pecastel
Cisco Employee
Cisco Employee

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