I have two tables and I am trying to find data gaps in them where the dates do not overlap.
Item Table:
id unique start_date end_date data
1 a 2019-01-01 2019-01-31 X
2 a 2019-02-01 2019-02-28 Y
3 b 2019-01-01 2019-06-30 Y
Plan Table:
id item_unique start_date end_date
1 a 2019-01-01 2019-01-10
2 a 2019-01-15 'infinity'
I am trying to find a way to produce the following
Missing:
item_unique from to
a 2019-01-11 2019-01-14
b 2019-01-01 2019-06-30
step-by-step demo:db<>fiddle
WITH excepts AS (
SELECT
item,
generate_series(start_date, end_date, interval '1 day') gs
FROM items
EXCEPT
SELECT
item,
generate_series(start_date, CASE WHEN end_date = 'infinity' THEN ( SELECT MAX(end_date) as max_date FROM items) ELSE end_date END, interval '1 day')
FROM plan
)
SELECT
item,
MIN(gs::date) AS start_date,
MAX(gs::date) AS end_date
FROM (
SELECT
*,
SUM(same_day) OVER (PARTITION BY item ORDER BY gs)
FROM (
SELECT
item,
gs,
COALESCE((gs - LAG(gs) OVER (PARTITION BY item ORDER BY gs) >= interval '2 days')::int, 0) as same_day
FROM excepts
) s
) s
GROUP BY item, sum
ORDER BY 1,2
Finding the missing days is quite simple. This is done within the WITH clause:
Generating all days of the date range and subtract this result from the expanded list of the second table. All dates that not occur in the second table are keeping. The infinity end is a little bit tricky, so I replaced the infinity occurrence with the max date of the first table. This avoids expanding an infinite list of dates.
The more interesting part is to reaggregate this list again, which is the part outside the WITH clause:
The lag() window function take the previous date. If the previous date in the list is the last day then give out true (here a time changing issue occurred: This is why I am not asking for a one day difference, but a 2-day-difference. Between 2019-03-31 and 2019-04-01 there are only 23 hours because of daylight saving time)
These 0 and 1 values are aggregated cumulatively. If there is one gap greater than one day, it is a new interval (the days between are covered)
This results in a groupable column which can be used to aggregate and find the max and min date of each interval
Tried something with date ranges which seems to be a better way, especially for avoiding to expand long date lists. But didn't come up with a proper solution. Maybe someone else?
Related
I have a list of dates each with a value in Postgresql.
For each date I want to sum the value for this date and the previous 4 days.
I also want to sum the values for the start of that month to the present date. So for example:
For 07/02/2021 sum all values from 07/02/2021 to 01/02/2021
For 06/02/2021 sum all values from 06/02/2021 to 01/02/2021
For 31/01/2021 sum all values from 31/01/2021 to 01/01/2021
The output should look like, will be created as two separate tables:
Output
Any help would be appreciated.
Thanks
Sample data and structure: dbfiddle
For first part of query:
select date,
value,
sum(value) over (
order by to_date(date, 'DD/MM/YYYY')
rows between 4 preceding and current row) as five_day_period
from your_table_name
order by to_date(date, 'DD/MM/YYYY') desc;
For second part of query:
select date,
value,
sum(value)
over (
partition by regexp_replace(date, '[0-9]{2}/(.+)', '\1')
order by to_date(date, 'DD/MM/YYYY')
rows between unbounded preceding and current row) as month_to_date
from your_table_name
order by to_date(date, 'DD/MM/YYYY') desc;
I'm currently writing an application which shows the growth of the total number of events in my table over time, I currently have the following query to do this:
query = session.query(
count(Event.id).label('count'),
extract('year', Event.date).label('year'),
extract('month', Event.date).label('month')
).filter(
Event.date.isnot(None)
).group_by('year', 'month').all()
This results in the following output:
Count
Year
Month
100
2021
1
50
2021
2
75
2021
3
While this is okay on it's own, I want it to display the total number over time, so not just the number of events that month, so the desired outpout should be:
Count
Year
Month
100
2021
1
150
2021
2
225
2021
3
I read on various places I should use a window function using SqlAlchemy's over function, however I can't seem to wrap my head around it and every time I try using it I get the following error:
sqlalchemy.exc.ProgrammingError: (psycopg2.errors.GroupingError) column "event.id" must appear in the GROUP BY clause or be used in an aggregate function
LINE 1: SELECT count(event.id) OVER (PARTITION BY event.date ORDER...
^
[SQL: SELECT count(event.id) OVER (PARTITION BY event.date ORDER BY EXTRACT(year FROM event.date), EXTRACT(month FROM event.date)) AS count, EXTRACT(year FROM event.date) AS year, EXTRACT(month FROM event.date) AS month
FROM event
WHERE event.date IS NOT NULL GROUP BY year, month]
This is the query I used:
session.query(
count(Event.id).over(
order_by=(
extract('year', Event.date),
extract('month', Event.date)
),
partition_by=Event.date
).label('count'),
extract('year', Event.date).label('year'),
extract('month', Event.date).label('month')
).filter(
Event.date.isnot(None)
).group_by('year', 'month').all()
Could someone show me what I'm doing wrong? I've been searching for hours but can't figure out how to get the desired output as adding event.id in the group by would stop my rows from getting grouped by month and year
The final query I ended up using:
query = session.query(
extract('year', Event.date).label('year'),
extract('month', Event.date).label('month'),
func.sum(func.count(Event.id)).over(order_by=(
extract('year', Event.date),
extract('month', Event.date)
)).label('count'),
).filter(
Event.date.isnot(None)
).group_by('year', 'month')
I'm not 100% sure what you want, but I'm assuming you want the number of events up to that month for each month. You're going to first need to calculate the # of events per month and also sum them with the postgresql window function.
You can do that with in a single select statement:
SELECT extract(year FROM events.date) AS year
, extract(month FROM events.date) AS month
, SUM(COUNT(events.id)) OVER(ORDER BY extract(year FROM events.date), extract(month FROM events.date)) AS total_so_far
FROM events
GROUP BY 1,2
but it might be easier to think about if you split it into two:
SELECT year, month, SUM(events_count) OVER(ORDER BY year, month)
FROM (
SELECT extract(year FROM events.date) AS year
, extract(month FROM events.date) AS month
, COUNT(events.id) AS events_count
FROM events
GROUP BY 1,2
)
but not sure how to do that in SqlAlchemy
I'm having some major issues with the following query formation:
I have projects with start and end dates
Name Start End
---------------------------------------
Project 1 2020-08-01 2020-09-10
Project 2 2020-01-01 2025-01-01
and I'm trying to count the monthly working days within each project with the following subquery
select datetrunc('month', days) as d_month, count(days) as d_count
from generate_series(greatest('2020-08-01'::date, p.start), least('2020-09-14'::date, p.end), '1 day'::interval) days
where extract(DOW from days) not IN (0, 6)
group by d_month
where p.start is from the aliased main query and the dates are hard-coded for now, this correctly gives me the following result:
{"d_month"=>2020-08-01 00:00:00 +0000, "d_count"=>21}
{"d_month"=>2020-09-01 00:00:00 +0000, "d_count"=>10}
However subqueries can't return multiple values. The date range for the query is dynamic, so I would either need to somehow return the query as:
Name Start End 2020-08-01 2020-09-01 ...
-------------------------------------------------------------------------
Project 1 2020-08-01 2020-09-10 21 8
Project 2 2020-01-01 2025-01-01 21 10
Or simply return the whole subquery as JSON, but it doesn't seem to working either.
Any idea on how to achieve this or whether there are simpler solutions for this?
The most correct solution would be to create an actual calendar table that holds every possible day of interest to your business and, at a minimum for your purpose here, marks work days.
Ideally you would have columns to hold fiscal quarters, periods, and weeks to match your industry. You would also mark holidays. Joining to this table makes these kinds of calculations a snap.
create table calendar (
ddate date not null primary key,
is_work_day boolean default true
);
insert into calendar
select ts::date as ddate,
extract(dow from ts) not in (0,6) as is_work_day
from generate_series(
'2000-01-01'::timestamp,
'2099-12-31'::timestamp,
interval '1 day'
) as gs(ts);
Assuming a calendar table is not within scope, you can do this:
with bounds as (
select min(start) as first_start, max("end") as last_end
from my_projects
), cal as (
select ts::date as ddate,
extract(dow from ts) not in (0,6) as is_work_day
from bounds
cross join generate_series(
first_start,
last_end,
interval '1 day'
) as gs(ts)
), bymonth as (
select p.name, p.start, p.end,
date_trunc('month', c.ddate) as month_start,
count(*) as work_days
from my_projects p
join cal c on c.ddate between p.start and p.end
where c.is_work_day
group by p.name, p.start, p.end, month_start
)
select jsonb_object_agg(to_char(month_start, 'YYYY-MM-DD'), work_days)
|| jsonb_object_agg('name', name)
|| jsonb_object_agg('start', start)
|| jsonb_object_agg('end', "end") as result
from bymonth
group by name;
Doing a pivot from rows to columns in SQL is usually a bad idea, so the query produces json for you.
I am trying to figure out a way to report how many people are in a location at the same time, down to the second.
I have a table with the id for the person, the date they entered, the time they entered, the date they left and the time they left.
example:
select unique_id, start_date, start_time, end_date, end_time
from My_Table
where start_date between '09/01/2019' and '09/02/2019'
limit 3
"unique_id" "start_date" "start_time" "end_date" "end_time"
989179 "2019-09-01" "06:03:13" "2019-09-01" "06:03:55"
995203 "2019-09-01" "11:29:27" "2019-09-01" "11:30:13"
917637 "2019-09-01" "11:06:46" "2019-09-01" "11:06:59"
i've concatenated the start_date & start_time as well as end_date & end_time so they are 2 fields
select unique_id, ((start_date + start_time)::timestamp without time zone) as start_date,
((end_date + end_time)::timestamp without time zone) as end_date
result example:
"start_date"
"2019-09-01 09:28:54"
so i'm making that a CTE, then using a second CTE that uses generate_series between dates down to the second.
The goal being, the generate series will have a row for every second between the two dates. Then when i join my data sets, i can count how many records exist in my_table where the start_date(plus time) is equal or greater than the generate_series date_time field, and the end_date(plus time) is less than or equal to the generate_series date_time field.
i feel that was harder to explain than it needed to be.
in theory, if a person was in the room from 2019-09-01 00:01:01 and left at 2019-09-01 00:01:03, i would count that record in the generate_series rows 2019-09-01 00:01:01, 2019-09-01 00:01:02 & 2019-09-01 00:01:03.
When i look at the data i can see that i should be returning hundreds of people in the room at specific peak periods. but the query returns all 0's.
is this possibly a field formatting issue i need to adjust?
Here is the query:
with CTE as (
select unique_id, ((start_date+start_time)::timestamp without time zone) as start_date,
((end_date+end_time)::timestamp without time zone) as end_date
from My_table
where start_date between '09/01/2019' and '09/02/2019'
),
time_series as (
select generate_series( (date '2019-09-01')::timestamp, (date '2019-09-02')::timestamp, interval '1 second') as date_time
)
/*FINAL SELECT*/
select date_time, count(B.unique_id) as NumPpl
FROM (
select A.date_time
FROM time_series a
)x
left join CTE b on b.start_date >= x.date_time AND b.end_date <= x.date_time
GROUP BY 1
ORDER BY 1
(partial) result screenshot
Thank you in advance
i should also add i have read only access to this database so i'm not able to create functions.
Simple version: b.start_date >= x.date_time AND b.end_date <= x.date_time will never be true assuming end_date is always after start_date.
Longer version: You also do not need a CTE for the generate_series() and there is no reason for selecting all columns and all rows of this CTE as a subquery. I would also drop the CTE for your original data and just join it to the seconds (NOTE: this does somehow change the query, since you might now take those entries into account, where start_date is earlier than 2019-09-01. If you do not want this, you can add your condition again to the join condition. But I guess this is what you really wanted). I also removed some casts which were not needed. Try this:
SELECT gs.second, COUNT(my.unique_id)
FROM generate_series('2019-09-01'::timestamp, '2019-09-02'::timestamp, interval '1 second') gs (second)
LEFT JOIN my_table my ON (my.start_date + my.start_time) <= gs.second
AND (my.end_date + my.end_time) >= gs.second
GROUP BY 1
ORDER BY 1
A question like this may have already been asked & answered, but I'm having trouble finding anything (it's tough to know what exactly to search for / how to phrase this).
If I have a table of values by date:
select *
from (values
(date '2018-05-11', 'lorem'),
(date '2018-05-10', 'ipsum'),
(date '2018-05-07', 'dolor'),
(date '2018-05-05', 'hello'),
(date '2018-05-04', 'world'),
(date '2018-04-30', 'foo'),
(date '2018-04-15', 'bar')
) as v(date, name)
order by date desc
How can I aggregate the values by date groups (e.g. "5 days") — grouping dynamically by the first value onwards (e.g. May 11-7, 6-1, Apr 30-26, etc.), not statically (e.g. modulo 5 days)?
Desired result:
min_date | max_date | names
-----------+------------+--------------------
2018-05-07 | 2018-05-11 | lorem, ipsum, dolor
2018-05-04 | 2018-05-05 | hello, world
2018-04-30 | 2018-04-30 | foo
2018-04-15 | 2018-04-15 | bar
————
I believe I need to first derive the max date to group each row under, which would be , e.g. 2018-05-11, 2018-05-05, etc.
I've tried two conceptual approaches for doing that, but neither work.
———
The first approach is to build up this rolling max date, but this isn't valid (column "groupbydate" does not exist):
select *,
case
when date > (lag(groupByDate) over w) - interval '5 days' then (lag(groupByDate) over w)
else date
end as groupByDate
from input
window w as (order by date desc)
————
The second approach is to "find" the max/"group by" for each row, but I'm not sure how to differentiate the current table row's date from the current window row's `date:
select *,
max(date) filter (where date < input.date + interval '5 days') over w
from input
window w as (order by date desc)
I think I can implement the second approach using a subquery, but I'm curious: is it possible to achieve this using window functions? Thank you!
EDIT: The second approach is wrong. It can find a different "group by" date for different dates that should be in the same group.
EDIT: Actually, this is wrong! This can find a different "group by" date for different dates that should be in the same group.
Here's how I achieved this with a subquery:
select date, name, (
select max(date)
from input as i2
where date < input.date + interval '5 days'
) as date_group
from input
And plugging into this outer query gets me my desired results:
select min_date, max_date, names
from (
select date_group, min(date) as min_date, max(date) as max_date, string_agg(name, ', ') as names
from groups -- results of above query, e.g. using CTE
group by date_group
order by date_group desc
) as x
Still curious if there's a way to do this with windowing functions. Thanks!