Edge Delta Aggregate Metric Processor

The Edge Delta aggregate metric processor creates metrics from metrics.

7 minute read

Overview

The aggregate metric function performs mathematical operations on metric items. It can aggregate, batch and group metrics to provide a higher level view of metrics while reducing metric data volume at the edge.

Note: Histogram metric items can not be aggregated. They must be cast into gauge or sum first.

See Extract and Aggregate Metrics for more information about using the extract metric processor.

Rollup Metric vs Aggregate Metric

The Edge Delta platform offers two ways to aggregate metrics:

Rollup Metric Processor: Aggregates metrics without grouping dimensions, producing a single summary value for all matching metrics in each interval. Use this when you need overall statistics like “total requests across all endpoints” or “median response time for the entire system.”
Aggregate Metric Processor (this page): Aggregates metrics with grouping dimensions using the group_by field, producing separate metrics for each unique dimension value. Use this when you need dimensional breakdowns like “requests per status code” or “response time by endpoint.”

Choose the rollup metric processor for simplified, single-value summaries. Choose the aggregate metric processor when you need to maintain visibility into different dimensions of your data.

Example Input

Consider this metric. It indicates that a single log had an HTTP status code of 200.

{
  "_type": "metric",
  "timestamp": 1744965983285,
  "resource": {
...
  },
  "attributes": {
    "level": "Notice",
    "msg": "processing completed with minor issues",
    "request": {
      "ip": "192.168.137.102",
      "method": "POST",
      "path": "/json/data"
    },
    "response_time_ms": 543,
    "status": 200,
    "timestamp": "2025-04-18T08:46:21.764083Z",
    "user": {
      "email": "wang.xiu@imaginarymail.com",
      "id": "99b4b6e8-b326-479f-b94f-aadf7a34b34c",
      "name": "8934f4b3-9539-470c-bd0b-3c4e58b73a8b"
    }
  },
  "description": "A count of each unique status code",
  "gauge": {
    "value": 200
  },
  "kind": "gauge",
  "name": "status_code",
  "unit": "1",
  "_stat_type": "value"
}

Note: The resource field has been redacted for brevity.

Configuration

You specify a name for the processor and a condition data items must fulfil for the processor to operate on them. You configure one or more rules. Each rule generates a metric. For each rule you can create conditions for that metric (in addition to the main condition), specify a unique metric name, a unique batching interval, and create dimension groups. For each dimension group, a unique metric data item is produced at every batching interval.

This example counts metrics per status. For example, there were 24 instances of the status 200 metric in the sample.

- name: Multiprocessor
  type: sequence
  processors:
  - type: aggregate_metric
    metadata: '{"id":"NoQMsANzEU7ltYMb5r1G0","type":"aggregate_metric","name":"Aggregate
      Metric"}'
    data_types:
    - metric
    aggregate_metric_rules:
    - name: Count per status code
      interval: 1m0s
      aggregation_type: count
      group_by:
      - attributes["status"]

Options

condition

The condition parameter contains a conditional phrase of an OTTL statement. It restricts operation of the processor to only data items where the condition is met. Those data items that do not match the condition are passed without processing. You configure it in the interface and an OTTL condition is generated. It is optional. You can select one of the following operators:

Operator	Name	Description	Example
`==`	Equal to	Returns `true` if both values are exactly the same	`attributes["status"] == "OK"`
`!=`	Not equal to	Returns `true` if the values are not the same	`attributes["level"] != "debug"`
`>`	Greater than	Returns `true` if the left value is greater than the right	`attributes["duration_ms"] > 1000`
`>=`	Greater than or equal	Returns `true` if the left value is greater than or equal to the right	`attributes["score"] >= 90`
`<`	Less than	Returns `true` if the left value is less than the right	`attributes["load"] < 0.75`
`<=`	Less than or equal	Returns `true` if the left value is less than or equal to the right	`attributes["retries"] <= 3`
`matches`	Regex match	Returns `true` if the string matches a regular expression	`isMatch(attributes["name"], ".*\\.name$"`