I have the folowing table:
| id | duty_id | date_start | date_end |
| 1 | 1 | 2015-07-16 07:00:00 | 2015-07-16 14:30:00 |
| 2 | 3 | 2015-07-17 03:30:00 | 2015-07-17 11:00:00 |
| 3 | 5 | 2015-07-17 12:00:00 | 2015-07-17 19:30:00 |
and i have a date: 2015-07-17.
and i need to select the rows that happens on my date. AKA i need these lines:
| 2 | 3 | 2015-07-17 03:30:00 | 2015-07-17 11:00:00 |
| 3 | 5 | 2015-07-17 12:00:00 | 2015-07-17 19:30:00 |
sadly the BETWEEN doesn't work:
SELECT * FROM table WHERE ('2015-07-17'::DATE BETWEEN date_start AND date_end)
gives back empty result.
How can i get those lines?
The problem is that when a DATE is coerced to a TIMESTAMP (as must be done here to compare the DATE of '2015-07-17' to the TIMESTAMPs in the data) the time portion of the coerced TIMESTAMP is set to 00:00:00, and thus since the test data doesn't have a time period which is valid at midnight on 2015-07-17 no rows are returned.
If you add an INTERVAL literal of 211 minutes (three hours and 31 minutes) to the converted date you'll get results returned because the test data DOES have a row which is valid at 2015-07-17 at 03:31 AM:
SELECT * FROM my_table
WHERE '2015-07-17'::DATE + INTERVAL '211' MINUTE BETWEEN date_start
AND date_end;
SQLFiddle here
Best of luck.
Please try something like this:
SELECT * FROM table
WHERE '2015-07-17'::DATE BETWEEN date_start::DATE AND date_end::DATE;
SQLFiddle excample
Related
Given a Postgres table with columns highwater_datetime::timestamp and highwater::integer, I am trying to construct a select statement for a given highwater_datetime range, that generates rows with a column for the max highwater for each hour (first occurrence when dups) and another column showing the highwater_datetime when it occurred (truncated to the minute and order by highwater_datetime asc). e.g.
| highwater_datetime | max_highwater |
+--------------------+---------------+
| 2021-01-27 20:05 | 8 |
| 2021-01-27 21:00 | 7 |
| 2021-01-27 22:00 | 7 |
| 2021-01-27 23:00 | 7 |
| 2021-01-28 00:00 | 7 |
| 2021-01-28 01:32 | 7 |
| 2021-01-28 02:00 | 7 |
| 2021-01-28 03:00 | 7 |
| 2021-01-28 04:22 | 9 |
DISTINCT ON should do the trick:
SELECT DISTINCT ON (date_trunc('hour', highwater_datetime))
highwater_datetime,
highwater
FROM mytable
ORDER BY date_trunc('hour', highwater_datetime),
highwater DESC,
highwater_datetime;
DISTINCT ON will output the first row for each entry with the same hour according to the ORDER BY clause.
Why the following query return false?
SELECT ('00:00:00'::TIME) = ('24:00:00'::TIME) AS "time", ('00:00:00'::TIMETZ) = ('24:00:00'::TIMETZ) AS "timetz"
Result:
+-------+--------+
| time | timetz |
+-------+--------+
| false | false |
+-------+--------+
While the result of the following query is same!!
SELECT ('00:00:00'::TIME) AS "time1", ('24:00:00'::TIME) AS "time2", ('00:00:00'::TIMETZ) AS "timetz1" , ('24:00:00'::TIMETZ) AS "timetz2"
Result:
+----------+----------+------------------------+------------------------+
| time1 | time2 | timetz1 | timetz2 |
+----------+----------+------------------------+------------------------+
| 00:00:00 | 00:00:00 | 00:00:00.000000 +00:00 | 00:00:00.000000 +00:00 |
+----------+----------+------------------------+------------------------+
What is difference between 00:00:00 and 24:00:00 in postgres?
Postgres' time type is defined to have a range of '00:00:00' to '24:00:00', inclusive on both ends. To see why the two endpoints are not the same, consider:
SELECT '00:00:00'::time AS start, '24:00:00'::time AS end;
For the end value, I see 1.00:00:00, contrary to what you see, indicating that 24 hours is actually one day, with zero hours, minutes, and seconds.
I have a table that has following columns:- local_id | time_in | time_out | date | employee_id
I have to calculate average working hours(which will be calculated by time_out and time_in) on a monthly basis in PSQL. I have no clue how to do that, was thinking about using date_part function...
here are the table details:
local_id | time_in | time_out | date | employee_id
---------+----------+----------+------------+-------------
7 | 08:00:00 | 17:00:00 | 2020-02-12 | 2
6 | 08:00:00 | 17:00:00 | 2020-02-12 | 4
8 | 09:00:00 | 17:00:00 | 2020-02-12 | 3
13 | 08:05:00 | 17:00:00 | 2020-02-17 | 3
12 | 08:00:00 | 18:09:00 | 2020-02-13 | 2
Click: demo:db<>fiddle; extended example covering two months
SELECT
employee_id,
date_trunc('month', the_date) AS month, -- 1
AVG(time_out - time_in) -- 2, 3
FROM
mytable
GROUP BY employee_id, month -- 3
date_trunc() "shortens" the date to a certain date part. In that case, all dates are truncated to the month. This gives the opportunity to group by month. (for your "monthly basis")
Calculate the working time by calculating the difference of both times
Grouping by employee_id and calculated month, calculating the average of the time differences.
Hello I have been trying to generate a report based on some db data.
I need to calculate per DAY (finished) so in this case lets say that the day for calculation will be : (2001-01-02) and in current date we are in the 2001-01-03.
So basically day before current date.
MAX count for locker_orders occupancy in that day + time of occurrence (peak max load of lockers per place)
Min count for locker_orders occupancy in that day + time of occurrence
(peak min load of lockers per place)
AVG count for locker_orders occupancy in that day (average load in that day based on min max and the number of lockers per place)
group PER place_id
group PER each minute in current day
NUMBER of all lockers in store on that day (may change in time)
Where there is no pickup date the locker is still occupied - it may move to another days span
I was able to perform a simple query to group by place and per minute the locker order was created at but currently i have a problem placing it in current day scope
here is a representation of the timeline (handmade ;))
Given a schema of data containing
DB DATA
LOCKERS
------------------------------------
| id | created_at |
------------------------------------
| 1 | 2001-01-01 00:00 (DATETIME) |
------------------------------------
| 2 | 2001-01-01 00:00 (DATETIME) |
------------------------------------
| 3 | 2001-01-01 00:00 (DATETIME) |
------------------------------------
| 4 | 2001-01-01 00:00 (DATETIME) |
------------------------------------
| 5 | 2001-01-01 00:00 (DATETIME) |
------------------------------------
LOCKER_ORDERS
------------------------------------------------------------------------------------
| id | created_at | pickup_date | place_id | locker_id |
------------------------------------------------------------------------------------
| 1 | 2001-01-02 10:00 (DATETIME) | 2001-01-02 13:25 (DATETIME) | 1 | 2 |
------------------------------------------------------------------------------------
| 2 | 2001-01-02 07:45 (DATETIME) | 2001-01-02 11:50 (DATETIME) | 1 | 1 |
------------------------------------------------------------------------------------
| 3 | 2001-01-02 19:30 (DATETIME) | NULL | 1 | 4 |
------------------------------------------------------------------------------------
| 4 | 2001-01-01 14:40 (DATETIME) | 2001-01-01 21:15 (DATETIME) | 1 | 5 |
-------------------------------------------------------------------------------------
| 5 | 2001-01-02 12:25 (DATETIME) | NULL | 1 | 3 |
-------------------------------------------------------------------------------------
| 6 | 2001-01-02 13:30 (DATETIME) | 2001-01-02 18:40 (DATETIME) | 1 | 2 |
-------------------------------------------------------------------------------------
| 7 | 2001-01-02 12:45 (DATETIME) | 2001-01-02 20:50 (DATETIME) | 1 | 1 |
-------------------------------------------------------------------------------------
| 8 | 2001-01-02 07:40 (DATETIME) | 2001-01-02 18:15 (DATETIME) | 1 | 5 |
-------------------------------------------------------------------------------------
OUTPUT DATA - the desired output
# | Date (day) | place_id | min | max | avg | NO of all lockers in that day in given place |
---------------------------------------------------------------------------------------------
# | 2001-01-02 | 1 | 0 | 4 | 2 | 8 |
I have a table in Netezza that looks like this
Date Stock Return
2015-01-01 A xxx
2015-01-02 A xxx
2015-01-03 A 0
2015-01-04 A 0
2015-01-05 A xxx
2015-01-06 A xxx
2015-01-07 A xxx
2015-01-08 A xxx
2015-01-09 A xxx
2015-01-10 A 0
2015-01-11 A 0
2015-01-12 A xxx
2015-01-13 A xxx
2015-01-14 A xxx
2015-01-15 A xxx
2015-01-16 A xxx
2015-01-17 A 0
2015-01-18 A 0
2015-01-19 A xxx
2015-01-20 A xxx
The data represents stock returns for various stocks and dates. what I need to do is group the data by a given interval, and day of that interval. Another difficulty is that weekends the (0s) will have to be discounted (ignoring public holidays). And the start date of the first interval should be an arbitrary date.
For example my out put should look sth like this
Interval Q01 Q02 Q03 Q04 Q05
1 xxx xxx xxx xxx xxx
2 xxx xxx xxx xxx xxx
3 xxx xxx xxx xxx xxx
4 xxx xxx xxx xxx xxx
This output would represent an interval of the length 5 working days, with averaged returns as results, in terms of the raw data from above,
start date 1st Jan, 1st Interval includes 1/2/5/6/7 (3 and 4 are weekends and are ignored) Q01 would be the 1st, Q02 the 2nd, Q03 the 5th etc. The second interval goes from 8/9/12/13/14.
What I tried unsuccessfully is using
CEIL(CAST(EXTRACT(DOY FROM DATE) AS FLOAT) / CAST (10 AS FLOAT)) AS interval
EXTRACT(DAY FROM DATE) % 10 AS DAYinInterval
I also tried playing around with rolling counters and for variable starting dates setting my DOY to zero with s.th like this
CEIL(CAST(EXTRACT(DOY FROM DATE) - EXTRACT(DOY FROM 'start-date' AS FLOAT) / CAST (10 AS FLOAT)) AS Interval
The one thing that came closest to what I would expect is this
SUM(Number) OVER(PARTITION BY STOCK ORDER BY DATE ASC rows 10 preceding) AS Counter
Unfortunately it goes from 1 to 10 followed by 11s where it should start from 1 to 10 again.
I would love to see how this can get implemented in an elegant way. thanks
I'm not entirely sure I understand the question, but I think I might, so I'm going to take a swing at this with some windowed aggregates and subqueries.
Here's the sample data, plugging in some random non-zero data for weekdays.
DATE | STOCK | RETURN
------------+-------+--------
2015-01-01 | A | 16
2015-01-02 | A | 80
2015-01-03 | A | 0
2015-01-04 | A | 0
2015-01-05 | A | 60
2015-01-06 | A | 25
2015-01-07 | A | 12
2015-01-08 | A | 1
2015-01-09 | A | 81
2015-01-10 | A | 0
2015-01-11 | A | 0
2015-01-12 | A | 35
2015-01-13 | A | 20
2015-01-14 | A | 69
2015-01-15 | A | 72
2015-01-16 | A | 89
2015-01-17 | A | 0
2015-01-18 | A | 0
2015-01-19 | A | 100
2015-01-20 | A | 67
(20 rows)
Here's my swing at it, with embedded comments.
select avg(return),
date_period,
day_period
from (
-- use row_number to generate a sequential value for each DOW,
-- with a WHERE to filter out the weekends
select date,
stock,
return,
date_period ,
row_number() over (partition by date_period order by date asc) day_period
from (
-- bin out the entries by date_period using the first_value of the entire set as the starting point
-- modulo 7
select date,
stock,
return,
date + (first_value(date) over (order by date asc) - date) % 7 date_period
from stocks
where date >= '2015-01-01'
-- setting the starting period date here
)
foo
where extract (dow from date) not in (1,7)
)
foo
group by date_period, day_period
order by date_period asc;
The results:
AVG | DATE_PERIOD | DAY_PERIOD
------------+-------------+------------
16.000000 | 2015-01-01 | 1
80.000000 | 2015-01-01 | 2
60.000000 | 2015-01-01 | 3
25.000000 | 2015-01-01 | 4
12.000000 | 2015-01-01 | 5
1.000000 | 2015-01-08 | 1
81.000000 | 2015-01-08 | 2
35.000000 | 2015-01-08 | 3
20.000000 | 2015-01-08 | 4
69.000000 | 2015-01-08 | 5
72.000000 | 2015-01-15 | 1
89.000000 | 2015-01-15 | 2
100.000000 | 2015-01-15 | 3
67.000000 | 2015-01-15 | 4
(14 rows)
Changing the starting date to '2015-01-03' to see if it adjusts properly:
...
from stocks
where date >= '2015-01-03'
...
And the results:
AVG | DATE_PERIOD | DAY_PERIOD
------------+-------------+------------
60.000000 | 2015-01-03 | 1
25.000000 | 2015-01-03 | 2
12.000000 | 2015-01-03 | 3
1.000000 | 2015-01-03 | 4
81.000000 | 2015-01-03 | 5
35.000000 | 2015-01-10 | 1
20.000000 | 2015-01-10 | 2
69.000000 | 2015-01-10 | 3
72.000000 | 2015-01-10 | 4
89.000000 | 2015-01-10 | 5
100.000000 | 2015-01-17 | 1
67.000000 | 2015-01-17 | 2
(12 rows)