I will be very grateful for your advice regarding the following issue.
Given:
PostgreSQL database
Initial (basic) query
select day, Value_1, Value_2, Value_3
from table
where day=current_date
which returns a row with following columns
Day | Value_1(int) | Value_2(int) | Value 3 (int)
2019-11-14 | 10 | 10 | 14
It is needed to create a view with this starting information and add a new row every day based on the outcome of initial query executed at 22:00.
The expected outcome tomorrow at 22:01 will be
Day | Value_1 | Value_2 | Value_3
2019-11-14 | 10 | 10 | 14
2019-11-15 | N | M | P
Many thanks in advance for your time and support.
Related
I have this table inside my postgresql database,
item_code | date | price
==============================
aaaaaa.1 |2019/12/08 | 3.04
bbbbbb.b |2019/12/08 | 19.48
261893.c |2019/12/08 | 7.15
aaaaaa.1 |2019/12/17 | 4.15
bbbbbb.2 |2019/12/17 | 20
xxxxxx.5 |2019/03/12 | 3
xxxxxx.5 |2019/03/18 | 4.5
how can i calculate the average per item, per month over the year. so i get the result something like:
item_code | month | price
==============================
aaaaaa.1 | 2019/12 | 3.59
bbbbbb.2 | 2019/12 | 19.74
261893.c | 2019/12 | 7.15
xxxxxx.5 | 2019/03 | 3.75
I have tried to look and apply many alternatives but i am still not get the point, would really appreciate your help because i am new to postgresql.
I don't see how the question relates to a moving average. It seems you just want group by:
select item_code, date_trunc('month', date) as date_month, avg(price) as price
from mytable
group by item_code, date_month
This gives date_month as a date, truncated to the first day of the month - which I find more useful that the format you suggested. But it you do want that:
to_char(date, 'YYYY/MM') as date_month
I have a file with a [start] and [end] date in Tableau and would like to create a calculated field that counts number of rows on a rolling basis that occur between [start] and [end] for each [person]. This data is like so:
| Start | End | Person
|1/1/2019 |1/7/2019 | A
|1/3/2019 |1/9/2019 | A
|1/8/2019 |1/15/2019| A
|1/1/2019 |1/7/2019 | B
I'd like to create a calculated field [count] with results like so:
| Start | End | Person | Count
|1/1/2019 |1/7/2019 | A | 1
|1/3/2019 |1/9/2019 | A | 2
|1/8/2019 |1/15/2019| A | 2
|1/1/2019 |1/7/2019 | B | 1
EDITED: A good analogy for what [count] represents is: "how many videos does each person rented at the same time as of that moment?" With the 1st row for person A, count is 1, with 1 item rented. As of row 2, person A has 2 items rented. But for the 3rd row [count]= 2 since the video rented in the first row is no longer rented.
Given a table that has a column of time ranges e.g.:
| <2015-10-02>--<2015-10-24> |
| <2015-10-05>--<2015-10-20> |
....
how can I create a column showing the results of org-evalute-time-range?
If I attempt something like:
#+TBLFM: $2='(org-evaluate-time-range $1)
the 2nd column is populated with
Time difference inserted
in every row.
It would also be nice to generate the same result from two different columns with, say, start date and end date instead of creating one column of time ranges out of those two.
If you have your date range split into 2 columns, a simple subtraction works and returns number of days:
| <2015-10-05> | <2015-10-20> | 15 |
| <2013-10-02 08:30> | <2015-10-24> | 751.64583 |
#+TBLFM: $3=$2-$1
Using org-evaluate-time-range is also possible, and you get a nice formatted output:
| <2015-10-02>--<2015-10-24> | 22 days |
| <2015-10-05>--<2015-10-20> | 15 days |
| <2015-10-22 Thu 21:08>--<2015-08-01> | 82 days 21 hours 8 minutes |
#+TBLFM: $2='(org-evaluate-time-range)
Note that the only optional argument that org-evaluate-time-range accepts is a flag to indicate insertion of the result in the current buffer, which you don't want.
Now, how does this function (without arguments) get the correct time range when evaluated is a complete mystery to me; pure magic(!)
I have some Postgres data like this:
date | count
2015-01-01 | 20
2015-01-02 | 15
2015-01-05 | 30
I want to run a query that pulls this data with 0s in place for the dates that are missing, like this:
date | count
2015-01-01 | 20
2015-01-02 | 15
2015-01-03 | 0
2015-01-04 | 0
2015-01-05 | 30
This is for a very large range of dates, and I need it to fill in all the gaps. How can I accomplish this with just SQL?
Given a table junk of:
d | c
------------+----
2015-01-01 | 20
2015-01-02 | 15
2015-01-05 | 30
Running
select fake.d, coalesce(j.c, 0) as c
from (select min(d) + generate_series(0,7,1) as d from junk) fake
left outer join junk j on fake.d=j.d;
gets us:
d | c
------------+----------
2015-01-01 | 20
2015-01-02 | 15
2015-01-03 | 0
2015-01-04 | 0
2015-01-05 | 30
2015-01-06 | 0
2015-01-07 | 0
2015-01-08 | 0
You could of course adjust the start date for the series, length it runs for, etc.
Where is this data going? To an outside source or another table or view?
There's probably a better solution but you could create a new table(or in excel wherever the data is going) that has the entire date-range you want with another integer column of null values. Then update that table with your current dataset then replace all nulls with zero.
It's a really roundabout way to do things but it'll work.
I don't have enough rep to comment :(
This is also a good reference
Using COALESCE to handle NULL values in PostgreSQL
I've got a database of messages which contain various pieces of information that come in. One such piece is a state change messages so my table looks like the following.
+-----------+--------------+---------+
| MessageId | RecievedUTC | State |
+-----------+--------------+---------+
| 1 | 1/1/2010 5pm | Off |
+-----------+--------------+---------+
| 2 | 1/2/2010 8am | Idle |
+-----------+--------------+---------+
| 3 | 1/2/2010 9am | Working |
+-----------+--------------+---------+
I'd like get a list of records which state how long I was in each state something like for a report and a maybe a pretty bar chart of how time was spent.
+---------+---------------+--------------+
| State | StartUTC | StopUTC |
+---------+---------------+--------------+
| Off | 1/1/2010 5pm | 1/2/2010 8am |
+---------+---------------+--------------+
| Idle | 1/1/2010 8am | 1/2/2010 9am |
+---------+---------------+--------------+
etc. In my mind its no harder than a join of the table with itself, offset by 1 record ordered by the RecievedUTC.
The best TSQL I could come up with is something to the effect of
SELECT m1.State, m1.RecievedUTC as StartUTC, MIN(m2.RecievedUTC) as StopUTC
FROM MessageStates as m1
INNER JOIN MessageStates as m2 ON MessageStates ON m2.RecievedUTC > m1.RecievedUTC
GROUP BY m1.MessageId, m1.State, m1.RecievedUTC
Or as a sub query to get StopUTC but both perform horribly with only 30-40k records taking almost 5 minutes to do this join.
If I wrote this in C# I would keep the track of the previous RecievedUTC and state so when I saw the next record I could combine the next RecievedUTC with it and in linear time have the data as I wanted it.
Try this:
WITH MsgStates AS
(
SELECT a.*, ROW_NUMBER() OVER(ORDER BY RecievedUTC ) RN
FROM MessageStates a
)
SELECT a.State, a.RecievedUTC StartUTC, b.RecievedUTC StartUTC
FROM MsgStates a, MsgStates b
WHERE a.rn = b.rn+1