aggregate-functions

Question is simple. Which query will be faster: SELECT TOP 1 value FROM table ORDER BY value or SELECT TOP 1 MIN(value) FROM table We can assume that we have two cases, Case 1. No index and Case 2. With index on value. Any insights are appreciated. Thanks! In the case where no index exists: MIN(value) should be implemented in O(N) time with a single scan; TOP 1 ... ORDER BY will require O(N Log N) time because of the specified sort (unless the DB engine is smart enough to read intent, which I would hate to rely on in production code). When an index does exist: Both should require only O(1)

I have a PySpark DataFrame with one column as one hot encoded vectors. I want to aggregate the different one hot encoded vectors by vector addition after groupby e.g. df[userid,action] Row1: ["1234","[1,0,0]] Row2: ["1234", [0 1 0]] I want the output as row: ["1234", [ 1 1 0]] so the vector is a sum of all vectors grouped by userid . How can I achieve this? PySpark sum aggregate operation does not support the vector addition. Assaf Mendelson You have several options: Create a user defined aggregate function. The problem is that you will need to write the user defined aggregate function in

I'm using an aggregate function with the OVER clause in PostgreSQL 9.1 and I want to return just the last row for each window. The last_value() window function sounds like it might do what I want - but it doesn't. It returns a row for each row in the window, whereas I want just one row per window A simplified example: SELECT a, some_func_like_last_value(b) OVER (PARTITION BY a ORDER BY b) FROM ( SELECT 1 AS a, 'do not want this' AS b UNION SELECT 1, 'just want this' ) sub I want this to return one row: 1, 'just want this' Erwin Brandstetter DISTINCT plus window function Add a DISTINCT clause:

I need to calculate sum of occurences of some data in two columns in one query. DB is in SQL Server 2005. For example I have this table: Person: Id, Name, Age And I need to get in one query those results: 1. Count of Persons that have name 'John' 2. Count of 'John' with age more than 30 y. I can do that with subqueries in this way (it is only example): SELECT (SELECT COUNT(Id) FROM Persons WHERE Name = 'John'), (SELECT COUNT (Id) FROM Persons WHERE Name = 'John' AND age > 30) FROM Persons But this is very slow, and I'm searching for faster method. I found this solution for MySQL (it almost

I'm using PostgreSQL 9.1 and I have this data structure: A B ------- 1 a 1 a 1 b 1 c 1 c 1 c 1 d 2 e 2 e I need a query that produces this result: 1 4 {{c,3},{a,2},{b,1},{d,1}} 2 1 {{e,2}} A=1, 4 rows total with A=1, the partial counts (3 rows with c value, 2 rows with a value, .....) The distinct values of column "A" The count of all rows related to the "A" value An array contains all the elements related to the "A" value and the relative count of itself The sort needed for the array is based of the count of each group (like the example 3,2,1,1). This should do the trick: SELECT a , sum(ab_ct

In the following example, why does the min() query return results, but the max() query does not? mysql> create table t(id int, a int); Query OK, 0 rows affected (0.10 sec) mysql> insert into t(id, a) values(1, 1); Query OK, 1 row affected (0.03 sec) mysql> insert into t(id, a) values(1, 2); Query OK, 1 row affected (0.02 sec) mysql> select * from t -> ; +------+------+ | id | a | +------+------+ | 1 | 1 | | 1 | 2 | +------+------+ 2 rows in set (0.00 sec) mysql> select * from t where a < 4; +------+------+ | id | a | +------+------+ | 1 | 1 | | 1 | 2 | +------+------+ 2 rows in set (0.00 sec)