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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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• 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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• 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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• 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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• 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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• Oracle Analytic Applications Strategy

  Chris Leone, Group Vice President of Applications Strategy at Oracle, talks to Fred about Analytic Application offerings, some product roadmaps and benefits to customers.

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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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• 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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• 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 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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• 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 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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• Working with Analytic Workflow Manager (AWM) - Part 8 Cube Metadata Analysis

  - by Mohan Ramanuja

  CUBE SIZEselect dbal.owner||'.'||substr(dbal.table_name,4) awname, sum(dbas.bytes)/1024/1024 as mb, dbas.tablespace_name from dba_lobs dbal, dba_segments dbas where dbal.column_name = 'AWLOB' and dbal.segment_name = dbas.segment_name group by dbal.owner, dbal.table_name, dbas.tablespace_name order by dbal.owner, dbal.table_name SESSION RESOURCES select vses.username||':'||vsst.sid username, vstt.name, max(vsst.value) valuefrom v$sesstat vsst, v$statname vstt, v$session vseswhere vstt.statistic# = vsst.statistic# and vsst.sid = vses.sid andVSES.USERNAME LIKE ('ATTRIBDW_OWN') ANDvstt.name in ('session pga memory', 'session pga memory max', 'session uga memory','session uga memory max', 'session cursor cache count', 'session cursor cache hits', 'session stored procedure space', 'opened cursors current', 'opened cursors cumulative') andvses.username is not null group by vsst.sid, vses.username, vstt.name order by vsst.sid, vses.username, vstt.name OLAP PGA USE select 'OLAP Pages Occupying: '|| round((((select sum(nvl(pool_size,1)) from v$aw_calc)) / (select value from v$pgastat where name = 'total PGA inuse')),2)*100||'%' info from dual union select 'Total PGA Inuse Size: '||value/1024||' KB' info from v$pgastat where name = 'total PGA inuse' union select 'Total OLAP Page Size: '|| round(sum(nvl(pool_size,1))/1024,0)||' KB' info from v$aw_calc order by info desc OLAP PGA USAGE PER USER select vs.username, vs.sid, round(pga_used_mem/1024/1024,2)||' MB' pga_used, round(pga_max_mem/1024/1024,2)||' MB' pga_max, round(pool_size/1024/1024,2)||' MB' olap_pp, round(100*(pool_hits-pool_misses)/pool_hits,2) || '%' olap_ratio from v$process vp, v$session vs, v$aw_calc va where session_id=vs.sid and addr = paddr CUBE LOADING SCRIPT REM The 'set define off' statement is needed only if running this script through SQLPlus.REM If you are using another tool to run this script, the line below may be commented out.set define offBEGIN  DBMS_CUBE.BUILD(    'VALIDATE  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/BEGIN  DBMS_CUBE.BUILD(    '  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/ VISUALIZATION OBJECT - AW$ATTRIBDW_OWN  CREATE TABLE "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"        (            "PS#"    NUMBER(10,0),            "GEN#"   NUMBER(10,0),            "EXTNUM" NUMBER(8,0),            "AWLOB" BLOB,            "OBJNAME"  VARCHAR2(256 BYTE),            "PARTNAME" VARCHAR2(256 BYTE)        )        PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE        (            BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "AWLOB"        )        STORE AS SECUREFILE        (            TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        PARTITION BY RANGE        (            "GEN#"        )        SUBPARTITION BY HASH        (            "PS#",            "EXTNUM"        )        SUBPARTITIONS 8        (            PARTITION "PTN1" VALUES LESS THAN (1) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE READS LOGGING NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP661" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP662" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP663" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP664" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP665" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP666" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP667" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP668" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" ) ,            PARTITION "PTNN" VALUES LESS THAN (MAXVALUE) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP669" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP670" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP671" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP672" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP673" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP674" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP675" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP676" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" )        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."ATTRIBDW_OWN_I$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        "PS#", "GEN#", "EXTNUM"    )    PCTFREE 10 INITRANS 4 MAXTRANS 255 COMPUTE STATISTICS STORAGE    (        INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT    )    TABLESPACE "ATTRIBDW_DATA" ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000406980C00004$$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOCAL (PARTITION "SYS_IL_P711" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP695" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP696" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP697" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP698" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP699" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP700" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP701" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP702" TABLESPACE "ATTRIBDW_DATA" ) , PARTITION "SYS_IL_P712" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP703" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP704" TABLESPACE        "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP705" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP706" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP707" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP708" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP709" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP710" TABLESPACE "ATTRIBDW_DATA" ) ) PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE BUILD LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_BUILD_LOG"        (            "BUILD_ID"          NUMBER,            "SLAVE_NUMBER"      NUMBER,            "STATUS"            VARCHAR2(10 BYTE),            "COMMAND"           VARCHAR2(25 BYTE),            "BUILD_OBJECT"      VARCHAR2(30 BYTE),            "BUILD_OBJECT_TYPE" VARCHAR2(10 BYTE),            "OUTPUT" CLOB,            "AW"            VARCHAR2(30 BYTE),            "OWNER"         VARCHAR2(30 BYTE),            "PARTITION"     VARCHAR2(50 BYTE),            "SCHEDULER_JOB" VARCHAR2(100 BYTE),            "TIME" TIMESTAMP (6)WITH TIME ZONE,        "BUILD_SCRIPT" CLOB,        "BUILD_TYPE"            VARCHAR2(22 BYTE),        "COMMAND_DEPTH"         NUMBER(2,0),        "BUILD_SUB_OBJECT"      VARCHAR2(30 BYTE),        "REFRESH_METHOD"        VARCHAR2(1 BYTE),        "SEQ_NUMBER"            NUMBER,        "COMMAND_NUMBER"        NUMBER,        "IN_BRANCH"             NUMBER(1,0),        "COMMAND_STATUS_NUMBER" NUMBER,        "BUILD_NAME"            VARCHAR2(100 BYTE)        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "OUTPUT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        LOB        (            "BUILD_SCRIPT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00013$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00007$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG" ( PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE DIMENSION COMPILE  CREATE TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"        (            "ID"               NUMBER,            "SEQ_NUMBER"       NUMBER,            "ERROR#"           NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE"    VARCHAR2(2000 BYTE),            "DIMENSION"        VARCHAR2(100 BYTE),            "DIMENSION_MEMBER" VARCHAR2(100 BYTE),            "MEMBER_ANCESTOR"  VARCHAR2(100 BYTE),            "HIERARCHY1"       VARCHAR2(100 BYTE),            "HIERARCHY2"       VARCHAR2(100 BYTE),            "ERROR_CONTEXT" CLOB        )        SEGMENT CREATION DEFERRED PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING TABLESPACE "ATTRIBDW_DATA" LOB        (            "ERROR_CONTEXT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION"IS    'Name of dimension being compiled';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION_MEMBER"IS    'Problem dimension member';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."MEMBER_ANCESTOR"IS    'Problem dimension member''s parent';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY1"IS    'First hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY2"IS    'Second hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_CONTEXT"IS    'Extra information for error';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"IS    'Cube dimension compile log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407307C00010$$" ON "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE( INITIAL 1048576 NEXT 1048576 MAXEXTENTS 2147483645) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE OPERATING LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"        (            "INST_ID"    NUMBER NOT NULL ENABLE,            "SID"        NUMBER NOT NULL ENABLE,            "SERIAL#"    NUMBER NOT NULL ENABLE,            "USER#"      NUMBER NOT NULL ENABLE,            "SQL_ID"     VARCHAR2(13 BYTE),            "JOB"        NUMBER,            "ID"         NUMBER,            "PARENT_ID"  NUMBER,            "SEQ_NUMBER" NUMBER,            "TIME" TIMESTAMP (6)WITH TIME ZONE NOT NULL ENABLE,        "LOG_LEVEL"    NUMBER(4,0) NOT NULL ENABLE,        "DEPTH"        NUMBER(4,0),        "OPERATION"    VARCHAR2(15 BYTE) NOT NULL ENABLE,        "SUBOPERATION" VARCHAR2(20 BYTE),        "STATUS"       VARCHAR2(10 BYTE) NOT NULL ENABLE,        "NAME"         VARCHAR2(20 BYTE) NOT NULL ENABLE,        "VALUE"        VARCHAR2(4000 BYTE),        "DETAILS" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "DETAILS"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."INST_ID"IS    'Instance ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SID"IS    'Session ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SERIAL#"IS    'Session serial#';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."USER#"IS    'User ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SQL_ID"IS    'Executing SQL statement ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."JOB"IS    'Identifier of job';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."PARENT_ID"IS    'Parent operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."TIME"IS    'Time of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."LOG_LEVEL"IS    'Verbosity level of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DEPTH"IS    'Nesting depth of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."OPERATION"IS    'Current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SUBOPERATION"IS    'Current suboperation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."STATUS"IS    'Status of current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."NAME"IS    'Name of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."VALUE"IS    'Value of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DETAILS"IS    'Extra information for record';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"IS    'Cube operations log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407301C00018$$" ON "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE REJECTED RECORDS CREATE TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"        (            "ID"            NUMBER,            "SEQ_NUMBER"    NUMBER,            "ERROR#"        NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE" VARCHAR2(2000 BYTE),            "RECORD#"       NUMBER(38,0),            "SOURCE_ROW" ROWID,            "REJECTED_RECORD" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "REJECTED_RECORD"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."RECORD#"IS    'Rejected record number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SOURCE_ROW"IS    'Rejected record''s ROWID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."REJECTED_RECORD"IS    'Rejected record copy';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"IS    'Cube rejected records log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407304C00007$$" ON "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;

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• Name Changes for the Business Analytic My Oracle Support Communities

  - by THE

  (guest post by Mel) Please let us welcome the new names for the EPM communities!You will shortly be seeing the following names when looking at your communities:Business Intelligence            OBIEE            OBIAOracle Hyperion EPM            Hyperion FDM            Hyperion Enterprise & Hyperion Enterprise Reporting            Hyperion Essbase            HFM            Hyperion Other Products            Hyperion Planning            HPCM            Hyperion Reporting Products             Hyperion Shared Services            Hyperion Patch ReviewsWe would also like to take this opportunity to mention that externally kept bookmarks may not work after the change, as the name of the community is part of the URL.So in case you have bookmarked discussions whitepaper-lists etc in your browser, you may want to re-visit these after the name-change. We hope that you continue your contribution to your community.Thank you for your ongoing support.

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• Custom iPhone analytic tool

  - by Ondrej

  Do you think that if I'll build my own custom analytic tool (Flurry, Pinchmedia) and I'll host that on the same server where I have my data source for the application, will Apple consider this as a thirdparty analytic tool or not? ... Problem is that Flurry and Pinch are being banned from Appstore by the newest T&C ... than I thought that I'll build an open source library that will allow anyone to have their own analytic installed on their server ... Thanks, Ondrej

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• Does your analytic solution tell you what questions to ask?

  - by Manan Goel

  Analytic solutions exist to answer business questions. Conventional wisdom holds that if you can answer business questions quickly and accurately, you can take better business decisions and therefore achieve better business results and outperform the competition. Most business questions are well understood (read structured) so they are relatively easy to ask and answer. Questions like what were the revenues, cost of goods sold, margins, which regions and products outperformed/underperformed are relatively well understood and as a result most analytics solutions are well equipped to answer such questions. Things get really interesting when you are looking for answers but you don’t know what questions to ask in the first place? That’s like an explorer looking to make new discoveries by exploration. An example of this scenario is the Center of Disease Control (CDC) in United States trying to find the vaccine for the latest strand of the swine flu virus. The researchers at CDC may try hundreds of options before finally discovering the vaccine. The exploration process is inherently messy and complex. The process is fraught with false starts, one question or a hunch leading to another and the final result may look entirely different from what was envisioned in the beginning. Speed and flexibility is the key; speed so the hundreds of possible options can be explored quickly and flexibility because almost everything about the problem, solutions and the process is unknown.  Come to think of it, most organizations operate in an increasingly unknown or uncertain environment. Business Leaders have to take decisions based on a largely unknown view of the future. And since the value proposition of analytic solutions is to help the business leaders take better business decisions, for best results, consider adding information exploration and discovery capabilities to your analytic solution. Such exploratory analysis capabilities will help the business leaders perform even better by empowering them to refine their hunches, ask better questions and take better decisions. That’s your analytic system not only answering the questions but also suggesting what questions to ask in the first place. Today, most leading analytic software vendors offer exploratory analysis products as part of their analytic solutions offerings. So, what characteristics should be top of mind while evaluating the various solutions? The answer is quite simply the same characteristics that are essential for exploration and analysis – speed & flexibility. Speed is required because the system inherently has to be agile to handle hundreds of different scenarios with large volumes of data across large user populations. Exploration happens at the speed of thought so make sure that you system is capable of operating at speed of thought. Flexibility is required because the exploration process from start to finish is full of unknowns; unknown questions, answers and hunches. So, make sure that the system is capable of managing and exploring all relevant data – structured or unstructured like databases, enterprise applications, tweets, social media updates, documents, texts, emails etc. and provides flexible Google like user interface to quickly explore all relevant data. Getting Started You can help business leaders become “Decision Masters” by augmenting your analytic solution with information discovery capabilities. For best results make sure that the solution you choose is enterprise class and allows advanced, yet intuitive, exploration and analysis of complex and varied data including structured, semi-structured and unstructured data.  You can learn more about Oracle’s exploratory analysis solutions by clicking here.

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• Google Analytic Metric to use for off-site banner click comparison

  - by EricPatterson

  I have all my off-site banner ads correctly campaign tracked/tagged but I want to know what metrics I should be looking at in the GA for the closest comparison to the ad servers clicks. I am pretty sure it wouldn't be Visits but I also see there is PageViews and UniquePageViews. My GA data is coming in way off from what the ad server manager people are telling my clicks are for said banners on their site. My other question is what type of percentage are other people seeing there data being off?

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• how to Google Analytic dat through YQL or Yahoo pipes??

  - by Nok Imchen

  I use Google analytic, i want to retrieve the last 100 search string from my Google analytic account using YQL or Yahoo pipes, how can i do it?? Please help..

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• Displaying a single rank in MySQL table

  - by MichaelInno

  I have a table called 'highscores' that looks like this. id udid name score 1 1111 Mike 200 2 3333 Joe 300 3 4444 Billy 50 4 0000 Loser 10 5 DDDD Face 400 Given a specific udid, I want to return the rank of that row by their score value. i.e. if udid given = 0000, I should return 5. Any idea how to write this query for a MySQL database?

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• How would I duplicate the Rank function in a Sql Server Compact Edition SELECT statement?

  - by AMissico

  It doesn't look like SQL Server Compact Edition supports the RANK() function. (See Functions (SQL Server Compact Edition) at http://msdn.microsoft.com/en-us/library/ms174077(SQL.90).aspx). How would I duplicate the RANK() function in a SQL Server Compact Edition SELECT statement. (Please use Northwind.sdf for any sample select statements, as it is the only one I can open with SQL Server 2005 Management Studio.)

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• How do I select a fixed number of rows for each group?

  - by Maiasaura

  Here is some example data in a mysql table a b distance 15 44 250 94 31 250 30 41 250 6 1 250 95 18 250 72 84 500 14 23 500 55 24 500 95 8 500 59 25 500 40 73 500 65 85 500 32 50 500 31 39 500 22 25 500 37 11 750 98 39 750 15 57 750 9 22 750 14 44 750 69 22 750 62 50 750 89 35 750 67 65 750 74 37 750 52 36 750 66 53 750 82 74 1000 79 22 1000 98 41 1000 How do I query this table such that I get 2 rows per distance selected at random? A successful query will produce something like a b distance 30 41 250 95 18 250 59 25 500 65 85 500 15 57 750 89 35 750 79 22 1000 98 41 1000

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