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Time Series

Concepts

Term Description
Daily Active Time Series The number of Time Series that generate new metric data on the current day. The count is based on Time Series that had data produced during the day. If data collection stops, no Time Series fees will be incurred for the new day, but previously collected metric data can still be queried.
Measurement A collection of statistical values, analogous to a table in a relational database.
Data Point A sample of metric data, analogous to a row in a relational database.
Time The timestamp when a data point was generated, i.e., the time when DataKit collected the metric data.
Field A numeric value that changes over time. For example, in the CPU measurement, cpu_total, cpu_use, cpu_use_percent, etc.
Tags Attribute information that does not change over time. For example, in the CPU measurement, fields such as host and project are used to identify the actual object attributes of the metric.

Billing Item Statistics

The number of Time Series newly added on the current day is counted at hourly intervals. After 24 data points are obtained, the maximum value is taken as the actual billable quantity.

Example

Take the measurement CPU as an example. Based on a single Field cpu_use_percent, there are 6 data points. Each data point includes the following fields:

  • Time field: time

  • Field: cpu_use_percent

  • Tags: host and project

The distribution of data points is as follows:

  • Rows 1 and 4: host is Hangzhou_test1, project belongs to Guance, representing the CPU usage of the Hangzhou server.

  • Rows 2 and 5: host is Ningxia_test1, project belongs to Guance, representing the CPU usage of the Ningxia server.

  • Rows 3 and 6: host is Singapore_test1, project belongs to Guance_oversea, representing the CPU usage of the Singapore server.

Based on the above data, there are 3 Time Series combinations for the cpu_use_percent Field:

  1. "host":"Hangzhou_test1","project":"Guance"

  2. "host":"Ningxia_test1","project":"Guance"

  3. "host":"Singapore_test1","project":"Guance_oversea"

To calculate the total number of Time Series for all metrics in the current workspace, simply sum the Time Series count of each metric that is actually collected.

Cost Calculation Formula

Daily cost = Actual billable quantity / 1000 × Unit price (the unit price is applied based on the data storage policy)

Assume a user has installed one host DataKit and enabled default metric collection. This host generates 600 daily active Time Series each day. The cost can be estimated as follows:

  1. Determine the number of hosts with DataKit installed:

    For example, 1 host installed

  2. Calculate the number of daily active Time Series:

    Number of hosts × 600 = Number of daily active Time Series

    For example, 1 host × 600 = 600 daily active Time Series

  3. Calculate the estimated daily cost:

    Unit price of the corresponding data storage policy × Number of daily active Time Series / 1000

    For example, assuming the unit price is 1 RMB per thousand, the cost is 1 RMB per thousand × 600 / 1000 = 0.6 RMB

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