![]() With the example StatsD mapping rules, all metrics are You can also access Kong Vitals metrics in Prometheus and display on Grafana Useful, but they are of limited value with respect to monitoring Kong behavior. To the StatsD exporter process itself, not Kong. These data points are related specifically Metrics as provided by the standard Vitals configuration.Īdditionally, the exporter process provides access to the default metrics exposed by the Golang With the above configuration, the Prometheus StatsD exporter will make available all statsd_exporter -statsd.mapping-config = \ -statsd.read-buffer =30000000 \ -statsd.listen-unixgram = '/tmp/statsd.sock' \ -statsd.unixsocket-umask = "777" Exported Metrics Using the following example to set read buffer to around 3 MB: To increase the UDP read buffer for the StatsD exporter process, run the binary It’s necessary to increase the UDP read buffer size to avoid possible packet Unprocessed UDP events will grow as well. Tuning and Optimization StatsD exporter UDP bufferĪs the amount of concurrent requests increases, the length of the queue to store On the next screen, select the Prometheus data source that is configured to scrape statsd-exporter, thenĬlick Import. Optionally, youĬan also download the JSON model from and import it manually. On the Import screen, find the Dashboard field and enter 11870. In your Grafana installation, click the + button in the sidebar, and choose Import. If you use Grafana, you can import the Kong Vitals Prometheus dashboard to visualize the data. # in les mappings : # by API - match : kong-vitals.api.*.unt name : " kong_requests_proxy" labels : job : " kong_metrics" # follows other metrics #. StatsD exporter is distributed as a Docker image. Using default config that listens on port 9090. In this guide, we assume Prometheus is running on hostname prometheus-node Possible to use existing Prometheus nodes as Vitals storage backend. Prometheus should be running on a separate node from the one running Kong.įor users that are already using Prometheus in their infrastructure, it’s The latest release of Prometheus can be found at the Prometheus download page. Set up Prometheus environment for Vitals Download Prometheus Run Kong and StatsD processes on separate hardware/VM/container environments to avoid saturating CPU usage. Within the StatsD exporter process can cause significant CPU usage. Note that in high-traffic environments, data aggregation Or as a sidecar/adjacent process within a VM. In either case, the StatsD exporter process can be run either as a standalone process/container The data to a StatsD exporter, and the node responsible for Admin API reads from Prometheus: In this case, the node responsible for proxy traffic writes One or more nodes serve only proxy traffic, while another node is responsible for serving the It is not uncommon to separate Kong functionality amongst a cluster of nodes. Prometheus does not ever directly scrape the Kong ![]() Kong then queries Prometheus to retrieve andĭisplay Vitals data via the API and Kong Manager. Scrapes this exporter as it would any other endpoint. In this design, Kong writes Vitals metrics to the StatsD exporter This integration allows Kong to efficiently ship Vitals metrics to an outside process where data pointsĬan be aggregated and made available for consumption by Prometheus, without impeding performance Kong Vitals integrates with Prometheus using an intermediary data exporter, the Prometheus StatsD exporter. Requests per second), without placing addition write load on the databaseįor using Vitals with a database as the backend, refer to Kong Vitals. Leveraging a time-series database for Vitals dataĬan improve request and Vitals performance in very-high traffic Kong GatewayĬlusters (such as environments handling tens or hundreds of thousands of This document covers integrating Kong Vitals with a new or existing Prometheus ![]()
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