Cluster Discovery
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.
uv run krkn_ai discover -k ./tmp/kubeconfig.yaml \
-n "robot-.*,etcd" \
-pl "service,env" \
-nl "disktype" \
-o ./krkn-ai.yaml
The above command generates a config file that contains the basic setup to help you get started. You can customize the parameters as described in the configs documentation. If you want to exclude any cluster components—such as a pod, node, or namespace—from being considered for Krkn-AI testing, simply remove them from the cluster_components YAML field.
# Path to your kubeconfig file
kubeconfig_file_path: "./path/to/kubeconfig.yaml"
# Genetic algorithm parameters
generations: 5
population_size: 10
composition_rate: 0.3
population_injection_rate: 0.1
scenario_mutation_rate: 0.6
# Duration to wait before running next scenario (seconds)
wait_duration: 30
# Specify how result filenames are formatted
output:
result_name_fmt: "scenario_%s.yaml"
graph_name_fmt: "scenario_%s.png"
log_name_fmt: "scenario_%s.log"
# Fitness function configuration for defining SLO
# In the below example, we use Total Restarts in "robot-shop" namespace as the SLO
fitness_function:
query: 'sum(kube_pod_container_status_restarts_total{namespace="robot-shop"})'
type: point
include_krkn_failure: true
# Chaos scenarios to consider during testing
scenario:
pod-scenarios:
enable: true
application-outages:
enable: true
container-scenarios:
enable: false
node-cpu-hog:
enable: false
node-memory-hog:
enable: false
# Cluster components to consider for Krkn-AI testing
cluster_components:
namespaces:
- name: robot-shop
pods:
- containers:
- name: cart
labels:
service: cart
env: dev
name: cart-7cd6c77dbf-j4gsv
- containers:
- name: catalogue
labels:
service: catalogue
env: dev
name: catalogue-94df6b9b-pjgsr
services:
- labels:
app.kubernetes.io/managed-by: Helm
name: cart
ports:
- port: 8080
protocol: TCP
target_port: 8080
- labels:
app.kubernetes.io/managed-by: Helm
service: catalogue
name: catalogue
ports:
- port: 8080
protocol: TCP
target_port: 8080
- name: etcd
pods:
- containers:
- name: etcd
labels:
service: etcd
name: etcd-0
- containers:
- name: etcd
labels:
service: etcd
name: etcd-1
nodes:
- labels:
kubernetes.io/hostname: node-1
disktype: SSD
name: node-1
taints: []
- labels:
kubernetes.io/hostname: node-2
disktype: HDD
name: node-2
taints: []
