docs: Add opentelemetry docs

docs/SUMMARY.md

@@ -36,6 +36,7 @@
   * [Batch Materialization Engine](getting-started/components/batch-materialization-engine.md)
   * [Provider](getting-started/components/provider.md)
   * [Authorization Manager](getting-started/components/authz_manager.md)
+  * [OpenTelemetry Integration](getting-started/components/open-telemetry.md)
 * [Third party integrations](getting-started/third-party-integrations.md)
 * [FAQ](getting-started/faq.md)
 

docs/getting-started/components/README.md

@@ -27,3 +27,7 @@
 {% content-ref url="authz_manager.md" %}
 [authz_manager.md](authz_manager.md)
 {% endcontent-ref %}
+
+{% content-ref url="open-telemetry.md" %}
+[open-telemetry.md](open-telemetry.md)
+{% endcontent-ref %}

docs/getting-started/components/open-telemetry.md

@@ -0,0 +1,149 @@
+# OpenTelemetry Integration
+
+The OpenTelemetry integration in Feast provides comprehensive monitoring and observability capabilities for your feature serving infrastructure. This component enables you to track key metrics, traces, and logs from your Feast deployment.
+
+## Motivation
+
+Monitoring and observability are critical for production machine learning systems. The OpenTelemetry integration addresses these needs by:
+
+1. **Performance Monitoring:** Track CPU and memory usage of feature servers
+2. **Operational Insights:** Collect metrics to understand system behavior and performance
+3. **Troubleshooting:** Enable effective debugging through distributed tracing
+4. **Resource Optimization:** Monitor resource utilization to optimize deployments
+5. **Production Readiness:** Provide enterprise-grade observability capabilities
+
+## Architecture
+
+The OpenTelemetry integration in Feast consists of several components working together:
+
+- **OpenTelemetry Collector:** Receives, processes, and exports telemetry data
+- **Prometheus Integration:** Enables metrics collection and monitoring
+- **Instrumentation:** Automatic Python instrumentation for tracking metrics
+- **Exporters:** Components that send telemetry data to monitoring systems
+
+## Key Features
+
+1. **Automated Instrumentation:** Python auto-instrumentation for comprehensive metric collection
+2. **Metric Collection:** Track key performance indicators including:
+   - Memory usage
+   - CPU utilization
+   - Request latencies
+   - Feature retrieval statistics
+3. **Flexible Configuration:** Customizable metric collection and export settings
+4. **Kubernetes Integration:** Native support for Kubernetes deployments
+5. **Prometheus Compatibility:** Integration with Prometheus for metrics visualization
+
+## Setup and Configuration
+
+To add monitoring to the Feast Feature Server, follow these steps:
+
+### 1. Deploy Prometheus Operator
+Follow the [Prometheus Operator documentation](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/user-guides/getting-started.md) to install the operator.
+
+### 2. Deploy OpenTelemetry Operator
+Before installing the OpenTelemetry Operator:
+1. Install `cert-manager`
+2. Validate that the `pods` are running
+3. Apply the OpenTelemetry operator:
+```bash
+kubectl apply -f https://github.com/open-telemetry/opentelemetry-operator/releases/latest/download/opentelemetry-operator.yaml
+```
+
+For additional installation steps, refer to the [OpenTelemetry Operator documentation](https://github.com/open-telemetry/opentelemetry-operator).
+
+### 3. Configure OpenTelemetry Collector
+Add the OpenTelemetry Collector configuration under the metrics section in your values.yaml file:
+
+```yaml
+metrics:
+  enabled: true
+  otelCollector:
+    endpoint: "otel-collector.default.svc.cluster.local:4317"  # sample
+    headers:
+      api-key: "your-api-key"
+```
+
+### 4. Add Instrumentation Configuration
+Add the following annotations and environment variables to your deployment.yaml:
+
+```yaml
+template:
+  metadata:
+    annotations:
+      instrumentation.opentelemetry.io/inject-python: "true"
+```
+
+```yaml
+- name: OTEL_EXPORTER_OTLP_ENDPOINT
+  value: http://{{ .Values.service.name }}-collector.{{ .Release.namespace }}.svc.cluster.local:{{ .Values.metrics.endpoint.port}}
+- name: OTEL_EXPORTER_OTLP_INSECURE
+  value: "true"
+```
+
+### 5. Add Metric Checks
+Add metric checks to all manifests and deployment files:
+
+```yaml
+{{ if .Values.metrics.enabled }}
+apiVersion: opentelemetry.io/v1alpha1
+kind: Instrumentation
+metadata:
+  name: feast-instrumentation
+spec:
+  exporter:
+    endpoint: http://{{ .Values.service.name }}-collector.{{ .Release.Namespace }}.svc.cluster.local:4318
+  env:
+  propagators:
+    - tracecontext
+    - baggage
+  python:
+    env:
+      - name: OTEL_METRICS_EXPORTER
+        value: console,otlp_proto_http
+      - name: OTEL_LOGS_EXPORTER
+        value: otlp_proto_http
+      - name: OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED
+        value: "true"
+{{end}}
+```
+
+### 6. Add Required Manifests
+Add the following components to your chart:
+- Instrumentation
+- OpenTelemetryCollector
+- ServiceMonitors
+- Prometheus Instance
+- RBAC rules
+
+### 7. Deploy Feast
+Deploy Feast with metrics enabled:
+
+```bash
+helm install feast-release infra/charts/feast-feature-server --set metric=true --set feature_store_yaml_base64=""
+```
+
+## Usage
+
+To enable OpenTelemetry monitoring in your Feast deployment:
+
+1. Set `metrics.enabled=true` in your Helm values
+2. Configure the OpenTelemetry Collector endpoint
+3. Deploy with proper annotations and environment variables
+
+Example configuration:
+```yaml
+metrics:
+  enabled: true
+  otelCollector:
+    endpoint: "otel-collector.default.svc.cluster.local:4317"
+```
+
+## Monitoring
+
+Once configured, you can monitor various metrics including:
+
+- `feast_feature_server_memory_usage`: Memory utilization of the feature server
+- `feast_feature_server_cpu_usage`: CPU usage statistics
+- Additional custom metrics based on your configuration
+
+These metrics can be visualized using Prometheus and other compatible monitoring tools.