From fb099b3653e4775742d74b869f989e4e0eca9335 Mon Sep 17 00:00:00 2001 From: Reese Lee Date: Wed, 4 Sep 2024 02:17:16 -0700 Subject: [PATCH] Create blog post about Prometheus and OpenTelemetry (#4119) Signed-off-by: svrnm Co-authored-by: Fabrizio Ferri-Benedetti Co-authored-by: Arve Knudsen Co-authored-by: Cijo Thomas Co-authored-by: Goutham Veeramachaneni Co-authored-by: Jacob Aronoff Co-authored-by: Adriana Villela <50256412+avillela@users.noreply.github.com> Co-authored-by: Severin Neumann Co-authored-by: Patrice Chalin Co-authored-by: opentelemetrybot <107717825+opentelemetrybot@users.noreply.github.com> --- .../OTel-collector-refresher.png | Bin 0 -> 448692 bytes .../prom-and-otel/Prom-and-otel-logos.png | Bin 0 -> 2287017 bytes content/en/blog/2024/prom-and-otel/index.md | 683 ++++++++++++++++++ .../prom-and-otel/target-allocator-promcr.png | Bin 0 -> 190917 bytes .../target-allocator-sharding.png | Bin 0 -> 112239 bytes static/refcache.json | 74 +- 6 files changed, 756 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z3w-2hcFMz#F*ixilI6S}Ca=AD7r*g&mxa?$eoQt$ zeDx^bKfgC&+=-V{1$H( z<70y?)O$z$BMY+GrT-Ns-%LP$4!T05s}Jhl(vrRBIs+~vUeY_1@e44im1pTP?{uza5%f_2Pd z_U4u6rl|=|^rth5TY1f)uFAmq+Mp^mNCkt!LBYSRy1-BS5 zaegM2?8uk+B0D0fU+=5Q5;y<=1KUYNK~y{dd3I1yyB&Sv*>6SEMcBvk>7Q5yXXq-8PkUk*K8ne-gKUJxylOpuWmp>9oey`AwOL$@nA#zVX3EaFeb1 zz6v3`C%yjQRTk^#)i+u62yx3^5pEXj`HX;(GY^2N$vK+vw>xg5vj|iu>mR zI;vkfp5`eJNNeVg@?LLY2|#ln11{hPhwG^yzUUv4vDt9S#(hN(;8ZtvjXWiD_yIF> z(6lA*1D@O0z~x_M!4H_hP}s(W*n)6axX7vvx;VPDn|gRRm(!lVDFa8moyRmr=em70 zpXNJw(YA0QP%3G=KCh2e!qSLuL_UZ9h*oJF_*MibEd8Hy{FiY&_nbHjwz5rSV?lGQ z!dx^meCP+$pgMuPb2T{1k63^g1&3z7f~h{MGYIC;xAD^Xe&EGex~VO~?#A2Afgjp& z>}y_@K9ktAuyjgi#G%U`WC&MC`e#y(QZ~Pm$rQ4X9ez|iNxb=5^ zn&q0G9s1euY=1_+rQ;71rw*?XV5 z-?&k34C5pD9k=Al^YpHbMi!nkHrWuACrqC`O+K5AFOL7@X@TGJB=Rqb=bxgp$9a8= zSbm*NE^m@gzsb|NpOXLHWIpl3k3U_#%9E_#S+C3uT5#{>`|+tTkTbfBzB@llSvTiI zIgMMXC%ab@0mbu-hxroOTpN=jYW2$Q-M|%o#HPkz8==Ct*Mqni#i*c#0&eesF#+u-Dvjh&g se4l)dt@xQ}QQM?ttU;LORCeb77jwe1yw#%Q)&Kwi07*qoM6N<$f-S- + [Reese Lee](https://github.com/reese-lee) (New Relic), [Adriana + Villela](https://github.com/avillela) (ServiceNow) +cSpell:ignore: hashmod kubelet sharded targetallocator +canonical_url: https://newrelic.com/blog/how-to-relic/prometheus-and-opentelemetry-better-together +--- + +![Image of a Greek god holding a torch with the Prometheus logo, and OTel logo](Prom-and-otel-logos.png) + +Tools such as [Prometheus](https://prometheus.io/) and +[OpenTelemetry](/docs/what-is-opentelemetry/) help us monitor the health, +performance, and availability of our complex distributed systems. Both are open +source projects under the +[Cloud Native Computing Foundation (CNCF)](https://www.cncf.io/) umbrella – but +what role does each play in observability? + +OpenTelemetry (OTel for short), is a vendor-neutral open standard for +instrumenting, generating, collecting, and exporting telemetry data. Prometheus +is a fixture of the observability landscape, widely relied upon for monitoring +and alerting within organizations. + +While both Prometheus and OTel emit metrics, there is a lot to cover on the +differences and similarities, and is out of scope for this article. Rather, we +want to show you how OTel supports Prometheus, specifically in a Kubernetes +environment. You'll learn: + +- How the OTel Collector's + [Prometheus Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/prometheusreceiver) + can be used to ingest Prometheus metrics. +- Alternative methods for Prometheus metric collection through OTel-native + options such as the K8s cluster receiver and Kubelet stats receiver. + +We'll also do a technical dive into the OTel Operator's Target Allocator (TA) +and learn: + +- How it can be used for Prometheus service discovery. +- How it ensures even distribution of Prometheus targets. + +## OTel and Prometheus + +Since OTel is primarily focused on the instrumentation part of observability, it +doesn't provide a backend for storing telemetry; you have to forward the data to +a backend vendor for storage, alerting, and querying. + +Prometheus, on the other hand, provides a time-series data store you can use for +your metrics, in addition to instrumentation clients. You can view graphs and +charts, set up alerts, and query your data via their web user interface. It also +encompasses a data format, known as +[Prometheus text-based exposition format](https://prometheus.io/docs/instrumenting/exposition_formats/#exposition-formats). + +Prometheus _data_ is stored as a dimensional time-series, meaning that the data +has attributes (for example, labels or dimensions) and a timestamp. + +The Prometheus _server_ collects Prometheus metrics data from targets defined in +a configuration file. A target is an endpoint that exposes metrics for the +Prometheus server to store. + +Prometheus is so ubiquitous in the monitoring space that many tools natively +emit metrics in Prometheus format, including +[Kubernetes](https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/) +and +[HashiCorp's Nomad](https://developer.hashicorp.com/nomad/docs/operations/monitoring-nomad). +And for those that don’t, there are a number of vendor- and community-built +[Prometheus exporters](https://prometheus.io/docs/instrumenting/exporters/) to +aggregate and import data into Prometheus. + +While you can use Prometheus to monitor a variety of infrastructure and +application metrics, one of its most popular use cases is to monitor Kubernetes. +This is the aspect of Prometheus monitoring that we will focus on in this +article. + +## Prometheus metrics with OpenTelemetry + +In this section, you'll learn about a couple of OTel Collector components that +demonstrate the interoperability between OTel and Prometheus. + +First, let’s do a quick refresher on the [Collector](/docs/collector/) – it’s an +OTel component that can be used to collect telemetry from multiple sources and +export data to multiple destinations. The Collector also handles telemetry +processing, such as modifying data attributes and scrubbing personally +identifiable information. For example, you can use Prometheus SDKs to generate +metrics, ingest them with the Collector, do some processing (if desired) and +then forward them to your chosen backend. + +![Diagram showing the different components of the OTel Collector](OTel-collector-refresher.png) + +The +[Prometheus receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/prometheusreceiver) +allows you to collect metrics from any software that exposes Prometheus metrics. +It serves as a drop-in replacement for Prometheus to scrape your services, and +supports the +[full set](https://github.com/prometheus/prometheus/blob/v2.28.1/docs/configuration/configuration.md#scrape_config) +of configurations in `scrape_config`. + +If you are interested in +[exemplars](/docs/specs/otel/metrics/data-model/#exemplars), which is a recorded +value that associates OTel context with a metric event, you can also use the +Prometheus receiver. Note that exemplars are currently only available in +[OpenMetrics](/docs/specs/otel/compatibility/prometheus_and_openmetrics/) +format. + +Something to consider with this component is that it is under active +development; as such, it has several +[limitations](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/receiver/prometheusreceiver/README.md#%EF%B8%8F-warning), +including that it’s a stateful component. Additionally, it is not recommended to +use this component when multiple replicas of the Collector are run _without the +target allocator_, because in this state: + +- The Collector is unable to auto-scale the scraping +- If the replicas are running with the same config, it will scrape the targets + multiple times +- You will need to configure each replica with a different scraping config if + you want to manually shard the scraping + +For exporting metrics from the OTel Collector to Prometheus, you have the +following options: the +[Prometheus exporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/exporter/prometheusexporter#prometheus-exporter), +and the +[Prometheus Remote Write exporter](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/exporter/prometheusremotewriteexporter/README.md). +You can also use the +[OTLP HTTP exporter](https://github.com/open-telemetry/opentelemetry-collector/tree/main/exporter/otlphttpexporter), +which comes with the Collector by default, and use Prometheus' native OTLP +endpoint. Note that +[Prometheus also now supports OTLP natively](https://prometheus.io/blog/2024/03/14/commitment-to-opentelemetry/). + +The Prometheus exporter allows you to ship data in the Prometheus format, which +is then scraped by a Prometheus server. It's used to report metrics via the +Prometheus scrape HTTP endpoint. You can learn more by trying out this +[example](https://github.com/open-telemetry/opentelemetry-go/tree/main/example/prometheus). +However, the scraping won't really scale, as all the metrics are sent in a +single scrape. + +To get around the scaling concern, you can alternatively use the Prometheus +Remote Write exporter, which allows you to push data to Prometheus from multiple +Collector instances with no issues. Since Prometheus also accepts remote write +ingestion, you can also use this exporter if you are generating OTel metrics and +want to ship them to a backend that is compatible with Prometheus remote write. + +Note that the Prometheus Remote Write in the Prometheus Server doesn't currently +support metadata, such as Help and Type. For more information, check out +[issue #13163](https://github.com/prometheus/prometheus/issues/13163) as well as +[issue #12608](https://github.com/prometheus/prometheus/issues/12608). This will +be addressed in +[Prometheus Remote Write v2.0](https://prometheus.io/docs/specs/remote_write_spec_2_0/#io-prometheus-write-v2-request). + +To learn more about the architecture of both exporters, see +[Use Prometheus Remote Write exporter](https://grafana.com/blog/2023/07/20/a-practical-guide-to-data-collection-with-opentelemetry-and-prometheus/#6-use-prometheus-remote-write-exporter). + +## Using the Target Allocator + +Scalability is a common challenge with Prometheus; that's the ability to +effectively maintain performance and resource allocation while managing an +increasing number of monitored targets and metrics. One option to help with this +is sharding the workload based on labels or dimensions, +[which means using multiple Prometheus instances to handle your metrics according to specific parameters](https://medium.com/wish-engineering/horizontally-scaling-prometheus-at-wish-ea4b694318dd). +This could help decrease the burden on individual instances. However, there are +two things to consider with this approach. + +The first is that to get around querying sharded instances, you need a +management instance; this means that you need to have N+1 Prometheus instances, +where the +1’s memory is equal to N, thereby doubling your memory requests. +Secondly, Prometheus sharding requires that each instance scrape the target, +even if it’s going to be dropped. + +Something to note is that if you can have a Prometheus instance with the +combined amount of memory of individual instances, there is not much benefit to +sharding, since you can scrape everything directly using the larger instance. A +reason that people shard is usually for some amount of fault tolerance. For +example, if one Prometheus instance is out of memory (OOM), then your entire +alerting pipeline won't be offline. + +Luckily, the OTel Operator’s Target Allocator (TA) is able to help with some of +this. For instance, it can automatically drop any targets it knows won’t be +scraped. The TA also automatically shards the targets for you, whereas if you +shard with `hashmod` you'll need to +[update your config based on the number of replicas you have](https://www.robustperception.io/scaling-and-federating-prometheus/). +The TA also allows you to continue using resources like PodMonitor and +ServiceMonitor to continue collecting Prometheus metrics about your Kubernetes +infrastructure. + +The Target Allocator is part of the OTel Operator. The +[OTel Operator](https://github.com/open-telemetry/opentelemetry-operator) is a +[Kubernetes Operator](https://kubernetes.io/docs/concepts/extend-kubernetes/operator/) +that: + +- Manages the [OpenTelemetry Collector](/docs/collector/) +- Injects and configures + [auto-instrumentation](https://www.honeycomb.io/blog/what-is-auto-instrumentation) + into your pods + +In fact, the Operator creates two new +[custom resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) +(CR) types in Kubernetes to support this functionality: the +[OpenTelemetry Collector CR](https://github.com/open-telemetry/opentelemetry-operator#getting-started), +and the +[Auto-Instrumentation CR](https://github.com/open-telemetry/opentelemetry-operator#opentelemetry-auto-instrumentation-injection). + +Today, we will be focusing on the Target Allocator. The TA is an optional +component of the Operator’s OTel Collector management capabilities. + +In a nutshell, the Target Allocator is a mechanism for decoupling the service +discovery and metric collection functions of Prometheus in a way that allows +them to be scaled independently. The OTel Collector manages Prometheus Metrics +without needing to install Prometheus. The TA manages the configuration of the +Collector’s Prometheus Receiver. + +The Target Allocator serves two functions: + +- Even distribution of Prometheus targets among a pool of OTel Collectors +- Discovery of Prometheus custom resources + +Let’s dig into each of these. + +### Even distribution of Prometheus targets + +The Target Allocator’s first job is to discover targets to scrape and OTel +Collectors to allocate targets to. It does so as follows: + +1. The Target Allocator finds all of the the metrics targets to scrape +2. The Target Allocator finds all of the available Collectors +3. The Target Allocator determines which Collectors scrape which metrics +4. The Collectors query the Target Allocator to find out what metrics to scrape +5. The Collectors scrape their assigned targets + +This means that the OTel collectors -- not a Prometheus scraper -- collect the +metrics. + +A **Target** is an endpoint that supplies Metrics for Prometheus to store. A +**Scrape** is the action of collecting Metrics through an HTTP request from a +targeted instance, parsing the response, and ingesting the collected samples to +storage. + +```mermaid +sequenceDiagram + participant Target Allocator + participant Metrics Targets + participant OTel Collectors + Target Allocator ->>Metrics Targets: 1. Discover Metrics targets + Target Allocator ->>OTel Collectors: 2. Discover available Collectors + Target Allocator ->>Target Allocator: 3. Assign Metrics targets + OTel Collectors ->>Target Allocator: 4. Query TA for Metrics endpoints scrape + OTel Collectors ->>Metrics Targets: 5. Scrape Metrics target +``` + +### Discovery of Prometheus custom resources + +The Target Allocator’s second job is to provide the discovery of Prometheus +Operator CRs, namely the +[ServiceMonitor and PodMonitor](https://github.com/open-telemetry/opentelemetry-operator/tree/main/cmd/otel-allocator#target-allocator). + +In the past, all Prometheus scrape configurations had to be done via the +Prometheus Receiver. When the Target Allocator’s service discovery feature is +enabled, the TA simplifies the configuration of the Prometheus receiver, by +creating scrape configurations in the Prometheus receiver from the PodMonitor +and ServiceMonitor instances deployed in your cluster. + +```mermaid +flowchart RL + pm(PodMonitor) + sm(ServiceMonitor) + ta(Target Allocator) + oc1(OTel Collector) + oc2(OTel Collector) + oc3(OTel Collector) + ta --> pm + ta --> sm + oc1 --> ta + oc2 --> ta + oc3 --> ta + sm ~~~|"1. Discover Prometheus Operator CRs"| sm + ta ~~~|"2. Add job to TA scrape configuration"| ta + oc3 ~~~|"3. Add job to OTel Collector scrape configuration"| oc3 +``` + +Even though Prometheus is not required to be installed in your Kubernetes +cluster to use the Target Allocator for Prometheus CR discovery, the TA does +require that the ServiceMonitor and PodMonitor be installed. These CRs are +bundled with Prometheus Operator; however, they can be installed standalone as +well. The easiest way to do this is to grab a copy of the individual +[PodMonitor YAML](https://github.com/prometheus-community/helm-charts/blob/main/charts/kube-prometheus-stack/charts/crds/crds/crd-podmonitors.yaml) +and +[ServiceMonitor YAML](https://github.com/prometheus-community/helm-charts/blob/main/charts/kube-prometheus-stack/charts/crds/crds/crd-servicemonitors.yaml) +custom resource definitions (CRDs). + +OTel supports the PodMonitor and ServiceMonitor Prometheus resources because +these are widely-used in Kubernetes infrastructure monitoring. As a result, the +OTel Operator developers wanted to make it easy to add them to the OTel +ecosystem. + +PodMonitor and ServiceMonitor are limited to collecting metrics from pods, and +are unable to scrape other endpoints, such as the kubelet. In that case, you +still have to rely on Prometheus scrape configs in the Collector’s +[Prometheus Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/blob/main/receiver/prometheusreceiver/README.md). + +### Configuration + +The following is the YAML config for the OTel Collector CR. Note that this +Collector is running in a namespace called `opentelemetry`, but it can run in +whatever namespace you like. + +The main components are: + +- **mode:** This is one of four + [OTel Collector deployment modes supported by the Operator](https://github.com/open-telemetry/opentelemetry-operator?tab=readme-ov-file#deployment-modes): + Sidecar, Deployment, StatefulSet and DaemonSet. +- **targetallocator:** This is where you configure the Target Allocator. Note + that the + [Target Allocator only works for the Deployment, DaemonSet, and StatefulSet modes](https://www.youtube.com/watch?v=Uwq4EPaMJFM). +- **config:** This is where you configure the OTel Collector’s config YAML. + +```yaml +apiVersion: opentelemetry.io/v1alpha1 +kind: OpenTelemetryCollector +metadata: + name: otelcol + namespace: opentelemetry +spec: + mode: statefulset + targetAllocator: + enabled: true + serviceAccount: opentelemetry-targetallocator-sa + prometheusCR: + enabled: true + config: | + receivers: + otlp: + protocols: + grpc: + http: + prometheus: + config: + scrape_configs: + - job_name: 'otel-collector' + scrape_interval: 30s + static_configs: + - targets: [ '0.0.0.0:8888' ] + target_allocator: + endpoint: http://otelcol-targetallocator + interval: 30s + collector_id: "${POD_NAME}" +… +``` + +To use the Target Allocator, you need to set `spec.targetallocator.enabled` to +`true`. (See previous note about supported modes.) + +Next, you need to make sure that the Prometheus receiver of the deployed +Collector is made aware of the Target Allocator in the Collector config section +of the spec by setting the `target_allocator.endpoint`: + +```yaml +receivers: + prometheus: + config: + scrape_configs: + - job_name: 'otel-collector' + scrape_interval: 30s + static_configs: + - targets: ['0.0.0.0:8888'] + target_allocator: + endpoint: http://otelcol-targetallocator + interval: 30s + collector_id: '${POD_NAME}' +``` + +The Target Allocator endpoint that the Prometheus receiver config is pointing to +is a concatenation of the OTel Collector’s name (`otelcol`, in our case) and the +`-targetallocator` suffix. + +To use the Prometheus service discovery functionality, you’ll need to enable it +by setting `spec.targetallocator.prometheusCR.enabled` to `true`. + +Finally, if you want to enable the Prometheus CR functionality of the Target +Allocator, you’ll need to define your own ServiceMonitor and PodMonitor +instances. The following is a sample ServiceMonitor definition that says, find +me a service with the label `app: my-app`, with an endpoint that’s a port named +`prom`, and scrape it every 15 seconds. + +```yaml +apiVersion: monitoring.coreos.com/v1 +kind: ServiceMonitor +metadata: + name: sm-example + namespace: opentelemetry + labels: + app.kubernetes.io/name: py-prometheus-app + release: prometheus +spec: + selector: + matchLabels: + app: my-app + namespaceSelector: + matchNames: + - opentelemetry + endpoints: + - port: prom + interval: 15s +``` + +The corresponding `Service` definition, which is just a standard +[Kubernetes Service](https://kubernetes.io/docs/concepts/services-networking/service/) +definition, is as follows: + +```yaml +apiVersion: v1 +kind: Service +metadata: + name: py-prometheus-app + namespace: opentelemetry + labels: + app: my-app + app.kubernetes.io/name: py-prometheus-app +spec: + selector: + app: my-app + app.kubernetes.io/name: py-prometheus-app + ports: + - name: prom + port: 8080 +``` + +Because the `Service` has a label called `app: my-app` and a port named `prom`, +it will get picked up by the ServiceMonitor. + +You can either create separate ServiceMonitors for each service you wish to +monitor, or create a single ServiceMonitor to encompass all of your services. +The same applies for the PodMonitor. + +Before the Target Allocator can start scraping, you need to set up Kubernetes +role-based access controls (RBAC). This means that you need to have a +[ServiceAccount](https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/) +and corresponding cluster roles so that the Target Allocator has access to all +of the necessary resources to pull metrics from. + +You can create your own ServiceAccount, and reference it as +`spec.targetAllocator.serviceAccount` in the OTel Collector CR. You’ll then need +to configure the +[ClusterRole](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-and-clusterrole) +and +[ClusterRoleBinding](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#rolebinding-and-clusterrolebinding) +for this service account. + +If you omit the ServiceAccount configuration, the Target Allocator creates a +ServiceAccount automatically for you. The ServiceAccount’s default name is a +concatenation of the Collector name and the `-collector` suffix. By default, +this ServiceAccount has no defined policy, so you’ll need to create your own +[ClusterRole](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-and-clusterrole) +and +[ClusterRoleBinding](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#rolebinding-and-clusterrolebinding). + +The following is an example RBAC configuration taken from the +[OTel Target Allocator readme](https://github.com/open-telemetry/opentelemetry-operator/tree/main/cmd/otel-allocator#rbac). +It includes the ServiceAccount, ClusterRole, and ClusterRoleBinding +configurations: + +```yaml +apiVersion: v1 +kind: ServiceAccount +metadata: + name: opentelemetry-targetallocator-sa + namespace: opentelemetry +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + name: opentelemetry-targetallocator-role +rules: + - apiGroups: + - monitoring.coreos.com + resources: + - servicemonitors + - podmonitors + verbs: + - '*' + - apiGroups: [''] + resources: + - namespaces + verbs: ['get', 'list', 'watch'] + - apiGroups: [''] + resources: + - nodes + - nodes/metrics + - services + - endpoints + - pods + verbs: ['get', 'list', 'watch'] + - apiGroups: [''] + resources: + - configmaps + verbs: ['get'] + - apiGroups: + - discovery.k8s.io + resources: + - endpointslices + verbs: ['get', 'list', 'watch'] + - apiGroups: + - networking.k8s.io + resources: + - ingresses + verbs: ['get', 'list', 'watch'] + - nonResourceURLs: ['/metrics'] + verbs: ['get'] +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: opentelemetry-targetallocator-rb +subjects: + - kind: ServiceAccount + name: opentelemetry-targetallocator-sa + namespace: opentelemetry +roleRef: + kind: ClusterRole + name: opentelemetry-targetallocator-role + apiGroup: rbac.authorization.k8s.io +``` + +Zooming in a bit on the previous +[ClusterRole](https://kubernetes.io/docs/reference/access-authn-authz/rbac/#role-and-clusterrole), +the following rules will provide the minimum access required for the Target +Allocator to query all the targets it needs based on any Prometheus +configurations: + +```yaml +- apiGroups: [''] + resources: + - nodes + - nodes/metrics + - services + - endpoints + - pods + verbs: ['get', 'list', 'watch'] +- apiGroups: [''] + resources: + - configmaps + verbs: ['get'] +- apiGroups: + - discovery.k8s.io + resources: + - endpointslices + verbs: ['get', 'list', 'watch'] +- apiGroups: + - networking.k8s.io + resources: + - ingresses + verbs: ['get', 'list', 'watch'] +- nonResourceURLs: ['/metrics'] + verbs: ['get'] +``` + +If you enable the `prometheusCR` (set +`spec.targetAllocator.prometheusCR.enabled` to `true`) in the +OpenTelemetryCollector CR, you will also need to define the following roles. +These give the Target Allocator access to the PodMonitor and ServiceMonitor CRs. +It also gives namespace access to the PodMonitor and ServiceMonitor. + +```yaml +- apiGroups: + - monitoring.coreos.com + resources: + - servicemonitors + - podmonitors + verbs: + - '*' +- apiGroups: [''] + resources: + - namespaces + verbs: ['get', 'list', 'watch'] +``` + +## Additional OTel components for Kubernetes + +This section covers additional OTel Collector components you can use to capture +Kubernetes metrics. + +Receiving data: + +- [Kubernetes Cluster Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/k8sclusterreceiver): + collects cluster-level metrics and entity events from the + [Kubernetes API server](https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/) +- [Kubernetes Objects Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/k8sobjectsreceiver): + collects (pull/watch) objects from the Kubernetes API server +- [Kubelet Stats Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/kubeletstatsreceiver): + pulls metrics from the + [Kubelet](https://kubernetes.io/docs/reference/command-line-tools-reference/kubelet/) + and sends it down the metric pipeline for further processing +- [Host Metrics Receiver](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/receiver/hostmetricsreceiver): + scrapes system metrics from hosts that make up the cluster + +Processing data: + +- [Kubernetes Attributes Processor](https://github.com/open-telemetry/opentelemetry-collector-contrib/tree/main/processor/k8sattributesprocessor): + adds Kubernetes context, thereby +- enabling you to correlate application telemetry with your Kubernetes telemetry +- – considered one of the most important components for monitoring Kubernetes +- with OpenTelemetry + +You can also use the Kubernetes attributes processor to set custom resource +attributes for traces, metrics, and logs using the Kubernetes labels and +annotations you’ve added to your pods and namespaces. + +There are a few more Collector components you can implement to monitor +Kubernetes, including Kubernetes-specific ones as well as general-use +processors, such as the batch, memory limiter, and resource processors. To learn +more, see +[Important Components for Kubernetes](/docs/kubernetes/collector/components/). + +After you’ve configured the components in your Collector config file, you need +to enable them within the [pipelines](/docs/collector/configuration/#pipelines) +section. A data pipeline enables you to +[collect](/docs/collector/configuration/#receivers), +[process](/docs/collector/configuration/#processors), and route data from any +source [to one destination or more](/docs/collector/configuration/#exporters). + +## Pros and cons + +The following are pros and cons of the setup we covered in this article. + +**Pros:** + +- Not having to maintain Prometheus as your data store, which means less + infrastructure overall to maintain -- especially if you go with an all-in-one + observability backend to ingest OTel data (traces, metrics, logs). +- While you would still have to maintain the ServiceMonitor and PodMonitor, it’s + a lot less work than keeping the Prometheus Operator up-to-date. +- Allows you to end up with a full OTel solution while still obtaining your + Prometheus metrics +- OTel can provide traces and logs in addition to metrics, as well as + correlation of these signals, thus enhancing the observability of Kubernetes + environments. +- OTel provides handy tools, such as the Target Allocator and OTel Collector + components, to provide flexibility for configuration and deployment options. + +**Cons:** + +- Adopting and managing a new observability tool involves a steep learning curve + for users unfamiliar with OTel concepts, components, and workflows. +- Users of PromQL, Prometheus’ powerful query language, can still use it **if** + they send metrics to a Prometheus-compatible backend. +- OTel itself contains many moving parts, and presents its own challenges with + scalability and adoption. +- Maturity and stability within OTel varies; Prometheus has a mature ecosystem. +- Additional computational and human resources needed to maintain OTel + components. +- Managing and maintaining both Prometheus and OTel components introduces + operational complexity in your monitoring infrastructure. + +## Conclusion + +Prometheus maintainers have also been further developing the interoperability +between the two projects from the Prometheus side to make it easier for it to be +the backend for OTLP metrics. For instance, Prometheus can now accept OTLP, and +soon, you’ll be able to use Prometheus exporters to export OTLP. So if you have +a service instrumented with a Prometheus SDK, you’ll be able to _push_ OTLP and +take advantage of the rich Prometheus exporter ecosystem for OTel users. The +maintainers are also working on adding support for delta temporality. This +[component](https://github.com/open-telemetry/opentelemetry-collector-contrib/issues/30479) +will aggregate delta samples to their respective cumulative counterparts. For +more about Prometheus' commitment to OTel, see +[Our commitment to OpenTelemetry](https://prometheus.io/blog/2024/03/14/commitment-to-opentelemetry/). + +However you decide to use OTel to gather Prometheus metrics, ultimately what is +right for your organization depends on your business needs. Using the OTel +components discussed previously, you could convert all your metrics into the +Prometheus format, or you could convert your Prometheus metrics into OTLP. +Although Prometheus itself was not built for long-term data storage and presents +scaling challenges, there are open source projects such as +[Mimir](https://grafana.com/oss/mimir/), +[Thanos](https://thanos.io/v0.10/thanos/getting-started.md/), and +[Cortex](https://cortexmetrics.io/docs/guides/running-cortex-on-kubernetes/) +that can help with these concerns. + +Whether or not you choose to implement these solutions in your organization, +it’s nice to know that there are additional options out there to lead you to +observability greatness with OTel and Prometheus. + +_A version of this article was +[originally posted](https://newrelic.com/blog/how-to-relic/prometheus-and-opentelemetry-better-together) +on the New Relic blog._ diff --git a/content/en/blog/2024/prom-and-otel/target-allocator-promcr.png 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"https://cortexmetrics.io/docs/guides/tracing/#opentelemetry": { "StatusCode": 206, "LastSeen": "2024-01-30T16:04:53.700061-05:00" @@ -815,6 +819,10 @@ "StatusCode": 206, "LastSeen": "2024-01-18T19:03:01.390387-05:00" }, + "https://developer.hashicorp.com/nomad/docs/operations/monitoring-nomad": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:10.832204893Z" + }, "https://developer.hashicorp.com/nomad/downloads": { "StatusCode": 206, "LastSeen": "2024-01-18T19:11:16.793463-05:00" @@ -3655,6 +3663,10 @@ "StatusCode": 200, "LastSeen": "2024-05-15T19:23:46.151980372+03:00" }, + "https://github.com/open-telemetry/opentelemetry-collector-contrib/issues/30479": { + "StatusCode": 200, + "LastSeen": "2024-06-24T18:06:37.991639153Z" + }, "https://github.com/open-telemetry/opentelemetry-collector-contrib/issues/31959": { "StatusCode": 200, "LastSeen": "2024-05-15T19:23:47.587898763+03:00" @@ -5095,6 +5107,14 @@ "StatusCode": 200, "LastSeen": "2024-01-18T19:37:11.409183-05:00" }, + "https://github.com/prometheus/prometheus/issues/12608": { + "StatusCode": 200, + "LastSeen": "2024-09-04T09:48:39.868883+02:00" + }, + "https://github.com/prometheus/prometheus/issues/13163": { + "StatusCode": 200, + "LastSeen": "2024-09-04T09:48:35.996317+02:00" + }, "https://github.com/purview-dev/purview-telemetry-sourcegenerator/": { "StatusCode": 200, "LastSeen": "2024-04-26T09:57:49.687508317+01:00" @@ -5507,6 +5527,10 @@ "StatusCode": 200, "LastSeen": "2024-08-15T15:44:21.083848+02:00" }, + "https://grafana.com/blog/2023/07/20/a-practical-guide-to-data-collection-with-opentelemetry-and-prometheus/#6-use-prometheus-remote-write-exporter": { + "StatusCode": 200, + "LastSeen": "2024-06-24T18:06:20.970906267Z" + }, "https://grafana.com/docs/agent/latest/": { "StatusCode": 200, "LastSeen": "2024-01-24T14:54:56.149229+01:00" @@ -5535,6 +5559,10 @@ "StatusCode": 200, "LastSeen": "2024-05-21T06:04:46.742171356Z" }, + "https://grafana.com/oss/mimir/": { + "StatusCode": 200, + "LastSeen": "2024-06-24T18:06:40.436832671Z" + }, "https://grafana.com/oss/opentelemetry/": { "StatusCode": 200, "LastSeen": "2024-08-09T10:43:44.997369-04:00" @@ -5907,6 +5935,10 @@ "StatusCode": 206, "LastSeen": "2024-01-30T06:02:31.465356-05:00" }, + "https://kubernetes.io/docs/concepts/cluster-administration/system-metrics/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:09.622137396Z" + }, "https://kubernetes.io/docs/concepts/cluster-administration/system-traces/": { "StatusCode": 206, "LastSeen": "2024-08-09T10:44:30.895853-04:00" @@ -5987,6 +6019,14 @@ "StatusCode": 206, "LastSeen": "2024-04-25T00:01:11.609121-04:00" }, + "https://kubernetes.io/docs/reference/command-line-tools-reference/kube-apiserver/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:29.521450883Z" + }, + "https://kubernetes.io/docs/reference/command-line-tools-reference/kubelet/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:32.862427557Z" + }, "https://kubernetes.io/docs/reference/kubectl/": { "StatusCode": 206, "LastSeen": "2024-01-30T06:02:57.628168-05:00" @@ -6327,6 +6367,10 @@ "StatusCode": 200, "LastSeen": "2024-08-09T10:46:36.774164-04:00" }, + "https://medium.com/wish-engineering/horizontally-scaling-prometheus-at-wish-ea4b694318dd": { + "StatusCode": 200, + "LastSeen": "2024-06-24T18:06:23.663092635Z" + }, "https://microcks.io/": { "StatusCode": 206, "LastSeen": "2024-03-12T09:44:49.348708+01:00" @@ -6375,6 +6419,10 @@ "StatusCode": 206, "LastSeen": "2024-03-27T00:26:34.094985195Z" }, + "https://newrelic.com/blog/how-to-relic/prometheus-and-opentelemetry-better-together": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:05:59.442618131Z" + }, "https://newrelic.com/solutions/opentelemetry": { "StatusCode": 206, "LastSeen": "2024-01-18T19:08:01.196826-05:00" @@ -7071,6 +7119,10 @@ "StatusCode": 206, "LastSeen": "2024-06-04T17:29:58.393305846+02:00" }, + "https://opentelemetry.io/docs/specs/otel/compatibility/prometheus_and_openmetrics/": { + "StatusCode": 206, + "LastSeen": "2024-09-04T09:48:32.91926+02:00" + }, "https://opentelemetry.io/docs/specs/otel/glossary/#instrumentation-library": { "StatusCode": 206, "LastSeen": "2024-04-30T09:31:37.929550219Z" @@ -8405,7 +8457,7 @@ }, "https://prometheus.io/blog/2024/03/14/commitment-to-opentelemetry/": { "StatusCode": 206, - "LastSeen": "2024-06-18T14:24:38.978819371Z" + "LastSeen": "2024-06-24T18:06:18.104195195Z" }, "https://prometheus.io/blog/2024/03/14/commitment-to-opentelemetry/#support-utf-8-metric-and-label-names": { "StatusCode": 206, @@ -8427,6 +8479,10 @@ "StatusCode": 206, "LastSeen": "2024-04-11T14:54:50.705990785Z" }, + "https://prometheus.io/docs/instrumenting/exporters/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:13.076959824Z" + }, "https://prometheus.io/docs/instrumenting/exposition_formats/#basic-info": { "StatusCode": 206, "LastSeen": "2024-04-11T14:54:57.142101041Z" @@ -8435,6 +8491,10 @@ "StatusCode": 206, "LastSeen": "2024-04-11T14:54:55.03691741Z" }, + "https://prometheus.io/docs/instrumenting/exposition_formats/#exposition-formats": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:07.50964971Z" + }, "https://prometheus.io/docs/instrumenting/writing_clientlibs/#overall-structure": { "StatusCode": 206, "LastSeen": "2024-04-11T14:54:52.880727432Z" @@ -8475,6 +8535,10 @@ "StatusCode": 206, "LastSeen": "2024-01-18T19:10:46.603367-05:00" }, + "https://prometheus.io/docs/specs/remote_write_spec_2_0/#io-prometheus-write-v2-request": { + "StatusCode": 206, + "LastSeen": "2024-09-04T09:48:44.106256+02:00" + }, "https://protobuf.dev/": { "StatusCode": 206, "LastSeen": "2024-05-24T10:11:26.948009-05:00" @@ -9219,6 +9283,10 @@ "StatusCode": 206, "LastSeen": "2024-01-30T16:07:05.720805-05:00" }, + "https://thanos.io/v0.10/thanos/getting-started.md/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:42.349083709Z" + }, "https://tinygo.org/": { "StatusCode": 206, "LastSeen": "2024-01-18T19:07:44.513364-05:00" @@ -10475,6 +10543,10 @@ "StatusCode": 200, "LastSeen": "2024-01-18T19:36:42.607433-05:00" }, + "https://www.robustperception.io/scaling-and-federating-prometheus/": { + "StatusCode": 206, + "LastSeen": "2024-06-24T18:06:26.04748648Z" + }, "https://www.rubydoc.info/gems/opentelemetry-sdk/OpenTelemetry/SDK/Trace/Samplers": { "StatusCode": 200, "LastSeen": "2024-01-18T20:05:29.811032-05:00"

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