On Tue, Oct 3, 2023 at 7:33 PM Lincoln Swaine-Moore
<lswainemo...@gmail.com> wrote:
Hi all,
I'm attempting to generate some reports using user-driven
timezones for UTC data, and I'm having trouble writing a query
that meets the following criteria:
1) Data should be averaged at one of daily, hourly, or 15 minute
granularities (user-driven).
2) Data over a given period should reflect the localized value of
the UTC data for a user-provided time zone. E.g.:
a) The 1 hour period starting at '2023-10-03
12:00:00.000000-0400' (America/New_York) should reflect data with
timestamps between '2023-10-03 16:00:00.000000Z' and '2023-10-03
17:00:00.000000Z'.
b) The 1 day period starting at '2023-10-03
00:00:00.000000-0400' should reflect data with timestamps between
'2023-10-03 04:00:00.000000Z' and '2023-10-04 04:00:00.000000Z'.
3) When a period interacts with a DST change in the given
timezone, the data should not be clumped together. E.g.:
a) Data points occurring at 2023-11-05 05:30:00.000000Z and
2023-11-05 06:30:00.000000Z should be treated as falling into
separate buckets when the time zone is America/New_York:
(2023-11-05 01:30:00.000000-0400 and 2023-11-05
01:30:00.000000-0500, respectively). This should be true for
either the 15 minute or 1 hour intervals.
b) Some clumping for day resolution seems ok! E.g. the 1 day
period starting at '2023-11-05 00:00:00.000000-0400' can and
probably should contain 25 hours' worth of data. Certainly it
should not reflect the data falling between '2023-11-05
00:04:00.000000Z' and '2023-11-05 00:04:00.000000Z' + '24
hours'::interval (= '2023-11-06 00:04:00.000000Z'), because that
would be the local times of '2023-11-05 00:00:00.000000-0400' and
'2023-11-04 23:00:00.000000-0500'.
4) It would be relatively simple to do 15 minute and 1 hour
periods were all timezone offsets multiples of 1 hour (in that
case, all operations could be done in UTC and then converted after
the fact), but unfortunately some time zones have 30 min-based
offsets, which interferes with this approach.
5) Ideally, the solution would not involve messing with the
server/connection's value of timezone. (Though I would be
interested if there was a solution that relaxed this constraint
and was relatively safe/compatible with transactions and psycopg2.)
6) Ideally, my query would return periods that are missing data
(though could plausibly fill these in in the layer above). This
points toward generate_series but...
7) Sigh: I can't upgrade to 16. I gather that 16 has a timezone
parameter for generate_series, which I believe might help. But
tragically, Digitalocean doesn't yet support 16, and it's not
practical to migrate elsewhere. Based on historical release ->
support timing, I'd imagine they will not support it until Q2
2024, which is too late for this feature. If anyone had the inside
scoop about when they'd likely support it, I'd welcome it!
This looks pretty hairy written out as above, but I actually think
it reflects most people's intuitions about what data a local
period "should" correspond to (though I'd welcome feedback on this
point).
Here are some thoughts about approaches that I've tried, and what
their drawbacks seem to be. For all these, I'll use the following
CTE to demonstrate some data that crosses a DST boundary:
```
with original_data as (
select
('2023-11-05 00:00:00.000000Z'::timestamptz) + (15 * x ||
' minutes')::interval as "t"
from
generate_series(0, 1000) as x
)
```
1) date_trunc: it seems like as of v12, date_trunc accepts a third
argument of timezone, which essentially plays the role of the
server timezone setting for the scope of the function. This is
very handy, and *I believe* solves my issues for the hour/day periods:
```
[etc]
select
date_trunc('day', t, 'America/New_York'),
min(t),
max(t),
count(*)
from original_data
group by 1
order by 1;
date_trunc | min | max
| count
------------------------+------------------------+------------------------+-------
2023-11-04 04:00:00+00 | 2023-11-05 00:00:00+00 | 2023-11-05
03:45:00+00 | 16
2023-11-05 04:00:00+00 | 2023-11-05 04:00:00+00 | 2023-11-06
04:45:00+00 | 100
2023-11-06 05:00:00+00 | 2023-11-06 05:00:00+00 | 2023-11-07
04:45:00+00 | 96
2023-11-07 05:00:00+00 | 2023-11-07 05:00:00+00 | 2023-11-08
04:45:00+00 | 96
2023-11-08 05:00:00+00 | 2023-11-08 05:00:00+00 | 2023-11-09
04:45:00+00 | 96
2023-11-09 05:00:00+00 | 2023-11-09 05:00:00+00 | 2023-11-10
04:45:00+00 | 96
[etc]
```
This checks out, but unfortunately doesn't seem to work for 15
minutes. I think, by the way, that this behavior is identical to
what I would've gotten if my server timezone was
"America/New_York" and I ran it without the 3rd argument, though
I'd be curious to hear if there are discrepancies.
On this point, I've read up on some of the history around this
feature, and was a little puzzled by this assertion in this
thread:
https://www.postgresql.org/message-id/87in1k73nr....@news-spur.riddles.org.uk:
> If you have a timestamp-in-UTC column and want to do a
date_trunc in some other specified zone (that's not the session
timezone), you need FOUR uses of AT TIME ZONE to do it correctly:
> date_trunc('day', col AT TIME ZONE 'UTC' AT TIME ZONE
'Asia/Kathmandu') AT TIME ZONE 'Asia/Kathmandu' AT TIME ZONE 'UTC'
I *think* that this doesn't comport with behavior I've seen,
because at a DST boundary the initial `col AT TIME ZONE 'UTC' AT
TIME ZONE 'Asia/Kathmandu'` would drop the offset, which from then
on would be unrecoverable. For example:
```
select date_trunc('hour', '2023-11-05 05:30:00.000000'::timestamp
AT TIME ZONE 'UTC' AT TIME ZONE 'America/New_York') AT TIME ZONE
'America/New_York' AT TIME ZONE 'UTC';
timezone
---------------------
2023-11-05 06:00:00
select date_trunc('hour', '2023-11-05 06:30:00.000000'::timestamp
AT TIME ZONE 'UTC' AT TIME ZONE 'America/New_York') AT TIME ZONE
'America/New_York' AT TIME ZONE 'UTC';
timezone
---------------------
2023-11-05 06:00:00
```
This is what I meant above by "clumping" in point (3). I believe
this issue is ameliorated by setting the database timezone:
```
set timezone to 'America/New_York';
select date_trunc('hour', '2023-11-05 06:30:00.000000'::timestamp
AT TIME ZONE 'UTC');
date_trunc
------------------------
2023-11-05 01:00:00-05
select date_trunc('hour', '2023-11-05 05:30:00.000000'::timestamp
AT TIME ZONE 'UTC');
date_trunc
------------------------
2023-11-05 01:00:00-04
```
The same (correct, in my opinion) behavior seems to occur when
using the third argument to date_trunc, so all is well on that
front, but I'm being pedantic about this because if I was
misunderstanding and usage of four `at time zone`s was a
legitimate strategy, it could plausibly be applied to the below.
Hoping to be incorrect here!
2) date_bin: this will take a "stride", which should accommodate
all the periods I'm interested in, and "origin" to help deal with
initial day/hour offsets. But I'm having trouble getting it to
respect time zone/DST stuff. For example:
```
[etc]
select
date_bin('24 hours', t, '2023-11-03
00:00:00.000000-0400'::timestamptz),
min(t),
max(t),
count(*)
from
original_data
group by 1
order by 1;
date_bin | min | max
| count
------------------------+------------------------+------------------------+-------
2023-11-04 00:00:00-04 | 2023-11-04 20:00:00-04 | 2023-11-04
23:45:00-04 | 16
2023-11-05 00:00:00-04 | 2023-11-05 00:00:00-04 | 2023-11-05
22:45:00-05 | 96
2023-11-05 23:00:00-05 | 2023-11-05 23:00:00-05 | 2023-11-06
22:45:00-05 | 96
2023-11-06 23:00:00-05 | 2023-11-06 23:00:00-05 | 2023-11-07
22:45:00-05 | 96
2023-11-07 23:00:00-05 | 2023-11-07 23:00:00-05 | 2023-11-08
22:45:00-05 | 96
2023-11-08 23:00:00-05 | 2023-11-08 23:00:00-05 | 2023-11-09
22:45:00-05 | 96
2023-11-09 23:00:00-05 | 2023-11-09 23:00:00-05 | 2023-11-10
22:45:00-05 | 96
[etc]
```
As you can see, the days get "bumped" by DST, and are off by one
(until the spring). I actually think this makes intuitive sense
for this concept of "stride"--it just means that it's not as
useful for timezones with DST.
3) generate_series: If I could get this working, it would be
ideal, because it would also help fill in gaps in my data with
null rows. But again, I can't seem to, except in v16, have it
respect timezones other than the server's:
```
set timezone to 'UTC';
select
days as start_time,
lead(days) over (order BY days) as end_time
from generate_series(
'2023-11-03 00:00:00.000000-0400'::timestamptz,
'2023-11-07 00:00:00.000000-0500'::timestamptz,
'1 day'::interval
) days;
start_time | end_time
------------------------+------------------------
2023-11-03 04:00:00+00 | 2023-11-04 04:00:00+00
2023-11-04 04:00:00+00 | 2023-11-05 04:00:00+00
2023-11-05 04:00:00+00 | 2023-11-06 04:00:00+00
2023-11-06 04:00:00+00 | 2023-11-07 04:00:00+00
[etc.]
set timezone to 'America/New_York';
[same as above]
start_time | end_time
------------------------+------------------------
2023-11-03 00:00:00-04 | 2023-11-04 00:00:00-04
2023-11-04 00:00:00-04 | 2023-11-05 00:00:00-04
2023-11-05 00:00:00-04 | 2023-11-06 00:00:00-05
2023-11-06 00:00:00-05 | 2023-11-07 00:00:00-05
[etc.]
```
The latter being correct for these purposes, but not seeming super
reliable/practical (see: point 5).
Can anyone think of any other approaches to this problem? I'd be
thrilled if I could manage it without resorting to manually
post-processing in pandas or something, because I have a number of
datapoints, and each individual one is quite wide.
Apologies for the length of this message--just trying to be
thorough. I sincerely appreciate any help or pointers!
Best,
Lincoln
--
Lincoln Swaine-Moore
That's a long email to digest but a couple thoughts that may help.
I've always disliked the term "timestamp with time zone" and always
mentally translate that to "point in time". As long as data is stored
as a timestamp with time zone (point in time), it can be manipulated
in any time zone you desire including handling DST.
Time calculation always involves some assumption of meaning and
PostgreSQL makes some reasonable ones. I'm in US Pacific time and if I
run:
select '2023-11-05'::timestamptz ;
I get:
timestamptz
------------------------
2023-11-05 00:00:00-07
If I add a day (crossing the DST boundary):
select '2023-11-05'::timestamptz + '1 day'::interval;
?column?
------------------------
2023-11-06 00:00:00-08
Observe that I end up at midnight the following day. But if I instead
add 24 hours:
select '2023-11-05'::timestamptz + '24 hours'::interval;
?column?
------------------------
2023-11-05 23:00:00-08
24 hours is exactly what I get.
You are generating 15-minute intervals the hard way. You can do it
directly and have DST handled for you:
select generate_series('2023-11-05'::timestamptz,
'2023-11-06'::timestamptz, '15 minutes'::interval);
generate_series
------------------------
2023-11-05 00:00:00-07
2023-11-05 00:15:00-07
2023-11-05 00:30:00-07
2023-11-05 00:45:00-07
2023-11-05 01:00:00-07
2023-11-05 01:15:00-07
2023-11-05 01:30:00-07
2023-11-05 01:45:00-07
2023-11-05 01:00:00-08
2023-11-05 01:15:00-08
...
2023-11-06 00:00:00-08
(101 rows)
Note that 01:00:00-07 is a different point in time than 01:00:00-08.
Sticking with timestamp with time zone, aka a fully qualified point in
time, removes any ambiguity. Also observe that I get the correct
number of "bins". If I run the same thing but for March 12 2023
(spring forward) I'll get 93 rows but on "normal days" there will be 97.
I suspect your best bet will be to store the data as type time
stamp with time zone and to set the time zone before running your queries.
Note that setting the time zone is a client/connection setting so if
you set it within a transaction, it will stay set when the transaction
concludes. But time manipulation is tricky and trying to DIY reinvent
the wheel is painful and often buggy. Let PostgreSQL do the work for you.
Cheers,
Steve
