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• Linq to Sql statement for multiple aggregate values in single expression

  - by Jeff Langston

  How would I write a linq to sql statement for the following tsql? select count(*), sum(Amount), avg(Amount), min(Amount), max(Amount) from TableName

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• Aggregate path counts using HierarchyID

  - by austincav

  Business problem - understand process fallout using analytics data. Here is what we have done so far: Build a dictionary table with every possible process step Find each process "start" Find the last step for each start Join dictionary table to last step to find path to final step In the final report output we end up with a list of paths for each start to each final step: User Fallout Step HierarchyID.ToString() A 1/1/1 B 1/1/1/1/1 C 1/1/1/1 D 1/1/1 E 1/1 What this means is that five users (A-E) started the process. Assume only User B finished, the other four did not. Since this is a simple example (without branching) we want the output to look as follows: Step Unique Users 1 5 2 5 3 4 4 2 5 1 The easiest solution I could think of is to take each hierarchyID.ToString(), parse that out into a set of subpaths, JOIN back to the dictionary table, and output using GROUP BY. Given the volume of data, I'd like to use the built-in HierarchyID functions, e.g. IsAncestorOf. Any ideas or thoughts how I could write this? Maybe a recursive CTE?

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• Linq Aggregate on object and List

  - by Kris-I

  I do this query with NHibernate: var test = _session.CreateCriteria(typeof(Estimation)) .SetFetchMode("EstimationItems", FetchMode.Eager) .List(); An "Estimation" can have several "EstimationItems" (Quantity, Price and ProductId) I'd like a list of "Estimation" with these constraints : One line by "Estimation" code on the picture (ex : 2011/0001 and 2011/0003) By estimation (means on each line) the number of "EstimationItems" By Estimation (means on each line) the total price (Quantity * Price) for each "EstimationItems" I hope the structure will be clearer with the picture below. Thanks,

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• How cast in XML for aggregate functions

  - by renegm

  In SQL Server 2008. I need execute a query like that: DECLARE @x AS xml SET @x=N'<r><c>First Text</c></r><r><c>Other Text</c></r>' SELECT @x.query('fn:max(r/c)') But return nothing (apparently because convert xdt:untypedAtomic to numeric) How to "cast" r/c to varchar? Something like SELECT @x.query('fn:max(«CAST(r/c «AS varchar(20))»)') Edit: Using Nodes the function MAX is from T-SQL no fn:max function In this code: DECLARE @x xml; SET @x = ''; SELECT @x.query('fn:max((1, 2))'); SELECT @x.query('fn:max(("First Text", "Other Text"))'); both query return expected: 2 and "Other Text" fn:max can evaluate string expression ad hoc. But the first query dont work. How to force string arguments to fn:max?

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• Returning multiple aggregate functions as rows

  - by SDLFunTimes

  I need some help formulating a select statement. I need to select the total quantity shipped for each part with a distinct color. So the result should be a row with the color name and the total. Here's my schema: create table s ( sno char(5) not null, sname char(20) not null, status smallint, city char(15), primary key (sno) ); create table p ( pno char(6) not null, pname char(20) not null, color char(6), weight smallint, city char(15), primary key (pno) ); create table sp ( sno char(5) not null, pno char(6) not null, qty integer not null, primary key (sno, pno) );

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• Using reshape + cast to aggregate over multiple columns

  - by DamonJW

  In R, I have a data frame with columns for Seat (factor), Party (factor) and Votes (numeric). I want to create a summary data frame with columns for Seat, Winning party, and Vote share. For example, from the data frame df <- data.frame(party=rep(c('Lab','C','LD'),times=4), votes=c(1,12,2,11,3,10,4,9,5,8,6,15), seat=rep(c('A','B','C','D'),each=3)) I want to get the output seat winner voteshare 1 A C 0.8000000 2 B Lab 0.4583333 3 C C 0.5000000 4 D LD 0.5172414 I can figure out how to achieve this. But I'm sure there must be a better way, probably a cunning one-liner using Hadley Wickham's reshape package. Any suggestions? For what it's worth, my solution uses a function from my package djwutils_2.10.zip and is invoked as follows. But there are all sorts of special cases it doesn't deal with, so I'd rather rely on someone else's code. aggregateList(df, by=list(seat=seat), FUN=list(winner=function(x) x$party[which.max(x$votes)], voteshare=function(x) max(x$votes)/sum(x$votes)))

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• Linq order by aggregate in the select { }

  - by Joe Pitz

  Here is one I am working on: var fStep = from insp in sq.Inspections where insp.TestTimeStamp > dStartTime && insp.TestTimeStamp < dEndTime && insp.Model == "EP" && insp.TestResults != "P" group insp by new { insp.TestResults, insp.FailStep } into grp select new { FailedCount = (grp.Key.TestResults == "F" ? grp.Count() : 0), CancelCount = (grp.Key.TestResults == "C" ? grp.Count() : 0), grp.Key.TestResults, grp.Key.FailStep, PercentFailed = Convert.ToDecimal(1.0 * grp.Count() /tcount*100) } ; I would like to orderby one or more of the fields in the select projection.

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• SQL aggregate query question

  - by Phil

  Hi, Can anyone help me with a SQL query in Apache Derby SQL to get a "simple" count. Given a table ABC that looks like this... id a b c 1 1 1 1 2 1 1 2 3 2 1 3 4 2 1 1 ** 5 2 1 2 ** ** 6 2 2 1 ** 7 3 1 2 8 3 1 3 9 3 1 1 How can I write a query to get a count of how may distinct values of 'a' have both (b=1 and c=2) AND (b=2 and c=1) to get the correct result of 1. (the two rows marked match the criteria and both have a value of a=2, there is only 1 distinct value of a in this table that match the criteria) The tricky bit is that (b=1 and c=2) AND (b=2 and c=1) are obviously mutually exclusive when applied to a single row. .. so how do I apply that expression across multiple rows of distinct values for a? These queries are wrong but to illustrate what I'm trying to do... "SELECT DISTINCT COUNT(a) WHERE b=1 AND c=2 AND b=2 AND c=1 ..." .. (0) no go as mutually exclusive "SELECT DISTINCT COUNT(a) WHERE b=1 AND c=2 OR b=2 AND c=1 ..." .. (3) gets me the wrong result. SELECT COUNT(a) (CASE WHEN b=1 AND c=10 THEN 1 END) FROM ABC WHERE b=2 AND c=1 .. (0) no go as mutually exclusive Cheers, Phil.

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• Need help optimizing this Django aggregate query

  - by Chris Lawlor

  I have the following model class Plugin(models.Model): name = models.CharField(max_length=50) # more fields which represents a plugin that can be downloaded from my site. To track downloads, I have class Download(models.Model): plugin = models.ForiegnKey(Plugin) timestamp = models.DateTimeField(auto_now=True) So to build a view showing plugins sorted by downloads, I have the following query: # pbd is plugins by download - commented here to prevent scrolling pbd = Plugin.objects.annotate(dl_total=Count('download')).order_by('-dl_total') Which works, but is very slow. With only 1,000 plugins, the avg. response is 3.6 - 3.9 seconds (devserver with local PostgreSQL db), where a similar view with a much simpler query (sorting by plugin release date) takes 160 ms or so. I'm looking for suggestions on how to optimize this query. I'd really prefer that the query return Plugin objects (as opposed to using values) since I'm sharing the same template for the other views (Plugins by rating, Plugins by release date, etc.), so the template is expecting Plugin objects - plus I'm not sure how I would get things like the absolute_url without a reference to the plugin object. Or, is my whole approach doomed to failure? Is there a better way to track downloads? I ultimately want to provide users some nice download statistics for the plugins they've uploaded - like downloads per day/week/month. Will I have to calculate and cache Downloads at some point? EDIT: In my test dataset, there are somewhere between 10-20 Download instances per Plugin - in production I expect this number would be much higher for many of the plugins.

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• need help in aggregate select

  - by eugeneK

  Hi, i have a problem with selecting some values from my DB. DB is in design stages so i can redesign it a bit of needed. You can see the Diagram on this image Basically what i want to select is select c.campaignID, ct.campaignTypeName, c.campaignName, c.campaignDailyBudget, c.campaignTotalBudget, c.campaignCPC, c.date, cs.campaignStatusName ***impressions, ***clicks, ***cast(campaignTotalBudget-(clicks*campaignCPC) as decimal(18,1)) as remainingFunds from Campaigns as c left join CampaignTypes as ct on c.campaignTypeID=ct.campaignTypeID left join CampaignStatuses as cs on c.campaignStatusID=cs.campaignStatusID left join CampaignVariants as cv on c.campaignID=cv.campaignID left join CampaignVariants2Visitors as c2v on cv.campaignVariantID=c2v.campaignVariantID left join Visitors as v on c2v.visitorID=v.visitorID ..... order by c.campaignID desc Problem is that Visitors table has column named isClick so i don't know the way to separate what is impression with isClick=false and what is click isClick=true so i can show nice form with all the stuff about campaign and visitors... I don't think to split Visitors to two tables like Impressions and Click is a good idea because again i would need to have Visitors with two more tables thanks

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• T-SQL Self Join in combination with aggregate function

  - by Nick

  Hi, i have the following table. CREATE TABLE [dbo].[Tree]( [AutoID] [int] IDENTITY(1,1) NOT NULL, [Category] [varchar](10) NULL, [Condition] [varchar](10) NULL, [Description] [varchar](50) NULL, CONSTRAINT [PK_Tree] PRIMARY KEY CLUSTERED ( [AutoID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO the data looks like this: INSERT INTO [Test].[dbo].[Tree] ([Category] ,[Condition] ,[Description]) VALUES ('1','Alpha','Type 1') INSERT INTO [Test].[dbo].[Tree] ([Category] ,[Condition] ,[Description]) VALUES ('1','Alpha','Type 1') INSERT INTO [Test].[dbo].[Tree] ([Category] ,[Condition] ,[Description]) VALUES ('2','Alpha','Type 2') INSERT INTO [Test].[dbo].[Tree] ([Category] ,[Condition] ,[Description]) VALUES ('2','Alpha','Type 2') go I try now to do the following: SELECT Category,COUNT(*) as CategoryCount FROM Tree where Condition = 'Alpha' group by Category but i wish also to get the Description for each Element. I tried several subqueries, self joins etc. i always come to the problem that the subquery cannot return more than one record. The problem is caused by a poor database design which i cannot change and i run out of ideas to get this done in a single query ;-(

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• Elegant way to aggregate multi-dimensional array by index key

  - by Stephen J. Fuhry

  How can I recursively find the total value of all children of an array that looks something like this? [0] => Array ( [value] => ? // 8590.25 + 200.5 + 22.4 [children] => Array ( [0] => Array ( [value] => ? // 8590.25 + 200.5 [children] => Array ( [0] => Array ( [value] => 8590.25 // leaf node ) [1] => Array ( [value] => 200.05 // leaf node ) ) ) [1] => Array ( [value] => 22.4 // leaf node ) ) )

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• Aggregate SQL column values by time period

  - by user305688

  I have some numerical data that comes in every 5 minutes (i.e. 288 values per day, and quite a few days worth of data). I need to write a query that can return the sums of all values for each day. So currently the table looks like this: 03/30/2010 00:01:00 -- 553 03/30/2010 00:06:00 -- 558 03/30/2010 00:11:00 -- 565 03/30/2010 00:16:00 -- 565 03/30/2010 00:21:00 -- 558 03/30/2010 00:26:00 -- 566 03/30/2010 00:31:00 -- 553 ... And this goes on for 'x' number of days, I'd like the query to return 'x' number of rows, each of which containing the sum of all the values on each day. Something like this: 03/30/2010 -- <sum> 03/31/2010 -- <sum> 04/01/2010 -- <sum> The query will go inside a Dundas webpart, so unfortunately I can't write custom user functions to assist it. All the logic needs to be in just the one big query. Any help would be appreciated, thanks. I'm trying to get it to work using GROUP BY and DATEPART at the moment, not sure if it's the right way to go about it.

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• Elegant way to aggregate multi-demensional array by index key

  - by Stephen J. Fuhry

  How can I recursively find the total value of all children of an array that looks something like this? [0] => Array ( [value] => ? // 8590.25 + 200.5 + 22.4 [children] => Array ( [0] => Array ( [value] => ? // 8590.25 + 200.5 [children] => Array ( [0] => Array ( [value] => 8590.25 // leaf node ) [1] => Array ( [value] => 200.05 // leaf node ) ) ) [1] => Array ( [value] => 22.4 // leaf node ) ) )

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• how to aggregate information on UIImage?

  - by user1582281

  I want to draw on each drawing cycle 1000 more lines on my UIIMage, right now I do it by : -(void)drawRect { for(int i=0;i<1000;i++) { UIGraphicsBeginImageContext(myImage.size); code to draw line on current context... draw previous info from myImage: [myImage drawInRect:myRect]; //store info from context back to myImage myImage=UIGraphicsGetImageFromCurrentImageContext(); UIGraphicsEndImageContext(); } //append the image on the right side of current context: [myImage drawInRect:myRightRect]; } problem is that I think that drawing entire image each time just for the few lines added is very expensive, anyone has any idea how to optimize it?

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• Jocuri friv pentru toti

  - by haioase

  Jocurile online sunt o modalitate foarte simpla de a te relaxa in timpul liber si spun acest lucru deoarece nu ai altceva de facut decat sa te asezi in fata calculatorului, sa cauti pe internet ceea ce iti place si sa te joci cat vrei - sau cat de mult timp ai la dispozitie.Poate si din cauza ca sunt atat de cautate, industria jocurilor de pe internet a devenit tot mai infloritoare in ultima decada.Un site unde poti sa gasesti o multitudine de jocuri flash este si friv.me.uk games - Play your favorite online game, site dedicat in exclusivitate jocurilor friv de tot felul. Dupa cum se vede din titlu este in limba engleza, dar acest lucru nu cred ca reprezinta un impediment pentru vizitatorii din lumea intreaga care viziteaza www.friv.me.uk , deoarece astazi pana si copiii de gradinita stiu semnificatia cuvintelor play this game sau click here to play.Daca va intrebati ce sa alegeti din multele jocuri de acolo, v-as sugera sa incercati noile jocuri friv de strategie, deoarece sunt atat haioase, cat si interesante si educative pentru cei mici. Nu iti trebuie dexteritate in apasarea tastelor, ci o minte organizata, deoarece trebuie sa iti faci un plan de aparare foarte bun pentru a putea castiga un joc de genul lui Bloons tower defense, de exemplu.Fetelor care vor sa se joace pe friv.me.uk as vrea sa le sugerez cateva jocuri speciale pentru ele, cum ar fi cele de gatit impreuna cu Dora. Se vor distra copios, preparand cea mai gustoasa pizza in bucataria virtuala a Dorei si, in acelasi timp, vor invata fractiile, deoarece trebuie sa imparta pizza in felii egale pentru toti cei aflati la masa.Acestea au fost doar cateva idei despre ce jocuri friv puteti sa va jucati in fiecare zi pe friv.me.uk. Voi alegeti orice va place si stati oricat vreti acolo, pentru ca este un site unde va puteti amuza foarte tare impreuna cu prietenii sau familia. Distractie placuta tuturor!

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• Fun with Aggregates

  - by Paul White

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

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• Pre-rentrée Oracle Open World 2012 : à vos agendas

  - by Eric Bezille

  A maintenant moins d'un mois de l’événement majeur d'Oracle, qui se tient comme chaque année à San Francisco, fin septembre, début octobre, les spéculations vont bon train sur les annonces qui vont y être dévoilées... Et sans lever le voile, je vous engage à prendre connaissance des sujets des "Key Notes" qui seront tenues par Larry Ellison, Mark Hurd, Thomas Kurian (responsable des développements logiciels) et John Fowler (responsable des développements systèmes) afin de vous donner un avant goût. Stratégie et Roadmaps Oracle Bien entendu, au-delà des séances plénières qui vous donnerons  une vision précise de la stratégie, et pour ceux qui seront sur place, je vous engage à ne pas manquer les séances d'approfondissement qui auront lieu dans la semaine, dont voici quelques morceaux choisis : "Accelerate your Business with the Oracle Hardware Advantage" avec John Fowler, le lundi 1er Octobre, 3:15pm-4:15pm "Why Oracle Softwares Runs Best on Oracle Hardware" , avec Bradley Carlile, le responsable des Benchmarks, le lundi 1er Octobre, 12:15pm-13:15pm "Engineered Systems - from Vision to Game-changing Results", avec Robert Shimp, le lundi 1er Octobre 1:45pm-2:45pm "Database and Application Consolidation on SPARC Supercluster", avec Hugo Rivero, responsable dans les équipes d'intégration matériels et logiciels, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle’s SPARC Server Strategy Update", avec Masood Heydari, responsable des développements serveurs SPARC, le mardi 2 Octobre, 10:15am - 11:15am "Oracle Solaris 11 Strategy, Engineering Insights, and Roadmap", avec Markus Flier, responsable des développements Solaris, le mercredi 3 Octobre, 10:15am - 11:15am "Oracle Virtualization Strategy and Roadmap", avec Wim Coekaerts, responsable des développement Oracle VM et Oracle Linux, le lundi 1er Octobre, 12:15pm-1:15pm "Big Data: The Big Story", avec Jean-Pierre Dijcks, responsable du développement produits Big Data, le lundi 1er Octobre, 3:15pm-4:15pm "Scaling with the Cloud: Strategies for Storage in Cloud Deployments", avec Christine Rogers,  Principal Product Manager, et Chris Wood, Senior Product Specialist, Stockage , le lundi 1er Octobre, 10:45am-11:45am Retours d'expériences et témoignages Si Oracle Open World est l'occasion de partager avec les équipes de développement d'Oracle en direct, c'est aussi l'occasion d'échanger avec des clients et experts qui ont mis en oeuvre  nos technologies pour bénéficier de leurs retours d'expériences, comme par exemple : "Oracle Optimized Solution for Siebel CRM at ACCOR", avec les témoignages d'Eric Wyttynck, directeur IT Multichannel & CRM  et Pascal Massenet, VP Loyalty & CRM systems, sur les bénéfices non seulement métiers, mais également projet et IT, le mercredi 3 Octobre, 1:15pm-2:15pm "Tips from AT&T: Oracle E-Business Suite, Oracle Database, and SPARC Enterprise", avec le retour d'expérience des experts Oracle, le mardi 2 Octobre, 11:45am-12:45pm "Creating a Maximum Availability Architecture with SPARC SuperCluster", avec le témoignage de Carte Wright, Database Engineer à CKI, le mercredi 3 Octobre, 11:45am-12:45pm "Multitenancy: Everybody Talks It, Oracle Walks It with Pillar Axiom Storage", avec le témoignage de Stephen Schleiger, Manager Systems Engineering de Navis, le lundi 1er Octobre, 1:45pm-2:45pm "Oracle Exadata for Database Consolidation: Best Practices", avec le retour d'expérience des experts Oracle ayant participé à la mise en oeuvre d'un grand client du monde bancaire, le lundi 1er Octobre, 4:45pm-5:45pm "Oracle Exadata Customer Panel: Packaged Applications with Oracle Exadata", animé par Tim Shetler, VP Product Management, mardi 2 Octobre, 1:15pm-2:15pm "Big Data: Improving Nearline Data Throughput with the StorageTek SL8500 Modular Library System", avec le témoignage du CTO de CSC, Alan Powers, le jeudi 4 Octobre, 12:45pm-1:45pm "Building an IaaS Platform with SPARC, Oracle Solaris 11, and Oracle VM Server for SPARC", avec le témoignage de Syed Qadri, Lead DBA et Michael Arnold, System Architect d'US Cellular, le mardi 2 Octobre, 10:15am-11:15am "Transform Data Center TCO with Oracle Optimized Servers: A Customer Panel", avec les témoignages notamment d'AT&T et Liberty Global, le mardi 2 Octobre, 11:45am-12:45pm "Data Warehouse and Big Data Customers’ View of the Future", avec The Nielsen Company US, Turkcell, GE Retail Finance, Allianz Managed Operations and Services SE, le lundi 1er Octobre, 4:45pm-5:45pm "Extreme Storage Scale and Efficiency: Lessons from a 100,000-Person Organization", le témoignage de l'IT interne d'Oracle sur la transformation et la migration de l'ensemble de notre infrastructure de stockage, mardi 2 Octobre, 1:15pm-2:15pm Echanges avec les groupes d'utilisateurs et les équipes de développement Oracle Si vous avez prévu d'arriver suffisamment tôt, vous pourrez également échanger dès le dimanche avec les groupes d'utilisateurs, ou tous les soirs avec les équipes de développement Oracle sur des sujets comme : "To Exalogic or Not to Exalogic: An Architectural Journey", avec Todd Sheetz - Manager of DBA and Enterprise Architecture, Veolia Environmental Services, le dimanche 30 Septembre, 2:30pm-3:30pm "Oracle Exalytics and Oracle TimesTen for Exalytics Best Practices", avec Mark Rittman, de Rittman Mead Consulting Ltd, le dimanche 30 Septembre, 10:30am-11:30am "Introduction of Oracle Exadata at Telenet: Bringing BI to Warp Speed", avec Rudy Verlinden & Eric Bartholomeus - Managers IT infrastructure à Telenet, le dimanche 30 Septembre, 1:15pm-2:00pm "The Perfect Marriage: Sun ZFS Storage Appliance with Oracle Exadata", avec Melanie Polston, directeur, Data Management, de Novation et Charles Kim, Managing Director de Viscosity, le dimanche 30 Septembre, 9:00am-10am "Oracle’s Big Data Solutions: NoSQL, Connectors, R, and Appliance Technologies", avec Jean-Pierre Dijcks et les équipes de développement Oracle, le lundi 1er Octobre, 6:15pm-7:00pm Testez et évaluez les solutions Et pour finir, vous pouvez même tester les technologies au travers du Oracle DemoGrounds, (1133 Moscone South pour la partie Systèmes Oracle, OS, et Virtualisation) et des "Hands-on-Labs", comme : "Deploying an IaaS Environment with Oracle VM", le mardi 2 Octobre, 10:15am-11:15am "Virtualize and Deploy Oracle Applications in Minutes with Oracle VM: Hands-on Lab", le mardi 2 Octobre, 11:45am-12:45pm (il est fortement conseillé d'avoir suivi le "Hands-on-Labs" précédent avant d'effectuer ce Lab. "x86 Enterprise Cloud Infrastructure with Oracle VM 3.x and Sun ZFS Storage Appliance", le mercredi 3 Octobre, 5:00pm-6:00pm "StorageTek Tape Analytics: Managing Tape Has Never Been So Simple", le mercredi 3 Octobre, 1:15pm-2:15pm "Oracle’s Pillar Axiom 600 Storage System: Power and Ease", le lundi 1er Octobre, 12:15pm-1:15pm "Enterprise Cloud Infrastructure for SPARC with Oracle Enterprise Manager Ops Center 12c", le lundi 1er Octobre, 1:45pm-2:45pm "Managing Storage in the Cloud", le mardi 2 Octobre, 5:00pm-6:00pm "Learn How to Write MapReduce on Oracle’s Big Data Platform", le lundi 1er Octobre, 12:15pm-1:15pm "Oracle Big Data Analytics and R", le mardi 2 Octobre, 1:15pm-2:15pm "Reduce Risk with Oracle Solaris Access Control to Restrain Users and Isolate Applications", le lundi 1er Octobre, 10:45am-11:45am "Managing Your Data with Built-In Oracle Solaris ZFS Data Services in Release 11", le lundi 1er Octobre, 4:45pm-5:45pm "Virtualizing Your Oracle Solaris 11 Environment", le mardi 2 Octobre, 1:15pm-2:15pm "Large-Scale Installation and Deployment of Oracle Solaris 11", le mercredi 3 Octobre, 3:30pm-4:30pm En conclusion, une semaine très riche en perspective, et qui vous permettra de balayer l'ensemble des sujets au coeur de vos préoccupations, de la stratégie à l'implémentation... Cette semaine doit se préparer, pour tailler votre agenda sur mesure, à travers les plus de 2000 sessions dont je ne vous ai fait qu'un extrait, et dont vous pouvez retrouver l'ensemble en ligne.

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• Fun with Aggregates

  - by Paul White

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

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• Les IRC (et chatrooms) de développeurs sont-ils élitistes ? Et le repère du manque de savoir vivre ?

  Les IRC (et chatrooms) de développeurs sont-ils élitistes ? Et le repère du manque de savoir vivre ? L'Internet est un lieu d'échange et parfois aussi, d'entraide. C'est du moins ce que l'on souhaiterait. Tout programmeur, aussi doué soit-il, a déjà connu dans sa vie un "blanc", un moment où il a besoin des conseils d'un spécialiste sur telle ou telle question spécifique. Que faire dans ces cas là ? Le réflexe le plus répandu : aller chercher de l'aide en posant ses questions sur un chat spécialisé, en espérant y trouver une solution. Sur IRC, poser une question "simple" (comprenez : qui peut-être résolue par un manuel) est un motif de châtiment immédiat. Mais, même en respectant c...

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• Configurer un module Scaime eNod3-C en CANopen sur M340 en Unity, par Bruno Guérangé

  Contrairement à la série TSX57 Premium, la gamme M340 de Schneider ne comporte pas de carte de pesage. Pour ajouter des voies de pesage sur une configuration il faut donc installer et raccorder des modules externes sur l'automate. Nous avons donc cherché des modules raccordables sur le réseau CANopen, CANopen est un bus de terrain ouvert et rapide, de plus il est intégré de série sur certaines CPU de la gamme M340. Un des modules connectable sur le réseau CANopen est le transmetteur de pesage Scaime eNod3-C. Scaime est une marque reconnue dans le domaine du pesage et le module eNod3-C présente de très bonnes caractéristiques de mesure et filtrage. ...

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• Le marché des serveurs HTC attend les nouveaux processeurs Intel, qui seront dévoilés dans quelques

  Le marché des serveurs HTC attend les nouveaux processeurs Intel, qui seront dévoilés dans quelques jours Le mardi 30 mars devrait logiquement être la date de présentation par Intel d'une volée de nouveaux processeurs, y compris de la gamme Xeon. De cette "annonciation" attendue fébrilement par les plus grands constructeurs, devrait découler le visage des serveurs de nouvelle génération. D'après les déclarations de ces groupes, on peut s'attendre à une nouvelle génération de serveurs-calculateurs extrêmement puissants, dont certains seraient conçus autour des processeurs Xeon de milieu de gamme. Les nouvelles architectures multi-coeurs des puces promettent de belles avancées et ouvrent de nou...

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• Quel est votre mentor informatique ? Quel développeur célèbre est votre plus grande inspiration ?

  Quel est votre mentor informatique ? Quel développeur célèbre est votre plus grande inspiration ? Il n'y a pas que dans le showbizness qu'il y a des héros, dans l'industrie du développement de logiciels, certains noms sortent également du lot. Et ces programmeurs hors du commun deviennent parfois les modèles de certains. Il y a des fans de Madonna, de Brad Pitt, de Ribéry, de Claude Monet, etc... (ou de Chantal Goya, mais c'est plus rare). Et en informatique ? Quelle personnalité de ce milieu est la plus admirée ? Voici une liste des principaux acteurs de ce secteur : James Gosling Un développeur Java qui vient de quitter Oracle. Il a déjà fait tellement de choses dans sa carrière,...

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• Plataforma social Google+ innovación para desarrolladores

  Plataforma social Google+ innovación para desarrolladores En este programa presentaremos una visión general de las novedades tecnológicas desde el equipo de relaciones para desarrolladores de la región de sur de Latinoamérica. Seguiremos presentando nuestro enfoque de desarrollo, ingeniería y las mejores prácticas para implementar tecnología Google favoreciendo la evolución de soluciones tecnológicas. Luego nos introduciremos en un escenario técnico en donde analizaremos la solución de social de Google+ para desarrolladores, opciones y posibilidad de innovar en este ecosistema. Finalmente estaremos conversando con la comunidad de desarrollo, resolviendo un desafío técnico y premiando todo el talento regional. From: GoogleDevelopers Views: 0 0 ratings Time: 00:00 More in Education

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• RIM se recentre sur les entreprises, mais n'abandonne pas le grand public, la sombre période continue pour BlackBerry

  RIM se recentre sur les entreprises mais n'abandonne pas le grand public, la sombre période continue pour le constructeur du BlackBerry Ça va de mal en pire pour RIM. Le constructeur canadien du BlackBerry a du mal à sortir de la période sombre qu'il traverse depuis plusieurs mois, et accumule des résultats de plus en plus mitigés. Le constructeur a révélé lors de la présentation de son résultat annuel que son chiffre d'affaires a reculé de 23% (soit une baisse de 5,6 milliards de dollars) au dernier trimestre fiscal par rapport à un an plus tôt. Au quatrième trimestre, le constructeur a réalisé une perte de 125 millions de dollars avec un chiffre d'affaires de 4,5 millia...

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