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。

# 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)
```