What is krkn-ai? on Krkn

Getting Started

Mon, 01 Jan 0001 00:00:00 +0000

Getting Started with Krkn-AI

This documentation details how to deploy a sample microservice application on Kubernetes Cluster and run Krkn-AI test.

Prerequisites

Follow this guide to install Krkn-AI CLI.
Krkn-AI uses Thanos Querier to fetch SLO metrics by PromQL. You can easily install it by setting up prometheus-operator in your cluster.

Deploy Sample Microservice

For demonstration purpose, we will deploy a sample microservice called robot-shop on the cluster:

Cluster Discovery

Mon, 01 Jan 0001 00:00:00 +0000

Krkn-AI uses a genetic algorithm to generate Chaos scenarios. These scenarios require information about the components available in the cluster, which is obtained from the cluster_components YAML field of the Krkn-AI configuration.

CLI Usage

$ uv run krkn_ai discover --help
Usage: krkn_ai discover [OPTIONS]

 Discover components for Krkn-AI tests

Options:
 -k, --kubeconfig TEXT Path to cluster kubeconfig file.
 -o, --output TEXT Path to save config file.
 -n, --namespace TEXT Namespace(s) to discover components in. Supports
 Regex and comma separated values.
 -pl, --pod-label TEXT Pod Label Keys(s) to filter. Supports Regex and
 comma separated values.
 -nl, --node-label TEXT Node Label Keys(s) to filter. Supports Regex and
 comma separated values.
 -v, --verbose Increase verbosity of output.
 --skip-pod-name TEXT Pod name to skip. Supports comma separated values
 with regex.
 --help Show this message and exit.

Example

The example below filters cluster components from namespaces that match the patterns robot-.* and etcd. In addition to namespaces, we also provide filters for pod labels and node labels. This allows us to narrow down the necessary components to consider when running a Krkn-AI test.

Run Krkn-AI

Mon, 01 Jan 0001 00:00:00 +0000

The run command executes automated resilience and chaos testing using Krkn-AI. It initializes a random population samples containing Chaos Experiments based on your Krkn-AI configuration file, then starts the evolutionary algorithm to run the experiments, gather feedback, and continue evolving existing scenarios until stopping criteria is met.

CLI Usage

$ uv run krkn_ai run --help
Usage: krkn_ai run [OPTIONS]

 Run Krkn-AI tests

Options:
 -c, --config TEXT Path to Krkn-AI config file.
 -o, --output TEXT Directory to save results.
 -f, --format [json|yaml] Format of the output file. [default: yaml]
 -r, --runner-type [krknctl|krknhub] Type of chaos engine to use.
 -p, --param TEXT Additional parameters for config file in key=value format.
 -v, --verbose Increase verbosity of output. [default: 0]
 --help Show this message and exit.

Example

The following command runs Krkn-AI with verbose output (-vv), specifies the configuration file (-c), sets the output directory for results (-o), and passes an additional parameter (-p) to override the HOST variable in the config file:

Run Krkn-AI (Container)

Mon, 01 Jan 0001 00:00:00 +0000

Krkn-AI can be run inside containers, which simplifies integration with continuous testing workflows.

Container Image

A pre-built container image is available on Quay.io:

podman pull quay.io/krkn-chaos/krkn-ai:latest

Running the Container

The container supports two modes controlled by the MODE environment variable:

1. Discovery Mode

Discovers cluster components and generates a configuration file.

Usage:

# create a folder
mkdir -p ./tmp/container/

# copy kubeconfig to ./tmp/container

# execute discover command
podman run --rm \
 --net="host" \
 -v ./tmp/container:/mount:Z \
 -e MODE="discover" \
 -e KUBECONFIG="/mount/kubeconfig.yaml" \
 -e OUTPUT_DIR="/mount" \
 -e NAMESPACE="robot-shop" \
 -e POD_LABEL="service" \
 -e NODE_LABEL="kubernetes.io/hostname" \
 -e SKIP_POD_NAME="nginx-proxy.*" \
 -e VERBOSE="2" \
 quay.io/krkn-chaos/krkn-ai:latest

Environment Variables (Discovery):