lttb() - Tiger Data

Since 1.10.1 Downsample your data with the Largest Triangle Three Buckets algorithm. This algorithm tries to retain visual similarity between the downsampled data and the original dataset.

Samples

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.

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))

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

Arguments

The syntax is:

lttb(
  ts TIMESTAMPTZ,
  value DOUBLE PRECISION,
  resolution INT
) RETURNS Timevector

Name	Type	Default	Required	Description
ts	TIMESTAMPTZ	-	✔	Timestamps for each data point
value	DOUBLE PRECISION	-	✔	The value at each timestamp
resolution	INT	-	✔	The approximate number of points to return. Determines the horizontal resolution of the resulting graph.

Returns

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.