![]() ![]() (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. 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. : įor most use-cases, the default mapping is sufficient.Observability is a critical part of Kong’s API Gateway. StatsD metric path is organized using the following schema: Metrics which are not mapped will log an error. If the user does not specify their own JSON file, a default mapping is used. This conversion can be enabled by setting the optional "convertRange" field true in the JSON mapping file. To accommodate these metrics they are converted Druid emits some metrics with values between the range 0 and 1. StatsD expects that metric values be integers. ![]() Additionally, this mapping specifies which dimensions should be included for each metric. StatsD Emitter expects this mapping toīe provided as a JSON file. If and are true, Alert events are reported to DogStatsD.Įach metric sent to StatsD must specify a type, one of. ) and druid is used as metric prefix (e.g. druid_service:druid/broker) instead of being included in metric name (e.g. druid/broker, druid/coordinator, etc) is reported as a tag (e.g. If is true, the tags in the JSON list of strings will be sent with every event.ĭ Causes dimensions to be included as tags, not as a part of the metric name. The blank character replacement as StatsD does not support path with blank characterįlag to enable DogStatsD support. JSON file defining the StatsD type, and desired dimensions for every Druid metric propertyįlag to include the hostname as part of the metric name. This extension emits druid metrics to a StatsD server.Īll the configuration parameters for the StatsD emitter are under. To use this Apache Druid extension, include statsd-emitter in the extensions load list. Moment Sketches for Approximate Quantiles module.Key/Value Stores (HBase/Cassandra/OpenTSDB)
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