Para recursos semelhantes aos do Amazon Timestream para, considere o Amazon Timestream LiveAnalytics para InfluxDB. Ele oferece ingestão de dados simplificada e tempos de resposta de consulta de um dígito em milissegundos para análises em tempo real. Saiba mais [aqui](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html).

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# Consultas com funções agregadas
<a name="sample-queries.iot-scenarios"></a>

Abaixo está um exemplo de conjunto de dados em cenário IoT para ilustrar consultas com funções agregadas.

**Topics**
+ [Exemplo de dados](#sample-queries.iot-scenarios.example-data)
+ [Consultas de exemplo](#sample-queries.iot-scenarios.example-queries)

## Exemplo de dados
<a name="sample-queries.iot-scenarios.example-data"></a>

O Timestream permite que você armazene e analise dados de sensores de IoT, como localização, consumo de combustível, velocidade e capacidade de carga de uma ou mais frotas de caminhões para permitir o gerenciamento eficaz da frota. Abaixo está o esquema e alguns dos dados de uma tabela iot\_trucks que armazena telemetria, como localização, consumo de combustível, velocidade e capacidade de carga dos caminhões.


| Hora | truck\_id | Make | Modelo | Frota | fuel\_capacity | load\_capacity | measure\_name | measure\_value::double | measure\_value::varchar | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
| 2019-12-04 19:00:00.000000000 | 123456781 | GMC | Astro | Alpha | 100 | 500 | fuel\_reading | 65,2 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456781 | GMC | Astro | Alpha | 100 | 500 | balanceamento | 400,0 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456781 | GMC | Astro | Alpha | 100 | 500 | velocidade | 90,2 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456781 | GMC | Astro | Alpha | 100 | 500 | location | nulo | 47.6062 N, 122.3321 W | 
| 2019-12-04 19:00:00.000000000 | 123456782 | Kenworth | W900 | Alpha | 150 | 1000 | fuel\_reading | 10.1 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456782 | Kenworth | W900 | Alpha | 150 | 1000 | balanceamento | 950,3 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456782 | Kenworth | W900 | Alpha | 150 | 1000 | velocidade | 50,8 | nulo | 
| 2019-12-04 19:00:00.000000000 | 123456782 | Kenworth | W900 | Alpha | 150 | 1000 | location | nulo | 40,7128 graus N, 74,0060 graus W | 

## Consultas de exemplo
<a name="sample-queries.iot-scenarios.example-queries"></a>

Obtenha uma lista de todos os atributos e valores dos sensores que estão sendo monitorados para cada caminhão da frota.

```
SELECT
    truck_id,
    fleet,
    fuel_capacity,
    model,
    load_capacity,
    make,
    measure_name
FROM "sampleDB".IoT
GROUP BY truck_id, fleet, fuel_capacity, model, load_capacity, make, measure_name
```

Obtenha a leitura de combustível mais recente de cada caminhão da frota nas últimas 24 horas.

```
WITH latest_recorded_time AS (
    SELECT
        truck_id,
        max(time) as latest_time
    FROM "sampleDB".IoT
    WHERE measure_name = 'fuel-reading'
    AND time >= ago(24h)
    GROUP BY truck_id
)
SELECT
    b.truck_id,
    b.fleet,
    b.make,
    b.model,
    b.time,
    b.measure_value::double as last_reported_fuel_reading
FROM
latest_recorded_time a INNER JOIN "sampleDB".IoT b
ON a.truck_id = b.truck_id AND b.time = a.latest_time
WHERE b.measure_name = 'fuel-reading'
AND b.time > ago(24h)
ORDER BY b.truck_id
```

Identificar caminhões que estão com pouco combustível (menos de 10%) nas últimas 48 horas:

```
WITH low_fuel_trucks AS (
    SELECT time, truck_id, fleet, make, model, (measure_value::double/cast(fuel_capacity as double)*100) AS fuel_pct
    FROM "sampleDB".IoT
    WHERE time >= ago(48h)
    AND (measure_value::double/cast(fuel_capacity as double)*100) < 10
    AND measure_name = 'fuel-reading'
),
other_trucks AS (
SELECT time, truck_id, (measure_value::double/cast(fuel_capacity as double)*100) as remaining_fuel
    FROM "sampleDB".IoT
    WHERE time >= ago(48h)
    AND truck_id IN (SELECT truck_id FROM low_fuel_trucks)
    AND (measure_value::double/cast(fuel_capacity as double)*100) >= 10
    AND measure_name = 'fuel-reading'
),
trucks_that_refuelled AS (
    SELECT a.truck_id
    FROM low_fuel_trucks a JOIN other_trucks b
    ON a.truck_id = b.truck_id AND b.time >= a.time
)
SELECT DISTINCT truck_id, fleet, make, model, fuel_pct
FROM low_fuel_trucks
WHERE truck_id NOT IN (
    SELECT truck_id FROM trucks_that_refuelled
)
```

Encontre a carga média e a velocidade máxima de cada caminhão na última semana:

```
SELECT
    bin(time, 1d) as binned_time,
    fleet,
    truck_id,
    make,
    model,
    AVG(
        CASE WHEN measure_name = 'load' THEN measure_value::double ELSE NULL END
    ) AS avg_load_tons,
    MAX(
        CASE WHEN measure_name = 'speed' THEN measure_value::double ELSE NULL END
    ) AS max_speed_mph
FROM "sampleDB".IoT
WHERE time >= ago(7d)
AND measure_name IN ('load', 'speed')
GROUP BY fleet, truck_id, make, model, bin(time, 1d)
ORDER BY truck_id
```

Obtenha a eficiência de carga de cada caminhão na última semana:

```
WITH average_load_per_truck AS (
    SELECT
        truck_id,
        avg(measure_value::double)  AS avg_load
    FROM "sampleDB".IoT
    WHERE measure_name = 'load'
    AND time >= ago(7d)
    GROUP BY truck_id, fleet, load_capacity, make, model
),
truck_load_efficiency AS (
    SELECT
        a.truck_id,
        fleet,
        load_capacity,
        make,
        model,
        avg_load,
        measure_value::double,
        time,
        (measure_value::double*100)/avg_load as load_efficiency -- , approx_percentile(avg_load_pct, DOUBLE '0.9')
    FROM "sampleDB".IoT a JOIN average_load_per_truck b
    ON a.truck_id = b.truck_id
    WHERE a.measure_name = 'load'
)
SELECT
    truck_id,
    time,
    load_efficiency
FROM truck_load_efficiency
ORDER BY truck_id, time
```