Per funzionalità simili a Amazon Timestream for, prendi in considerazione Amazon Timestream LiveAnalytics per InfluxDB. Offre un'acquisizione semplificata dei dati e tempi di risposta alle query di una sola cifra di millisecondi per analisi in tempo reale. [Scopri](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html) di più qui.

Le traduzioni sono generate tramite traduzione automatica. In caso di conflitto tra il contenuto di una traduzione e la versione originale in Inglese, quest'ultima prevarrà.

# Query di esempio
<a name="sample-queries"></a>

Questa sezione include esempi di casi d'uso del linguaggio di interrogazione LiveAnalytics di Timestream for.

**Topics**
+ [Interrogazioni semplici](sample-queries.basic-scenarios.md)
+ [Interrogazioni con funzioni di serie temporali](sample-queries.devops-scenarios.md)
+ [Interrogazioni con funzioni aggregate](sample-queries.iot-scenarios.md)

# Interrogazioni semplici
<a name="sample-queries.basic-scenarios"></a>

Di seguito vengono ottenuti i 10 punti dati aggiunti più di recente per una tabella.

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

Di seguito vengono ottenuti i 5 punti dati più vecchi per una misura specifica.

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

Quanto segue funziona con timestamp di granularità in nanosecondi.

```
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
```

I valori di misura per i record con più misure sono identificati dal nome della colonna. I valori di misura per i record a misura singola sono identificati da`measure_value::<data_type>`, dove `<data_type>` è uno di`double`, `bigint``boolean`, o `varchar` come descritto in. [Tipi di dati supportati](supported-data-types.md) Per ulteriori informazioni su come vengono modellati i valori di misura, consulta [Tabella singola e tabelle multiple.](https://docs.aws.amazon.com/timestream/latest/developerguide/data-modeling.html#data-modeling-multiVsinglerecords)

Di seguito vengono recuperati i valori per una misura richiamata `speed` da record con più misure con un di. `measure_name` `IoTMulti-stats`

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

Quanto segue recupera `double` i valori dai record a misura singola con un di. `measure_name` `load`

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

# Interrogazioni con funzioni di serie temporali
<a name="sample-queries.devops-scenarios"></a>

**Topics**
+ [Set di dati e interrogazioni di esempio](#sample-queries.devops-scenarios.example)

## Set di dati e interrogazioni di esempio
<a name="sample-queries.devops-scenarios.example"></a>

È possibile utilizzare Timestream per LiveAnalytics comprendere e migliorare le prestazioni e la disponibilità dei servizi e delle applicazioni. Di seguito è riportato un esempio di tabella e delle query di esempio eseguite su tale tabella. 

La tabella `ec2_metrics` memorizza i dati di telemetria, come l'utilizzo della CPU e altre metriche delle istanze EC2. È possibile visualizzare la tabella riportata di seguito.


| Orario | region | az | Hostname (Nome host) | measure\$1name | measure\$1value::double | measure\$1value::bigint | 
| --- | --- | --- | --- | --- | --- | --- | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  cpu\$1utilization  |  35.1  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  memory\$1utilization  |  5.3  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1in  |  null  |  1.500  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1out  |  null  |  6.700  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  cpu\$1utilization  |  38,5  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  memory\$1utilization  |  58,4  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1in  |  null  |  23.000  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1out  |  null  |  12.000  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  cpu\$1utilization  |  45.0  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  memory\$1utilization  |  65,8  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1in  |  null  |  15.000  | 
|  2019-12-04 19:00:00.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1out  |  null  |  836.000  | 
|  2019-12-04 19:00:05.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  cpu\$1utilization  |  55.2  |  null  | 
|  2019-12-04 19:00:05.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  memory\$1utilization  |  75.0  |  null  | 
|  2019-12-04 19:00:05.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1in  |  null  |  1.245  | 
|  2019-12-04 19:00:05.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1out  |  null  |  68.432  | 
|  2019-12-04 19:00:08000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  cpu\$1utilization  |  65,6  |  null  | 
|  2019-12-04 19:00:08000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  memory\$1utilization  |  85,3  |  null  | 
|  2019-12-04 19:00:08000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1in  |  null  |  1.245  | 
|  2019-12-04 19:00:08000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1out  |  null  |  68.432  | 
|  2019-12-04 19:00:20000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  cpu\$1utilization  |  12.1  |  null  | 
|  2019-12-04 19:00:20000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  memory\$1utilization  |  32,0  |  null  | 
|  2019-12-04 19:00:20000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1in  |  null  |  1.400  | 
|  2019-12-04 19:00:20000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1out  |  null  |  345  | 
|  2019-12-04 19:00:10.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  cpu\$1utilization  |  15.3  |  null  | 
|  2019-12-04 19:00:10.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  memory\$1utilization  |  35,4  |  null  | 
|  2019-12-04 19:00:10.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1in  |  null  |  23  | 
|  2019-12-04 19:00:10.000 000000  |  us-east-1  |  us-east-1a  |  front-end 01  |  network\$1bytes\$1out  |  null  |  0  | 
|  2019-12-04 19:00:16.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  cpu\$1utilization  |  44.0  |  null  | 
|  2019-12-04 19:00:16.000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  memory\$1utilization  |  64.2  |  null  | 
|  2019-12-04 19:00:16000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1in  |  null  |  1.450  | 
|  2019-12-04 19:00:16000 000000  |  us-east-1  |  us-east-1b  |  front-end 02  |  network\$1bytes\$1out  |  null  |  200  | 
|  2019-12-04 19:00:40.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  cpu\$1utilization  |  6.4  |  null  | 
|  2019-12-04 19:00:40,000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  memory\$1utilization  |  86,3  |  null  | 
|  2019-12-04 19:00:40,000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1in  |  null  |  300  | 
|  2019-12-04 19:00:40.000 000000  |  us-east-1  |  us-east-1c  |  front-end 03  |  network\$1bytes\$1out  |  null  |  423  | 

Trova l'utilizzo medio della CPU p90, p95 e p99 per uno specifico host EC2 nelle ultime 2 ore:

```
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
```

Identifica gli host EC2 con un utilizzo della CPU superiore del 10% o più rispetto all'utilizzo medio della CPU dell'intera flotta nelle ultime 2 ore:

```
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
```

Trova l'utilizzo medio della CPU suddiviso a intervalli di 30 secondi per uno specifico host EC2 nelle ultime 2 ore:

```
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
```

Trova l'utilizzo medio della CPU eseguito a intervalli di 30 secondi per uno specifico host EC2 nelle ultime 2 ore, inserendo i valori mancanti utilizzando l'interpolazione lineare:

```
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)
```

Trova l'utilizzo medio della CPU eseguito a intervalli di 30 secondi per uno specifico host EC2 nelle ultime 2 ore, inserendo i valori mancanti utilizzando l'interpolazione basata sull'ultima osservazione riportata:

```
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)
```

# Interrogazioni con funzioni aggregate
<a name="sample-queries.iot-scenarios"></a>

Di seguito è riportato un esempio di set di dati di esempio di scenario IoT per illustrare le query con funzioni aggregate.

**Topics**
+ [Dati di esempio](#sample-queries.iot-scenarios.example-data)
+ [Query di esempio](#sample-queries.iot-scenarios.example-queries)

## Dati di esempio
<a name="sample-queries.iot-scenarios.example-data"></a>

Timestream consente di archiviare e analizzare i dati dei sensori IoT come la posizione, il consumo di carburante, la velocità e la capacità di carico di una o più flotte di camion per consentire una gestione efficace della flotta. Di seguito sono riportati lo schema e alcuni dati di una tabella iot\$1trucks che memorizza dati di telemetria come posizione, consumo di carburante, velocità e capacità di carico dei camion.


| Orario | truck\$1id | Make | Modello | Parco istanze | capacità\$1carburante | capacità\$1di carico | measure\$1name | measure\$1value::double | measure\$1value::varchar | 
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
|  2019-12-04 19:00:00.000 000000  |  123456781  |  GMC  |  Astro  |  Alpha (Afa)  |  100  |  500  |  fuel\$1reading  |  65,2  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456781  |  GMC  |  Astro  |  Alpha (Afa)  |  100  |  500  |  caricare  |  400,0  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456781  |  GMC  |  Astro  |  Alpha (Afa)  |  100  |  500  |  speed  |  90,2  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456781  |  GMC  |  Astro  |  Alpha (Afa)  |  100  |  500  |  location  |  null  |  47,6062 NM, 122.321 W  | 
|  2019-12-04 19:00:00.000 000000  |  123456782  |  Kenworth  |  W900  |  Alpha (Afa)  |  150  |  1000  |  lettura del carburante  |  10.1  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456782  |  Kenworth  |  W900  |  Alpha (Afa)  |  150  |  1000  |  caricare  |  950,3  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456782  |  Kenworth  |  W900  |  Alpha (Afa)  |  150  |  1000  |  speed  |  50,8  |  null  | 
|  2019-12-04 19:00:00.000 000000  |  123456782  |  Kenworth  |  W900  |  Alpha (Afa)  |  150  |  1000  |  location  |  null  |  40.7128 gradi N, 74.0060 gradi W  | 

## Query di esempio
<a name="sample-queries.iot-scenarios.example-queries"></a>

Ottieni un elenco di tutti gli attributi e i valori dei sensori monitorati per ogni camion della flotta.

```
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
```

Ottieni i dati più recenti relativi al carburante di ogni camion della flotta nelle ultime 24 ore.

```
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
```

Identifica i camion che hanno utilizzato poco carburante (meno del 10%) nelle ultime 48 ore:

```
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
)
```

Calcola il carico medio e la velocità massima di ogni camion nell'ultima settimana:

```
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
```

Calcola l'efficienza di carico di ogni camion nell'ultima settimana:

```
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
```