Amazon Timestream for LiveAnalytics に類似した機能をご希望の場合は Amazon Timestream for InfluxDB をご検討ください。リアルタイム分析に適した、シンプルなデータインジェストと 1 桁ミリ秒のクエリ応答時間を特徴としています。詳細については、[こちら](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html)を参照してください。

翻訳は機械翻訳により提供されています。提供された翻訳内容と英語版の間で齟齬、不一致または矛盾がある場合、英語版が優先します。

# サンプルクエリ
<a name="sample-queries"></a>

このセクションでは、Timestream for LiveAnalytics クエリ言語のユースケースの例を示します。

**Topics**
+ [シンプルなクエリ](sample-queries.basic-scenarios.md)
+ [時系列関数を使用したクエリ](sample-queries.devops-scenarios.md)
+ [集計関数を使用したクエリ](sample-queries.iot-scenarios.md)

# シンプルなクエリ
<a name="sample-queries.basic-scenarios"></a>

以下は、テーブルに最近追加された 10 個のデータポイントを取得します。

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

以下は、特定のメジャーの最も古い 5 つのデータポイントを取得します。

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

以下は、ナノ秒の粒度のタイムスタンプで機能します。

```
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
```

マルチメジャーレコードのメジャー値は、列名によって識別されます。単一メジャーレコードのメジャー値は `measure_value::<data_type>` によって識別されます。ここで、`<data_type>` は、「[サポートされているデータ型](supported-data-types.md)」で説明されているように `double`、`bigint`、`boolean`、`varchar` のいずれかです。メジャー値のモデル化方法の詳細については、「[単一テーブルとマルチテーブル](https://docs.aws.amazon.com/timestream/latest/developerguide/data-modeling.html#data-modeling-multiVsinglerecords)」を参照してください。

以下は、`measure_name` が `IoTMulti-stats` のマルチメジャーレコードから `speed` というメジャーの値を取得します。

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

以下は、`measure_name` が `load` の単一メジャーレコードから `double` 値を取得します。

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

# 時系列関数を使用したクエリ
<a name="sample-queries.devops-scenarios"></a>

**Topics**
+ [データセットとクエリの例](#sample-queries.devops-scenarios.example)

## データセットとクエリの例
<a name="sample-queries.devops-scenarios.example"></a>

Timestream for LiveAnalytics は、サービスとアプリケーションのパフォーマンスと可用性を理解し改善するために使用できます。以下は、テーブルの例と、そのテーブルで実行されるサンプルクエリです。

このテーブル `ec2_metrics` には、CPU 使用率といった EC2 インスタンスのメトリクスなどのテレメトリデータが保存されます。以下の表を表示できます。


| Time | リージョン | 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  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  55.3  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  null  |  1,500  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  null  |  6,700  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  38.5  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  58.4  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  null  |  23,000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  null  |  12,000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  45.0  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  65.8  |  null  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  null  |  15,000  | 
|  2019-12-04 19:00:00.000000000  |  us-east-1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  null  |  836,000  | 
|  2019-12-04 19:00:05.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  cpu\$1utilization  |  55.2  |  null  | 
|  2019-12-04 19:00:05.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  75.0  |  null  | 
|  2019-12-04 19:00:05.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  null  |  1,245  | 
|  2019-12-04 19:00:05.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  null  |  68,432  | 
|  2019-12-04 19:00:08.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  65.6  |  null  | 
|  2019-12-04 19:00:08.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  85.3  |  null  | 
|  2019-12-04 19:00:08.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  null  |  1,245  | 
|  2019-12-04 19:00:08.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  null  |  68,432  | 
|  2019-12-04 19:00:20.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  12.1  |  null  | 
|  2019-12-04 19:00:20.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  32.0  |  null  | 
|  2019-12-04 19:00:20.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  null  |  1,400  | 
|  2019-12-04 19:00:20.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  null  |  345  | 
|  2019-12-04 19:00:10.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  cpu\$1utilization  |  15.3  |  null  | 
|  2019-12-04 19:00:10.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  memory\$1utilization  |  35.4  |  null  | 
|  2019-12-04 19:00:10.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1in  |  null  |  23  | 
|  2019-12-04 19:00:10.000000000  |  us–east–1  |  us-east-1a  |  frontend01  |  network\$1bytes\$1out  |  null  |  0  | 
|  2019-12-04 19:00:16.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  cpu\$1utilization  |  44.0  |  null  | 
|  2019-12-04 19:00:16.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  memory\$1utilization  |  64.2  |  null  | 
|  2019-12-04 19:00:16.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1in  |  null  |  1,450  | 
|  2019-12-04 19:00:16.000000000  |  us–east–1  |  us-east-1b  |  frontend02  |  network\$1bytes\$1out  |  null  |  200  | 
|  2019-12-04 19:00:40.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  cpu\$1utilization  |  66.4  |  null  | 
|  2019-12-04 19:00:40.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  memory\$1utilization  |  86.3  |  null  | 
|  2019-12-04 19:00:40.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1in  |  null  |  300  | 
|  2019-12-04 19:00:40.000000000  |  us–east–1  |  us-east-1c  |  frontend03  |  network\$1bytes\$1out  |  null  |  423  | 

過去 2 時間における特定の EC2 ホストの p90、p95、p99 の平均 CPU 使用率を調べます。

```
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
```

過去 2 時間のフリート全体の平均 CPU 使用率と比較して、CPU 使用率が 10% 以上高い EC2 ホストを特定します。

```
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
```

過去 2 時間における特定の EC2 ホストの 30 秒間隔でビニングされた平均 CPU 使用率を調べます。

```
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
```

過去 2 時間における特定の EC2 ホストの 30 秒間隔でビニングされた平均 CPU 使用率を調べ、線形補間を使用して欠落している値を入力します。

```
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)
```

過去 2 時間における特定の EC2 ホストの 30 秒間隔でビニングされた平均 CPU 使用率を調べ、locf に基づく補間を使用して欠落値を入力します。

```
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)
```

# 集計関数を使用したクエリ
<a name="sample-queries.iot-scenarios"></a>

以下は、集計関数を使用したクエリを示すための IoT シナリオデータセットの例です。

**Topics**
+ [サンプルデータ](#sample-queries.iot-scenarios.example-data)
+ [クエリの例](#sample-queries.iot-scenarios.example-queries)

## サンプルデータ
<a name="sample-queries.iot-scenarios.example-data"></a>

Timestream を使用すると、1 つ以上のトラックフリートの場所、燃料消費量、速度、積載量などの IoT センサーデータを保存および分析して、効果的なフリート管理が可能になります。以下は、トラックの場所、燃料消費量、速度、積載量などのテレメトリを保存するテーブル iot\$1trucks のスキーマとデータの一部です。


| Time | truck\$1id | Make | モデル | Fleet | 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  |  load  |  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  |  Kenworth  |  W900  |  Alpha  |  150  |  1,000  |  fuel\$1reading  |  10.1  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1,000  |  load  |  950.3  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1,000  |  speed  |  50.8  |  null  | 
|  2019-12-04 19:00:00.000000000  |  123456782  |  Kenworth  |  W900  |  Alpha  |  150  |  1,000  |  location  |  null  |  40.7128 degrees N, 74.0060 degrees W  | 

## クエリの例
<a name="sample-queries.iot-scenarios.example-queries"></a>

フリート内の各トラックでモニタリングされているすべてのセンサー属性と値のリストを取得します。

```
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
```

過去 24 時間におけるフリート内トラックそれぞれの直近の燃料測定値を取得します。

```
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
```

過去 48 時間に低燃料 (10% 未満) で走行しているトラックを特定します。

```
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
)
```

過去 1 週間の各トラックの平均負荷と最大速度を調べます。

```
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
```

過去 1 週間の各トラックの負荷効率を取得します。

```
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
```