timescaledb_experimental.time_bucket_ng()
The time_bucket_ng() (next generation) experimental function is an updated
version of the original time_bucket() function. While
time_bucket works only with small units of time, time_bucket_ng()
supports years and months in addition to small units of time.
warning
| Functionality | time_bucket() | time_bucket_ng() |
|---|---|---|
| Buckets by seconds, minutes, hours, days and weeks | YES | YES |
| Buckets by months and years | NO | YES |
| Timezones support | NO | YES |
warning
The time_bucket() and time_bucket_ng() functions are similar, but not completely compatible. There are two main differences.
Firstly, time_bucket_ng() doesn't work with timestamps prior to origin,
while time_bucket() does.
Secondly, the default origin values differ. time_bucket() uses an origin
date of 3 Jan 2000, because that date is a Monday. This works better with
weekly buckets. time_bucket_ng() uses an origin date of 1 Jan 2000, because
it is the first day of the month and the year. This works better with monthly
or annual aggregates.
Required Arguments
| Name | Type | Description |
|---|---|---|
bucket_width | INTERVAL | A PostgreSQL time interval for how long each bucket is |
ts | DATE, TIMESTAMP or TIMESTAMPTZ | The timestamp to bucket |
Optional Arguments
| Name | Type | Description |
|---|---|---|
origin | Should be the same as ts | Buckets are aligned relative to this timestamp |
timezone | TEXT | The name of the timezone. The argument can be specified only if the type of ts is TIMESTAMPTZ |
For backward compatibility with time_bucket() the timezone argument is
optional. Note that if you call the TIMESTAMPTZ-version of the function
without the timezone argument, the timezone defaults to the session's
timezone and so the function can't be used with continuous aggregates.
Return value
The function returns the bucket's start time. The return value type is the
same as ts.
Sample Usage
In this example, time_bucket_ng() is used to create bucket data in three month
intervals:
SELECT timescaledb_experimental.time_bucket_ng('3 month', date '2021-08-01');
time_bucket_ng
----------------
2021-07-01
(1 row)This example uses time_bucket_ng() to bucket data in one year intervals:
SELECT timescaledb_experimental.time_bucket_ng('1 year', date '2021-08-01');
time_bucket_ng
----------------
2021-01-01
(1 row)To split time into buckets, time_bucket_ng() uses a starting point in time
called origin. The default origin is 2000-01-01. time_bucket_ng cannot use
timestamps earlier than origin:
SELECT timescaledb_experimental.time_bucket_ng('100 years', timestamp '1988-05-08');
ERROR: origin must be before the given dateGoing back in time from origin isn't usually possible, especially when you
consider timezones and daylight savings time (DST). Note also that there is no
reasonable way to split time in variable-sized buckets (such as months) from an
arbitrary origin, so origin defaults to the first day of the month.
To bypass named limitations, you can override the default origin:
-- working with timestamps before 2000-01-01
SELECT timescaledb_experimental.time_bucket_ng('100 years', timestamp '1988-05-08', origin => '1900-01-01');
time_bucket_ng
---------------------
1900-01-01 00:00:00
-- unlike the default origin, which is Saturday, 2000-01-03 is Monday
SELECT timescaledb_experimental.time_bucket_ng('1 week', timestamp '2021-08-26', origin => '2000-01-03');
time_bucket_ng
---------------------
2021-08-23 00:00:00This example shows how time_bucket_ng() is used to bucket data
by months in a specified timezone:
-- note that timestamptz is displayed differently depending on the session parameters
SET TIME ZONE 'Europe/Moscow';
SET
SELECT timescaledb_experimental.time_bucket_ng('1 month', timestamptz '2001-02-03 12:34:56 MSK', timezone => 'Europe/Moscow');
time_bucket_ng
------------------------
2001-02-01 00:00:00+03You can use time_bucket_ng() with continuous aggregates. This example tracks
the temperature in Moscow over seven day intervals:
CREATE TABLE conditions(
day DATE NOT NULL,
city text NOT NULL,
temperature INT NOT NULL);
SELECT create_hypertable(
'conditions', 'day',
chunk_time_interval => INTERVAL '1 day'
);
INSERT INTO conditions (day, city, temperature) VALUES
('2021-06-14', 'Moscow', 26),
('2021-06-15', 'Moscow', 22),
('2021-06-16', 'Moscow', 24),
('2021-06-17', 'Moscow', 24),
('2021-06-18', 'Moscow', 27),
('2021-06-19', 'Moscow', 28),
('2021-06-20', 'Moscow', 30),
('2021-06-21', 'Moscow', 31),
('2021-06-22', 'Moscow', 34),
('2021-06-23', 'Moscow', 34),
('2021-06-24', 'Moscow', 34),
('2021-06-25', 'Moscow', 32),
('2021-06-26', 'Moscow', 32),
('2021-06-27', 'Moscow', 31);
CREATE MATERIALIZED VIEW conditions_summary_weekly
WITH (timescaledb.continuous) AS
SELECT city,
timescaledb_experimental.time_bucket_ng('7 days', day) AS bucket,
MIN(temperature),
MAX(temperature)
FROM conditions
GROUP BY city, bucket;
SELECT to_char(bucket, 'YYYY-MM-DD'), city, min, max
FROM conditions_summary_weekly
ORDER BY bucket;
to_char | city | min | max
------------+--------+-----+-----
2021-06-12 | Moscow | 22 | 27
2021-06-19 | Moscow | 28 | 34
2021-06-26 | Moscow | 31 | 32
(3 rows)For more information, see the continuous aggregates documentation.
important
time_bucket_ng() supports months and timezones, continuous aggregates cannot always be used with monthly buckets or buckets with timezones.This table shows which time_bucket_ng() functions can be used in a continuous aggregate:
| Function | Available in continuous aggregate | TimescaleDB version |
|---|---|---|
| Buckets by seconds, minutes, hours, days, and weeks | ✅ | 2.4.0 and later |
| Buckets by months and years | ✅ | 2.6.0 or later |
| Specify custom origin | ❌ | To be determined |
| Timezones support | ❌ | To be determined |
Found an issue on this page?
Report an issue!