timescaledb_experimental.time_bucket_ng()
ExperimentalExperimental features are not suitable for production environments. They are included under the TimescaleDB experimental schema. Click to learn more.The time_bucket_ng()
function is an experimental version of the
time_bucket()
function. It introduced some new capabilities,
such as monthly buckets and timezone support. Those features are now part of the
regular time_bucket()
function.
Deprecation
This section describes a feature that is deprecated on Timescale. We strongly recommend that you do not use this feature in a production environment. If you need more information, contact us.
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 January 3, 2000, for buckets shorter than a month. time_bucket_ng()
uses an origin date of January 1, 2000, for all bucket sizes.
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 |
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. However, it is required for time buckets that are less than 24 hours.
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. Best practice is to use
time_bucket_ng(interval, timestamptz, text)
and specify the timezone.
The function returns the bucket's start time. The return value type is the
same as ts
.
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 date
Going 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 2000-01-01SELECT 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 MondaySELECT timescaledb_experimental.time_bucket_ng('1 week', timestamp '2021-08-26', origin => '2000-01-03');time_bucket_ng---------------------2021-08-23 00:00:00
This 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 parametersSET TIME ZONE 'Europe/Moscow';SETSELECT 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+03
You 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', by_range('day', 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_weeklyWITH (timescaledb.continuous) ASSELECT city,timescaledb_experimental.time_bucket_ng('7 days', day) AS bucket,MIN(temperature),MAX(temperature)FROM conditionsGROUP BY city, bucket;SELECT to_char(bucket, 'YYYY-MM-DD'), city, min, maxFROM conditions_summary_weeklyORDER BY bucket;to_char | city | min | max------------+--------+-----+-----2021-06-12 | Moscow | 22 | 272021-06-19 | Moscow | 28 | 342021-06-26 | Moscow | 31 | 32(3 rows)
Note
The by_range
dimension builder is an addition to TimescaleDB 2.13. For simpler cases, like this one, you can also create the hypertable using the old syntax:
SELECT create_hypertable('<table name>', '<time column name>');
For more information, see the continuous aggregates documentation.
Important
While 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 - 2.14.2 |
Buckets by months and years | ✅ | 2.6.0 - 2.14.2 |
Timezones support | ✅ | 2.6.0 - 2.14.2 |
Specify custom origin | ✅ | 2.7.0 - 2.14.2 |
Keywords
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