PetClinic Monolith Workshop

The goal is to walk through the basic steps to configure the following components of the Splunk Observability Cloud platform:

• Splunk Infrastructure Monitoring (IM)
• Splunk Automatic Discovery for Java (APM)
  • Database Query Performance
  • AlwaysOn Profiling
• Splunk Real User Monitoring (RUM)
• RUM to APM Correlation
• Splunk Log Observer (LO)

We will also show the steps about how to clone (download) a sample Java application (Spring PetClinic), as well as how to compile, package and run the application.

Once the application is up and running, we will instantly start seeing metrics, traces and logs via the automatic discovery and configuration for Java 2.x that will be used by the Splunk APM product.

After that, we will instrument PetClinic’s end user interface (HTML pages rendered by the application) with the Splunk OpenTelemetry Javascript Libraries (RUM) that will generate RUM traces around all the individual clicks and page loads executed by an end user.

Lastly, we will view the logs generated by the automatic injection of trace metadata into the PetClinic application logs.

Spring PetClinic SpringBoot Based Microservices On Kubernetes

The goal of this workshop is to introduce the features of Splunk’s automatic discovery and configuration for Java.

The workshop scenario will be created by installing a simple (un-instrumented) Java microservices application in Kubernetes.

By following the simple steps to install the Splunk OpenTelemetry Collector and enabling automatic discovery and configuration for existing Java based deployments you will learn how easy it is to send metrics, traces and logs to Splunk Observability Cloud.

During this workshop we will cover the following components:

• Splunk Infrastructure Monitoring (IM)
• Splunk automatic discovery and configuration for Java (APM)
  • Database Query Performance
  • AlwaysOn Profiling
• Splunk Log Observer (LO)
• Splunk Real User Monitoring (RUM)

Splunk Synthetics is feeling a little left out here, but we cover that in other workshops

Deploying the OpenTelemetry Collector in Kubernetes

1. Connect to EC2 instance

You will be able to connect to the workshop instance by using SSH from your Mac, Linux or Windows device. Open the link to the sheet provided by your instructor. This sheet contains the IP addresses and the password for the workshop instances.

2. Install Splunk OTel using Helm

Install the OpenTelemetry Collector using the Splunk Helm chart. First, add the Splunk Helm chart repository and update:

helm repo add splunk-otel-collector-chart https://signalfx.github.io/splunk-otel-collector-chart && helm repo update

Using ACCESS_TOKEN=<REDACTED>
Using REALM=eu0
"splunk-otel-collector-chart" has been added to your repositories
Using ACCESS_TOKEN=<REDACTED>
Using REALM=eu0
Hang tight while we grab the latest from your chart repositories...
...Successfully got an update from the "splunk-otel-collector-chart" chart repository
Update Complete. ⎈Happy Helming!⎈

Install the OpenTelemetry Collector Helm with the following commands, do NOT edit this:

helm install splunk-otel-collector \
--set="splunkObservability.realm=$REALM" \
--set="splunkObservability.accessToken=$ACCESS_TOKEN" \
--set="clusterName=$INSTANCE-k3s-cluster" \
--set="logsEngine=otel" \
--set="splunkPlatform.endpoint=$HEC_URL" \
--set="splunkPlatform.token=$HEC_TOKEN" \
--set="splunkPlatform.index=splunk4rookies-workshop" \
splunk-otel-collector-chart/splunk-otel-collector \
-f ~/workshop/k3s/otel-collector.yaml

3. Verify Deployment

You can monitor the progress of the deployment by running kubectl get pods which should typically report that the new pods are up and running after about 30 seconds.

Ensure the status is reported as Running before continuing.

kubectl get pods

NAME                                                          READY   STATUS    RESTARTS   AGE
splunk-otel-collector-agent-pvstb                             2/2     Running   0          19s
splunk-otel-collector-k8s-cluster-receiver-6c454894f8-mqs8n   1/1     Running   0          19s

Use the label set by the helm install to tail logs (You will need to press ctrl + c to exit).

kubectl logs -l app=splunk-otel-collector -f --container otel-collector

Or use the installed k9s terminal UI.

helm delete splunk-otel-collector

Tour of the Kubernetes Navigator

1. Cluster vs Workload View

The Kubernetes Navigator offers you two separate use cases to view your Kubernetes data.

• The K8s workloads are focusing on providing information in regards to workloads a.k.a. your deployments.
• The K8s nodes are focusing on providing insight into the performance of clusters, nodes, pods and containers.

You will initially select either view depending on your need (you can switch between the view on the fly if required). The most common one we will use in this workshop is the workload view and we will focus on that specifically.

1.1 Finding your K8s Cluster Name

Your first task is to identify and find your cluster. The cluster will be named as determined by the preconfigured environment variable INSTANCE. To confirm the cluster name enter the following command in your terminal:

echo $INSTANCE-k3s-cluster

Please make a note of your cluster name as you will need this later in the workshop for filtering.

2. Workloads & Workload Details Pane

Go to the Infrastructure page in the Observability UI and select Kubernetes, this will offer you a set of Kubernetes services, one of them being the Kubernetes workloads pane. The pane will show a tiny graph giving you a bird’s eye view of the load being handled across all workloads. Click on the Kubernetes workloads pane and you will be taken to the workload view.

Initially, you will see all the workloads for all clusters that are reported into your Observability Cloud Org. If an alert has fired for any of the workloads, it will be highlighted on the top right in the image below.

Now, let’s find your cluster by filtering on Cluster in the filter toolbar.

You will now just see data just for your own cluster.

2.1 Using the Navigator Selection Chart

By default, the Kubernetes Workloads table filters by # Pods Failed grouped by k8s.namespace.name. Go ahead and expand the default namespace to see the workloads in the namespace.

Now, let’s change the list view to a heatmap view by selecting Map icon (next to the Table icon). Changing this option will result in the following visualization (or similar):

In this view, you will note that each workload is now a colored square. These squares will change color according to the Color by option you select. The colors give a visual indication of health and/or usage. You can check the meaning by hovering over the legend exclamation icon bottom right of the heatmaps.

Another valuable option in this screen is Find outliers which provides historical analytics of your clusters based on what is selected in the Color by dropdown.

Now, let’s select the Network transferred (bytes) from the Color by drop-down box, then click on the Find outliers and change the Scope in the dialog to Per k8s.namespace.name and Deviation from Median as below:

The Find Outliers view is very useful when you need to view a selection of your workloads (or any service depending on the Navigator used) and quickly need to figure out if something has changed.

It will give you fast insight into items (workloads in our case) that are performing differently (both increased or decreased) which helps to make it easier to spot problems.

2.2 The Deployment Overview pane

The Deployment Overview pane gives you a quick insight into the status of your deployments. You can see at once if the pods of your deployments are Pending, Running, Succeeded, Failed or in an Unknown state.

• Running: Pod is deployed and in a running state
• Pending: Waiting to be deployed
• Succeeded: Pod has been deployed and completed its job and is finished
• Failed: Containers in the pod have run and returned some kind of error
• Unknown: Kubernetes isn’t reporting any of the known states. (This may be during the starting or stopping of pods, for example).

You can expand the Workload name by hovering your mouse on it, in case the name is longer than the chart allows.

To filter to a specific workload, you can click on three dots … next to the workload name in the k8s.workload.name column and choose Filter from the dropdown box:

This will add the selected workload to your filters. It would then list a single workload in the default namespace:

From the Heatmap above find the splunk-otel-collector-k8s-cluster-receiver in the default namespace and click on the square to see more information about the workload:

At this point, you can drill into the information of the pods, but that is outside the scope of this workshop.

3. Navigator Sidebar

Later in the workshop, you will deploy an Apache server into your cluster which will display an icon in the Navigator Sidebar.

In navigators for Kubernetes, you can track dependent services and containers in the navigator sidebar. To get the most out of the navigator sidebar you configure the services you want to track by configuring an extra dimension called service.name. For this workshop, we have already configured the extraDimensions in the collector configuration for monitoring Apache e.g.

extraDimensions:
  service.name: php-apache

The Navigator Sidebar will expand and a link to the discovered service will be added as seen in the image below:

This will allow for easy switching between Navigators. The same applies to your Apache server instance, it will have a Navigator Sidebar allowing you to quickly jump back to the Kubernetes Navigator.

Deploying PHP/Apache

1. Namespaces in Kubernetes

Most of our customers will make use of some kind of private or public cloud service to run Kubernetes. They often choose to have only a few large Kubernetes clusters as it is easier to manage centrally.

Namespaces are a way to organize these large Kubernetes clusters into virtual sub-clusters. This can be helpful when different teams or projects share a Kubernetes cluster as this will give them the easy ability to just see and work with their resources.

Any number of namespaces are supported within a cluster, each logically separated from others but with the ability to communicate with each other. Components are only visible when selecting a namespace or when adding the --all-namespaces flag to kubectl instead of allowing you to view just the components relevant to your project by selecting your namespace.

Most customers will want to install the applications into a separate namespace. This workshop will follow that best practice.

2. DNS and Services in Kubernetes

The Domain Name System (DNS) is a mechanism for linking various sorts of information with easy-to-remember names, such as IP addresses. Using a DNS system to translate request names into IP addresses makes it easy for end-users to reach their target domain name effortlessly.

Most Kubernetes clusters include an internal DNS service configured by default to offer a lightweight approach for service discovery. Even when Pods and Services are created, deleted, or shifted between nodes, built-in service discovery simplifies applications to identify and communicate with services on the Kubernetes clusters.

In short, the DNS system for Kubernetes will create a DNS entry for each Pod and Service. In general, a Pod has the following DNS resolution:

pod-name.my-namespace.pod.cluster-domain.example

For example, if a Pod in the default namespace has the Pod name my_pod, and the domain name for your cluster is cluster.local, then the Pod has a DNS name:

my_pod.default.pod.cluster.local

Any Pods exposed by a Service have the following DNS resolution available:

my_pod.service-name.my-namespace.svc.cluster-domain.example

More information can be found here: DNS for Service and Pods

3. Review OTel receiver for PHP/Apache

Inspect the YAML file ~/workshop/k3s/otel-apache.yaml and validate the contents using the following command:

cat ~/workshop/k3s/otel-apache.yaml

This file contains the configuration for the OpenTelemetry agent to monitor the PHP/Apache deployment.

agent:
  config:
    receivers:
      receiver_creator:
        receivers:
          smartagent/apache:
            rule: type == "port" && pod.name matches "apache" && port == 80
            config:
              type: collectd/apache
              url: http://php-apache-svc.apache.svc.cluster.local/server-status?auto
              extraDimensions:
                service.name: php-apache

4. Observation Rules in the OpenTelemetry config

The above file contains an observation rule for Apache using the OTel receiver_creator. This receiver can instantiate other receivers at runtime based on whether observed endpoints match a configured rule.

The configured rules will be evaluated for each endpoint discovered. If the rule evaluates to true, then the receiver for that rule will be started as configured against the matched endpoint.

In the file above we tell the OpenTelemetry agent to look for Pods that match the name apache and have port 80 open. Once found, the agent will configure an Apache receiver to read Apache metrics from the configured URL. Note, the K8s DNS-based URL in the above YAML for the service.

To use the Apache configuration, you can upgrade the existing Splunk OpenTelemetry Collector Helm chart to use the otel-apache.yaml file with the following command:

helm upgrade splunk-otel-collector \
--set="splunkObservability.realm=$REALM" \
--set="splunkObservability.accessToken=$ACCESS_TOKEN" \
--set="clusterName=$INSTANCE-k3s-cluster" \
--set="logsEngine=otel" \
--set="splunkPlatform.endpoint=$HEC_URL" \
--set="splunkPlatform.token=$HEC_TOKEN" \
--set="splunkPlatform.index=splunk4rookies-workshop" \
splunk-otel-collector-chart/splunk-otel-collector \
-f ~/workshop/k3s/otel-collector.yaml \
-f ~/workshop/k3s/otel-apache.yaml

Release "splunk-otel-collector" has been upgraded. Happy Helming!
NAME: splunk-otel-collector
LAST DEPLOYED: Mon Nov  4 14:56:25 2024
NAMESPACE: default
STATUS: deployed
REVISION: 2
TEST SUITE: None
NOTES:
Splunk OpenTelemetry Collector is installed and configured to send data to Splunk Platform endpoint "https://http-inputs-workshop.splunkcloud.com:443/services/collector/event".

Splunk OpenTelemetry Collector is installed and configured to send data to Splunk Observability realm eu0.

5. Kubernetes ConfigMaps

A ConfigMap is an object in Kubernetes consisting of key-value pairs that can be injected into your application. With a ConfigMap, you can separate configuration from your Pods.

Using ConfigMap, you can prevent hardcoding configuration data. ConfigMaps are useful for storing and sharing non-sensitive, unencrypted configuration information.

The OpenTelemetry collector/agent uses ConfigMaps to store the configuration of the agent and the K8s Cluster receiver. You can/will always verify the current configuration of an agent after a change by running the following commands:

kubectl get cm

When you have a list of ConfigMaps from the namespace, select the one for the otel-agent and view it with the following command:

kubectl get cm splunk-otel-collector-otel-agent -o yaml

6. Review PHP/Apache deployment YAML

Inspect the YAML file ~/workshop/k3s/php-apache.yaml and validate the contents using the following command:

cat ~/workshop/k3s/php-apache.yaml

This file contains the configuration for the PHP/Apache deployment and will create a new StatefulSet with a single replica of the PHP/Apache image.

A stateless application does not care which network it is using, and it does not need permanent storage. Examples of stateless apps may include web servers such as Apache, Nginx, or Tomcat.

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: php-apache
spec:
  updateStrategy:
    type: RollingUpdate
  selector:
    matchLabels:
      run: php-apache
  serviceName: "php-apache-svc"
  replicas: 1
  template:
    metadata:
      labels:
        run: php-apache
    spec:
      containers:
      - name: php-apache
        image: ghcr.io/splunk/php-apache:latest
        ports:
        - containerPort: 80
        resources:
          limits:
            cpu: "8"
            memory: "8Mi"
          requests:
            cpu: "6"
            memory: "4Mi"

---
apiVersion: v1
kind: Service
metadata:
  name: php-apache-svc
  labels:
    run: php-apache
spec:
  ports:
  - port: 80
  selector:
    run: php-apache

7. Deploy PHP/Apache

Create an apache namespace then deploy the PHP/Apache application to the cluster.

Create the apache namespace:

kubectl create namespace apache

Deploy the PHP/Apache application:

kubectl apply -f ~/workshop/k3s/php-apache.yaml -n apache

Ensure the deployment has been created:

kubectl get statefulset -n apache

Fix PHP/Apache Issue

1. Kubernetes Resources

Especially in Production Kubernetes Clusters, CPU and Memory are considered precious resources. Cluster Operators will normally require you to specify the amount of CPU and Memory your Pod or Service will require in the deployment, so they can have the Cluster automatically manage on which Node(s) your solution will be placed.

You do this by placing a Resource section in the deployment of your application/Pod

Example:

resources:
  limits:         # Maximum amount of CPU & memory for peek use
    cpu: "8"      # Maximum of 8 cores of CPU allowed at for peek use
    memory: "8Mi" # Maximum allowed 8Mb of memory
  requests:       # Request are the expected amount of CPU & memory for normal use
    cpu: "6"      # Requesting 4 cores of a CPU
    memory: "4Mi" # Requesting 4Mb of memory

More information can be found here: Resource Management for Pods and Containers

If your application or Pod will go over the limits set in your deployment, Kubernetes will kill and restart your Pod to protect the other applications on the Cluster.

Another scenario that you will run into is when there is not enough Memory or CPU on a Node. In that case, the Cluster will try to reschedule your Pod(s) on a different Node with more space.

If that fails, or if there is not enough space when you deploy your application, the Cluster will put your workload/deployment in schedule mode until there is enough room on any of the available Nodes to deploy the Pods according to their limits.

2. Fix PHP/Apache Deployment

To fix the PHP/Apache StatefulSet, edit ~/workshop/k3s/php-apache.yaml using the following commands to reduce the CPU resources:

vim ~/workshop/k3s/php-apache.yaml

Find the resources section and reduce the CPU limits to 1 and the CPU requests to 0.5:

resources:
  limits:
    cpu: "1"
    memory: "8Mi"
  requests:
    cpu: "0.5"
    memory: "4Mi"

Save the changes you have made. (Hint: Use Esc followed by :wq! to save your changes).

Now, we must delete the existing StatefulSet and re-create it. StatefulSets are immutable, so we must delete the existing one and re-create it with the new changes.

kubectl delete statefulset php-apache -n apache

Now, deploy your changes:

kubectl apply -f ~/workshop/k3s/php-apache.yaml -n apache

3. Validate the changes

You can validate the changes have been applied by running the following command:

kubectl describe statefulset php-apache -n apache

Validate the Pod is now running in Splunk Observability Cloud.

Monitor the Apache web servers Navigator dashboard for a few minutes.

4. Fix the memory issue

If you navigate back to the Apache dashboard, you will notice that metrics are no longer coming in. We have another resource issue and this time we are Out of Memory. Let’s edit the stateful set and increase the memory to what is shown in the image below:

kubectl edit statefulset php-apache -n apache

resources:
  limits:
    cpu: "1"
    memory: 16Mi
  requests:
    cpu: 500m
    memory: 12Mi

Save the changes you have made.

Because StatefulSets are immutable, we must delete the existing Pod and let the StatefulSet re-create it with the new changes.

kubectl delete pod php-apache-0 -n apache

Validate the changes have been applied by running the following command:

kubectl describe statefulset php-apache -n apache

Deploy Load Generator

Now let’s apply some load against the php-apache pod. To do this, you will need to start a different Pod to act as a client. The container within the client Pod runs in an infinite loop, sending HTTP GETs to the php-apache service.

1. Review loadgen YAML

Inspect the YAML file ~/workshop/k3s/loadgen.yaml and validate the contents using the following command:

cat ~/workshop/k3s/loadgen.yaml

This file contains the configuration for the load generator and will create a new ReplicaSet with two replicas of the load generator image.

apiVersion: apps/v1
kind: ReplicaSet
metadata:
  name: loadgen
  labels:
    app: loadgen
spec:
  replicas: 2
  selector:
    matchLabels:
      app: loadgen
  template:
    metadata:
      name: loadgen
      labels:
        app: loadgen
    spec:
      containers:
      - name: infinite-calls
        image: busybox
        command:
        - /bin/sh
        - -c
        - "while true; do wget -q -O- http://php-apache-svc.apache.svc.cluster.local; done"

2. Create a new namespace

kubectl create namespace loadgen

3. Deploy the loadgen YAML

kubectl apply -f ~/workshop/k3s/loadgen.yaml --namespace loadgen

Once you have deployed the load generator, you can see the Pods running in the loadgen namespace. Use previous similar commands to check the status of the Pods from the command line.

4. Scale the load generator

A ReplicaSet is a process that runs multiple instances of a Pod and keeps the specified number of Pods constant. Its purpose is to maintain the specified number of Pod instances running in a cluster at any given time to prevent users from losing access to their application when a Pod fails or is inaccessible.

ReplicaSet helps bring up a new instance of a Pod when the existing one fails, scale it up when the running instances are not up to the specified number, and scale down or delete Pods if another instance with the same label is created. A ReplicaSet ensures that a specified number of Pod replicas are running continuously and helps with load-balancing in case of an increase in resource usage.

Let’s scale our ReplicaSet to 4 replicas using the following command:

kubectl scale replicaset/loadgen --replicas 4 -n loadgen

Validate the replicas are running from both the command line and Splunk Observability Cloud:

kubectl get replicaset loadgen -n loadgen

Let the load generator run for around 2-3 minutes and keep observing the metrics in the Kubernetes Navigator and the Apache Navigator.

Setup Horizontal Pod Autoscaling (HPA)

In Kubernetes, a HorizontalPodAutoscaler automatically updates a workload resource (such as a Deployment or StatefulSet), to automatically scale the workload to match demand.

Horizontal scaling means that the response to increased load is to deploy more Pods. This is different from vertical scaling, which for Kubernetes would mean assigning more resources (for example: memory or CPU) to the Pods that are already running for the workload.

If the load decreases, and the number of Pods is above the configured minimum, the HorizontalPodAutoscaler instructs the workload resource (the Deployment, StatefulSet, or other similar resource) to scale back down.

1. Setup HPA

Inspect the ~/workshop/k3s/hpa.yaml file and validate the contents using the following command:

cat ~/workshop/k3s/hpa.yaml

This file contains the configuration for the Horizontal Pod Autoscaler and will create a new HPA for the php-apache deployment.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: php-apache
  namespace: apache
spec:
  maxReplicas: 4
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        averageUtilization: 50
        type: Utilization
  - type: Resource
    resource:
      name: memory
      target:
        averageUtilization: 75
        type: Utilization
  minReplicas: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: StatefulSet
    name: php-apache

Once deployed, php-apache will autoscale when either the average CPU usage goes above 50% or the average memory usage for the deployment goes above 75%, with a minimum of 1 pod and a maximum of 4 pods.

kubectl apply -f ~/workshop/k3s/hpa.yaml

2. Validate HPA

kubectl get hpa -n apache

Go to the Workloads or Node Detail tab in Kubernetes and check the HPA deployment.

3. Increase the HPA replica count

Increase the maxReplicas to 8

kubectl edit hpa php-apache -n apache

Save the changes you have made. (Hint: Use Esc followed by :wq! to save your changes).

Congratulations! You have completed the workshop.

Installing OpenTelemetry Collector Contrib

Download the OpenTelemetry Collector Contrib distribution

The first step in installing the Open Telemetry Collector is downloading it. For our lab, we will use the wget command to download the .deb package from the OpenTelemetry Github repository.

Obtain the .deb package for your platform from the OpenTelemetry Collector Contrib releases page

wget https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v0.111.0/otelcol-contrib_0.111.0_linux_amd64.deb

Install the OpenTelemetry Collector Contrib distribution

Install the .deb package using dpkg. Take a look at the dpkg Output tab below to see what the example output of a successful install will look like:

sudo dpkg -i otelcol-contrib_0.111.0_linux_amd64.deb

Selecting previously unselected package otelcol-contrib.
(Reading database ... 89232 files and directories currently installed.)
Preparing to unpack otelcol-contrib_0.111.0_linux_amd64.deb ...
Unpacking otelcol-contrib (0.111.0) ...
Setting up otelcol-contrib (0.111.0) ...
Created symlink /etc/systemd/system/multi-user.target.wants/otelcol-contrib.service → /lib/systemd/system/otelcol-contrib.service.

OpenTelemetry Collector Extensions

Now that we have the OpenTelemetry Collector installed, let’s take a look at extensions for the OpenTelemetry Collector. Extensions are optional and available primarily for tasks that do not involve processing telemetry data. Examples of extensions include health monitoring, service discovery, and data forwarding.

%%{
  init:{
    "theme": "base",
    "themeVariables": {
      "primaryColor": "#ffffff",
      "clusterBkg": "#eff2fb",
      "defaultLinkColor": "#333333"
    }
  }
}%%

flowchart LR;
    style E fill:#e20082,stroke:#333,stroke-width:4px,color:#fff
    subgraph Collector
    A[OTLP] --> M(Receivers)
    B[JAEGER] --> M(Receivers)
    C[Prometheus] --> M(Receivers)
    end
    subgraph Processors
    M(Receivers) --> H(Filters, Attributes, etc)
    E(Extensions)
    end
    subgraph Exporters
    H(Filters, Attributes, etc) --> S(OTLP)
    H(Filters, Attributes, etc) --> T(JAEGER)
    H(Filters, Attributes, etc) --> U(Prometheus)
    end

OpenTelemetry Collector Receivers

Welcome to the receiver portion of the workshop! This is the starting point of the data pipeline of the OpenTelemetry Collector. Let’s dive in.

A receiver, which can be push or pull based, is how data gets into the Collector. Receivers may support one or more data sources. Generally, a receiver accepts data in a specified format, translates it into the internal format and passes it to processors and exporters defined in the applicable pipelines.

%%{
  init:{
    "theme":"base",
    "themeVariables": {
      "primaryColor": "#ffffff",
      "clusterBkg": "#eff2fb",
      "defaultLinkColor": "#333333"
    }
  }
}%%

flowchart LR;
    style M fill:#e20082,stroke:#333,stroke-width:4px,color:#fff
    subgraph Collector
    A[OTLP] --> M(Receivers)
    B[JAEGER] --> M(Receivers)
    C[Prometheus] --> M(Receivers)
    end
    subgraph Processors
    M(Receivers) --> H(Filters, Attributes, etc)
    E(Extensions)
    end
    subgraph Exporters
    H(Filters, Attributes, etc) --> S(OTLP)
    H(Filters, Attributes, etc) --> T(JAEGER)
    H(Filters, Attributes, etc) --> U(Prometheus)
    end

OpenTelemetry Collector Processors

Processors are run on data between being received and being exported. Processors are optional though some are recommended. There are a large number of processors included in the OpenTelemetry contrib Collector.

%%{
  init:{
    "theme":"base",
    "themeVariables": {
      "primaryColor": "#ffffff",
      "clusterBkg": "#eff2fb",
      "defaultLinkColor": "#333333"
    }
  }
}%%

flowchart LR;
    style Processors fill:#e20082,stroke:#333,stroke-width:4px,color:#fff
    subgraph Collector
    A[OTLP] --> M(Receivers)
    B[JAEGER] --> M(Receivers)
    C[Prometheus] --> M(Receivers)
    end
    subgraph Processors
    M(Receivers) --> H(Filters, Attributes, etc)
    E(Extensions)
    end
    subgraph Exporters
    H(Filters, Attributes, etc) --> S(OTLP)
    H(Filters, Attributes, etc) --> T(JAEGER)
    H(Filters, Attributes, etc) --> U(Prometheus)
    end