Assuming data such as the following:
ID EffDate Rate
1 12/12/2011 100
1 01/01/2012 110
1 02/01/2012 120
2 01/01/2012 40
2 02/01/2012 50
3 01/01/2012 25
3 03/01/2012 30
3 05/01/2012 35
How would I find the rate for ID 2 as of 1/15/2012?
Or, the rate for ID 1 for 1/15/2012?
In other words, how do I do a query that finds the correct rate when the date falls between the EffDate for two records? (Rate should be for the date prior to the selected date).
Thanks,
John
How about this:
SELECT Rate
FROM Table1
WHERE ID = 1 AND EffDate = (
SELECT MAX(EffDate)
FROM Table1
WHERE ID = 1 AND EffDate <= '2012-15-01');
Here's an SQL Fiddle to play with. I assume here that 'ID/EffDate' pair is unique for all table (at least the opposite doesn't make sense).
SELECT TOP 1 Rate FROM the_table
WHERE ID=whatever AND EffDate <='whatever'
ORDER BY EffDate DESC
if I read you right.
(edited to suit my idea of ms-sql which I have no idea about).
Related
I need a query to return the initial and final numeric value of the number of listeners of some artists of the last 30 days ordered from the highest increase of listeners to the lowest.
To better understand what I mean, here are the tables involved.
artist table saves the information of a Spotify artist.
id
name
Spotify_id
1
Shakira
0EmeFodog0BfCgMzAIvKQp
2
Bizarrap
716NhGYqD1jl2wI1Qkgq36
platform_information table save the information that I want to get from the artists and on which platform.
id
platform
information
1
spotify
monthly_listeners
2
spotify
followers
platform_information_artist table stores information for each artist on a platform and information on a specific date.
id
platform_information_id
artist_id
date
value
1
1
1
2022-11-01
100000
2
1
1
2022-11-15
101000
3
1
1
2022-11-30
102000
4
1
2
2022-11-02
85000
5
1
2
2022-11-06
90000
6
1
2
2022-11-26
100000
Right now have this query:
SELECT (SELECT value
FROM platform_information_artist
WHERE artist_id = 1
AND platform_information_id =
(SELECT id from platform_information WHERE platform = 'spotify' AND information = 'monthly_listeners')
AND DATE(date) >= DATE(NOW()) - INTERVAL 30 DAY
ORDER BY date ASC
LIMIT 1) as month_start,
(SELECT value
FROM platform_information_artist
WHERE artist_id = 1
AND platform_information_id =
(SELECT id from platform_information WHERE platform = 'spotify' AND information = 'monthly_listeners')
AND DATE(date) >= DATE(NOW()) - INTERVAL 30 DAY
ORDER BY date DESC
LIMIT 1) as month_end,
(SELECT month_end - month_start) as diference
ORDER BY month_start;
Which returns the following:
month_start
month_end
difference
100000
102000
2000
The problem is that this query only returns the artist I specify.
And I need the information like this:
artist_id
name
platform_information_id
month_start_value
month_end_value
difference
2
Bizarrap
1
85000
100000
15000
1
Shakira
1
100000
102000
2000
The query should return the 5 artists that have grown the most in number of monthly listeners over the last 30 days, along with the starting value 30 days ago, and the current value.
Thanks for the help.
I am working on a query to return the next 7 days worth of data every time an event happens indicated by "where event = 1". The goal is to then group all the data by the user id and perform aggregate functions on this data after the event happens - the event is encoded as binary [0, 1].
So far, I have been attempting to use nested select statements to structure the data how I would like to have it, but using the window functions is starting to restrict me. I am now thinking a self join could be more appropriate but need help in constructing such a query.
The query currently first creates daily aggregate values grouped by user and date (3rd level nested select). Then, the 2nd level sums the data "value_x" to obtain an aggregate value grouped by the user. Then, the 1st level nested select statement uses the lead function to grab the next rows value over and partitioned by each user which acts as selecting the next day's value when event = 1. Lastly, the select statement uses an aggregate function to calculate the average "sum_next_day_value_after_event" grouped by user and where event = 1. Put together, where event = 1, the query returns the avg(value_x) of the next row's total value_x.
However, this doesn't follow my time rule; "where event = 1", return the next 7 days worth of data after the event happens. If there is not 7 days worth of data, then return whatever data is <= 7 days. Yes, I currently only have one lead with the offset as 1, but you could just put 6 more of these functions to grab the next 6 rows. But, the lead function currently just grabs the next row without regard to date. So theoretically, the next row's "value_x" could actually be 15 days from where "event = 1". Also, as can be seen below in the data table, a user may have more than one row per day.
Here is the following query I have so far:
select
f.user_id
avg(f.sum_next_day_value_after_event) as sum_next_day_values
from (
select
bld.user_id,
lead(bld.value_x, 1) over(partition by bld.user_id order by bld.daily) as sum_next_day_value_after_event
from (
select
l.user_id,
l.daily,
sum(l.value_x) as sum_daily_value_x
from (
select
user_id, value_x, date_part('day', day_ts) as daily
from table_1
group by date_part('day', day_ts), user_id, value_x) l
group by l.user_id, l.day_ts
order by l.user_id) bld) f
group by f.user_id
Below is a snippet of the data from table_1:
user_id
day_ts
value_x
event
50
4/2/21 07:37
25
0
50
4/2/21 07:42
45
0
50
4/2/21 09:14
67
1
50
4/5/21 10:09
8
0
50
4/5/21 10:24
75
0
50
4/8/21 11:08
34
0
50
4/15/21 13:09
32
1
50
4/16/21 14:23
12
0
50
4/29/21 14:34
90
0
55
4/4/21 15:31
12
0
55
4/5/21 15:23
34
0
55
4/17/21 18:58
32
1
55
4/17/21 19:00
66
1
55
4/18/21 19:57
54
0
55
4/23/21 20:02
34
0
55
4/29/21 20:39
57
0
55
4/30/21 21:46
43
0
Technical details:
PostgreSQL, supported by EDB, version = 14.1
pgAdmin4, version 5.7
Thanks for the help!
"The query currently first creates daily aggregate values"
I don't see any aggregate function in your first query, so that the GROUP BY clause is useless.
select
user_id, value_x, date_part('day', day_ts) as daily
from table_1
group by date_part('day', day_ts), user_id, value_x
could be simplified as
select
user_id, value_x, date_part('day', day_ts) as daily
from table_1
which in turn provides no real added value, so this first query could be removed and the second query would become :
select user_id
, date_part('day', day_ts) as daily
, sum(value_x) as sum_daily_value_x
from table_1
group by user_id, date_part('day', day_ts)
The order by user_id clause can also be removed at this step.
Now if you want to calculate the average value of the sum_daily_value_x in the period of 7 days after the event (I'm referring to the avg() function in your top query), you can use avg() as a window function that you can restrict to the period of 7 days after the event :
select f.user_id
, avg(f.sum_daily_value_x) over (order by f.daily range between current row and '7 days' following) as sum_next_day_values
from (
select user_id
, date_part('day', day_ts) as daily
, sum(value_x) as sum_daily_value_x
from table_1
group by user_id, date_part('day', day_ts)
) AS f
group by f.user_id
The partition by f.user_id clause in the window function is useless because the rows have already been grouped by f.user_id before the window function is applied.
You can replace the avg() window function by any other one, for instance sum() which could better fit with the alias sum_next_day_values
I have a table like this
item_id date number
1 2000-01-01 100
1 2003-03-08 50
1 2004-04-21 10
1 2004-12-11 10
1 2010-03-03 10
2 2000-06-29 1
2 2002-05-22 2
2 2002-07-06 3
2 2008-10-20 4
I'm trying to get the average for each uniq Item_id over the last 3 dates.
It's difficult because there are missing date in between so a range of hardcoded dates doesn't always work.
I expect a result like :
item_id MyAverage
1 10
2 3
I don't really know how to do this. Currently i manage to do it for one item but i have trouble extending it to multiples items :
SELECT AVG(MyAverage.number) FROM (
SELECT date,number
FROM item_list
where item_id = 1
ORDER BY date DESC limit 3
) as MyAverage;
My main problem is with generalising the "DESC limit 3" over a group by id.
attempt :
SELECT item_id,AVG(MyAverage.number)
FROM (
SELECT item_id,date,number
FROM item_list
ORDER BY date DESC limit 3) as MyAverage
GROUP BY item_id;
The limit is messing things up there.
I have made it " work " using between date and date but it's not working as i want because i need a limit and not an hardcoded date..
Can anybody help
You can use row_number() to assign 1 to 3 for the records with the last date for an ID an then filter for that.
SELECT x.item_id,
avg(x.number)
FROM (SELECT il.item_id,
il.number,
row_number() OVER (PARTITION BY il.item_id
ORDER BY il.date DESC) rn
FROM item_list il) x
WHERE x.rn BETWEEN 1 AND 3
GROUP BY x.item_id;
I'm trying to write a query that calculates the number of days between the first and last score per id.
The data sample:
id date score
11 1/1/2017 25.34
4 1/2/2017 34.34
25 1/2/2017 15.78
4 3/2/2017 47.2
25 7/3/2017 65.21
11 9/3/2017 96.09
25 10/3/2017 11.3
4 10/3/2017 27.12
Which is far from what I need, but I'm really lost. Clueless to be honest. Any idea?
Thanks
Try this:
SELECT
customer_id,
date(last_score) - date(first_score) AS days_between_last_and_first_score,
total_score::float/(date(last_score) - date(first_score)) AS score_per_day
FROM
(
select customer_id,
MAX(date(purchase_date)) as last_score,
MIN(date(purchase_date)) as first_score,
SUM(score) AS total_score
FROM candidate_test_q1
group by customer_id
) AS sub_query
A fair amount of material is available detailing methods utilising dense_rank() and the like to count distinct somethings per month, however, I've been unable to find anything that allows a count of distinct per month which also removes/discounts any id's that have been seen in prior month groups.
The data can be imagined like so:
id (int8 type) | observed time (timestamp utc)
------------------
1 | 2017-01-01
2 | 2017-01-02
1 | 2017-01-02
1 | 2017-02-02
2 | 2017-02-03
3 | 2017-02-04
1 | 2017-03-01
3 | 2017-03-01
4 | 2017-03-01
5 | 2017-03-02
The process of the count can be seen as:
1: in 2017-01 we saw devices 1 and 2 so the count is 2
2: in 2017-02 we saw devices 1, 2 and 3. We know already about devices 1 and 2, but not 3, so the count is 1
3: in 2017-03 we saw devices 1, 3, 4 and 5. We already know about 1 and 3, but not 4 or 5, so the count is 2.
with the desired output being something like:
observed time | count of new id
--------------------------
2017-01 | 2
2017-02 | 1
2017-03 | 2
Explicitly, I am looking to have a new table, with an aggregated month per row, with a count of how many new ids occur within that month that have not been seen at all before.
The IRL case allows devices to be seen more than once in a month, but this shouldn't impact the count. It also uses integer for storage (both positive and negative) of the id, and time periods will be to the second in true timestamps. The size of the data set is also significant.
My initial attempt is along the lines of:
WITH records_months AS (
SELECT *,
date_trunc('month', observed_time) AS month_group
FROM my_table
WHERE observed_time > '2017-01-01')
id_months AS (
SELECT DISTINCT
month_group,
id
FROM records_months
GROUP BY month_group, id)
SELECT *
FROM id-months
However, I'm stuck on the next part i.e counting the number of new ID that were not seen in prior months. I believe the solution might be a window function, but I'm having trouble working out which or how.
First thing I thought of. The idea is to
(innermost query) calculate the earliest month that each id was seen,
(next level up) join that back to the main my_table dataset, and then
(outer query) count distinct ids by month after nulling out the already-seen ids.
I tested it out and got the desired result set. Joining the earliest month back to the original table seemed like the most natural thing to do (vs. a window function). Hopefully this is performant enough for your Redshift!
select observed_month,
-- Null out the id if the observed_month that we're grouping by
-- is NOT the earliest month that the id was seen.
-- Then count distinct id
count(distinct(case when observed_month != earliest_month then null else id end)) as num_new_ids
from (
select t.id,
date_trunc('month', t.observed_time) as observed_month,
earliest.earliest_month
from my_table t
join (
-- What's the earliest month an id was seen?
select id,
date_trunc('month', min(observed_time)) as earliest_month
from my_table
group by 1
) earliest
on t.id = earliest.id
)
group by 1
order by 1;