timeSeriesInstantDeltaToGrid

Aggregate function that calculates PromQL-like idelta over time series data on the specified grid.

timeSeriesInstantDeltaToGrid

Introduced in: v25.6.0

Aggregate function that takes time series data as pairs of timestamps and values and calculates PromQL-like idelta from this data on a regular time grid described by start timestamp, end timestamp and step. For each point on the grid the samples for calculating idelta are considered within the specified time window.

This function is experimental, enable it by setting allow_experimental_ts_to_grid_aggregate_function=true.

Syntax

timeSeriesInstantDeltaToGrid(start_timestamp, end_timestamp, grid_step, staleness)(timestamp, value)

Parameters

• start_timestamp — Specifies start of the grid. UInt32 or DateTime
• end_timestamp — Specifies end of the grid. UInt32 or DateTime
• grid_step — Specifies step of the grid in seconds. UInt32
• staleness — Specifies the maximum staleness in seconds of the considered samples. The staleness window is a left-open and right-closed interval. UInt32

Arguments

• timestamp — Timestamp of the sample. Can be individual values or arrays. UInt32 or DateTime or Array(UInt32) or Array(DateTime)
• value — Value of the time series corresponding to the timestamp. Can be individual values or arrays. Float* or Array(Float*)

Returned value

Returns idelta values on the specified grid. The returned array contains one value for each time grid point. The value is NULL if there are not enough samples within the window to calculate the instant delta value for a particular grid point. Array(Nullable(Float64))

Examples

Basic usage with individual timestamp-value pairs

Query

WITH
    -- NOTE: the gap between 140 and 190 is to show how values are filled for ts = 150, 165, 180 according to window parameter
    [110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps,
    [1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values, -- array of values corresponding to timestamps above
    90 AS start_ts,       -- start of timestamp grid
    90 + 120 AS end_ts,   -- end of timestamp grid
    15 AS step_seconds,   -- step of timestamp grid
    45 AS window_seconds  -- "staleness" window
SELECT timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamp, value)
FROM
(
    -- This subquery converts arrays of timestamps and values into rows of `timestamp`, `value`
    SELECT
        arrayJoin(arrayZip(timestamps, values)) AS ts_and_val,
        ts_and_val.1 AS timestamp,
        ts_and_val.2 AS value
);

Response

┌─timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamp, value)─┐
│ [NULL,NULL,0,2,1,1,NULL,NULL,3]                                                               │
└───────────────────────────────────────────────────────────────────────────────────────────────┘

Using array arguments

Query

-- it is possible to pass multiple samples of timestamps and values as Arrays of equal size
WITH
    [110, 120, 130, 140, 190, 200, 210, 220, 230]::Array(DateTime) AS timestamps,
    [1, 1, 3, 4, 5, 5, 8, 12, 13]::Array(Float32) AS values,
    90 AS start_ts,
    90 + 120 AS end_ts,
    15 AS step_seconds,
    45 AS window_seconds
SELECT timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamps, values);

Response

┌─timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamps, values)─┐
│ [NULL,NULL,0,2,1,1,NULL,NULL,3]                                                                 │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘