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+PLEASE NOTE: This document applies to the HEAD of the source tree
+
+If you are using a released version of Kubernetes, you should
+refer to the docs that go with that version.
+
+Documentation for other releases can be found at
+[releases.k8s.io](http://releases.k8s.io).
+
+--
+
+
+
+
+
+# Kubernetes monitoring architecture
+
+## Executive Summary
+
+Monitoring is split into two pipelines:
+
+* A **core metrics pipeline** consisting of Kubelet, a resource estimator, a slimmed-down
+Heapster called metrics-server, and the API server serving the master metrics API. These
+metrics are used by core system components, such as scheduling logic (e.g. scheduler and
+horizontal pod autoscaling based on system metrics) and simple out-of-the-box UI components
+(e.g. `kubectl top`). This pipeline is not intended for integration with third-party
+monitoring systems.
+* A **monitoring pipeline** used for collecting various metrics from the system and exposing
+them to end-users, as well as to the Horizontal Pod Autoscaler (for custom metrics) and Infrastore
+via adapters. Users can choose from many monitoring system vendors, or run none at all. In
+open-source, Kubernetes will not ship with a monitoring pipeline, but third-party options
+will be easy to install. We expect that such pipelines will typically consist of a per-node
+agent and a cluster-level aggregator.
+
+The architecture is illustrated in the diagram in the Appendix of this doc.
+
+## Introduction and Objectives
+
+This document proposes a high-level monitoring architecture for Kubernetes. It covers
+a subset of the issues mentioned in the “Kubernetes Monitoring Architecture” doc,
+specifically focusing on an architecture (components and their interactions) that
+hopefully meets the numerous requirements. We do not specify any particular timeframe
+for implementing this architecture, nor any particular roadmap for getting there.
+
+### Terminology
+
+There are two types of metrics, system metrics and service metrics. System metrics are
+generic metrics that are generally available from every entity that is monitored (e.g.
+usage of CPU and memory by container and node). Service metrics are explicitly defined
+in application code and exported (e.g. number of 500s served by the API server). Both
+system metrics and service metrics can originate from users’ containers or from system
+infrastructure components (master components like the API server, addon pods running on
+the master, and addon pods running on user nodes).
+
+We divide system metrics into
+
+* *core metrics*, which are metrics that Kubernetes understands and uses for operation
+of its internal components and core utilities -- for example, metrics used for scheduling
+(including the inputs to the algorithms for resource estimation, initial resources/vertical
+autoscaling, cluster autoscaling, and horizontal pod autoscaling excluding custom metrics),
+the kube dashboard, and “kubectl top.” As of now this would consist of cpu cumulative usage,
+memory instantaneous usage, disk usage of pods, disk usage of containers
+* *non-core metrics*, which are not interpreted by Kubernetes; we generally assume they
+include the core metrics (though not necessarily in a format Kubernetes understands) plus
+additional metrics.
+
+Service metrics can be divided into those produced by Kubernetes infrastructure components
+(and thus useful for operation of the Kubernetes cluster) and those produced by user applications.
+Service metrics used as input to horizontal pod autoscaling are sometimes called custom metrics.
+Of course horizontal pod autoscaling also uses core metrics.
+
+We consider logging to be separate from monitoring, so logging is outside the scope of
+this doc.
+
+### Requirements
+
+The monitoring architecture should
+
+* include a solution that is part of core Kubernetes and
+ * makes core system metrics about nodes, pods, and containers available via a standard
+ master API (today the master metrics API), such that core Kubernetes features do not
+ depend on non-core components
+ * requires Kubelet to only export a limited set of metrics, namely those required for
+ core Kubernetes components to correctly operate (this is related to #18770)
+ * can scale up to at least 5000 nodes
+ * is small enough that we can require that all of its components be running in all deployment
+ configurations
+* include an out-of-the-box solution that can serve historical data, e.g. to support Initial
+Resources and vertical pod autoscaling as well as cluster analytics queries, that depends
+only on core Kubernetes
+* allow for third-party monitoring solutions that are not part of core Kubernetes and can
+be integrated with components like Horizontal Pod Autoscaler that require service metrics
+
+## Architecture
+
+We divide our description of the long-term architecture plan into the core metrics pipeline
+and the monitoring pipeline. For each, it is necessary to think about how to deal with each
+type of metric (core metrics, non-core metrics, and service metrics) from both the master
+and minions.
+
+### Core metrics pipeline
+
+The core metrics pipeline collects a set of core system metrics. There are two sources for
+these metrics
+
+* Kubelet, providing per-node/pod/container usage information (the current cAdvisor that
+is part of Kubelet will be slimmed down to provide only core system metrics)
+* a resource estimator that runs as a DaemonSet and turns raw usage values scraped from
+Kubelet into resource estimates (values used by scheduler for a more advanced usage-based
+scheduler)
+
+These sources are scraped by a component we call *metrics-server* which is like a slimmed-down
+version of today's Heapster. metrics-server stores locally only latest values and has no sinks.
+metrics-server exposes the master metrics API. (The configuration described here is similar
+to the current Heapster in “standalone” mode.)
+[Discovery summarizer](../../docs/proposals/federated-api-servers.md)
+makes the master metrics API available to external clients such that from the client’s perspective
+it looks the same as talking to the API server.
+
+Core (system) metrics are handled as described above in all deployment environments. The only
+easily replaceable part is resource estimator, which could be replaced by power users. In
+theory, metric-server itself can also be substituted, but it’d be similar to substituting
+apiserver itself or controller-manager - possible, but not recommended and not supported.
+
+Eventually the core metrics pipeline might also collect metrics from Kubelet and Docker daemon
+themselves (e.g. CPU usage of Kubelet), even though they do not run in containers.
+
+The core metrics pipeline is intentionally small and not designed for third-party integrations.
+“Full-fledged” monitoring is left to third-party systems, which provide the monitoring pipeline
+(see next section) and can run on Kubernetes without having to make changes to upstream components.
+In this way we can remove the burden we have today that comes with maintaining Heapster as the
+integration point for every possible metrics source, sink, and feature.
+
+#### Infrastore
+
+We will build an open-source Infrastore component (most likely reusing existing technologies)
+for serving historical queries over core system metrics and events, which it will fetch from
+the master APIs. Infrastore will expose one or more APIs (possibly just SQL-like queries --
+this is TBD) to handle the following use cases
+
+* initial resources
+* vertical autoscaling
+* oldtimer API
+* decision-support queries for debugging, capacity planning, etc.
+* usage graphs in the [Kubernetes Dashboard](https://github.com/kubernetes/dashboard)
+
+In addition, it may collect monitoring metrics and service metrics (at least from Kubernetes
+infrastructure containers), described in the upcoming sections.
+
+### Monitoring pipeline
+
+One of the goals of building a dedicated metrics pipeline for core metrics, as described in the
+previous section, is to allow for a separate monitoring pipeline that can be very flexible
+because core Kubernetes components do not need to rely on it. By default we will not provide
+one, but we will provide an easy way to install one (using a single command, most likely using
+Helm). We described the monitoring pipeline in this section.
+
+Data collected by the monitoring pipeline may contain any sub- or superset of the following groups
+of metrics:
+
+* core system metrics
+* non-core system metrics
+* service metrics from user application containers
+* service metrics from Kubernetes infrastructure containers; these metrics are exposed using
+Prometheus instrumentation
+
+It is up to the monitoring solution to decide which of these are collected.
+
+In order to enable horizontal pod autoscaling based on custom metrics, the provider of the
+monitoring pipeline would also have to create a stateless API adapter that pulls the custom
+metrics from the monitoring pipeline and exposes them to the Horizontal Pod Autoscaler. Such
+API will be a well defined, versioned API similar to regular APIs. Details of how it will be
+exposed or discovered will be covered in a detailed design doc for this component.
+
+The same approach applies if it is desired to make monitoring pipeline metrics available in
+Infrastore. These adapters could be standalone components, libraries, or part of the monitoring
+solution itself.
+
+There are many possible combinations of node and cluster-level agents that could comprise a
+monitoring pipeline, including
+cAdvisor + Heapster + InfluxDB (or any other sink)
+* cAdvisor + collectd + Heapster
+* cAdvisor + Prometheus
+* snapd + Heapster
+* snapd + SNAP cluster-level agent
+* Sysdig
+
+As an example we’ll describe a potential integration with cAdvisor + Prometheus.
+
+Prometheus has the following metric sources on a node:
+* core and non-core system metrics from cAdvisor
+* service metrics exposed by containers via HTTP handler in Prometheus format
+* [optional] metrics about node itself from Node Exporter (a Prometheus component)
+
+All of them are polled by the Prometheus cluster-level agent. We can use the Prometheus
+cluster-level agent as a source for horizontal pod autoscaling custom metrics by using a
+standalone API adapter that proxies/translates between the Prometheus Query Language endpoint
+on the Prometheus cluster-level agent and an HPA-specific API. Likewise an adapter can be
+used to make the metrics from the monitoring pipeline available in Infrastore. Neither
+adapter is necessary if the user does not need the corresponding feature.
+
+The command that installs cAdvisor+Prometheus should also automatically set up collection
+of the metrics from infrastructure containers. This is possible because the names of the
+infrastructure containers and metrics of interest are part of the Kubernetes control plane
+configuration itself, and because the infrastructure containers export their metrics in
+Prometheus format.
+
+## Appendix: Architecture diagram
+
+### Open-source monitoring pipeline
+
+
+
+
+
+
+[]()
+
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