Aggregate

Aggregate metrics passing through a topology

status: stable egress: stream state: stateful
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Aggregates multiple metric events into a single metric event based on a defined interval window. This helps to reduce metric volume at the cost of granularity.

Configuration

Example configurations 

{
  "transforms": {
    "my_transform_id": {
      "type": "aggregate",
      "inputs": [
        "my-source-or-transform-id"
      ]
    }
  }
}
[transforms.my_transform_id]
type = "aggregate"
inputs = [ "my-source-or-transform-id" ]

transforms:
  my_transform_id:
    type: aggregate
    inputs:
      - my-source-or-transform-id

{
  "transforms": {
    "my_transform_id": {
      "type": "aggregate",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "interval_ms": 10000,
      "mode": "Auto"
    }
  }
}
[transforms.my_transform_id]
type = "aggregate"
inputs = [ "my-source-or-transform-id" ]
interval_ms = 10_000
mode = "Auto"

transforms:
  my_transform_id:
    type: aggregate
    inputs:
      - my-source-or-transform-id
    interval_ms: 10000
    mode: Auto

graph

optional object

Extra graph configuration

Configure output for component when generated with graph command

graph.node_attributes

optional object

Node attributes to add to this component’s node in resulting graph

They are added to the node as provided

graph.node_attributes.*
required string literal
A single graph node attribute in graphviz DOT language.
Examples 
{
  "color": "red",
  "name": "Example Node",
  "width": "5.0"
}

inputs

required [string]

A list of upstream source or transform IDs.

Wildcards (*) are supported.

See configuration for more info.

Array string literal
Examples 
[
  "my-source-or-transform-id",
  "prefix-*"
]

interval_ms

optional uint

The interval between flushes, in milliseconds.

During this time frame, metrics (beta) with the same series data (name, namespace, tags, and so on) are aggregated.

default: 10000

mode

optional string literal enum

Function to use for aggregation.

Some of the functions may only function on incremental and some only on absolute metrics.

Enum options string literal
OptionDescription
AutoDefault mode. Sums incremental metrics and uses the latest value for absolute metrics.
CountCounts metrics for incremental and absolute metrics
DiffReturns difference between latest value for absolute, ignores incremental
LatestReturns the latest value for absolute metrics, ignores incremental
MaxMax value of absolute metric, ignores incremental
MeanMean value of absolute metric, ignores incremental
MinMin value of absolute metric, ignores incremental
StdevStdev value of absolute metric, ignores incremental
SumSums incremental metrics, ignores absolute
default: Auto

Outputs

<component_id>

Default output stream of the component. Use this component’s ID as an input to downstream transforms and sinks.

Telemetry

Metrics

link

aggregate_events_recorded_total

counter
The number of events recorded by the aggregate transform.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.

aggregate_failed_updates

counter
The number of failed metric updates, incremental adds, encountered by the aggregate transform.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.

aggregate_flushes_total

counter
The number of flushes done by the aggregate transform.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.

component_discarded_events_total

counter
The number of events dropped by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
intentional
True if the events were discarded intentionally, like a filter transform, or false if due to an error.
pid optional
The process ID of the Vector instance.

component_errors_total

counter
The total number of errors encountered by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
error_type
The type of the error
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.
stage
The stage within the component at which the error occurred.

component_received_event_bytes_total

counter
The number of event bytes accepted by this component either from tagged origins like file and uri, or cumulatively from other origins.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_received_events_count

histogram

A histogram of the number of events passed in each internal batch in Vector’s internal topology.

Note that this is separate than sink-level batching. It is mostly useful for low level debugging performance issues in Vector due to small internal batches.

component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_received_events_total

counter
The number of events accepted by this component either from tagged origins like file and uri, or cumulatively from other origins.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
container_name optional
The name of the container from which the data originated.
file optional
The file from which the data originated.
host optional
The hostname of the system Vector is running on.
mode optional
The connection mode used by the component.
peer_addr optional
The IP from which the data originated.
peer_path optional
The pathname from which the data originated.
pid optional
The process ID of the Vector instance.
pod_name optional
The name of the pod from which the data originated.
uri optional
The sanitized URI from which the data originated.

component_sent_event_bytes_total

counter
The total number of event bytes emitted by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
output optional
The specific output of the component.
pid optional
The process ID of the Vector instance.

component_sent_events_total

counter
The total number of events emitted by this component.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
output optional
The specific output of the component.
pid optional
The process ID of the Vector instance.

utilization

gauge
A ratio from 0 to 1 of the load on a component. A value of 0 would indicate a completely idle component that is simply waiting for input. A value of 1 would indicate a that is never idle. This value is updated every 5 seconds.
component_id
The Vector component ID.
component_kind
The Vector component kind.
component_type
The Vector component type.
host optional
The hostname of the system Vector is running on.
pid optional
The process ID of the Vector instance.

Examples

Aggregate over 5 seconds

Given this event...
[{"metric":{"counter":{"value":1.1},"kind":"incremental","name":"counter.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:44.223543Z"}},{"metric":{"counter":{"value":2.2},"kind":"incremental","name":"counter.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}},{"metric":{"counter":{"value":1.1},"kind":"incremental","name":"counter.1","tags":{"host":"different.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}},{"metric":{"counter":{"value":22.33},"kind":"absolute","name":"gauge.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:47.223543Z"}},{"metric":{"counter":{"value":44.55},"kind":"absolute","name":"gauge.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}}]
...and this configuration...
transforms:
  my_transform_id:
    type: aggregate
    inputs:
      - my-source-or-transform-id
    interval_ms: 5000

[transforms.my_transform_id]
type = "aggregate"
inputs = [ "my-source-or-transform-id" ]
interval_ms = 5_000

{
  "transforms": {
    "my_transform_id": {
      "type": "aggregate",
      "inputs": [
        "my-source-or-transform-id"
      ],
      "interval_ms": 5000
    }
  }
}
...this Vector event is produced:
[{"metric":{"counter":{"value":3.3},"kind":"incremental","name":"counter.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}},{"metric":{"counter":{"value":1.1},"kind":"incremental","name":"counter.1","tags":{"host":"different.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}},{"metric":{"counter":{"value":44.55},"kind":"absolute","name":"gauge.1","tags":{"host":"my.host.com"},"timestamp":"2021-07-12T07:58:45.223543Z"}}]

How it works

Advantages of Use

The major advantage to aggregation is the reduction of volume. It may reduce costs directly in situations that charge by metric event volume, or indirectly by requiring less CPU to process and/or less network bandwidth to transmit and receive. In systems that are constrained by the processing required to ingest metric events it may help to reduce the processing overhead. This may apply to transforms and sinks downstream of the aggregate transform as well.

Aggregation Behavior

Metrics are aggregated based on their kind. During an interval, incremental metrics are “added” and newer absolute metrics replace older ones in the same series. This results in a reduction of volume and less granularity, while maintaining numerical correctness. As an example, two incremental counter metrics with values 10 and 13 processed by the transform during a period would be aggregated into a single incremental counter with a value of 23. Two absolute gauge metrics with values 93 and 95 would result in a single absolute gauge with the value of 95. More complex types like distribution, histogram, set, and summary behave similarly with incremental values being combined in a manner that makes sense based on their type.

State

This component is stateful, meaning its behavior changes based on previous inputs (events). State is not preserved across restarts, therefore state-dependent behavior will reset between restarts and depend on the inputs (events) received since the most recent restart.
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