211 lines
9.9 KiB
Go
211 lines
9.9 KiB
Go
// Copyright 2014 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// Package prometheus is the core instrumentation package. It provides metrics
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// primitives to instrument code for monitoring. It also offers a registry for
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// metrics. Sub-packages allow to expose the registered metrics via HTTP
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// (package promhttp) or push them to a Pushgateway (package push). There is
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// also a sub-package promauto, which provides metrics constructors with
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// automatic registration.
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//
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// All exported functions and methods are safe to be used concurrently unless
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// specified otherwise.
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//
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// # A Basic Example
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//
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// As a starting point, a very basic usage example:
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//
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// package main
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//
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// import (
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// "log"
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// "net/http"
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//
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// "github.com/prometheus/client_golang/prometheus"
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// "github.com/prometheus/client_golang/prometheus/promhttp"
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// )
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//
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// type metrics struct {
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// cpuTemp prometheus.Gauge
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// hdFailures *prometheus.CounterVec
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// }
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//
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// func NewMetrics(reg prometheus.Registerer) *metrics {
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// m := &metrics{
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// cpuTemp: prometheus.NewGauge(prometheus.GaugeOpts{
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// Name: "cpu_temperature_celsius",
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// Help: "Current temperature of the CPU.",
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// }),
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// hdFailures: prometheus.NewCounterVec(
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// prometheus.CounterOpts{
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// Name: "hd_errors_total",
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// Help: "Number of hard-disk errors.",
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// },
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// []string{"device"},
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// ),
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// }
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// reg.MustRegister(m.cpuTemp)
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// reg.MustRegister(m.hdFailures)
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// return m
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// }
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//
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// func main() {
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// // Create a non-global registry.
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// reg := prometheus.NewRegistry()
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//
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// // Create new metrics and register them using the custom registry.
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// m := NewMetrics(reg)
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// // Set values for the new created metrics.
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// m.cpuTemp.Set(65.3)
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// m.hdFailures.With(prometheus.Labels{"device":"/dev/sda"}).Inc()
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//
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// // Expose metrics and custom registry via an HTTP server
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// // using the HandleFor function. "/metrics" is the usual endpoint for that.
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// http.Handle("/metrics", promhttp.HandlerFor(reg, promhttp.HandlerOpts{Registry: reg}))
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// log.Fatal(http.ListenAndServe(":8080", nil))
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// }
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//
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// This is a complete program that exports two metrics, a Gauge and a Counter,
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// the latter with a label attached to turn it into a (one-dimensional) vector.
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// It register the metrics using a custom registry and exposes them via an HTTP server
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// on the /metrics endpoint.
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//
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// # Metrics
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//
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// The number of exported identifiers in this package might appear a bit
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// overwhelming. However, in addition to the basic plumbing shown in the example
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// above, you only need to understand the different metric types and their
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// vector versions for basic usage. Furthermore, if you are not concerned with
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// fine-grained control of when and how to register metrics with the registry,
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// have a look at the promauto package, which will effectively allow you to
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// ignore registration altogether in simple cases.
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//
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// Above, you have already touched the Counter and the Gauge. There are two more
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// advanced metric types: the Summary and Histogram. A more thorough description
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// of those four metric types can be found in the Prometheus docs:
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// https://prometheus.io/docs/concepts/metric_types/
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//
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// In addition to the fundamental metric types Gauge, Counter, Summary, and
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// Histogram, a very important part of the Prometheus data model is the
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// partitioning of samples along dimensions called labels, which results in
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// metric vectors. The fundamental types are GaugeVec, CounterVec, SummaryVec,
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// and HistogramVec.
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//
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// While only the fundamental metric types implement the Metric interface, both
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// the metrics and their vector versions implement the Collector interface. A
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// Collector manages the collection of a number of Metrics, but for convenience,
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// a Metric can also “collect itself”. Note that Gauge, Counter, Summary, and
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// Histogram are interfaces themselves while GaugeVec, CounterVec, SummaryVec,
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// and HistogramVec are not.
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//
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// To create instances of Metrics and their vector versions, you need a suitable
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// …Opts struct, i.e. GaugeOpts, CounterOpts, SummaryOpts, or HistogramOpts.
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//
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// # Custom Collectors and constant Metrics
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//
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// While you could create your own implementations of Metric, most likely you
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// will only ever implement the Collector interface on your own. At a first
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// glance, a custom Collector seems handy to bundle Metrics for common
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// registration (with the prime example of the different metric vectors above,
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// which bundle all the metrics of the same name but with different labels).
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//
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// There is a more involved use case, too: If you already have metrics
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// available, created outside of the Prometheus context, you don't need the
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// interface of the various Metric types. You essentially want to mirror the
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// existing numbers into Prometheus Metrics during collection. An own
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// implementation of the Collector interface is perfect for that. You can create
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// Metric instances “on the fly” using NewConstMetric, NewConstHistogram, and
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// NewConstSummary (and their respective Must… versions). NewConstMetric is used
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// for all metric types with just a float64 as their value: Counter, Gauge, and
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// a special “type” called Untyped. Use the latter if you are not sure if the
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// mirrored metric is a Counter or a Gauge. Creation of the Metric instance
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// happens in the Collect method. The Describe method has to return separate
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// Desc instances, representative of the “throw-away” metrics to be created
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// later. NewDesc comes in handy to create those Desc instances. Alternatively,
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// you could return no Desc at all, which will mark the Collector “unchecked”.
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// No checks are performed at registration time, but metric consistency will
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// still be ensured at scrape time, i.e. any inconsistencies will lead to scrape
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// errors. Thus, with unchecked Collectors, the responsibility to not collect
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// metrics that lead to inconsistencies in the total scrape result lies with the
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// implementer of the Collector. While this is not a desirable state, it is
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// sometimes necessary. The typical use case is a situation where the exact
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// metrics to be returned by a Collector cannot be predicted at registration
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// time, but the implementer has sufficient knowledge of the whole system to
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// guarantee metric consistency.
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//
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// The Collector example illustrates the use case. You can also look at the
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// source code of the processCollector (mirroring process metrics), the
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// goCollector (mirroring Go metrics), or the expvarCollector (mirroring expvar
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// metrics) as examples that are used in this package itself.
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//
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// If you just need to call a function to get a single float value to collect as
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// a metric, GaugeFunc, CounterFunc, or UntypedFunc might be interesting
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// shortcuts.
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//
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// # Advanced Uses of the Registry
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//
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// While MustRegister is the by far most common way of registering a Collector,
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// sometimes you might want to handle the errors the registration might cause.
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// As suggested by the name, MustRegister panics if an error occurs. With the
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// Register function, the error is returned and can be handled.
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//
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// An error is returned if the registered Collector is incompatible or
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// inconsistent with already registered metrics. The registry aims for
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// consistency of the collected metrics according to the Prometheus data model.
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// Inconsistencies are ideally detected at registration time, not at collect
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// time. The former will usually be detected at start-up time of a program,
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// while the latter will only happen at scrape time, possibly not even on the
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// first scrape if the inconsistency only becomes relevant later. That is the
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// main reason why a Collector and a Metric have to describe themselves to the
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// registry.
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//
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// So far, everything we did operated on the so-called default registry, as it
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// can be found in the global DefaultRegisterer variable. With NewRegistry, you
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// can create a custom registry, or you can even implement the Registerer or
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// Gatherer interfaces yourself. The methods Register and Unregister work in the
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// same way on a custom registry as the global functions Register and Unregister
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// on the default registry.
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//
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// There are a number of uses for custom registries: You can use registries with
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// special properties, see NewPedanticRegistry. You can avoid global state, as
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// it is imposed by the DefaultRegisterer. You can use multiple registries at
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// the same time to expose different metrics in different ways. You can use
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// separate registries for testing purposes.
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//
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// Also note that the DefaultRegisterer comes registered with a Collector for Go
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// runtime metrics (via NewGoCollector) and a Collector for process metrics (via
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// NewProcessCollector). With a custom registry, you are in control and decide
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// yourself about the Collectors to register.
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//
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// # HTTP Exposition
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//
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// The Registry implements the Gatherer interface. The caller of the Gather
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// method can then expose the gathered metrics in some way. Usually, the metrics
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// are served via HTTP on the /metrics endpoint. That's happening in the example
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// above. The tools to expose metrics via HTTP are in the promhttp sub-package.
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//
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// # Pushing to the Pushgateway
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//
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// Function for pushing to the Pushgateway can be found in the push sub-package.
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//
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// # Graphite Bridge
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//
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// Functions and examples to push metrics from a Gatherer to Graphite can be
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// found in the graphite sub-package.
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//
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// # Other Means of Exposition
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//
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// More ways of exposing metrics can easily be added by following the approaches
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// of the existing implementations.
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package prometheus
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