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  • Delphi Clientdataset Lookup/Aggregate

    - by TheRoadrunner
    Hi, I need a little help with ClientDatasets in Delphi. What I want to achieve is a grid showing customers, where one of the columns shows the number of orders for each customer. I put a ClientDataset on a form and load Customers.xml from Delphi demo-data. Another ClienDataset is loaded with orders.xml. Relatively simple, I can define an aggregate on the orders CDS showing the total amount per customer (or the count). (See Cary Jensens article on this: http://edn.embarcadero.com/article/29272) The problem is getting this aggregate result from orders dataset into the customer dataset. It is kind of an reverse lookup, since there is a 1-n relationship between customers and orders, not an n-1 as normally in lookup scenarios. Any ideas ? Søren

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  • Non-trivial functions that operate on any monad

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

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  • Oracle User definied aggregate function for varray of varchar

    - by baju
    I am trying to write some aggregate function for the varray and I get this error code when I'm trying to use it with data from the DB: ORA-00600 internal error code, arguments: [kodpunp1], [], [], [], [], [], [], [], [], [], [], [] [koxsihread1], [0], [3989], [45778], [], [], [], [], [], [], [], [] Code of the function is really simple(in fact it does nothing ): create or replace TYPE "TEST_VECTOR" as varray(10) of varchar(20) ALTER TYPE "TEST_VECTOR" MODIFY LIMIT 4000 CASCADE create or replace type Test as object( lastVector TEST_VECTOR, STATIC FUNCTION ODCIAggregateInitialize(sctx in out Test) return number, MEMBER FUNCTION ODCIAggregateIterate(self in out Test, value in TEST_VECTOR) return number, MEMBER FUNCTION ODCIAggregateMerge(self IN OUT Test, ctx2 IN Test) return number, MEMBER FUNCTION ODCIAggregateTerminate(self IN Test, returnValue OUT TEST_VECTOR, flags IN number) return number ); create or replace type body Test is STATIC FUNCTION ODCIAggregateInitialize(sctx in out Test) return number is begin sctx := Test(TEST_VECTOR()); return ODCIConst.Success; end; MEMBER FUNCTION ODCIAggregateIterate(self in out Test, value in TEST_VECTOR) return number is begin self.lastVector := value; return ODCIConst.Success; end; MEMBER FUNCTION ODCIAggregateMerge(self IN OUT Test, ctx2 IN Test) return number is begin return ODCIConst.Success; end; MEMBER FUNCTION ODCIAggregateTerminate(self IN Test, returnValue OUT TEST_VECTOR, flags IN number) return number is begin returnValue := self.lastVector; return ODCIConst.Success; end; end; create or replace FUNCTION test_fn (input TEST_VECTOR) RETURN TEST_VECTOR PARALLEL_ENABLE AGGREGATE USING Test; Next I create some test data: create table t1_test_table( t1_id number not null, t1_value TEST_VECTOR not null, Constraint PRIMARY_KEY_1 PRIMARY KEY (t1_id) ) Next step is to put some data to the table insert into t1_test_table (t1_id,t1_value) values (1,TEST_VECTOR('x','y','z')) Now everything is prepared to perform queries: Select test_fn(TEST_VECTOR('y','x')) from dual Query above work well Select test_fn(t1_value) from t1_test_table where t1_id = 1 Version of Oracle DBMS I use: 11.2.0.3.0 Does anyone tried do such a thing? What can be the reason that it does not work? How to solve it? Thanks in advance for help.

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  • Functional Methods on Collections

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

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  • LINQ and Aggregate function

    - by vik20000in
    LINQ also provides with itself important aggregate function. Aggregate function are function that are applied over a sequence like and return only one value like Average, count, sum, Maximum etc…Below are some of the Aggregate functions provided with LINQ and example of their implementation. Count     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count();The below example provided count for only odd number.     int[] primeFactorsOf300 = { 2, 2, 3, 5, 5 };     int uniqueFactors = primeFactorsOf300.Distinct().Count(n => n%2 = 1);  Sum     int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 };        double numSum = numbers.Sum();  Minimum      int minNum = numbers.Min(); Maximum      int maxNum = numbers.Max();Average      double averageNum = numbers.Average();  Aggregate      double[] doubles = { 1.7, 2.3, 1.9, 4.1, 2.9 };     double product = doubles.Aggregate((runningProduct, nextFactor) => runningProduct * nextFactor);  Vikram

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  • SPARC T4-2 Produces World Record Oracle Essbase Aggregate Storage Benchmark Result

    - by Brian
    Significance of Results Oracle's SPARC T4-2 server configured with a Sun Storage F5100 Flash Array and running Oracle Solaris 10 with Oracle Database 11g has achieved exceptional performance for the Oracle Essbase Aggregate Storage Option benchmark. The benchmark has upwards of 1 billion records, 15 dimensions and millions of members. Oracle Essbase is a multi-dimensional online analytical processing (OLAP) server and is well-suited to work well with SPARC T4 servers. The SPARC T4-2 server (2 cpus) running Oracle Essbase 11.1.2.2.100 outperformed the previous published results on Oracle's SPARC Enterprise M5000 server (4 cpus) with Oracle Essbase 11.1.1.3 on Oracle Solaris 10 by 80%, 32% and 2x performance improvement on Data Loading, Default Aggregation and Usage Based Aggregation, respectively. The SPARC T4-2 server with Sun Storage F5100 Flash Array and Oracle Essbase running on Oracle Solaris 10 achieves sub-second query response times for 20,000 users in a 15 dimension database. The SPARC T4-2 server configured with Oracle Essbase was able to aggregate and store values in the database for a 15 dimension cube in 398 minutes with 16 threads and in 484 minutes with 8 threads. The Sun Storage F5100 Flash Array provides more than a 20% improvement out-of-the-box compared to a mid-size fiber channel disk array for default aggregation and user-based aggregation. The Sun Storage F5100 Flash Array with Oracle Essbase provides the best combination for large Oracle Essbase databases leveraging Oracle Solaris ZFS and taking advantage of high bandwidth for faster load and aggregation. Oracle Fusion Middleware provides a family of complete, integrated, hot pluggable and best-of-breed products known for enabling enterprise customers to create and run agile and intelligent business applications. Oracle Essbase's performance demonstrates why so many customers rely on Oracle Fusion Middleware as their foundation for innovation. Performance Landscape System Data Size(millions of items) Database Load(minutes) Default Aggregation(minutes) Usage Based Aggregation(minutes) SPARC T4-2, 2 x SPARC T4 2.85 GHz 1000 149 398* 55 Sun M5000, 4 x SPARC64 VII 2.53 GHz 1000 269 526 115 Sun M5000, 4 x SPARC64 VII 2.4 GHz 400 120 448 18 * – 398 mins with CALCPARALLEL set to 16; 484 mins with CALCPARALLEL threads set to 8 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 2 x 2.85 GHz SPARC T4 processors 128 GB memory 2 x 300 GB 10000 RPM SAS internal disks Storage Configuration: 1 x Sun Storage F5100 Flash Array 40 x 24 GB flash modules SAS HBA with 2 SAS channels Data Storage Scheme Striped - RAID 0 Oracle Solaris ZFS Software Configuration: Oracle Solaris 10 8/11 Installer V 11.1.2.2.100 Oracle Essbase Client v 11.1.2.2.100 Oracle Essbase v 11.1.2.2.100 Oracle Essbase Administration services 64-bit Oracle Database 11g Release 2 (11.2.0.3) HP's Mercury Interactive QuickTest Professional 9.5.0 Benchmark Description The objective of the Oracle Essbase Aggregate Storage Option benchmark is to showcase the ability of Oracle Essbase to scale in terms of user population and data volume for large enterprise deployments. Typical administrative and end-user operations for OLAP applications were simulated to produce benchmark results. The benchmark test results include: Database Load: Time elapsed to build a database including outline and data load. Default Aggregation: Time elapsed to build aggregation. User Based Aggregation: Time elapsed of the aggregate views proposed as a result of tracked retrieval queries. Summary of the data used for this benchmark: 40 flat files, each of size 1.2 GB, 49.4 GB in total 10 million rows per file, 1 billion rows total 28 columns of data per row Database outline has 15 dimensions (five of them are attribute dimensions) Customer dimension has 13.3 million members 3 rule files Key Points and Best Practices The Sun Storage F5100 Flash Array has been used to accelerate the application performance. Setting data load threads (DLTHREADSPREPARE) to 64 and Load Buffer to 6 improved dataloading by about 9%. Factors influencing aggregation materialization performance are "Aggregate Storage Cache" and "Number of Threads" (CALCPARALLEL) for parallel view materialization. The optimal values for this workload on the SPARC T4-2 server were: Aggregate Storage Cache: 32 GB CALCPARALLEL: 16   See Also Oracle Essbase Aggregate Storage Option Benchmark on Oracle's SPARC T4-2 Server oracle.com Oracle Essbase oracle.com OTN SPARC T4-2 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 28 August 2012.

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  • Using Groovy Aggregate Functions in ADF BC

    - by Sireesha Pinninti
    This article explains how groovy aggregate functions(sum, count, min, max and avg) can be used in ADF Business components and demonstrates how these can be used at entity and view level Let's consider EMP and DEPT tables and an usecase to track number of employees in each department   Entity-Level To use aggregate functions at entity level, we need to have association between entities representing master and child relationship and the destination accessor name is what we are going to use in our groovy Syntax: <Accessor>.count(Groovyexpression) - Note down the destination accessor name(EMP) in the association or AccessorAttribute name in source entity - Add a transient attribute in source entity with persistent property set to false and provide the groovy expression in the syntax provided above - Finally, Add newly added attribute to view object View-Level To use aggregate functions at view level, we need to have a view link between viewobjects representing master and child relationship and the destination accessor name is what we are going to use in our groovy Syntax: <ViewLinkAccessor>.count(Groovyexpression) - Note down the destination accessor name(EmpView) in the view link or viewLinkAccessor name in source view - Add a transient attribute in view object and provide a groovy aggregate function count as a value to it in the syntax provided above Now, If you run application module tester and execute DeptView / ViewLink, you should see employee count in EmpCount field  In similar way, one can use other groovy aggregate functions sum, avg, min and max.

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  • self join- how to use the aggregate functions

    - by Ranjana
    self join- how to use the aggregate functions select a.tablename, b.TableName,b.UserName from Employee a inner join Employee b on a.ColumnValue=b.ColumnValue and and a.TableName <> b.TableName and a.UserName=b.UserName and also to check whether the same user has count of records i.e Employee a = count of records of Employee b. how to add count function over here

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  • how to use the order by and aggregate function together in sql query

    - by Ranjana
    SELECT count(distinct req.requirementid), req.requirementid, org.organizationid,req. locationofposting,org.registereddate FROM OrganizationRegisteredDetails AS org, RequirementsDetailsforOrganization AS req WHERE org.organizationid =req.requirementid order by org.RegisteredDate desc this shows me the error : Column 'RequirementsDetailsforOrganization.RequirementID' is invalid in the select list because it is not contained in either an aggregate function or the GROUP BY clause. how to do the 'order by org.RegisteredDate desc' in this Query ....

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  • Using multiple aggregate functions in an algebraic expression in (ANSI) SQL statement

    - by morpheous
    I have the following aggregate functions (AGG FUNCs): foo(), foobar(), fredstats(), barneystats(). I want to know if I can use multiple AGG FUNCs in an algebraic expression. This may seem a strange/simplistic question for seasoned SQL developers - however, the but the reason I ask is that so far, all AGG FUNCs examples I have seen are of the simplistic variety e.g. max(salary) < 100, rather than using the AGG FUNCs in an expression which involves using multiple AGG FUNCs in an expression (like agg_func1() agg_func2()). The information below should help clarify further. Given tables with the following schemas: CREATE TABLE item (id int, length float, weight float); CREATE TABLE item_info (item_id, name varchar(32)); # Is it legal (ANSI) SQL to write queries of this format ? SELECT id, name, foo, foobar, fredstats FROM A, B (SELECT id, foo(123) as foo, foobar('red') as foobar, fredstats('weight') as fredstats FROM item GROUP BY id HAVING [ALGEBRAIC EXPRESSION] ORDER BY id AS A), item_info AS B WHERE item.id = B.id Where: ALGEBRAIC EXPRESSION is the type of expression that can be used in a WHERE clause - for example: ((foo(x) < foobar(y)) AND foobar(y) IN (1,2,3)) OR (fredstats(x) <> 0)) I am using PostgreSQL as the db, but I would prefer to use ANSI SQL wherever possible. Assuming it is legal to include AGG FUNCS in the way I have done above, I'd like to know: Is there a more efficient way to write the above query ? Is there any way I can speed up the query in terms of a judicious choice of indexes on the tables item and item_info ? Is there a performance hit of using AGG FUNCs in an algebraic expression like I am (i.e. an expression involving the output of aggregate functions rather than constants? Can the expression also include 'scaled' AGG FUNC? (for example: 2*foo(123) < -3*foobar(456) ) - will scaling (i.e. multiplying an AGG FUNC by a number have an effect on performance?) How can I write the query above using INNER JOINS instead?

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  • Linq Aggregate function

    - by Nyla Pareska
    I have a List like "test", "bla", "something", "else" But when I use the Aggrate on it and in the mean time call a function it seems to me that after 2 'iterations' the result of the first gets passed in? I am using it like : myList.Aggregate((current, next) => someMethod(current) + ", "+ someMethod(next)); and while I put a breakpoint in the someMethod function where some transformation on the information in the myList occurs, I notice that after the 3rd call I get a result from a former transformation as input paramter.

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  • Linq TakeWhile depending on sum (or aggregate) of elements

    - by martinweser
    I have a list of elements and want to takeWhile the sum (or any aggregation of the elements) satisfy a certain condition. The following code does the job, but i am pretty sure this is not an unusual problem for which a proper pattern should exist. var list = new List<int> { 1, 2, 3, 4, 5, 6, 7 }; int tmp = 0; var listWithSum = from x in list let sum = tmp+=x select new {x, sum}; int MAX = 10; var result = from x in listWithSum where x.sum < MAX select x.x; Does somebody know how to solve the task in nicer way, probably combining TakeWhile and Aggregate into one query? Thx

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  • NSPredicate aggregate function [SIZE] gives 'unsupported function expression' error

    - by jinglesthula
    iOS 4: I have entities in Core Data (using SQLite, which is a requirement) of: Request Response (which has a property personId) Revision Relationships are: Request <-- Revision Request <-- Response Revision <-- Response (e.g. each request may have many responses; each request/response pair may have many revisions) I'm trying to do a predicate to get all Responses with a given personId that have zero Revisions. Using: (personId == %d) && (Request.Revision[SIZE] == 0) in my predicate string gives me a runtime exception "Unsupported function expression Request.Revision[SIZE]" The documentation seems pretty sparse on aggregate functions, only noting that they exist, but with no syntax or examples. Not sure if it's my syntax or if the SIZE function really isn't supported in iOS.

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  • Using data.table to aggregate

    - by dayne
    After multiple suggestions from SO users, I am finally trying to convert my code over to using data.tables. library(data.table) DT <- data.table(plate = paste0("plate",rep(1:2,each=5)), id = rep(c("CTRL","CTRL","ID1","ID2","ID3"),2), val = 1:10) > DT plate id val 1: plate1 CTRL 1 2: plate1 CTRL 2 3: plate1 ID1 3 4: plate1 ID2 4 5: plate1 ID3 5 6: plate2 CTRL 6 7: plate2 CTRL 7 8: plate2 ID1 8 9: plate2 ID2 9 10: plate2 ID3 10 What I would like to do is take the average of DT[,val] by plate when the id is "CTRL". I would normally aggregate the data frame, then use match to map the values back to a new column, 'ctrl'. Using the data.table package I can get: DT[id=="CTRL",ctrl:=mean(val),by=plate] > DT plate id val ctrl 1: plate1 CTRL 1 1.5 2: plate1 CTRL 2 1.5 3: plate1 ID1 3 NA 4: plate1 ID2 4 NA 5: plate1 ID3 5 NA 6: plate2 CTRL 6 6.5 7: plate2 CTRL 7 6.5 8: plate2 ID1 8 NA 9: plate2 ID2 9 NA 10: plate2 ID3 10 NA What I need is really: DT <- data.table(plate = paste0("plate",rep(1:2,each=5)), id = rep(c("CTRL","CTRL","ID1","ID2","ID3"),2), val = 1:10, ctrl = rep(c(1.5,6.5),each=5)) > DT plate id val ctrl 1: plate1 CTRL 1 1.5 2: plate1 CTRL 2 1.5 3: plate1 ID1 3 1.5 4: plate1 ID2 4 1.5 5: plate1 ID3 5 1.5 6: plate2 CTRL 6 6.5 7: plate2 CTRL 7 6.5 8: plate2 ID1 8 6.5 9: plate2 ID2 9 6.5 10: plate2 ID3 10 6.5 Eventually I would like to use much more complicated selections of the values, but I do not know how to select specific values, run some function, then map those values back to the appropriate row using data frames.

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  • What method do you use to identify the Aggregate Roots in Domain Drive Design?

    - by Robert
    When applying Domain Driven Design to a project, how do you identify the Aggregate Roots? For example, in a standard E-Commerce website, you might say that the Order is one, and the User is the other. But what if your Users belong to a Company? Does that make your Company the aggregate root? I'm interested in hearing people's approaches to working out the Aggregate roots, and how to identify poorly chosen aggregate roots.

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  • Using Aggregate functions in DataView filters

    - by Shrewd Demon
    hi, i have a DataTable that has a column ("Profit"). What i want is to get the Sum of all the values in this table. I tried to do this in the following manner... DataTable dsTemp = new DataTable(); dsTemp.Columns.Add("Profit"); DataRow dr = null; dr = dsTemp.NewRow(); dr["Profit"] = 100; dsTemp.Rows.Add(dr); dr = dsTemp.NewRow(); dr["Profit"] = 200; dsTemp.Rows.Add(dr); DataView dvTotal = dsTemp.DefaultView; dvTotal.RowFilter = " SUM ( Profit ) "; DataTable dt = dvTotal.ToTable(); But i get an error while applying the filter... how can i get the Sum of the Profit column in a variable thank you...

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  • Semi-complex aggregate select statement confusion

    - by Ian Henry
    Alright, this problem is a little complicated, so bear with me. I have a table full of data. One of the table columns is an EntryDate. There can be multiple entries per day. However, I want to select all rows that are the latest entry on their respective days, and I want to select all the columns of said table. One of the columns is a unique identifier column, but it is not the primary key (I have no idea why it's there; this is a pretty old system). For purposes of demonstration, say the table looks like this: create table ExampleTable ( ID int identity(1,1) not null, PersonID int not null, StoreID int not null, Data1 int not null, Data2 int not null, EntryDate datetime not null ) The primary key is on PersonID and StoreID, which logically defines uniqueness. Now, like I said, I want to select all the rows that are the latest entries on that particular day (for each Person-Store combination). This is pretty easy: --Figure 1 select PersonID, StoreID, max(EntryDate) from ExampleTable group by PersonID, StoreID, dbo.dayof(EntryDate) Where dbo.dayof() is a simple function that strips the time component from a datetime. However, doing this loses the rest of the columns! I can't simply include the other columns, because then I'd have to group by them, which would produce the wrong results (especially since ID is unique). I have found a dirty hack that will do what I want, but there must be a better way -- here's my current solution: select cast(null as int) as ID, PersonID, StoreID, cast(null as int) as Data1, cast(null as int) as Data2, max(EntryDate) as EntryDate into #StagingTable from ExampleTable group by PersonID, StoreID, dbo.dayof(EntryDate) update Target set ID = Source.ID, Data1 = Source.Data1, Data2 = Source.Data2, from #StagingTable as Target inner join ExampleTable as Source on Source.PersonID = Target.PersonID and Source.StoreID = Target.StoreID and Source.EntryDate = Target.EntryDate This gets me the correct data in #StagingTable but, well, look at it! Creating a table with null values, then doing an update to get the values back -- surely there's a better way to do this? A single statement that will get me all the values the first time? It is my belief that the correct join on that original select (Figure 1) would do the trick, like a self-join or something... but how do you do that with the group by clause? I cannot find the right syntax to make the query execute. I am pretty new with SQL, so it's likely that I'm missing something obvious. Any suggestions? (Working in T-SQL, if it makes any difference)

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  • Simple LINQ Aggregate Query

    - by Steven
    What is the vb.net equivalent of the following psuedo-code using LINQ? select min(credits) minCredits, max(credits) maxCredits, min(dollars) minDollars, max(dollars) maxDollars from players minCredits_lbl.Text = minCredits ... maxDollars_lbl.Text = maxDollars I have the following, but I can't figure out how to get any further. Dim query = From row in myDataSet.Tables("Players") _ Select credits = row("credits"), dollars = row("dollars")

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  • How to aggregate over few types with linq?

    - by Shimmy
    Can someone help me translate the following to one liner: Dim items As New List(Of Object) For Each c In ph.Contacts items.Add(New With {.Type = "Contact", .Id = c.ContactId, .Title = c.Title}) Next For Each c In ph.Persons items.Add(New With {.Type = "Person", .Id = c.PersonId, .Title = c.Title}) Next For Each c In ph.Jobs items.Add(New With {.Type = "Job", .Id = c.JobId, .Title = c.Title}) Next Is it possible to merge them all into one query or method line, I don't really care if this will be done with something other than linq, I am just looking for a more efficient way as I have a long list coming ahead, and the aggregating list will be strongly-typed using Dim list = blah blah

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  • Core Data @sum aggregate

    - by nasim
    I am getting an exception when I try to get @sum on a column in iPhone Core-Data application. My two models are following - Task model: @interface Task : NSManagedObject { } @property (nonatomic, retain) NSString * taskName; @property (nonatomic, retain) NSSet* completion; @end @interface Task (CoreDataGeneratedAccessors) - (void)addCompletionObject:(NSManagedObject *)value; - (void)removeCompletionObject:(NSManagedObject *)value; - (void)addCompletion:(NSSet *)value; - (void)removeCompletion:(NSSet *)value; @end Completion model: @interface Completion : NSManagedObject { } @property (nonatomic, retain) NSNumber * percentage; @property (nonatomic, retain) NSDate * time; @property (nonatomic, retain) Task * task; @end And here is the fetch: NSFetchRequest *request = [[NSFetchRequest alloc] init]; request.entity = [NSEntityDescription entityForName:@"Task" inManagedObjectContext:context]; NSSortDescriptor *sortDescriptor = [[NSSortDescriptor alloc] initWithKey:@"taskName" ascending:YES]; request.sortDescriptors = [NSArray arrayWithObject:sortDescriptor]; NSError *error; NSArray *results = [context executeFetchRequest:request error:&error]; NSArray *parents = [results valueForKeyPath:@"taskName"]; NSArray *children = [results valueForKeyPath:@"[email protected]"]; NSLog(@"%@ %@", parents, children); [request release]; [sortDescriptor release]; The exception is thrown at the fourth line from bottom. The thrown exception is: *** -[NSCFSet decimalValue]: unrecognized selector sent to instance 0x3b25a30 *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: '*** -[NSCFSet decimalValue]: unrecognized selector sent to instance 0x3b25a30' I would very much appreciate any kind of help. Thanks.

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  • Aggregate Functions on subsets of data based on current row values with SQL

    - by aasukisuki
    Hopefully that title makes sense... Let's say I have an employee table: ID | Name | Title | Salary ---------------------------- 1 | Bob | Manager | 15285 2 | Joe | Worker | 10250 3 | Al | Worker | 11050 4 | Paul | Manager | 16025 5 | John | Worker | 10450 What I'd like to do is write a query that will give me the above table, along with an averaged salary column, based on the employee title: ID | Name | Title | Salary | Pos Avg -------------------------------------- 1 | Bob | Manager | 15285 | 15655 2 | Joe | Worker | 10250 | 10583 3 | Al | Worker | 11050 | 10583 4 | Paul | Manager | 16025 | 15655 5 | John | Worker | 10450 | 10583 I've tried doing this with a sub-query along the lines of: Select *, (select Avg(e2.salary) from employee e2 where e2.title = e.title) from employee e But I've come to realize that the sub-query is executed first, and has no knowledge of the table alias'd e I'm sure I'm missing something REALLY obvious here, can anyone point me in the right diretion?

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  • MYSQL : First and last record of a grouped record (aggregate functions)

    - by Jimmy
    I am trying to do fectch the first and the last record of a 'grouped' record. More precisely, I am doing a query like this SELECT MIN(low_price), MAX(high_price), open, close FROM symbols WHERE date BETWEEN(.. ..) GROUP BY YEARWEEK(date) but I'd like to get the first and the last record of the group. It could by done by doing tons of requests but I have a quite large table. Is there a [low processing time if possible] way to do this with MySQL?

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  • Sql: simultaneous aggregate from two tables

    - by Ash
    I have two tables: a Files table, which includes the file type, and a File Properties table, which references the file table via a foreign key. Sample Files table: | id | name | type | --------------------- | 1 | file1 | zip | | 2 | file2 | zip | | 3 | file3 | zip | | 4 | file4 | jpg | And the Properties table: | file_id | property | ----------------------- | 1 | x | | 2 | x | I want to make a query, which shows the count of each file type, and how many files of that type have a property. So in the example, the result would be | type | filecount | prop count | ---------------------------------- | zip | 3 | 2 | | jpg | 1 | 0 | I could accomplish this by select f.type, (select count(id) from files where type = f.type), count(fp.id) from files as f, file_properties as fp where f.id = fp.file_id group by f.type; But this seems very suboptimal and is very slow. Any better way to do this?

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  • aggregate over several variables in r

    - by Misha
    Dear overflowers, I have a rather large dataset in a long format where I need to count the number of instances of the ID due to two different variables, A & B. E.g. The same person can be represented in multiple rows due to either A or B. What I need to do is to count the number of instances of ID which is not too hard, but also count the number of ID due to A and B and return these as variables in the dataset. Regards, //Mi

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  • For each level of factor aggregate values over all levels except the current one (in R)

    - by Andrey Chetverikov
    For each level of factor I need to extract values aggregated over all subsets of data.frame except the current one. For example, there is a several subjects doing a reaction time task during several days, and I need to compute mean reaction time for all subjects and all days, but not including the subject for whom the mean is computed. Currently, I do it like this: library(lme4) ddply(sleepstudy, .(Subject, Days), summarise , avg_rt=mean(sleepstudy[sleepstudy$Subject!=Subject&sleepstudy$Days==Days,"Reaction"]), .progress="text") It works fine for small data sets, but for large ones it can be very slow. Is there a way to do it faster?

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