However a middleware service ( StatsD Exporter) needs to be deployed to collect and aggregate metrics sent by Kong. The only overhead is using OpenResty cosocket to asynchronously send data over the network. When using StatsD plugin to push metrics, Kong Gateway doesn’t need to store the content of the metrics in memory, and therefore this plugin has minimal impact on the proxy path. In Kong Gateway 3.0, we moved all StatsD Advanced functions to StatsD plugin, so community users can use StatsD plugin to achieve more complex and functional things that are previously only available to Enterprise offerings. Import Kong statsd exporter dashboard to your grafana. Config your Prometheus config # add this line to your prometheus.yaml Enable Prometheus plugin # enable prometheus plugin (or you can use global plugin) Enabling those metrics will impact the performance if you have a lot of Kong entities, therefore we recommend using the StatsD plugin with the push model for those cases. They can still be turned on manually by setting config status_code_metrics, lantency_metrics, bandwidth_metrics, and upstream_health_metrics respectively. So unlike the old release version, in Kong Gateway 3.0 the Prometheus plugin doesn’t export status codes, latencies, bandwidth, and upstream health check metrics by default to avoid the costly overhead of collecting metrics.īecause these metrics will need to be added up or reset during the life of each connection - and these metrics have some different labels - they take up a lot of memory space, which needs to be traversed when Prometheus polls for metrics leading to higher latencies. (See the issues on GitHub to get more information). Therefore when the Prometheus service performed its periodic pull for the metrics, it triggered high overhead in Nginx and affected the real request latency. For example, in the production environment of one of our enterprise customers, they found that the request from Prometheus to pull metrics caused sporadic spikes in latencies for other requests - sometimes as much as three seconds.Īs the investigation progressed, we found that the Prometheus plugin collects metrics with some expensive function calls because it stores many high cardinality metrics in Nginx’s shared memory. In a previous version of Kong Gateway, we found some performance issues with the Prometheus plugin.
#Statsd exporter how to#
We’ll explain how to use these two plugins in the following sections.
We recommend using the StatsD plugin as an alternative for such use cases. When the plugin is used to monitor metrics with high cardinality, it can affect Kong Gateway performance significantly, especially increasing the long tail latencies. However, every read and write operation to the shared memory requires a mutex lock to lock the critical section and will block all worker processes from processing requests. Nginx handles requests in a non-blocking way and is normally very efficient. To collect and aggregate metrics from different processes, we implement the Prometheus plugin with shared memory. Kong Gateway is built on top of OpenResty/Nginx, which is a multi-process single-threaded architecture. This enables Prometheus to pull metrics directly from the gateway or using a StatsD exporter in between to offload some work from the gateway. Kong Gateway supports both of the above for integrating with Prometheus. Prometheus provides a StatsD exporter to collect metrics that are sent in StatsD format. StatsD was originally a simple daemon developed by Etsy to aggregate and summarize application metrics. Prometheus is an open source system monitor toolkit built at SoundCloud that is now widely adopted. In this post, we’ll describe two options to monitor Kong Gateway using Prometheus. Observability is a critical part of Kong’s API Gateway.
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