telegraf/plugins/aggregators/histogram
Olli-Pekka Lehto 13a00eeca5 Add option to reset buckets on flush to histogram aggregator (#5641) 2019-04-01 11:53:50 -07:00
..
README.md Add option to reset buckets on flush to histogram aggregator (#5641) 2019-04-01 11:53:50 -07:00
histogram.go Add option to reset buckets on flush to histogram aggregator (#5641) 2019-04-01 11:53:50 -07:00
histogram_test.go Add option to reset buckets on flush to histogram aggregator (#5641) 2019-04-01 11:53:50 -07:00

README.md

Histogram Aggregator Plugin

The histogram aggregator plugin creates histograms containing the counts of field values within a range.

Values added to a bucket are also added to the larger buckets in the distribution. This creates a cumulative histogram.

Like other Telegraf aggregators, the metric is emitted every period seconds. By default bucket counts are not reset between periods and will be non-strictly increasing while Telegraf is running. This behavior can be changed by setting the reset parameter to true.

Design

Each metric is passed to the aggregator and this aggregator searches histogram buckets for those fields, which have been specified in the config. If buckets are found, the aggregator will increment +1 to the appropriate bucket otherwise it will be added to the +Inf bucket. Every period seconds this data will be forwarded to the outputs.

The algorithm of hit counting to buckets was implemented on the base of the algorithm which is implemented in the Prometheus client.

Configuration

# Configuration for aggregate histogram metrics
[[aggregators.histogram]]
  ## The period in which to flush the aggregator.
  period = "30s"

  ## If true, the original metric will be dropped by the
  ## aggregator and will not get sent to the output plugins.
  drop_original = false

  ## If true, the histogram will be reset on flush instead
  ## of accumulating the results.
  reset = false

  ## Example config that aggregates all fields of the metric.
  # [[aggregators.histogram.config]]
  #   ## The set of buckets.
  #   buckets = [0.0, 15.6, 34.5, 49.1, 71.5, 80.5, 94.5, 100.0]
  #   ## The name of metric.
  #   measurement_name = "cpu"

  ## Example config that aggregates only specific fields of the metric.
  # [[aggregators.histogram.config]]
  #   ## The set of buckets.
  #   buckets = [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
  #   ## The name of metric.
  #   measurement_name = "diskio"
  #   ## The concrete fields of metric
  #   fields = ["io_time", "read_time", "write_time"]

The user is responsible for defining the bounds of the histogram bucket as well as the measurement name and fields to aggregate.

Each histogram config section must contain a buckets and measurement_name option. Optionally, if fields is set only the fields listed will be aggregated. If fields is not set all fields are aggregated.

The buckets option contains a list of floats which specify the bucket boundaries. Each float value defines the inclusive upper bound of the bucket. The +Inf bucket is added automatically and does not need to be defined.

Measurements & Fields:

The postfix bucket will be added to each field key.

  • measurement1
    • field1_bucket
    • field2_bucket

Tags:

All measurements are given the tag le. This tag has the border value of bucket. It means that the metric value is less than or equal to the value of this tag. For example, let assume that we have the metric value 10 and the following buckets: [5, 10, 30, 70, 100]. Then the tag le will have the value 10, because the metrics value is passed into bucket with right border value 10.

Example Output:

cpu,cpu=cpu1,host=localhost,le=0.0 usage_idle_bucket=0i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=10.0 usage_idle_bucket=0i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=20.0 usage_idle_bucket=1i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=30.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=40.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=50.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=60.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=70.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=80.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=90.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=100.0 usage_idle_bucket=2i 1486998330000000000
cpu,cpu=cpu1,host=localhost,le=+Inf usage_idle_bucket=2i 1486998330000000000