aggregate-functions

问题 I have been altering a query for a while now, reading many posts on this wonderful site to get to where I am with this so far. But, alas, now I am stuck :( This query and relevant part of the database I have designed is similar to the youtube comment liking system. Relevant tables and fields are: USRS usr_id int (PK) COMMENTS comment_id int (PK) usr_id int (FK) references usrs(usr_id) topic_id int (FK) references topics(topic_id) descr varchar created varchar COMMENT_LIKERS comment_id int (PK

问题 I have below referenced query which groups studies counts by teacher, study year-month, and room for the past 12 months (including current month). The result I get is correct, however, I would like to include rows with zero counts for when the data is missing. I looked at several other related posts but could not get desired output: Postgres - how to return rows with 0 count for missing data? Postgresql group month wise with missing values Best way to count records by arbitrary time intervals

问题 Let say I have the following table: id column_a column_b column_c 1 t f t 2 t f f 3 f t f From the above table, I want to: select rows from id = 1,2; The result should be: column_a column_b column_c t f t If any of the rows among the defined id has a true for a particular column we assume the result to be true. 回答1: Use the aggregate function bool_or(). SELECT bool_or(column_a) AS column_a , bool_or(column_b) AS column_b , bool_or(column_c) AS column_c FROM tbl WHERE id IN (1,2); The manual:

问题 Before you mark this as duplicate please take a look at this SQLFiddle. I have this schema: CREATE TABLE book(book_id int, book_name varchar(100), author_id int, editor_id varchar(100), isbn varchar(100)); INSERT INTO book VALUES (1 , 'Book1 Title' , 12 , 'Editor1' , '8000-9000' ), (2 , 'Book2 Title' , 98 , 'Editor1' , '8000-9001' ), (1 , 'Book1 Title' , 12 , 'Editor1' , '8000-9002' ), (3 , 'Book3 Title' , 3 , 'Editor1' , '8000-9003' ); CREATE TABLE author(author_id int, fn varchar(100), ln

问题 I have a data frame with two columns. I want to add an additional two columns to the data set with counts based on aggregates. df <- structure(list(ID = c(1045937900, 1045937900), SMS.Type = c("DF1", "WCB14"), SMS.Date = c("12/02/2015 19:51", "13/02/2015 08:38"), Reply.Date = c("", "13/02/2015 09:52") ), row.names = 4286:4287, class = "data.frame") I want to simply count the number of Instances of SMS.Type and Reply.Date where there is no null. So in the toy example below, i will generate the

问题 I tried this query on MySQL server (5.1.41)... SELECT max(volume), dateofclose, symbol, volume, close, market FROM daily group by market I got this result: max(volume) dateofclose symbol volume close market 287031500 2010-07-20 AA.P 500 66.41 AMEX 242233000 2010-07-20 AACC 16200 3.98 NASDAQ 1073538000 2010-07-20 A 4361000 27.52 NYSE 2147483647 2010-07-20 AAAE.OB 400 0.01 OTCBB 437462400 2010-07-20 AAB.TO 31400 0.37 TSX 61106320 2010-07-20 AA.V 0 0.24 TSXV As you can see, the maximum volume is

问题 I have a table which looks like this: Date | User_ID 2017-1-1 | 1 2017-1-1 | 2 2017-1-1 | 4 2017-1-2 | 3 2017-1-2 | 2 ... | .. ... | .. ... | .. ... | .. 2017-2-1 | 1 2017-2-2 | 2 ... | .. ... | .. ... | .. I'd like to calculate the monthly active users over a rolling 30 day period. I know Redshift does not do COUNT(DISTINCT)) windowing. What can I do to get the following output? Date | MAU 2017-1-1 | 3 2017-1-2 | 4 <- We don't want to count user_id 2 twice. ... | .. ... | .. ... | .. 2017-2

问题 I have a table which looks like this: Date | User_ID 2017-1-1 | 1 2017-1-1 | 2 2017-1-1 | 4 2017-1-2 | 3 2017-1-2 | 2 ... | .. ... | .. ... | .. ... | .. 2017-2-1 | 1 2017-2-2 | 2 ... | .. ... | .. ... | .. I'd like to calculate the monthly active users over a rolling 30 day period. I know Redshift does not do COUNT(DISTINCT)) windowing. What can I do to get the following output? Date | MAU 2017-1-1 | 3 2017-1-2 | 4 <- We don't want to count user_id 2 twice. ... | .. ... | .. ... | .. 2017-2

问题 We have a table with the following columns: SESSION_ID USER_ID CONNECT_TS -------------- --------------- --------------- 1 99 2013-01-01 2:23:33 2 101 2013-01-01 2:23:55 3 104 2013-01-01 2:24:41 4 101 2013-01-01 2:24:43 5 233 2013-01-01 2:25:01 We need to get a distinct count of users for each day and a count of "active users" which are defined as users that have used the application in the last 45 days. Here is what we have come up with, but I feel like there has to be a better way: select

问题 We have a table with the following columns: SESSION_ID USER_ID CONNECT_TS -------------- --------------- --------------- 1 99 2013-01-01 2:23:33 2 101 2013-01-01 2:23:55 3 104 2013-01-01 2:24:41 4 101 2013-01-01 2:24:43 5 233 2013-01-01 2:25:01 We need to get a distinct count of users for each day and a count of "active users" which are defined as users that have used the application in the last 45 days. Here is what we have come up with, but I feel like there has to be a better way: select