aggregate-functions

I have a field in a table which contains bitwise flags. Let's say for the sake of example there are three flags: 4 => read, 2 => write, 1 => execute and the table looks like this * : user_id | file | permissions -----------+--------+--------------- 1 | a.txt | 6 ( <-- 6 = 4 + 2 = read + write) 1 | b.txt | 4 ( <-- 4 = 4 = read) 2 | a.txt | 4 2 | c.exe | 1 ( <-- 1 = execute) I'm interested to find all users who have a particular flag set (eg: write) on ANY record. To do this in one query, I figured that if you OR'd all the user's permissions together you'd get a single value which is the "sum

i am trying to solve this super simple problem and i am already sick of it, I hope somebody can help my out with this. I have a dataframe of shape like this: --------------------------- | Category | Product_ID | |------------+------------+ | a | product 1 | | a | product 2 | | a | product 3 | | a | product 1 | | a | product 4 | | b | product 5 | | b | product 6 | --------------------------- How do i group these rows by category and apply complicated function in Scala? Maybe something like this: val result = df.groupBy("Category").apply(myComplexFunction) This myComplexFunction should produce

I get the following warning when I use min or max in the dcast function from the reshape2 package. What is it telling me? I can't find anything that explains the warning message and I'm a bit confused about why I get it when I use max but not when I use mean or other aggregate functions. Warning message: In .fun(.value[0], ...) : no non-missing arguments to min; returning Inf Here's a reproducible example: data(iris) library(reshape2) molten.iris <- melt(iris,id.var="Species") summary(molten.iris) str(molten.iris) #------------------------------------------------------------ # Both return

This question already has an answer here: is there a PRODUCT function like there is a SUM function in Oracle SQL? 6 answers When there are Sum(), min(), max(), avg(), count() functions, can someone help understand why there is no product() built-in function. And what will be the most efficient user-implementation of this aggregate function ? Thanks, Trinity If you have exponential and log functions available, then: PRODUCT(TheColumn) = EXP(SUM(LN(TheColumn))) One can make a user-defined aggregate in SQL 2005 and up by using CLR . In Postgresql, you can do it in Postgres itself , likewise with

I have a table temp defined like this: id | name | body | group_id ------------------------------- 1 | test_1 | body_1 | 1 2 | test_2 | body_2 | 1 3 | test_3 | body_3 | 2 4 | test_4 | body_4 | 2 I would like to produce a result grouped by group_id and aggregated to json. However, query like this: SELECT group_id, json_agg(ROW(id, name, body)) FROM temp GROUP BY group_id; Produces this result: 1;[{"f1":1,"f2":"test_1","f3":"body_1"}, {"f1":2,"f2":"test_2","f3":"body_2"}] 2;[{"f1":3,"f2":"test_3","f3":"body_3"}, {"f1":4,"f2":"test_4","f3":"body_4"}] The attributes in the json objects are named

I have the following query: select distinct profile_id from userprofile_... union select distinct profile_id from productions_... How would I get the count of the total number of results? If you want a total count for all records, then you would do this: SELECT COUNT(*) FROM ( select distinct profile_id from userprofile_... union all select distinct profile_id from productions_... ) x you should use Union All if there are equals rows in both tables, because Union makes a distinct select count(*) from (select distinct profile_id from userprofile_... union ALL select distinct profile_id from