I originally posted this as a question only about destructors, but now I&#39;m adding consideration of the default constructor.  Here&#39;s the original question:

&gt; If I want to give my class a destructor that is virtual, but is
&gt; otherwise the same as what the compiler would generate, I can use `=default`:
&gt; 
&gt;     class Widget {
&gt;     public:
&gt;        virtual ~Widget() = default;
&gt;     };
&gt; 
&gt; But it seems that I can get the same effect with less typing using an
&gt; empty definition:
&gt; 
&gt;     class Widget {
&gt;     public:
&gt;        virtual ~Widget() {}
&gt;     };
&gt; 
&gt; Is there any way in which these two definitions behave differently?

Based on the replies posted for this question, the situation for the default constructor seems similar.  Given that there is almost no difference in meaning between &quot;`=default`&quot; and &quot;`{}`&quot; for destructors, is there similarly almost no difference in meaning between these options for default constructors?  That is, assuming I want to create a type where the objects of that type will be both created and destroyed, why would I want to say

    Widget() = default;

instead of
   
    Widget() {}

?

I apologize if extending this question after its original posting is violating some SO rules.  Posting an almost-identical question for default constructors struck me as the less desirable option.


How is &quot;=default&quot; different from &quot;{}&quot; for default constructor and destructor?

A few examples to show, just incase:

*Inline Table Valued*

    CREATE FUNCTION MyNS.GetUnshippedOrders()
    RETURNS TABLE
    AS 
    RETURN SELECT a.SaleId, a.CustomerID, b.Qty
        FROM Sales.Sales a INNER JOIN Sales.SaleDetail b
            ON a.SaleId = b.SaleId
            INNER JOIN Production.Product c ON b.ProductID = c.ProductID
        WHERE a.ShipDate IS NULL
    GO

*Multi Statement Table Valued*

    CREATE FUNCTION MyNS.GetLastShipped(@CustomerID INT)
    RETURNS @CustomerOrder TABLE
    (SaleOrderID    INT         NOT NULL,
    CustomerID      INT         NOT NULL,
    OrderDate       DATETIME    NOT NULL,
    OrderQty        INT         NOT NULL)
    AS
    BEGIN
        DECLARE @MaxDate DATETIME
       
        SELECT @MaxDate = MAX(OrderDate)
        FROM Sales.SalesOrderHeader
        WHERE CustomerID = @CustomerID
    
        INSERT @CustomerOrder
        SELECT a.SalesOrderID, a.CustomerID, a.OrderDate, b.OrderQty
        FROM Sales.SalesOrderHeader a INNER JOIN Sales.SalesOrderHeader b
            ON a.SalesOrderID = b.SalesOrderID
            INNER JOIN Production.Product c ON b.ProductID = c.ProductID
        WHERE a.OrderDate = @MaxDate
            AND a.CustomerID = @CustomerID
        RETURN
    END
    GO
       
Is there an advantage to using one type (in-line or multi statement) over the other? Is there certain scenarios when one is better than the other or are the differences purely syntactical? I realise the two example queries are doing different things but is there a reason I would write them in that way?

Reading about them and the advantages/differences haven&#39;t really been explained.

Multi-statement Table Valued Function vs Inline Table Valued Function

How could you remove all characters that are not alphabetic from a string? 

What about non-alphanumeric?

Does this have to be a custom function or are there also more generalizable solutions?

How to strip all non-alphabetic characters from string in SQL Server?

I have a function, `a()`, that I want to override, but also have the original `a()` be performed in an order depending on the context. For example, sometimes when I&#39;m generating a page I&#39;ll want to override like this:

    function a() {
        new_code();
        original_a();
    }

and sometimes like this:

    function a() {
        original_a();
        other_new_code();
    }

How do I get that `original_a()` from within the over-riding `a()`? Is it even possible?

Please don&#39;t suggest alternatives to over-riding in this way, I know of many. I&#39;m asking about this way specifically.

Overriding a JavaScript function while referencing the original

I&#39;m writing a user-defined function in SQL Server 2008. I know that functions cannot raise errors in the usual way - if you try to include the RAISERROR statement SQL returns:

    Msg 443, Level 16, State 14, Procedure ..., Line ...
    Invalid use of a side-effecting operator &#39;RAISERROR&#39; within a function.

But the fact is, the function takes some input, which may be invalid and, if it is, there is no meaningful value the function can return. What do I do then?

I could, of course, return NULL, but it would be difficult for any developer using the function to troubleshoot this. I could also cause a division by zero or something like that - this would generate an error message, but a misleading one. Is there any way I can have my own error message reported somehow?

How to report an error from a SQL Server user-defined function

How can I declare a variable in a table valued function? (like in my title)

Declare variable in table valued function

I&#39;m attempting to customize an existing JS library without modifying the original JS code.  This code loads in a few external JS files which I do have access to, and what I&#39;d like to do is change one of the functions contained in the original file without copying and pasting the whole thing into the second JS file.
&lt;br&gt;
So for example, the off limits JS might have a function like this:

    var someFunction = function(){
        alert(&quot;done&quot;);
    }

I&#39;d like to be able to somehow append or prepend some JS code into that function.  The reason is primarily that in the original untouchable JS the function is pretty enormous and if that JS ever gets updated, the function I overwrite it with will be out of date.

I&#39;m not entirely sure this is possible, but I figured I&#39;d check.


Adding code to a javascript function programmatically

How do I call functions defined in abc.R file in another file, say xyz.R?

A supplementary question is, how do I call functions defined in abc.R from the R prompt/command line?



Define all functions in one .R file, call them from another .R file. How, if possible?

I need to determine the number of days in a month for a given date in SQL Server.

Is there a built-in function? If not, what should I use as the user-defined function?

How to determine the number of days in a month in SQL Server?

How can I get a user-defined function to re-evaluate itself based on changed data in the spreadsheet?

I tried **&lt;kbd&gt;F9&lt;/kbd&gt;** and **&lt;kbd&gt;Shift&lt;/kbd&gt;+&lt;kbd&gt;F9&lt;/kbd&gt;**.

The only thing that seems to work is editing the cell with the function call and then pressing Enter.

Refresh Excel VBA Function Results

Basically I want to use `PRINT` statement inside a user defined function to aide my debugging.

However I&#39;m getting the following error;

&gt; Invalid use of side-effecting or time-dependent operator in &#39;PRINT&#39;
&gt; within a function.

Can this not be done?

Anyway to aid my user defined function debugging?


TSQL How do you output PRINT in a user defined function?

I have a user defined function in SQL called getBuisnessDays it takes @startdate and @enddate and returns the number of business days between the two dates. How can I call that function within my select?

Here&#39;s what I&#39;d like to do.. 

    SELECT getBusinessDays(a.opendate,a.closedate) 
    FROM account a
    WHERE ...

SQL User Defined Function Within Select

Hai guys,

  I ve used the following split function,

    CREATE FUNCTION dbo.Splitfn(@String varchar(8000), @Delimiter char(1))       
    returns @temptable TABLE (items varchar(8000))       
     as       
    begin       
    declare @idx int       
    declare @slice varchar(8000)       
      
    select @idx = 1       
        if len(@String)&lt;1 or @String is null  return       
      
    while @idx!= 0       
    begin       
        set @idx = charindex(@Delimiter,@String)       
        if @idx!=0       
            set @slice = left(@String,@idx - 1)       
        else       
            set @slice = @String       
          
        if(len(@slice)&gt;0)  
            insert into @temptable(Items) values(@slice)       
  
        set @String = right(@String,len(@String) - @idx)       
        if len(@String) = 0 break       
    end   
    return      

    end  

and i used this function in a query and it was executed

    ALTER PROCEDURE [dbo].[Employees_Delete] 
	-- Add the parameters for the stored procedure here
	@Id varchar(50)
    AS
    BEGIN
	-- SET NOCOUNT ON added to prevent extra result sets from
	-- interfering with SELECT statements.
	SET NOCOUNT ON;

    -- Insert statements for procedure here
	
     if exists( select Emp_Id from Employee where Emp_Id=dbo.Splitfn(@Id,&#39;,&#39;))
    begin
		update Employee set Is_Deleted=1 where Emp_Id=dbo.Splitfn(@Id,&#39;,&#39;)
        select &#39;deleted&#39; as message
    end 
    END

but when i excute my store procedure giving values say (1,2) i got the error



    Cannot find either column &quot;dbo&quot; or the user-defined 
    function or aggregate &quot;dbo.Splitfn&quot;, or the name is ambiguous.

I ve checked my tablevalued functions the function &#39;splitfn&#39; was there but i dont know what is going wrong? Any suggestions..



Cannot find either column &quot;dbo&quot; or the user-defined function or aggregate &quot;dbo.Splitfn&quot;, or the name is ambiguous

I&#39;m using pyspark, loading a large csv file into a dataframe with spark-csv, and as a pre-processing step I need to apply a variety of operations to the data available in one of the columns (that contains a json string). That will return X values, each of which needs to be stored in their own separate column.

That functionality will be implemented in a UDF. However, I am not sure how to return a list of values from that UDF and feed these into individual columns. Below is a simple example:

    (...)
    from pyspark.sql.functions import udf
    def udf_test(n):
        return [n/2, n%2]

    test_udf=udf(udf_test)


    df.select(&#39;amount&#39;,&#39;trans_date&#39;).withColumn(&quot;test&quot;, test_udf(&quot;amount&quot;)).show(4)

That produces the following:

    +------+----------+--------------------+
    |amount|trans_date|                test|
    +------+----------+--------------------+
    |  28.0|2016-02-07|         [14.0, 0.0]|
    | 31.01|2016-02-07|[15.5050001144409...|
    | 13.41|2016-02-04|[6.70499992370605...|
    | 307.7|2015-02-17|[153.850006103515...|
    | 22.09|2016-02-05|[11.0450000762939...|
    +------+----------+--------------------+
    only showing top 5 rows

What would be the best way to store the two (in this example) values being returned by the udf on separate columns? Right now they are being typed as strings:

    df.select(&#39;amount&#39;,&#39;trans_date&#39;).withColumn(&quot;test&quot;, test_udf(&quot;amount&quot;)).printSchema()

    root
     |-- amount: float (nullable = true)
     |-- trans_date: string (nullable = true)
     |-- test: string (nullable = true)


Apache Spark -- Assign the result of UDF to multiple dataframe columns

Spark now offers predefined functions that can be used in dataframes, and it seems they are highly optimized. My original question was going to be on which is faster, but I did some testing myself and found the spark functions to be about 10 times faster at least in one instance. Does anyone know why this is so, and when would a udf be faster (only for instances that an identical spark function exists)?

Here is my testing code (ran on Databricks community ed):

&lt;!-- language: python --&gt;

    # UDF vs Spark function
    from faker import Factory
    from pyspark.sql.functions import lit, concat
    fake = Factory.create()
    fake.seed(4321)

    # Each entry consists of last_name, first_name, ssn, job, and age (at least 1)
    from pyspark.sql import Row
    def fake_entry():
      name = fake.name().split()
      return (name[1], name[0], fake.ssn(), fake.job(), abs(2016 - fake.date_time().year) + 1)

    # Create a helper function to call a function repeatedly
    def repeat(times, func, *args, **kwargs):
        for _ in xrange(times):
            yield func(*args, **kwargs)
    data = list(repeat(500000, fake_entry))
    print len(data)
    data[0]

    dataDF = sqlContext.createDataFrame(data, (&#39;last_name&#39;, &#39;first_name&#39;, &#39;ssn&#39;, &#39;occupation&#39;, &#39;age&#39;))
    dataDF.cache()

UDF function:


&lt;!-- language: python --&gt;

    concat_s = udf(lambda s: s+ &#39;s&#39;)
    udfData = dataDF.select(concat_s(dataDF.first_name).alias(&#39;name&#39;))
    udfData.count()

Spark Function:


&lt;!-- language: python --&gt;

    spfData = dataDF.select(concat(dataDF.first_name, lit(&#39;s&#39;)).alias(&#39;name&#39;))
    spfData.count()

Ran both multiple times, the udf usually took about 1.1 - 1.4 s, and the Spark `concat` function always took under 0.15 s. 

Spark functions vs UDF performance?

I know this has been asked to death, and I know why SQL Server doesn&#39;t let you do it.


But is there any workaround for this, other than using Extended Stored Procedures?

And please don&#39;t tell me to convert my function into a procedure...

So what I&#39;m really asking is: Is there ANY way to run a Stored Procedure from within a Function?


**EDIT:**

Point proven: there is a way around it, but it&#39;s so **WRONG** I wouldn&#39;t do it. 
I&#39;m gonna change it to a Stored Procedure and execute it elsewhere.


Execute Stored Procedure from a Function

I&#39;d like to pass a table as a parameter into a scaler UDF. 

I&#39;d also prefer to restrict the parameter to tables with only one column. (optional)

Is this possible?

**EDIT**

I don&#39;t want to pass a table name, I&#39;d like to pass the table of data (as a reference I presume)

**EDIT**

I would want my Scaler UDF to basically take a table of values and return a CSV list of the rows.

IE

    col1  
    &quot;My First Value&quot;  
    &quot;My Second Value&quot;
    ...
    &quot;My nth Value&quot;

would return

    &quot;My First Value, My Second Value,... My nth Value&quot;

I&#39;d like to do some filtering on the table though, IE ensuring that there are no nulls and to ensure there are no duplicates. I was expecting something along the lines of:

    SELECT dbo.MyFunction(SELECT DISTINCT myDate FROM myTable WHERE myDate IS NOT NULL)

Pass table as parameter into sql server UDF

Here&#39;s my user-defined table type...

    CREATE TYPE [dbo].[FooType] AS TABLE(
     [Bar] [INT],
    )

This is what ive had to do in my table-valued function to return the type:

    CREATE FUNCTION [dbo].[GetFoos]
    RETURN @FooTypes TABLE ([Bar] [INT])
    INSERT INTO @FooTypes (1)
    RETURN

Basically, im having to re-declare my type definition in the RETURN statement of the function. Isnt there a way i can simply declare the type in the RETURN statement?

I would have thought this would work:

    CREATE FUNCTION [dbo].[GetFoos]
    RETURN @FooTypes [FooType]
    INSERT INTO @FooTypes (1)
    RETURN

Cannot find any help on MSDN/Google regarding this....anyone?

**EDIT**

I unmarked my answer, and bumping this question - as i am encountering the same scenario 6 months later.

Does anyone have any idea if it&#39;s possible to return a user defined table type from a table valued function? If not, is there a better workaround other than what i have done? (re-declare the type again).



SQL Server 2008 - How do i return a User-Defined Table Type from a Table-Valued Function?

I am looking for a Built-in UDF to convert values of a string column to integer in my hive table for sorting using SELECT and ORDER BY. I searched in the Language Manual, but no use. Any other suggestions also welcome.

Hive: Convert String to Integer

I have a DF with a huge parseable metadata as a single string column in a Dataframe, lets call it DFA, with ColmnA.

I would like to break this column, ColmnA into multiple columns thru a function, ClassXYZ = Func1(ColmnA). This function returns a class ClassXYZ, with multiple variables, and each of these variables now has to be mapped to new Column, such a ColmnA1, ColmnA2 etc.

How would I do such a transformation from 1 Dataframe to another with these additional columns by calling this Func1 just once, and not have to repeat-it to create all the columns.

Its easy to solve if I were to call this huge function every time to add a new column, but that what I wish to avoid.

Kindly please advise with a working or pseudo code.

Thanks

Sanjay

Derive multiple columns from a single column in a Spark DataFrame

How would I return multiple values (say, a number and a string) from a user-defined function in SQL Server?

All User Defined-Functions Solutions on Gang of Coders

Total of 21 User Defined-Functions Solutions

How is "=default" different from "{}" for default constructor and destructor?