2013-04-16 22:50:21 +02:00
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# The form of each line in this file should be as follows:
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#
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# output_template (frequency) = method input_pattern
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#
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# This will capture any received metrics that match 'input_pattern'
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# for calculating an aggregate metric. The calculation will occur
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# every 'frequency' seconds and the 'method' can specify 'sum' or
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# 'avg'. The name of the aggregate metric will be derived from
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# 'output_template' filling in any captured fields from 'input_pattern'.
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#
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# For example, if you're metric naming scheme is:
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#
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# <env>.applications.<app>.<server>.<metric>
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#
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# You could configure some aggregations like so:
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#
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# <env>.applications.<app>.all.requests (60) = sum <env>.applications.<app>.*.requests
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# <env>.applications.<app>.all.latency (60) = avg <env>.applications.<app>.*.latency
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#
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# As an example, if the following metrics are received:
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#
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# prod.applications.apache.www01.requests
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# prod.applications.apache.www01.requests
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#
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# They would all go into the same aggregation buffer and after 60 seconds the
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# aggregate metric 'prod.applications.apache.all.requests' would be calculated
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# by summing their values.
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#
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# Note that any time this file is modified, it will be re-read automatically.
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2013-05-20 22:29:59 +02:00
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# NOTE: If you use the `sum` aggregation method, make sure the aggregation period is
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# 5 seconds unless you know what you are doing. statsd pushes to carbon
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# every 5 seconds (see local.js), so aggregating over a longer period of time
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# will inflate the output value
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2016-03-31 03:39:51 +02:00
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# Aggregate all per-bucket remote cache stats into a generic hit/miss stat
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2013-05-20 22:29:59 +02:00
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stats.<app>.cache.all.hit (5) = sum stats.<app>.cache.*.hit
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stats.<app>.cache.all.miss (5) = sum stats.<app>.cache.*.miss
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2013-05-01 17:33:38 +02:00
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2016-03-31 03:39:51 +02:00
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# Aggregate all per-bucket remote cache stats counts into a generic hit/miss stat
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2013-09-17 20:18:15 +02:00
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stats_counts.<app>.cache.all.hit (5) = sum stats_counts.<app>.cache.*.hit
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stats_counts.<app>.cache.all.miss (5) = sum stats_counts.<app>.cache.*.miss
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2013-05-01 17:33:38 +02:00
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# Aggregate all per-domain active stats to overall active stats
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2013-05-20 22:29:59 +02:00
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stats.gauges.<app>.users.active.all.<bucket> (5) = sum stats.gauges.<app>.users.active.*.<bucket>
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2013-09-05 21:39:57 +02:00
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stats.gauges.<app>.users.reading.all.<bucket> (5) = sum stats.gauges.<app>.users.reading.*.<bucket>
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2013-11-06 17:23:19 +01:00
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# Aggregate all per-realm end-to-end send stats to overall
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2013-12-06 00:22:21 +01:00
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stats.timers.<app>.endtoend.send_time.all.<type> (5) = sum stats.timers.<app>.endtoend.send_time.*.<type>
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stats.timers.<app>.endtoend.receive_time.all.<type> (5) = sum stats.timers.<app>.endtoend.receive_time.*.<type>
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stats.timers.<app>.endtoend.displayed_time.all.<type> (5) = sum stats.timers.<app>.endtoend.displayed_time.*.<type>
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2013-12-06 00:22:25 +01:00
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# Aggregate all per-realm narrow timing stats
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stats.timers.<app>.narrow.initial_core.all.<type> (5) = sum stats.timers.<app>.narrow.initial_core.*.<type>
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stats.timers.<app>.narrow.initial_free.all.<type> (5) = sum stats.timers.<app>.narrow.initial_free.*.<type>
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stats.timers.<app>.narrow.network.all.<type> (5) = sum stats.timers.<app>.narrow.network.*.<type>
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2014-02-13 18:49:44 +01:00
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# Do the same for unnarrow times
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stats.timers.<app>.unnarrow.initial_core.all.<type> (5) = sum stats.timers.<app>.unnarrow.initial_core.*.<type>
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stats.timers.<app>.unnarrow.initial_free.all.<type> (5) = sum stats.timers.<app>.unnarrow.initial_free.*.<type>
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