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  • replace selfjoin with analytic functions

    - by edwards
    Hi Any ideas how i go about replacing the following self join using analytics SELECT t1.col1 col1, t1.col2 col2, SUM((extract(hour FROM (t1.times_stamp - t2.times_stamp)) * 3600 + extract(minute FROM ( t1.times_stamp - t2.times_stamp)) * 60 + extract(second FROM ( t1.times_stamp - t2.times_stamp)) ) ) div, COUNT(*) tot_count FROM tab1 t1, tab1 t2 WHERE t2.col1 = t1.col1 AND t2.col2 = t1.col2 AND t2.col3 = t1.sequence_num AND t2.times_stamp < t1.times_stamp AND t2.col4 = 3 AND t1.col4 = 4 AND t2.col5 NOT IN(103,123) AND t1.col5 != 549 GROUP BY t1.col1, t1.col2

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  • SQL SERVER – Solution to Puzzle – Simulate LEAD() and LAG() without Using SQL Server 2012 Analytic Function

    - by pinaldave
    Earlier I wrote a series on SQL Server Analytic Functions of SQL Server 2012. During the series to keep the learning maximum and having fun, we had few puzzles. One of the puzzle was simulating LEAD() and LAG() without using SQL Server 2012 Analytic Function. Please read the puzzle here first before reading the solution : Write T-SQL Self Join Without Using LEAD and LAG. When I was originally wrote the puzzle I had done small blunder and the question was a bit confusing which I corrected later on but wrote a follow up blog post on over here where I describe the give-away. Quick Recap: Generate following results without using SQL Server 2012 analytic functions. I had received so many valid answers. Some answers were similar to other and some were very innovative. Some answers were very adaptive and some did not work when I changed where condition. After selecting all the valid answer, I put them in table and ran RANDOM function on the same and selected winners. Here are the valid answers. No Joins and No Analytic Functions Excellent Solution by Geri Reshef – Winner of SQL Server Interview Questions and Answers (India | USA) WITH T1 AS (SELECT Row_Number() OVER(ORDER BY SalesOrderDetailID) N, s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663)) SELECT SalesOrderID,SalesOrderDetailID,OrderQty, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY N/2) END LeadVal, CASE WHEN N%2=1 THEN MAX(CASE WHEN N%2=0 THEN SalesOrderDetailID END) OVER (Partition BY N/2) ELSE MAX(CASE WHEN N%2=1 THEN SalesOrderDetailID END) OVER (Partition BY (N+1)/2) END LagVal FROM T1 ORDER BY SalesOrderID, SalesOrderDetailID, OrderQty; GO No Analytic Function and Early Bird Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription -- a query to emulate LEAD() and LAG() ;WITH s AS ( SELECT 1 AS ldOffset, -- equiv to 2nd param of LEAD 1 AS lgOffset, -- equiv to 2nd param of LAG NULL AS ldDefVal, -- equiv to 3rd param of LEAD NULL AS lgDefVal, -- equiv to 3rd param of LAG ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLd.SalesOrderDetailID, s.ldDefVal) AS LeadValue, ISNULL( sLg.SalesOrderDetailID, s.lgDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLd ON s.row = sLd.row - s.ldOffset LEFT OUTER JOIN s AS sLg ON s.row = sLg.row + s.lgOffset ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and Partition By Excellent Solution by DHall – Winner of Pluralsight 30 days Subscription /* a query to emulate LEAD() and LAG() */ ;WITH s AS ( SELECT 1 AS LeadOffset, /* equiv to 2nd param of LEAD */ 1 AS LagOffset, /* equiv to 2nd param of LAG */ NULL AS LeadDefVal, /* equiv to 3rd param of LEAD */ NULL AS LagDefVal, /* equiv to 3rd param of LAG */ /* Try changing the values of the 4 integer values above to see their effect on the results */ /* The values given above of 0, 0, null and null behave the same as the default 2nd and 3rd parameters to LEAD() and LAG() */ ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS row, SalesOrderID, SalesOrderDetailID, OrderQty FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty, ISNULL( sLead.SalesOrderDetailID, s.LeadDefVal) AS LeadValue, ISNULL( sLag.SalesOrderDetailID, s.LagDefVal) AS LagValue FROM s LEFT OUTER JOIN s AS sLead ON s.row = sLead.row - s.LeadOffset /* Try commenting out this next line when LeadOffset != 0 */ AND s.SalesOrderID = sLead.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LEAD() function */ LEFT OUTER JOIN s AS sLag ON s.row = sLag.row + s.LagOffset /* Try commenting out this next line when LagOffset != 0 */ AND s.SalesOrderID = sLag.SalesOrderID /* The additional join criteria on SalesOrderID above is equivalent to PARTITION BY SalesOrderID in the OVER clause of the LAG() function */ ORDER BY s.SalesOrderID, s.SalesOrderDetailID, s.OrderQty No Analytic Function and CTE Usage Excellent Solution by Pravin Patel - Winner of SQL Server Interview Questions and Answers (India | USA) --CTE based solution ; WITH cteMain AS ( SELECT SalesOrderID, SalesOrderDetailID, OrderQty, ROW_NUMBER() OVER (ORDER BY SalesOrderDetailID) AS sn FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ) SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, sLead.SalesOrderDetailID AS leadvalue, sLeg.SalesOrderDetailID AS leagvalue FROM cteMain AS m LEFT OUTER JOIN cteMain AS sLead ON sLead.sn = m.sn+1 LEFT OUTER JOIN cteMain AS sLeg ON sLeg.sn = m.sn-1 ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty No Analytic Function and Co-Related Subquery Usage Excellent Solution by Pravin Patel – Winner of SQL Server Interview Questions and Answers (India | USA) -- Co-Related subquery SELECT m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty, ( SELECT MIN(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID >= m.SalesOrderID AND l.SalesOrderDetailID > m.SalesOrderDetailID ) AS lead, ( SELECT MAX(SalesOrderDetailID) FROM Sales.SalesOrderDetail AS l WHERE l.SalesOrderID IN (43670, 43669, 43667, 43663) AND l.SalesOrderID <= m.SalesOrderID AND l.SalesOrderDetailID < m.SalesOrderDetailID ) AS leag FROM Sales.SalesOrderDetail AS m WHERE m.SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY m.SalesOrderID, m.SalesOrderDetailID, m.OrderQty This was one of the most interesting Puzzle on this blog. Giveaway Winners will get following giveaways. Geri Reshef and Pravin Patel SQL Server Interview Questions and Answers (India | USA) DHall Pluralsight 30 days Subscription Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, Readers Contribution, Readers Question, SQL, SQL Authority, SQL Function, SQL Puzzle, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Functions inside page using Razor View Engine – ASP.NET MVC

    - by hajan
    As we already know, Razor is probably the best view engine for ASP.NET MVC so far. It keeps your code fluid and very expressive. Besides the other functionalities Razor has, it also supports writing local functions. If you want to write a function, you can’t just open new @{ } razor block and write it there… it won’t work. Instead, you should specify @functions { } so that inside the brackets you will write your own C#/VB.NET functions/methods. Lets see an example: 1. I have the following loop that prints data using Razor <ul> @{     int N = 10;     for (int i = 1; i<=N; i++)     {         <li>Number @i</li>     }     } </ul> This code will print the numbers from 1 to 10: Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7 Number 8 Number 9 Number 10 So, now lets write a function that will check if current number is even, if yes… add Even before Number word. Function in Razor @functions{     public bool isEven(int number)     {         return number % 2 == 0 ? true : false;     } } The modified code which creates unordered list is: <ul> @{     int N = 10;     for (int i = 1; i<=N; i++)     {         if (isEven(@i)) {             <li>Even number @i</li>         }         else {             <li>Number @i</li>         }                 }             } </ul> As you can see, in the modified code we use the isEven(@i) function to check if the current number is even or not… The result is: Number 1 Even number 2 Number 3 Even number 4 Number 5 Even number 6 Number 7 Even number 8 Number 9 Even number 10 So, the main point of this blog was to show how you can define your own functions inside page using Razor View Engine. Of course you can define multiple functions inside the same @functions { } defined razor statement. The complete code: @{     Layout = null; } <!DOCTYPE html> <html> <head>     <title>ASP.NET MVC - Razor View Engine :: Functions</title> </head> <body>     <div>         <ul>         @{             int N = 10;             for (int i = 1; i<=N; i++)             {                 if (isEven(@i)) {                     <li>Even number @i</li>                 }                 else {                     <li>Number @i</li>                 }                         }                     }         </ul>         @functions{             public bool isEven(int number)             {                 return number % 2 == 0 ? true : false;             }         }     </div> </body> </html> Hope you like it. Regards, Hajan

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  • Network programming under windows: is WSA functions can be more complete than pSock functions [on hold]

    - by Kane
    I plan to make a set of classic socket functions to simplify their usages. Since i work under windows and linux indifferently i usually make it portable (it's not my first version of this set of functions), but i want to do something different this time and dedicate one version to windows, and one other to linux. With that i wonder for the windows version, if the WSA* functions can have any interest using them instead of the psock ones. I have found nothing about a comparison between them, so if any of you have any idea, suggestion, link or benchmark ?

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  • Oracle analytic functions for "the attribute from the row with the max date"

    - by tpdi
    I'm refactoring a colleague's code, and I have several cases where he's using a cursor to get "the latest row that matches some predicate": His technique is to write the join as a cursor, order it by the date field descending, open the cursor, get the first row, and close the cursor. This requires calling a cursor for each row of the result set that drives this, which is costly for many rows. I'd prefer to be able to join, but what something cheaper than a correlated subquery: select a.id_shared_by_several_rows, a.foo from audit_trail a where a.entry_date = (select max(a.entry_date) from audit_trail b where b.id_shared_by_several_rows = a.id_shared_by_several_rows ); I'm guessing that since this is a common need, there's an Oracle analytic function that does this?

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  • Oracle analytic functions for "the atatrbute from the row with the max date"

    - by tpdi
    I'm refactoring a colleague's code, and I have several cases where he's using a cursor to get "the latest row that matches some predicate": His technique is to write the join as a cursor, order it by the date field descending, open the cursor, get the first row, and close the cursor. This requires calling a cursor for each row of the result set that drives this, which is costly for many rows. I'd prefer to be able to join, but what something cheaper than a correlated subquery: select a.id_shared_by_several_rows, a.foo from audit_trail a where a.entry_date = (select max(a.entry_date) from audit_trail b where b.id_shared_by_several_rows = a.id_shared_by_several_rows ); I'm guessing that since this is a common need, there's an Oracle analytic function that does this?

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  • SQL SERVER – 2012 – Summary of All the Analytic Functions – MSDN and SQLAuthority

    - by pinaldave
    SQL Server 2012 (RC0 Available here) has introduced new analytic functions. These functions were long awaited and I am glad that they are here. Previously when any of this function was needed people use to write long T-SQL code to simulate that and now no need of the same. Having available native function also helps performance as well readability. In last few days I have written many articles on this subject on my blog. The goal was make these complex analytic functions easy to understand and make it widely accepted. As this new functions are available and as awareness spreads we should start using the new functions. Here is the quick list of the new function and relevant MSDN site. Function SQLAuthority MSDN CUME_DIST CUME_DIST CUME_DIST FIRST_VALUE FIRST_VALUE FIRST_VALUE LAST_VALUE LAST_VALUE LAST_VALUE LEAD LEAD LEAD LAG LAG LAG PERCENTILE_CONT PERCENTILE_CONT PERCENTILE_CONT PERCENTILE_DISC PERCENTILE_DISC PERCENTILE_DISC PERCENT_RANK PERCENT_RANK PERCENT_RANK I also enjoyed three different puzzles during the course of this series which gave clear idea to the SQL Server 2012 analytic functions. SQL SERVER – Puzzle to Win Print Book – Functions FIRST_VALUE and LAST_VALUE with OVER clause and ORDER BY SQL SERVER – Puzzle to Win Print Book – Write T-SQL Self Join Without Using LEAD and LAG SQL SERVER – Puzzle to Win Print Book – Explain Value of PERCENTILE_CONT() Using Simple Example This series will be always my dear series as during this series I had went through very unique experience of my book going out of stock and becoming available after 48 hours. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Analytic functions – they’re not aggregates

    - by Rob Farley
    SQL 2012 brings us a bunch of new analytic functions, together with enhancements to the OVER clause. People who have known me over the years will remember that I’m a big fan of the OVER clause and the types of things that it brings us when applied to aggregate functions, as well as the ranking functions that it enables. The OVER clause was introduced in SQL Server 2005, and remained frustratingly unchanged until SQL Server 2012. This post is going to look at a particular aspect of the analytic functions though (not the enhancements to the OVER clause). When I give presentations about the analytic functions around Australia as part of the tour of SQL Saturdays (starting in Brisbane this Thursday), and in Chicago next month, I’ll make sure it’s sufficiently well described. But for this post – I’m going to skip that and assume you get it. The analytic functions introduced in SQL 2012 seem to come in pairs – FIRST_VALUE and LAST_VALUE, LAG and LEAD, CUME_DIST and PERCENT_RANK, PERCENTILE_CONT and PERCENTILE_DISC. Perhaps frustratingly, they take slightly different forms as well. The ones I want to look at now are FIRST_VALUE and LAST_VALUE, and PERCENTILE_CONT and PERCENTILE_DISC. The reason I’m pulling this ones out is that they always produce the same result within their partitions (if you’re applying them to the whole partition). Consider the following query: SELECT     YEAR(OrderDate),     FIRST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING),     LAST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING),     PERCENTILE_CONT(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)),     PERCENTILE_DISC(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)) FROM Sales.SalesOrderHeader ; This is designed to get the TotalDue for the first order of the year, the last order of the year, and also the 95% percentile, using both the continuous and discrete methods (‘discrete’ means it picks the closest one from the values available – ‘continuous’ means it will happily use something between, similar to what you would do for a traditional median of four values). I’m sure you can imagine the results – a different value for each field, but within each year, all the rows the same. Notice that I’m not grouping by the year. Nor am I filtering. This query gives us a result for every row in the SalesOrderHeader table – 31465 in this case (using the original AdventureWorks that dates back to the SQL 2005 days). The RANGE BETWEEN bit in FIRST_VALUE and LAST_VALUE is needed to make sure that we’re considering all the rows available. If we don’t specify that, it assumes we only mean “RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW”, which means that LAST_VALUE ends up being the row we’re looking at. At this point you might think about other environments such as Access or Reporting Services, and remember aggregate functions like FIRST. We really should be able to do something like: SELECT     YEAR(OrderDate),     FIRST_VALUE(TotalDue)         OVER (PARTITION BY YEAR(OrderDate)               ORDER BY OrderDate, SalesOrderID               RANGE BETWEEN UNBOUNDED PRECEDING                         AND UNBOUNDED FOLLOWING) FROM Sales.SalesOrderHeader GROUP BY YEAR(OrderDate) ; But you can’t. You get that age-old error: Msg 8120, Level 16, State 1, Line 5 Column 'Sales.SalesOrderHeader.OrderDate' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause. Msg 8120, Level 16, State 1, Line 5 Column 'Sales.SalesOrderHeader.SalesOrderID' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause. Hmm. You see, FIRST_VALUE isn’t an aggregate function. None of these analytic functions are. There are too many things involved for SQL to realise that the values produced might be identical within the group. Furthermore, you can’t even surround it in a MAX. Then you get a different error, telling you that you can’t use windowed functions in the context of an aggregate. And so we end up grouping by doing a DISTINCT. SELECT DISTINCT     YEAR(OrderDate),         FIRST_VALUE(TotalDue)              OVER (PARTITION BY YEAR(OrderDate)                   ORDER BY OrderDate, SalesOrderID                   RANGE BETWEEN UNBOUNDED PRECEDING                             AND UNBOUNDED FOLLOWING),         LAST_VALUE(TotalDue)             OVER (PARTITION BY YEAR(OrderDate)                   ORDER BY OrderDate, SalesOrderID                   RANGE BETWEEN UNBOUNDED PRECEDING                             AND UNBOUNDED FOLLOWING),     PERCENTILE_CONT(0.95)          WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)),     PERCENTILE_DISC(0.95)         WITHIN GROUP (ORDER BY TotalDue)         OVER (PARTITION BY YEAR(OrderDate)) FROM Sales.SalesOrderHeader ; I’m sorry. It’s just the way it goes. Hopefully it’ll change the future, but for now, it’s what you’ll have to do. If we look in the execution plan, we see that it’s incredibly ugly, and actually works out the results of these analytic functions for all 31465 rows, finally performing the distinct operation to convert it into the four rows we get in the results. You might be able to achieve a better plan using things like TOP, or the kind of calculation that I used in http://sqlblog.com/blogs/rob_farley/archive/2011/08/23/t-sql-thoughts-about-the-95th-percentile.aspx (which is how PERCENTILE_CONT works), but it’s definitely convenient to use these functions, and in time, I’m sure we’ll see good improvements in the way that they are implemented. Oh, and this post should be good for fellow SQL Server MVP Nigel Sammy’s T-SQL Tuesday this month.

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  • helper functions as static functions or procedural functions?

    - by fayer
    i wonder if one should create a helper function in a class as a static function or just have it declared as a procedural function? i tend to think that a static helper function is the right way to go cause then i can see what kind of helper function it is eg. Database::connect(), File::create(). what is best practice?

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  • F#: any way to use member functions as unbound functions?

    - by gatoatigrado
    Is there a way to extract member functions, and use them as F# functions? I'd like to be able to write the following: mystring |> string.Split '\n' |> Array.filter (string.Length >> (=) 0 >> not) The code above works if you [let] let mystring = "a c\nb\n" let stringSplit (y:char) (x:string) = x.Split(y) let stringLength (x:string) = x.Length mystring |> stringSplit '\n' |> Array.filter (stringLength >> (=) 0 >> not)

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  • Python — Time complexity of built-in functions versus manually-built functions in finite fields

    - by stackuser
    Generally, I'm wondering about the advantages versus disadvantages of using the built-in arithmetic functions versus rolling your own in Python. Specifically, I'm taking in GF(2) finite field polynomials in string format, converting to base 2 values, performing arithmetic, then output back into polynomials as string format. So a small example of this is in multiplication: Rolling my own: def multiply(a,b): bitsa = reversed("{0:b}".format(a)) g = [(b<<i)*int(bit) for i,bit in enumerate(bitsa)] return reduce(lambda x,y: x+y,g) Versus the built-in: def multiply(a,b): # a,b are GF(2) polynomials in binary form .... return a*b #returns product of 2 polynomials in gf2 Currently, operations like multiplicative inverse (with for example 20 bit exponents) take a long time to run in my program as it's using all of Python's built-in mathematical operations like // floor division and % modulus, etc. as opposed to making my own division, remainder, etc. I'm wondering how much of a gain in efficiency and performance I can get by building these manually (as shown above). I realize the gains are dependent on how well the manual versions are built, that's not the question. I'd like to find out 'basically' how much advantage there is over the built-in's. So for instance, if multiplication (as in the example above) is well-suited for base 10 (decimal) arithmetic but has to jump through more hoops to change bases to binary and then even more hoops in operating (so it's lower efficiency), that's what I'm wondering. Like, I'm wondering if it's possible to bring the time down significantly by building them myself in ways that maybe some professionals here have already come across.

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  • What can procs and lambdas do that functions can't in ruby

    - by SecurityGate
    I've been working in Ruby for the last couple weeks, and I've come to the subject of procs, lambdas and blocks. After reading a fair share of examples from a variety of sources, I don't how they're much different from small, specialized functions. It's entirely possible that the examples I've read aren't showing the power behind procs and lambdas. def zero_function(x) x = x.to_s if x.length == 1 return x = "0" + x else return x end end zero_lambda = lambda {|x| x = x.to_s if x.length == 1 return x = "0" + x else return x end } zero_proc = Proc.new {|x| x = x.to_s if x.length == 1 puts x = "0" + x else puts x end } puts zero_function(4) puts zero_lambda.call(3) zero_proc.call(2) This function, proc, and lambda do the exact same thing, just slightly different. Is there any reason to choose one over another?

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  • Non-trivial functions that operate on any monad

    - by Strilanc
    I'm looking for examples of interesting methods that take an arbitrary monad and do something useful with it. Monads are extremely general, so methods that operate on monads are widely applicable. On the other hand, methods I know of that can apply to any monad tend to be... really, really trivial. Barely worth extracting into a function. Here's a really boring example: joinTwice. It just flattens an m m m t into an m t: join n = n >>= id joinTwice n = (join . join) n main = print (joinTwice [[[1],[2, 3]], [[4]]]) -- prints [1,2,3,4] The only non-trivial method for monads that I know of is bindFold (see my answer below). Are there more?

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  • Functional Methods on Collections

    - by GlenPeterson
    I'm learning Scala and am a little bewildered by all the methods (higher-order functions) available on the collections. Which ones produce more results than the original collection, which ones produce less, and which are most appropriate for a given problem? Though I'm studying Scala, I think this would pertain to most modern functional languages (Clojure, Haskell) and also to Java 8 which introduces these methods on Java collections. Specifically, right now I'm wondering about map with filter vs. fold/reduce. I was delighted that using foldRight() can yield the same result as a map(...).filter(...) with only one traversal of the underlying collection. But a friend pointed out that foldRight() may force sequential processing while map() is friendlier to being processed by multiple processors in parallel. Maybe this is why mapReduce() is so popular? More generally, I'm still sometimes surprised when I chain several of these methods together to get back a List(List()) or to pass a List(List()) and get back just a List(). For instance, when would I use: collection.map(a => a.map(b => ...)) vs. collection.map(a => ...).map(b => ...) The for/yield command does nothing to help this confusion. Am I asking about the difference between a "fold" and "unfold" operation? Am I trying to jam too many questions into one? I think there may be an underlying concept that, if I understood it, might answer all these questions, or at least tie the answers together.

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  • SQL SERVER – Introduction to FIRST _VALUE and LAST_VALUE – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical functions FIRST_VALUE() and LAST_VALUE(). This function returns first and last value from the list. It will be very difficult to explain this in words so I’d like to attempt to explain its function through a brief example. Instead of creating a new table, I will be using the AdventureWorks sample database as most developers use that for experiment purposes. Now let’s have fun following query: USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, FIRST_VALUE(SalesOrderDetailID) OVER (ORDER BY SalesOrderDetailID) FstValue, LAST_VALUE(SalesOrderDetailID) OVER (ORDER BY SalesOrderDetailID) LstValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO The above query will give us the following result: What’s the most interesting thing here is that as we go from row 1 to row 10, the value of the FIRST_VALUE() remains the same but the value of the LAST_VALUE is increasing. The reason behind this is that as we progress in every line – considering that line and all the other lines before it, the last value will be of the row where we are currently looking at. To fully understand this statement, see the following figure: This may be useful in some cases; but not always. However, when we use the same thing with PARTITION BY, the same query starts showing the result which can be easily used in analytical algorithms and needs. Let us have fun through the following query: Let us fun following query. USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, FIRST_VALUE(SalesOrderDetailID) OVER (PARTITION BY SalesOrderID ORDER BY SalesOrderDetailID) FstValue, LAST_VALUE(SalesOrderDetailID) OVER (PARTITION BY SalesOrderID ORDER BY SalesOrderDetailID) LstValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO The above query will give us the following result: Let us understand how PARTITION BY windows the resultset. I have used PARTITION BY SalesOrderID in my query. This will create small windows of the resultset from the original resultset and will follow the logic or FIRST_VALUE and LAST_VALUE in this resultset. Well, this is just an introduction to these functions. In the future blog posts we will go deeper to discuss the usage of these two functions. By the way, these functions can be applied over VARCHAR fields as well and are not limited to the numeric field only. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Introduction to LEAD and LAG – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical function LEAD() and LAG(). This functions accesses data from a subsequent row (for lead) and previous row (for lag) in the same result set without the use of a self-join . It will be very difficult to explain this in words so I will attempt small example to explain you this function. Instead of creating new table, I will be using AdventureWorks sample database as most of the developer uses that for experiment. Let us fun following query. USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, LEAD(SalesOrderDetailID) OVER (ORDER BY SalesOrderDetailID ) LeadValue, LAG(SalesOrderDetailID) OVER (ORDER BY SalesOrderDetailID ) LagValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO Above query will give us following result. When we look at above resultset it is very clear that LEAD function gives us value which is going to come in next line and LAG function gives us value which was encountered in previous line. If we have to generate the same result without using this function we will have to use self join. In future blog post we will see the same. Let us explore this function a bit more. This function not only provide previous or next line but it can also access any line before or after using offset. Let us fun following query, where LEAD and LAG function accesses the row with offset of 2. USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, LEAD(SalesOrderDetailID,2) OVER (ORDER BY SalesOrderDetailID ) LeadValue, LAG(SalesOrderDetailID,2) OVER (ORDER BY SalesOrderDetailID ) LagValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO Above query will give us following result. You can see the LEAD and LAG functions  now have interval of  rows when they are returning results. As there is interval of two rows the first two rows in LEAD function and last two rows in LAG function will return NULL value. You can easily replace this NULL Value with any other default value by passing third parameter in LEAD and LAG function. Let us fun following query. USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, LEAD(SalesOrderDetailID,2,0) OVER (ORDER BY SalesOrderDetailID ) LeadValue, LAG(SalesOrderDetailID,2,0) OVER (ORDER BY SalesOrderDetailID ) LagValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO Above query will give us following result, where NULL are now replaced with value 0. Just like any other analytic function we can easily partition this function as well. Let us see the use of PARTITION BY in this clause. USE AdventureWorks GO SELECT s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty, LEAD(SalesOrderDetailID) OVER (PARTITION BY SalesOrderID ORDER BY SalesOrderDetailID ) LeadValue, LAG(SalesOrderDetailID) OVER (PARTITION BY SalesOrderID ORDER BY SalesOrderDetailID ) LagValue FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID,s.SalesOrderDetailID,s.OrderQty GO Above query will give us following result, where now the data is partitioned by SalesOrderID and LEAD and LAG functions are returning the appropriate result in that window. As now there are smaller partition in my query, you will see higher presence of NULL. In future blog post we will see how this functions are compared to SELF JOIN. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • anyone familiar with these analytic questions?

    - by Moon
    So...my recruiter just called me to confirm my interview on Thurs. He also mentioned that I am going to be asked to answer for two analytic questions. He gave me a little bit about those questions. There are eight balls. One of them is defective. There are three incandescent light bulbs inside a room, but switches are placed outside. These are all he said. I think that these are not completed question. Anyone knows what questions these are?? Does my question belong to programmer.stackexchange.com? I thought it would because it is related to interview questions.

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  • Google reverse an analytic

    - by Dan
    I am confused about what code must be executed to reverse a google analytic. I have the following code pasted within a test page: <body onLoad=”function()”> <script type="text/javascript"> var _gaq = _gaq || []; _gaq.push(['_setAccount', 'UA-25305776-3']); _gaq.push(['_trackPageview']); _gaq.push(['_addTrans', '11455', // order ID - required '-42.38', // total - required '-2.38', // tax '-15.00' // shipping ]); _gaq.push(['_addItem', '11455', // order ID - necessary to associate item with transaction 'Evan Turner Turningpoint™ Basketball Pants', // product name '25.00', // unit price - required '-1' // quantity - required ]); _gaq.push(['_trackTrans']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); </script> Is this correct? Thanks!

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  • SQL SERVER – Introduction to PERCENTILE_DISC() – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical function PERCENTILE_DISC(). The book online gives following definition of this function: Computes a specific percentile for sorted values in an entire rowset or within distinct partitions of a rowset in Microsoft SQL Server 2012 Release Candidate 0 (RC 0). For a given percentile value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest CUME_DIST value (with respect to the same sort specification) that is greater than or equal to P. If you are clear with understanding of the function – no need to read further. If you got lost here is the same in simple words – find value of the column which is equal or more than CUME_DIST. Before you continue reading this blog I strongly suggest you read about CUME_DIST function over here Introduction to CUME_DIST – Analytic Functions Introduced in SQL Server 2012. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, CUME_DIST() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS CDist, PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY ProductID) OVER (PARTITION BY SalesOrderID) AS PercentileDisc FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY SalesOrderID DESC GO The above query will give us the following result: You can see that I have used PERCENTILE_DISC(0.5) in query, which is similar to finding median but not exactly. PERCENTILE_DISC() function takes a percentile as a passing parameters. It returns the value as answer which value is equal or great to the percentile value which is passed into the example. For example in above example we are passing 0.5 into the PERCENTILE_DISC() function. It will go through the resultset and identify which rows has values which are equal to or great than 0.5. In first example it found two rows which are equal to 0.5 and the value of ProductID of that row is the answer of PERCENTILE_DISC(). In some third windowed resultset there is only single row with the CUME_DIST() value as 1 and that is for sure higher than 0.5 making it as a answer. To make sure that we are clear with this example properly. Here is one more example where I am passing 0.6 as a percentile. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, CUME_DIST() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS CDist, PERCENTILE_DISC(0.6) WITHIN GROUP (ORDER BY ProductID) OVER (PARTITION BY SalesOrderID) AS PercentileDisc FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY SalesOrderID DESC GO The above query will give us the following result: The result of the PERCENTILE_DISC(0.6) is ProductID of which CUME_DIST() is more than 0.6. This means for SalesOrderID 43670 has row with CUME_DIST() 0.75 is the qualified row, resulting answer 773 for ProductID. I hope this explanation makes it further clear. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • SQL SERVER – Introduction to CUME_DIST – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    This blog post is written in response to the T-SQL Tuesday post of Prox ‘n’ Funx. This is a very interesting subject. By the way Brad Schulz is my favorite guy when it is about blogging. I respect him as well learn a lot from him. Everybody is writing something new his subject, I decided to start SQL Server 2012 analytic functions series. SQL Server 2012 introduces new analytical function CUME_DIST(). This function provides cumulative distribution value. It will be very difficult to explain this in words so I will attempt small example to explain you this function. Instead of creating new table, I will be using AdventureWorks sample database as most of the developer uses that for experiment. Let us fun following query. USE AdventureWorks GO SELECT SalesOrderID, OrderQty, CUME_DIST() OVER(ORDER BY SalesOrderID) AS CDist FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY CDist DESC GO Above query will give us following result. Now let us understand what is the formula behind CUME_DIST and why the values in SalesOrderID = 43670 are 1. Let us take more example and be clear about why the values in SalesOrderID = 43667 are 0.5. Now let us enhence the same example and use PARTITION BY into the OVER clause and see the results. Run following query in SQL Server 2012. USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, CUME_DIST() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS CDist FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY s.SalesOrderID DESC, CDist DESC GO Now let us see the result of this query. We are have changed the ORDER BY clause as well partitioning by SalesOrderID. You can see that CUME_DIST() function provides us different results. Additionally now we see value 1 multiple times. As we are using partitioning for each group of SalesOrderID we get the CUME_DIST() value. CUME_DIST() was long awaited Analytical function and I am glad to see it in SQL Server 2012. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • New Analytic settings for the new code

    - by Steve Tunstall
    If you have upgraded to the new 2011.1.3.0 code, you may find some very useful settings for the Analytics. If you didn't already know, the analytic datasets have the potential to fill up your OS hard drives. The more datasets you use and create, that faster this can happen. Since they take a measurement every second, forever, some of these metrics can get in the multiple GB size in a matter of weeks. The traditional 'fix' was that you had to go into Analytics -> Datasets about once a month and clean up the largest datasets. You did this by deleting them. Ouch. Now you lost all of that historical data that you might have wanted to check out many months from now. Or, you had to export each metric individually to a CSV file first. Not very easy or fun. You could also suspend a dataset, and have it not collect data at all. Well, that fixed the problem, didn't it? of course you now had no data to go look at. Hmmmm.... All of this is no longer a concern. Check out the new Settings tab under Analytics... Now, I can tell the ZFSSA to keep every second of data for, say, 2 weeks, and then average those 60 seconds of each minute into a single 'minute' value. I can go even further and ask it to average those 60 minutes of data into a single 'hour' value.  This allows me to effectively shrink my older datasets by a factor of 1/3600 !!! Very cool. I can now allow my datasets to go forever, and really never have to worry about them filling up my OS drives. That's great going forward, but what about those huge datasets you already have? No problem. Another new feature in 2011.1.3.0 is the ability to shrink the older datasets in the same way. Check this out. I have here a dataset called "Disk: I/O opps per second" that is about 6.32M on disk (You need not worry so much about the "In Core" value, as that is in RAM, and it fluctuates all the time. Once you stop viewing a particular metric, you will see that shrink over time, just relax).  When one clicks on the trash can icon to the right of the dataset, it used to delete the whole thing, and you would have to re-create it from scratch to get the data collecting again. Now, however, it gives you this prompt: As you can see, this allows you to once again shrink the dataset by averaging the second data into minutes or hours. Here is my new dataset size after I do this. So it shrank from 6.32MB down to 2.87MB, but i can still see my metrics going back to the time I began the dataset. Now, you do understand that once you do this, as you look back in time to the minute or hour data metrics, that you are going to see much larger time values, right? You will need to decide what size of granularity you can live with, and for how long. Check this out. Here is my Disk: Percent utilized from 5-21-2012 2:42 pm to 4:22 pm: After I went through the delete process to change everything older than 1 week to "Minutes", the same date and time looks like this: Just understand what this will do and how you want to use it. Right now, I'm thinking of keeping the last 6 weeks of data as "seconds", and then the last 3 months as "Minutes", and then "Hours" forever after that. I'll check back in six months and see how the sizes look. Steve 

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  • SQL SERVER – Introduction to PERCENT_RANK() – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical functions PERCENT_RANK(). This function returns relative standing of a value within a query result set or partition. It will be very difficult to explain this in words so I’d like to attempt to explain its function through a brief example. Instead of creating a new table, I will be using the AdventureWorks sample database as most developers use that for experiment purposes. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, RANK() OVER(ORDER BY SalesOrderID) Rnk, PERCENT_RANK() OVER(ORDER BY SalesOrderID) AS PctDist FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY PctDist DESC GO The above query will give us the following result: Now let us understand the resultset. You will notice that I have also included the RANK() function along with this query. The reason to include RANK() function was as this query is infect uses RANK function and find the relative standing of the query. The formula to find PERCENT_RANK() is as following: PERCENT_RANK() = (RANK() – 1) / (Total Rows – 1) If you want to read more about this function read here. Now let us attempt the same example with PARTITION BY clause USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, RANK() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) Rnk, PERCENT_RANK() OVER(PARTITION BY SalesOrderID ORDER BY ProductID ) AS PctDist FROM Sales.SalesOrderDetail s WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY PctDist DESC GO Now you will notice that the same logic is followed in follow result set. I have now quick question to you – how many of you know the logic/formula of PERCENT_RANK() before this blog post? Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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  • Why isn't there a typeclass for functions?

    - by Steve314
    I already tried this on Reddit, but there's no sign of a response - maybe it's the wrong place, maybe I'm too impatient. Anyway... In a learning problem I've been messing around with, I realised I needed a typeclass for functions with operations for applying, composing etc. Reasons... It can be convenient to treat a representation of a function as if it were the function itself, so that applying the function implicitly uses an interpreter, and composing functions derives a new description. Once you have a typeclass for functions, you can have derived typeclasses for special kinds of functions - in my case, I want invertible functions. For example, functions that apply integer offsets could be represented by an ADT containing an integer. Applying those functions just means adding the integer. Composition is implemented by adding the wrapped integers. The inverse function has the integer negated. The identity function wraps zero. The constant function cannot be provided because there's no suitable representation for it. Of course it doesn't need to spell things as if it the values were genuine Haskell functions, but once I had the idea, I thought a library like that must already exist and maybe even using the standard spellings. But I can't find such a typeclass in the Haskell library. I found the Data.Function module, but there's no typeclass - just some common functions that are also available from the Prelude. So - why isn't there a typeclass for functions? Is it "just because there isn't" or "because it's not so useful as you think"? Or maybe there's a fundamental problem with the idea? The biggest possible problem I've thought of so far is that function application on actual functions would probably have to be special-cased by the compiler to avoid a looping problem - in order to apply this function I need to apply the function application function, and to do that I need to call the function application function, and to do that...

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  • SQL SERVER – Introduction to PERCENTILE_CONT() – Analytic Functions Introduced in SQL Server 2012

    - by pinaldave
    SQL Server 2012 introduces new analytical function PERCENTILE_CONT(). The book online gives following definition of this function: Computes a specific percentile for sorted values in an entire rowset or within distinct partitions of a rowset in Microsoft SQL Server 2012 Release Candidate 0 (RC 0). For a given percentile value P, PERCENTILE_DISC sorts the values of the expression in the ORDER BY clause and returns the value with the smallest CUME_DIST value (with respect to the same sort specification) that is greater than or equal to P. If you are clear with understanding of the function – no need to read further. If you got lost here is the same in simple words – it is lot like finding median with percentile value. Now let’s have fun following query: USE AdventureWorks GO SELECT SalesOrderID, OrderQty, ProductID, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY ProductID) OVER (PARTITION BY SalesOrderID) AS MedianCont FROM Sales.SalesOrderDetail WHERE SalesOrderID IN (43670, 43669, 43667, 43663) ORDER BY SalesOrderID DESC GO The above query will give us the following result: You can see that I have used PERCENTILE_COUNT(0.5) in query, which is similar to finding median. Let me explain above diagram with little more explanation. The defination of median is as following: In case of Even Number of elements = In ordered list add the two digits from the middle and devide by 2 In case of Odd Numbers of elements = In ordered list select the digits from the middle I hope this example gives clear idea how PERCENTILE_CONT() works. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Function, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, T SQL, Technology

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