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I need find percentile(50) value and its timestamp using timescale db time-bucket. Finding P50 is easy but I don't know how to get the time stamp.
Select time_bucket('120 sec',timestamp_utc) as interval_size,
first(timestamp_utc,int_val) as minTime,
min(int_val) as minVal,
last(timestamp_utc,int_val) as maxTime,
max(int_val) as maxVal,
-- timestamp of percentile value below.
percentile_disc(0.5) within group (order by int_val) as medianVal
from timeseries.raw
where timestamp_utc > NOW() - INTERVAL '10 min'
AND tag_id = 59560544877390423
group by interval_size
order by interval_size desc
I think what you're looking for we can do by selecting where the int_val is equal to the median value in a lateral (percentile_disc does ensure that there is a value exactly equal to that value, there may be more than one depending on what you want there you could deal with the more than one case in different ways), building on a previous answer and making it work a bit better I think would look something like this:
WITH p50 AS (
Select time_bucket('120 sec',timestamp_utc) as interval_size,
first(timestamp_utc,int_val) as minTime,
min(int_val) as minVal,
last(timestamp_utc,int_val) as maxTime,
max(int_val) as maxVal,
-- timestamp of percentile value below.
percentile_disc(0.5) within group (order by int_val) as medianVal
from timeseries.raw
where timestamp_utc > NOW() - INTERVAL '10 min'
AND tag_id = 59560544877390423
group by interval_size
order by interval_size desc
) SELECT p50.*, rmed.*
FROM p50, LATERAL (SELECT * FROM timeseries.raw r
-- copy over the same where clause from above so we're dealing with the same subset of data
WHERE timestamp_utc > NOW() - INTERVAL '10 min'
AND tag_id = 59560544877390423
-- add a where clause on the median value
AND r.int_val = p50.medianVal
-- now add a where clause to account for the time bucket
AND r.timestamp_utc >= p50.interval_size
AND r.timestamp_utc < p50.interval_size + '120 sec'::interval
-- Can add an order by something desc limit 1 if you want to avoid ties
) rmed;
Note that this will do a second scan of the table, it should be reasonably efficient, especially if you have an index on that column, but it will cause another scan, there isn't a great way that I know of of doing it without a second scan.
I have a table in Postgres which looks like below:
CREATE TABLE my_features
(
id uuid NOT NULL,
feature_id uuid NOT NULL,
begin_time timestamptz NOT NULL,
duration integer NOT NULL
)
For each feature_id there may be multiple rows with time ranges specified by begin_time .. (begin_time + duration). duration is in milliseconds. They may overlap. I'm looking for a fast way to find all feature_ids that have any overlaps.
I have referred to this - Query Overlapping time range which is similar but works on a fixed time end time.
I have tried the below query but it is throwing an error.
Query:
select c1.*
from my_features c1
where exists (select 1
from my_features c2
where tsrange(c2.begin_time, c2.begin_time + '30 minutes'::INTERVAL, '[]') && tsrange(c1.begin_time, c1.begin_time + '30 minutes'::INTERVAL, '[]')
and c2.feature_id = c1.feature_id
and c2.id <> c1.id);
Error:
ERROR: function tsrange(timestamp with time zone, timestamp with time zone, unknown) does not exist
LINE 5: where tsrange(c2.begin_time, c2.begin_time...
I have used a default time interval here because I did not understand how to convert the time into minutes and substitute it with 'n minutes'.
If you need a solution faster than O(n²), then you can use constraints on ranges with btree_gist extension, possibly on a temporary table:
CREATE TEMPORARY TABLE my_features_ranges (
id uuid NOT NULL,
feature_id uuid NOT NULL,
range tstzrange NOT NULL,
EXCLUDE USING GIST (feature_id WITH =, range WITH &&)
);
INSERT INTO my_features_ranges (id, feature_id, range)
select id, feature_id, tstzrange(begin_time, begin_time+duration*'1ms'::interval)
from my_features
on conflict do nothing;
select id from my_features except select id from my_features_ranges;
Using OVERLAPS predicate:
SELECT * -- DISTINCT f1.*
FROM my_features f1
JOIN my_features f2
ON f1.feature_id = f2.feature_id
AND f1.id <> f2.id
AND (f1.begin_time::date, f1.begin_time::date + '30 minutes'::INTERVAL)
OVERLAPS (f2.begin_time::date, f2.begin_time::date + '30 minutes'::INTERVAL);
db<>fiddle demo
Or try this
select c1.*
from jak.my_features c1
where exists (select 1
from jak.my_features c2
where tsrange(c2.begin_time::date, c2.begin_time::date + '30 minutes'::INTERVAL, '[]') && tsrange(c1.begin_time::date, c1.begin_time::date + '30 minutes'::INTERVAL, '[]') and
c2.feature_id = c1.feature_id
and c2.id <> c1.id);
The problem was, I was using tsrange on a column with timezone and for timestamp with timezone, there exist another function called tstzrange
Below worked for me:
EDIT: Added changes suggested by #a_horse_with_no_name
select c1.*
from my_features c1
where exists (select 1
from my_features c2
where tstzrange(c2.begin_time, c2.begin_time + make_interval(secs => c2.duration / 1000), '[]') && tstzrange(c1.begin_time, c1.begin_time + make_interval(secs => c1.duration / 1000), '[]')
and c2.feature_id = c1.feature_id
and c2.id <> c1.id);
However, the part of calculating interval dynamically is still pending
I need to select the rows for which the difference between max(date) and the date just before max(date) is smaller than 366 days. I know about SELECT MAX(date) FROM table to get the last date from now, but how could I get the date before?
I would need a query of this kind:
SELECT code, MAX(date) - before_date FROM troncon WHERE MAX(date) - before_date < 366 ;
NB : before_date does not refer to anything and is to be replaced by a functionnal stuff.
Edit : Example of the table I'm testing it on:
CREATE TABLE troncon (code INTEGER, ope_date DATE) ;
INSERT INTO troncon (code, ope_date) VALUES
('C086000-T10001', '2014-11-11'),
('C086000-T10001', '2014-11-11'),
('C086000-T10002', '2014-12-03'),
('C086000-T10002', '2014-01-03'),
('C086000-T10003', '2014-08-11'),
('C086000-T10003', '2014-03-03'),
('C086000-T10003', '2012-02-27'),
('C086000-T10004', '2014-08-11'),
('C086000-T10004', '2013-12-30'),
('C086000-T10004', '2013-06-01'),
('C086000-T10004', '2012-07-31'),
('C086000-T10005', '2013-10-01'),
('C086000-T10005', '2012-11-01'),
('C086000-T10006', '2014-04-01'),
('C086000-T10006', '2014-05-15'),
('C086000-T10001', '2014-07-05'),
('C086000-T10003', '2014-03-03');
Many thanks!
The sub query contains all rows joined with the unique max date, and you select only ones which there differente with the max date is smaller than 366 days:
select * from
(
SELECT id, date, max(date) over(partition by code) max_date FROM your_table
) A
where max_date - date < interval '366 day'
PS: As #a_horse_with_no_name said, you can partition by code to get maximum_date for each code.
Im a new in TSQL.
I have a table with a field called ODOMETER of a vehicle. I have to get the quantity of km in a period of time from 1st of the month to the end.
SELECT MAX(Odometer) - MIN(Odometer) as TotalKm FROM Table
This will work in ideal test scenary, but the Odomometer can be reset to 0 in anytime.
Someone can help to solve my problem, thank you.
I'm working with MS SQL 2012
EXAMPLE of records:
Date Odometer value
datetime var, 37210
datetime var, 37340
datetime var, 0
datetime var, 220
Try something like this using the LAG. There are other ways, but this should be easy.
EDIT: Changing the sample data to include records outside of the desired month range. Also simplifying that Reading for easy hand calc. Will shows a second option as siggested by OP.
DECLARE #tbl TABLE (stamp DATETIME, Reading INT)
INSERT INTO #tbl VALUES
('02/28/2014',0)
,('03/01/2014',10)
,('03/10/2014',20)
,('03/22/2014',0)
,('03/30/2014',10)
,('03/31/2014',20)
,('04/01/2014',30)
--Original solution with WHERE on the "outer" SELECT.
--This give a result of 40 as it include the change of 10 between 2/28 and 3/31.
;WITH cte AS (
SELECT Reading
,LAG(Reading,1,Reading) OVER (ORDER BY stamp ASC) LastReading
,Reading - LAG(Reading,1,Reading) OVER (ORDER BY stamp ASC) ChangeSinceLastReading
,CONVERT(date, stamp) stamp
FROM #tbl
)
SELECT SUM(CASE WHEN Reading = 0 THEN 0 ELSE ChangeSinceLastReading END)
FROM cte
WHERE stamp BETWEEN '03/01/2014' AND '03/31/2014'
--Second option with WHERE on the "inner" SELECT (within the CTE)
--This give a result of 30 as it include the change of 10 between 2/28 and 3/31 is by the filtered lag.
;WITH cte AS (
SELECT Reading
,LAG(Reading,1,Reading) OVER (ORDER BY stamp ASC) LastReading
,Reading - LAG(Reading,1,Reading) OVER (ORDER BY stamp ASC) ChangeSinceLastReading
,CONVERT(date, stamp) stamp
FROM #tbl
WHERE stamp BETWEEN '03/01/2014' AND '03/31/2014'
)
SELECT SUM(CASE WHEN Reading = 0 THEN 0 ELSE ChangeSinceLastReading END)
FROM cte
I think Karl solution using LAG is better than mine, but anyway:
;WITH [Rows] AS
(
SELECT o1.[Date], o1.[Value] as CurrentValue,
(SELECT TOP 1 o2.[Value]
FROM #tbl o2 WHERE o1.[Date] < o2.[Date]) as NextValue
FROM #tbl o1
)
SELECT SUM (CASE WHEN [NextValue] IS NULL OR [NextValue] < [CurrentValue] THEN 0 ELSE [NextValue] - [CurrentValue] END )
FROM [Rows]
I have my measurement data stored into the following structure:
CREATE TABLE measurements(
measured_at TIMESTAMPTZ,
val INTEGER
);
I already know that using
(a) date_trunc('hour',measured_at)
AND
(b) generate_series
I would be able to aggregate my data by:
microseconds,
milliseconds
.
.
.
But is it possible to aggregate the data by 5 minutes or let's say an arbitrary amount of seconds? Is it possible to aggregate measured data by an arbitrary multiple of seconds?
I need the data aggregated by different time resolutions to feed them into a FFT or an AR-Model in order to see possible seasonalities.
You can generate a table of "buckets" by adding intervals created by generate_series(). This SQL statement will generate a table of five-minute buckets for the first day (the value of min(measured_at)) in your data.
select
(select min(measured_at)::date from measurements) + ( n || ' minutes')::interval start_time,
(select min(measured_at)::date from measurements) + ((n+5) || ' minutes')::interval end_time
from generate_series(0, (24*60), 5) n
Wrap that statement in a common table expression, and you can join and group on it as if it were a base table.
with five_min_intervals as (
select
(select min(measured_at)::date from measurements) + ( n || ' minutes')::interval start_time,
(select min(measured_at)::date from measurements) + ((n+5) || ' minutes')::interval end_time
from generate_series(0, (24*60), 5) n
)
select f.start_time, f.end_time, avg(m.val) avg_val
from measurements m
right join five_min_intervals f
on m.measured_at >= f.start_time and m.measured_at < f.end_time
group by f.start_time, f.end_time
order by f.start_time
Grouping by an arbitrary number of seconds is similar--use date_trunc().
A more general use of generate_series() lets you avoid guessing the upper limit for five-minute buckets. In practice, you'd probably build this as a view or a function. You might get better performance from a base table.
select
(select min(measured_at)::date from measurements) + ( n || ' minutes')::interval start_time,
(select min(measured_at)::date from measurements) + ((n+5) || ' minutes')::interval end_time
from generate_series(0, ((select max(measured_at)::date - min(measured_at)::date from measurements) + 1)*24*60, 5) n;
Catcall has a great answer. My example of using it demonstrates having fixed buckets - in this case 30 minute intervals starting at midnight. It also shows that there can be one extra bucket generated in Catcall's first version and how to eliminate it. I wanted exactly 48 buckets in a day. In my problem, observations have separate date and time columns and I want to average the observations within a 30 minute period across the month for a number of different services.
with intervals as (
select
(n||' minutes')::interval as start_time,
((n+30)|| ' minutes')::interval as end_time
from generate_series(0, (23*60+30), 30) n
)
select i.start_time, o.service, avg(o.o)
from
observations o right join intervals i
on o.time >= i.start_time and o.time < i.end_time
where o.date between '2013-01-01' and '2013-01-31'
group by i.start_time, i.end_time, o.service
order by i.start_time
How about
SELECT MIN(val),
EXTRACT(epoch FROM measured_at) / EXTRACT(epoch FROM INTERVAL '5 min') AS int
FROM measurements
GROUP BY int
where '5 min' can be any expression supported by INTERVAL
The following will give you buckets of any size, even if they don't aline well with a nice minute/hour/whatever boundary. The value "300" is for a 5 minute grouping, but any value can be substituted:
select measured_at,
val,
(date_trunc('seconds', (measured_at - timestamptz 'epoch') / 300) * 300 + timestamptz 'epoch') as aligned_measured_at
from measurements;
You can then use whatever aggregate you need around "val", and use "group by aligned_measured_at" as required.
This is based on Mike Sherrill's answer, except that it uses timestamp intervals instead of separate start/end columns.
with intervals as (
select tstzrange(s, s + '5 minutes') das_interval
from (select generate_series(min(lower(time_range)), max(upper(time_rage)), '5 minutes') s
from your_table) x)
select das_interval, your_table.*
from your_table
right join intervals on time_range && das_interval
order by das_interval;
From PostgreSQL v14 on, you can use the date_bin function for that:
SELECT date_bin(
INTERVAL '5 minutes',
measured_at,
TIMSTAMPTZ '2000-01-01'
),
sum(val)
FROM measurements
GROUP BY 1;
I wanted to look at the past 24 hours of data and count things in hourly increments. I started Cat Recall's solution, which is pretty slick. It's bound to the data, though, rather than just what's happened in the past 24H. So I refactored and ended up with something pretty close to Julian's solution, but with more CTE. So it's sort of the marriage of the 2 answers.
WITH interval_query AS (
SELECT (ts ||' hour')::INTERVAL AS hour_interval
FROM generate_series(0,23) AS ts
), time_series AS (
SELECT date_trunc('hour', now()) + INTERVAL '60 min' * ROUND(date_part('minute', now()) / 60.0) - interval_query.hour_interval AS start_time
FROM interval_query
), time_intervals AS (
SELECT start_time, start_time + '1 hour'::INTERVAL AS end_time
FROM time_series ORDER BY start_time
), reading_counts AS (
SELECT f.start_time, f.end_time, br.minor, count(br.id) readings
FROM beacon_readings br
RIGHT JOIN time_intervals f
ON br.reading_timestamp >= f.start_time AND br.reading_timestamp < f.end_time AND br.major = 4
GROUP BY f.start_time, f.end_time, br.minor
ORDER BY f.start_time, br.minor
)
SELECT * FROM reading_counts
Note that any additional limiting I wanted in the final query needed to be done in the RIGHT JOIN. I'm not suggesting this is necessarily the best (or even a good approach), but it is something I'm running with (at least at the moment) in a dashboard.
The Timescale extension for PostgreSQL gives the ability to group by arbitrary time intervals. The function is called time_bucket() and has the same syntax as the date_trunc() function but takes an interval instead of a time precision as first parameter. Here you can find its API Docs. This is an example:
SELECT
time_bucket('5 minutes', observation_time) as bucket,
device_id,
avg(metric) as metric_avg,
max(metric) - min(metric) as metric_spread
FROM
device_readings
GROUP BY bucket, device_id;
You may also take a look at the continuous aggregate views if you want the 'grouped by an interval' views be updated automatically with new ingested data and if you want to query these views on a frequent basis. This can save you a lot of resources and will make your queries a lot faster.
I've taken a synthesis of all the above to try and come up with something slightly easier to use;
create or replace function interval_generator(start_ts timestamp with TIME ZONE, end_ts timestamp with TIME ZONE, round_interval INTERVAL)
returns TABLE(start_time timestamp with TIME ZONE, end_time timestamp with TIME ZONE) as $$
BEGIN
return query
SELECT
(n) start_time,
(n + round_interval) end_time
FROM generate_series(date_trunc('minute', start_ts), end_ts, round_interval) n;
END
$$
LANGUAGE 'plpgsql';
This function is a timestamp abstraction of Mikes answer, which (IMO) makes things a little cleaner, especially if you're generating queries on the client end.
Also using an inner join gets rid of the sea of NULLs that appeared previously.
with intervals as (select * from interval_generator(NOW() - INTERVAL '24 hours' , NOW(), '30 seconds'::INTERVAL))
select f.start_time, m.session_id, m.metric, min(m.value) min_val, avg(m.value) avg_val, max(m.value) max_val
from ts_combined as m
inner JOIN intervals f
on m.time >= f.start_time and m.time < f.end_time
GROUP BY f.start_time, f.end_time, m.metric, m.session_id
ORDER BY f.start_time desc
(Also for my purposes I added in a few more aggregation fields)
Perhaps, you can extract(epoch from measured_at) and go from that?