timeSeriesLastTwoSamples

Introduced in: v25.6.0 Aggregate function for re-sampling time series data for PromQL-like irate and idelta calculation. Aggregate function that takes time series data as pairs of timestamps and values and stores only at most 2 recent samples. This aggregate function is intended to be used with a Materialized View and Aggregated table that stores re-sampled time series data for grid-aligned timestamps. The aggregated table stores only last 2 values for each aligned timestamp. This allows to calculate PromQL-like irate and idelta by reading much less data then is stored in the raw table.

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

Syntax

timeSeriesLastTwoSamples(timestamp, value)

Arguments

timestamp — Timestamp of the sample. DateTime or DateTime64 or (U)Int* or Int*
value — Value of the time series corresponding to the timestamp. Float32 or Float64

Returned value Returns a pair of arrays of equal length from 0 to 2. The first array contains the timestamps of sampled time series, the second array contains the corresponding values of the time series. Tuple(Array(DateTime), Array(Float64)) Examples Example table for raw data, and a table for storing re-sampled data

Query

-- Table for raw data
CREATE TABLE t_raw_timeseries
(
    metric_id UInt64,
    timestamp DateTime64(3, 'UTC') CODEC(DoubleDelta, ZSTD),
    value Float64 CODEC(DoubleDelta)
)
ENGINE = MergeTree()
ORDER BY (metric_id, timestamp);

-- Table with data re-sampled to bigger (15 sec) time steps
CREATE TABLE t_resampled_timeseries_15_sec
(
    metric_id UInt64,
    grid_timestamp DateTime('UTC') CODEC(DoubleDelta, ZSTD), -- Timestamp aligned to 15 sec
    samples AggregateFunction(timeSeriesLastTwoSamples, DateTime64(3, 'UTC'), Float64)
)
ENGINE = AggregatingMergeTree()
ORDER BY (metric_id, grid_timestamp);

-- MV for populating re-sampled table
CREATE MATERIALIZED VIEW mv_resampled_timeseries TO t_resampled_timeseries_15_sec
(
    metric_id UInt64,
    grid_timestamp DateTime('UTC') CODEC(DoubleDelta, ZSTD),
    samples AggregateFunction(timeSeriesLastTwoSamples, DateTime64(3, 'UTC'), Float64)
)
AS SELECT
    metric_id,
    ceil(toUnixTimestamp(timestamp + interval 999 millisecond) / 15, 0) * 15 AS grid_timestamp, -- Round timestamp up to the next grid point
    initializeAggregation('timeSeriesLastTwoSamplesState', timestamp, value) AS samples
FROM t_raw_timeseries
ORDER BY metric_id, grid_timestamp;

-- Insert some data
INSERT INTO t_raw_timeseries(metric_id, timestamp, value) SELECT number%10 AS metric_id, '2024-12-12 12:00:00'::DateTime64(3, 'UTC') + interval ((number/10)%100)*900 millisecond as timestamp, number%3+number%29 AS value FROM numbers(1000);

-- Check raw data
SELECT *
FROM t_raw_timeseries
WHERE metric_id = 3 AND timestamp BETWEEN '2024-12-12 12:00:12' AND '2024-12-12 12:00:31'
ORDER BY metric_id, timestamp;

Response

  2024-12-12 12:00:12.870    29
  2024-12-12 12:00:13.770    8
  2024-12-12 12:00:14.670    19
  2024-12-12 12:00:15.570    30
  2024-12-12 12:00:16.470    9
  2024-12-12 12:00:17.370    20
  2024-12-12 12:00:18.270    2
  2024-12-12 12:00:19.170    10
  2024-12-12 12:00:20.070    21
  2024-12-12 12:00:20.970    3
  2024-12-12 12:00:21.870    11
  2024-12-12 12:00:22.770    22
  2024-12-12 12:00:23.670    4
  2024-12-12 12:00:24.570    12
  2024-12-12 12:00:25.470    23
  2024-12-12 12:00:26.370    5
  2024-12-12 12:00:27.270    13
  2024-12-12 12:00:28.170    24
  2024-12-12 12:00:29.069    6
  2024-12-12 12:00:29.969    14
  2024-12-12 12:00:30.869    25

Query the last 2 sample for timestamps ‘2024-12-12 12:00:15’ and ‘2024-12-12 12:00:30’

Query

-- Check re-sampled data
SELECT metric_id, grid_timestamp, (finalizeAggregation(samples).1 as timestamp, finalizeAggregation(samples).2 as value)
FROM t_resampled_timeseries_15_sec
WHERE metric_id = 3 AND grid_timestamp BETWEEN '2024-12-12 12:00:15' AND '2024-12-12 12:00:30'
ORDER BY metric_id, grid_timestamp;

Response

3    2024-12-12 12:00:15    (['2024-12-12 12:00:14.670','2024-12-12 12:00:13.770'],[19,8])
3    2024-12-12 12:00:30    (['2024-12-12 12:00:29.969','2024-12-12 12:00:29.069'],[14,6])

Calculate idelta and irate from the raw data

Query

-- The aggregated table stores only last 2 values for each 15-second aligned timestamp.
-- This allows to calculate PromQL-like irate and idelta by reading much less data then is stored in the raw table.

WITH
    '2024-12-12 12:00:15'::DateTime64(3,'UTC') AS start_ts,       -- start of timestamp grid
    start_ts + INTERVAL 60 SECOND AS end_ts,   -- end of timestamp grid
    15 AS step_seconds,   -- step of timestamp grid
    45 AS window_seconds  -- "staleness" window
SELECT
    metric_id,
    timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamp, value),
    timeSeriesInstantRateToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamp, value)
FROM t_raw_timeseries
WHERE metric_id = 3 AND timestamp BETWEEN start_ts - interval window_seconds seconds AND end_ts
GROUP BY metric_id;

Response

3    [11,8,-18,8,11]    [12.222222222222221,8.88888888888889,1.1111111111111112,8.88888888888889,12.222222222222221]

Calculate idelta and irate from the re-sampled data

Query

WITH
    '2024-12-12 12:00:15'::DateTime64(3,'UTC') AS start_ts,       -- start of timestamp grid
    start_ts + INTERVAL 60 SECOND AS end_ts,   -- end of timestamp grid
    15 AS step_seconds,   -- step of timestamp grid
    45 AS window_seconds  -- "staleness" window
SELECT
    metric_id,
    timeSeriesInstantDeltaToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamps, values),
    timeSeriesInstantRateToGrid(start_ts, end_ts, step_seconds, window_seconds)(timestamps, values)
FROM (
    SELECT
        metric_id,
        finalizeAggregation(samples).1 AS timestamps,
        finalizeAggregation(samples).2 AS values
    FROM t_resampled_timeseries_15_sec
    WHERE metric_id = 3 AND grid_timestamp BETWEEN start_ts - interval window_seconds seconds AND end_ts
)
GROUP BY metric_id;

Response

3    [11,8,-18,8,11]    [12.222222222222221,8.88888888888889,1.1111111111111112,8.88888888888889,12.222222222222221]

​timeSeriesLastTwoSamples

timeSeriesLastTwoSamples