chore(aggregators): migrate sample configs into separate files (#11130)
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# Keep the aggregate basicstats of each metric passing through.
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[[aggregators.basicstats]]
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## The period on which to flush & clear the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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## Configures which basic stats to push as fields
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# stats = ["count","diff","rate","min","max","mean","non_negative_diff","non_negative_rate","stdev","s2","sum","interval"]
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# Calculates a derivative for every field.
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[[aggregators.derivative]]
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## Specific Derivative Aggregator Arguments:
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## Configure a custom derivation variable. Timestamp is used if none is given.
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# variable = ""
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## Suffix to add to the field name for the derivative name.
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# suffix = "_rate"
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## Roll-Over last measurement to first measurement of next period
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# max_roll_over = 10
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## General Aggregator Arguments:
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## calculate derivative every 30 seconds
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period = "30s"
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# Report the final metric of a series
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[[aggregators.final]]
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## The period on which to flush & clear the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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## The time that a series is not updated until considering it final.
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series_timeout = "5m"
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# Configuration for aggregate histogram metrics
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[[aggregators.histogram]]
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## The period in which to flush the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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## If true, the histogram will be reset on flush instead
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## of accumulating the results.
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reset = false
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## Whether bucket values should be accumulated. If set to false, "gt" tag will be added.
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## Defaults to true.
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cumulative = true
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## Expiration interval for each histogram. The histogram will be expired if
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## there are no changes in any buckets for this time interval. 0 == no expiration.
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# expiration_interval = "0m"
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## If true, aggregated histogram are pushed to output only if it was updated since
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## previous push. Defaults to false.
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# push_only_on_update = false
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## Example config that aggregates all fields of the metric.
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# [[aggregators.histogram.config]]
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# ## Right borders of buckets (with +Inf implicitly added).
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# buckets = [0.0, 15.6, 34.5, 49.1, 71.5, 80.5, 94.5, 100.0]
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# ## The name of metric.
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# measurement_name = "cpu"
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## Example config that aggregates only specific fields of the metric.
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# [[aggregators.histogram.config]]
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# ## Right borders of buckets (with +Inf implicitly added).
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# buckets = [0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0]
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# ## The name of metric.
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# measurement_name = "diskio"
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# ## The concrete fields of metric
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# fields = ["io_time", "read_time", "write_time"]
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# Merge metrics into multifield metrics by series key
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[[aggregators.merge]]
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = true
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# Keep the aggregate min/max of each metric passing through.
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[[aggregators.minmax]]
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## General Aggregator Arguments:
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## The period on which to flush & clear the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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# Keep the aggregate quantiles of each metric passing through.
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[[aggregators.quantile]]
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## General Aggregator Arguments:
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## The period on which to flush & clear the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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## Quantiles to output in the range [0,1]
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# quantiles = [0.25, 0.5, 0.75]
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## Type of aggregation algorithm
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## Supported are:
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## "t-digest" -- approximation using centroids, can cope with large number of samples
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## "exact R7" -- exact computation also used by Excel or NumPy (Hyndman & Fan 1996 R7)
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## "exact R8" -- exact computation (Hyndman & Fan 1996 R8)
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## NOTE: Do not use "exact" algorithms with large number of samples
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## to not impair performance or memory consumption!
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# algorithm = "t-digest"
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## Compression for approximation (t-digest). The value needs to be
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## greater or equal to 1.0. Smaller values will result in more
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## performance but less accuracy.
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# compression = 100.0
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# Aggregate metrics using a Starlark script
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[[aggregators.starlark]]
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## The Starlark source can be set as a string in this configuration file, or
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## by referencing a file containing the script. Only one source or script
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## should be set at once.
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##
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## Source of the Starlark script.
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source = '''
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state = {}
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def add(metric):
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state["last"] = metric
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def push():
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return state.get("last")
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def reset():
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state.clear()
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'''
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## File containing a Starlark script.
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# script = "/usr/local/bin/myscript.star"
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## The constants of the Starlark script.
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# [aggregators.starlark.constants]
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# max_size = 10
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# threshold = 0.75
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# default_name = "Julia"
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# debug_mode = true
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# Count the occurrence of values in fields.
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[[aggregators.valuecounter]]
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## General Aggregator Arguments:
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## The period on which to flush & clear the aggregator.
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period = "30s"
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## If true, the original metric will be dropped by the
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## aggregator and will not get sent to the output plugins.
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drop_original = false
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## The fields for which the values will be counted
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fields = ["status"]
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