self-join

Im trying to calculate the amount of money won by all the offspring of a male race horse (Sire) over a time period. Listed by the Sire with the most amount of money won. I run the query and get the result Im after with one problem, I cant display the sires name, only their ID. SELECT `horses`.`SireID` AS `SireID` , `horses`.`HorseName` AS `Sire Name`, COUNT( `runs`.`HorsesID` ) AS `Runs` , COUNT( CASE WHEN `runs`.`Finish` =1 THEN 1 ELSE NULL END ) AS `Wins` , CONCAT( FORMAT( ( COUNT( CASE WHEN `runs`.`Finish` =1 THEN 1 ELSE NULL END ) / COUNT ( `runs`.`TrainersID` ) ) *100, 0 ) , '%' ) AS

I have a query requirement from ----. Trying to solve it with CONNECT BY , but can't seem to get the results I need. Table (simplified): create table CSS.USER_DESC ( USER_ID VARCHAR2(30) not null, NEW_USER_ID VARCHAR2(30), GLOBAL_HR_ID CHAR(8) ) -- USER_ID is the primary key -- NEW_USER_ID is a self-referencing key -- GLOBAL_HR_ID is an ID field from another system There are two sources of user data (datafeeds)... I have to watch for mistakes in either of them when updating information. Scenarios: A user is given a new User ID... The old record is set accordingly and deactivated (typically a

While preparing an answer to the question dplyr or data.table to calculate time series aggregations in R I noticed that I do get different results depending on whether the table is updated in place or returned as a new object. Also, I do get different result when I change the order of columns in the non-equi join conditions. Currently, I don't have an explanation for this, perhaps due to a major misunderstanding on my side or a simple coding error. Please, note that this question is asking particularly for explanations of the observed behaviour of data.table joins. If you have alternative

I have a component list made of 3 columns: product, component and quantity of component used: a <- structure(list(prodName = c("prod1", "prod1", "prod2", "prod3", "prod3", "int1", "int1", "int2", "int2"), component = c("a", "int1", "b", "b", "int2", "a", "b", "int1", "d"), qty = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L)), row.names = c(NA, -9L), class = c("data.table", "data.frame")) prodName component qty 1 prod1 a 1 2 prod1 int1 2 3 prod2 b 3 4 prod3 b 4 5 prod3 int2 5 6 int1 a 6 7 int1 b 7 8 int2 int1 8 9 int2 d 9 Products with names starting with prod are final products, those with names like

It is common task to make some evaluation on pairs of items: Examples: de-duplication, collaborative filtering, similar items etc This is basically self-join or cross-product with the same source of data. To do a self join, you can follow the "reduce-side join" pattern. The mapper emits the join/foreign key as key, and the record as the value. So, let's say we wanted to do a self-join on "city" (the middle column) on the following data: don,baltimore,12 jerry,boston,19 bob,baltimore,99 cameron,baltimore,13 james,seattle,1 peter,seattle,2 The mapper would emit the key->value pairs: (baltimore -

I have an existing InnoDB table which already has foreign keys pointing to different tables. But when I try to create a foreign key pointing to the Primary index, I get an error (check data type). The table is User with User_Id as the Primary. I want a foreign key Manager_ID which is a FK to User_Id. Both of INT Both of Length 10 Unsigned... But I still get a data check error...? Powerlord Make sure that Manager_ID is not set to NOT NULL . You have to allow nulls on that field, as the top-most person in the company will have no manager. I found a post over on the MySQL boards that might help.

I would like some help with the following join. I have one table (with about 20 million rows) that consists of: MemberId (Primary Key) | Id (Primary Key) | TransactionDate | Balance I would like to get the latest Balance for all the customers in one query. I know I could do something like this (I just wrote it from my memory). But this way is terribly slow. SELECT * FROM money WHERE money.Id = (SELECT MAX(Id) FROM money AS m WHERE m.MemberId = money.MemberId) Are there any other (faster/smarter) options? In all optimization tutorials and screencasts that I've endured through, joins are always

Possible Duplicate: combinations (not permutations) from cross join in sql I've currently got a table with the following records: A1 A2 A3 B1 B2 C1 C2 Where the same letter denotes some criteria in common (e.g. a common value for the column 'letter'). I do a self join on the criteria as follows: SELECT mytable.*, self.* FROM mytable INNER JOIN mytable AS self ON (mytable.letter = self.letter and mytable.number != self.number); This join gives something like the following: A1 A2 A2 A1 A1 A3 A3 A1 A2 A3 A3 A2 B1 B2 B2 B1 C1 C2 C2 C1 However, I only want to include each pair once (a combination

I'm trying to get the top row by a group of three variables using a data.table. I have a working solution: col1 <- c(1,1,1,1,2,2,2,2,3,3,3,3) col2 <- c(2000,2000,2001,2001,2000,2000,2001,2001,2000,2000,2001,2001) col4 <- c(1,2,3,4,5,6,7,8,9,10,11,12) data <- data.frame(store=col1,year=col2,month=12,sales=col4) solution1 <- data.table(data)[,.SD[1,],by="store,year,month"] I used the slower approach suggested by Matthew Dowle in the following link: https://stats.stackexchange.com/questions/7884/fast-ways-in-r-to-get-the-first-row-of-a-data-frame-grouped-by-an-identifier I'm trying to implement