Monitoring Poc for Openshift

Creating a proof of concept for the Spanmetrics Connector involves deploying a basic system that integrates Grafana Tempo, Spanmetrics Connector, Prometheus, and Grafana. Here’s a step-by-step guide to help you implement a simple version of this in an OpenShift or Kubernetes environment.

1. Set Up the Environment

You will need the following components:

• Tempo for tracing.

• Prometheus for metrics scraping.

• Spanmetrics Connector to convert spans to metrics.

• Grafana for visualization.

2. Deploy Grafana Tempo

First, deploy Tempo to collect tracing data.

Tempo Deployment YAML:

apiVersion: apps/v1
kind: Deployment
metadata:
name: tempo
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: tempo
template:
metadata:
labels:
app: tempo
spec:
containers:
- name: tempo
image: grafana/tempo:latest
ports:
- containerPort: 3200

Apply this configuration:

oc apply -f tempo-deployment.yaml

3. Deploy Prometheus

Next, deploy Prometheus to scrape metrics.

Prometheus Deployment YAML:

apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
template:
metadata:
labels:
app: prometheus
spec:
containers:
- name: prometheus
image: prom/prometheus:latest
ports:
- containerPort: 9090
volumeMounts:
- name: prometheus-config
mountPath: /etc/prometheus
subPath: prometheus.yml
volumes:
- name: prometheus-config
configMap:
name: prometheus-config

You also need to create a ConfigMap for Prometheus that includes the Spanmetrics scraping endpoint:

Prometheus ConfigMap:

apiVersion: v1
kind: ConfigMap
metadata:
name: prometheus-config
namespace: monitoring
data:
prometheus.yml: |
scrape_configs:
- job_name: 'spanmetrics-connector'
static_configs:
- targets: ['spanmetrics-connector:8080']

Apply this configuration:

oc apply -f prometheus-deployment.yaml
oc apply -f prometheus-configmap.yaml

4. Deploy Spanmetrics Connector

The Spanmetrics Connector will convert tracing spans into metrics for Prometheus to scrape.

Spanmetrics Connector Deployment YAML:

apiVersion: apps/v1
kind: Deployment
metadata:
name: spanmetrics-connector
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: spanmetrics-connector
template:
metadata:
labels:
app: spanmetrics-connector
spec:
containers:
- name: spanmetrics-connector
image: grafana/tempo-spanmetrics-connector:latest
ports:
- containerPort: 8080
env:
- name: TEMPO_ENDPOINT
value: http://tempo.monitoring.svc.cluster.local:3200
- name: SCRAPE_PORT
value: "8080"

This configuration assumes:

• The Spanmetrics Connector is configured to pull data from Tempo at its endpoint (http://tempo.monitoring.svc.cluster.local:3200).

• Metrics will be exposed on port 8080, which Prometheus will scrape.

Apply this configuration:

oc apply -f spanmetrics-connector.yaml

5. Deploy Grafana

Grafana will be used to visualize the metrics scraped by Prometheus.

Grafana Deployment YAML:

apiVersion: apps/v1
kind: Deployment
metadata:
name: grafana
namespace: monitoring
spec:
replicas: 1
selector:
matchLabels:
app: grafana
template:
metadata:
labels:
app: grafana
spec:
containers:
- name: grafana
image: grafana/grafana:latest
ports:
- containerPort: 3000

Once deployed, access Grafana by exposing a route or port-forwarding:

oc expose svc/grafana

6. Configure Grafana Data Source

Once you have Grafana running, you will need to add Prometheus as a data source:

• Go to Grafana UI.

• Navigate to Configuration > Data Sources.

• Add Prometheus and specify the Prometheus URL (e.g., http://prometheus:9090).

7. Generate Sample Traces

To simulate traffic and generate spans that will be picked up by Tempo and converted into metrics by the Spanmetrics Connector, you can use Jaeger or OpenTelemetry SDKs to send trace data to Tempo.

8. Create a Dashboard in Grafana

After setting up the data source, you can create a Grafana dashboard to visualize the metrics generated from spans. Some useful metrics include:

• Request Latency: The average latency of traces.

• Error Rates: The number of errors encountered across traces.

• Request Count: The total number of traces.

Summary of Workflow:

• Tempo collects distributed trace spans.

• Spanmetrics Connector pulls the trace spans from Tempo and converts them into Prometheus-compatible metrics.

• Prometheus scrapes the metrics from the Spanmetrics Connector.

• Grafana visualizes the metrics for monitoring and troubleshooting.

This proof of concept demonstrates the entire workflow and integration between tracing, metrics generation, and visualization.

Imported from rifaterdemsahin.com · 2024