topk

README

TopK Processor Plugin

The TopK processor plugin is a filter designed to get the top series over a period of time. It can be tweaked to calculate the top metrics via different aggregation functions.

This processor goes through these steps when processing a batch of metrics:

1. Groups measurements in buckets based on their tags and name
2. Every N seconds, for each bucket, for each selected field: aggregate all the measurements using a given aggregation function (min, sum, mean, etc) and the field.
3. For each computed aggregation: order the buckets by the aggregation, then returns all measurements in the top K buckets

Notes:

• The deduplicates metrics
• The name of the measurement is always used when grouping it
• Depending on the amount of metrics on each bucket, more than K series may be returned
• If a measurement does not have one of the selected fields, it is dropped from the aggregation

Global configuration options

In addition to the plugin-specific configuration settings, plugins support additional global and plugin configuration settings. These settings are used to modify metrics, tags, and field or create aliases and configure ordering, etc. See the CONFIGURATION.md for more details.

Configuration

# Print all metrics that pass through this filter.
[[processors.topk]]
  ## How many seconds between aggregations
  # period = 10

  ## How many top buckets to return per field
  ## Every field specified to aggregate over will return k number of results.
  ## For example, 1 field with k of 10 will return 10 buckets. While 2 fields
  ## with k of 3 will return 6 buckets.
  # k = 10

  ## Over which tags should the aggregation be done. Globs can be specified, in
  ## which case any tag matching the glob will aggregated over. If set to an
  ## empty list is no aggregation over tags is done
  # group_by = ['*']

  ## The field(s) to aggregate
  ## Each field defined is used to create an independent aggregation. Each
  ## aggregation will return k buckets. If a metric does not have a defined
  ## field the metric will be dropped from the aggregation. Considering using
  ## the defaults processor plugin to ensure fields are set if required.
  # fields = ["value"]

  ## What aggregation function to use. Options: sum, mean, min, max
  # aggregation = "mean"

  ## Instead of the top k largest metrics, return the bottom k lowest metrics
  # bottomk = false

  ## The plugin assigns each metric a GroupBy tag generated from its name and
  ## tags. If this setting is different than "" the plugin will add a
  ## tag (which name will be the value of this setting) to each metric with
  ## the value of the calculated GroupBy tag. Useful for debugging
  # add_groupby_tag = ""

  ## These settings provide a way to know the position of each metric in
  ## the top k. The 'add_rank_field' setting allows to specify for which
  ## fields the position is required. If the list is non empty, then a field
  ## will be added to each and every metric for each string present in this
  ## setting. This field will contain the ranking of the group that
  ## the metric belonged to when aggregated over that field.
  ## The name of the field will be set to the name of the aggregation field,
  ## suffixed with the string '_topk_rank'
  # add_rank_fields = []

  ## These settings provide a way to know what values the plugin is generating
  ## when aggregating metrics. The 'add_aggregate_field' setting allows to
  ## specify for which fields the final aggregation value is required. If the
  ## list is non empty, then a field will be added to each every metric for
  ## each field present in this setting. This field will contain
  ## the computed aggregation for the group that the metric belonged to when
  ## aggregated over that field.
  ## The name of the field will be set to the name of the aggregation field,
  ## suffixed with the string '_topk_aggregate'
  # add_aggregate_fields = []

Tags

This processor does not add tags by default. But the setting add_groupby_tag will add a tag if set to anything other than ""

Fields

This processor does not add fields by default. But the settings add_rank_fields and add_aggregation_fields will add one or several fields if set to anything other than ""

Example

Below is an example configuration:

[[processors.topk]]
  period = 20
  k = 3
  group_by = ["pid"]
  fields = ["cpu_usage"]

Output difference with topk:

< procstat,pid=2088,process_name=Xorg cpu_usage=7.296576662282613 1546473820000000000
< procstat,pid=2780,process_name=ibus-engine-simple cpu_usage=0 1546473820000000000
< procstat,pid=2554,process_name=gsd-sound cpu_usage=0 1546473820000000000
< procstat,pid=3484,process_name=chrome cpu_usage=4.274300361942799 1546473820000000000
< procstat,pid=2467,process_name=gnome-shell-calendar-server cpu_usage=0 1546473820000000000
< procstat,pid=2525,process_name=gvfs-goa-volume-monitor cpu_usage=0 1546473820000000000
< procstat,pid=2888,process_name=gnome-terminal-server cpu_usage=1.0224991500287577 1546473820000000000
< procstat,pid=2454,process_name=ibus-x11 cpu_usage=0 1546473820000000000
< procstat,pid=2564,process_name=gsd-xsettings cpu_usage=0 1546473820000000000
< procstat,pid=12184,process_name=docker cpu_usage=0 1546473820000000000
< procstat,pid=2432,process_name=pulseaudio cpu_usage=9.892858669796528 1546473820000000000
---
> procstat,pid=2432,process_name=pulseaudio cpu_usage=11.486933087507786 1546474120000000000
> procstat,pid=2432,process_name=pulseaudio cpu_usage=10.056503212060552 1546474130000000000
> procstat,pid=23620,process_name=chrome cpu_usage=2.098690278123081 1546474120000000000
> procstat,pid=23620,process_name=chrome cpu_usage=17.52514619948493 1546474130000000000
> procstat,pid=2088,process_name=Xorg cpu_usage=1.6016732172309973 1546474120000000000
> procstat,pid=2088,process_name=Xorg cpu_usage=8.481040931533833 1546474130000000000

Documentation

Index

• Variables
• type MetricAggregation
• type TopK
  • func New() *TopK
  • func (t *TopK) Apply(in ...telegraf.Metric) []telegraf.Metric
  • func (t *TopK) Reset()
  • func (*TopK) SampleConfig() string

Variables

var MetricsSet1 = []telegraf.Metric{metric11, metric12, metric13, metric14, metric15}

var MetricsSet2 = []telegraf.Metric{metric21, metric22, metric23, metric24, metric25, metric26}

Types

type MetricAggregation

type MetricAggregation struct {
	// contains filtered or unexported fields
}

type TopK

type TopK struct {
	Period             config.Duration `toml:"period"`
	K                  int             `toml:"k"`
	GroupBy            []string        `toml:"group_by"`
	Fields             []string        `toml:"fields"`
	Aggregation        string          `toml:"aggregation"`
	Bottomk            bool            `toml:"bottomk"`
	AddGroupByTag      string          `toml:"add_groupby_tag"`
	AddRankFields      []string        `toml:"add_rank_fields"`
	AddAggregateFields []string        `toml:"add_aggregate_fields"`
	Log                telegraf.Logger `toml:"-"`
	// contains filtered or unexported fields
}

func New

func New() *TopK

func (*TopK) Apply

func (t *TopK) Apply(in ...telegraf.Metric) []telegraf.Metric

func (*TopK) Reset

func (t *TopK) Reset()

func (*TopK) SampleConfig

func (*TopK) SampleConfig() string