Metric to Log Transform

The Vector metric_to_log transform converts a metric event into a log event, which can be useful for sending metrics to log-support downstream components.

Configuration

• Common
• Advanced

• vector.toml
• vector.yaml
• vector.json

[transforms.my_transform_id]
  type = "metric_to_log" # required
  inputs = ["my-source-or-transform-id", "prefix-*"] # required
  host_tag = "hostname" # optional, default

• commonoptionalstring

  host_tag

  Tag key that identifies the source host.

  • Syntax: literal
  • Default: "hostname"
  • View examples
• optionalstring

  timezone

  The name of the time zone to apply to timestamp conversions that do not contain an explicit time zone. This overrides the global timezone option. The time zone name may be any name in the TZ database, or local to indicate system local time.

  • Syntax: literal
  • Default: "local"
  • View examples

Telemetry

This component provides the following metrics that can be retrieved through the internal_metrics source. See the metrics section in the monitoring page for more info.

• counter

  processing_errors_total

  The total number of processing errors encountered by this component. This metric includes the following tags:

  • component_kind - The Vector component kind.

  • component_name - The Vector component ID.

  • component_type - The Vector component type.

  • error_type - The type of the error

  • instance - The Vector instance identified by host and port.

  • job - The name of the job producing Vector metrics.

• counter

  events_in_total

  The total number of events accepted by this component. This metric includes the following tags:

  • component_kind - The Vector component kind.

  • component_name - The Vector component ID.

  • component_type - The Vector component type.

  • instance - The Vector instance identified by host and port.

  • job - The name of the job producing Vector metrics.

• counter

  processed_events_total

  The total number of events processed by this component. This metric includes the following tags:

  • component_kind - The Vector component kind.

  • component_name - The Vector component ID.

  • component_type - The Vector component type.

  • file - The file that produced the error

  • instance - The Vector instance identified by host and port.

  • job - The name of the job producing Vector metrics.

• counter

  events_out_total

  The total number of events emitted by this component. This metric includes the following tags:

  • component_kind - The Vector component kind.

  • component_name - The Vector component ID.

  • component_type - The Vector component type.

  • instance - The Vector instance identified by host and port.

  • job - The name of the job producing Vector metrics.

• counter

  processed_bytes_total

  The total number of bytes processed by the component. This metric includes the following tags:

  • component_kind - The Vector component kind.

  • component_name - The Vector component ID.

  • component_type - The Vector component type.

  • instance - The Vector instance identified by host and port.

  • job - The name of the job producing Vector metrics.

Examples

Given the following Vector metric event:

{
  "kind": "absolute",
  "name": "histogram",
  "timestamp": "2020-08-01T21:15:47+00:00",
  "tags": {
    "host": "my.host.com",
    "code": "200"
  },
  "histogram": {
    "buckets": [
      {
        "upper_limit": 1.0,
        "count": 10
      },
      {
        "upper_limit": 2.0,
        "count": 20
      }
    ],
    "count": 30,
    "sum": 50.0
  }
}

And the following configuration:

vector.toml

[transforms.metric_to_log]
type = "metric_to_log"
host_tag = "host"

The following Vector log event will be output:

{
  "name": "histogram",
  "timestamp": "2020-08-01T21:15:47+00:00",
  "host": "my.host.com",
  "tags": {
    "code": "200"
  },
  "kind": "absolute",
  "histogram": {
    "buckets": [
      {
        "count": 10,
        "upper_limit": 1.0
      },
      {
        "count": 20,
        "upper_limit": 2.0
      }
    ],
    "count": 30,
    "sum": 50.0
  }
}

How It Works

State

This component is stateless, meaning its behavior is consistent across each input.