Designing Microservices With Cassandra. You can find that in github here. Expvar values that are not numbers or bools are silently ignored. // hasUpdates checks an alert against the last notification that was made // about it. This // will match this library's version, which subscribes to the Semantic // Versioning scheme. To demonstrate Prometheus metrics code instrumentation in Golang, we’re going to use the official Prometheus library to instrument a simple application. To do that, you can either configure a summary with a 0.95-quantile and (for example) a 5-minute decay time, or you configure a histogram with a few buckets around the 300ms mark, e.g. const ( // APIVersion is the version of the format of the exported data. There’s quite a few ways to deploy Prometheus on Kubernetes (which we won’t deep dive on). Example. Prometheus metrics code examples Golang code instrumentation with Prometheus metrics / OpenMetrics. Prometheus is a pull-based system, if you want push-based monitoring, you need to use a gateway of some sort.
{le="0.1"}, {le="0.2"}, {le="0.3"}, and {le="0.45"}. // By wrapping the handler with InstrumentHandler, request count, // request and response sizes, and request latency are automatically // exported to Prometheus, partitioned by HTTP status code and method // and by the handler name (here "fileserver"). Starting up some sample targets. Configure Prometheus. rate(prometheus_tsdb_head_chunks_created_total[1m]) Experiment with the graph range parameters and other settings. APIVersion = "0.0.4" // DelimitedTelemetryContentType is the content type set on telemetry // data responses in delimited protobuf format. Create Versatile Microservices in Golang — Part 6 (Web Clients) Free DZone Refcard.

Let us make this more interesting and start some example targets for Prometheus to scrape.
Luckily, client libraries make this pretty easy, which is one of the reasons behind Prometheus’ wide adoption. But we need to tell Prometheus to pull metrics from the /metrics endpoint from the Go application. 4.

An absolute high level of how prometheus does its thing is necessary as well. So far in this Prometheus blog series, we have looked into Prometheus metrics and labels (see Part 1 & 2), as well as how Prometheus integrates in a distributed architecture (see Part 3). To do that, let’s create a prometheus.yml file with the following content. Code: // Handle the "/doc" endpoint with the standard http.FileServer handler. The number is then directly exported as the Prometheus sample value. At this time, we’re using Prometheus with a default configuration. (For a bool, 'false' translates to 0 and 'true' to 1). The Node Exporter is used as an example target, for more information on using it see these instructions. A minimal example (without actually doing anything useful like starting an HTTP listener, or actually doing anything to a metric) follows: For descriptors without variable labels, the expvar value must be a number or a bool. Monitor and instrument a sample Golang application; Understand how to deploy Prometheus with Docker stacks; Get a quick-start with PromQL examples; Prometheus is a leading cloud-native time-series database and monitoring solution. (Make sure to replace 192.168.1.61 with your application IP—don’t use localhost if using Docker.) You just need to create and register your metrics and update their values. In this 4th part, it is time to look at code to create custom instrumentation. First, know that there’s already a golang SDK for prometheus which makes the process quite nice.