![]() įor example, say you have a counter metric named event.login with a sample_rate of 0.1. The Splunk platform responds to this by multiplying the value of a downsampled counter metric by 1/. The StatsD client puts the sample_rate value in the counter metric data point to indicate to the Splunk platform the actual downsampling percentage that it employed. The StatsD client manages this by implementing a sample rate to reduce the network traffic that it sends to the Splunk platform. When large numbers of data points are being produced for a particular counter metric, it can be expensive for the Splunk platform to aggregate them. This means that the original metric sent from the StatsD client looked like that the original metric event had a numeric value of 3. The Splunk platform adjusts for this by multiplying the metric value by 1/0.5, or 2. This means that this counter metric is sampled only 50% of the time by the StatsD client. Here is an example of an counter metric that has been processed by the Splunk that this counter metric has a sample rate of 0.5. For more information about counter metrics, see Investigate counter metrics. For example, you can use a counter to represent a number of requests served, tasks completed, or errors. ![]() Its value can only increase or be reset to zero. Performance.os.disk:1099511627776|g|#region:us-west-1,datacenter:us-west-1a,rack:63,os:Ubuntu16.10,arch:圆4,team:LON,service:6,service_version:0,service_environment:test, path:/dev/sdal,fstype:ext3Įxample counter metric, after processing by the Splunk platformĪ counter metric counts occurrences of an event. For example, you can use a gauge to represent the number of currently running search jobs, or the temperature in your server room. Forįor more about formats for metric names and dimensions, see Best practices for metrics.Ī gauge is a metric that represents a single numerical value that can arbitrarily go up and down. Sample rates only apply to counter metrics, meaning they have a metric_type of c. The expanded StatsD data line metric protocol supports dimensions and a sample rate. The basic StatsD data line metric protocol just has three fields: the metric_name, the metric _value, and the metric_type.Įxample metric performance.os.disk:1099511627776|g Expanded StatsD metric protocol See Create Special StatsD Input Customizations for more information. If you need the Splunk software to convert StatsD data into a metric data point format that supports multiple metric measurements per data point, add STATSD_EMIT_SINGLE_MEASUREMENT_FORMAT=false to a stanza for the metric source type in nf. Splunk supports two metric_type values for StatsD metric data points: g, for gauge metrics, and c, for counter metrics.įor ease of use, by default the Splunk platform converts StatsD data into single-measurement metric data points, where each metric data point has a key-value pair for the metric name and another key-value pair for the metric measurement. Expanded StatsD data line metric protocol, which adds sample rate and dimensions.Basic StatsD data line metric protocol, which includes metric_name, _value and metric_type.The Splunk platform supports the following formats natively: StatsD has several metric protocol formats, some of which encode dimensions in different ways. For an overview of StatsD, see Measure Anything, Measure Everything on the Code as Craft website. Register_histogram_vec!("example_random_hist", "Random value.", &, vec!).StatsD is a network daemon that runs on the Node.js platform, sending metrics over UDP or TCP. Using the native rust client on a single thread I was able to get in the ~15m observations/second range, dropping down into the 5-10m observations/second range depending on the number of labels on a single core ( **Intel** (R) Core(TM) i7-8750H CPU 2.20GHz):Įxample code for anyone interested: # Going to link this presentation on rust-prometheus here. Yeah using the native client worked out much better.
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