要获得与亚马逊 Timestream 类似的功能 LiveAnalytics，可以考虑适用于 InfluxDB 的亚马逊 Timestream。适用于 InfluxDB 的 Amazon Timestream 提供简化的数据摄取和个位数毫秒级的查询响应时间，以实现实时分析。点击[此处](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html)了解更多信息。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 使用聚合函数查询
<a name="sample-queries.iot-scenarios"></a>

以下是 IoT 场景示例数据集的示例，用于说明使用聚合函数的查询。

**Topics**
+ [示例数据](#sample-queries.iot-scenarios.example-data)
+ [示例查询](#sample-queries.iot-scenarios.example-queries)

## 示例数据
<a name="sample-queries.iot-scenarios.example-data"></a>

Timestream 使您能够存储和分析 IoT 传感器数据，例如一个或多个卡车车队的位置、油耗、速度和载重，从而实现有效的车队管理。以下是 iot\_trucks 表的架构和部分数据，该表存储卡车的位置、油耗、速度和载重等遥测数据。


| 时间 | truck\_id | Make | 模型 | Fleet | 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  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456781  |  GMC  |  Astro  |  Alpha  |  100  |  500  |  负载  |  400.0  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456781  |  GMC  |  Astro  |  Alpha  |  100  |  500  |  speed  |  90.2  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456781  |  GMC  |  Astro  |  Alpha  |  100  |  500  |  地点  |  null  |  47.6062 N, 122.3321 W  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1000  |  fuel\_reading  |  10.1  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1000  |  负载  |  950.3  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1000  |  speed  |  50.8  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1000  |  地点  |  null  |  北纬 40.7128 度，西经 74.0060 度  | 

## 示例查询
<a name="sample-queries.iot-scenarios.example-queries"></a>

获取车队中每辆卡车正在监控的所有传感器属性及值的列表。

```
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
```

获取车队中每辆卡车过去 24 小时内的最新燃油读数。

```
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
```

确定过去 48 小时内燃油量低于 10% 的卡车：

```
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
)
```

计算过去一周每辆卡车的平均载重和最高速度：

```
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
```

获取过去一周每辆卡车的装载效率：

```
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
```