包含时间序列函数的查询 - Amazon Timestream

有关与适用于 LiveAnalytics 的 Amazon Timestream 类似的功能,可以考虑使用适用于 InfluxDB 的 Amazon 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。点击此处了解更多信息。

包含时间序列函数的查询

示例数据集和查询

您可以使用适用于 LiveAnalytics 的 Timestream,以了解和提升服务和应用程序的性能与可用性。以下是示例表及其上运行的示例查询。

ec2_metrics 存储遥测数据,例如 EC2 实例的 CPU 利用率及其他指标。您可以查看下表。

Time 区域 az 主机名 measure_name measure_value::double measure_value::bigint

2019-12-04 19:00:00.000000000

us-east-1

us–east–1a

frontend01

cpu_utilization

35.1

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1a

frontend01

memory_utilization

55.3

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1a

frontend01

network_bytes_in

null

1500

2019-12-04 19:00:00.000000000

us-east-1

us–east–1a

frontend01

network_bytes_out

null

6,700

2019-12-04 19:00:00.000000000

us-east-1

us–east–1b

frontend02

cpu_utilization

38.5

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1b

frontend02

memory_utilization

58.4

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1b

frontend02

network_bytes_in

null

23000

2019-12-04 19:00:00.000000000

us-east-1

us–east–1b

frontend02

network_bytes_out

null

12000

2019-12-04 19:00:00.000000000

us-east-1

us–east–1c

frontend03

cpu_utilization

45.0

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1c

frontend03

memory_utilization

65.8

null

2019-12-04 19:00:00.000000000

us-east-1

us–east–1c

frontend03

network_bytes_in

null

15000

2019-12-04 19:00:00.000000000

us-east-1

us–east–1c

frontend03

network_bytes_out

null

836000

2019-12-04 19:00:05.000000000

us-east-1

us–east–1a

frontend01

cpu_utilization

55.2

null

2019-12-04 19:00:05.000000000

us-east-1

us–east–1a

frontend01

memory_utilization

75.0

null

2019-12-04 19:00:05.000000000

us-east-1

us–east–1a

frontend01

network_bytes_in

null

1245

2019-12-04 19:00:05.000000000

us-east-1

us–east–1a

frontend01

network_bytes_out

null

68432

2019-12-04 19:00:08.000000000

us-east-1

us–east–1b

frontend02

cpu_utilization

65.6

null

2019-12-04 19:00:08.000000000

us-east-1

us–east–1b

frontend02

memory_utilization

85.3

null

2019-12-04 19:00:08.000000000

us-east-1

us–east–1b

frontend02

network_bytes_in

null

1245

2019-12-04 19:00:08.000000000

us-east-1

us–east–1b

frontend02

network_bytes_out

null

68432

2019-12-04 19:00:20.000000000

us-east-1

us–east–1c

frontend03

cpu_utilization

12.1

null

2019-12-04 19:00:20.000000000

us-east-1

us–east–1c

frontend03

memory_utilization

32.0

null

2019-12-04 19:00:20.000000000

us-east-1

us–east–1c

frontend03

network_bytes_in

null

1400

2019-12-04 19:00:20.000000000

us-east-1

us–east–1c

frontend03

network_bytes_out

null

345

2019-12-04 19:00:10.000000000

us-east-1

us–east–1a

frontend01

cpu_utilization

15.3

null

2019-12-04 19:00:10.000000000

us-east-1

us–east–1a

frontend01

memory_utilization

35.4

null

2019-12-04 19:00:10.000000000

us-east-1

us–east–1a

frontend01

network_bytes_in

null

23

2019-12-04 19:00:10.000000000

us-east-1

us–east–1a

frontend01

network_bytes_out

null

0

2019-12-04 19:00:16.000000000

us-east-1

us–east–1b

frontend02

cpu_utilization

44.0

null

2019-12-04 19:00:16.000000000

us-east-1

us–east–1b

frontend02

memory_utilization

64.2

null

2019-12-04 19:00:16.000000000

us-east-1

us–east–1b

frontend02

network_bytes_in

null

1450

2019-12-04 19:00:16.000000000

us-east-1

us–east–1b

frontend02

network_bytes_out

null

200

2019-12-04 19:00:40.000000000

us-east-1

us–east–1c

frontend03

cpu_utilization

66.4

null

2019-12-04 19:00:40.000000000

us-east-1

us–east–1c

frontend03

memory_utilization

86.3

null

2019-12-04 19:00:40.000000000

us-east-1

us–east–1c

frontend03

network_bytes_in

null

300

2019-12-04 19:00:40.000000000

us-east-1

us–east–1c

frontend03

network_bytes_out

null

423

计算过去 2 小时内特定 EC2 主机的平均 CPU 利用率、p90、p95 和 p99:

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

确定 CPU 利用率比过去 2 小时整个实例集平均 CPU 利用率高出 10% 或以上的 EC2 主机:

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

计算过去 2 小时内特定 EC2 主机的 CPU 平均利用率,按 30 秒间隔进行分箱:

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

计算过去 2 小时内特定 EC2 主机的 CPU 平均利用率,按 30 秒间隔进行分箱,并使用线性插值填补缺失值:

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)

计算过去 2 小时内特定 EC2 主机的 CPU 平均利用率,按 30 秒间隔进行分箱,并使用基于末次观测值结转的插值填补缺失值:

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)