window-functions

问题 Backstory: I have a database that has data points of drivers in trucks which also contain the. While in a truck, the driver can have a 'driverstatus'. What I'd like to do is group these statuses by driver, truck. As of now, I've tried using LAG/LEAD to help. The reason for this is so I can tell when a driverstatus change occurs, and then I can mark that row as having the last datetime of that status. That in itself is insufficient, because I need to group the statuses by their status and date

问题 I'm looking to aggregate data by the end date of a dataset with some leading period rather than the start. For example, I want to query a table and return the count of matching results 30 days PRIOR to the end date of the date shown in the results. The original table would contain ONLY the date a sale was made (timestamp). Example: sales_timestamp ------------------ 2015-08-05 12:00:00 2015-08-06 13:00:00 2015-08-25 12:31:00 2015-08-26 01:02:00 2015-08-27 02:03:00 2015-08-29 04:23:00 2015-09

问题 I must apply a certain transformation fn(argument) . Here argument is equal to value , but not when it is negative. When you get a first negative value , then you "wait" until it sums up with consecutive values and this sum becomes positive. Then you do fn(argument) . See the table I want to get: value argument --------------------- 2 2 3 3 -10 0 4 0 3 0 10 7 1 1 I could have summed all values and apply fn to the sum, but fn can be different for different rows and it is essential to know the

问题 I have the following data : rowid uid time code 1 1 5 a 2 1 6 b 3 1 7 c 4 2 8 a 5 2 9 c 6 2 9 c 7 2 10 c 8 2 11 a 9 2 12 c Now I wanted to filter the data in such a way that I can remove the rows 6 and 7 as for a particular uid i want to keep just one row with value 'c' in code So the expected data should be : rowid uid time code 1 1 5 a 2 1 6 b 3 1 7 c 4 2 8 a 5 2 9 c 8 2 11 a 9 2 12 c I'm using window function something like this : val window = Window.partitionBy("uid").orderBy("time") val

问题 I have a connected set of edges with unique nodes. They are connected using a parent node. Consider the following example code and illustration: CREATE TABLE network ( node integer PRIMARY KEY, parent integer REFERENCES network(node), length numeric NOT NULL ); CREATE INDEX ON network (parent); INSERT INTO network (node, parent, length) VALUES (1, NULL, 1.3), (2, 1, 1.2), (3, 2, 0.9), (4, 3, 1.4), (5, 4, 1.6), (6, 2, 1.5), (7, NULL, 1.0); Visually, two groups of edges can be identified. How

问题 Following result set is derived from a sql query with a few joins and a union. The sql query already groups rows on Date and game. I need a column to describe the number of attempts at a game partitioned by date column. Username Game ID Date johndoe1 Game_1 100 7/22/14 1:52 AM johndoe1 Game_1 100 7/22/14 1:52 AM johndoe1 Game_1 100 7/22/14 1:52 AM johndoe1 Game_1 100 7/22/14 1:52 AM johndoe1 Game_1 121 7/22/14 1:56 AM johndoe1 Game_1 121 7/22/14 1:56 AM johndoe1 Game_1 121 7/22/14 1:56 AM

问题 I have a PostgreSQL 9.1 database with a table containing a timestamp and a measuring value '2012-10-25 01:00' 2 '2012-10-25 02:00' 5 '2012-10-25 03:00' 12 '2012-10-25 04:00' 7 '2012-10-25 05:00' 1 ... ... I need to average the value over a range of 8 hours, every hour. In other words, I need the average of 1h-8h, 2h-9h, 3h-10h etc. I have no idea how to proceed for such a query. I have looked everywhere but have also no clue what functionalities to look for. The closes I find are hourly/daily

问题 I've successfully create a row_number() partitionBy by in Spark using Window, but would like to sort this by descending, instead of the default ascending. Here is my working code: from pyspark import HiveContext from pyspark.sql.types import * from pyspark.sql import Row, functions as F from pyspark.sql.window import Window data_cooccur.select("driver", "also_item", "unit_count", F.rowNumber().over(Window.partitionBy("driver").orderBy("unit_count")).alias("rowNum")).show() That gives me this

问题 Here is my problem.. Actual Auction Ammanat id 7000 500 100 228,229 7000 100 100 228,229 7000 900 100 228,229 5000 0 0 230 I want result as given below Actual Auction Ammanat Remaining id 7000 500 100 5550 228,229 7000 100 100 5550 228,229 7000 900 100 5550 228,229 5000 0 0 5000 230 Here, Remaining is (sum(auction)-actual) . I am using PostgreSQL. But if anyone know solution in SQL Server, it will be OK. 回答1: You need a to use a window function - http://www.postgresql.org/docs/9.3/static

问题 I need to calculate percentages of various dimensions in a table. I'd like to simplify things by using window functions to calculate the denominator, however I am having an issue because the numerator has to be an aggregate as well. As a simple example, take the following table: create temp table test (d1 text, d2 text, v numeric); insert into test values ('a','x',5), ('a','y',5), ('a','y',10), ('b','x',20); If I just want to calculate the share of each individual row out of d1, then