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‎content/en/bootcamp/docs/apm/_index.md‎

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---
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title: 'Lab: Application Performance Monitoring'
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linkTitle: Microserverices APM
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weight: 6
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---
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## Goals
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1. Understand GDI path for APM for common tech stacks (Docker, K8s).
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1. Be able to instrument an app from scratch (traces, custom metadata).
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1. Understand how distributed tracing works across tech stacks (header propagation, …)

‎content/en/bootcamp/docs/apm/api.md‎

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---
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title: Using OpenTelementry in Instrumentation
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weight: 9
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---
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The business teams want to add the service version, the customer profile that is defined by a color and the name of the analyzed file.
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Switch to the provided milestone `12microservices-k8s-autoi` with the instructions from "Getting Started".
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Implement the requested changes to the `public-api` microservice.
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The Kubernetes manifests are located in the `k8s` folder. Add the service version by [configuring the OpenTelemetry resource attributes][splunk-py-otel-cfg].
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The customer profile and the file name vary by request. Create attributes and assign them to the current span with the [OpenTelemetry Python API][otel-py-api].
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[splunk-py-otel-cfg]: https://github.com/signalfx/splunk-otel-python/blob/main/docs/advanced-config.md#trace-configuration
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[otel-py-api]: https://opentelemetry-python.readthedocs.io/en/stable/faq-and-cookbook.html
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You can use a random function to generate the customer profile (e.g. red, blue, green) with this snippet:
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```python
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color = random.choice(['red', 'blue', 'green'])
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```
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Note 1: Do not use a temporary variable to retrieve the current span. Use the `trace` directly.
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Note 2: Make sure to import modules.
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Rebuild the container images for the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose build{{< /tab >}}
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{{< /tabpane >}}
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Push the images to the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose push{{< /tab >}}
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{{< /tabpane >}}
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Delete the `public-api` deployment:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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kubectl delete deploy public-api{{< /tab >}}
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{{< /tabpane >}}
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Redeploy to the cluster with
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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kubectl apply -f k8s{{< /tab >}}
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{{< /tabpane >}}
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Test the service with
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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ENDPOINT=$(kubectl get service/public-api -o jsonpath='{.spec.clusterIP}')
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curl http://$ENDPOINT:8000/api -F text=@hamlet.txt{{< /tab >}}
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{{< /tabpane >}}
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Verify in Splunk APM that traces contain the desired informations: TODO screenshot
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[Create a new indexed span tag][index-span-tag] so that the business team is able to breakdown performance per customer profile.
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The milestone for this task is `13custom-instr`. It adds the described custom instrumentation.
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[span-tag]: (https://docs.splunk.com/Observability/apm/span-tags/index-span-tags.html#index-a-new-span-tag)

‎content/en/bootcamp/docs/apm/k8s.md‎

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---
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title: Instrumentation in Kubernetes
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weight: 7
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---
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TODO Note on .env being overwritten
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TODO change name of environment from YOURENV to something else.
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The development team has started using Kubernetes for container orchestration. Switch to the provided milestone `12microservices-k8s` with the instructions from "Getting Started".
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The Kubernetes manifests are located in the `k8s` folder. Add auto-instrumentation to the `public_api` microservice `deployment` by configuring the [Splunk distribution of OpenTelemetry Python][splunk-otel-python]. The `Dockerfile` has already been prepared.
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Install the OpenTelemetry Collector to the environment using [Splunk's helm chart][splunk-otel-helm] and use the provided `values.yaml`:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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helm repo add splunk-otel-collector-chart https://signalfx.github.io/splunk-otel-collector-chart
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helm install my-splunk-otel-collector --set="splunkObservability.realm=${SPLUNK_REALM},splunkObservability.accessToken=${SPLUNK_ACCESS_TOKEN},clusterName=${CLUSTER_NAME}" splunk-otel-collector-chart/splunk-otel-collector -f values.yaml{{< /tab >}}{{< /tabpane >}}
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Rebuild the container images for the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose build{{< /tab >}}
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{{< /tabpane >}}
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Push the images to the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose push{{< /tab >}}
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{{< /tabpane >}}
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Deploy to the cluster with
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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kubectl apply -f k8s{{< /tab >}}
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{{< /tabpane >}}
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Test the service with
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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ENDPOINT=$(kubectl get service/public-api -o jsonpath='{.spec.clusterIP}')
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curl http://$ENDPOINT:8000/api -F text=@hamlet.txt{{< /tab >}}
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{{< /tabpane >}}
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The milestone for this task is `12microservices-k8s-autoi`. It has auto-instrumentation applied for *all* microservices.
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[splunk-otel-helm]: https://github.com/signalfx/splunk-otel-collector-chart

‎content/en/bootcamp/docs/gdi/k8s.md‎

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---
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title: Kubernetes
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weight: 21
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---
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The development team has started using [Kubernetes][kubernetes] for container orchestration. Switch to the provided milestone `09k8s` with the instructions from "Getting Started".
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Rebuild the container images for the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose build{{< /tab >}}
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{{< /tabpane >}}
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Push the images to the private registry:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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docker-compose push
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{{< /tab >}}
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{{< /tabpane >}}
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Then deploy the services into the cluster:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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kubectl apply -f k8s
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{{< /tab >}}
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{{< /tabpane >}}
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Test the service with
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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ENDPOINT=$(kubectl get service/wordcount -o jsonpath='{.spec.clusterIP}')
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curl http://$ENDPOINT:8000/wordcount -F text=@hamlet.txt
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{{< /tab >}}
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{{< /tabpane >}}
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Configure and install an OpenTelemetry Collector using [Splunk\'s helm chart][splunk-otel-helm]:
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1. Review the [configuration how-to][otel-docs] and the [advanced configuration][otel-adv-cfg] to create a `values.yaml` that adds the required receivers for redis and prometheus.
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1. Use the environment variables for realm,token and cluster name and pass them to `helm` as arguments.
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The milestone for this task is `09k8s-otel`.
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[kubernetes]: https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/
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[splunk-otel-helm]: https://github.com/signalfx/splunk-otel-collector-chart
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[otel-docs]: https://github.com/signalfx/splunk-otel-collector-chart#how-to-install
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[otel-adv-cfg]: https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/advanced-configuration.md

‎content/en/bootcamp/docs/gdi/otel-container-svc.md‎

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---
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title: Monitor Containerized Services
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weight: 19
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---
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The development team has started using other containerized services with docker compose. Switch to the provided milestone `08docker-compose-redis` with the instructions from "Getting Started".
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Add the [redis monitor][redis-mon] to the OpenTelemetry Collector configuration in `collector.yaml` to get metrics from the [redis cache].
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Rebuild the docker-compose stack and run it.
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Check that you are getting data in the Redis dashboard:
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![Redis dashboard](../../../images/redis-dashboard.png)
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The milestone for this task is `08docker-compose-redis-otel`.
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[redis]: https://redis.io/
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[redis-mon]: https://docs.splunk.com/Observability/gdi/redis/redis.html

‎content/en/bootcamp/docs/gdi/prometheus.md‎

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---
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title: Add Prometheus metrics
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weight: 7
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---
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We need visibility into performance - let us add metrics with [Prometheus][prometheus].
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Install the [Python Prometheus client][py-prom] as a dependency:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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echo "prometheus-client" >> requirements.txt
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python3 -m venv .venv
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source .venv/bin/activate
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.venv/bin/pip install -r requirements.txt
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{{< /tab >}}
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{{< /tabpane >}}
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Import the modules by editing `app.py`. These imports go towards the top of the file:
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```python
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import prometheus_client
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from prometheus_client.exposition import CONTENT_TYPE_LATEST
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from prometheus_client import Counter
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```
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Define a metrics endpoint before `@app.route('/wordcount', methods=['POST'])`:
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```python
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@app.route('/metrics')
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def metrics():
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return Response(prometheus_client.generate_latest(), mimetype=CONTENT_TYPE_LATEST)
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```
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And use this python snippet after `app = Flask(__name__)` to define a new counter metric:
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```python
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c_recv = Counter('characters_recv', 'Number of characters received')
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```
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Increase the counter metric after `data = request.files['text'].read().decode('utf-8')`:
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```python
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c_recv.inc(len(data))
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```
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Test that the application exposes metrics by hitting the endpoint while the app is running:
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{{< tabpane >}}
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{{< tab header="Shell Command" lang="bash" >}}
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curl http://127.0.0.1:5000/metrics{{< /tab >}}
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{{< /tabpane >}}
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The milestone for this task is `02service-metrics`.
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[prometheus]: https://prometheus.io/docs/introduction/overview/#architecture
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[py-prom]: https://pypi.org/project/prometheus-client/

‎content/en/imt/docs/dashboards/dashboarding.md‎

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---
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title: Adding Notes and Dashboard Layout
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linkTitle: Notes and layout
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weight: 13
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---
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## 1. Adding Notes
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Often on dashboards it makes sense to place a short "instruction" pane that helps users of a dashboard.
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Lets add one now by clicking on the {{% labelbutton color="ui-button-blue" %}}New Text Note{{% /labelbutton %}}
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Button.
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![three charts](../../../images/M-Notes-0.png)
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This will open the notes editor.
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![Notes 1](../../../images/M-Notes-1.png)
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To allow you to add more then just text to you notes, Splunk is allowing you to use Markdown in these notes/panes.
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Markdown is a lightweight markup language for creating formatted text using plain-text often used in Webpages.
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This includes (but not limited to):
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* Headers. (in various sizes)
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* Emphasis styles.
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* Lists and Tables.
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* Links. These can be external webpages (for documentation for example) or directly to other Splunk IMT Dashboards
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Below is an example of above Markdown options you can use in your note.
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{{< tabpane >}}
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{{< tab header="Sample Markdown text" lang="markdown" >}}
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# h1 Big headings
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###### h6 To small headings
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##### Emphasis
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**This is bold text**, *This is italic text* , ~~Strikethrough~~
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##### Lists
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Unordered
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+ Create a list by starting a line with `+`, `-`, or `*`
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- Sub-lists are made by indenting 2 spaces:
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- Marker character change forces new list start:
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* Ac tristique libero volutpat at
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+ Facilisis in pretium nisl aliquet
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* Very easy!
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Ordered
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1. Lorem ipsum dolor sit amet
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2. Consectetur adipiscing elit
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3. Integer molestie lorem at massa
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##### Tables
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| Option | Description |
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| ------ | ----------- |
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| chart | path to data files to supply the data that will be passed into templates. |
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| engine | engine to be used for processing templates. Handlebars is the default. |
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| ext | extension to be used for dest files. |
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#### Links
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[link to webpage](https://www.splunk.com)
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[link to dashboard with title](https://app.eu0.signalfx.com/#/dashboard/EaJHrbPAEAA?groupId=EaJHgrsAIAA&configId=EaJHsHzAEAA "Link to the Sample chart Dashboard!")
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{{< /tab >}}
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{{< /tabpane >}}
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Copy the above by using the copy button and paste it in the *Edit* box.
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the preview will show you how it will look.
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---
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## 2. Saving our chart
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Give the Note chart a name, in our example we used *Example text chart*, then press the {{< labelbutton >}}Save And Close{{< /labelbutton >}} Button.
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![saving note](../../../images/M-Notes-2.png)
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This will bring you back to you Dashboard, that now includes the note.
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![three charts and note](../../../images/M-Notes-3.png)
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---
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## 3. Ordering & sizing of charts
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If you do not like the default order and sizes of your charts you can simply use window dragging technique to move and size them to the desired location.
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Grab the **top** border of a chart and you should see the mouse pointer change to a drag icon (see picture below).
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![dragging charts](../../../images/M-Notes-4.png)
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Now drag the **Latency vs Load** chart to sit between the **Latency History** Chart and the **Example text chart**.
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![sizing](../../../images/M-Notes-5.png)
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You can also resize windows by dragging from the left, right and bottom edges.
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As a last exercise reduce the width of the note chart to about a third of the other charts. The chart will automatically snap to one of the sizes it supports. Widen the 3 other charts to about a third of the Dashboard. Drag the notes to the right of the others and resize it to match it to the 3 others.
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Set the time to -1 h hour and you should have the following dashboard!
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![TaDA!](../../../images/M-Notes-6.png)
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On to Detectors!

‎content/en/imt/docs/detectors/_index.md‎

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---
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title: Working with Detectors
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linkTitle: Detectors
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weight: 5
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description: >
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**10 minutes**
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---

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