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Author SHA1 Message Date
Janos Bonic
d8c4c27a7d Fixes #265: Replace powerfulseal 2022-06-07 17:11:53 +02:00
253 changed files with 3494 additions and 17344 deletions

40
.github/workflows/build.yml vendored Normal file
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@@ -0,0 +1,40 @@
name: Build Krkn
on:
push:
branches:
- main
pull_request:
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v3
- name: Create multi-node KinD cluster
uses: chaos-kubox/actions/kind@main
- name: Install environment
run: |
sudo apt-get install build-essential python3-dev
pip install -r requirements.txt
- name: Run unit tests
run: python -m unittest discover
- name: Run e2e tests
run: ./CI/run.sh
- name: Build the Docker images
run: docker build --no-cache -t quay.io/chaos-kubox/krkn containers/
- name: Login in quay
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker login quay.io -u ${QUAY_USER} -p ${QUAY_TOKEN}
env:
QUAY_USER: ${{ secrets.QUAY_USER_1 }}
QUAY_TOKEN: ${{ secrets.QUAY_TOKEN_1 }}
- name: Push the Docker images
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker push quay.io/chaos-kubox/krkn
- name: Rebuild krkn-hub
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
uses: chaos-kubox/actions/krkn-hub@main
with:
QUAY_USER: ${{ secrets.QUAY_USER_1 }}
QUAY_TOKEN: ${{ secrets.QUAY_TOKEN_1 }}

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@@ -1,41 +0,0 @@
name: Docker Image CI
on:
push:
branches:
- main
pull_request:
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v3
- name: Build the Docker images
run: |
docker build --no-cache -t quay.io/krkn-chaos/krkn containers/
docker tag quay.io/krkn-chaos/krkn quay.io/redhat-chaos/krkn
- name: Login in quay
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker login quay.io -u ${QUAY_USER} -p ${QUAY_TOKEN}
env:
QUAY_USER: ${{ secrets.QUAY_USERNAME }}
QUAY_TOKEN: ${{ secrets.QUAY_PASSWORD }}
- name: Push the KrknChaos Docker images
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker push quay.io/krkn-chaos/krkn
- name: Login in to redhat-chaos quay
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker login quay.io -u ${QUAY_USER} -p ${QUAY_TOKEN}
env:
QUAY_USER: ${{ secrets.QUAY_USER_1 }}
QUAY_TOKEN: ${{ secrets.QUAY_TOKEN_1 }}
- name: Push the RedHat Chaos Docker images
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: docker push quay.io/redhat-chaos/krkn
- name: Rebuild krkn-hub
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
uses: redhat-chaos/actions/krkn-hub@main
with:
QUAY_USER: ${{ secrets.QUAY_USERNAME }}
QUAY_TOKEN: ${{ secrets.QUAY_PASSWORD }}

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@@ -1,198 +0,0 @@
name: Functional & Unit Tests
on:
pull_request:
push:
branches:
- main
jobs:
tests:
# Common steps
name: Functional & Unit Tests
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v3
- name: Create multi-node KinD cluster
uses: redhat-chaos/actions/kind@main
- name: Install Helm & add repos
run: |
curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo add stable https://charts.helm.sh/stable
helm repo update
- name: Deploy prometheus & Port Forwarding
run: |
kubectl create namespace prometheus-k8s
helm install \
--wait --timeout 360s \
kind-prometheus \
prometheus-community/kube-prometheus-stack \
--namespace prometheus-k8s \
--set prometheus.service.nodePort=30000 \
--set prometheus.service.type=NodePort \
--set grafana.service.nodePort=31000 \
--set grafana.service.type=NodePort \
--set alertmanager.service.nodePort=32000 \
--set alertmanager.service.type=NodePort \
--set prometheus-node-exporter.service.nodePort=32001 \
--set prometheus-node-exporter.service.type=NodePort
SELECTOR=`kubectl -n prometheus-k8s get service kind-prometheus-kube-prome-prometheus -o wide --no-headers=true | awk '{ print $7 }'`
POD_NAME=`kubectl -n prometheus-k8s get pods --selector="$SELECTOR" --no-headers=true | awk '{ print $1 }'`
kubectl -n prometheus-k8s port-forward $POD_NAME 9090:9090 &
sleep 5
- name: Install Python
uses: actions/setup-python@v4
with:
python-version: '3.9'
architecture: 'x64'
- name: Install environment
run: |
sudo apt-get install build-essential python3-dev
pip install --upgrade pip
pip install -r requirements.txt
- name: Deploy test workloads
run: |
kubectl apply -f CI/templates/outage_pod.yaml
kubectl wait --for=condition=ready pod -l scenario=outage --timeout=300s
kubectl apply -f CI/templates/container_scenario_pod.yaml
kubectl wait --for=condition=ready pod -l scenario=container --timeout=300s
kubectl create namespace namespace-scenario
kubectl apply -f CI/templates/time_pod.yaml
kubectl wait --for=condition=ready pod -l scenario=time-skew --timeout=300s
kubectl apply -f CI/templates/service_hijacking.yaml
kubectl wait --for=condition=ready pod -l "app.kubernetes.io/name=proxy" --timeout=300s
- name: Get Kind nodes
run: |
kubectl get nodes --show-labels=true
# Pull request only steps
- name: Run unit tests
if: github.event_name == 'pull_request'
run: python -m coverage run -a -m unittest discover -s tests -v
- name: Setup Pull Request Functional Tests
if: |
github.event_name == 'pull_request'
run: |
yq -i '.kraken.port="8081"' CI/config/common_test_config.yaml
yq -i '.kraken.signal_address="0.0.0.0"' CI/config/common_test_config.yaml
yq -i '.kraken.performance_monitoring="localhost:9090"' CI/config/common_test_config.yaml
echo "test_service_hijacking" > ./CI/tests/functional_tests
echo "test_app_outages" >> ./CI/tests/functional_tests
echo "test_container" >> ./CI/tests/functional_tests
echo "test_namespace" >> ./CI/tests/functional_tests
echo "test_net_chaos" >> ./CI/tests/functional_tests
echo "test_time" >> ./CI/tests/functional_tests
echo "test_arca_cpu_hog" >> ./CI/tests/functional_tests
echo "test_arca_memory_hog" >> ./CI/tests/functional_tests
echo "test_arca_io_hog" >> ./CI/tests/functional_tests
# Push on main only steps + all other functional to collect coverage
# for the badge
- name: Configure AWS Credentials
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
uses: aws-actions/configure-aws-credentials@v4
with:
aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
aws-region : ${{ secrets.AWS_REGION }}
- name: Setup Post Merge Request Functional Tests
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
run: |
yq -i '.kraken.port="8081"' CI/config/common_test_config.yaml
yq -i '.kraken.signal_address="0.0.0.0"' CI/config/common_test_config.yaml
yq -i '.kraken.performance_monitoring="localhost:9090"' CI/config/common_test_config.yaml
yq -i '.telemetry.username="${{secrets.TELEMETRY_USERNAME}}"' CI/config/common_test_config.yaml
yq -i '.telemetry.password="${{secrets.TELEMETRY_PASSWORD}}"' CI/config/common_test_config.yaml
echo "test_telemetry" > ./CI/tests/functional_tests
echo "test_service_hijacking" >> ./CI/tests/functional_tests
echo "test_app_outages" >> ./CI/tests/functional_tests
echo "test_container" >> ./CI/tests/functional_tests
echo "test_namespace" >> ./CI/tests/functional_tests
echo "test_net_chaos" >> ./CI/tests/functional_tests
echo "test_time" >> ./CI/tests/functional_tests
echo "test_arca_cpu_hog" >> ./CI/tests/functional_tests
echo "test_arca_memory_hog" >> ./CI/tests/functional_tests
echo "test_arca_io_hog" >> ./CI/tests/functional_tests
# Final common steps
- name: Run Functional tests
env:
AWS_BUCKET: ${{ secrets.AWS_BUCKET }}
run: |
./CI/run.sh
cat ./CI/results.markdown >> $GITHUB_STEP_SUMMARY
echo >> $GITHUB_STEP_SUMMARY
- name: Upload CI logs
uses: actions/upload-artifact@v3
with:
name: ci-logs
path: CI/out
if-no-files-found: error
- name: Collect coverage report
run: |
python -m coverage html
python -m coverage json
- name: Publish coverage report to job summary
run: |
pip install html2text
html2text --ignore-images --ignore-links -b 0 htmlcov/index.html >> $GITHUB_STEP_SUMMARY
- name: Upload coverage data
uses: actions/upload-artifact@v3
with:
name: coverage
path: htmlcov
if-no-files-found: error
- name: Upload json coverage
uses: actions/upload-artifact@v3
with:
name: coverage.json
path: coverage.json
if-no-files-found: error
- name: Check CI results
run: grep Fail CI/results.markdown && false || true
badge:
permissions:
contents: write
name: Generate Coverage Badge
runs-on: ubuntu-latest
needs:
- tests
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
steps:
- name: Check out doc repo
uses: actions/checkout@master
with:
repository: krkn-chaos/krkn-lib-docs
path: krkn-lib-docs
ssh-key: ${{ secrets.KRKN_LIB_DOCS_PRIV_KEY }}
- name: Download json coverage
uses: actions/download-artifact@v3
with:
name: coverage.json
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.9
- name: Copy badge on GitHub Page Repo
env:
COLOR: yellow
run: |
# generate coverage badge on previously calculated total coverage
# and copy in the docs page
export TOTAL=$(python -c "import json;print(json.load(open('coverage.json'))['totals']['percent_covered_display'])")
[[ $TOTAL > 40 ]] && COLOR=green
echo "TOTAL: $TOTAL"
echo "COLOR: $COLOR"
curl "https://img.shields.io/badge/coverage-$TOTAL%25-$COLOR" > ./krkn-lib-docs/coverage_badge_krkn.svg
- name: Push updated Coverage Badge
run: |
cd krkn-lib-docs
git add .
git config user.name "krkn-chaos"
git config user.email "<>"
git commit -m "[KRKN] Coverage Badge ${GITHUB_REF##*/}" || echo "no changes to commit"
git push

9
.gitignore vendored
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@@ -16,7 +16,6 @@ __pycache__/*
*.out
kube-burner*
kube_burner*
recommender_*.json
# Project files
.ropeproject
@@ -24,8 +23,6 @@ recommender_*.json
.pydevproject
.settings
.idea
.vscode
config/debug.yaml
tags
# Package files
@@ -62,9 +59,5 @@ inspect.local.*
!CI/config/common_test_config.yaml
CI/out/*
CI/ci_results
CI/legacy/*node.yaml
CI/scenarios/*node.yaml
CI/results.markdown
#env
chaos/*

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@@ -1,6 +0,0 @@
[allowlist]
description = "Global Allowlist"
paths = [
'''kraken/arcaflow_plugin/fixtures/*'''
]

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@@ -1,7 +1,7 @@
## CI Tests
### First steps
Edit [functional_tests](tests/functional_tests) with tests you want to run
Edit [my_tests](tests/my_tests) with tests you want to run
### How to run
```./CI/run.sh```
@@ -11,7 +11,7 @@ This will run kraken using python, make sure python3 is set up and configured pr
### Adding a test case
1. Add in simple scenario yaml file to execute under [../CI/scenarios/](legacy)
1. Add in simple scenario yaml file to execute under [../CI/scenarios/](scenarios)
2. Copy [test_application_outages.sh](tests/test_app_outages.sh) for example on how to get started
@@ -27,7 +27,7 @@ This will run kraken using python, make sure python3 is set up and configured pr
e. 15: Make sure name of config in line 14 matches what you pass on this line
4. Add test name to [functional_tests](../CI/tests/functional_tests) file
4. Add test name to [my_tests](../CI/tests/my_tests) file
a. This will be the name of the file without ".sh"

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@@ -1,6 +1,6 @@
kraken:
distribution: kubernetes # Distribution can be kubernetes or openshift.
kubeconfig_path: ~/.kube/config # Path to kubeconfig.
distribution: openshift # Distribution can be kubernetes or openshift.
kubeconfig_path: /root/.kube/config # Path to kubeconfig.
exit_on_failure: False # Exit when a post action scenario fails.
litmus_version: v1.13.6 # Litmus version to install.
litmus_uninstall: False # If you want to uninstall litmus if failure.
@@ -15,38 +15,17 @@ cerberus:
performance_monitoring:
deploy_dashboards: False # Install a mutable grafana and load the performance dashboards. Enable this only when running on OpenShift.
repo: "https://github.com/cloud-bulldozer/performance-dashboards.git"
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
capture_metrics: False
config_path: config/kube_burner.yaml # Define the Elasticsearch url and index name in this config.
metrics_profile_path: config/metrics-aggregated.yaml
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set.
enable_alerts: False # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error.
alert_profile: config/alerts.yaml # Path to alert profile with the prometheus queries.
alert_profile: config/alerts # Path to alert profile with the prometheus queries.
tunings:
wait_duration: 6 # Duration to wait between each chaos scenario.
iterations: 1 # Number of times to execute the scenarios.
daemon_mode: False # Iterations are set to infinity which means that the kraken will cause chaos forever.
telemetry:
enabled: False # enable/disables the telemetry collection feature
api_url: https://yvnn4rfoi7.execute-api.us-west-2.amazonaws.com/test #telemetry service endpoint
username: $TELEMETRY_USERNAME # telemetry service username
password: $TELEMETRY_PASSWORD # telemetry service password
prometheus_namespace: 'prometheus-k8s' # prometheus namespace
prometheus_pod_name: 'prometheus-kind-prometheus-kube-prome-prometheus-0' # prometheus pod_name
prometheus_container_name: 'prometheus'
prometheus_backup: True # enables/disables prometheus data collection
full_prometheus_backup: False # if is set to False only the /prometheus/wal folder will be downloaded.
backup_threads: 5 # number of telemetry download/upload threads
archive_path: /tmp # local path where the archive files will be temporarly stored
max_retries: 0 # maximum number of upload retries (if 0 will retry forever)
run_tag: '' # if set, this will be appended to the run folder in the bucket (useful to group the runs)
archive_size: 10000 # the size of the prometheus data archive size in KB. The lower the size of archive is
logs_backup: True
logs_filter_patterns:
- "(\\w{3}\\s\\d{1,2}\\s\\d{2}:\\d{2}:\\d{2}\\.\\d+).+" # Sep 9 11:20:36.123425532
- "kinit (\\d+/\\d+/\\d+\\s\\d{2}:\\d{2}:\\d{2})\\s+" # kinit 2023/09/15 11:20:36 log
- "(\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d+Z).+" # 2023-09-15T11:20:36.123425532Z log
oc_cli_path: /usr/bin/oc # optional, if not specified will be search in $PATH
events_backup: True # enables/disables cluster events collection
telemetry_group: "funtests"

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@@ -1,25 +1,9 @@
#!/bin/bash
MAX_RETRIES=60
set -x
KUBECTL=`which kubectl 2>/dev/null`
[[ $? != 0 ]] && echo "[ERROR]: kubectl missing, please install it and try again" && exit 1
ci_tests_loc="CI/tests/my_tests"
wait_cluster_become_ready() {
COUNT=1
until `$KUBECTL get namespace > /dev/null 2>&1`
do
echo "[INF] waiting Kubernetes to become ready, after $COUNT check"
sleep 3
[[ $COUNT == $MAX_RETRIES ]] && echo "[ERR] max retries exceeded, failing" && exit 1
((COUNT++))
done
}
ci_tests_loc="CI/tests/functional_tests"
echo -e "********* Running Functional Tests Suite *********\n\n"
echo "running test suit consisting of ${ci_tests}"
rm -rf CI/out
@@ -36,32 +20,7 @@ echo 'Test | Result | Duration' >> $results
echo '-----------------------|--------|---------' >> $results
# Run each test
failed_tests=()
for test_name in `cat CI/tests/functional_tests`
for test_name in `cat CI/tests/my_tests`
do
#wait_cluster_become_ready
return_value=`./CI/run_test.sh $test_name $results`
if [[ $return_value == 1 ]]
then
echo "Failed"
failed_tests+=("$test_name")
fi
wait_cluster_become_ready
./CI/run_test.sh $test_name $results
done
if (( ${#failed_tests[@]}>0 ))
then
echo -e "\n\n======================================================================"
echo -e "\n FUNCTIONAL TESTS FAILED ${failed_tests[*]} ABORTING"
echo -e "\n======================================================================\n\n"
for test in "${failed_tests[@]}"
do
echo -e "\n********** $test KRKN RUN OUTPUT **********\n"
cat "CI/out/$test.out"
echo -e "\n********************************************\n\n\n\n"
done
exit 1
fi

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@@ -1,4 +1,5 @@
#!/bin/bash
set -x
readonly SECONDS_PER_HOUR=3600
readonly SECONDS_PER_MINUTE=60
function get_time_format() {
@@ -13,7 +14,9 @@ ci_test=`echo $1`
results_file=$2
echo -e "test: ${ci_test}" >&2
echo -e "\n======================================================================"
echo -e " CI test for ${ci_test} "
echo -e "======================================================================\n"
ci_results="CI/out/$ci_test.out"
# Test ci
@@ -25,16 +28,13 @@ then
# if the test passes update the results and complete
duration=$SECONDS
duration=$(get_time_format $duration)
echo -e "> $ci_test: Successful\n" >&2
echo "$ci_test: Successful"
echo "$ci_test | Pass | $duration" >> $results_file
count=$retries
# return value for run.sh
echo 0
else
duration=$SECONDS
duration=$(get_time_format $duration)
echo -e "> $ci_test: Failed\n" >&2
echo "$ci_test: Failed"
echo "$ci_test | Fail | $duration" >> $results_file
# return value for run.sh
echo 1
echo "Logs for "$ci_test
fi

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@@ -0,0 +1,5 @@
application_outage: # Scenario to create an outage of an application by blocking traffic
duration: 10 # Duration in seconds after which the routes will be accessible
namespace: openshift-monitoring # Namespace to target - all application routes will go inaccessible if pod selector is empty
pod_selector: {} # Pods to target
block: [Ingress, Egress] # It can be Ingress or Egress or Ingress, Egress

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@@ -0,0 +1,8 @@
scenarios:
- name: "kill machine config container"
namespace: "openshift-machine-config-operator"
label_selector: "k8s-app=machine-config-server"
container_name: "hello-openshift"
action: "kill 1"
count: 1
retry_wait: 60

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@@ -0,0 +1,31 @@
---
kind: Pod
apiVersion: v1
metadata:
name: hello-pod
creationTimestamp:
labels:
name: hello-openshift
spec:
containers:
- name: hello-openshift
image: openshift/hello-openshift
ports:
- containerPort: 5050
protocol: TCP
resources: {}
volumeMounts:
- name: tmp
mountPath: "/tmp"
terminationMessagePath: "/dev/termination-log"
imagePullPolicy: IfNotPresent
securityContext:
capabilities: {}
privileged: false
volumes:
- name: tmp
emptyDir: {}
restartPolicy: Always
dnsPolicy: ClusterFirst
serviceAccount: ''
status: {}

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@@ -0,0 +1,31 @@
config:
runStrategy:
runs: 1
maxSecondsBetweenRuns: 30
minSecondsBetweenRuns: 1
scenarios:
- name: "delete hello pods"
steps:
- podAction:
matches:
- labels:
namespace: "default"
selector: "hello-openshift"
filters:
- randomSample:
size: 1
actions:
- kill:
probability: 1
force: true
- podAction:
matches:
- labels:
namespace: "default"
selector: "hello-openshift"
retries:
retriesTimeout:
timeout: 180
actions:
- checkPodCount:
count: 1

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@@ -0,0 +1,7 @@
scenarios:
- action: delete
namespace: "^.*ingress.*$"
label_selector:
runs: 1
sleep: 15
wait_time: 30

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@@ -0,0 +1,6 @@
network_chaos: # Scenario to create an outage by simulating random variations in the network.
duration: 10 # seconds
instance_count: 1
execution: serial
egress:
bandwidth: 100mbit

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@@ -0,0 +1,34 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be true/false
annotationCheck: 'false'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
monitoring: false
# It can be delete/retain
jobCleanUpPolicy: 'delete'
experiments:
- name: node-cpu-hog
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
# Number of cores of node CPU to be consumed
- name: NODE_CPU_CORE
value: '1'
# percentage of total nodes to target
- name: NODES_AFFECTED_PERC
value: '30'
# ENTER THE COMMA SEPARATED TARGET NODES NAME
- name: TARGET_NODES
value: $WORKER_NODE

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@@ -0,0 +1,34 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be true/false
annotationCheck: 'false'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
monitoring: false
# It can be delete/retain
jobCleanUpPolicy: 'delete'
experiments:
- name: node-cpu-hog
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
# Number of cores of node CPU to be consumed
- name: NODE_CPU_CORE
value: '1'
# percentage of total nodes to target
- name: NODES_AFFECTED_PERC
value: '30'
# ENTER THE COMMA SEPARATED TARGET NODES NAME
- name: TARGET_NODES
value:

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@@ -0,0 +1,35 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be delete/retain
jobCleanUpPolicy: 'retain'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
experiments:
- name: node-io-stress
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
## specify the size as percentage of free space on the file system
- name: FILESYSTEM_UTILIZATION_PERCENTAGE
value: '100'
## Number of core of CPU
- name: CPU
value: '1'
## Total number of workers default value is 4
- name: NUMBER_OF_WORKERS
value: '3'
## enter the comma separated target nodes name
- name: TARGET_NODES
value: $WORKER_NODE

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@@ -0,0 +1,35 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be delete/retain
jobCleanUpPolicy: 'retain'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
experiments:
- name: node-io-stress
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
## specify the size as percentage of free space on the file system
- name: FILESYSTEM_UTILIZATION_PERCENTAGE
value: '100'
## Number of core of CPU
- name: CPU
value: '1'
## Total number of workers default value is 4
- name: NUMBER_OF_WORKERS
value: '3'
## enter the comma separated target nodes name
- name: TARGET_NODES
value:

View File

@@ -0,0 +1,28 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be delete/retain
jobCleanUpPolicy: 'retain'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
experiments:
- name: node-memory-hog
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
## Specify the size as percent of total node capacity Ex: '30'
## Note: For consuming memory in mebibytes change the variable to MEMORY_CONSUMPTION_MEBIBYTES
- name: MEMORY_CONSUMPTION_PERCENTAGE
value: '30'
# ENTER THE COMMA SEPARATED TARGET NODES NAME
- name: TARGET_NODES
value: $WORKER_NODE

View File

@@ -0,0 +1,28 @@
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: nginx-chaos
namespace: litmus
spec:
# It can be delete/retain
jobCleanUpPolicy: 'retain'
# It can be active/stop
engineState: 'active'
chaosServiceAccount: litmus-sa
experiments:
- name: node-memory-hog
spec:
components:
env:
# set chaos duration (in sec) as desired
- name: TOTAL_CHAOS_DURATION
value: '10'
## Specify the size as percent of total node capacity Ex: '30'
## Note: For consuming memory in mebibytes change the variable to MEMORY_CONSUMPTION_MEBIBYTES
- name: MEMORY_CONSUMPTION_PERCENTAGE
value: '30'
# ENTER THE COMMA SEPARATED TARGET NODES NAME
- name: TARGET_NODES
value:

View File

@@ -0,0 +1,5 @@
time_scenarios:
- action: skew_time
object_type: pod
label_selector: k8s-app=etcd
container_name: ""

View File

@@ -1,20 +1,7 @@
apiVersion: v1
kind: Namespace
metadata:
labels:
kubernetes.io/metadata.name: kraken
pod-security.kubernetes.io/audit: privileged
pod-security.kubernetes.io/enforce: privileged
pod-security.kubernetes.io/enforce-version: v1.24
pod-security.kubernetes.io/warn: privileged
security.openshift.io/scc.podSecurityLabelSync: "false"
name: kraken
---
apiVersion: v1
kind: PersistentVolume
metadata:
name: kraken-test-pv
namespace: kraken
labels:
type: local
spec:
@@ -30,7 +17,6 @@ apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: kraken-test-pvc
namespace: kraken
spec:
storageClassName: manual
accessModes:
@@ -43,7 +29,6 @@ apiVersion: v1
kind: Pod
metadata:
name: kraken-test-pod
namespace: kraken
spec:
volumes:
- name: kraken-test-pv
@@ -51,7 +36,7 @@ spec:
claimName: kraken-test-pvc
containers:
- name: kraken-test-container
image: 'quay.io/centos7/httpd-24-centos7:latest'
image: 'image-registry.openshift-image-registry.svc:5000/openshift/httpd:latest'
volumeMounts:
- mountPath: "/home/krake-dir/"
name: kraken-test-pv

View File

@@ -1,16 +0,0 @@
apiVersion: v1
kind: Pod
metadata:
name: container
labels:
scenario: container
spec:
hostNetwork: true
containers:
- name: fedtools
image: docker.io/fedora/tools
command:
- /bin/sh
- -c
- |
sleep infinity

View File

@@ -1,16 +0,0 @@
apiVersion: v1
kind: Pod
metadata:
name: outage
labels:
scenario: outage
spec:
hostNetwork: true
containers:
- name: fedtools
image: docker.io/fedora/tools
command:
- /bin/sh
- -c
- |
sleep infinity

View File

@@ -1,29 +0,0 @@
apiVersion: v1
kind: Pod
metadata:
name: nginx
labels:
app.kubernetes.io/name: proxy
spec:
containers:
- name: nginx
image: nginx:stable
ports:
- containerPort: 80
name: http-web-svc
---
apiVersion: v1
kind: Service
metadata:
name: nginx-service
spec:
selector:
app.kubernetes.io/name: proxy
type: NodePort
ports:
- name: name-of-service-port
protocol: TCP
port: 80
targetPort: http-web-svc
nodePort: 30036

View File

@@ -1,16 +0,0 @@
apiVersion: v1
kind: Pod
metadata:
name: time-skew
labels:
scenario: time-skew
spec:
hostNetwork: true
containers:
- name: fedtools
image: docker.io/fedora/tools
command:
- /bin/sh
- -c
- |
sleep infinity

View File

@@ -1,26 +1,18 @@
ERRORED=false
function finish {
if [ $? != 0 ] && [ $ERRORED != "true" ]
if [ $? -eq 1 ] && [ $ERRORED != "true" ]
then
error
fi
}
function error {
exit_code=$?
if [ $exit_code == 1 ]
then
echo "Error caught."
ERRORED=true
elif [ $exit_code == 2 ]
then
echo "Run with exit code 2 detected, it is expected, wrapping the exit code with 0 to avoid pipeline failure"
exit 0
fi
echo "Error caught."
ERRORED=true
}
function get_node {
worker_node=$(kubectl get nodes --no-headers | grep worker | head -n 1)
worker_node=$(oc get nodes --no-headers | grep worker | head -n 1)
export WORKER_NODE=$worker_node
}

View File

@@ -1 +0,0 @@

12
CI/tests/my_tests Normal file
View File

@@ -0,0 +1,12 @@
test_pods
test_nodes
test_time
test_app_outages
test_container
test_zone
test_io_hog
test_mem_hog
test_cpu_hog
test_shut_down
test_net_chaos
test_namespace

View File

@@ -7,14 +7,12 @@ trap finish EXIT
function functional_test_app_outage {
yq -i '.application_outage.duration=10' scenarios/openshift/app_outage.yaml
yq -i '.application_outage.pod_selector={"scenario":"outage"}' scenarios/openshift/app_outage.yaml
yq -i '.application_outage.namespace="default"' scenarios/openshift/app_outage.yaml
export scenario_type="application_outages"
export scenario_file="scenarios/openshift/app_outage.yaml"
export scenario_file="CI/scenarios/app_outage.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/app_outage.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/app_outage.yaml
python3 run_kraken.py -c CI/config/app_outage.yaml
echo "App outage scenario test: Success"
}

View File

@@ -1,19 +0,0 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_arca_cpu_hog {
yq -i '.input_list[0].node_selector={"kubernetes.io/hostname":"kind-worker2"}' scenarios/arcaflow/cpu-hog/input.yaml
export scenario_type="arcaflow_scenarios"
export scenario_file="scenarios/arcaflow/cpu-hog/input.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/arca_cpu_hog.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/arca_cpu_hog.yaml
echo "Arcaflow CPU Hog: Success"
}
functional_test_arca_cpu_hog

View File

@@ -1,19 +0,0 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_arca_io_hog {
yq -i '.input_list[0].node_selector={"kubernetes.io/hostname":"kind-worker2"}' scenarios/arcaflow/io-hog/input.yaml
export scenario_type="arcaflow_scenarios"
export scenario_file="scenarios/arcaflow/io-hog/input.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/arca_io_hog.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/arca_io_hog.yaml
echo "Arcaflow IO Hog: Success"
}
functional_test_arca_io_hog

View File

@@ -1,19 +0,0 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_arca_memory_hog {
yq -i '.input_list[0].node_selector={"kubernetes.io/hostname":"kind-worker2"}' scenarios/arcaflow/memory-hog/input.yaml
export scenario_type="arcaflow_scenarios"
export scenario_file="scenarios/arcaflow/memory-hog/input.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/arca_memory_hog.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/arca_memory_hog.yaml
echo "Arcaflow Memory Hog: Success"
}
functional_test_arca_memory_hog

View File

@@ -8,15 +8,13 @@ trap finish EXIT
pod_file="CI/scenarios/hello_pod.yaml"
function functional_test_container_crash {
yq -i '.scenarios[0].namespace="default"' scenarios/openshift/container_etcd.yml
yq -i '.scenarios[0].label_selector="scenario=container"' scenarios/openshift/container_etcd.yml
yq -i '.scenarios[0].container_name="fedtools"' scenarios/openshift/container_etcd.yml
export scenario_type="container_scenarios"
export scenario_file="- scenarios/openshift/container_etcd.yml"
export scenario_file="- CI/scenarios/container_scenario.yml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/container_config.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/container_config.yaml
python3 run_kraken.py -c CI/config/container_config.yaml
echo "Container scenario test: Success"
}

20
CI/tests/test_cpu_hog.sh Executable file
View File

@@ -0,0 +1,20 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_litmus_cpu {
export scenario_type="litmus_scenarios"
export scenario_file="- scenarios/templates/litmus-rbac.yaml"
export post_config="- CI/scenarios/node_cpu_hog_engine_node.yaml"
envsubst < CI/config/common_test_config.yaml > CI/config/litmus_config.yaml
envsubst < CI/scenarios/node_cpu_hog_engine.yaml > CI/scenarios/node_cpu_hog_engine_node.yaml
python3 run_kraken.py -c CI/config/litmus_config.yaml
echo "Litmus scenario $1 test: Success"
}
functional_test_litmus_cpu

20
CI/tests/test_io_hog.sh Executable file
View File

@@ -0,0 +1,20 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_litmus_io {
export scenario_type="litmus_scenarios"
export scenario_file="- scenarios/templates/litmus-rbac.yaml"
export post_config="- CI/scenarios/node_io_engine_node.yaml"
envsubst < CI/config/common_test_config.yaml > CI/config/litmus_config.yaml
envsubst < CI/scenarios/node_io_engine.yaml > CI/scenarios/node_io_engine_node.yaml
python3 run_kraken.py -c CI/config/litmus_config.yaml
echo "Litmus scenario $1 test: Success"
}
functional_test_litmus_io

20
CI/tests/test_mem_hog.sh Executable file
View File

@@ -0,0 +1,20 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_litmus_mem {
export scenario_type="litmus_scenarios"
export scenario_file="- scenarios/templates/litmus-rbac.yaml"
export post_config="- CI/scenarios/node_mem_engine_node.yaml"
envsubst < CI/config/common_test_config.yaml > CI/config/litmus_config.yaml
envsubst < CI/scenarios/node_mem_engine.yaml > CI/scenarios/node_mem_engine_node.yaml
python3 run_kraken.py -c CI/config/litmus_config.yaml
echo "Litmus scenario $1 test: Success"
}
functional_test_litmus_mem "- CI/scenarios/node_mem_engine.yaml"

View File

@@ -7,13 +7,12 @@ trap finish EXIT
function funtional_test_namespace_deletion {
export scenario_type="namespace_scenarios"
export scenario_file="- scenarios/openshift/ingress_namespace.yaml"
export scenario_file="- CI/scenarios/ingress_namespace.yaml"
export post_config=""
yq '.scenarios[0].namespace="^namespace-scenario$"' -i scenarios/openshift/ingress_namespace.yaml
yq '.scenarios[0].wait_time=30' -i scenarios/openshift/ingress_namespace.yaml
yq '.scenarios[0].action="delete"' -i scenarios/openshift/ingress_namespace.yaml
envsubst < CI/config/common_test_config.yaml > CI/config/namespace_config.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/namespace_config.yaml
python3 run_kraken.py -c CI/config/namespace_config.yaml
echo $?
echo "Namespace scenario test: Success"
}

View File

@@ -7,19 +7,12 @@ trap finish EXIT
function functional_test_network_chaos {
yq -i '.network_chaos.duration=10' scenarios/openshift/network_chaos.yaml
yq -i '.network_chaos.node_name="kind-worker2"' scenarios/openshift/network_chaos.yaml
yq -i '.network_chaos.egress.bandwidth="100mbit"' scenarios/openshift/network_chaos.yaml
yq -i 'del(.network_chaos.interfaces)' scenarios/openshift/network_chaos.yaml
yq -i 'del(.network_chaos.label_selector)' scenarios/openshift/network_chaos.yaml
yq -i 'del(.network_chaos.egress.latency)' scenarios/openshift/network_chaos.yaml
yq -i 'del(.network_chaos.egress.loss)' scenarios/openshift/network_chaos.yaml
export scenario_type="network_chaos"
export scenario_file="scenarios/openshift/network_chaos.yaml"
export scenario_file="CI/scenarios/network_chaos.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/network_chaos.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/network_chaos.yaml
python3 run_kraken.py -c CI/config/network_chaos.yaml
echo "Network Chaos test: Success"
}

View File

@@ -13,7 +13,7 @@ function funtional_test_node_crash {
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/node_config.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/node_config.yaml
python3 run_kraken.py -c CI/config/node_config.yaml
echo "Node scenario test: Success"
}

19
CI/tests/test_pods.sh Executable file
View File

@@ -0,0 +1,19 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function funtional_test_pod_deletion {
export scenario_type="pod_scenarios"
export scenario_file="- CI/scenarios/hello_pod_killing.yml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/pod_config.yaml
python3 run_kraken.py -c CI/config/pod_config.yaml
echo $?
echo "Pod scenario test: Success"
}
funtional_test_pod_deletion

View File

@@ -1,107 +0,0 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
# port mapping has been configured in kind-config.yml
SERVICE_URL=http://localhost:8888
PAYLOAD_GET_1="{ \
\"status\":\"internal server error\" \
}"
STATUS_CODE_GET_1=500
PAYLOAD_PATCH_1="resource patched"
STATUS_CODE_PATCH_1=201
PAYLOAD_POST_1="{ \
\"status\": \"unauthorized\" \
}"
STATUS_CODE_POST_1=401
PAYLOAD_GET_2="{ \
\"status\":\"resource created\" \
}"
STATUS_CODE_GET_2=201
PAYLOAD_PATCH_2="bad request"
STATUS_CODE_PATCH_2=400
PAYLOAD_POST_2="not found"
STATUS_CODE_POST_2=404
JSON_MIME="application/json"
TEXT_MIME="text/plain; charset=utf-8"
function functional_test_service_hijacking {
export scenario_type="service_hijacking"
export scenario_file="scenarios/kube/service_hijacking.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/service_hijacking.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/service_hijacking.yaml > /dev/null 2>&1 &
PID=$!
#Waiting the hijacking to have effect
while [ `curl -X GET -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/list/index.php` == 404 ]; do echo "waiting scenario to kick in."; sleep 1; done;
#Checking Step 1 GET on /list/index.php
OUT_GET="`curl -X GET -s $SERVICE_URL/list/index.php`"
OUT_CONTENT=`curl -X GET -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/list/index.php`
OUT_STATUS_CODE=`curl -X GET -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/list/index.php`
[ "${PAYLOAD_GET_1//[$'\t\r\n ']}" == "${OUT_GET//[$'\t\r\n ']}" ] && echo "Step 1 GET Payload OK" || (echo "Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_GET_1" ] && echo "Step 1 GET Status Code OK" || (echo " Step 1 GET status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$JSON_MIME" ] && echo "Step 1 GET MIME OK" || (echo " Step 1 GET MIME did not match. Test failed." && exit 1)
#Checking Step 1 POST on /list/index.php
OUT_POST="`curl -s -X POST $SERVICE_URL/list/index.php`"
OUT_STATUS_CODE=`curl -X POST -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/list/index.php`
OUT_CONTENT=`curl -X POST -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/list/index.php`
[ "${PAYLOAD_POST_1//[$'\t\r\n ']}" == "${OUT_POST//[$'\t\r\n ']}" ] && echo "Step 1 POST Payload OK" || (echo "Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_POST_1" ] && echo "Step 1 POST Status Code OK" || (echo "Step 1 POST status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$JSON_MIME" ] && echo "Step 1 POST MIME OK" || (echo " Step 1 POST MIME did not match. Test failed." && exit 1)
#Checking Step 1 PATCH on /patch
OUT_PATCH="`curl -s -X PATCH $SERVICE_URL/patch`"
OUT_STATUS_CODE=`curl -X PATCH -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/patch`
OUT_CONTENT=`curl -X PATCH -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/patch`
[ "${PAYLOAD_PATCH_1//[$'\t\r\n ']}" == "${OUT_PATCH//[$'\t\r\n ']}" ] && echo "Step 1 PATCH Payload OK" || (echo "Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_PATCH_1" ] && echo "Step 1 PATCH Status Code OK" || (echo "Step 1 PATCH status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$TEXT_MIME" ] && echo "Step 1 PATCH MIME OK" || (echo " Step 1 PATCH MIME did not match. Test failed." && exit 1)
# wait for the next step
sleep 16
#Checking Step 2 GET on /list/index.php
OUT_GET="`curl -X GET -s $SERVICE_URL/list/index.php`"
OUT_CONTENT=`curl -X GET -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/list/index.php`
OUT_STATUS_CODE=`curl -X GET -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/list/index.php`
[ "${PAYLOAD_GET_2//[$'\t\r\n ']}" == "${OUT_GET//[$'\t\r\n ']}" ] && echo "Step 2 GET Payload OK" || (echo "Step 2 GET Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_GET_2" ] && echo "Step 2 GET Status Code OK" || (echo "Step 2 GET status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$JSON_MIME" ] && echo "Step 2 GET MIME OK" || (echo " Step 2 GET MIME did not match. Test failed." && exit 1)
#Checking Step 2 POST on /list/index.php
OUT_POST="`curl -s -X POST $SERVICE_URL/list/index.php`"
OUT_CONTENT=`curl -X POST -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/list/index.php`
OUT_STATUS_CODE=`curl -X POST -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/list/index.php`
[ "${PAYLOAD_POST_2//[$'\t\r\n ']}" == "${OUT_POST//[$'\t\r\n ']}" ] && echo "Step 2 POST Payload OK" || (echo "Step 2 POST Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_POST_2" ] && echo "Step 2 POST Status Code OK" || (echo "Step 2 POST status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$TEXT_MIME" ] && echo "Step 2 POST MIME OK" || (echo " Step 2 POST MIME did not match. Test failed." && exit 1)
#Checking Step 2 PATCH on /patch
OUT_PATCH="`curl -s -X PATCH $SERVICE_URL/patch`"
OUT_CONTENT=`curl -X PATCH -s -o /dev/null -I -w "%{content_type}" $SERVICE_URL/patch`
OUT_STATUS_CODE=`curl -X PATCH -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL/patch`
[ "${PAYLOAD_PATCH_2//[$'\t\r\n ']}" == "${OUT_PATCH//[$'\t\r\n ']}" ] && echo "Step 2 PATCH Payload OK" || (echo "Step 2 PATCH Payload did not match. Test failed." && exit 1)
[ "$OUT_STATUS_CODE" == "$STATUS_CODE_PATCH_2" ] && echo "Step 2 PATCH Status Code OK" || (echo "Step 2 PATCH status code did not match. Test failed." && exit 1)
[ "$OUT_CONTENT" == "$TEXT_MIME" ] && echo "Step 2 PATCH MIME OK" || (echo " Step 2 PATCH MIME did not match. Test failed." && exit 1)
wait $PID
# now checking if service has been restore correctly and nginx responds correctly
curl -s $SERVICE_URL | grep nginx! && echo "BODY: Service restored!" || (echo "BODY: failed to restore service" && exit 1)
OUT_STATUS_CODE=`curl -X GET -s -o /dev/null -I -w "%{http_code}" $SERVICE_URL`
[ "$OUT_STATUS_CODE" == "200" ] && echo "STATUS_CODE: Service restored!" || (echo "STATUS_CODE: failed to restore service" && exit 1)
echo "Service Hijacking Chaos test: Success"
}
functional_test_service_hijacking

View File

@@ -12,7 +12,7 @@ function functional_test_shut_down {
export scenario_file="- CI/scenarios/cluster_shut_down_scenario.yml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/shut_down.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/shut_down.yaml
python3 run_kraken.py -c CI/config/shut_down.yaml
echo "Cluster shut down scenario test: Success"
}

View File

@@ -1,37 +0,0 @@
set -xeEo pipefail
source CI/tests/common.sh
trap error ERR
trap finish EXIT
function functional_test_telemetry {
AWS_CLI=`which aws`
[ -z "$AWS_CLI" ]&& echo "AWS cli not found in path" && exit 1
[ -z "$AWS_BUCKET" ] && echo "AWS bucket not set in environment" && exit 1
export RUN_TAG="funtest-telemetry"
yq -i '.telemetry.enabled=True' CI/config/common_test_config.yaml
yq -i '.telemetry.full_prometheus_backup=True' CI/config/common_test_config.yaml
yq -i '.performance_monitoring.check_critical_alerts=True' CI/config/common_test_config.yaml
yq -i '.performance_monitoring.prometheus_url="http://localhost:9090"' CI/config/common_test_config.yaml
yq -i '.telemetry.run_tag=env(RUN_TAG)' CI/config/common_test_config.yaml
export scenario_type="arcaflow_scenarios"
export scenario_file="scenarios/arcaflow/cpu-hog/input.yaml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/telemetry.yaml
retval=$(python3 -m coverage run -a run_kraken.py -c CI/config/telemetry.yaml)
RUN_FOLDER=`cat CI/out/test_telemetry.out | grep amazonaws.com | sed -rn "s#.*https:\/\/.*\/files/(.*)#\1#p"`
$AWS_CLI s3 ls "s3://$AWS_BUCKET/$RUN_FOLDER/" | awk '{ print $4 }' > s3_remote_files
echo "checking if telemetry files are uploaded on s3"
cat s3_remote_files | grep events-00.json || ( echo "FAILED: events-00.json not uploaded" && exit 1 )
cat s3_remote_files | grep critical-alerts-00.log || ( echo "FAILED: critical-alerts-00.log not uploaded" && exit 1 )
cat s3_remote_files | grep prometheus-00.tar || ( echo "FAILED: prometheus backup not uploaded" && exit 1 )
cat s3_remote_files | grep telemetry.json || ( echo "FAILED: telemetry.json not uploaded" && exit 1 )
echo "all files uploaded!"
echo "Telemetry Collection: Success"
}
functional_test_telemetry

View File

@@ -7,16 +7,12 @@ trap finish EXIT
function functional_test_time_scenario {
yq -i '.time_scenarios[0].label_selector="scenario=time-skew"' scenarios/openshift/time_scenarios_example.yml
yq -i '.time_scenarios[0].container_name=""' scenarios/openshift/time_scenarios_example.yml
yq -i '.time_scenarios[0].namespace="default"' scenarios/openshift/time_scenarios_example.yml
yq -i '.time_scenarios[1].label_selector="kubernetes.io/hostname=kind-worker2"' scenarios/openshift/time_scenarios_example.yml
export scenario_type="time_scenarios"
export scenario_file="scenarios/openshift/time_scenarios_example.yml"
export scenario_file="CI/scenarios/time_scenarios.yml"
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/time_config.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/time_config.yaml
python3 run_kraken.py -c CI/config/time_config.yaml
echo "Time scenario test: Success"
}

View File

@@ -13,7 +13,7 @@ function functional_test_zone_crash {
export post_config=""
envsubst < CI/config/common_test_config.yaml > CI/config/zone3_config.yaml
envsubst < CI/scenarios/zone_outage.yaml > CI/scenarios/zone_outage_env.yaml
python3 -m coverage run -a run_kraken.py -c CI/config/zone3_config.yaml
python3 run_kraken.py -c CI/config/zone3_config.yaml
echo "zone3 scenario test: Success"
}

View File

@@ -1,104 +0,0 @@
## CNCF Community Code of Conduct v1.3
Other languages available:
- [Arabic/العربية](code-of-conduct-languages/ar.md)
- [Bulgarian/Български](code-of-conduct-languages/bg.md)
- [Chinese/中文](code-of-conduct-languages/zh.md)
- [Czech/Česky](code-of-conduct-languages/cs.md)
- [Farsi/فارسی](code-of-conduct-languages/fa.md)
- [French/Français](code-of-conduct-languages/fr.md)
- [German/Deutsch](code-of-conduct-languages/de.md)
- [Hindi/हिन्दी](code-of-conduct-languages/hi.md)
- [Indonesian/Bahasa Indonesia](code-of-conduct-languages/id.md)
- [Italian/Italiano](code-of-conduct-languages/it.md)
- [Japanese/日本語](code-of-conduct-languages/jp.md)
- [Korean/한국어](code-of-conduct-languages/ko.md)
- [Polish/Polski](code-of-conduct-languages/pl.md)
- [Portuguese/Português](code-of-conduct-languages/pt.md)
- [Russian/Русский](code-of-conduct-languages/ru.md)
- [Spanish/Español](code-of-conduct-languages/es.md)
- [Turkish/Türkçe](code-of-conduct-languages/tr.md)
- [Ukrainian/Українська](code-of-conduct-languages/uk.md)
- [Vietnamese/Tiếng Việt](code-of-conduct-languages/vi.md)
### Community Code of Conduct
As contributors, maintainers, and participants in the CNCF community, and in the interest of fostering
an open and welcoming community, we pledge to respect all people who participate or contribute
through reporting issues, posting feature requests, updating documentation,
submitting pull requests or patches, attending conferences or events, or engaging in other community or project activities.
We are committed to making participation in the CNCF community a harassment-free experience for everyone, regardless of age, body size, caste, disability, ethnicity, level of experience, family status, gender, gender identity and expression, marital status, military or veteran status, nationality, personal appearance, race, religion, sexual orientation, socioeconomic status, tribe, or any other dimension of diversity.
## Scope
This code of conduct applies:
* within project and community spaces,
* in other spaces when an individual CNCF community participant's words or actions are directed at or are about a CNCF project, the CNCF community, or another CNCF community participant.
### CNCF Events
CNCF events that are produced by the Linux Foundation with professional events staff are governed by the Linux Foundation [Events Code of Conduct](https://events.linuxfoundation.org/code-of-conduct/) available on the event page. This is designed to be used in conjunction with the CNCF Code of Conduct.
## Our Standards
The CNCF Community is open, inclusive and respectful. Every member of our community has the right to have their identity respected.
Examples of behavior that contributes to a positive environment include but are not limited to:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
* Using welcoming and inclusive language
Examples of unacceptable behavior include but are not limited to:
* The use of sexualized language or imagery
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment in any form
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Violence, threatening violence, or encouraging others to engage in violent behavior
* Stalking or following someone without their consent
* Unwelcome physical contact
* Unwelcome sexual or romantic attention or advances
* Other conduct which could reasonably be considered inappropriate in a
professional setting
The following behaviors are also prohibited:
* Providing knowingly false or misleading information in connection with a Code of Conduct investigation or otherwise intentionally tampering with an investigation.
* Retaliating against a person because they reported an incident or provided information about an incident as a witness.
Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct.
By adopting this Code of Conduct, project maintainers commit themselves to fairly and consistently applying these principles to every aspect
of managing a CNCF project.
Project maintainers who do not follow or enforce the Code of Conduct may be temporarily or permanently removed from the project team.
## Reporting
For incidents occurring in the Kubernetes community, contact the [Kubernetes Code of Conduct Committee](https://git.k8s.io/community/committee-code-of-conduct) via <conduct@kubernetes.io>. You can expect a response within three business days.
For other projects, or for incidents that are project-agnostic or impact multiple CNCF projects, please contact the [CNCF Code of Conduct Committee](https://www.cncf.io/conduct/committee/) via <conduct@cncf.io>. Alternatively, you can contact any of the individual members of the [CNCF Code of Conduct Committee](https://www.cncf.io/conduct/committee/) to submit your report. For more detailed instructions on how to submit a report, including how to submit a report anonymously, please see our [Incident Resolution Procedures](https://github.com/cncf/foundation/blob/main/code-of-conduct/coc-incident-resolution-procedures.md). You can expect a response within three business days.
For incidents occurring at CNCF event that is produced by the Linux Foundation, please contact <eventconduct@cncf.io>.
## Enforcement
Upon review and investigation of a reported incident, the CoC response team that has jurisdiction will determine what action is appropriate based on this Code of Conduct and its related documentation.
For information about which Code of Conduct incidents are handled by project leadership, which incidents are handled by the CNCF Code of Conduct Committee, and which incidents are handled by the Linux Foundation (including its events team), see our [Jurisdiction Policy](https://github.com/cncf/foundation/blob/main/code-of-conduct/coc-committee-jurisdiction-policy.md).
## Amendments
Consistent with the CNCF Charter, any substantive changes to this Code of Conduct must be approved by the Technical Oversight Committee.
## Acknowledgements
This Code of Conduct is adapted from the Contributor Covenant
(http://contributor-covenant.org), version 2.0 available at
http://contributor-covenant.org/version/2/0/code_of_conduct/

View File

@@ -1,12 +0,0 @@
## Overview
This document contains a list of maintainers in this repo.
## Current Maintainers
| Maintainer | GitHub ID | Email |
|---------------------| --------------------------------------------------------- | ----------------------- |
| Ravi Elluri | [chaitanyaenr](https://github.com/chaitanyaenr) | nelluri@redhat.com |
| Pradeep Surisetty | [psuriset](https://github.com/psuriset) | psuriset@redhat.com |
| Paige Rubendall | [paigerube14](https://github.com/paigerube14) | prubenda@redhat.com |
| Tullio Sebastiani | [tsebastiani](https://github.com/tsebastiani) | tsebasti@redhat.com |

View File

@@ -1,12 +1,10 @@
# Krkn aka Kraken
![Workflow-Status](https://github.com/krkn-chaos/krkn/actions/workflows/docker-image.yml/badge.svg)
![coverage](https://krkn-chaos.github.io/krkn-lib-docs/coverage_badge_krkn.svg)
![action](https://github.com/krkn-chaos/krkn/actions/workflows/tests.yml/badge.svg)
[![Docker Repository on Quay](https://quay.io/repository/chaos-kubox/krkn?tab=tags&tag=latest "Docker Repository on Quay")](https://quay.io/chaos-kubox/krkn)
![Krkn logo](media/logo.png)
Chaos and resiliency testing tool for Kubernetes.
Kraken injects deliberate failures into Kubernetes clusters to check if it is resilient to turbulent conditions.
Chaos and resiliency testing tool for Kubernetes and OpenShift.
Kraken injects deliberate failures into Kubernetes/OpenShift clusters to check if it is resilient to turbulent conditions.
### Workflow
@@ -19,30 +17,28 @@ Kraken injects deliberate failures into Kubernetes clusters to check if it is re
### Chaos Testing Guide
[Guide](docs/index.md) encapsulates:
- Test methodology that needs to be embraced.
- Best practices that an Kubernetes cluster, platform and applications running on top of it should take into account for best user experience, performance, resilience and reliability.
- Best practices that an OpenShift cluster, platform and applications running on top of it should take into account for best user experience, performance, resilience and reliability.
- Tooling.
- Scenarios supported.
- Test environment recommendations as to how and where to run chaos tests.
- Chaos testing in practice.
The guide is hosted at https://krkn-chaos.github.io/krkn.
The guide is hosted at [https://chaos-kubox.github.io/krkn/](https://chaos-kubox.github.io/krkn/).
### How to Get Started
Instructions on how to setup, configure and run Kraken can be found at [Installation](docs/installation.md).
You may consider utilizing the chaos recommendation tool prior to initiating the chaos runs to profile the application service(s) under test. This tool discovers a list of Krkn scenarios with a high probability of causing failures or disruptions to your application service(s). The tool can be accessed at [Chaos-Recommender](utils/chaos_recommender/README.md).
See the [getting started doc](docs/getting_started.md) on support on how to get started with your own custom scenario or editing current scenarios for your specific usage.
After installation, refer back to the below sections for supported scenarios and how to tweak the kraken config to load them on your cluster.
#### Running Kraken with minimal configuration tweaks
For cases where you want to run Kraken with minimal configuration changes, refer to [krkn-hub](https://github.com/krkn-chaos/krkn-hub). One use case is CI integration where you do not want to carry around different configuration files for the scenarios.
For cases where you want to run Kraken with minimal configuration changes, refer to [Kraken-hub](https://github.com/chaos-kubox/krkn-hub). One use case is CI integration where you do not want to carry around different configuration files for the scenarios.
### Setting up infrastructure dependencies
Kraken indexes the metrics specified in the profile into Elasticsearch in addition to leveraging Cerberus for understanding the health of the Kubernetes cluster under test. More information on the features is documented below. The infrastructure pieces can be easily installed and uninstalled by running:
Kraken indexes the metrics specified in the profile into Elasticsearch in addition to leveraging Cerberus for understanding the health of the Kubernetes/OpenShift cluster under test. More information on the features is documented below. The infrastructure pieces can be easily installed and uninstalled by running:
```
$ cd kraken
@@ -58,31 +54,28 @@ This will manage the Cerberus and Elasticsearch containers on the host on which
Instructions on how to setup the config and the options supported can be found at [Config](docs/config.md).
### Kubernetes chaos scenarios supported
### Kubernetes/OpenShift chaos scenarios supported
Scenario type | Kubernetes
--------------------------- | ------------- |
[Pod Scenarios](docs/pod_scenarios.md) | :heavy_check_mark: |
[Pod Network Scenarios](docs/pod_network_scenarios.md) | :x: |
[Container Scenarios](docs/container_scenarios.md) | :heavy_check_mark: |
[Node Scenarios](docs/node_scenarios.md) | :heavy_check_mark: |
[Time Scenarios](docs/time_scenarios.md) | :heavy_check_mark: |
[Hog Scenarios: CPU, Memory](docs/arcaflow_scenarios.md) | :heavy_check_mark: |
[Cluster Shut Down Scenarios](docs/cluster_shut_down_scenarios.md) | :heavy_check_mark: |
[Service Disruption Scenarios](docs/service_disruption_scenarios.md.md) | :heavy_check_mark: |
[Zone Outage Scenarios](docs/zone_outage.md) | :heavy_check_mark: |
[Application_outages](docs/application_outages.md) | :heavy_check_mark: |
[PVC scenario](docs/pvc_scenario.md) | :heavy_check_mark: |
[Network_Chaos](docs/network_chaos.md) | :heavy_check_mark: |
[ManagedCluster Scenarios](docs/managedcluster_scenarios.md) | :heavy_check_mark: |
[Service Hijacking Scenarios](docs/service_hijacking_scenarios.md) | :heavy_check_mark: |
Scenario type | Kubernetes | OpenShift
--------------------------- | ------------- | -------------------- |
[Pod Scenarios](docs/pod_scenarios.md) | :heavy_check_mark: | :heavy_check_mark: |
[Container Scenarios](docs/container_scenarios.md) | :heavy_check_mark: | :heavy_check_mark: |
[Node Scenarios](docs/node_scenarios.md) | :heavy_check_mark: | :heavy_check_mark: |
[Time Scenarios](docs/time_scenarios.md) | :x: | :heavy_check_mark: |
[Litmus Scenarios](docs/litmus_scenarios.md) | :x: | :heavy_check_mark: |
[Cluster Shut Down Scenarios](docs/cluster_shut_down_scenarios.md) | :heavy_check_mark: | :heavy_check_mark: |
[Namespace Scenarios](docs/namespace_scenarios.md) | :heavy_check_mark: | :heavy_check_mark: |
[Zone Outage Scenarios](docs/zone_outage.md) | :heavy_check_mark: | :heavy_check_mark: |
[Application_outages](docs/application_outages.md) | :heavy_check_mark: | :heavy_check_mark: |
[PVC scenario](docs/pvc_scenario.md) | :heavy_check_mark: | :heavy_check_mark: |
[Network_Chaos](docs/network_chaos.md) | :heavy_check_mark: | :heavy_check_mark: |
### Kraken scenario pass/fail criteria and report
It is important to make sure to check if the targeted component recovered from the chaos injection and also if the Kubernetes cluster is healthy as failures in one component can have an adverse impact on other components. Kraken does this by:
It is important to make sure to check if the targeted component recovered from the chaos injection and also if the Kubernetes/OpenShift cluster is healthy as failures in one component can have an adverse impact on other components. Kraken does this by:
- Having built in checks for pod and node based scenarios to ensure the expected number of replicas and nodes are up. It also supports running custom scripts with the checks.
- Leveraging [Cerberus](https://github.com/krkn-chaos/cerberus) to monitor the cluster under test and consuming the aggregated go/no-go signal to determine pass/fail post chaos. It is highly recommended to turn on the Cerberus health check feature available in Kraken. Instructions on installing and setting up Cerberus can be found [here](https://github.com/openshift-scale/cerberus#installation) or can be installed from Kraken using the [instructions](https://github.com/krkn-chaos/krkn#setting-up-infrastructure-dependencies). Once Cerberus is up and running, set cerberus_enabled to True and cerberus_url to the url where Cerberus publishes go/no-go signal in the Kraken config file. Cerberus can monitor [application routes](https://github.com/redhat-chaos/cerberus/blob/main/docs/config.md#watch-routes) during the chaos and fails the run if it encounters downtime as it is a potential downtime in a customers, or users environment as well. It is especially important during the control plane chaos scenarios including the API server, Etcd, Ingress etc. It can be enabled by setting `check_applicaton_routes: True` in the [Kraken config](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml) provided application routes are being monitored in the [cerberus config](https://github.com/redhat-chaos/krkn/blob/main/config/cerberus.yaml).
- Leveraging built-in alert collection feature to fail the runs in case of critical alerts.
- Leveraging [Cerberus](https://github.com/openshift-scale/cerberus) to monitor the cluster under test and consuming the aggregated go/no-go signal to determine pass/fail post chaos. It is highly recommended to turn on the Cerberus health check feature available in Kraken. Instructions on installing and setting up Cerberus can be found [here](https://github.com/openshift-scale/cerberus#installation) or can be installed from Kraken using the [instructions](https://github.com/chaos-kubox/krkn#setting-up-infrastructure-dependencies). Once Cerberus is up and running, set cerberus_enabled to True and cerberus_url to the url where Cerberus publishes go/no-go signal in the Kraken config file. Cerberus can monitor [application routes](https://github.com/chaos-kubox/cerberus/blob/main/docs/config.md#watch-routes) during the chaos and fails the run if it encounters downtime as it is a potential downtime in a customers, or users environment as well. It is especially important during the control plane chaos scenarios including the API server, Etcd, Ingress etc. It can be enabled by setting `check_applicaton_routes: True` in the [Kraken config](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml) provided application routes are being monitored in the [cerberus config](https://github.com/chaos-kubox/krkn/blob/main/config/cerberus.yaml).
- Leveraging [kube-burner](docs/alerts.md) alerting feature to fail the runs in case of critical alerts.
### Signaling
In CI runs or any external job it is useful to stop Kraken once a certain test or state gets reached. We created a way to signal to kraken to pause the chaos or stop it completely using a signal posted to a port of your choice.
@@ -96,29 +89,27 @@ More detailed information on enabling and leveraging this feature can be found [
Monitoring the Kubernetes/OpenShift cluster to observe the impact of Kraken chaos scenarios on various components is key to find out the bottlenecks as it is important to make sure the cluster is healthy in terms if both recovery as well as performance during/after the failure has been injected. Instructions on enabling it can be found [here](docs/performance_dashboards.md).
### SLOs validation during and post chaos
- In addition to checking the recovery and health of the cluster and components under test, Kraken takes in a profile with the Prometheus expressions to validate and alerts, exits with a non-zero return code depending on the severity set. This feature can be used to determine pass/fail or alert on abnormalities observed in the cluster based on the metrics.
- Kraken also provides ability to check if any critical alerts are firing in the cluster post chaos and pass/fail's.
Information on enabling and leveraging this feature can be found [here](docs/SLOs_validation.md)
### Scraping and storing metrics long term
Kraken supports capturing metrics for the duration of the scenarios defined in the config and indexes then into Elasticsearch to be able to store and evaluate the state of the runs long term. The indexed metrics can be visualized with the help of Grafana. It uses [Kube-burner](https://github.com/cloud-bulldozer/kube-burner) under the hood. The metrics to capture need to be defined in a metrics profile which Kraken consumes to query prometheus ( installed by default in OpenShift ) with the start and end timestamp of the run. Information on enabling and leveraging this feature can be found [here](docs/metrics.md).
### OCM / ACM integration
Kraken supports injecting faults into [Open Cluster Management (OCM)](https://open-cluster-management.io/) and [Red Hat Advanced Cluster Management for Kubernetes (ACM)](https://www.krkn.com/en/technologies/management/advanced-cluster-management) managed clusters through [ManagedCluster Scenarios](docs/managedcluster_scenarios.md).
### Alerts
In addition to checking the recovery and health of the cluster and components under test, Kraken takes in a profile with the Prometheus expressions to validate and alerts, exits with a non-zero return code depending on the severity set. This feature can be used to determine pass/fail or alert on abnormalities observed in the cluster based on the metrics. Information on enabling and leveraging this feature can be found [here](docs/alerts.md).
### Blogs and other useful resources
- Blog post on introduction to Kraken: https://www.openshift.com/blog/introduction-to-kraken-a-chaos-tool-for-openshift/kubernetes
- Discussion and demo on how Kraken can be leveraged to ensure OpenShift is reliable, performant and scalable: https://www.youtube.com/watch?v=s1PvupI5sD0&ab_channel=OpenShift
- Blog post emphasizing the importance of making Chaos part of Performance and Scale runs to mimic the production environments: https://www.openshift.com/blog/making-chaos-part-of-kubernetes/openshift-performance-and-scalability-tests
- Blog post on findings from Chaos test runs: https://cloud.redhat.com/blog/openshift/kubernetes-chaos-stories
- Discussion with CNCF TAG App Delivery on Krkn workflow, features and addition to CNCF sandbox: [Github](https://github.com/cncf/sandbox/issues/44), [Tracker](https://github.com/cncf/tag-app-delivery/issues/465), [recording](https://www.youtube.com/watch?v=nXQkBFK_MWc&t=722s)
- Blog post on supercharging chaos testing using AI integration in Krkn: https://www.redhat.com/en/blog/supercharging-chaos-testing-using-ai
- Blog post announcing Krkn joining CNCF Sandbox: https://www.redhat.com/en/blog/krknchaos-joining-cncf-sandbox
### Roadmap
Enhancements being planned can be found in the [roadmap](ROADMAP.md).
Following is a list of enhancements that we are planning to work on adding support in Kraken. Of course any help/contributions are greatly appreciated.
- [Ability to visualize the metrics that are being captured by Kraken and stored in Elasticsearch](https://github.com/chaos-kubox/krkn/issues/124)
- Ability to shape the ingress network similar to how Kraken supports [egress traffic shaping](https://github.com/chaos-kubox/krkn/blob/main/docs/network_chaos.md) today.
- Continue to improve [Chaos Testing Guide](https://cloud-bulldozer.github.io/kraken/) in terms of adding best practices, test environment recommendations and scenarios to make sure the OpenShift platform, as well the applications running on top it, are resilient and performant under chaotic conditions.
- Support for running Kraken on Kubernetes distribution - see https://github.com/chaos-kubox/krkn/issues/185, https://github.com/chaos-kubox/krkn/issues/186
- Sweet logo for Kraken - see https://github.com/chaos-kubox/krkn/issues/195
### Contributions
@@ -131,7 +122,6 @@ Please read [this file]((CI/README.md#adding-a-test-case)) for more information
### Community
Key Members(slack_usernames/full name): paigerube14/Paige Rubendall, mffiedler/Mike Fiedler, tsebasti/Tullio Sebastiani, yogi/Yogananth Subramanian, sahil/Sahil Shah, pradeep/Pradeep Surisetty and ravielluri/Naga Ravi Chaitanya Elluri.
* [**#krkn on Kubernetes Slack**](https://kubernetes.slack.com/messages/C05SFMHRWK1)
The Linux Foundation® (TLF) has registered trademarks and uses trademarks. For a list of TLF trademarks, see [Trademark Usage](https://www.linuxfoundation.org/legal/trademark-usage).
Key Members(slack_usernames/full name): paigerube14/Paige Rubendall, mffiedler/Mike Fiedler, ravielluri/Naga Ravi Chaitanya Elluri.
* [**#sig-scalability on Kubernetes Slack**](https://kubernetes.slack.com)
* [**#forum-chaos on CoreOS Slack internal to Red Hat**](https://coreos.slack.com)

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@@ -1,15 +0,0 @@
## Krkn Roadmap
Following are a list of enhancements that we are planning to work on adding support in Krkn. Of course any help/contributions are greatly appreciated.
- [ ] [Ability to run multiple chaos scenarios in parallel under load to mimic real world outages](https://github.com/krkn-chaos/krkn/issues/424)
- [x] [Centralized storage for chaos experiments artifacts](https://github.com/krkn-chaos/krkn/issues/423)
- [ ] [Support for causing DNS outages](https://github.com/krkn-chaos/krkn/issues/394)
- [x] [Chaos recommender](https://github.com/krkn-chaos/krkn/tree/main/utils/chaos-recommender) to suggest scenarios having probability of impacting the service under test using profiling results
- [ ] Chaos AI integration to improve and automate test coverage
- [x] [Support for pod level network traffic shaping](https://github.com/krkn-chaos/krkn/issues/393)
- [ ] [Ability to visualize the metrics that are being captured by Kraken and stored in Elasticsearch](https://github.com/krkn-chaos/krkn/issues/124)
- [ ] Support for running all the scenarios of Kraken on Kubernetes distribution - see https://github.com/krkn-chaos/krkn/issues/185, https://github.com/redhat-chaos/krkn/issues/186
- [ ] Continue to improve [Chaos Testing Guide](https://krkn-chaos.github.io/krkn) in terms of adding best practices, test environment recommendations and scenarios to make sure the OpenShift platform, as well the applications running on top it, are resilient and performant under chaotic conditions.
- [ ] [Switch documentation references to Kubernetes](https://github.com/krkn-chaos/krkn/issues/495)
- [ ] [OCP and Kubernetes functionalities segregation](https://github.com/krkn-chaos/krkn/issues/497)

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@@ -8,13 +8,13 @@ orchestration_user: "{{ lookup('env', 'ORCHESTRATION_USER')|default('root', true
###############################################################################
# kube config location
kubeconfig_path: "{{ lookup('env', 'KUBECONFIG_PATH')|default('~/.kube/config', true) }}"
kubeconfig_path: "{{ lookup('env', 'KUBECONFIG_PATH')|default('/root/.kube/config', true) }}"
# kraken dir location on jump host
kraken_dir: "{{ lookup('env', 'KRAKEN_DIR')|default('~/kraken', true) }}"
kraken_dir: "{{ lookup('env', 'KRAKEN_DIR')|default('/root/kraken', true) }}"
# kraken config path location
kraken_config: "{{ lookup('env', 'KRAKEN_CONFIG')|default('~/kraken/config/config.yaml', true) }}"
kraken_config: "{{ lookup('env', 'KRAKEN_CONFIG')|default('/root/kraken/config/config.yaml', true) }}"
# kraken repository location
kraken_repository: "{{ lookup('env', 'KRAKEN_REPOSITORY')|default('https://github.com/openshift-scale/kraken.git', true) }}"

11
config/alerts Normal file
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@@ -0,0 +1,11 @@
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[5m:]) > 0.01
description: 5 minutes avg. etcd fsync latency on {{$labels.pod}} higher than 10ms {{$value}}
severity: error
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_network_peer_round_trip_time_seconds_bucket[5m]))[5m:]) > 0.1
description: 5 minutes avg. etcd netowrk peer round trip on {{$labels.pod}} higher than 100ms {{$value}}
severity: info
- expr: increase(etcd_server_leader_changes_seen_total[2m]) > 0
description: etcd leader changes observed
severity: critical

View File

@@ -1,90 +0,0 @@
# etcd
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[10m:]) > 0.01
description: 10 minutes avg. 99th etcd fsync latency on {{$labels.pod}} higher than 10ms. {{$value}}s
severity: warning
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[10m:]) > 1
description: 10 minutes avg. 99th etcd fsync latency on {{$labels.pod}} higher than 1s. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_backend_commit_duration_seconds_bucket[2m]))[10m:]) > 0.03
description: 10 minutes avg. 99th etcd commit latency on {{$labels.pod}} higher than 30ms. {{$value}}s
severity: warning
- expr: rate(etcd_server_leader_changes_seen_total[2m]) > 0
description: etcd leader changes observed
severity: warning
- expr: (last_over_time(etcd_mvcc_db_total_size_in_bytes[5m]) / last_over_time(etcd_server_quota_backend_bytes[5m]))*100 > 95
description: etcd cluster database is running full.
severity: critical
- expr: (last_over_time(etcd_mvcc_db_total_size_in_use_in_bytes[5m]) / last_over_time(etcd_mvcc_db_total_size_in_bytes[5m])) < 0.5
description: etcd database size in use is less than 50% of the actual allocated storage.
severity: warning
- expr: rate(etcd_server_proposals_failed_total{job=~".*etcd.*"}[15m]) > 5
description: etcd cluster has high number of proposal failures.
severity: warning
- expr: histogram_quantile(0.99, rate(etcd_network_peer_round_trip_time_seconds_bucket{job=~".*etcd.*"}[5m])) > 0.15
description: etcd cluster member communication is slow.
severity: warning
- expr: histogram_quantile(0.99, sum(rate(grpc_server_handling_seconds_bucket{job=~".*etcd.*", grpc_method!="Defragment", grpc_type="unary"}[5m])) without(grpc_type)) > 0.15
description: etcd grpc requests are slow.
severity: critical
- expr: 100 * sum(rate(grpc_server_handled_total{job=~".*etcd.*", grpc_code=~"Unknown|FailedPrecondition|ResourceExhausted|Internal|Unavailable|DataLoss|DeadlineExceeded"}[5m])) without (grpc_type, grpc_code) / sum(rate(grpc_server_handled_total{job=~".*etcd.*"}[5m])) without (grpc_type, grpc_code) > 5
description: etcd cluster has high number of failed grpc requests.
severity: critical
- expr: etcd_server_has_leader{job=~".*etcd.*"} == 0
description: etcd cluster has no leader.
severity: warning
- expr: sum(up{job=~".*etcd.*"} == bool 1) without (instance) < ((count(up{job=~".*etcd.*"}) without (instance) + 1) / 2)
description: etcd cluster has insufficient number of members.
severity: warning
- expr: max without (endpoint) ( sum without (instance) (up{job=~".*etcd.*"} == bool 0) or count without (To) ( sum without (instance) (rate(etcd_network_peer_sent_failures_total{job=~".*etcd.*"}[120s])) > 0.01 )) > 0
description: etcd cluster members are down.
severity: warning
# API server
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"POST|PUT|DELETE|PATCH", subresource!~"log|exec|portforward|attach|proxy"}[2m])) by (le, resource, verb))[10m:]) > 1
description: 10 minutes avg. 99th mutating API call latency for {{$labels.verb}}/{{$labels.resource}} higher than 1 second. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="resource"}[2m])) by (le, resource, verb, scope))[5m:]) > 1
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 1 second. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="namespace"}[2m])) by (le, resource, verb, scope))[5m:]) > 5
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 5 seconds. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="cluster"}[2m])) by (le, resource, verb, scope))[5m:]) > 30
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 30 seconds. {{$value}}s
severity: error
# Control plane pods
- expr: up{job=~"crio|kubelet"} == 0
description: "{{$labels.node}}/{{$labels.job}} down"
severity: warning
- expr: up{job="ovnkube-node"} == 0
description: "{{$labels.instance}}/{{$labels.pod}} {{$labels.job}} down"
severity: warning
# Service sync latency
- expr: histogram_quantile(0.99, sum(rate(kubeproxy_network_programming_duration_seconds_bucket[2m])) by (le)) > 10
description: 99th Kubeproxy network programming latency higher than 10 seconds. {{$value}}s
severity: warning
# Prometheus alerts
- expr: ALERTS{severity="critical", alertstate="firing"} > 0
description: Critical prometheus alert. {{$labels.alertname}}
severity: warning

View File

@@ -1,101 +0,0 @@
# etcd
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[10m:]) > 0.01
description: 10 minutes avg. 99th etcd fsync latency on {{$labels.pod}} higher than 10ms. {{$value}}s
severity: warning
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[10m:]) > 1
description: 10 minutes avg. 99th etcd fsync latency on {{$labels.pod}} higher than 1s. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_backend_commit_duration_seconds_bucket[2m]))[10m:]) > 0.03
description: 10 minutes avg. 99th etcd commit latency on {{$labels.pod}} higher than 30ms. {{$value}}s
severity: warning
- expr: rate(etcd_server_leader_changes_seen_total[2m]) > 0
description: etcd leader changes observed
severity: warning
- expr: (last_over_time(etcd_mvcc_db_total_size_in_bytes[5m]) / last_over_time(etcd_server_quota_backend_bytes[5m]))*100 > 95
description: etcd cluster database is running full.
severity: critical
- expr: (last_over_time(etcd_mvcc_db_total_size_in_use_in_bytes[5m]) / last_over_time(etcd_mvcc_db_total_size_in_bytes[5m])) < 0.5
description: etcd database size in use is less than 50% of the actual allocated storage.
severity: warning
- expr: rate(etcd_server_proposals_failed_total{job=~".*etcd.*"}[15m]) > 5
description: etcd cluster has high number of proposal failures.
severity: warning
- expr: histogram_quantile(0.99, rate(etcd_network_peer_round_trip_time_seconds_bucket{job=~".*etcd.*"}[5m])) > 0.15
description: etcd cluster member communication is slow.
severity: warning
- expr: histogram_quantile(0.99, sum(rate(grpc_server_handling_seconds_bucket{job=~".*etcd.*", grpc_method!="Defragment", grpc_type="unary"}[5m])) without(grpc_type)) > 0.15
description: etcd grpc requests are slow.
severity: critical
- expr: 100 * sum(rate(grpc_server_handled_total{job=~".*etcd.*", grpc_code=~"Unknown|FailedPrecondition|ResourceExhausted|Internal|Unavailable|DataLoss|DeadlineExceeded"}[5m])) without (grpc_type, grpc_code) / sum(rate(grpc_server_handled_total{job=~".*etcd.*"}[5m])) without (grpc_type, grpc_code) > 5
description: etcd cluster has high number of failed grpc requests.
severity: critical
- expr: etcd_server_has_leader{job=~".*etcd.*"} == 0
description: etcd cluster has no leader.
severity: warning
- expr: sum(up{job=~".*etcd.*"} == bool 1) without (instance) < ((count(up{job=~".*etcd.*"}) without (instance) + 1) / 2)
description: etcd cluster has insufficient number of members.
severity: warning
- expr: max without (endpoint) ( sum without (instance) (up{job=~".*etcd.*"} == bool 0) or count without (To) ( sum without (instance) (rate(etcd_network_peer_sent_failures_total{job=~".*etcd.*"}[120s])) > 0.01 )) > 0
description: etcd cluster members are down.
severity: warning
# API server
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"POST|PUT|DELETE|PATCH", subresource!~"log|exec|portforward|attach|proxy"}[2m])) by (le, resource, verb))[10m:]) > 1
description: 10 minutes avg. 99th mutating API call latency for {{$labels.verb}}/{{$labels.resource}} higher than 1 second. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="resource"}[2m])) by (le, resource, verb, scope))[5m:]) > 1
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 1 second. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="namespace"}[2m])) by (le, resource, verb, scope))[5m:]) > 5
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 5 seconds. {{$value}}s
severity: error
- expr: avg_over_time(histogram_quantile(0.99, sum(irate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb=~"LIST|GET", subresource!~"log|exec|portforward|attach|proxy", scope="cluster"}[2m])) by (le, resource, verb, scope))[5m:]) > 30
description: 5 minutes avg. 99th read-only API call latency for {{$labels.verb}}/{{$labels.resource}} in scope {{$labels.scope}} higher than 30 seconds. {{$value}}s
severity: error
# Control plane pods
- expr: up{apiserver=~"kube-apiserver|openshift-apiserver"} == 0
description: "{{$labels.apiserver}} {{$labels.instance}} down"
severity: warning
- expr: up{namespace=~"openshift-etcd"} == 0
description: "{{$labels.namespace}}/{{$labels.pod}} down"
severity: warning
- expr: up{namespace=~"openshift-.*(kube-controller-manager|scheduler|controller-manager|sdn|ovn-kubernetes|dns)"} == 0
description: "{{$labels.namespace}}/{{$labels.pod}} down"
severity: warning
- expr: up{job=~"crio|kubelet"} == 0
description: "{{$labels.node}}/{{$labels.job}} down"
severity: warning
- expr: up{job="ovnkube-node"} == 0
description: "{{$labels.instance}}/{{$labels.pod}} {{$labels.job}} down"
severity: warning
# Service sync latency
- expr: histogram_quantile(0.99, sum(rate(kubeproxy_network_programming_duration_seconds_bucket[2m])) by (le)) > 10
description: 99th Kubeproxy network programming latency higher than 10 seconds. {{$value}}s
severity: warning
# Prometheus alerts
- expr: ALERTS{severity="critical", alertstate="firing"} > 0
description: Critical prometheus alert. {{$labels.alertname}}
severity: warning

View File

@@ -1,6 +1,6 @@
cerberus:
distribution: openshift # Distribution can be kubernetes or openshift
kubeconfig_path: ~/.kube/config # Path to kubeconfig
kubeconfig_path: /root/.kube/config # Path to kubeconfig
port: 8080 # http server port where cerberus status is published
watch_nodes: True # Set to True for the cerberus to monitor the cluster nodes
watch_cluster_operators: True # Set to True for cerberus to monitor cluster operators

View File

@@ -1,49 +1,49 @@
kraken:
distribution: kubernetes # Distribution can be kubernetes or openshift
kubeconfig_path: ~/.kube/config # Path to kubeconfig
distribution: openshift # Distribution can be kubernetes or openshift
kubeconfig_path: /root/.kube/config # Path to kubeconfig
exit_on_failure: False # Exit when a post action scenario fails
port: 8081
publish_kraken_status: True # Can be accessed at http://0.0.0.0:8081
signal_state: RUN # Will wait for the RUN signal when set to PAUSE before running the scenarios, refer docs/signal.md for more details
signal_address: 0.0.0.0 # Signal listening address
port: 8081 # Signal port
chaos_scenarios:
# List of policies/chaos scenarios to load
- arcaflow_scenarios:
- scenarios/arcaflow/cpu-hog/input.yaml
- scenarios/arcaflow/memory-hog/input.yaml
- scenarios/arcaflow/io-hog/input.yaml
- application_outages:
- scenarios/openshift/app_outage.yaml
- container_scenarios: # List of chaos pod scenarios to load
- - scenarios/openshift/container_etcd.yml
- plugin_scenarios:
- scenarios/openshift/etcd.yml
- scenarios/openshift/regex_openshift_pod_kill.yml
- scenarios/openshift/vmware_node_scenarios.yml
- scenarios/openshift/network_chaos_ingress.yml
- scenarios/openshift/prom_kill.yml
- node_scenarios: # List of chaos node scenarios to load
- scenarios/openshift/node_scenarios_example.yml
- plugin_scenarios:
- scenarios/openshift/openshift-apiserver.yml
- scenarios/openshift/openshift-kube-apiserver.yml
- time_scenarios: # List of chaos time scenarios to load
litmus_version: v1.13.6 # Litmus version to install
litmus_uninstall: False # If you want to uninstall litmus if failure
litmus_uninstall_before_run: True # If you want to uninstall litmus before a new run starts
chaos_scenarios: # List of policies/chaos scenarios to load
- container_scenarios: # List of chaos pod scenarios to load
- - scenarios/openshift/container_etcd.yml
- pod_scenarios:
- - scenarios/openshift/etcd.yml
- - scenarios/openshift/regex_openshift_pod_kill.yml
- scenarios/openshift/post_action_regex.py
- node_scenarios: # List of chaos node scenarios to load
- scenarios/openshift/node_scenarios_example.yml
- pod_scenarios:
- - scenarios/openshift/openshift-apiserver.yml
- - scenarios/openshift/openshift-kube-apiserver.yml
- time_scenarios: # List of chaos time scenarios to load
- scenarios/openshift/time_scenarios_example.yml
- cluster_shut_down_scenarios:
- litmus_scenarios: # List of litmus scenarios to load
- - scenarios/openshift/templates/litmus-rbac.yaml
- scenarios/openshift/node_cpu_hog_engine.yaml
- - scenarios/openshift/templates/litmus-rbac.yaml
- scenarios/openshift/node_mem_engine.yaml
- - scenarios/openshift/templates/litmus-rbac.yaml
- scenarios/openshift/node_io_engine.yaml
- cluster_shut_down_scenarios:
- - scenarios/openshift/cluster_shut_down_scenario.yml
- scenarios/openshift/post_action_shut_down.py
- service_disruption_scenarios:
- namespace_scenarios:
- - scenarios/openshift/regex_namespace.yaml
- - scenarios/openshift/ingress_namespace.yaml
- scenarios/openshift/post_action_namespace.py
- zone_outages:
- zone_outages:
- scenarios/openshift/zone_outage.yaml
- pvc_scenarios:
- application_outages:
- scenarios/openshift/app_outage.yaml
- pvc_scenarios:
- scenarios/openshift/pvc_scenario.yaml
- network_chaos:
- network_chaos:
- scenarios/openshift/network_chaos.yaml
- service_hijacking:
- scenarios/kube/service_hijacking.yaml
cerberus:
cerberus_enabled: False # Enable it when cerberus is previously installed
@@ -53,49 +53,17 @@ cerberus:
performance_monitoring:
deploy_dashboards: False # Install a mutable grafana and load the performance dashboards. Enable this only when running on OpenShift
repo: "https://github.com/cloud-bulldozer/performance-dashboards.git"
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
capture_metrics: False
config_path: config/kube_burner.yaml # Define the Elasticsearch url and index name in this config
metrics_profile_path: config/metrics-aggregated.yaml
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set
enable_alerts: False # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error
alert_profile: config/alerts.yaml # Path or URL to alert profile with the prometheus queries
check_critical_alerts: False # When enabled will check prometheus for critical alerts firing post chaos
alert_profile: config/alerts # Path to alert profile with the prometheus queries
tunings:
wait_duration: 60 # Duration to wait between each chaos scenario
iterations: 1 # Number of times to execute the scenarios
daemon_mode: False # Iterations are set to infinity which means that the kraken will cause chaos forever
telemetry:
enabled: False # enable/disables the telemetry collection feature
api_url: https://ulnmf9xv7j.execute-api.us-west-2.amazonaws.com/production #telemetry service endpoint
username: username # telemetry service username
password: password # telemetry service password
prometheus_backup: True # enables/disables prometheus data collection
prometheus_namespace: "" # namespace where prometheus is deployed (if distribution is kubernetes)
prometheus_container_name: "" # name of the prometheus container name (if distribution is kubernetes)
prometheus_pod_name: "" # name of the prometheus pod (if distribution is kubernetes)
full_prometheus_backup: False # if is set to False only the /prometheus/wal folder will be downloaded.
backup_threads: 5 # number of telemetry download/upload threads
archive_path: /tmp # local path where the archive files will be temporarly stored
max_retries: 0 # maximum number of upload retries (if 0 will retry forever)
run_tag: '' # if set, this will be appended to the run folder in the bucket (useful to group the runs)
archive_size: 500000
telemetry_group: '' # if set will archive the telemetry in the S3 bucket on a folder named after the value, otherwise will use "default"
# the size of the prometheus data archive size in KB. The lower the size of archive is
# the higher the number of archive files will be produced and uploaded (and processed by backup_threads
# simultaneously).
# For unstable/slow connection is better to keep this value low
# increasing the number of backup_threads, in this way, on upload failure, the retry will happen only on the
# failed chunk without affecting the whole upload.
logs_backup: True
logs_filter_patterns:
- "(\\w{3}\\s\\d{1,2}\\s\\d{2}:\\d{2}:\\d{2}\\.\\d+).+" # Sep 9 11:20:36.123425532
- "kinit (\\d+/\\d+/\\d+\\s\\d{2}:\\d{2}:\\d{2})\\s+" # kinit 2023/09/15 11:20:36 log
- "(\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d+Z).+" # 2023-09-15T11:20:36.123425532Z log
oc_cli_path: /usr/bin/oc # optional, if not specified will be search in $PATH
events_backup: True # enables/disables cluster events collection
elastic:
elastic_url: "" # To track results in elasticsearch, give url to server here; will post telemetry details when url and index not blank
elastic_index: "" # Elastic search index pattern to post results to

View File

@@ -1,32 +0,0 @@
kraken:
distribution: kubernetes # Distribution can be kubernetes or openshift
kubeconfig_path: ~/.kube/config # Path to kubeconfig
exit_on_failure: False # Exit when a post action scenario fails
port: 8081
publish_kraken_status: True # Can be accessed at http://0.0.0.0:8081
signal_state: RUN # Will wait for the RUN signal when set to PAUSE before running the scenarios, refer docs/signal.md for more details
signal_address: 0.0.0.0 # Signal listening address
chaos_scenarios: # List of policies/chaos scenarios to load
- plugin_scenarios:
- scenarios/kind/scheduler.yml
- node_scenarios:
- scenarios/kind/node_scenarios_example.yml
cerberus:
cerberus_enabled: False # Enable it when cerberus is previously installed
cerberus_url: # When cerberus_enabled is set to True, provide the url where cerberus publishes go/no-go signal
check_applicaton_routes: False # When enabled will look for application unavailability using the routes specified in the cerberus config and fails the run
performance_monitoring:
deploy_dashboards: False # Install a mutable grafana and load the performance dashboards. Enable this only when running on OpenShift
repo: "https://github.com/cloud-bulldozer/performance-dashboards.git"
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set
enable_alerts: False # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error
alert_profile: config/alerts.yaml # Path to alert profile with the prometheus queries
tunings:
wait_duration: 60 # Duration to wait between each chaos scenario
iterations: 1 # Number of times to execute the scenarios
daemon_mode: False # Iterations are set to infinity which means that the kraken will cause chaos forever

View File

@@ -1,15 +1,18 @@
kraken:
distribution: kubernetes # Distribution can be kubernetes or openshift
kubeconfig_path: ~/.kube/config # Path to kubeconfig
distribution: kubernetes # Distribution can be kubernetes or openshift
kubeconfig_path: /root/.kube/config # Path to kubeconfig
exit_on_failure: False # Exit when a post action scenario fails
port: 8081
publish_kraken_status: True # Can be accessed at http://0.0.0.0:8081
signal_state: RUN # Will wait for the RUN signal when set to PAUSE before running the scenarios, refer docs/signal.md for more details
litmus_version: v1.13.6 # Litmus version to install
litmus_uninstall: False # If you want to uninstall litmus if failure
litmus_uninstall_before_run: True # If you want to uninstall litmus before a new run starts
chaos_scenarios: # List of policies/chaos scenarios to load
- container_scenarios: # List of chaos pod scenarios to load
- - scenarios/kube/container_dns.yml
- plugin_scenarios:
- scenarios/kube/scheduler.yml
- pod_scenarios:
- - scenarios/kube/scheduler.yml
cerberus:
cerberus_enabled: False # Enable it when cerberus is previously installed
@@ -19,12 +22,16 @@ cerberus:
performance_monitoring:
deploy_dashboards: False # Install a mutable grafana and load the performance dashboards. Enable this only when running on OpenShift
repo: "https://github.com/cloud-bulldozer/performance-dashboards.git"
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
capture_metrics: False
config_path: config/kube_burner.yaml # Define the Elasticsearch url and index name in this config
metrics_profile_path: config/metrics-aggregated.yaml
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set
enable_alerts: False # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error
alert_profile: config/alerts.yaml # Path to alert profile with the prometheus queries
check_critical_alerts: False # When enabled will check prometheus for critical alerts firing post chaos after soak time for the cluster to settle down
alert_profile: config/alerts # Path to alert profile with the prometheus queries
tunings:
wait_duration: 60 # Duration to wait between each chaos scenario
iterations: 1 # Number of times to execute the scenarios

View File

@@ -1,27 +1,32 @@
kraken:
distribution: openshift # Distribution can be kubernetes or openshift
kubeconfig_path: ~/.kube/config # Path to kubeconfig
kubeconfig_path: /root/.kube/config # Path to kubeconfig
exit_on_failure: False # Exit when a post action scenario fails
port: 8081
publish_kraken_status: True # Can be accessed at http://0.0.0.0:8081
signal_state: RUN # Will wait for the RUN signal when set to PAUSE before running the scenarios, refer docs/signal.md for more details
signal_address: 0.0.0.0 # Signal listening address
port: 8081 # Signal port
litmus_version: v1.13.6 # Litmus version to install
litmus_uninstall: False # If you want to uninstall litmus if failure
litmus_uninstall_before_run: True # If you want to uninstall litmus before a new run starts
chaos_scenarios: # List of policies/chaos scenarios to load
- plugin_scenarios: # List of chaos pod scenarios to load
- scenarios/openshift/etcd.yml
- scenarios/openshift/regex_openshift_pod_kill.yml
- scenarios/openshift/prom_kill.yml
- pod_scenarios: # List of chaos pod scenarios to load
- - scenarios/openshift/etcd.yml
- - scenarios/openshift/regex_openshift_pod_kill.yml
- scenarios/openshift/post_action_regex.py
- node_scenarios: # List of chaos node scenarios to load
- scenarios/openshift/node_scenarios_example.yml
- plugin_scenarios:
- scenarios/openshift/openshift-apiserver.yml
- scenarios/openshift/openshift-kube-apiserver.yml
- pod_scenarios:
- - scenarios/openshift/openshift-apiserver.yml
- - scenarios/openshift/openshift-kube-apiserver.yml
- time_scenarios: # List of chaos time scenarios to load
- scenarios/openshift/time_scenarios_example.yml
- litmus_scenarios: # List of litmus scenarios to load
- - https://hub.litmuschaos.io/api/chaos/1.10.0?file=charts/generic/node-cpu-hog/rbac.yaml
- scenarios/openshift/node_cpu_hog_engine.yaml
- cluster_shut_down_scenarios:
- - scenarios/openshift/cluster_shut_down_scenario.yml
- scenarios/openshift/post_action_shut_down.py
- service_disruption_scenarios:
- namespace_scenarios:
- scenarios/openshift/regex_namespace.yaml
- scenarios/openshift/ingress_namespace.yaml
- zone_outages:
@@ -41,44 +46,17 @@ cerberus:
performance_monitoring:
deploy_dashboards: True # Install a mutable grafana and load the performance dashboards. Enable this only when running on OpenShift
repo: "https://github.com/cloud-bulldozer/performance-dashboards.git"
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
capture_metrics: True
config_path: config/kube_burner.yaml # Define the Elasticsearch url and index name in this config
metrics_profile_path: config/metrics-aggregated.yaml
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set
enable_alerts: True # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error
alert_profile: config/alerts.yaml # Path to alert profile with the prometheus queries
alert_profile: config/alerts # Path to alert profile with the prometheus queries
tunings:
wait_duration: 60 # Duration to wait between each chaos scenario
iterations: 1 # Number of times to execute the scenarios
daemon_mode: False # Iterations are set to infinity which means that the kraken will cause chaos forever
telemetry:
enabled: False # enable/disables the telemetry collection feature
api_url: https://ulnmf9xv7j.execute-api.us-west-2.amazonaws.com/production #telemetry service endpoint
username: username # telemetry service username
password: password # telemetry service password
prometheus_backup: True # enables/disables prometheus data collection
full_prometheus_backup: False # if is set to False only the /prometheus/wal folder will be downloaded.
backup_threads: 5 # number of telemetry download/upload threads
archive_path: /tmp # local path where the archive files will be temporarly stored
max_retries: 0 # maximum number of upload retries (if 0 will retry forever)
run_tag: '' # if set, this will be appended to the run folder in the bucket (useful to group the runs)
archive_size: 500000 # the size of the prometheus data archive size in KB. The lower the size of archive is
# the higher the number of archive files will be produced and uploaded (and processed by backup_threads
# simultaneously).
# For unstable/slow connection is better to keep this value low
# increasing the number of backup_threads, in this way, on upload failure, the retry will happen only on the
# failed chunk without affecting the whole upload.
logs_backup: True
logs_filter_patterns:
- "(\\w{3}\\s\\d{1,2}\\s\\d{2}:\\d{2}:\\d{2}\\.\\d+).+" # Sep 9 11:20:36.123425532
- "kinit (\\d+/\\d+/\\d+\\s\\d{2}:\\d{2}:\\d{2})\\s+" # kinit 2023/09/15 11:20:36 log
- "(\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d+Z).+" # 2023-09-15T11:20:36.123425532Z log
oc_cli_path: /usr/bin/oc # optional, if not specified will be search in $PATH
elastic:
elastic_url: "" # To track results in elasticsearch, give url to server here; will post telemetry details when url and index not blank
elastic_index: "" # Elastic search index pattern to post results to

15
config/kube_burner.yaml Normal file
View File

@@ -0,0 +1,15 @@
---
global:
writeToFile: true
metricsDirectory: collected-metrics
measurements:
- name: podLatency
esIndex: kraken
indexerConfig:
enabled: true
esServers: [http://0.0.0.0:9200] # Please change this to the respective Elasticsearch in use if you haven't run the podman-compose command to setup the infrastructure containers
insecureSkipVerify: true
defaultIndex: kraken
type: elastic

View File

@@ -139,39 +139,6 @@ metrics:
- query: histogram_quantile(0.99,sum(rate(etcd_request_duration_seconds_bucket[2m])) by (le,operation,apiserver)) > 0
metricName: P99APIEtcdRequestLatency
- query: sum(grpc_server_started_total{namespace="openshift-etcd",grpc_service="etcdserverpb.Watch",grpc_type="bidi_stream"}) - sum(grpc_server_handled_total{namespace="openshift-etcd",grpc_service="etcdserverpb.Watch",grpc_type="bidi_stream"})
metricName: ActiveWatchStreams
- query: sum(grpc_server_started_total{namespace="openshift-etcd",grpc_service="etcdserverpb.Lease",grpc_type="bidi_stream"}) - sum(grpc_server_handled_total{namespace="openshift-etcd",grpc_service="etcdserverpb.Lease",grpc_type="bidi_stream"})
metricName: ActiveLeaseStreams
- query: sum(rate(etcd_debugging_snap_save_total_duration_seconds_sum{namespace="openshift-etcd"}[2m]))
metricName: snapshotSaveLatency
- query: sum(rate(etcd_server_heartbeat_send_failures_total{namespace="openshift-etcd"}[2m]))
metricName: HeartBeatFailures
- query: sum(rate(etcd_server_health_failures{namespace="openshift-etcd"}[2m]))
metricName: HealthFailures
- query: sum(rate(etcd_server_slow_apply_total{namespace="openshift-etcd"}[2m]))
metricName: SlowApplies
- query: sum(rate(etcd_server_slow_read_indexes_total{namespace="openshift-etcd"}[2m]))
metricName: SlowIndexRead
- query: sum(etcd_server_proposals_pending)
metricName: PendingProposals
- query: histogram_quantile(1.0, sum(rate(etcd_debugging_mvcc_db_compaction_pause_duration_milliseconds_bucket[1m])) by (le, instance))
metricName: CompactionMaxPause
- query: sum by (instance) (apiserver_storage_objects)
metricName: etcdTotalObjectCount
- query: topk(500, max by(resource) (apiserver_storage_objects))
metricName: etcdTopObectCount
# Cluster metrics
- query: count(kube_namespace_created)
metricName: namespaceCount

View File

@@ -1,35 +0,0 @@
application: openshift-etcd
namespaces: openshift-etcd
labels: app=openshift-etcd
kubeconfig: ~/.kube/config.yaml
prometheus_endpoint: <Prometheus_Endpoint>
auth_token: <Auth_Token>
scrape_duration: 10m
chaos_library: "kraken"
log_level: INFO
json_output_file: False
json_output_folder_path:
# for output purpose only do not change if not needed
chaos_tests:
GENERIC:
- pod_failure
- container_failure
- node_failure
- zone_outage
- time_skew
- namespace_failure
- power_outage
CPU:
- node_cpu_hog
NETWORK:
- application_outage
- node_network_chaos
- pod_network_chaos
MEM:
- node_memory_hog
- pvc_disk_fill
threshold: .7
cpu_threshold: .5
mem_threshold: .5

View File

@@ -1,49 +1,28 @@
# azure-client
FROM mcr.microsoft.com/azure-cli:latest as azure-cli
# Dockerfile for kraken
# oc build
FROM golang:1.22.2 AS oc-build
RUN apt-get update && apt-get install -y libkrb5-dev
WORKDIR /tmp
RUN git clone --branch release-4.18 https://github.com/openshift/oc.git
WORKDIR /tmp/oc
RUN make GO_REQUIRED_MIN_VERSION:= oc
FROM quay.io/openshift/origin-tests:latest as origintests
FROM registry.access.redhat.com/ubi9/ubi:latest
FROM quay.io/centos/centos:stream9
# krkn version that will be built
ENV KRKN_VERSION v1.6.0
LABEL org.opencontainers.image.authors="Red Hat OpenShift Chaos Engineering"
ENV KUBECONFIG /root/.kube/config
# update yum and install dependencies
RUN yum update -y glibc glibc-common glibc-minimal-langpack runc
RUN rpm -e --allmatches --nodeps --noscripts --notriggers python3-requests
RUN yum install -y git python39 python3-pip jq gettext wget
# Copy OpenShift CLI, Kubernetes CLI from origin-tests image
COPY --from=origintests /usr/bin/oc /usr/bin/oc
COPY --from=origintests /usr/bin/kubectl /usr/bin/kubectl
# get yq
RUN wget https://github.com/mikefarah/yq/releases/latest/download/yq_linux_amd64 -O /usr/bin/yq && chmod +x /usr/bin/yq
# get kubectl
RUN curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" &&\
cp kubectl /usr/local/bin/kubectl && chmod +x /usr/local/bin/kubectl &&\
cp kubectl /usr/bin/kubectl && chmod +x /usr/bin/kubectl
# copy azure client binary from azure-cli image
COPY --from=azure-cli /usr/local/bin/az /usr/bin/az
# copy oc client binary from oc-build image
COPY --from=oc-build /tmp/oc/oc /usr/bin/oc
COPY --from=oc-build /tmp/oc/oc /usr/local/bin/oc
# krkn build
RUN python3.9 -m pip install -U pip
RUN git clone https://github.com/krkn-chaos/krkn.git --branch $KRKN_VERSION /root/kraken && \
mkdir -p /root/.kube
WORKDIR /root/kraken
RUN pip3.9 install -r requirements.txt
RUN pip3.9 install virtualenv
# Install dependencies
RUN yum install epel-release -y && \
yum install -y git python python3-pip jq gettext && \
python3 -m pip install -U pip && \
rpm --import https://packages.microsoft.com/keys/microsoft.asc && \
echo -e "[azure-cli]\nname=Azure CLI\nbaseurl=https://packages.microsoft.com/yumrepos/azure-cli\nenabled=1\ngpgcheck=1\ngpgkey=https://packages.microsoft.com/keys/microsoft.asc" > /etc/yum.repos.d/azure-cli.repo && yum install -y azure-cli && \
git clone https://github.com/openshift-scale/kraken /root/kraken && \
mkdir -p /root/.kube && cd /root/kraken && \
pip3 install -r requirements.txt
WORKDIR /root/kraken
ENTRYPOINT ["python3.9", "run_kraken.py"]
CMD ["--config=config/config.yaml"]
ENTRYPOINT ["python3", "run_kraken.py"]
CMD ["--config=config/config.yaml"]

View File

@@ -2,28 +2,24 @@
FROM ppc64le/centos:8
FROM mcr.microsoft.com/azure-cli:latest as azure-cli
LABEL org.opencontainers.image.authors="Red Hat OpenShift Chaos Engineering"
MAINTAINER Red Hat OpenShift Performance and Scale
ENV KUBECONFIG /root/.kube/config
# Copy azure client binary from azure-cli image
COPY --from=azure-cli /usr/local/bin/az /usr/bin/az
RUN curl -L -o kubernetes-client-linux-ppc64le.tar.gz https://dl.k8s.io/v1.19.0/kubernetes-client-linux-ppc64le.tar.gz \
&& tar xf kubernetes-client-linux-ppc64le.tar.gz && mv kubernetes/client/bin/kubectl /usr/bin/ && rm -rf kubernetes-client-linux-ppc64le.tar.gz
RUN curl -L -o openshift-client-linux.tar.gz https://mirror.openshift.com/pub/openshift-v4/ppc64le/clients/ocp/stable/openshift-client-linux.tar.gz \
&& tar xf openshift-client-linux.tar.gz -C /usr/bin && rm -rf openshift-client-linux.tar.gz
# Install dependencies
RUN yum install -y git python39 python3-pip jq gettext wget && \
python3.9 -m pip install -U pip && \
git clone https://github.com/redhat-chaos/krkn.git --branch v1.5.14 /root/kraken && \
mkdir -p /root/.kube && cd /root/kraken && \
pip3.9 install -r requirements.txt && \
pip3.9 install virtualenv && \
wget https://github.com/mikefarah/yq/releases/latest/download/yq_linux_amd64 -O /usr/bin/yq && chmod +x /usr/bin/yq
# Get Kubernetes and OpenShift clients from stable releases
WORKDIR /tmp
RUN wget https://mirror.openshift.com/pub/openshift-v4/clients/ocp/stable/openshift-client-linux.tar.gz && tar -xvf openshift-client-linux.tar.gz && cp oc /usr/local/bin/oc && cp oc /usr/bin/oc && cp kubectl /usr/local/bin/kubectl && cp kubectl /usr/bin/kubectl
RUN yum install epel-release -y && \
yum install -y git python36 python3-pip gcc libffi-devel python36-devel openssl-devel gcc-c++ make jq gettext && \
git clone https://github.com/cloud-bulldozer/kraken /root/kraken && \
mkdir -p /root/.kube && cd /root/kraken && \
pip3 install cryptography==3.3.2 && \
pip3 install -r requirements.txt setuptools==40.3.0 urllib3==1.25.4
WORKDIR /root/kraken
ENTRYPOINT python3.9 run_kraken.py --config=config/config.yaml
ENTRYPOINT python3 run_kraken.py --config=config/config.yaml

View File

@@ -1,46 +1,28 @@
### Kraken image
Container image gets automatically built by quay.io at [Kraken image](https://quay.io/redhat-chaos/krkn).
Container image gets automatically built by quay.io at [Kraken image](https://quay.io/chaos-kubox/krkn).
### Run containerized version
Refer [instructions](https://github.com/redhat-chaos/krkn/blob/main/docs/installation.md#run-containerized-version) for information on how to run the containerized version of kraken.
Refer [instructions](https://github.com/chaos-kubox/krkn/blob/main/docs/installation.md#run-containerized-version) for information on how to run the containerized version of kraken.
### Run Custom Kraken Image
Refer to [instructions](https://github.com/redhat-chaos/krkn/blob/main/containers/build_own_image-README.md) for information on how to run a custom containerized version of kraken using podman.
Refer to [instructions](https://github.com/chaos-kubox/krkn/blob/main/containers/build_own_image-README.md) for information on how to run a custom containerized version of kraken using podman.
### Kraken as a KubeApp ( Unsupported and not recommended )
#### GENERAL NOTES:
- It is not generally recommended to run Kraken internal to the cluster as the pod which is running Kraken might get disrupted, the suggested use case to run kraken from inside k8s/OpenShift is to target **another** cluster (eg. to bypass network restrictions or to leverage cluster's computational resources)
- your kubeconfig might contain several cluster contexts and credentials so be sure, before creating the ConfigMap, to keep **only** the credentials related to the destination cluster. Please refer to the [Kubernetes documentation](https://kubernetes.io/docs/tasks/access-application-cluster/configure-access-multiple-clusters/) for more details
- to add privileges to the service account you must be logged in the cluster with an highly privileged account (ideally kubeadmin)
### Kraken as a KubeApp
To run containerized Kraken as a Kubernetes/OpenShift Deployment, follow these steps:
1. Configure the [config.yaml](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml) file according to your requirements.
**NOTE**: both the scenarios ConfigMaps are needed regardless you're running kraken in Kubernetes or OpenShift
1. Configure the [config.yaml](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml) file according to your requirements.
2. Create a namespace under which you want to run the kraken pod using `kubectl create ns <namespace>`.
3. Switch to `<namespace>` namespace:
- In Kubernetes, use `kubectl config set-context --current --namespace=<namespace>`
- In OpenShift, use `oc project <namespace>`
4. Create a ConfigMap named kube-config using `kubectl create configmap kube-config --from-file=<path_to_kubeconfig>` *(eg. ~/.kube/config)*
5. Create a ConfigMap named kraken-config using `kubectl create configmap kraken-config --from-file=<path_to_kraken>/config`
6. Create a ConfigMap named scenarios-config using `kubectl create configmap scenarios-config --from-file=<path_to_kraken>/scenarios`
7. Create a ConfigMap named scenarios-openshift-config using `kubectl create configmap scenarios-openshift-config --from-file=<path_to_kraken>/scenarios/openshift`
8. Create a ConfigMap named scenarios-kube-config using `kubectl create configmap scenarios-kube-config --from-file=<path_to_kraken>/scenarios/kube`
9. Create a service account to run the kraken pod `kubectl create serviceaccount useroot`.
10. In Openshift, add privileges to service account and execute `oc adm policy add-scc-to-user privileged -z useroot`.
11. Create a Job using `kubectl apply -f <path_to_kraken>/containers/kraken.yml` and monitor the status using `oc get jobs` and `oc get pods`.
- In Kubernetes, use `kubectl config set-context --current --namespace=<namespace>`
- In OpenShift, use `oc project <namespace>`
4. Create a ConfigMap named kube-config using `kubectl create configmap kube-config --from-file=<path_to_kubeconfig>`
5. Create a ConfigMap named kraken-config using `kubectl create configmap kraken-config --from-file=<path_to_kraken_config>`
6. Create a ConfigMap named scenarios-config using `kubectl create configmap scenarios-config --from-file=<path_to_scenarios_folder>`
7. Create a service account to run the kraken pod `kubectl create serviceaccount useroot`.
8. In Openshift, add privileges to service account and execute `oc adm policy add-scc-to-user privileged -z useroot`.
9. Create a Job using `kubectl apply -f kraken.yml` and monitor the status using `oc get jobs` and `oc get pods`.
NOTE: It is not recommended to run Kraken internal to the cluster as the pod which is running Kraken might get disrupted.

View File

@@ -1,13 +1,13 @@
# Building your own Kraken image
1. Git clone the Kraken repository using `git clone https://github.com/redhat-chaos/krkn.git`.
1. Git clone the Kraken repository using `git clone https://github.com/openshift-scale/kraken.git`.
2. Modify the python code and yaml files to address your needs.
3. Execute `podman build -t <new_image_name>:latest .` in the containers directory within kraken to build an image from a Dockerfile.
4. Execute `podman run --detach --name <container_name> <new_image_name>:latest` to start a container based on your new image.
# Building the Kraken image on IBM Power (ppc64le)
1. Git clone the Kraken repository using `git clone https://github.com/redhat-chaos/krkn.git` on an IBM Power Systems server.
1. Git clone the Kraken repository using `git clone https://github.com/cloud-bulldozer/kraken.git` on an IBM Power Systems server.
2. Modify the python code and yaml files to address your needs.
3. Execute `podman build -t <new_image_name>:latest -f Dockerfile-ppc64le` in the containers directory within kraken to build an image from the Dockerfile for Power.
4. Execute `podman run --detach --name <container_name> <new_image_name>:latest` to start a container based on your new image.

View File

@@ -16,9 +16,9 @@ spec:
- name: kraken
securityContext:
privileged: true
image: quay.io/redhat-chaos/krkn
image: quay.io/chaos-kubox/krkn
command: ["/bin/sh", "-c"]
args: ["python3.9 run_kraken.py -c config/config.yaml"]
args: ["python3 run_kraken.py -c config/config.yaml"]
volumeMounts:
- mountPath: "/root/.kube"
name: config
@@ -26,10 +26,6 @@ spec:
name: kraken-config
- mountPath: "/root/kraken/scenarios"
name: scenarios-config
- mountPath: "/root/kraken/scenarios/openshift"
name: scenarios-openshift-config
- mountPath: "/root/kraken/scenarios/kube"
name: scenarios-kube-config
restartPolicy: Never
volumes:
- name: config
@@ -41,9 +37,3 @@ spec:
- name: scenarios-config
configMap:
name: scenarios-config
- name: scenarios-openshift-config
configMap:
name: scenarios-openshift-config
- name: scenarios-kube-config
configMap:
name: scenarios-kube-config

View File

@@ -1,28 +1,18 @@
## SLOs validation
## Alerts
Pass/fail based on metrics captured from the cluster is important in addition to checking the health status and recovery. Kraken supports:
### Checking for critical alerts post chaos
If enabled, the check runs at the end of each scenario ( post chaos ) and Kraken exits in case critical alerts are firing to allow user to debug. You can enable it in the config:
```
performance_monitoring:
check_critical_alerts: False # When enabled will check prometheus for critical alerts firing post chaos
```
### Validation and alerting based on the queries defined by the user during chaos
Takes PromQL queries as input and modifies the return code of the run to determine pass/fail. It's especially useful in case of automated runs in CI where user won't be able to monitor the system. This feature can be enabled in the [config](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml) by setting the following:
Pass/fail based on metrics captured from the cluster is important in addition to checking the health status and recovery. Kraken supports alerting based on the queries defined by the user and modifies the return code of the run to determine pass/fail. It's especially useful in case of automated runs in CI where user won't be able to monitor the system. It uses [Kube-burner](https://kube-burner.readthedocs.io/en/latest/) under the hood. This feature can be enabled in the [config](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml) by setting the following:
```
performance_monitoring:
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
enable_alerts: True # Runs the queries specified in the alert profile and displays the info or exits 1 when severity=error.
alert_profile: config/alerts.yaml # Path to alert profile with the prometheus queries.
alert_profile: config/alerts # Path to alert profile with the prometheus queries.
```
#### Alert profile
A couple of [alert profiles](https://github.com/redhat-chaos/krkn/tree/main/config) [alerts](https://github.com/redhat-chaos/krkn/blob/main/config/alerts.yaml) are shipped by default and can be tweaked to add more queries to alert on. User can provide a URL or path to the file in the [config](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml). The following are a few alerts examples:
### Alert profile
A couple of [alert profiles](https://github.com/chaos-kubox/krkn/tree/main/config) [alerts](https://github.com/chaos-kubox/krkn/blob/main/config/alerts) are shipped by default and can be tweaked to add more queries to alert on. The following are a few alerts examples:
```
- expr: avg_over_time(histogram_quantile(0.99, rate(etcd_disk_wal_fsync_duration_seconds_bucket[2m]))[5m:]) > 0.01

View File

@@ -1,70 +0,0 @@
## Arcaflow Scenarios
Arcaflow is a workflow engine in development which provides the ability to execute workflow steps in sequence, in parallel, repeatedly, etc. The main difference to competitors such as Netflix Conductor is the ability to run ad-hoc workflows without an infrastructure setup required.
The engine uses containers to execute plugins and runs them either locally in Docker/Podman or remotely on a Kubernetes cluster. The workflow system is strongly typed and allows for generating JSON schema and OpenAPI documents for all data formats involved.
### Available Scenarios
#### Hog scenarios:
- [CPU Hog](arcaflow_scenarios/cpu_hog.md)
- [Memory Hog](arcaflow_scenarios/memory_hog.md)
- [I/O Hog](arcaflow_scenarios/io_hog.md)
### Prequisites
Arcaflow supports three deployment technologies:
- Docker
- Podman
- Kubernetes
#### Docker
In order to run Arcaflow Scenarios with the Docker deployer, be sure that:
- Docker is correctly installed in your Operating System (to find instructions on how to install docker please refer to [Docker Documentation](https://www.docker.com/))
- The Docker daemon is running
#### Podman
The podman deployer is built around the podman CLI and doesn't need necessarily to be run along with the podman daemon.
To run Arcaflow Scenarios in your Operating system be sure that:
- podman is correctly installed in your Operating System (to find instructions on how to install podman refer to [Podman Documentation](https://podman.io/))
- the podman CLI is in your shell PATH
#### Kubernetes
The kubernetes deployer integrates directly the Kubernetes API Client and needs only a valid kubeconfig file and a reachable Kubernetes/OpenShift Cluster.
### Usage
To enable arcaflow scenarios edit the kraken config file, go to the section `kraken -> chaos_scenarios` of the yaml structure
and add a new element to the list named `arcaflow_scenarios` then add the desired scenario
pointing to the `input.yaml` file.
```
kraken:
...
chaos_scenarios:
- arcaflow_scenarios:
- scenarios/arcaflow/cpu-hog/input.yaml
```
#### input.yaml
The implemented scenarios can be found in *scenarios/arcaflow/<scenario_name>* folder.
The entrypoint of each scenario is the *input.yaml* file.
In this file there are all the options to set up the scenario accordingly to the desired target
### config.yaml
The arcaflow config file. Here you can set the arcaflow deployer and the arcaflow log level.
The supported deployers are:
- Docker
- Podman (podman daemon not needed, suggested option)
- Kubernetes
The supported log levels are:
- debug
- info
- warning
- error
### workflow.yaml
This file contains the steps that will be executed to perform the scenario against the target.
Each step is represented by a container that will be executed from the deployer and its options.
Note that we provide the scenarios as a template, but they can be manipulated to define more complex workflows.
To have more details regarding the arcaflow workflows architecture and syntax it is suggested to refer to the [Arcaflow Documentation](https://arcalot.io/arcaflow/).
This edit is no longer in quay image
Working on fix in ticket: https://issues.redhat.com/browse/CHAOS-494
This will effect all versions 4.12 and higher of OpenShift

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@@ -1,19 +0,0 @@
# CPU Hog
This scenario is based on the arcaflow [arcaflow-plugin-stressng](https://github.com/arcalot/arcaflow-plugin-stressng) plugin.
The purpose of this scenario is to create cpu pressure on a particular node of the Kubernetes/OpenShift cluster for a time span.
To enable this plugin add the pointer to the scenario input file `scenarios/arcaflow/cpu-hog/input.yaml` as described in the
Usage section.
This scenario takes a list of objects named `input_list` with the following properties:
- **kubeconfig :** *string* the kubeconfig needed by the deployer to deploy the sysbench plugin in the target cluster
- **namespace :** *string* the namespace where the scenario container will be deployed
**Note:** this parameter will be automatically filled by kraken if the `kubeconfig_path` property is correctly set
- **node_selector :** *key-value map* the node label that will be used as `nodeSelector` by the pod to target a specific cluster node
- **duration :** *string* stop stress test after N seconds. One can also specify the units of time in seconds, minutes, hours, days or years with the suffix s, m, h, d or y.
- **cpu_count :** *int* the number of CPU cores to be used (0 means all)
- **cpu_method :** *string* a fine-grained control of which cpu stressors to use (ackermann, cfloat etc. see [manpage](https://manpages.org/sysbench) for all the cpu_method options)
- **cpu_load_percentage :** *int* the CPU load by percentage
To perform several load tests in the same run simultaneously (eg. stress two or more nodes in the same run) add another item
to the `input_list` with the same properties (and eventually different values eg. different node_selectors
to schedule the pod on different nodes). To reduce (or increase) the parallelism change the value `parallelism` in `workload.yaml` file

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@@ -1,21 +0,0 @@
# I/O Hog
This scenario is based on the arcaflow [arcaflow-plugin-stressng](https://github.com/arcalot/arcaflow-plugin-stressng) plugin.
The purpose of this scenario is to create disk pressure on a particular node of the Kubernetes/OpenShift cluster for a time span.
The scenario allows to attach a node path to the pod as a `hostPath` volume.
To enable this plugin add the pointer to the scenario input file `scenarios/arcaflow/io-hog/input.yaml` as described in the
Usage section.
This scenario takes a list of objects named `input_list` with the following properties:
- **kubeconfig :** *string* the kubeconfig needed by the deployer to deploy the sysbench plugin in the target cluster
- **namespace :** *string* the namespace where the scenario container will be deployed
**Note:** this parameter will be automatically filled by kraken if the `kubeconfig_path` property is correctly set
- **node_selector :** *key-value map* the node label that will be used as `nodeSelector` by the pod to target a specific cluster node
- **duration :** *string* stop stress test after N seconds. One can also specify the units of time in seconds, minutes, hours, days or years with the suffix s, m, h, d or y.
- **target_pod_folder :** *string* the path in the pod where the volume is mounted
- **target_pod_volume :** *object* the `hostPath` volume definition in the [Kubernetes/OpenShift](https://docs.openshift.com/container-platform/3.11/install_config/persistent_storage/using_hostpath.html) format, that will be attached to the pod as a volume
- **io_write_bytes :** *string* writes N bytes for each hdd process. The size can be expressed as % of free space on the file system or in units of Bytes, KBytes, MBytes and GBytes using the suffix b, k, m or g
- **io_block_size :** *string* size of each write in bytes. Size can be from 1 byte to 4m.
To perform several load tests in the same run simultaneously (eg. stress two or more nodes in the same run) add another item
to the `input_list` with the same properties (and eventually different values eg. different node_selectors
to schedule the pod on different nodes). To reduce (or increase) the parallelism change the value `parallelism` in `workload.yaml` file

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@@ -1,18 +0,0 @@
# Memory Hog
This scenario is based on the arcaflow [arcaflow-plugin-stressng](https://github.com/arcalot/arcaflow-plugin-stressng) plugin.
The purpose of this scenario is to create Virtual Memory pressure on a particular node of the Kubernetes/OpenShift cluster for a time span.
To enable this plugin add the pointer to the scenario input file `scenarios/arcaflow/memory-hog/input.yaml` as described in the
Usage section.
This scenario takes a list of objects named `input_list` with the following properties:
- **kubeconfig :** *string* the kubeconfig needed by the deployer to deploy the sysbench plugin in the target cluster
- **namespace :** *string* the namespace where the scenario container will be deployed
**Note:** this parameter will be automatically filled by kraken if the `kubeconfig_path` property is correctly set
- **node_selector :** *key-value map* the node label that will be used as `nodeSelector` by the pod to target a specific cluster node
- **duration :** *string* stop stress test after N seconds. One can also specify the units of time in seconds, minutes, hours, days or years with the suffix s, m, h, d or y.
- **vm_bytes :** *string* N bytes per vm process or percentage of memory used (using the % symbol). The size can be expressed in units of Bytes, KBytes, MBytes and GBytes using the suffix b, k, m or g.
- **vm_workers :** *int* Number of VM stressors to be run (0 means 1 stressor per CPU)
To perform several load tests in the same run simultaneously (eg. stress two or more nodes in the same run) add another item
to the `input_list` with the same properties (and eventually different values eg. different node_selectors
to schedule the pod on different nodes). To reduce (or increase) the parallelism change the value `parallelism` in `workload.yaml` file

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@@ -1,12 +1,10 @@
Supported Cloud Providers:
- [AWS](#aws)
- [GCP](#gcp)
- [Openstack](#openstack)
- [Azure](#azure)
- [Alibaba](#alibaba)
- [VMware](#vmware)
- [IBMCloud](#ibmcloud)
* [AWS](#aws)
* [GCP](#gcp)
* [Openstack](#openstack)
* [Azure](#azure)
* [Alibaba](#alibaba)
## AWS
@@ -55,35 +53,3 @@ See the [Installation guide](https://www.alibabacloud.com/help/en/alibaba-cloud-
Refer to [region and zone page](https://www.alibabacloud.com/help/en/elastic-compute-service/latest/regions-and-zones#concept-2459516) to get the region id for the region you are running on.
Set cloud_type to either alibaba or alicloud in your node scenario yaml file.
## VMware
Set the following environment variables
1. ```export VSPHERE_IP=<vSphere_client_IP_address>```
2. ```export VSPHERE_USERNAME=<vSphere_client_username>```
3. ```export VSPHERE_PASSWORD=<vSphere_client_password>```
These are the credentials that you would normally use to access the vSphere client.
## IBMCloud
If no api key is set up with proper VPC resource permissions, use the following to create:
* Access group
* Service id with the following access
* With policy **VPC Infrastructure Services**
* Resources = All
* Roles:
* Editor
* Administrator
* Operator
* Viewer
* API Key
Set the following environment variables
1. ```export IBMC_URL=https://<region>.iaas.cloud.ibm.com/v1```
2. ```export IBMC_APIKEY=<ibmcloud_api_key>```

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@@ -1,5 +1,5 @@
#### Kubernetes cluster shut down scenario
Scenario to shut down all the nodes including the masters and restart them after specified duration. Cluster shut down scenario can be injected by placing the shut_down config file under cluster_shut_down_scenario option in the kraken config. Refer to [cluster_shut_down_scenario](https://github.com/krkn-chaos/krkn/blob/main/scenarios/cluster_shut_down_scenario.yml) config file.
#### Kubernetes/OpenShift cluster shut down scenario
Scenario to shut down all the nodes including the masters and restart them after specified duration. Cluster shut down scenario can be injected by placing the shut_down config file under cluster_shut_down_scenario option in the kraken config. Refer to [cluster_shut_down_scenario](https://github.com/chaos-kubox/krkn/blob/main/scenarios/cluster_shut_down_scenario.yml) config file.
Refer to [cloud setup](cloud_setup.md) to configure your cli properly for the cloud provider of the cluster you want to shut down.

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@@ -1,65 +1,4 @@
### Config
Set the scenarios to inject and the tunings like duration to wait between each scenario in the config file located at [config/config.yaml](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml).
**NOTE**: [config](https://github.com/redhat-chaos/krkn/blob/main/config/config_performance.yaml) can be used if leveraging the [automated way](https://github.com/redhat-chaos/krkn#setting-up-infrastructure-dependencies) to install the infrastructure pieces.
Config components:
* [Kraken](#kraken)
* [Cerberus](#cerberus)
* [Performance Monitoring](#performance-monitoring)
* [Tunings](#tunings)
# Kraken
This section defines scenarios and specific data to the chaos run
## Distribution
Either **openshift** or **kubernetes** depending on the type of cluster you want to run chaos on.
The prometheus url/route and bearer token are automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
## Exit on failure
**exit_on_failure**: Exit when a post action check or cerberus run fails
## Publish kraken status
**publish_kraken_status**: Can be accessed at http://0.0.0.0:8081 (or what signal_address and port you set in signal address section)
**signal_state**: State you want kraken to start at; will wait for the RUN signal to start running a chaos iteration. When set to PAUSE before running the scenarios, refer to [signal.md](signal.md) for more details
## Signal Address
**signal_address**: Address to listen/post the signal state to
**port**: port to listen/post the signal state to
## Chaos Scenarios
**chaos_scenarios**: List of different types of chaos scenarios you want to run with paths to their specific yaml file configurations
If a scenario has a post action check script, it will be run before and after each scenario to validate the component under test starts and ends at the same state
Currently the scenarios are run one after another (in sequence) and will exit if one of the scenarios fail, without moving onto the next one
Chaos scenario types:
- container_scenarios
- plugin_scenarios
- node_scenarios
- time_scenarios
- cluster_shut_down_scenarios
- namespace_scenarios
- zone_outages
- application_outages
- pvc_scenarios
- network_chaos
# Cerberus
Parameters to set for enabling of cerberus checks at the end of each executed scenario. The given url will pinged after the scenario and post action check have been completed for each scenario and iteration.
**cerberus_enabled**: Enable it when cerberus is previously installed
**cerberus_url**: When cerberus_enabled is set to True, provide the url where cerberus publishes go/no-go signal
**check_applicaton_routes**: When enabled will look for application unavailability using the routes specified in the cerberus config and fails the run
# Performance Monitoring
There are 2 main sections defined in this part of the config [metrics](metrics.md) and [alerts](alerts.md); read more about each of these configurations in their respective docs
# Tunings
**wait_duration**: Duration to wait between each chaos scenario
**iterations**: Number of times to execute the scenarios
**daemon_mode**: True or False; If true, iterations are set to infinity which means that the kraken will cause chaos forever and number of iterations is ignored
Set the scenarios to inject and the tunings like duration to wait between each scenario in the config file located at [config/config.yaml](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml).
**NOTE**: [config](https://github.com/chaos-kubox/krkn/blob/main/config/config_performance.yaml) can be used if leveraging the [automated way](https://github.com/chaos-kubox/krkn#setting-up-infrastructure-dependencies) to install the infrastructure pieces.

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@@ -4,19 +4,17 @@ This can be based on the pods namespace or labels. If you know the exact object
These scenarios are in a simple yaml format that you can manipulate to run your specific tests or use the pre-existing scenarios to see how it works.
#### Example Config
The following are the components of Kubernetes for which a basic chaos scenario config exists today.
The following are the components of Kubernetes/OpenShift for which a basic chaos scenario config exists today.
```
scenarios:
- name: "<name of scenario>"
- name: "<Name of scenario>"
namespace: "<specific namespace>" # can specify "*" if you want to find in all namespaces
label_selector: "<label of pod(s)>"
container_name: "<specific container name>" # This is optional, can take out and will kill all containers in all pods found under namespace and label
pod_names: # This is optional, can take out and will select all pods with given namespace and label
- <pod_name>
count: <number of containers to disrupt, default=1>
action: <kill signal to run. For example 1 ( hang up ) or 9. Default is set to 1>
expected_recovery_time: <number of seconds to wait for container to be running again> (defaults to 120seconds)
retry_wait: <number of seconds to wait for container to be running again> (defaults to 120seconds)
```
#### Post Action
@@ -25,7 +23,7 @@ In all scenarios we do a post chaos check to wait and verify the specific compon
Here there are two options:
1. Pass a custom script in the main config scenario list that will run before the chaos and verify the output matches post chaos scenario.
See [scenarios/post_action_etcd_container.py](https://github.com/krkn-chaos/krkn/blob/main/scenarios/post_action_etcd_container.py) for an example.
See [scenarios/post_action_etcd_container.py](https://github.com/chaos-kubox/krkn/blob/main/scenarios/post_action_etcd_container.py) for an example.
```
- container_scenarios: # List of chaos pod scenarios to load.
- - scenarios/container_etcd.yml
@@ -36,5 +34,5 @@ See [scenarios/post_action_etcd_container.py](https://github.com/krkn-chaos/krkn
containers that were killed as well as the namespaces and pods to verify all containers that were affected recover properly.
```
expected_recovery_time: <seconds to wait for container to recover>
retry_wait: <seconds to wait for container to recover>
```

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@@ -62,7 +62,7 @@ If changes go into the main repository while you're working on your code it is b
If not already configured, set the upstream url for kraken.
```
git remote add upstream https://github.com/krkn-chaos/krkn.git
git remote add upstream https://github.com/cloud-bulldozer/kraken.git
```
Rebase to upstream master branch.

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@@ -1,22 +1,52 @@
## Getting Started Running Chaos Scenarios
#### Adding New Scenarios
Adding a new scenario is as simple as adding a new config file under [scenarios directory](https://github.com/redhat-chaos/krkn/tree/main/scenarios) and defining it in the main kraken [config](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml#L8).
Adding a new scenario is as simple as adding a new config file under [scenarios directory](https://github.com/chaos-kubox/krkn/tree/main/scenarios) and defining it in the main kraken [config](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml#L8).
You can either copy an existing yaml file and make it your own, or fill in one of the templates below to suit your needs.
### Templates
#### Pod Scenario Yaml Template
For example, for adding a pod level scenario for a new application, refer to the sample scenario below to know what fields are necessary and what to add in each location:
```
# yaml-language-server: $schema=../plugin.schema.json
- id: kill-pods
config:
namespace_pattern: ^<namespace>$
label_selector: <pod label>
kill: <number of pods to kill>
krkn_pod_recovery_time: <expected time for the pod to become ready>
config:
runStrategy:
runs: <number of times to execute the scenario>
#This will choose a random number to wait between min and max
maxSecondsBetweenRuns: 30
minSecondsBetweenRuns: 1
scenarios:
- name: "delete pods example"
steps:
- podAction:
matches:
- labels:
namespace: "<namespace>"
selector: "<pod label>" # This can be left blank.
filters:
- randomSample:
size: <number of pods to kill>
actions:
- kill:
probability: 1
force: true
- podAction:
matches:
- labels:
namespace: "<namespace>"
selector: "<pod label>" # This can be left blank.
retries:
retriesTimeout:
# Amount of time to wait with retrying, before failing if pod count does not match expected
# timeout: 180.
actions:
- checkPodCount:
count: <expected number of pods that match namespace and label"
```
More information on specific items that you can add to the pod killing scenarios can be found in the [powerfulseal policies](https://powerfulseal.github.io/powerfulseal/policies) documentation
#### Node Scenario Yaml Template
```

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@@ -10,9 +10,7 @@
* [Cluster recovery checks, metrics evaluation and pass/fail criteria](#cluster-recovery-checks-metrics-evaluation-and-passfail-criteria)
* [Scenarios](#scenarios)
* [Test Environment Recommendations - how and where to run chaos tests](#test-environment-recommendations---how-and-where-to-run-chaos-tests)
* [Chaos testing in Practice](#chaos-testing-in-practice)
* [OpenShift oraganization](#openshift-organization)
* [startx-lab](#startx-lab)
* [Chaos testing in Practice within the OpenShift Organization](#chaos-testing-in-practice-within-the-OpenShift-Organization)
### Introduction
@@ -48,7 +46,7 @@ Failures in production are costly. To help mitigate risk to service health, cons
### Best Practices
Now that we understand the test methodology, let us take a look at the best practices for an Kubernetes cluster. On that platform there are user applications and cluster workloads that need to be designed for stability and to provide the best user experience possible:
Now that we understand the test methodology, let us take a look at the best practices for an OpenShift cluster. On that platform there are user applications and cluster workloads that need to be designed for stability and to provide the best user experience possible:
- Alerts with appropriate severity should get fired.
- Alerts are key to identify when a component starts degrading, and can help focus the investigation effort on affected system components.
@@ -77,11 +75,11 @@ We want to look at this in terms of CPU, Memory, Disk, Throughput, Network etc.
- The controller watching the component should recognize a failure as soon as possible. The component needs to have minimal initialization time to avoid extended downtime or overloading the replicas if it is a highly available configuration. The cause of failure can be because of issues with the infrastructure on top of which it is running, application failures, or because of service failures that it depends on.
- High Availability deployment strategy.
- There should be multiple replicas ( both Kubernetes and application control planes ) running preferably in different availability zones to survive outages while still serving the user/system requests. Avoid single points of failure.
- There should be multiple replicas ( both OpenShift and application control planes ) running preferably in different availability zones to survive outages while still serving the user/system requests. Avoid single points of failure.
- Backed by persistent storage
- It is important to have the system/application backed by persistent storage. This is especially important in cases where the application is a database or a stateful application given that a node, pod, or container failure will wipe off the data.
- There should be fallback routes to the backend in case of using CDN, for example, Akamai in case of console.redhat.com - a managed service deployed on top of Kubernetes dedicated:
- There should be fallback routes to the backend in case of using CDN, for example, Akamai in case of console.redhat.com - a managed service deployed on top of OpenShift dedicated:
- Content delivery networks (CDNs) are commonly used to host resources such as images, JavaScript files, and CSS. The average web page is nearly 2 MB in size, and offloading heavy resources to third-parties is extremely effective for reducing backend server traffic and latency. However, this makes each CDN an additional point of failure for every site that relies on it. If the CDN fails, its customers could also fail.
- To test how the application reacts to failures, drop all network traffic between the system and CDN. The application should still serve the content to the user irrespective of the failure.
@@ -92,18 +90,18 @@ We want to look at this in terms of CPU, Memory, Disk, Throughput, Network etc.
### Tooling
Now that we looked at the best practices, In this section, we will go through how [Kraken](https://github.com/redhat-chaos/krkn) - a chaos testing framework can help test the resilience of Kubernetes and make sure the applications and services are following the best practices.
Now that we looked at the best practices, In this section, we will go through how [Kraken](https://github.com/chaos-kubox/krkn) - a chaos testing framework can help test the resilience of OpenShift and make sure the applications and services are following the best practices.
#### Workflow
Let us start by understanding the workflow of kraken: the user will start by running kraken by pointing to a specific Kubernetes cluster using kubeconfig to be able to talk to the platform on top of which the Kubernetes cluster is hosted. This can be done by either the oc/kubectl API or the cloud API. Based on the configuration of kraken, it will inject specific chaos scenarios as shown below, talk to [Cerberus](https://github.com/redhat-chaos/cerberus) to get the go/no-go signal representing the overall health of the cluster ( optional - can be turned off ), scrapes metrics from in-cluster prometheus given a metrics profile with the promql queries and stores them long term in Elasticsearch configured ( optional - can be turned off ), evaluates the promql expressions specified in the alerts profile ( optional - can be turned off ) and aggregated everything to set the pass/fail i.e. exits 0 or 1. More about the metrics collection, cerberus and metrics evaluation can be found in the next section.
Let us start by understanding the workflow of kraken: the user will start by running kraken by pointing to a specific OpenShift cluster using kubeconfig to be able to talk to the platform on top of which the OpenShift cluster is hosted. This can be done by either the oc/kubectl API or the cloud API. Based on the configuration of kraken, it will inject specific chaos scenarios as shown below, talk to [Cerberus](https://github.com/chaos-kubox/cerberus) to get the go/no-go signal representing the overall health of the cluster ( optional - can be turned off ), scrapes metrics from in-cluster prometheus given a metrics profile with the promql queries and stores them long term in Elasticsearch configured ( optional - can be turned off ), evaluates the promql expressions specified in the alerts profile ( optional - can be turned off ) and aggregated everything to set the pass/fail i.e. exits 0 or 1. More about the metrics collection, cerberus and metrics evaluation can be found in the next section.
![Kraken workflow](../media/kraken-workflow.png)
#### Cluster recovery checks, metrics evaluation and pass/fail criteria
- Most of the scenarios have built in checks to verify if the targeted component recovered from the failure after the specified duration of time but there might be cases where other components might have an impact because of a certain failure and its extremely important to make sure that the system/application is healthy as a whole post chaos. This is exactly where [Cerberus](https://github.com/redhat-chaos/cerberus) comes to the rescue.
- Most of the scenarios have built in checks to verify if the targeted component recovered from the failure after the specified duration of time but there might be cases where other components might have an impact because of a certain failure and its extremely important to make sure that the system/application is healthy as a whole post chaos. This is exactly where [Cerberus](https://github.com/chaos-kubox/cerberus) comes to the rescue.
If the monitoring tool, cerberus is enabled it will consume the signal and continue running chaos or not based on that signal.
- Apart from checking the recovery and cluster health status, its equally important to evaluate the performance metrics like latency, resource usage spikes, throughput, etcd health like disk fsync, leader elections etc. To help with this, Kraken has a way to evaluate promql expressions from the incluster prometheus and set the exit status to 0 or 1 based on the severity set for each of the query. Details on how to use this feature can be found [here](https://github.com/redhat-chaos/krkn#alerts).
- Apart from checking the recovery and cluster health status, its equally important to evaluate the performance metrics like latency, resource usage spikes, throughput, etcd health like disk fsync, leader elections etc. To help with this, Kraken has a way to evaluate promql expressions from the incluster prometheus and set the exit status to 0 or 1 based on the severity set for each of the query. Details on how to use this feature can be found [here](https://github.com/chaos-kubox/krkn#alerts).
- The overall pass or fail of kraken is based on the recovery of the specific component (within a certain amount of time), the cerberus health signal which tracks the health of the entire cluster and metrics evaluation from incluster prometheus.
@@ -112,19 +110,19 @@ If the monitoring tool, cerberus is enabled it will consume the signal and conti
### Scenarios
Let us take a look at how to run the chaos scenarios on your Kubernetes clusters using Kraken-hub - a lightweight wrapper around Kraken to ease the runs by providing the ability to run them by just running container images using podman with parameters set as environment variables. This eliminates the need to carry around and edit configuration files and makes it easy for any CI framework integration. Here are the scenarios supported:
Let us take a look at how to run the chaos scenarios on your OpenShift clusters using Kraken-hub - a lightweight wrapper around Kraken to ease the runs by providing the ability to run them by just running container images using podman with parameters set as environment variables. This eliminates the need to carry around and edit configuration files and makes it easy for any CI framework integration. Here are the scenarios supported:
- Pod Scenarios ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/pod-scenarios.md))
- Disrupts Kubernetes/Kubernetes and applications deployed as pods:
- Pod Scenarios ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/pod-scenarios.md))
- Disrupts OpenShift/Kubernetes and applications deployed as pods:
- Helps understand the availability of the application, the initialization timing and recovery status.
- [Demo](https://asciinema.org/a/452351?speed=3&theme=solarized-dark)
- Container Scenarios ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/container-scenarios.md))
- Disrupts Kubernetes/Kubernetes and applications deployed as containers running as part of a pod(s) using a specified kill signal to mimic failures:
- Container Scenarios ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/container-scenarios.md))
- Disrupts OpenShift/Kubernetes and applications deployed as containers running as part of a pod(s) using a specified kill signal to mimic failures:
- Helps understand the impact and recovery timing when the program/process running in the containers are disrupted - hangs, paused, killed etc., using various kill signals, i.e. SIGHUP, SIGTERM, SIGKILL etc.
- [Demo](https://asciinema.org/a/BXqs9JSGDSEKcydTIJ5LpPZBM?speed=3&theme=solarized-dark)
- Node Scenarios ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/node-scenarios.md))
- Node Scenarios ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/node-scenarios.md))
- Disrupts nodes as part of the cluster infrastructure by talking to the cloud API. AWS, Azure, GCP, OpenStack and Baremetal are the supported platforms as of now. Possible disruptions include:
- Terminate nodes
- Fork bomb inside the node
@@ -133,18 +131,18 @@ Let us take a look at how to run the chaos scenarios on your Kubernetes clusters
- etc.
- [Demo](https://asciinema.org/a/ANZY7HhPdWTNaWt4xMFanF6Q5)
- Zone Outages ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/zone-outages.md))
- Creates outage of availability zone(s) in a targeted region in the public cloud where the Kubernetes cluster is running by tweaking the network acl of the zone to simulate the failure, and that in turn will stop both ingress and egress traffic from all nodes in a particular zone for the specified duration and reverts it back to the previous state.
- Helps understand the impact on both Kubernetes/Kubernetes control plane as well as applications and services running on the worker nodes in that zone.
- Zone Outages ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/zone-outages.md))
- Creates outage of availability zone(s) in a targeted region in the public cloud where the OpenShift cluster is running by tweaking the network acl of the zone to simulate the failure, and that in turn will stop both ingress and egress traffic from all nodes in a particular zone for the specified duration and reverts it back to the previous state.
- Helps understand the impact on both Kubernetes/OpenShift control plane as well as applications and services running on the worker nodes in that zone.
- Currently, only set up for AWS cloud platform: 1 VPC and multiples subnets within the VPC can be specified.
- [Demo](https://asciinema.org/a/452672?speed=3&theme=solarized-dark)
- Application Outages ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/application-outages.md))
- Application Outages ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/application-outages.md))
- Scenario to block the traffic ( Ingress/Egress ) of an application matching the labels for the specified duration of time to understand the behavior of the service/other services which depend on it during the downtime.
- Helps understand how the dependent services react to the unavailability.
- [Demo](https://asciinema.org/a/452403?speed=3&theme=solarized-dark)
- Power Outages ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/power-outages.md))
- Power Outages ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/power-outages.md))
- This scenario imitates a power outage by shutting down of the entire cluster for a specified duration of time, then restarts all the nodes after the specified time and checks the health of the cluster.
- There are various use cases in the customer environments. For example, when some of the clusters are shutdown in cases where the applications are not needed to run in a particular time/season in order to save costs.
- The nodes are stopped in parallel to mimic a power outage i.e., pulling off the plug
@@ -153,23 +151,24 @@ Let us take a look at how to run the chaos scenarios on your Kubernetes clusters
- Resource Hog
- Hogs CPU, Memory and IO on the targeted nodes
- Helps understand if the application/system components have reserved resources to not get disrupted because of rogue applications, or get performance throttled.
- CPU Hog ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/node-cpu-hog.md), [Demo](https://asciinema.org/a/452762))
- Memory Hog ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/node-memory-hog.md), [Demo](https://asciinema.org/a/452742?speed=3&theme=solarized-dark))
- CPU Hog ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/node-cpu-hog.md), [Demo](https://asciinema.org/a/452762))
- Memory Hog ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/node-memory-hog.md), [Demo](https://asciinema.org/a/452742?speed=3&theme=solarized-dark))
- IO Hog ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/node-io-hog.md))
- Time Skewing ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/time-scenarios.md))
- Time Skewing ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/time-scenarios.md))
- Manipulate the system time and/or date of specific pods/nodes.
- Verify scheduling of objects so they continue to work.
- Verify time gets reset properly.
- Namespace Failures ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/namespace-scenarios.md))
- Namespace Failures ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/namespace-scenarios.md))
- Delete namespaces for the specified duration.
- Helps understand the impact on other components and tests/improves recovery time of the components in the targeted namespace.
- Persistent Volume Fill ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/pvc-scenarios.md))
- Persistent Volume Fill ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/pvc-scenarios.md))
- Fills up the persistent volumes, up to a given percentage, used by the pod for the specified duration.
- Helps understand how an application deals when it is no longer able to write data to the disk. For example, kafkas behavior when it is not able to commit data to the disk.
- Network Chaos ([Documentation](https://github.com/redhat-chaos/krkn-hub/blob/main/docs/network-chaos.md))
- Network Chaos ([Documentation](https://github.com/chaos-kubox/krkn-hub/blob/main/docs/network-chaos.md))
- Scenarios supported includes:
- Network latency
- Packet loss
@@ -200,7 +199,7 @@ Let us take a look at few recommendations on how and where to run the chaos test
- Enable Observability:
- Chaos Engineering Without Observability ... Is Just Chaos.
- Make sure to have logging and monitoring installed on the cluster to help with understanding the behaviour as to why it is happening. In case of running the tests in the CI where it is not humanly possible to monitor the cluster all the time, it is recommended to leverage Cerberus to capture the state during the runs and metrics collection in Kraken to store metrics long term even after the cluster is gone.
- Kraken ships with dashboards that will help understand API, Etcd and Kubernetes cluster level stats and performance metrics.
- Kraken ships with dashboards that will help understand API, Etcd and OpenShift cluster level stats and performance metrics.
- Pay attention to Prometheus alerts. Check if they are firing as expected.
- Run multiple chaos tests at once to mimic the production outages:
@@ -208,9 +207,8 @@ Let us take a look at few recommendations on how and where to run the chaos test
- You might have existing test cases, be it related to Performance, Scalability or QE. Run the chaos in the background during the test runs to observe the impact. Signaling feature in Kraken can help with coordinating the chaos runs i.e., start, stop, pause the scenarios based on the state of the other test jobs.
#### Chaos testing in Practice
#### Chaos testing in Practice within the OpenShift Organization
##### OpenShift organization
Within the OpenShift organization we use kraken to perform chaos testing throughout a release before the code is available to customers.
1. We execute kraken during our regression test suite.
@@ -228,83 +226,3 @@ Within the OpenShift organization we use kraken to perform chaos testing through
iii. This test can be seen here: https://github.com/openshift/svt/tree/master/reliability-v2
3. We are starting to add in test cases that perform chaos testing during an upgrade (not many iterations of this have been completed).
##### startx-lab
**NOTE**: Requests for enhancements and any issues need to be filed at the mentioned links given that they are not natively supported in Kraken.
The following content covers the implementation details around how Startx is leveraging Kraken:
* Using kraken as part of a tekton pipeline
You can find on [artifacthub.io](https://artifacthub.io/packages/search?kind=7&ts_query_web=kraken) the
[kraken-scenario](https://artifacthub.io/packages/tekton-task/startx-tekton-catalog/kraken-scenario) `tekton-task`
which can be used to start a kraken chaos scenarios as part of a chaos pipeline.
To use this task, you must have :
- Openshift pipeline enabled (or tekton CRD loaded for Kubernetes clusters)
- 1 Secret named `kraken-aws-creds` for scenarios using aws
- 1 ConfigMap named `kraken-kubeconfig` with credentials to the targeted cluster
- 1 ConfigMap named `kraken-config-example` with kraken configuration file (config.yaml)
- 1 ConfigMap named `kraken-common-example` with all kraken related files
- The `pipeline` SA with be autorized to run with priviveged SCC
You can create theses resources using the following sequence :
```bash
oc project default
oc adm policy add-scc-to-user privileged -z pipeline
oc apply -f https://github.com/startxfr/tekton-catalog/raw/stable/task/kraken-scenario/0.1/samples/common.yaml
```
Then you must change content of `kraken-aws-creds` secret, `kraken-kubeconfig` and `kraken-config-example` configMap
to reflect your cluster configuration. Refer to the [kraken configuration](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml)
and [configuration examples](https://github.com/startxfr/tekton-catalog/blob/stable/task/kraken-scenario/0.1/samples/)
for details on how to configure theses resources.
* Start as a single taskrun
```bash
oc apply -f https://github.com/startxfr/tekton-catalog/raw/stable/task/kraken-scenario/0.1/samples/taskrun.yaml
```
* Start as a pipelinerun
```yaml
oc apply -f https://github.com/startxfr/tekton-catalog/raw/stable/task/kraken-scenario/0.1/samples/pipelinerun.yaml
```
* Deploying kraken using a helm-chart
You can find on [artifacthub.io](https://artifacthub.io/packages/search?kind=0&ts_query_web=kraken) the
[chaos-kraken](https://artifacthub.io/packages/helm/startx/chaos-kraken) `helm-chart`
which can be used to deploy a kraken chaos scenarios.
Default configuration create the following resources :
- 1 project named **chaos-kraken**
- 1 scc with privileged context for kraken deployment
- 1 configmap with kraken 21 generic scenarios, various scripts and configuration
- 1 configmap with kubeconfig of the targeted cluster
- 1 job named kraken-test-xxx
- 1 service to the kraken pods
- 1 route to the kraken service
```bash
# Install the startx helm repository
helm repo add startx https://startxfr.github.io/helm-repository/packages/
# Install the kraken project
helm install --set project.enabled=true chaos-kraken-project startx/chaos-kraken
# Deploy the kraken instance
helm install \
--set kraken.enabled=true \
--set kraken.aws.credentials.region="eu-west-3" \
--set kraken.aws.credentials.key_id="AKIAXXXXXXXXXXXXXXXX" \
--set kraken.aws.credentials.secret="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \
--set kraken.kubeconfig.token.server="https://api.mycluster:6443" \
--set kraken.kubeconfig.token.token="sha256~XXXXXXXXXX_PUT_YOUR_TOKEN_HERE_XXXXXXXXXXXX" \
-n chaos-kraken \
chaos-kraken-instance startx/chaos-kraken
```

View File

@@ -3,52 +3,55 @@
The following ways are supported to run Kraken:
- Standalone python program through Git.
- Containerized version using either Podman or Docker as the runtime via [Krkn-hub](https://github.com/krkn-chaos/krkn-hub)
- Kubernetes or OpenShift deployment ( unsupported )
- Containerized version using either Podman or Docker as the runtime.
- Kubernetes or OpenShift deployment.
**NOTE**: It is recommended to run Kraken external to the cluster ( Standalone or Containerized ) hitting the Kubernetes/OpenShift API as running it internal to the cluster might be disruptive to itself and also might not report back the results if the chaos leads to cluster's API server instability.
**NOTE**: To run Kraken on Power (ppc64le) architecture, build and run a containerized version by following the
instructions given [here](https://github.com/krkn-chaos/krkn/blob/main/containers/build_own_image-README.md).
**NOTE**: Helper functions for interactions in Krkn are part of [krkn-lib](https://github.com/redhat-chaos/krkn-lib).
Please feel free to reuse and expand them as you see fit when adding a new scenario or expanding
the capabilities of the current supported scenarios.
instructions given [here](https://github.com/chaos-kubox/krkn/blob/main/containers/build_own_image-README.md).
### Git
#### Clone the repository
Pick the latest stable release to install [here](https://github.com/krkn-chaos/krkn/releases).
```
$ git clone https://github.com/krkn-chaos/krkn.git --branch <release version>
$ cd krkn
$ git clone https://github.com/openshift-scale/krkn.git
$ cd kraken
```
#### Install the dependencies
```
$ python3.9 -m venv chaos
$ python3 -m venv chaos
$ source chaos/bin/activate
$ pip3.9 install -r requirements.txt
$ pip3 install -r requirements.txt
```
**NOTE**: Make sure python3-devel and latest pip versions are installed on the system. The dependencies install has been tested with pip >= 21.1.3 versions.
#### Run
```
$ python3.9 run_kraken.py --config <config_file_location>
$ python3 run_kraken.py --config <config_file_location>
```
### Run containerized version
[Krkn-hub](https://github.com/krkn-chaos/krkn-hub) is a wrapper that allows running Krkn chaos scenarios via podman or docker runtime with scenario parameters/configuration defined as environment variables.
Assuming that the latest docker ( 17.05 or greater with multi-build support ) is installed on the host, run:
```
$ docker pull quay.io/chaos-kubox/krkn:latest
$ docker run --name=kraken --net=host -v <path_to_kubeconfig>:/root/.kube/config:Z -v <path_to_kraken_config>:/root/kraken/config/config.yaml:Z -d quay.io/chaos-kubox/krkn:latest
$ docker run --name=kraken --net=host -v <path_to_kubeconfig>:/root/.kube/config:Z -v <path_to_kraken_config>:/root/kraken/config/config.yaml:Z -v <path_to_scenarios_directory>:/root/kraken/scenarios:Z -d quay.io/chaos-kubox/krkn:latest #custom or tweaked scenario configs
$ docker logs -f kraken
```
Refer [instructions](https://github.com/krkn-chaos/krkn-hub#supported-chaos-scenarios) to get started.
Similarly, podman can be used to achieve the same:
```
$ podman pull quay.io/chaos-kubox/krkn
$ podman run --name=kraken --net=host -v <path_to_kubeconfig>:/root/.kube/config:Z -v <path_to_kraken_config>:/root/kraken/config/config.yaml:Z -d quay.io/chaos-kubox/krkn:latest
$ podman run --name=kraken --net=host -v <path_to_kubeconfig>:/root/.kube/config:Z -v <path_to_kraken_config>:/root/kraken/config/config.yaml:Z -v <path_to_scenarios_directory>:/root/kraken/scenarios:Z -d quay.io/chaos-kubox/krkn:latest #custom or tweaked scenario configs
$ podman logs -f kraken
```
If you want to build your own kraken image see [here](https://github.com/chaos-kubox/krkn/blob/main/containers/build_own_image-README.md)
### Run Kraken as a Kubernetes deployment ( unsupported option - standalone or containerized deployers are recommended )
Refer [Instructions](https://github.com/krkn-chaos/krkn/blob/main/containers/README.md) on how to deploy and run Kraken as a Kubernetes/OpenShift deployment.
Refer to the [chaos-kraken chart manpage](https://artifacthub.io/packages/helm/startx/chaos-kraken)
and especially the [kraken configuration values](https://artifacthub.io/packages/helm/startx/chaos-kraken#chaos-kraken-values-dictionary)
for details on how to configure this chart.
### Run Kraken as a Kubernetes deployment
Refer [Instructions](https://github.com/chaos-kubox/krkn/blob/main/containers/README.md) on how to deploy and run Kraken as a Kubernetes/OpenShift deployment.

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@@ -0,0 +1,41 @@
### Litmus Scenarios
Kraken consumes [Litmus](https://github.com/litmuschaos/litmus) under the hood for some scenarios
Official Litmus documentation and specifics of Litmus resources can be found [here](https://docs.litmuschaos.io/docs/next/getstarted/)
#### Litmus Chaos Custom Resources
There are 3 custom resources that are created during each Litmus scenario. Below is a description of the resources:
* ChaosEngine: A resource to link a Kubernetes application or Kubernetes node to a ChaosExperiment. ChaosEngine is watched by Litmus' Chaos-Operator which then invokes Chaos-Experiments.
* ChaosExperiment: A resource to group the configuration parameters of a chaos experiment. ChaosExperiment CRs are created by the operator when experiments are invoked by ChaosEngine.
* ChaosResult : A resource to hold the results of a chaos-experiment. The Chaos-exporter reads the results and exports the metrics into a configured Prometheus server.
### Understanding Litmus Scenarios
To run Litmus scenarios we need to apply 3 different resources/yaml files to our cluster.
1. **Chaos experiments** contain the actual chaos details of a scenario.
i. This is installed automatically by Kraken (does not need to be specified in kraken scenario configuration).
2. **Service Account**: should be created to allow chaosengine to run experiments in your application namespace. Usually it sets just enough permissions to a specific namespace to be able to run the experiment properly.
i. This can be defined using either a link to a yaml file or a downloaded file in the scenarios' folder.
3. **Chaos Engine** connects the application instance to a Chaos Experiment. This is where you define the specifics of your scenario; i.e.: the node or pod name you want to cause chaos within.
i. This is a downloaded yaml file in the scenarios' folder. A full list of scenarios can be found [here](https://hub.litmuschaos.io/)
**NOTE**: By default, all chaos experiments will be installed based on the version you give in the config file.
Adding a new Litmus based scenario is as simple as adding references to 2 new yaml files (the Service Account and Chaos engine files for your scenario ) in the Kraken config.
### Supported scenarios
The following are the start of scenarios for which a chaos scenario config exists today.
Scenario | Description | Working
------------------------ |-----------------------------------------------------------------------------------------| ------------------------- |
[Node CPU Hog](https://github.com/chaos-kubox/krkn/blob/main/scenarios/node_cpu_hog_engine.yaml) | Chaos scenario that hogs up the CPU on a defined node for a specific amount of time. | :heavy_check_mark: |
[Node Memory Hog](https://github.com/chaos-kubox/krkn/blob/main/scenarios/node_mem_engine.yaml) | Chaos scenario that hogs up the memory on a defined node for a specific amount of time. | :heavy_check_mark: |
[Node IO Hog](https://github.com/chaos-kubox/krkn/blob/main/scenarios/node_io_engine.yaml) | Chaos scenario that hogs up the IO on a defined node for a specific amount of time. | :heavy_check_mark: |

View File

@@ -1,36 +0,0 @@
### ManagedCluster Scenarios
[ManagedCluster](https://open-cluster-management.io/concepts/managedcluster/) scenarios provide a way to integrate kraken with [Open Cluster Management (OCM)](https://open-cluster-management.io/) and [Red Hat Advanced Cluster Management for Kubernetes (ACM)](https://www.redhat.com/en/technologies/management/advanced-cluster-management).
ManagedCluster scenarios leverage [ManifestWorks](https://open-cluster-management.io/concepts/manifestwork/) to inject faults into the ManagedClusters.
The following ManagedCluster chaos scenarios are supported:
1. **managedcluster_start_scenario**: Scenario to start the ManagedCluster instance.
2. **managedcluster_stop_scenario**: Scenario to stop the ManagedCluster instance.
3. **managedcluster_stop_start_scenario**: Scenario to stop and then start the ManagedCluster instance.
4. **start_klusterlet_scenario**: Scenario to start the klusterlet of the ManagedCluster instance.
5. **stop_klusterlet_scenario**: Scenario to stop the klusterlet of the ManagedCluster instance.
6. **stop_start_klusterlet_scenario**: Scenario to stop and start the klusterlet of the ManagedCluster instance.
ManagedCluster scenarios can be injected by placing the ManagedCluster scenarios config files under `managedcluster_scenarios` option in the Kraken config. Refer to [managedcluster_scenarios_example](https://github.com/redhat-chaos/krkn/blob/main/scenarios/kube/managedcluster_scenarios_example.yml) config file.
```
managedcluster_scenarios:
- actions: # ManagedCluster chaos scenarios to be injected
- managedcluster_stop_start_scenario
managedcluster_name: cluster1 # ManagedCluster on which scenario has to be injected; can set multiple names separated by comma
# label_selector: # When managedcluster_name is not specified, a ManagedCluster with matching label_selector is selected for ManagedCluster chaos scenario injection
instance_count: 1 # Number of managedcluster to perform action/select that match the label selector
runs: 1 # Number of times to inject each scenario under actions (will perform on same ManagedCluster each time)
timeout: 420 # Duration to wait for completion of ManagedCluster scenario injection
# For OCM to detect a ManagedCluster as unavailable, have to wait 5*leaseDurationSeconds
# (default leaseDurationSeconds = 60 sec)
- actions:
- stop_start_klusterlet_scenario
managedcluster_name: cluster1
# label_selector:
instance_count: 1
runs: 1
timeout: 60
```

51
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@@ -0,0 +1,51 @@
## Scraping and storing metrics for the run
There are cases where the state of the cluster and metrics on the cluster during the chaos test run need to be stored long term to review after the cluster is terminated, for example CI and automation test runs. To help with this, Kraken supports capturing metrics for the duration of the scenarios defined in the config and indexes them into Elasticsearch. The indexed metrics can be visualized with the help of Grafana.
It uses [Kube-burner](https://github.com/cloud-bulldozer/kube-burner) under the hood. The metrics to capture need to be defined in a metrics profile which Kraken consumes to query prometheus ( installed by default in OpenShift ) with the start and end timestamp of the run. Each run has a unique identifier ( uuid ) and all the metrics/documents in Elasticsearch will be associated with it. The uuid is generated automatically if not set in the config. This feature can be enabled in the [config](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml) by setting the following:
```
performance_monitoring:
kube_burner_binary_url: "https://github.com/cloud-bulldozer/kube-burner/releases/download/v0.9.1/kube-burner-0.9.1-Linux-x86_64.tar.gz"
capture_metrics: True
config_path: config/kube_burner.yaml # Define the Elasticsearch url and index name in this config.
metrics_profile_path: config/metrics-aggregated.yaml
prometheus_url: # The prometheus url/route is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes.
prometheus_bearer_token: # The bearer token is automatically obtained in case of OpenShift, please set it when the distribution is Kubernetes. This is needed to authenticate with prometheus.
uuid: # uuid for the run is generated by default if not set.
```
### Metrics profile
A couple of [metric profiles](https://github.com/chaos-kubox/krkn/tree/main/config), [metrics.yaml](https://github.com/chaos-kubox/krkn/blob/main/config/metrics.yaml), and [metrics-aggregated.yaml](https://github.com/chaos-kubox/krkn/blob/main/config/metrics-aggregated.yaml) are shipped by default and can be tweaked to add more metrics to capture during the run. The following are the API server metrics for example:
```
metrics:
# API server
- query: histogram_quantile(0.99, sum(rate(apiserver_request_duration_seconds_bucket{apiserver="kube-apiserver", verb!~"WATCH", subresource!="log"}[2m])) by (verb,resource,subresource,instance,le)) > 0
metricName: API99thLatency
- query: sum(irate(apiserver_request_total{apiserver="kube-apiserver",verb!="WATCH",subresource!="log"}[2m])) by (verb,instance,resource,code) > 0
metricName: APIRequestRate
- query: sum(apiserver_current_inflight_requests{}) by (request_kind) > 0
metricName: APIInflightRequests
```
### Indexing
Define the Elasticsearch and index to store the metrics/documents in the kube_burner config:
```
global:
writeToFile: true
metricsDirectory: collected-metrics
measurements:
- name: podLatency
esIndex: kube-burner
indexerConfig:
enabled: true
esServers: [https://elastic.example.com:9200]
insecureSkipVerify: true
defaultIndex: kraken
type: elastic
```

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@@ -1,6 +1,6 @@
### Service Disruption Scenarios (Previously Delete Namespace Scenario)
### Delete Namespace Scenarios
Using this type of scenario configuration one is able to delete crucial objects in a specific namespace, or a namespace matching a certain regex string.
Using this type of scenario configuration one is able to delete a specific namespace, or a namespace matching a certain regex string.
Configuration Options:
@@ -16,7 +16,7 @@ Set to '^.*$' and label_selector to "" to randomly select any namespace in your
**sleep:** Number of seconds to wait between each iteration/count of killing namespaces. Defaults to 10 seconds if not set
Refer to [namespace_scenarios_example](https://github.com/krkn-chaos/krkn/blob/main/scenarios/regex_namespace.yaml) config file.
Refer to [namespace_scenarios_example](https://github.com/chaos-kubox/krkn/blob/main/scenarios/regex_namespace.yaml) config file.
```
scenarios:
@@ -27,20 +27,12 @@ scenarios:
sleep: 15
```
### Steps
This scenario will select a namespace (or multiple) dependent on the configuration and will kill all of the below object types in that namespace and will wait for them to be Running in the post action
1. Services
2. Daemonsets
3. Statefulsets
4. Replicasets
5. Deployments
**NOTE:** Many openshift namespaces have finalizers built that protect the namespace from being fully deleted: see documentation [here](https://kubernetes.io/blog/2021/05/14/using-finalizers-to-control-deletion/).
The namespaces that do have finalizers enabled will be in left in a terminating state but all the pods running on that namespace will get deleted.
#### Post Action
We do a post chaos check to wait and verify the specific objects in each namespace are Ready
In all scenarios we do a post chaos check to wait and verify the specific component.
Here there are two options:
@@ -55,8 +47,8 @@ See [scenarios/post_action_namespace.py](https://github.com/cloud-bulldozer/krak
```
1. Allow kraken to wait and check all killed objects in the namespaces become 'Running' again. Kraken keeps a list of the specific
objects in namespaces that were killed to verify all that were affected recover properly.
2. Allow kraken to wait and check the killed namespaces become 'Active' again. Kraken keeps a list of the specific
namespaces that were killed to verify all that were affected recover properly.
```
wait_time: <seconds to wait for namespace to recover>

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@@ -1,7 +1,7 @@
### Network chaos
Scenario to introduce network latency, packet loss, and bandwidth restriction in the Node's host network interface. The purpose of this scenario is to observe faults caused by random variations in the network.
##### Sample scenario config for egress traffic shaping
##### Sample scenario config
```
network_chaos: # Scenario to create an outage by simulating random variations in the network.
duration: 300 # In seconds - duration network chaos will be applied.
@@ -12,34 +12,11 @@ network_chaos: # Scenario to create an outage
- "ens5" # Interface name would be the Kernel host network interface name.
execution: serial|parallel # Execute each of the egress options as a single scenario(parallel) or as separate scenario(serial).
egress:
latency: 500ms
loss: 50% # percentage
bandwidth: 10mbit
latency: 50ms
loss: 0.02 # percentage
bandwidth: 100mbit
```
##### Sample scenario config for ingress traffic shaping (using a plugin)
'''
- id: network_chaos
config:
node_interface_name: # Dictionary with key as node name(s) and value as a list of its interfaces to test
ip-10-0-128-153.us-west-2.compute.internal:
- ens5
- genev_sys_6081
label_selector: node-role.kubernetes.io/master # When node_interface_name is not specified, nodes with matching label_selector is selected for node chaos scenario injection
instance_count: 1 # Number of nodes to perform action/select that match the label selector
kubeconfig_path: ~/.kube/config # Path to kubernetes config file. If not specified, it defaults to ~/.kube/config
execution_type: parallel # Execute each of the ingress options as a single scenario(parallel) or as separate scenario(serial).
network_params:
latency: 500ms
loss: '50%'
bandwidth: 10mbit
wait_duration: 120
test_duration: 60
'''
Note: For ingress traffic shaping, ensure that your node doesn't have any [IFB](https://wiki.linuxfoundation.org/networking/ifb) interfaces already present. The scenario relies on creating IFBs to do the shaping, and they are deleted at the end of the scenario.
##### Steps
- Pick the nodes to introduce the network anomaly either from node_name or label_selector.
- Verify interface list in one of the nodes or use the interface with a default route, as test interface, if no interface is specified by the user.

View File

@@ -4,7 +4,7 @@ The following node chaos scenarios are supported:
1. **node_start_scenario**: Scenario to stop the node instance.
2. **node_stop_scenario**: Scenario to stop the node instance.
3. **node_stop_start_scenario**: Scenario to stop and then start the node instance. Not supported on VMware.
3. **node_stop_start_scenario**: Scenario to stop and then start the node instance.
4. **node_termination_scenario**: Scenario to terminate the node instance.
5. **node_reboot_scenario**: Scenario to reboot the node instance.
6. **stop_kubelet_scenario**: Scenario to stop the kubelet of the node instance.
@@ -12,14 +12,13 @@ The following node chaos scenarios are supported:
8. **node_crash_scenario**: Scenario to crash the node instance.
9. **stop_start_helper_node_scenario**: Scenario to stop and start the helper node and check service status.
**NOTE**: If the node does not recover from the node_crash_scenario injection, reboot the node to get it back to Ready state.
**NOTE**: node_start_scenario, node_stop_scenario, node_stop_start_scenario, node_termination_scenario
, node_reboot_scenario and stop_start_kubelet_scenario are supported only on AWS, Azure, OpenStack, BareMetal, GCP
, VMware and Alibaba as of now.
, and Alibaba as of now.
**NOTE**: Node scenarios are supported only when running the standalone version of Kraken until https://github.com/redhat-chaos/krkn/issues/106 gets fixed.
**NOTE**: Node scenarios are supported only when running the standalone version of Kraken until https://github.com/chaos-kubox/krkn/issues/106 gets fixed.
#### AWS
@@ -38,14 +37,6 @@ See the example node scenario or the example below.
**NOTE**: Baremetal machines are fragile. Some node actions can occasionally corrupt the filesystem if it does not shut down properly, and sometimes the kubelet does not start properly.
#### Docker
The Docker provider can be used to run node scenarios against kind clusters.
[kind](https://kind.sigs.k8s.io/) is a tool for running local Kubernetes clusters using Docker container "nodes".
kind was primarily designed for testing Kubernetes itself, but may be used for local development or CI.
#### GCP
How to set up GCP cli to run node scenarios is defined [here](cloud_setup.md#gcp).
@@ -73,53 +64,13 @@ How to set up Alibaba cli to run node scenarios is defined [here](cloud_setup.md
. Releasing a node is 2 steps, stopping the node and then releasing it.
#### VMware
How to set up VMware vSphere to run node scenarios is defined [here](cloud_setup.md#vmware)
This cloud type uses a different configuration style, see actions below and [example config file](../scenarios/openshift/vmware_node_scenarios.yml)
*vmware-node-terminate, vmware-node-reboot, vmware-node-stop, vmware-node-start*
#### IBMCloud
How to set up IBMCloud to run node scenarios is defined [here](cloud_setup.md#ibmcloud)
This cloud type uses a different configuration style, see actions below and [example config file](../scenarios/openshift/ibmcloud_node_scenarios.yml)
*ibmcloud-node-terminate, ibmcloud-node-reboot, ibmcloud-node-stop, ibmcloud-node-start
*
#### IBMCloud and Vmware example
```
- id: ibmcloud-node-stop
config:
name: "<node_name>"
label_selector: "node-role.kubernetes.io/worker" # When node_name is not specified, a node with matching label_selector is selected for node chaos scenario injection
runs: 1 # Number of times to inject each scenario under actions (will perform on same node each time)
instance_count: 1 # Number of nodes to perform action/select that match the label selector
timeout: 30 # Duration to wait for completion of node scenario injection
skip_openshift_checks: False # Set to True if you don't want to wait for the status of the nodes to change on OpenShift before passing the scenario
- id: ibmcloud-node-start
config:
name: "<node_name>" #Same name as before
label_selector: "node-role.kubernetes.io/worker" # When node_name is not specified, a node with matching label_selector is selected for node chaos scenario injection
runs: 1 # Number of times to inject each scenario under actions (will perform on same node each time)
instance_count: 1 # Number of nodes to perform action/select that match the label selector
timeout: 30 # Duration to wait for completion of node scenario injection
skip_openshift_checks: False # Set to True if you don't want to wait for the status of the nodes to change on OpenShift before passing the scenario
```
#### General
**NOTE**: The `node_crash_scenario` and `stop_kubelet_scenario` scenario is supported independent of the cloud platform.
Use 'generic' or do not add the 'cloud_type' key to your scenario if your cluster is not set up using one of the current supported cloud types.
Node scenarios can be injected by placing the node scenarios config files under node_scenarios option in the kraken config. Refer to [node_scenarios_example](https://github.com/redhat-chaos/krkn/blob/main/scenarios/node_scenarios_example.yml) config file.
Node scenarios can be injected by placing the node scenarios config files under node_scenarios option in the kraken config. Refer to [node_scenarios_example](https://github.com/chaos-kubox/krkn/blob/main/scenarios/node_scenarios_example.yml) config file.
```

View File

@@ -1,46 +0,0 @@
## Pod network Scenarios
### Pod outage
Scenario to block the traffic ( Ingress/Egress ) of a pod matching the labels for the specified duration of time to understand the behavior of the service/other services which depend on it during downtime. This helps with planning the requirements accordingly, be it improving the timeouts or tweaking the alerts etc.
With the current network policies, it is not possible to explicitly block ports which are enabled by allowed network policy rule. This chaos scenario addresses this issue by using OVS flow rules to block ports related to the pod. It supports OpenShiftSDN and OVNKubernetes based networks.
##### Sample scenario config (using a plugin)
```
- id: pod_network_outage
config:
namespace: openshift-console # Required - Namespace of the pod to which filter need to be applied
direction: # Optioinal - List of directions to apply filters
- ingress # Blocks ingress traffic, Default both egress and ingress
ingress_ports: # Optional - List of ports to block traffic on
- 8443 # Blocks 8443, Default [], i.e. all ports.
label_selector: 'component=ui' # Blocks access to openshift console
```
### Pod Network shaping
Scenario to introduce network latency, packet loss, and bandwidth restriction in the Pod's network interface. The purpose of this scenario is to observe faults caused by random variations in the network.
##### Sample scenario config for egress traffic shaping (using plugin)
```
- id: pod_egress_shaping
config:
namespace: openshift-console # Required - Namespace of the pod to which filter need to be applied.
label_selector: 'component=ui' # Applies traffic shaping to access openshift console.
network_params:
latency: 500ms # Add 500ms latency to egress traffic from the pod.
```
##### Sample scenario config for ingress traffic shaping (using plugin)
```
- id: pod_ingress_shaping
config:
namespace: openshift-console # Required - Namespace of the pod to which filter need to be applied.
label_selector: 'component=ui' # Applies traffic shaping to access openshift console.
network_params:
latency: 500ms # Add 500ms latency to egress traffic from the pod.
```
##### Steps
- Pick the pods to introduce the network anomaly either from label_selector or pod_name.
- Identify the pod interface name on the node.
- Set traffic shaping config on pod's interface using tc and netem.
- Wait for the duration time.
- Remove traffic shaping config on pod's interface.
- Remove the job that spawned the pod.

View File

@@ -1,37 +1,14 @@
### Pod Scenarios
Krkn recently replaced PowerfulSeal with its own internal pod scenarios using a plugin system. You can run pod scenarios by adding the following config to Krkn:
```yaml
kraken:
chaos_scenarios:
- plugin_scenarios:
- path/to/scenario.yaml
```
You can then create the scenario file with the following contents:
```yaml
# yaml-language-server: $schema=../plugin.schema.json
- id: kill-pods
config:
namespace_pattern: ^kube-system$
label_selector: k8s-app=kube-scheduler
krkn_pod_recovery_time: 120
```
Please adjust the schema reference to point to the [schema file](../scenarios/plugin.schema.json). This file will give you code completion and documentation for the available options in your IDE.
Kraken consumes [Powerfulseal](https://github.com/powerfulseal/powerfulseal) under the hood to run the pod scenarios.
These scenarios are in a simple yaml format that you can manipulate to run your specific tests or use the pre-existing scenarios to see how it works.
#### Pod Chaos Scenarios
The following are the components of Kubernetes/OpenShift for which a basic chaos scenario config exists today.
| Component | Description | Working |
| ------------------------ |-------------| -------- |
| [Basic pod scenario](../scenarios/kube/pod.yml) | Kill a pod. | :heavy_check_mark: |
| [Etcd](../scenarios/openshift/etcd.yml) | Kills a single/multiple etcd replicas. | :heavy_check_mark: |
| [Kube ApiServer](../scenarios/openshift/openshift-kube-apiserver.yml)| Kills a single/multiple kube-apiserver replicas. | :heavy_check_mark: |
| [ApiServer](../scenarios/openshift/openshift-apiserver.yml) | Kills a single/multiple apiserver replicas. | :heavy_check_mark: |
| [Prometheus](../scenarios/openshift/prometheus.yml) | Kills a single/multiple prometheus replicas. | :heavy_check_mark: |
| [OpenShift System Pods](../scenarios/openshift/regex_openshift_pod_kill.yml) | Kills random pods running in the OpenShift system namespaces. | :heavy_check_mark: |
Component | Description | Working
------------------------ |----------------------------------------------------------------------------------------------| ------------------------- |
[Etcd](https://github.com/chaos-kubox/krkn/blob/main/scenarios/etcd.yml) | Kills a single/multiple etcd replicas for the specified number of times in a loop. | :heavy_check_mark: |
[Kube ApiServer](https://github.com/chaos-kubox/krkn/blob/main/scenarios/openshift-kube-apiserver.yml) | Kills a single/multiple kube-apiserver replicas for the specified number of times in a loop. | :heavy_check_mark: |
[ApiServer](https://github.com/chaos-kubox/krkn/blob/main/scenarios/openshift-apiserver.yml) | Kills a single/multiple apiserver replicas for the specified number of times in a loop. | :heavy_check_mark: |
[Prometheus](https://github.com/chaos-kubox/krkn/blob/main/scenarios/prometheus.yml) | Kills a single/multiple prometheus replicas for the specified number of times in a loop. | :heavy_check_mark: |
[OpenShift System Pods](https://github.com/chaos-kubox/krkn/blob/main/scenarios/regex_openshift_pod_kill.yml) | Kills random pods running in the OpenShift system namespaces. | :heavy_check_mark: |

View File

@@ -1,80 +0,0 @@
### Service Hijacking Scenarios
Service Hijacking Scenarios aim to simulate fake HTTP responses from a workload targeted by a
`Service` already deployed in the cluster.
This scenario is executed by deploying a custom-made web service and modifying the target `Service`
selector to direct traffic to this web service for a specified duration.
The web service's source code is available [here](https://github.com/krkn-chaos/krkn-service-hijacking).
It employs a time-based test plan from the scenario configuration file, which specifies the behavior of resources during the chaos scenario as follows:
```yaml
service_target_port: http-web-svc # The port of the service to be hijacked (can be named or numeric, based on the workload and service configuration).
service_name: nginx-service # The name of the service that will be hijacked.
service_namespace: default # The namespace where the target service is located.
image: quay.io/krkn-chaos/krkn-service-hijacking:v0.1.3 # Image of the krkn web service to be deployed to receive traffic.
chaos_duration: 30 # Total duration of the chaos scenario in seconds.
plan:
- resource: "/list/index.php" # Specifies the resource or path to respond to in the scenario. For paths, both the path and query parameters are captured but ignored. For resources, only query parameters are captured.
steps: # A time-based plan consisting of steps can be defined for each resource.
GET: # One or more HTTP methods can be specified for each step. Note: Non-standard methods are supported for fully custom web services (e.g., using NONEXISTENT instead of POST).
- duration: 15 # Duration in seconds for this step before moving to the next one, if defined. Otherwise, this step will continue until the chaos scenario ends.
status: 500 # HTTP status code to be returned in this step.
mime_type: "application/json" # MIME type of the response for this step.
payload: | # The response payload for this step.
{
"status":"internal server error"
}
- duration: 15
status: 201
mime_type: "application/json"
payload: |
{
"status":"resource created"
}
POST:
- duration: 15
status: 401
mime_type: "application/json"
payload: |
{
"status": "unauthorized"
}
- duration: 15
status: 404
mime_type: "text/plain"
payload: "not found"
```
The scenario will focus on the `service_name` within the `service_namespace`,
substituting the selector with a randomly generated one, which is added as a label in the mock service manifest.
This allows multiple scenarios to be executed in the same namespace, each targeting different services without
causing conflicts.
The newly deployed mock web service will expose a `service_target_port`,
which can be either a named or numeric port based on the service configuration.
This ensures that the Service correctly routes HTTP traffic to the mock web service during the chaos run.
Each step will last for `duration` seconds from the deployment of the mock web service in the cluster.
For each HTTP resource, defined as a top-level YAML property of the plan
(it could be a specific resource, e.g., /list/index.php, or a path-based resource typical in MVC frameworks),
one or more HTTP request methods can be specified. Both standard and custom request methods are supported.
During this time frame, the web service will respond with:
- `status`: The [HTTP status code](https://datatracker.ietf.org/doc/html/rfc7231#section-6) (can be standard or custom).
- `mime_type`: The [MIME type](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types) (can be standard or custom).
- `payload`: The response body to be returned to the client.
At the end of the step `duration`, the web service will proceed to the next step (if available) until
the global `chaos_duration` concludes. At this point, the original service will be restored,
and the custom web service and its resources will be undeployed.
__NOTE__: Some clients (e.g., cURL, jQuery) may optimize queries using lightweight methods (like HEAD or OPTIONS)
to probe API behavior. If these methods are not defined in the test plan, the web service may respond with
a `405` or `404` status code. If you encounter unexpected behavior, consider this use case.

View File

@@ -16,7 +16,7 @@ Configuration Options:
**object_name:** List of the names of pods or nodes you want to skew.
Refer to [time_scenarios_example](https://github.com/krkn-chaos/krkn/blob/main/scenarios/time_scenarios_example.yml) config file.
Refer to [time_scenarios_example](https://github.com/chaos-kubox/krkn/blob/main/scenarios/time_scenarios_example.yml) config file.
```
time_scenarios:

View File

@@ -1,5 +1,5 @@
### Zone outage scenario
Scenario to create outage in a targeted zone in the public cloud to understand the impact on both Kubernetes/OpenShift control plane as well as applications running on the worker nodes in that zone. It tweaks the network acl of the zone to simulate the failure and that in turn will stop both ingress and egress traffic from all the nodes in a particular zone for the specified duration and reverts it back to the previous state. Zone outage can be injected by placing the zone_outage config file under zone_outages option in the [kraken config](https://github.com/redhat-chaos/krkn/blob/main/config/config.yaml). Refer to [zone_outage_scenario](https://github.com/redhat-chaos/krkn/blob/main/scenarios/zone_outage.yaml) config file for the parameters that need to be defined.
Scenario to create outage in a targeted zone in the public cloud to understand the impact on both Kubernetes/OpenShift control plane as well as applications running on the worker nodes in that zone. It tweaks the network acl of the zone to simulate the failure and that in turn will stop both ingress and egress traffic from all the nodes in a particular zone for the specified duration and reverts it back to the previous state. Zone outage can be injected by placing the zone_outage config file under zone_outages option in the [kraken config](https://github.com/chaos-kubox/krkn/blob/main/config/config.yaml). Refer to [zone_outage_scenario](https://github.com/chaos-kubox/krkn/blob/main/scenarios/zone_outage.yaml) config file for the parameters that need to be defined.
Refer to [cloud setup](cloud_setup.md) to configure your cli properly for the cloud provider of the cluster you want to shut down.

View File

@@ -2,9 +2,6 @@ kind: Cluster
apiVersion: kind.x-k8s.io/v1alpha4
nodes:
- role: control-plane
extraPortMappings:
- containerPort: 30036
hostPort: 8888
- role: control-plane
- role: control-plane
- role: worker

View File

@@ -4,44 +4,25 @@ import time
import kraken.cerberus.setup as cerberus
from jinja2 import Template
import kraken.invoke.command as runcommand
from krkn_lib.k8s import KrknKubernetes
from krkn_lib.telemetry.k8s import KrknTelemetryKubernetes
from krkn_lib.models.telemetry import ScenarioTelemetry
from krkn_lib.utils.functions import get_yaml_item_value, log_exception
# Reads the scenario config, applies and deletes a network policy to
# block the traffic for the specified duration
def run(scenarios_list, config, wait_duration,kubecli: KrknKubernetes, telemetry: KrknTelemetryKubernetes) -> (list[str], list[ScenarioTelemetry]):
def run(scenarios_list, config, wait_duration):
failed_post_scenarios = ""
scenario_telemetries: list[ScenarioTelemetry] = []
failed_scenarios = []
for app_outage_config in scenarios_list:
scenario_telemetry = ScenarioTelemetry()
scenario_telemetry.scenario = app_outage_config
scenario_telemetry.start_timestamp = time.time()
telemetry.set_parameters_base64(scenario_telemetry, app_outage_config)
if len(app_outage_config) > 1:
try:
with open(app_outage_config, "r") as f:
app_outage_config_yaml = yaml.full_load(f)
scenario_config = app_outage_config_yaml["application_outage"]
pod_selector = get_yaml_item_value(
scenario_config, "pod_selector", "{}"
)
traffic_type = get_yaml_item_value(
scenario_config, "block", "[Ingress, Egress]"
)
namespace = get_yaml_item_value(
scenario_config, "namespace", ""
)
duration = get_yaml_item_value(
scenario_config, "duration", 60
)
with open(app_outage_config, "r") as f:
app_outage_config_yaml = yaml.full_load(f)
scenario_config = app_outage_config_yaml["application_outage"]
pod_selector = scenario_config.get("pod_selector", "{}")
traffic_type = scenario_config.get("block", "[Ingress, Egress]")
namespace = scenario_config.get("namespace", "")
duration = scenario_config.get("duration", 60)
start_time = int(time.time())
start_time = int(time.time())
network_policy_template = """---
network_policy_template = """---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
@@ -50,35 +31,28 @@ spec:
podSelector:
matchLabels: {{ pod_selector }}
policyTypes: {{ traffic_type }}
"""
t = Template(network_policy_template)
rendered_spec = t.render(pod_selector=pod_selector, traffic_type=traffic_type)
yaml_spec = yaml.safe_load(rendered_spec)
# Block the traffic by creating network policy
logging.info("Creating the network policy")
"""
t = Template(network_policy_template)
rendered_spec = t.render(pod_selector=pod_selector, traffic_type=traffic_type)
# Write the rendered template to a file
with open("kraken_network_policy.yaml", "w") as f:
f.write(rendered_spec)
# Block the traffic by creating network policy
logging.info("Creating the network policy")
runcommand.invoke(
"kubectl create -f %s -n %s --validate=false" % ("kraken_network_policy.yaml", namespace)
)
kubecli.create_net_policy(yaml_spec, namespace)
# wait for the specified duration
logging.info("Waiting for the specified duration in the config: %s" % (duration))
time.sleep(duration)
# wait for the specified duration
logging.info("Waiting for the specified duration in the config: %s" % (duration))
time.sleep(duration)
# unblock the traffic by deleting the network policy
logging.info("Deleting the network policy")
kubecli.delete_net_policy("kraken-deny", namespace)
# unblock the traffic by deleting the network policy
logging.info("Deleting the network policy")
runcommand.invoke("kubectl delete -f %s -n %s" % ("kraken_network_policy.yaml", namespace))
logging.info("End of scenario. Waiting for the specified duration: %s" % (wait_duration))
time.sleep(wait_duration)
end_time = int(time.time())
cerberus.publish_kraken_status(config, failed_post_scenarios, start_time, end_time)
except Exception as e :
scenario_telemetry.exit_status = 1
failed_scenarios.append(app_outage_config)
log_exception(app_outage_config)
else:
scenario_telemetry.exit_status = 0
scenario_telemetry.end_timestamp = time.time()
scenario_telemetries.append(scenario_telemetry)
return failed_scenarios, scenario_telemetries
logging.info("End of scenario. Waiting for the specified duration: %s" % (wait_duration))
time.sleep(wait_duration)
end_time = int(time.time())
cerberus.publish_kraken_status(config, failed_post_scenarios, start_time, end_time)

View File

@@ -1,2 +0,0 @@
from .arcaflow_plugin import *
from .context_auth import ContextAuth

View File

@@ -1,180 +0,0 @@
import time
import arcaflow
import os
import yaml
import logging
from pathlib import Path
from typing import List
from .context_auth import ContextAuth
from krkn_lib.telemetry.k8s import KrknTelemetryKubernetes
from krkn_lib.models.telemetry import ScenarioTelemetry
def run(scenarios_list: List[str], kubeconfig_path: str, telemetry: KrknTelemetryKubernetes) -> (list[str], list[ScenarioTelemetry]):
scenario_telemetries: list[ScenarioTelemetry] = []
failed_post_scenarios = []
for scenario in scenarios_list:
scenario_telemetry = ScenarioTelemetry()
scenario_telemetry.scenario = scenario
scenario_telemetry.start_timestamp = time.time()
telemetry.set_parameters_base64(scenario_telemetry,scenario)
engine_args = build_args(scenario)
status_code = run_workflow(engine_args, kubeconfig_path)
scenario_telemetry.end_timestamp = time.time()
scenario_telemetry.exit_status = status_code
scenario_telemetries.append(scenario_telemetry)
if status_code != 0:
failed_post_scenarios.append(scenario)
return failed_post_scenarios, scenario_telemetries
def run_workflow(engine_args: arcaflow.EngineArgs, kubeconfig_path: str) -> int:
set_arca_kubeconfig(engine_args, kubeconfig_path)
exit_status = arcaflow.run(engine_args)
return exit_status
def build_args(input_file: str) -> arcaflow.EngineArgs:
"""sets the kubeconfig parsed by setArcaKubeConfig as an input to the arcaflow workflow"""
current_path = Path().resolve()
context = f"{current_path}/{Path(input_file).parent}"
workflow = f"{context}/workflow.yaml"
config = f"{context}/config.yaml"
if not os.path.exists(context):
raise Exception(
"context folder for arcaflow workflow not found: {}".format(
context)
)
if not os.path.exists(input_file):
raise Exception(
"input file for arcaflow workflow not found: {}".format(input_file))
if not os.path.exists(workflow):
raise Exception(
"workflow file for arcaflow workflow not found: {}".format(
workflow)
)
if not os.path.exists(config):
raise Exception(
"configuration file for arcaflow workflow not found: {}".format(
config)
)
engine_args = arcaflow.EngineArgs()
engine_args.context = context
engine_args.config = config
engine_args.workflow = workflow
engine_args.input = f"{current_path}/{input_file}"
return engine_args
def set_arca_kubeconfig(engine_args: arcaflow.EngineArgs, kubeconfig_path: str):
context_auth = ContextAuth()
if not os.path.exists(kubeconfig_path):
raise Exception("kubeconfig not found in {}".format(kubeconfig_path))
with open(kubeconfig_path, "r") as stream:
try:
kubeconfig = yaml.safe_load(stream)
context_auth.fetch_auth_data(kubeconfig)
except Exception as e:
logging.error("impossible to read kubeconfig file in: {}".format(
kubeconfig_path))
raise e
kubeconfig_str = set_kubeconfig_auth(kubeconfig, context_auth)
with open(engine_args.input, "r") as stream:
input_file = yaml.safe_load(stream)
if "input_list" in input_file and isinstance(input_file["input_list"],list):
for index, _ in enumerate(input_file["input_list"]):
if isinstance(input_file["input_list"][index], dict):
input_file["input_list"][index]["kubeconfig"] = kubeconfig_str
else:
input_file["kubeconfig"] = kubeconfig_str
stream.close()
with open(engine_args.input, "w") as stream:
yaml.safe_dump(input_file, stream)
with open(engine_args.config, "r") as stream:
config_file = yaml.safe_load(stream)
if config_file["deployers"]["image"]["deployer_name"] == "kubernetes":
kube_connection = set_kubernetes_deployer_auth(config_file["deployers"]["image"]["connection"], context_auth)
config_file["deployers"]["image"]["connection"]=kube_connection
with open(engine_args.config, "w") as stream:
yaml.safe_dump(config_file, stream,explicit_start=True, width=4096)
def set_kubernetes_deployer_auth(deployer: any, context_auth: ContextAuth) -> any:
if context_auth.clusterHost is not None :
deployer["host"] = context_auth.clusterHost
if context_auth.clientCertificateData is not None :
deployer["cert"] = context_auth.clientCertificateData
if context_auth.clientKeyData is not None:
deployer["key"] = context_auth.clientKeyData
if context_auth.clusterCertificateData is not None:
deployer["cacert"] = context_auth.clusterCertificateData
if context_auth.username is not None:
deployer["username"] = context_auth.username
if context_auth.password is not None:
deployer["password"] = context_auth.password
if context_auth.bearerToken is not None:
deployer["bearerToken"] = context_auth.bearerToken
return deployer
def set_kubeconfig_auth(kubeconfig: any, context_auth: ContextAuth) -> str:
"""
Builds an arcaflow-compatible kubeconfig representation and returns it as a string.
In order to run arcaflow plugins in kubernetes/openshift the kubeconfig must contain client certificate/key
and server certificate base64 encoded within the kubeconfig file itself in *-data fields. That is not always the
case, infact kubeconfig may contain filesystem paths to those files, this function builds an arcaflow-compatible
kubeconfig file and returns it as a string that can be safely included in input.yaml
"""
if "current-context" not in kubeconfig.keys():
raise Exception(
"invalid kubeconfig file, impossible to determine current-context"
)
user_id = None
cluster_id = None
user_name = None
cluster_name = None
current_context = kubeconfig["current-context"]
for context in kubeconfig["contexts"]:
if context["name"] == current_context:
user_name = context["context"]["user"]
cluster_name = context["context"]["cluster"]
if user_name is None:
raise Exception(
"user not set for context {} in kubeconfig file".format(current_context)
)
if cluster_name is None:
raise Exception(
"cluster not set for context {} in kubeconfig file".format(current_context)
)
for index, user in enumerate(kubeconfig["users"]):
if user["name"] == user_name:
user_id = index
for index, cluster in enumerate(kubeconfig["clusters"]):
if cluster["name"] == cluster_name:
cluster_id = index
if cluster_id is None:
raise Exception(
"no cluster {} found in kubeconfig users".format(cluster_name)
)
if "client-certificate" in kubeconfig["users"][user_id]["user"]:
kubeconfig["users"][user_id]["user"]["client-certificate-data"] = context_auth.clientCertificateDataBase64
del kubeconfig["users"][user_id]["user"]["client-certificate"]
if "client-key" in kubeconfig["users"][user_id]["user"]:
kubeconfig["users"][user_id]["user"]["client-key-data"] = context_auth.clientKeyDataBase64
del kubeconfig["users"][user_id]["user"]["client-key"]
if "certificate-authority" in kubeconfig["clusters"][cluster_id]["cluster"]:
kubeconfig["clusters"][cluster_id]["cluster"]["certificate-authority-data"] = context_auth.clusterCertificateDataBase64
del kubeconfig["clusters"][cluster_id]["cluster"]["certificate-authority"]
kubeconfig_str = yaml.dump(kubeconfig)
return kubeconfig_str

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