Deploy the Splunk OpenTelemetry Collector

To get Observability signals (metrics, traces and logs) into Splunk Observability Cloud the Splunk OpenTelemetry Collector needs to be deployed into the Kubernetes cluster.

For this workshop, we will be using the Splunk OpenTelemetry Collector Helm Chart. First we need to add the Helm chart repository to Helm and update to ensure the latest version:

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!⎈

Splunk Observability Cloud offers wizards in the UI to walk you through the setup of the OpenTelemetry Collector on Kubernetes, but in the interest of time, we will use the Helm install command below. Additional parameters are set to enable the operator and automatic discovery and configuration.

• --set="operator.enabled=true" - this will install the Opentelemetry operator that will be used to handle automatic discovery and configuration.
• --set="certmanager.enabled=true" - this will install the required certificate manager for the operator.
• --set="splunkObservability.profilingEnabled=true" - this enables Code Profiling via the operator.

To install the collector run the following command, do NOT edit this:

helm install splunk-otel-collector --version 0.111.0 \
--set="operator.enabled=true", \
--set="certmanager.enabled=true", \
--set="splunkObservability.realm=$REALM" \
--set="splunkObservability.accessToken=$ACCESS_TOKEN" \
--set="clusterName=$INSTANCE-k3s-cluster" \
--set="splunkObservability.profilingEnabled=true" \
--set="agent.service.enabled=true"  \
--set="environment=$INSTANCE-workshop" \
--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

LAST DEPLOYED: Fri Apr 19 09:39:54 2024
NAMESPACE: default
STATUS: deployed
REVISION: 1
NOTES:
Splunk OpenTelemetry Collector is installed and configured to send data to Splunk Platform endpoint "https://http-inputs-o11y-workshop-eu0.splunkcloud.com:443/services/collector/event".

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

[INFO] You've enabled the operator's auto-instrumentation feature (operator.enabled=true)! The operator can automatically instrument Kubernetes hosted applications.
  - Status: Instrumentation language maturity varies. See `operator.instrumentation.spec` and documentation for utilized instrumentation details.
  - Splunk Support: We offer full support for Splunk distributions and best-effort support for native OpenTelemetry distributions of auto-instrumentation libraries.

Ensure the Pods are reported as Running before continuing (this typically takes around 30 seconds).

kubectl get pods | grep splunk-otel 

splunk-otel-collector-certmanager-cainjector-5c5dc4ff8f-95z49   1/1     Running   0          10m
splunk-otel-collector-certmanager-6d95596898-vjxss              1/1     Running   0          10m
splunk-otel-collector-certmanager-webhook-69f4ff754c-nghxz      1/1     Running   0          10m
splunk-otel-collector-k8s-cluster-receiver-6bd5567d95-5f8cj     1/1     Running   0          10m
splunk-otel-collector-agent-tspd2                               1/1     Running   0          10m
splunk-otel-collector-operator-69d476cb7-j7zwd                  2/2     Running   0          10m

Ensure there are no errors reported by the Splunk OpenTelemetry Collector (press ctrl + c to exit) or use the installed awesome k9s terminal UI for bonus points!

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

2021-03-21T16:11:10.900Z        INFO    service/service.go:364  Starting receivers...
2021-03-21T16:11:10.900Z        INFO    builder/receivers_builder.go:70 Receiver is starting... {"component_kind": "receiver", "component_type": "prometheus", "component_name": "prometheus"}
2021-03-21T16:11:11.009Z        INFO    builder/receivers_builder.go:75 Receiver started.       {"component_kind": "receiver", "component_type": "prometheus", "component_name": "prometheus"}
2021-03-21T16:11:11.009Z        INFO    builder/receivers_builder.go:70 Receiver is starting... {"component_kind": "receiver", "component_type": "k8s_cluster", "component_name": "k8s_cluster"}
2021-03-21T16:11:11.009Z        INFO    k8sclusterreceiver@v0.21.0/watcher.go:195       Configured Kubernetes MetadataExporter  {"component_kind": "receiver", "component_type": "k8s_cluster", "component_name": "k8s_cluster", "exporter_name": "signalfx"}
2021-03-21T16:11:11.009Z        INFO    builder/receivers_builder.go:75 Receiver started.       {"component_kind": "receiver", "component_type": "k8s_cluster", "component_name": "k8s_cluster"}
2021-03-21T16:11:11.009Z        INFO    healthcheck/handler.go:128      Health Check state change       {"component_kind": "extension", "component_type": "health_check", "component_name": "health_check", "status": "ready"}
2021-03-21T16:11:11.009Z        INFO    service/service.go:267  Everything is ready. Begin running and processing data.
2021-03-21T16:11:11.009Z        INFO    k8sclusterreceiver@v0.21.0/receiver.go:59       Starting shared informers and wait for initial cache sync.      {"component_kind": "receiver", "component_type": "k8s_cluster", "component_name": "k8s_cluster"}
2021-03-21T16:11:11.281Z        INFO    k8sclusterreceiver@v0.21.0/receiver.go:75       Completed syncing shared informer caches.       {"component_kind": "receiver", "component_type": "k8s_cluster", "component_name": "k8s_cluster"}

helm delete splunk-otel-collector

Deploy the PetClinic Application

The first deployment of the application will be using prebuilt containers to give the base scenario: a regular Java microservices-based application running in Kubernetes that we want to start observing. So let’s deploy the application:

kubectl apply -f ~/workshop/petclinic/petclinic-deploy.yaml

deployment.apps/config-server created
service/config-server created
deployment.apps/discovery-server created
service/discovery-server created
deployment.apps/api-gateway created
service/api-gateway created
service/api-gateway-external created
deployment.apps/customers-service created
service/customers-service created
deployment.apps/vets-service created
service/vets-service created
deployment.apps/visits-service created
service/visits-service created
deployment.apps/admin-server created
service/admin-server created
service/petclinic-db created
deployment.apps/petclinic-db created
configmap/petclinic-db-initdb-config created
deployment.apps/petclinic-loadgen-deployment created
configmap/scriptfile created

At this point, we can verify the deployment by checking that the Pods are running. The containers need to be downloaded and started so this may take a couple of minutes.

kubectl get pods

NAME                                                            READY   STATUS    RESTARTS   AGE
splunk-otel-collector-certmanager-dc744986b-z2gzw               1/1     Running   0          114s
splunk-otel-collector-certmanager-cainjector-69546b87d6-d2fz2   1/1     Running   0          114s
splunk-otel-collector-certmanager-webhook-78b59ffc88-r2j8x      1/1     Running   0          114s
splunk-otel-collector-k8s-cluster-receiver-655dcd9b6b-dcvkb     1/1     Running   0          114s
splunk-otel-collector-agent-dg2vj                               1/1     Running   0          114s
splunk-otel-collector-operator-57cbb8d7b4-dk5wf                 2/2     Running   0          114s
petclinic-db-64d998bb66-2vzpn                                   1/1     Running   0          58s
api-gateway-d88bc765-jd5lg                                      1/1     Running   0          58s
visits-service-7f97b6c579-bh9zj                                 1/1     Running   0          58s
admin-server-76d8b956c5-mb2zv                                   1/1     Running   0          58s
customers-service-847db99f79-mzlg2                              1/1     Running   0          58s
vets-service-7bdcd7dd6d-2tcfd                                   1/1     Running   0          58s
petclinic-loadgen-deployment-5d69d7f4dd-xxkn4                   1/1     Running   0          58s
config-server-67f7876d48-qrsr5                                  1/1     Running   0          58s
discovery-server-554b45cfb-bqhgt                                1/1     Running   0          58s

Make sure the output of kubectl get pods matches the output as shown above. Ensure all the services are shown as Running (or use k9s to continuously monitor the status).

To test the application you need to obtain the public IP address of the instance you are running on. You can do this by running the following command:

curl http://ifconfig.me

You can validate if the application is running by visiting http://<IP_ADDRESS>:81 (replace <IP_ADDRESS> with the IP address you obtained above). You should see the PetClinic application running. The application is also running on ports 80 & 443 if you prefer to use those or port 81 is unreachable.

Make sure the application is working correctly by visiting the All Owners (1) and Veterinarians (2) tabs, you should get a list of names in each case.

Patching the Deployment

To configure automatic discovery and configuration the deployments need to be patched to add the instrumentation annotation. Once patched, the OpenTelemetry Collector will inject the automatic discovery and configuration library and the Pods will be restarted in order to start sending traces and profiling data. First, confirm that the api-gateway does not have the splunk-otel-java image.

kubectl describe pods api-gateway | grep Image:

Image:         quay.io/phagen/spring-petclinic-api-gateway:0.0.2

Next, enable the Java automatic discovery and configuration for all of the services by adding the annotation to the deployments. The following command will patch the all deployments. This will trigger the OpenTelemetry Operator to inject the splunk-otel-java image into the Pods:

kubectl get deployments -l app.kubernetes.io/part-of=spring-petclinic -o name | xargs -I % kubectl patch % -p "{\"spec\": {\"template\":{\"metadata\":{\"annotations\":{\"instrumentation.opentelemetry.io/inject-java\":\"default/splunk-otel-collector\"}}}}}"

deployment.apps/config-server patched (no change)
deployment.apps/admin-server patched (no change)
deployment.apps/customers-service patched
deployment.apps/visits-service patched
deployment.apps/discovery-server patched (no change)
deployment.apps/vets-service patched
deployment.apps/api-gateway patched

There will be no change for the config-server, discovery-server and admin-server as these have already been patched.

To check the container image(s) of the api-gateway pod again, run the following command:

kubectl describe pods api-gateway | grep Image:

Image:         ghcr.io/signalfx/splunk-otel-java/splunk-otel-java:v1.30.0
Image:         quay.io/phagen/spring-petclinic-api-gateway:0.0.2

A new image has been added to the api-gateway which will pull splunk-otel-java from ghcr.io (if you see two api-gateway containers, the original one is probably still terminating, so give it a few seconds).

Navigate back to the Kubernetes Navigator in Splunk Observability Cloud. After a couple of minutes you will see that the Pods are being restarted by the operator and the automatic discovery and configuration container will be added. This will look similar to the screenshot below:

Wait for the Pods to turn green in the Kubernetes Navigator, then go tho the next section.

Viewing the data in Splunk APM

Log in to Splunk Observability Cloud, from the left-hand menu click on APM to see the data generated by the traces from the newly instrumented services. Change the Environment filter (1) to the name of your workshop instance in the dropdown box (this will be <INSTANCE>-workshop where INSTANCE is the value from the shell script you ran earlier) and make sure it is the only one selected.

You will see the name (2) of the api-gateway service and metrics in the Latency and Request & Errors charts (you can ignore the Critical Alert, as it is caused by the sudden request increase generated by the load generator). You will also see the rest of the services appear.

Once you see the Customer service, Vets service and Visits services like show in the screenshot above, let’s click on the Service Map (3) pane to get ready for the next section.

APM Service Map

The above map shows all the interactions between all of the services. The map may still be in an interim state as it will take the Petclinic Microservice application a few minutes to start up and fully synchronize. Reducing the time filter to a custom time of 2 minutes will help. You can click on the Refresh button (1) on the top right of the screen. The initial startup-related errors (red dots) will eventually disappear.

Next, let’s examine the metrics that are available for each service that is instrumented and visit the request, error, and duration (RED) metrics Dashboard

For this exercise we are going to use a common scenario you would use if the service operation was showing high latency, or errors for example.

Select (click) on the Customer Service in the Dependency map (1), then make sure the customers-service is selected in the Services dropdown box (2). Next, select GET /Owners from the Operations dropdown (3)**.

This should give you the workflow with a filter on GET /owners (1) as shown below.

APM Trace

To pick a trace, select a line in the Service Requests & Errors chart (1), when the dot appears click to get a list of sample traces:

Once you have the list of sample traces, click on the blue (2) Trace ID Link (make sure it has the same three services mentioned in the Service Column.)

This brings us the the Trace selected in the Waterfall view:

Here we find several sections:

• The actual Waterfall Pane (1), where you see the trace and all the instrumented functions visible as spans, with their duration representation and order/relationship showing.
• The Trace Info Pane (2), by default, shows the selected Span information (highlighted with a box around the Span in the Waterfall Pane).
• The Span Pane (3), here you can find all the Tags that have been sent in the selected Span, You can scroll down to see all of them.
• The process Pane, with tags related to the process that created the Span (scroll down to see as it is not in the screenshot).
• The Trace Properties at the top of the right-hand pane by default is collapsed as shown.

APM Span

While we examine our spans, let’s look at several features that you get out of the box without code modifications when using automatic discovery and configuration on top of tracing:

First, in the Waterfall Pane, make sure the customers-service:SELECT petclinic or similar span is selected as shown in the screenshot below:

• The basic latency information is shown as a bar for the instrumented function or call, in our example, it took 17.8 Milliseconds.
• Several similar Spans (1), are only visible if the span is repeated multiple times. In this case, there are 10 repeats in our example. (You can show/hide them all by clicking on the 10x and all spans will show in order)
• Inferred Services: Calls made to external systems that are not instrumented, show up as a grey ‘inferred’ span. The Inferred Service or span in our case here is a call to the Mysql Database mysql:petclinic SELECT petclinic (2) as shown above our selected span.
• Span Tags: In the Tag Pane, standard tags produced by the automatic discovery and configuration. In this case, the span is calling a Database, so it includes the db.statement tag (3). This tag will hold the DB query statement and is used by the Database call performed during this span. This will be used by the DB-Query Performance feature. We look at DB-Query Performance in the next section.
• Always-on Profiling: IF the system is configured to and has captured Profiling data during a Span life cycle, it will show the number of Call Stacks captured in the Spans timeline (18 Call Stacks for the customer-service:GET /owners Span shown above). (4)

We will look at Profiling in the next section.

Service Centric View

Splunk APM provide Service Centric Views that provide engineers a deep understanding of service performance in one centralized view. Now, across every service, engineers can quickly identify errors or bottlenecks from a service’s underlying infrastructure, pinpoint performance degradations from new deployments, and visualize the health of every third party dependency.

To see this dashboard for the api-gateway,Click on APM from the right hand menu bar and go to the Dependency Map. Make sure you have the api-gateway service selected in the Service Map, then click on the *View Service button in the top of the right-hand pane. This will bring you to the Service Centric View dashboard:

This view, which is available for each of your instrumented services, offers an overview of Service metrics, Runtime metrics and Infrastructure metrics.

You can select the Back function of you browser to go back to the previous view.

Always-On Profiling & Metrics

When we installed the Splunk Distribution of the OpenTelemetry Collector using the Helm chart earlier, we configured it to enable AlwaysOn Profiling and Metrics. This means that the collector will automatically generate CPU and Memory profiles for the application and send them to Splunk Observability Cloud.

When you deploy the PetClinic application and set the annotation, the collector automatically detects the application and instruments it for traces and profiling. We can verify this by examining the startup logs of one of the Java containers we are instrumenting by running the following script:

The logs should show what flags were picked up by the Java automatic discovery and configuration:

.  ~/workshop/petclinic/scripts/get_logs.sh

2024/02/15 09:42:00 Problem with dial: dial tcp 10.43.104.25:8761: connect: connection refused. Sleeping 1s
2024/02/15 09:42:01 Problem with dial: dial tcp 10.43.104.25:8761: connect: connection refused. Sleeping 1s
2024/02/15 09:42:02 Connected to tcp://discovery-server:8761
Picked up JAVA_TOOL_OPTIONS:  -javaagent:/otel-auto-instrumentation-java/javaagent.jar
Picked up _JAVA_OPTIONS: -Dspring.profiles.active=docker,mysql -Dsplunk.profiler.call.stack.interval=150
OpenJDK 64-Bit Server VM warning: Sharing is only supported for boot loader classes because bootstrap classpath has been appended
[otel.javaagent 2024-02-15 09:42:03:056 +0000] [main] INFO io.opentelemetry.javaagent.tooling.VersionLogger - opentelemetry-javaagent - version: splunk-1.30.1-otel-1.32.1
[otel.javaagent 2024-02-15 09:42:03:768 +0000] [main] INFO com.splunk.javaagent.shaded.io.micrometer.core.instrument.push.PushMeterRegistry - publishing metrics for SignalFxMeterRegistry every 30s
[otel.javaagent 2024-02-15 09:42:07:478 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger - -----------------------
[otel.javaagent 2024-02-15 09:42:07:478 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger - Profiler configuration:
[otel.javaagent 2024-02-15 09:42:07:480 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -                  splunk.profiler.enabled : true
[otel.javaagent 2024-02-15 09:42:07:505 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -                splunk.profiler.directory : /tmp
[otel.javaagent 2024-02-15 09:42:07:505 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -       splunk.profiler.recording.duration : 20s
[otel.javaagent 2024-02-15 09:42:07:506 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -               splunk.profiler.keep-files : false
[otel.javaagent 2024-02-15 09:42:07:510 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -            splunk.profiler.logs-endpoint : http://10.13.2.38:4317
[otel.javaagent 2024-02-15 09:42:07:513 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -              otel.exporter.otlp.endpoint : http://10.13.2.38:4317
[otel.javaagent 2024-02-15 09:42:07:513 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -           splunk.profiler.memory.enabled : true
[otel.javaagent 2024-02-15 09:42:07:515 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -             splunk.profiler.tlab.enabled : true
[otel.javaagent 2024-02-15 09:42:07:516 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -        splunk.profiler.memory.event.rate : 150/s
[otel.javaagent 2024-02-15 09:42:07:516 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -      splunk.profiler.call.stack.interval : PT0.15S
[otel.javaagent 2024-02-15 09:42:07:517 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -  splunk.profiler.include.internal.stacks : false
[otel.javaagent 2024-02-15 09:42:07:517 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger -      splunk.profiler.tracing.stacks.only : false
[otel.javaagent 2024-02-15 09:42:07:517 +0000] [main] INFO com.splunk.opentelemetry.profiler.ConfigurationLogger - -----------------------
[otel.javaagent 2024-02-15 09:42:07:518 +0000] [main] INFO com.splunk.opentelemetry.profiler.JfrActivator - Profiler is active.

We can see the various settings you can control, some that are useful depending on your use case like the splunk.profiler.directory - The location where the agent writes the call stacks before sending them to Splunk. This may be different depending on how you configure your containers.

Another parameter you may want to change is splunk.profiler.call.stack.interval This is how often the system takes a CPU Stack trace. You may want to reduce this if you have short spans like we have in our application. (we kept the default as the spans in this demo application are extremely short, so Span may not always have a CPU Call Stack related to it.)

You can find how to set these parameters here. Below, is how you set a higher collection rate for call stack in your deployment.yaml, exactly how to pass any JAVA option to the Java application running in your container:

env: 
- name: JAVA_OPTIONS
  value: "-Xdebug -Dsplunk.profiler.call.stack.interval=150"

If you don’t see those lines as a result of the script, the startup may have taken too long and generated too many connection errors, try looking at the logs directly with kubectl or the k9s utility that is installed.

Always-On Profiling in the Trace Waterfall

Make sure you have your original (or similar) Trace & Span (1) selected in the APM Waterfall view and select Memory Stack Traces (2) from the right-hand pane:

The pane should show you the Memory Stack Trace Flame Graph (3), you can scroll down and/or make the pane for a better view by dragging the right side of the pane.

As AlwaysOn Profiling is constantly taking snapshots, or stack traces, of your application’s code and reading through thousands of stack traces is not practical, AlwaysOn Profiling aggregates and summarizes profiling data, providing a convenient way to explore Call Stacks in a view called the Flame Graph. It represents a summary of all stack traces captured from your application. You can use the Flame Graph to discover which lines of code might be causing performance issues and to confirm whether the changes you make to the code have the intended effect.

To dive deeper into the Always-on Profiling, select Span (3) in the Profiling Pane under Memory Stack Traces This will bring you to the Always-on Profiling main screen, with the Memory view pre-selected:

• Time filter will be set to the time frame of the span we selected (1)
• Java Memory Metric Charts (2), Allow you to Monitor Heap Memory, Application Activity like Memory Allocation Rate and Garbage Collecting Metrics.
• Ability to focus/see metrics and Stack Traces only related to the Span (3), This will filter out background activities running in the Java application if required.
• Java Function calls identified. (4), allowing you to drill down into the Methods called from that function.
• The Flame Graph (5), with the visualization of hierarchy based on the stack traces of the profiled service.
• Ability to select the Service instance (6) in case the service spins up multiple version of it self.

For further investigation the UI let’s you grab the actual stack trace, by selecting a function and the relevant line from the flam chart, so you can use in your coding platform to go to the actual lines of code used at this point (depending of course on your preferred Coding platform)

Database Query Performance

With Database Query Performance, you can monitor the impact of your database queries on service availability directly in Splunk APM. This way, you can quickly identify long-running, un-optimized, or heavy queries and mitigate issues they might be causing, without having to instrument your databases.

To look at the performance of your database queries, make sure you are on the APM Service Map page either by going back in the browser or navigating to the APM section in the Menu bar, then click on the Service Map tile. Select the inferred database service mysql:petclinic Inferred Database server in the Dependency map (1), then scroll the right-hand pane to find the Database Query Performance Pane (2).

If the service you have selected in the map is indeed an (inferred) database server, this pane will populate with the top 90% (P90) database calls based on duration. To dive deeper into the db-query performance function click somewhere on the word Database Query Performance at the top of the pane.

This will bring us to the DB-query Performance overview screen:

This screen will show us all the Database queries (1) done towards our database from your application, based on the Traces & Spans sent to the Splunk Observability Cloud. Note that you can compare them across a time block or sort them on Total Time, P90 Latency & Requests (2).

For each Database query in the list, we see the highest latency, the total number of calls during the time window and the number of requests per second (3). This allows you to identify places where you might optimize your queries.

You can select traces containing Database Calls via the two charts in the right-hand pane (5). Use the Tag Spotlight pane (6) to drill down what tags are related to the database calls, based on endpoints or tags.

If you need to see a detailed view of a query:

Click on the specific Query (1), this wil give you a detailed query Details pane (2), which you can use for more detailed investigations:

Related Content

In the bottom pane is where any related content will be reported. In the screenshot below you can see that APM has found a trace that is related to this log line (1):

By clicking (2) on Trace for 960432ac9f16b98be84618778905af50 we will be taken to the waterfall in APM for this specific trace, where this log line was generated:

Note that you now have a Related Content pane for Logs appear (1). Clicking on this will take you back to Log Observer and will display all the log lines that are part of this trace.

Select the RUM view for the Petclinic App

Lets start a quick high level tour into RUM by clicking RUM in the left-hand menu. Then change the Environment filter (1) to the name of your workshop instance from the dropdown box, it will be <INSTANCE>-workshop (1) (where INSTANCE is the value from the shell script you ran earlier). Make sure it is the only one selected.

Then change the App (2) dropdown box to the name of your app, it will be <INSTANCE>-store

Once you have selected your Environment and App, you will see an overview page showing the RUM status of your App (if your Summary Dashboard is just a single row of numbers, you are looking at the condensed view. You can expand it by clicking on the > (1) in front of the Application name). If any JavaScript error occurred they will show up as shown below:

To continue, click on the blue link (with your workshop name) to get to the details page, this will bring up a new dashboard view breaking down the interactions by UX Metrics, Front-end Health, Back-end Health and Custom Events and comparing them to historic metrics (1 hour by default).

Normally you have only one line inside the first chart, Click on the link that relates to your Petclinic shop, http://198.19.249.202:81 in our example:

This will bring us to the Tag Spotlight page.

RUM trace Waterfall view & linking to APM

In the TAG Spotlight view, you are presented with all the tags associated with the RUM data. Tags are key-value pairs that are used to identify the data. In this case, the tags are automatically generated by the OpenTelemetry instrumentation. The tags are used to filter the data and to create the charts and tables. The Tag Spotlight view allows you detect trends in behavior and to drill down into a user session.

Click on User Sessions (1), this will show you the list of user session that occurred during the time window. We want to look at one of the session , so click on Duration (2) to sort on duration, and make sure you click on the link of one of the longer ones (3):

RUM trace Waterfall view & linking to APM

We are now looking at the RUM Trace waterfall, this will tell you what happened during the session on the user device as they visited the page of our petclinic application.

If you scroll down the waterfall find click on the #!/owners/details segment on the right (1), you see a list of action that occurred during the handling of the Vets request. Note, that the HTTP request have a blue APM link before the return code. Pick one, and click on the APM link. This will show you the APM info for this Ser vice call to our Microservices in Kubernetes.

Note , that there give you the information what happened during action in the Microservices, and if you want to drill down to verify what happened with the request, click on the Trace ID url.

This will show you the trace related to your request from RUM:

You can see that the entry point into your service now has a RUM (1) related content link added, allowing you to return back to your RUM session after you validated what happened in your Microservices.