To get the most out of this tutorial, you should have access to an environment with the Go programming language installed, and the R statistical computing language installed. You should have Docker installed on your laptop, and install the Jupyter Notebook. You should also clone the Data Science for Effective Operations Jupyter Notebook.
You will want to have access to a Kubernetes environment also. Google Cloud Platform offers a free Kubernetes Engine trial that you can use for this tutorial.
The goals of this tutorial are to:
- Get Istio installed
- Send requests to a sample application
- Use the Istio Grafana dashboard
- Create the example metrics adapter
- Generate a statistical distribution of request latency data using R and Istio telemetry
- Use the Jupyter notebook to analyze request latency data with histograms
- The docker image to pull for this takes a while - please run ./docker.sh --run before the tutorial
- Docker for Mac with Kubernetes
- Minikube
- Hello Minikube Tutorial (OS X)
- Google Kubernetes Engine
- Google Cloud SDK
- kubectl cheat sheet
- Project homepage
- Github
- Users mailing list
- Community Links
- Istio Setup
- Bookinfo Sample Application
- Developer Guide
- Compiled in Adapter Walkthrough
- gRPC Adapter Walkthrough
- Mixer Using a Custom Adapter
- Protoc (Google Protocol Buffers) binaries
- The Circonus Istio Mixer Adapter
- Istio 0.8.0 release
- Verified stable Istio release -
HUB=gcr.io/istio-release TAG=release-1.0-20180710-09-15
- How to Make a Histogram with Basic R
- R Tutorial
- Log linear histogram Go library
- Jupyter Notebook
- Data Science for Effective Operations Jupyter Notebook
Exercise 2 - Bookinfo Sample Application
Exercise 3 - Create a metrics adapter
Exercise 4 - Use R to plot a simple histogram of latency data
Exercise 5 - Use the Jupyter notebook to analyze latency data