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).

As traduções são geradas por tradução automática. Em caso de conflito entre o conteúdo da tradução e da versão original em inglês, a versão em inglês prevalecerá.

# Exemplos de consultas
<a name="sample-queries"></a>

Esta seção inclui exemplos de casos de uso da linguagem de consulta LiveAnalytics do Timestream for.

**Topics**
+ [Consultas simples](sample-queries.basic-scenarios.md)
+ [Consultas com funções de série temporal](sample-queries.devops-scenarios.md)
+ [Consultas com funções agregadas](sample-queries.iot-scenarios.md)

# Consultas simples
<a name="sample-queries.basic-scenarios"></a>

O seguinte retorna os 10 pontos de dados adicionados mais recentemente a uma tabela.

```
SELECT * FROM <database_name>.<table_name>
ORDER BY time DESC
LIMIT 10
```

O seguinte retorna os 5 pontos de dados mais antigos de uma medida específica.

```
SELECT * FROM <database_name>.<table_name>
WHERE measure_name = '<measure_name>'
ORDER BY time ASC
LIMIT 5
```

O seguinte funciona com registro de data e horas de granularidade de nanossegundos.

```
SELECT now() AS time_now
, now() - (INTERVAL '12' HOUR) AS twelve_hour_earlier -- Compatibility with ANSI SQL 
, now() - 12h AS also_twelve_hour_earlier -- Convenient time interval literals
, ago(12h) AS twelve_hours_ago -- More convenience with time functionality
, bin(now(), 10m) AS time_binned -- Convenient time binning support
, ago(50ns) AS fifty_ns_ago -- Nanosecond support
, now() + (1h + 50ns) AS hour_fifty_ns_future
```

Os valores de medida para registros de várias medidas são identificados pelo nome da coluna. Os valores de medida para registros de medida única são identificados por `measure_value::<data_type>`, onde `<data_type>` é um dentre `double`, `bigint`, `boolean` ou `varchar` conforme descrito em [Tipos de dados compatíveis](supported-data-types.md). Para obter mais informações sobre como os valores de medida são modelados, consulte [Tabela única versus várias tabelas](https://docs.aws.amazon.com/timestream/latest/developerguide/data-modeling.html#data-modeling-multiVsinglerecords).

O seguinte recupera valores de uma medida chamada `speed` de registros de várias medidas com um `measure_name` de `IoTMulti-stats`.

```
SELECT speed FROM <database_name>.<table_name> where measure_name = 'IoTMulti-stats'
```

O seguinte recupera valores `double` de registros de medida única com um `measure_name` de `load`.

```
SELECT measure_value::double FROM <database_name>.<table_name> WHERE measure_name = 'load'
```

# Consultas com funções de série temporal
<a name="sample-queries.devops-scenarios"></a>

**Topics**
+ [Exemplo de conjunto de dados e consultas](#sample-queries.devops-scenarios.example)

## Exemplo de conjunto de dados e consultas
<a name="sample-queries.devops-scenarios.example"></a>

Você pode usar o Timestream LiveAnalytics para entender e melhorar o desempenho e a disponibilidade de seus serviços e aplicativos. Abaixo está um exemplo de tabela e exemplos de consultas executadas nessa tabela. 

A tabela `ec2_metrics` armazena dados de telemetria, como utilização da CPU e outras métricas das instâncias do EC2. Você pode ver a tabela abaixo.


| Hora | region | az | Hostname | measure\$1name | measure\$1value::double | measure\$1value::bigint | 
| --- | --- | --- | --- | --- | --- | --- | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  cpu\$1utilization  |  35,1  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  5.3  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  nulo  |  1.500  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  nulo  |  6.700  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  38,5  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  58,4  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  nulo  |  23.000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  nulo  |  12.000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  45.0  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  65,8  |  nulo  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  nulo  |  15.000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  nulo  |  836.000  | 
|  2019-12-04 19:00:05.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  cpu\$1utilization  |  5.2  |  nulo  | 
|  2019-12-04 19:00:05.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  75.0  |  nulo  | 
|  2019-12-04 19:00:05.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  nulo  |  1.245  | 
|  2019-12-04 19:00:05.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  nulo  |  68.432  | 
|  2019-12-04 19:00:08.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  65,6  |  nulo  | 
|  2019-12-04 19:00:08.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  85,3  |  nulo  | 
|  2019-12-04 19:00:08.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  nulo  |  1.245  | 
|  2019-12-04 19:00:08.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  nulo  |  68.432  | 
|  2019-12-04 19:00:20.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  12.1  |  nulo  | 
|  2019-12-04 19:00:20.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  32,0  |  nulo  | 
|  2019-12-04 19:00:20.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  nulo  |  1.400  | 
|  2019-12-04 19:00:20.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  nulo  |  345  | 
|  2019-12-04 19:00:10.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  cpu\$1utilization  |  15.3  |  nulo  | 
|  2019-12-04 19:00:10.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  35,4  |  nulo  | 
|  2019-12-04 19:00:10.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  nulo  |  23  | 
|  2019-12-04 19:00:10.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  nulo  |  0  | 
|  2019-12-04 19:00:16.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  44.0  |  nulo  | 
|  2019-12-04 19:00:16.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  64.2  |  nulo  | 
|  2019-12-04 19:00:16.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  nulo  |  1.450  | 
|  2019-12-04 19:00:16.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  nulo  |  200  | 
|  2019-12-04 19:00:40.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  6.4  |  nulo  | 
|  2019-12-04 19:00:40.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  86,3  |  nulo  | 
|  2019-12-04 19:00:40.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  nulo  |  300  | 
|  2019-12-04 19:00:40.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  nulo  |  423  | 

Encontrar a utilização média de CPU p90, p95 e p99 para um host EC2 específico nas últimas 2 horas:

```
SELECT region, az, hostname, BIN(time, 15s) AS binned_timestamp,
    ROUND(AVG(measure_value::double), 2) AS avg_cpu_utilization,
    ROUND(APPROX_PERCENTILE(measure_value::double, 0.9), 2) AS p90_cpu_utilization,
    ROUND(APPROX_PERCENTILE(measure_value::double, 0.95), 2) AS p95_cpu_utilization,
    ROUND(APPROX_PERCENTILE(measure_value::double, 0.99), 2) AS p99_cpu_utilization
FROM "sampleDB".DevOps
WHERE measure_name = 'cpu_utilization'
    AND hostname = 'host-Hovjv'
    AND time > ago(2h)
GROUP BY region, hostname, az, BIN(time, 15s)
ORDER BY binned_timestamp ASC
```

Identificar hosts EC2 com utilização de CPU maior em 10% ou mais em comparação com a utilização média de CPU de toda a frota nas últimas 2 horas:

```
WITH avg_fleet_utilization AS (
    SELECT COUNT(DISTINCT hostname) AS total_host_count, AVG(measure_value::double) AS fleet_avg_cpu_utilization
    FROM "sampleDB".DevOps
    WHERE measure_name = 'cpu_utilization'
        AND time > ago(2h)
), avg_per_host_cpu AS (
    SELECT region, az, hostname, AVG(measure_value::double) AS avg_cpu_utilization
    FROM "sampleDB".DevOps
    WHERE measure_name = 'cpu_utilization'
        AND time > ago(2h)
    GROUP BY region, az, hostname
)
SELECT region, az, hostname, avg_cpu_utilization, fleet_avg_cpu_utilization
FROM avg_fleet_utilization, avg_per_host_cpu
WHERE avg_cpu_utilization > 1.1 * fleet_avg_cpu_utilization
ORDER BY avg_cpu_utilization DESC
```

Encontrar a utilização média da CPU armazenada em intervalos de 30 segundos para um host EC2 específico nas últimas 2 horas:

```
SELECT BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::double), 2) AS avg_cpu_utilization
FROM "sampleDB".DevOps
WHERE measure_name = 'cpu_utilization'
    AND hostname = 'host-Hovjv'
    AND time > ago(2h)
GROUP BY hostname, BIN(time, 30s)
ORDER BY binned_timestamp ASC
```

Encontre a utilização média da CPU armazenada em intervalos de 30 segundos para um host EC2 específico nas últimas 2 horas, preenchendo os valores ausentes usando a interpolação linear:

```
WITH binned_timeseries AS (
    SELECT hostname, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::double), 2) AS avg_cpu_utilization
    FROM "sampleDB".DevOps
    WHERE measure_name = 'cpu_utilization'
        AND hostname = 'host-Hovjv'
        AND time > ago(2h)
    GROUP BY hostname, BIN(time, 30s)
), interpolated_timeseries AS (
    SELECT hostname,
        INTERPOLATE_LINEAR(
            CREATE_TIME_SERIES(binned_timestamp, avg_cpu_utilization),
                SEQUENCE(min(binned_timestamp), max(binned_timestamp), 15s)) AS interpolated_avg_cpu_utilization
    FROM binned_timeseries
    GROUP BY hostname
)
SELECT time, ROUND(value, 2) AS interpolated_cpu
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_cpu_utilization)
```

Encontre a utilização média da CPU armazenada em intervalos de 30 segundos para um host EC2 específico nas últimas 2 horas, preenchendo os valores ausentes usando a interpolação com base na última observação realizada:

```
WITH binned_timeseries AS (
    SELECT hostname, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::double), 2) AS avg_cpu_utilization
    FROM "sampleDB".DevOps
    WHERE measure_name = 'cpu_utilization'
        AND hostname = 'host-Hovjv'
        AND time > ago(2h)
    GROUP BY hostname, BIN(time, 30s)
), interpolated_timeseries AS (
    SELECT hostname,
        INTERPOLATE_LOCF(
            CREATE_TIME_SERIES(binned_timestamp, avg_cpu_utilization),
                SEQUENCE(min(binned_timestamp), max(binned_timestamp), 15s)) AS interpolated_avg_cpu_utilization
    FROM binned_timeseries
    GROUP BY hostname
)
SELECT time, ROUND(value, 2) AS interpolated_cpu
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_cpu_utilization)
```

# 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\$1trucks que armazena telemetria, como localização, consumo de combustível, velocidade e capacidade de carga dos caminhões.


| Hora | truck\$1id | Make | Modelo | Frota | fuel\$1capacity | load\$1capacity | measure\$1name | measure\$1value::double | measure\$1value::varchar | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
|  2019-12-04 19:00:00.000000000  |  123456781  |  GMC  |  Astro  |  Alpha  |  100  |  500  |  fuel\$1reading  |  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\$1reading  |  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
```