.cpp:4: error: conversion from ‘const char [5]’ to non-scalar type ‘ustring’ requested Is there a way to workaround this error? It probably is a problem with my code, though. The following is what I've written so far for the library: #include <cstdlib> #include <cstring> class ustring { int * values; long len; public: long length() { return len; } ustring * operator=(ustring input) { len = input.len; values = (int *) malloc(sizeof(int) * len); for (long i = 0; i < len; i++) values[i] = input.values[i]; return this; } ustring * operator=(char input[]) { len = sizeof(input); values = (int *) malloc(0); long s = 0; // s = number of parsed chars int a, b, c, d, contNeed = 0, cont = 0; for (long i = 0; i < sizeof(input); i++) if (input[i] < 0x80) { // ASCII, direct copy (00-7f) values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = input[i]; } else if (input[i] < 0xc0) { // this is a continuation (80-bf) if (cont == contNeed) { // no need for continuation, use U+fffd values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = 0xfffd; } cont = cont + 1; values[s - 1] = values[s - 1] | ((input[i] & 0x3f) << ((contNeed - cont) * 6)); if (cont == contNeed) cont = contNeed = 0; } else if (input[i] < 0xc2) { // invalid byte, use U+fffd (c0-c1) values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = 0xfffd; } else if (input[i] < 0xe0) { // start of 2-byte sequence (c2-df) contNeed = 1; values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = (input[i] & 0x1f) << 6; } else if (input[i] < 0xf0) { // start of 3-byte sequence (e0-ef) contNeed = 2; values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = (input[i] & 0x0f) << 12; } else if (input[i] < 0xf5) { // start of 4-byte sequence (f0-f4) contNeed = 3; values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = (input[i] & 0x07) << 18; } else { // restricted or invalid (f5-ff) values = (int *) realloc(values, sizeof(int) * ++s); values[s - 1] = 0xfffd; } return this; } ustring operator+(ustring input) { ustring result; result.len = len + input.len; result.values = (int *) malloc(sizeof(int) * result.len); for (long i = 0; i < len; i++) result.values[i] = values[i]; for (long i = 0; i < input.len; i++) result.values[i + len] = input.values[i]; return result; } ustring operator[](long index) { ustring result; result.len = 1; result.values = (int *) malloc(sizeof(int)); result.values[0] = values[index]; return result; } char * encode() { char * r = (char *) malloc(0); long s = 0; for (long i = 0; i < len; i++) { if (values[i] < 0x80) r = (char *) realloc(r, s + 1), r[s + 0] = char(values[i]), s += 1; else if (values[i] < 0x800) r = (char *) realloc(r, s + 2), r[s + 0] = char(values[i] >> 6 | 0x60), r[s + 1] = char(values[i] & 0x3f | 0x80), s += 2; else if (values[i] < 0x10000) r = (char *) realloc(r, s + 3), r[s + 0] = char(values[i] >> 12 | 0xe0), r[s + 1] = char(values[i] >> 6 & 0x3f | 0x80), r[s + 2] = char(values[i] & 0x3f | 0x80), s += 3; else r = (char *) realloc(r, s + 4), r[s + 0] = char(values[i] >> 18 | 0xf0), r[s + 1] = char(values[i] >> 12 & 0x3f | 0x80), r[s + 2] = char(values[i] >> 6 & 0x3f | 0x80), r[s + 3] = char(values[i] & 0x3f | 0x80), s += 4; } return r; } };

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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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  • What pseudo-operators exist in Perl 5?

    - by Chas. Owens
    I am currently documenting all of Perl 5's operators (see the perlopref GitHub project) and I have decided to include Perl 5's pseudo-operators as well. To me, a pseudo-operator in Perl is anything that looks like an operator, but is really more than one operator or a some other piece of syntax. I have documented the four I am familiar with already: ()= the countof operator =()= the goatse/countof operator ~~ the scalar context operator }{ the Eskimo-kiss operator What other names exist for these pseudo-operators, and do you know of any pseudo-operators I have missed? =head1 Pseudo-operators There are idioms in Perl 5 that appear to be operators, but are really a combination of several operators or pieces of syntax. These pseudo-operators have the precedence of the constituent parts. =head2 ()= X =head3 Description This pseudo-operator is the list assignment operator (aka the countof operator). It is made up of two items C<()>, and C<=>. In scalar context it returns the number of items in the list X. In list context it returns an empty list. It is useful when you have something that returns a list and you want to know the number of items in that list and don't care about the list's contents. It is needed because the comma operator returns the last item in the sequence rather than the number of items in the sequence when it is placed in scalar context. It works because the assignment operator returns the number of items available to be assigned when its left hand side has list context. In the following example there are five values in the list being assigned to the list C<($x, $y, $z)>, so C<$count> is assigned C<5>. my $count = my ($x, $y, $z) = qw/a b c d e/; The empty list (the C<()> part of the pseudo-operator) triggers this behavior. =head3 Example sub f { return qw/a b c d e/ } my $count = ()= f(); #$count is now 5 my $string = "cat cat dog cat"; my $cats = ()= $string =~ /cat/g; #$cats is now 3 print scalar( ()= f() ), "\n"; #prints "5\n" =head3 See also L</X = Y> and L</X =()= Y> =head2 X =()= Y This pseudo-operator is often called the goatse operator for reasons better left unexamined; it is also called the list assignment or countof operator. It is made up of three items C<=>, C<()>, and C<=>. When X is a scalar variable, the number of items in the list Y is returned. If X is an array or a hash it it returns an empty list. It is useful when you have something that returns a list and you want to know the number of items in that list and don't care about the list's contents. It is needed because the comma operator returns the last item in the sequence rather than the number of items in the sequence when it is placed in scalar context. It works because the assignment operator returns the number of items available to be assigned when its left hand side has list context. In the following example there are five values in the list being assigned to the list C<($x, $y, $z)>, so C<$count> is assigned C<5>. my $count = my ($x, $y, $z) = qw/a b c d e/; The empty list (the C<()> part of the pseudo-operator) triggers this behavior. =head3 Example sub f { return qw/a b c d e/ } my $count =()= f(); #$count is now 5 my $string = "cat cat dog cat"; my $cats =()= $string =~ /cat/g; #$cats is now 3 =head3 See also L</=> and L</()=> =head2 ~~X =head3 Description This pseudo-operator is named the scalar context operator. It is made up of two bitwise negation operators. It provides scalar context to the expression X. It works because the first bitwise negation operator provides scalar context to X and performs a bitwise negation of the result; since the result of two bitwise negations is the original item, the value of the original expression is preserved. With the addition of the Smart match operator, this pseudo-operator is even more confusing. The C<scalar> function is much easier to understand and you are encouraged to use it instead. =head3 Example my @a = qw/a b c d/; print ~~@a, "\n"; #prints 4 =head3 See also L</~X>, L</X ~~ Y>, and L<perlfunc/scalar> =head2 X }{ Y =head3 Description This pseudo-operator is called the Eskimo-kiss operator because it looks like two faces touching noses. It is made up of an closing brace and an opening brace. It is used when using C<perl> as a command-line program with the C<-n> or C<-p> options. It has the effect of running X inside of the loop created by C<-n> or C<-p> and running Y at the end of the program. It works because the closing brace closes the loop created by C<-n> or C<-p> and the opening brace creates a new bare block that is closed by the loop's original ending. You can see this behavior by using the L<B::Deparse> module. Here is the command C<perl -ne 'print $_;'> deparsed: LINE: while (defined($_ = <ARGV>)) { print $_; } Notice how the original code was wrapped with the C<while> loop. Here is the deparsing of C<perl -ne '$count++ if /foo/; }{ print "$count\n"'>: LINE: while (defined($_ = <ARGV>)) { ++$count if /foo/; } { print "$count\n"; } Notice how the C<while> loop is closed by the closing brace we added and the opening brace starts a new bare block that is closed by the closing brace that was originally intended to close the C<while> loop. =head3 Example # count unique lines in the file FOO perl -nle '$seen{$_}++ }{ print "$_ => $seen{$_}" for keys %seen' FOO # sum all of the lines until the user types control-d perl -nle '$sum += $_ }{ print $sum' =head3 See also L<perlrun> and L<perlsyn> =cut

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  • NHibernate and Stored Procedures in C#

    - by Jess Nickson
    I was recently trying and failing to set up NHibernate (v1.2) in an ASP.NET project. The aim was to execute a stored procedure and return the results, but it took several iterations for me to end up with a working solution. In this post I am simply trying to put the required code in one place, in the hope that the snippets may be useful in guiding someone else through the same process. As it is kind’ve the first time I have had to play with NHibernate, there is a good chance that this solution is sub-optimal and, as such, I am open to suggestions on how it could be improved! There are four code snippets that I required: The stored procedure that I wanted to execute The C# class representation of the results of the procedure The XML mapping file that allows NHibernate to map from C# to the procedure and back again The C# code used to run the stored procedure The Stored Procedure The procedure was designed to take a UserId and, from this, go and grab some profile data for that user. Simple, right? We just need to do a join first, because the user’s site ID (the one we have access to) is not the same as the user’s forum ID. CREATE PROCEDURE [dbo].[GetForumProfileDetails] ( @userId INT ) AS BEGIN SELECT Users.UserID, forumUsers.Twitter, forumUsers.Facebook, forumUsers.GooglePlus, forumUsers.LinkedIn, forumUsers.PublicEmailAddress FROM Users INNER JOIN Forum_Users forumUsers ON forumUsers.UserSiteID = Users.UserID WHERE Users.UserID = @userId END I’d like to make a shout out to Format SQL for its help with, well, formatting the above SQL!   The C# Class This is just the class representation of the results we expect to get from the stored procedure. NHibernate requires a virtual property for each column of data, and these properties must be called the same as the column headers. You will also need to ensure that there is a public or protected parameterless constructor. public class ForumProfile : IForumProfile { public virtual int UserID { get; set; } public virtual string Twitter { get; set; } public virtual string Facebook { get; set; } public virtual string GooglePlus { get; set; } public virtual string LinkedIn { get; set; } public virtual string PublicEmailAddress { get; set; } public ForumProfile() { } }   The NHibernate Mapping File This is the XML I wrote in order to make NHibernate a) aware of the stored procedure, and b) aware of the expected results of the procedure. <?xml version="1.0" encoding="utf-8" ?> <hibernate-mapping xmlns="urn:nhibernate-mapping-2.2" namespace="[namespace]" assembly="[assembly]"> <sql-query name="GetForumProfileDetails"> <return-scalar column="UserID" type="Int32"/> <return-scalar column="Twitter" type="String"/> <return-scalar column="Facebook" type="String"/> <return-scalar column="GooglePlus" type="String"/> <return-scalar column="LinkedIn" type="String"/> <return-scalar column="PublicEmailAddress" type="String"/> exec GetForumProfileDetails :UserID </sql-query> </hibernate-mapping>   Calling the Stored Procedure Finally, to bring it all together, the C# code that I used in order to execute the stored procedure! public IForumProfile GetForumUserProfile(IUser user) { return NHibernateHelper .GetCurrentSession() .GetNamedQuery("GetForumProfileDetails") .SetInt32("UserID", user.UserID) .SetResultTransformer( Transformers.AliasToBean(typeof (ForumProfile))) .UniqueResult<ForumProfile>(); } There are a number of ‘Set’ methods (i.e. SetInt32) that allow you specify values for any parameters in the procedure. The AliasToBean method is then required to map the returned scalars (as specified in the XML) to the correct C# class.

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