如需與 Amazon Timestream for LiveAnalytics 類似的功能，請考慮使用 Amazon Timestream for InfluxDB。它提供簡化的資料擷取和單一位數毫秒查詢回應時間，以進行即時分析。[在這裡](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html)進一步了解。

本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 範例查詢
<a name="sample-queries"></a>

本節包含 Timestream for LiveAnalytics 查詢語言的範例使用案例。

**Topics**
+ [簡單查詢](sample-queries.basic-scenarios.md)
+ [具有時間序列函數的查詢](sample-queries.devops-scenarios.md)
+ [具有彙總函數的查詢](sample-queries.iot-scenarios.md)

# 簡單查詢
<a name="sample-queries.basic-scenarios"></a>

以下內容會取得資料表最近新增的 10 個資料點。

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

以下內容會取得特定量值的 5 個最舊資料點。

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

下列適用於奈秒精細度時間戳記。

```
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
```

多度量記錄的測量值由資料欄名稱識別。單一度量記錄的測量值由 識別`measure_value::<data_type>`，其中 `<data_type>`是 `double`、`boolean`、 `bigint`或 之一`varchar`，如 中所述[支援的資料類型](supported-data-types.md)。如需如何建立度量值模型的詳細資訊，請參閱[單一資料表與多個資料表。](https://docs.aws.amazon.com/timestream/latest/developerguide/data-modeling.html#data-modeling-multiVsinglerecords)

以下內容`speed`會從具有 的多度量記錄擷取稱為 `measure_name`的度量值`IoTMulti-stats`。

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

以下使用 `measure_name`的 從單一度量記錄擷取`double`值`load`。

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

# 具有時間序列函數的查詢
<a name="sample-queries.devops-scenarios"></a>

**Topics**
+ [資料集和查詢範例](#sample-queries.devops-scenarios.example)

## 資料集和查詢範例
<a name="sample-queries.devops-scenarios.example"></a>

您可以使用 Timestream for LiveAnalytics 來了解並改善服務和應用程式的效能和可用性。以下是在該資料表上執行的範例資料表和範例查詢。

資料表`ec2_metrics`存放遙測資料，例如 CPU 使用率和來自 EC2 執行個體的其他指標。您可以檢視下表。


| 時間 | region | az | Hostname (主機名稱) | measure\$1name | measure\$1value::double | measure\$1value::bigint | 
| --- | --- | --- | --- | --- | --- | --- | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  cpu\$1utilization  |  35.1  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  memory\$1utilization  |  55.3  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1in  |  null  |  1,500  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1out  |  null  |  6,700  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  cpu\$1utilization  |  38.5  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  memory\$1utilization  |  58.4  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1in  |  null  |  23，000  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1out  |  null  |  12,000  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  cpu\$1utilization  |  45.0  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  memory\$1utilization  |  65.8  |  null  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1in  |  null  |  15,000  | 
|  2019-12-04 19：00：00.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1out  |  null  |  836，000  | 
|  2019-12-04 19：00：05.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  cpu\$1utilization  |  55.2  |  null  | 
|  2019-12-04 19：00：05.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  memory\$1utilization  |  75.0  |  null  | 
|  2019-12-04 19：00：05.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1in  |  null  |  1，245  | 
|  2019-12-04 19：00：05.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1out  |  null  |  68，432  | 
|  2019-12-04 19：00：08.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  cpu\$1utilization  |  65.6  |  null  | 
|  2019-12-04 19：00：08.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  memory\$1utilization  |  85.3  |  null  | 
|  2019-12-04 19：00：08.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1in  |  null  |  1，245  | 
|  2019-12-04 19：00：08.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1out  |  null  |  68，432  | 
|  2019-12-04 19：00：20.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  cpu\$1utilization  |  12.1  |  null  | 
|  2019-12-04 19：00：20.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  memory\$1utilization  |  32.0  |  null  | 
|  2019-12-04 19：00：20.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1in  |  null  |  1，400  | 
|  2019-12-04 19：00：20.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1out  |  null  |  345  | 
|  2019-12-04 19：00：10.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  cpu\$1utilization  |  15.3  |  null  | 
|  2019-12-04 19：00：10.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  memory\$1utilization  |  35.4  |  null  | 
|  2019-12-04 19：00：10.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1in  |  null  |  23  | 
|  2019-12-04 19：00：10.000000000  |  us-east-1  |  us-east-1a  |  前端01  |  network\$1bytes\$1out  |  null  |  0  | 
|  2019-12-04 19：00：16.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  cpu\$1utilization  |  44.0  |  null  | 
|  2019-12-04 19：00：16.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  memory\$1utilization  |  64.2  |  null  | 
|  2019-12-04 19：00：16.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1in  |  null  |  1，450  | 
|  2019-12-04 19：00：16.000000000  |  us-east-1  |  us-east-1b  |  前端02  |  network\$1bytes\$1out  |  null  |  200  | 
|  2019-12-04 19：00：40.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  cpu\$1utilization  |  66.4  |  null  | 
|  2019-12-04 19：00：40.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  memory\$1utilization  |  86.3  |  null  | 
|  2019-12-04 19：00：40.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1in  |  null  |  300  | 
|  2019-12-04 19：00：40.000000000  |  us-east-1  |  us-east-1c  |  前端03  |  network\$1bytes\$1out  |  null  |  423  | 

尋找過去 2 小時內特定 EC2 主機的平均 p90、p95 和 p99 CPU 使用率：

```
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
```

識別 CPU 使用率高於整個機群過去 2 小時內平均 CPU 使用率 10% 或更高的 EC2 主機：

```
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
```

尋找過去 2 小時內特定 EC2 主機以 30 秒間隔固定的平均 CPU 使用率：

```
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
```

尋找過去 2 小時內特定 EC2 主機以 30 秒間隔固定的平均 CPU 使用率，並使用線性插補填入缺少的值：

```
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)
```

尋找過去 2 小時內特定 EC2 主機以 30 秒間隔固定的平均 CPU 使用率，根據前次的觀察使用插補填入缺少的值：

```
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)
```

# 具有彙總函數的查詢
<a name="sample-queries.iot-scenarios"></a>

以下是 IoT 案例範例資料集，以說明具有彙總函數的查詢。

**Topics**
+ [範例資料](#sample-queries.iot-scenarios.example-data)
+ [查詢範例](#sample-queries.iot-scenarios.example-queries)

## 範例資料
<a name="sample-queries.iot-scenarios.example-data"></a>

Timestream 可讓您存放和分析 IoT 感應器資料，例如一或多個卡車機群的位置、油耗、速度和負載容量，以實現有效的機群管理。以下是存放遙測的資料表 iot\$1trucks 結構描述和一些資料，例如卡車的位置、油耗、速度和負載容量。


| 時間 | truck\$1id | Make | 模型 | 機群 | 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  |  null  | 
|  2019-12-04 19：00：00.000000000  |  123456781  |  GMC  |  Astro  |  Alpha  |  100  |  500  |  載入  |  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  |  location  |  null  |  47.6062 N、122.3321 W  | 
|  2019-12-04 19：00：00.000000000  |  123456782  |  根沃思  |  W900  |  Alpha  |  150  |  1000  |  fuel\$1reading  |  10.1  |  null  | 
|  2019-12-04 19：00：00.000000000  |  123456782  |  根沃思  |  W900  |  Alpha  |  150  |  1000  |  載入  |  950.3  |  null  | 
|  2019-12-04 19：00：00.000000000  |  123456782  |  根沃思  |  W900  |  Alpha  |  150  |  1000  |  speed  |  50.8  |  null  | 
|  2019-12-04 19：00：00.000000000  |  123456782  |  根沃思  |  W900  |  Alpha  |  150  |  1000  |  location  |  null  |  40.7128 度 N、74.0060 度 W  | 

## 查詢範例
<a name="sample-queries.iot-scenarios.example-queries"></a>

取得機群中每輛卡車監控的所有感應器屬性和值清單。

```
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
```

取得過去 24 小時內機群中每輛卡車的最新燃料讀數。

```
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
```

識別過去 48 小時內以低油量 （低於 10 %) 執行的卡車：

```
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
)
```

尋找過去一週每輛卡車的平均負載和最高速度：

```
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
```

取得過去一週每輛卡車的負載效率：

```
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
```