lttb.md

api_name	excerpt	topics	api	hyperfunction	api_details
lttb()	Downsample a time series using the Largest Triangle Three Buckets method	hyperfunctions	license type toolkit version community function true experimental stable 0.2.0 1.10.1	family type downsampling function	summary signatures parameters examples Downsample your data with the [Largest Triangle Three Buckets algorithm](https://github.com/sveinn-steinarsson/flot-downsample). This algorithm tries to retain visual similarity between the downsampled data and the original dataset. language code sql lttb( ts TIMESTAMPTZ, value DOUBLE PRECISION, resolution INT ) RETURNS Timevector required returns name type description ts TIMESTAMPTZ Timestamps for each data point name type description value DOUBLE PRECISION The value at each timestamp name type description resolution INT The approximate number of points to return. Determines the horizontal resolution of the resulting graph. column type description lttb Timevector An object representing a series of values occurring at set intervals from a starting time. It can be unpacked with `unnest`. For more information, see the documentation on [timevectors](/use-timescale/latest/hyperfunctions/function-pipelines/#timevectors). description command return This example uses a table with raw data generated as a sine wave. You can use LTTB to dramatically reduce the number of points while still capturing the peaks and valleys in the data. code SET TIME ZONE 'UTC'; CREATE TABLE metrics(date TIMESTAMPTZ, reading DOUBLE PRECISION); INSERT INTO metrics SELECT '2020-1-1 UTC'::timestamptz + make_interval(hours=>foo), (5 + 5 * sin(foo / 24.0 * PI())) FROM generate_series(1,168) foo; SELECT time, value FROM unnest(( SELECT lttb(date, reading, 8) FROM metrics)) code time | value ------------------------+--------------------- 2020-01-01 01:00:00+00 | 5.652630961100257 2020-01-01 13:00:00+00 | 9.957224306869053 2020-01-02 11:00:00+00 | 0.04277569313094798 2020-01-03 11:00:00+00 | 9.957224306869051 2020-01-04 13:00:00+00 | 0.04277569313094709 2020-01-05 16:00:00+00 | 9.330127018922191 2020-01-06 20:00:00+00 | 2.4999999999999996 2020-01-08 00:00:00+00 | 5.000000000000004