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> Função de agregação para reamostragem de séries temporais para o cálculo de irate e idelta no estilo do PromQL

# timeSeriesLastTwoSamples

<div id="timeSeriesLastTwoSamples">
  ## timeSeriesLastTwoSamples
</div>

Introduzido em: v25.6.0

Função de agregação para reamostragem de séries temporais para o cálculo de irate e idelta no estilo do PromQL.

Função de agregação que recebe séries temporais como pares de timestamps e valores e armazena no máximo 2 amostras mais recentes. Esta função de agregação foi projetada para ser usada com uma visão materializada e uma tabela agregada que armazena séries temporais reamostradas para timestamps alinhados à grade.

A tabela agregada armazena apenas os 2 últimos valores de cada timestamp alinhado. Isso permite calcular `irate` e `idelta` no estilo do PromQL lendo muito menos dados do que os armazenados na tabela bruta.

<Warning>
  Esta função é experimental; habilite-a definindo `allow_experimental_ts_to_grid_aggregate_function=true`.
</Warning>

**Sintaxe**

```sql theme={null}
timeSeriesLastTwoSamples(timestamp, value)
```

**Argumentos**

* `timestamp` — Timestamp da amostra. [`DateTime`](/pt-BR/reference/data-types/datetime) ou [`DateTime64`](/pt-BR/reference/data-types/datetime64) ou [`(U)Int*`](/pt-BR/reference/data-types/int-uint) ou [`Int*`](/pt-BR/reference/data-types/int-uint)
* `value` — Valor da série temporal correspondente ao timestamp. [`Float32`](/pt-BR/reference/data-types/float) ou [`Float64`](/pt-BR/reference/data-types/float)

**Valor retornado**

Retorna um par de arrays de mesmo comprimento, de 0 a 2. O primeiro array contém os timestamps da série temporal amostrada; o segundo contém os valores correspondentes da série temporal. [`Tuple(Array(DateTime), Array(Float64))`](/pt-BR/reference/data-types/tuple)

**Exemplos**

**Tabela de exemplo para dados brutos e uma tabela para armazenar dados reamostrados**

```sql title=Query theme={null}
-- Tabela para dados brutos
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);

-- Tabela com dados reamostrados para passos de tempo maiores (15 seg)
CREATE TABLE t_resampled_timeseries_15_sec
(
    metric_id UInt64,
    grid_timestamp DateTime('UTC') CODEC(DoubleDelta, ZSTD), -- Timestamp alinhado a 15 seg
    samples AggregateFunction(timeSeriesLastTwoSamples, DateTime64(3, 'UTC'), Float64)
)
ENGINE = AggregatingMergeTree()
ORDER BY (metric_id, grid_timestamp);

-- MV para popular a tabela reamostrada
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, -- Arredonda o timestamp para cima até o próximo ponto da grade
    initializeAggregation('timeSeriesLastTwoSamplesState', timestamp, value) AS samples
FROM t_raw_timeseries
ORDER BY metric_id, grid_timestamp;

-- Inserir alguns dados
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);

-- Verificar dados brutos
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 title=Response theme={null}
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
```

**Consulte as 2 últimas amostras para os timestamps '2024-12-12 12:00:15' e '2024-12-12 12:00:30'**

```sql title=Query theme={null}
-- Verificar dados reamostrados
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 title=Response theme={null}
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])
```

**Calcular idelta e irate com base nos dados brutos**

```sql title=Query theme={null}
-- A tabela agregada armazena apenas os 2 últimos valores para cada timestamp alinhado de 15 segundos.
-- Isso permite calcular irate e idelta no estilo do PromQL lendo muito menos dados do que os armazenados na tabela bruta.

WITH
    '2024-12-12 12:00:15'::DateTime64(3,'UTC') AS start_ts,       -- início da grade de timestamps
    start_ts + INTERVAL 60 SECOND AS end_ts,   -- fim da grade de timestamps
    15 AS step_seconds,   -- passo da grade de timestamps
    45 AS window_seconds  -- janela de "staleness"
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 title=Response theme={null}
3    [11,8,-18,8,11]    [12.222222222222221,8.88888888888889,1.1111111111111112,8.88888888888889,12.222222222222221]
```

**Calcule `idelta` e `irate` com base nos dados reamostrados**

```sql title=Query theme={null}
WITH
    '2024-12-12 12:00:15'::DateTime64(3,'UTC') AS start_ts,       -- início da grade de timestamps
    start_ts + INTERVAL 60 SECOND AS end_ts,   -- fim da grade de timestamps
    15 AS step_seconds,   -- passo da grade de timestamps
    45 AS window_seconds  -- janela de "staleness"
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 title=Response theme={null}
3    [11,8,-18,8,11]    [12.222222222222221,8.88888888888889,1.1111111111111112,8.88888888888889,12.222222222222221]
```