Meters

The OpenMeter provides a robust and flexible way to define and manage meters. This configuration is designed to offer multiple aggregation methods and window sizes, making it highly adaptable to your specific needs.

#Meter Definition

Meters in OpenMeter are defined in the meters section of the configuration file.

Each meter comprises the following attributes:

• slug: A unique identifier for the meter. This is used to reference the meter in the OpenMeter API.
• description: A human-readable description of the meter.
• eventType: The event type that the meter is tracking. This is used to filter the events that are used to calculate the meter.
• valueProperty: The JSON path to the property that contains the value to be metered. This is optional when the aggregation is COUNT.
• aggregation: The aggregation type to use for the meter. See Aggregation Types for more information.
• groupBy: A map of JSON paths to group the metered data by. This is optional.
• windowSize: The size of the time window to use for the meter. This is optional and defaults to MINUTE.

#Examples

Counting API requests per method and path:

meters:
  - slug: api_request_count
    description: Counts the number of API requests
    eventType: api_request
    aggregation: COUNT
    groupBy:
      method: $.method
      path: $.path

Summing up the total tokens used in OpenAI calls per model:

meters:
  - slug: openai_token_usage
    description: Total tokens used in OpenAI calls
    eventType: openai
    valueProperty: $.total_tokens
    aggregation: SUM
    groupBy:
      model: $.model

#Aggregation Types

OpenMeter allows various types of aggregations, making it suitable for a wide range of applications.

#SUM

The SUM aggregation type calculates the total sum of the metered values for a specific time window. This is useful for accumulating metrics like total API calls made, total data transferred, LLM tokens used or total time spent on a service.

#COUNT

The COUNT aggregation type counts the number of events that occur within a specific time window. This is often used for metrics that are inherently countable, such as the the number of transactions processed. The COUNT aggregation type does not require a valueProperty to be defined.

#AVG

The AVG (Average) aggregation type calculates the mean value of the metered data points within a specific time window. This is useful for metrics where you want to understand the average behavior over time, such as average CPU usage or average transaction value.

#MIN

The MIN aggregation type identifies the minimum value among the metered data points within a specific time window. This is useful for metrics where the lowest value is of interest, such as minimum available storage or minimum response time.

#MAX

The MAX aggregation type identifies the maximum value among the metered data points within a specific time window. This is useful for metrics where the highest value is of interest, such as maximum load on a server or maximum transaction value.