Overview
This lab provides a practical approach to consume XR Model Driven Streaming telemetry, using open-source consumers in an automated environment. After exploring how to configure XR routers for Model Driven Streaming telemetry, the lab introduces a consumption pipeline based on InfluxDB, Grafana and Kapacitor – to store, render and alarm on the data being streamed. The last section of the lab introduces Apache Kafka Pub/Sub bus, to distribute the telemetry data to multiple subscribers and explains how to code a basic Python subscriber.
Scenarios
- Section 1: Understand Model Driven Telemetry
- Section 2: Configure gRPC Dialout
- Section 3: Configure gRPC Dialin
- Section 4: Staging a Telemetry Stack
- Section 5: Understanding InfluxDB
- Section 6: Exploring InfluxDB APIs
- Section 7: Exploring Grafana - My first dashboard
- Section 8: Exploring the environment reference dashboard
- Section 9: Exploring near real time measurements using Ostinato
- Section 10: Streaming Routing Metrics
- Section 11: Exploring Kapacitor
- Section 12: Understanding metric.json
- Section 13: Troubleshooting YANG model data
- Section 14: Apache Kafka Pub/Sub bus
- Section 15: Cleaning up
- Section 16: What Next
Components and Functionality
- Model Driven Telemetry on IOS XRv
- InfluxDB as time series database to store telemetry information
- Kapacitor as real-time stream processing engine for KPI
- Grafana as open platform for analytics and monitoring
- Apache Kafka as distributed streaming messaging bus
- Ansible as environment automation engine
Resources
