![]() ![]() Result: send two OTLP gauge metrics to the downstream component. StatsdTestMetric1:500|h|#mykey:myvalue statsdTestMetric1:400|h|#mykey:myvalue Histogram Example one(use observer_type as gauge): ![]() The first one has the value 500, the second one has the value 400. StatsdTestMetric1:500|ms|#mykey:myvalue statsdTestMetric1:400|ms|#mykey:myvalue Timer Example one(use observer_type as gauge): For gauge, it supports plus and minus for aggregation. Result: get the value after calculation: 501. StatsdTestMetric1:500|g|#mykey:myvalue statsdTestMetric1:+2|g|#mykey:myvalue statsdTestMetric1:-1|g|#mykey:myvalue For gauge, the newest value will cover the early value. StatsdTestMetric1:500|g|#mykey:myvalue statsdTestMetric1:400|g|#mykey:myvalue StatsdTestMetric1:3000|c|#mykey:myvalue get the value after incrementation with sample rate: 3080 (3000+20/0.25). For counter, the same metric with different value inĪn interval will be incremented. Result: get the value after incrementation: 7000 (3000+4000). StatsdTestMetric1:3000|c|#mykey:myvalue statsdTestMetric1:4000|c|#mykey:myvalue The examples below mean receiving metrics in the same interval. You can set the aggregation interval using configuration parameter: aggregation_interval. StatsD receiver will send the timer/histogram metric to the downstream component as OTLP gauge without doing any aggregation. For observer_type, you could choose gauge. For statsd_type, you could choose timer, timing or histogram. Timer_histogram_mapping is the configuration for timer/histogram.
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