timeSeriesLastTwoSamples

自 v25.6 引入

用于对时间序列数据进行重采样，以支持类似 PromQL 中 irate 和 idelta 的计算的聚合函数。

一个聚合函数，用于接收时间戳和值成对的时间序列数据，并且最多只存储最近的 2 个样本。该聚合函数旨在与物化视图和用于存储针对网格对齐时间戳的重采样时间序列数据的聚合表一起使用。

聚合表仅为每个对齐的时间戳存储最后 2 个值。这样可以在只读取远少于原始表中存储数据量的情况下，计算类似 PromQL 的 irate 和 idelta。

注意

该函数为实验性功能，可通过设置 allow_experimental_ts_to_grid_aggregate_function=true 启用。

语法

timeSeriesLastTwoSamples(timestamp, value)

参数

timestamp — 样本的时间戳。DateTime 或 DateTime64 或 (U)Int* 或 Int*
value — 对应时间戳的时间序列值。Float32 或 Float64

返回值

返回一对长度在 0 到 2 之间且等长的数组。第一个数组包含采样后时间序列的时间戳，第二个数组包含时间序列对应的数值。Tuple(Array(DateTime), Array(Float64))

示例

原始数据示例表，以及一个用于存储重采样数据的表

-- 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;

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

查询时间戳为 '2024-12-12 12:00:15' 和 '2024-12-12 12:00:30' 的最后 2 个样本

-- 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;

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])

从原始数据中计算 idelta 和 irate

-- 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;

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

基于重采样数据计算 idelta 和 irate

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;

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

timeSeriesLastTwoSamples​

timeSeriesLastTwoSamples