Overview
We conducted 40+ chaos experiments across 2 MariaDB topologies (Galera Cluster and MariaDB Replication with MaxScale) on KubeDB-managed 3-node clusters. The goal: validate that KubeDB MariaDB delivers zero data loss, automatic failover, and self-healing recovery under realistic failure conditions with production-level write loads.
The result: Most experiments passed with zero data loss. Three IO-chaos experiments (IOChaos fault and mistake on Master/Slave) exposed edge cases requiring operator intervention on current releases — documented with recovery procedures below.
This post summarizes the methodology, results, and key findings from comprehensive chaos testing of KubeDB MariaDB.
Why Chaos Testing?
Running databases on Kubernetes introduces failure modes that traditional infrastructure does not have — pods can be evicted, nodes can go down, network policies can partition traffic, and resource limits can trigger OOMKills at any time. Chaos engineering deliberately injects these failures to verify that the system recovers correctly before they happen in production.
For a MariaDB cluster managed by KubeDB (Galera Cluster or Replication with MaxScale), we needed to answer:
- Does the cluster lose data when a primary/master is killed mid-transaction?
- Does automatic failover work under network partitions?
- Can the cluster self-heal after a full outage with no manual intervention?
- Are replication GTIDs consistent across all nodes after recovery?
- Does the cluster survive combined failures (CPU + memory + load simultaneously)?
Setup Guide
Step 1: Create a kind Cluster
We used kind (Kubernetes IN Docker) as our local Kubernetes cluster. Follow the kind installation guide to install it, then create a cluster:
kind create cluster --name chaos-test
Step 2: Install KubeDB
Install KubeDB operator using Helm:
helm install kubedb oci://ghcr.io/appscode-charts/kubedb \
--version v2026.2.26 \
--namespace kubedb --create-namespace \
--set-file global.license=/path/to/license.txt \
--wait --burst-limit=10000 --debug
Step 3: Install Chaos Mesh
Install Chaos Mesh for fault injection:
helm repo add chaos-mesh https://charts.chaos-mesh.org
helm repo update chaos-mesh
helm upgrade -i chaos-mesh chaos-mesh/chaos-mesh \
-n chaos-mesh --create-namespace \
--set dashboard.create=true \
--set dashboard.securityMode=false \
--set chaosDaemon.runtime=containerd \
--set chaosDaemon.socketPath=/run/containerd/containerd.sock \
--set chaosDaemon.privileged=true
Step 4: Deploy MariaDB Galera Cluster
Create the namespace:
kubectl create namespace demo
Important Notes on Database Status:
Ready— Database is fully operational. All pods are Synced.Critical— Cluster has quorum but one or more nodes may be down or desynced.NotReady— Cluster has lost quorum. No writes can be accepted.You can read/write in your database in both
ReadyandCriticalstates. Even if your db is inCriticalstate, your uptime is not compromised.
Test Environment
| Component | Details |
|---|---|
| Kubernetes | kind (local cluster) |
| KubeDB Version | 2026.2.26 |
| Cluster Topology | 3-node Galera Cluster (all nodes read-write) |
| MariaDB Version | 11.8.5 |
| Storage | 2Gi PVC per node (Durable, ReadWriteOnce) |
| Memory Limit | 1.5Gi per MariaDB pod |
| CPU Request | 500m per pod |
| Chaos Engine | Chaos Mesh |
| Load Generator | sysbench oltp_read_write, 4 tables x 50k rows, 4 threads |
| Baseline TPS | ~1,039 |
All experiments were run under sustained sysbench read-write load to simulate production traffic during failures.
Create a High-Availability MariaDB Galera Cluster
First, we need to deploy a MariaDB cluster configured for High Availability. Unlike a Standalone instance, a Galera Cluster consists of 3 multi-master nodes that all accept reads and writes simultaneously. If any node fails, the remaining nodes continue serving traffic with zero downtime.
Save the following YAML as setup/kubedb-mariadb.yaml:
apiVersion: kubedb.com/v1
kind: MariaDB
metadata:
name: md
namespace: demo
spec:
deletionPolicy: Delete
replicas: 3
storage:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi
storageType: Durable
podTemplate:
spec:
containers:
- name: mariadb
resources:
limits:
memory: 1.5Gi
requests:
cpu: 500m
memory: 1.5Gi
topology:
mode: GaleraCluster
version: 11.8.5
Important Notes:
- You can read/write in your database in both
ReadyandCriticalstates. So it means even if your db is inCriticalstate, your uptime is not compromised.Criticalmeans one or more nodes are offline, but the remaining nodes have quorum and accept connections.- All the results/metrics shown in this blog are related to the chaos scenarios. In general, a Galera node recovery takes ~5-30 seconds with zero data loss, ensuring high availability and data safety.
- Each MariaDB pod runs a KubeDB coordinator sidecar (
md-coordinatorcontainer) alongside themariadbcontainer. The coordinator is the brain behind all recovery operations — it detects failures, triggers failovers, bootstraps clusters, and manages replication. This is what we are testing.
Now, create the namespace and apply the manifest:
kubectl create ns demo
kubectl apply -f setup/kubedb-mariadb.yaml
You can monitor the status until all pods are ready:
watch kubectl get mariadb,pods -n demo -L kubedb.com/role
See the database status is ready:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 68m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 68m Primary
pod/md-1 2/2 Running 0 68m Primary
pod/md-2 2/2 Running 0 68m Primary
Note: In Galera Cluster, all nodes have role Primary because every node accepts reads and writes. This is different from MySQL Group Replication or PostgreSQL where you have a single primary and standbys.
Deploy sysbench Load Generator
apiVersion: apps/v1
kind: Deployment
metadata:
name: sysbench-load
namespace: demo
labels:
app: sysbench
spec:
replicas: 1
selector:
matchLabels:
app: sysbench
template:
metadata:
labels:
app: sysbench
spec:
containers:
- name: sysbench
image: perconalab/sysbench:latest
command: ["/bin/sleep", "infinity"]
resources:
requests:
cpu: "500m"
memory: "512Mi"
limits:
cpu: "2"
memory: "2Gi"
env:
- name: MYSQL_HOST
value: "md.demo.svc.cluster.local"
- name: MYSQL_PORT
value: "3306"
- name: MYSQL_USER
value: "root"
- name: MYSQL_PASSWORD
valueFrom:
secretKeyRef:
name: md-auth
key: password
- name: MYSQL_DB
value: "sbtest"
kubectl apply -f sysbench.yaml
Prepare sysbench Tables
# Get the MariaDB root password
PASS=$(kubectl get secret md-auth -n demo -o jsonpath='{.data.password}' | base64 -d)
# Create the sbtest database
kubectl exec -n demo md-0 -c mariadb -- \
mariadb -uroot -p"$PASS" -e "CREATE DATABASE IF NOT EXISTS sbtest;"
# Get the sysbench pod name
SBPOD=$(kubectl get pods -n demo -l app=sysbench -o jsonpath='{.items[0].metadata.name}')
# Prepare tables (4 tables x 50k rows)
kubectl exec -n demo $SBPOD -- sysbench oltp_read_write \
--mysql-host=md --mysql-port=3306 \
--mysql-user=root --mysql-password="$PASS" \
--mysql-db=sbtest --tables=4 --table-size=50000 \
--threads=4 prepare
Run sysbench During Chaos
kubectl exec -n demo $SBPOD -- sysbench oltp_read_write \
--mysql-host=md --mysql-port=3306 \
--mysql-user=root --mysql-password="$PASS" \
--mysql-db=sbtest --tables=4 --table-size=50000 \
--threads=4 --time=60 --report-interval=10 run
Galera Cluster Key Concepts
Unlike MySQL Group Replication which has a single primary and secondaries, MariaDB Galera Cluster is multi-master — all nodes accept reads and writes simultaneously. Key status variables:
| Variable | Meaning |
|---|---|
wsrep_cluster_size |
Number of nodes in the cluster |
wsrep_cluster_status |
Primary = cluster has quorum and is operational |
wsrep_local_state_comment |
Synced / Joined / Donor / Desynced |
wsrep_ready |
ON = node accepts queries |
wsrep_connected |
ON = node connected to cluster |
wsrep_flow_control_paused |
Fraction of time paused for flow control (0.0 = healthy) |
Important Notes on Database Status:
Ready— Database is fully operational. All pods are Synced.Critical— Cluster has quorum but one or more nodes may be down or desynced.NotReady— Cluster has lost quorum. No writes can be accepted.You can read/write in your database in both
ReadyandCriticalstates. Even if your db is inCriticalstate, your uptime is not compromised.
Chaos Testing
We will run chaos experiments to see how our Galera cluster behaves under failure scenarios. We use sysbench to simulate high read-write load during each experiment.
Verify Cluster is Ready
Before starting chaos experiments, verify the cluster is healthy:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 68m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 1 (34m ago) 68m Primary
pod/md-1 2/2 Running 0 12m Primary
pod/md-2 2/2 Running 0 68m Primary
Note: In Galera Cluster, all nodes have role Primary because every node accepts reads and writes.
Inspect the Galera cluster status:
➤ kubectl exec -n demo md-0 -c mariadb -- \
mariadb -uroot -p"$PASS" -e "SHOW GLOBAL STATUS WHERE Variable_name IN (
'wsrep_cluster_size','wsrep_cluster_status',
'wsrep_local_state_comment','wsrep_ready',
'wsrep_connected','wsrep_flow_control_paused');"
Variable_name Value
wsrep_flow_control_paused 0
wsrep_local_state_comment Synced
wsrep_cluster_size 3
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
All 3 nodes Synced, cluster_size=3, wsrep_ready=ON. With the cluster ready and sysbench tables prepared, we are ready to run chaos experiments.
Chaos#1: Kill a Pod
We kill one MariaDB pod and see how fast the Galera cluster recovers. In Galera, since all nodes are equal, killing any node should be handled gracefully.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: mariadb-primary-pod-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
gracePeriod: 0
What this chaos does: Terminates one MariaDB pod abruptly with grace-period=0, forcing the remaining 2 nodes to handle all traffic while the killed pod recovers.
-
Expected behavior: One pod killed → cluster briefly
Critical(2/3 members left) → killed pod recreated by StatefulSet → rejoins via IST (Incremental State Transfer) →wsrep_cluster_sizereturns to 3, all nodesSynced→ clusterReady. Zero data loss — all 25 tracking rows and table checksums consistent. -
Actual result: md-0 killed (age=8s after recreation). Auto-rejoined via IST within ~5s. All 3 nodes
Synced,wsrep_cluster_size=3. 25/25 tracking rows preserved, checksums match across all 3 nodes. Post-recovery TPS 1061 (back to baseline). PASS.
Before running:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 68m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 1 (34m ago) 68m Primary
pod/md-1 2/2 Running 0 12m Primary
pod/md-2 2/2 Running 0 68m Primary
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/pod-kill-primary.yaml
podchaos.chaos-mesh.org/mariadb-primary-pod-kill created
Within seconds, one pod is killed and recreated. The database goes Critical briefly:
➤ kubectl get mariadb,pods -n demo
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 68m
NAME READY STATUS RESTARTS AGE
pod/md-0 2/2 Running 0 8s
pod/md-1 2/2 Running 0 12m
pod/md-2 2/2 Running 0 68m
md-0 was killed and recreated (age=8s). After about 5 seconds, the pod rejoins the Galera cluster and syncs via IST (Incremental State Transfer). Check Galera status:
➤ SHOW GLOBAL STATUS WHERE Variable_name IN ('wsrep_cluster_size','wsrep_cluster_status',
'wsrep_local_state_comment','wsrep_ready','wsrep_connected');
md-0:
Variable_name Value
wsrep_local_state_comment Synced
wsrep_cluster_size 3
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
md-1:
Variable_name Value
wsrep_local_state_comment Synced
wsrep_cluster_size 3
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
md-2:
Variable_name Value
wsrep_local_state_comment Synced
wsrep_cluster_size 3
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
All 3 nodes Synced. Run sysbench to verify the cluster is fully operational:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1063.05 qps: 21272.82 (r/w/o: 14891.32/2327.87/4053.63) lat (ms,95%): 5.00 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1059.67 qps: 21198.98 (r/w/o: 14840.36/2343.15/4015.46) lat (ms,95%): 4.91 err/s: 0.20 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1060.19 qps: 21202.99 (r/w/o: 14841.86/2407.58/3953.56) lat (ms,95%): 4.91 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 15919 (1060.99 per sec.)
queries: 318397 (21220.87 per sec.)
ignored errors: 1 (0.07 per sec.)
reconnects: 0 (0.00 per sec.)
Verify data integrity:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=2941988609, sbtest2=1454430013, sbtest3=496174579, sbtest4=1322761405
md-1: sbtest1=2941988609, sbtest2=1454430013, sbtest3=496174579, sbtest4=1322761405
md-2: sbtest1=2941988609, sbtest2=1454430013, sbtest3=496174579, sbtest4=1322761405
Result: PASS — Zero data loss. Pod recreated in ~5 seconds, auto-rejoined via IST. All 25 tracking rows preserved, checksums match across all 3 nodes. Post-recovery throughput: 1061 TPS (back to baseline).
Clean up:
➤ kubectl delete -f 1-single-experiments/pod-kill-primary.yaml
podchaos.chaos-mesh.org "mariadb-primary-pod-kill" deleted
Chaos#2: OOMKill (Memory Stress)
We stress-test memory on one node to see if the cluster survives under extreme memory pressure.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: mariadb-primary-memory-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
stressors:
memory:
workers: 2
size: "1200MB"
duration: "10m"
What this chaos does: Allocates 1200MB of extra memory on one pod. With MariaDB’s memory usage, this approaches the 1.5Gi limit.
-
Expected behavior: Memory stress pushes pod near 1.5Gi limit → either (a) pod OOMKilled → recreated → rejoins via IST, or (b) survives with degraded performance. Cluster stays operational at ≥ 2 nodes throughout. Zero data loss either way.
-
Actual result: MariaDB survived 1200MB stress without OOMKill. No pod restarts. Cluster ran at 1050 TPS (no degradation). 25/25 tracking rows preserved, checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/stress-memory-primary.yaml
stresschaos.chaos-mesh.org/mariadb-primary-memory-stress created
After 20 seconds, check pods — no OOMKill triggered:
➤ kubectl get mariadb,pods -n demo
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 76m
NAME READY STATUS RESTARTS AGE
pod/md-0 2/2 Running 0 7m54s
pod/md-1 2/2 Running 0 20m
pod/md-2 2/2 Running 0 76m
MariaDB survived at 1200MB stress — no OOMKill. Run sysbench during stress:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1051.05 qps: 21032.31 (r/w/o: 14723.04/2446.84/3862.43) lat (ms,95%): 4.91 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1046.48 qps: 20930.60 (r/w/o: 14652.32/2489.19/3789.09) lat (ms,95%): 5.00 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1052.99 qps: 21064.27 (r/w/o: 14745.71/2533.78/3784.78) lat (ms,95%): 4.91 err/s: 0.20 reconn/s: 0.00
SQL statistics:
transactions: 15757 (1050.21 per sec.)
queries: 315156 (21005.21 per sec.)
ignored errors: 1 (0.07 per sec.)
reconnects: 0 (0.00 per sec.)
Verify data integrity:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3400554968, sbtest2=1909458598
md-1: sbtest1=3400554968, sbtest2=1909458598
md-2: sbtest1=3400554968, sbtest2=1909458598
Result: PASS — MariaDB survived 1200MB memory stress without OOMKill. Cluster fully operational at 1050 TPS (no degradation). All 25 tracking rows preserved, checksums match.
Clean up:
➤ kubectl delete -f 1-single-experiments/stress-memory-primary.yaml
stresschaos.chaos-mesh.org "mariadb-primary-memory-stress" deleted
Chaos#3: Network Partition
We isolate one Galera node from the other two for 2 minutes. This tests whether the remaining nodes maintain quorum and continue serving traffic, and whether the isolated node rejoins cleanly.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-primary-network-partition
namespace: chaos-mesh
spec:
action: partition
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
target:
mode: all
selector:
namespaces:
- demo
labelSelectors:
"kubedb.com/role": "Primary"
direction: both
duration: "2m"
What this chaos does: Creates a complete network partition between one node and the rest of the cluster for 2 minutes. The isolated node loses quorum and becomes non-Primary. The remaining 2 nodes maintain quorum and continue accepting writes.
-
Expected behavior: One node isolated → loses quorum, becomes
non-Primary, stops accepting queries (no split-brain) → remaining 2 nodes keep quorum, continue serving reads/writes → after partition lifts, isolated node auto-rejoins and syncs. Zero data loss. -
Actual result: md-2 isolated, went
wsrep_cluster_size=1, non-Primary, wsrep_ready=OFF. md-0/md-1 kept quorum atwsrep_cluster_size=2, Primary. TPS actually increased from 1039 → 1430 (fewer nodes to certify through). After partition lifted, md-2 auto-rejoined, allSynced. 25/25 tracking rows and checksums match. PASS.
Before running:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 82m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 13m Primary
pod/md-1 2/2 Running 0 25m Primary
pod/md-2 2/2 Running 0 82m Primary
➤ SHOW GLOBAL STATUS ...
wsrep_flow_control_paused 0.0277989
wsrep_local_state_comment Synced
wsrep_cluster_size 3
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/network-partition-primary.yaml
networkchaos.chaos-mesh.org/mariadb-primary-network-partition created
Within ~15 seconds, the isolated node loses quorum. The database goes Critical:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Critical 82m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 14m Primary
pod/md-1 2/2 Running 0 26m Primary
pod/md-2 2/2 Running 0 82m non-Primary
Note: md-2 is non-Primary — it’s the isolated node. Let’s check Galera status from each node:
➤ # md-0 (in quorum):
wsrep_flow_control_paused 0.0257333
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
➤ # md-1 (in quorum):
wsrep_flow_control_paused 0.0209364
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
➤ # md-2 (ISOLATED):
wsrep_flow_control_paused 0.00670716
wsrep_local_state_comment Initialized
wsrep_cluster_size 1
wsrep_cluster_status non-Primary
wsrep_connected ON
wsrep_ready OFF
The isolated node (md-2) shows wsrep_cluster_size=1, wsrep_cluster_status=non-Primary, wsrep_ready=OFF — it cannot accept queries. The remaining 2 nodes still have quorum (wsrep_cluster_status=Primary) and accept both reads and writes.
Run sysbench during the partition:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1435.24 qps: 28715.89 (r/w/o: 20102.42/3503.22/5110.25) lat (ms,95%): 3.49 err/s: 0.20 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1437.64 qps: 28756.01 (r/w/o: 20129.37/3626.70/4999.94) lat (ms,95%): 3.43 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1416.81 qps: 28336.92 (r/w/o: 19836.08/3607.82/4893.02) lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 21453 (1429.89 per sec.)
queries: 429076 (28598.90 per sec.)
ignored errors: 1 (0.07 per sec.)
reconnects: 0 (0.00 per sec.)
TPS increased from 1039 to 1430 during partition — a 37% improvement! This is because with only 2 nodes, Galera certification has less overhead (fewer nodes to coordinate with).
After the 2-minute partition expires, the isolated node automatically rejoins:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 85m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 17m Primary
pod/md-1 2/2 Running 0 29m Primary
pod/md-2 2/2 Running 0 85m Primary
All 3 nodes back to Primary, cluster Ready. Verify:
➤ # Galera status — all Synced
md-0: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0.0208
md-1: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0.0186
md-2: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0.0064
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3192592587, sbtest2=1620218475, sbtest3=827673677, sbtest4=2199205073
md-1: sbtest1=3192592587, sbtest2=1620218475, sbtest3=827673677, sbtest4=2199205073
md-2: sbtest1=3192592587, sbtest2=1620218475, sbtest3=827673677, sbtest4=2199205073
Result: PASS — Network partition handled correctly. Isolated node became non-Primary and stopped accepting queries (no split-brain). Remaining 2 nodes maintained quorum at 1430 TPS. After partition expired, isolated node auto-rejoined and synced. Zero data loss — all 25 tracking rows preserved, checksums match.
Clean up:
➤ kubectl delete -f 1-single-experiments/network-partition-primary.yaml
networkchaos.chaos-mesh.org "mariadb-primary-network-partition" deleted
Chaos#4: IO Latency (100ms)
We inject 100ms latency on all disk operations on one node. This simulates degraded storage — a common issue with cloud block storage or overloaded disk controllers.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: mariadb-primary-io-latency
namespace: chaos-mesh
spec:
action: latency
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
volumePath: "/var/lib/mysql"
path: "/**"
delay: "100ms"
percent: 100
duration: "3m"
What this chaos does: Adds 100ms delay to every disk read/write on /var/lib/mysql for one node. This makes every InnoDB flush, WAL write, and data page read significantly slower.
-
Expected behavior: 100ms delay on every I/O → affected node becomes unresponsive → Galera flow control expels it (
wsrep_cluster_sizedrops to 2 on remaining nodes) → the 2 healthy nodes keep serving writes → after chaos ends, expelled node auto-recovers and rejoins via IST. Zero data loss. -
Actual result: md-1 became unreachable (socket errors). Remaining 2 nodes showed
wsrep_cluster_size=2, served 1450 TPS with zero errors. After chaos, md-1 auto-rejoined, all 3Synced. 25/25 tracking rows preserved, checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/io-latency-primary.yaml
iochaos.chaos-mesh.org/mariadb-primary-io-latency created
After ~10 seconds, the affected node (md-1) becomes completely unresponsive — MariaDB cannot handle 100ms latency on every disk operation:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 88m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 19m Primary
pod/md-1 2/2 Running 0 32m Primary
pod/md-2 2/2 Running 0 88m Primary
The pod shows Running/Ready (Kubernetes doesn’t know MariaDB inside is frozen), but the Galera cluster has already expelled the node:
➤ # md-0 (healthy):
wsrep_flow_control_paused 0.0189172
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
➤ # md-1 (IO latency target):
ERROR 2002 (HY000): Can't connect to local server through socket '/run/mysqld/mysqld.sock' (111)
➤ # md-2 (healthy):
wsrep_flow_control_paused 0.00631477
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_connected ON
wsrep_ready ON
The affected node is completely unreachable — wsrep_cluster_size=2 on the healthy nodes confirms Galera expelled it. Run sysbench against the 2 remaining nodes:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1452.19 qps: 29057.21 (r/w/o: 20341.06/3789.65/4926.49) lat (ms,95%): 3.36 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1456.70 qps: 29129.26 (r/w/o: 20390.44/3873.07/4865.74) lat (ms,95%): 3.30 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1440.25 qps: 28803.66 (r/w/o: 20162.34/3885.60/4755.71) lat (ms,95%): 3.43 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 21750 (1449.63 per sec.)
queries: 435000 (28992.63 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
1450 TPS on 2 nodes, zero errors. After the 3-minute IO latency expires, the affected node auto-recovers and rejoins:
➤ kubectl get mariadb -n demo md
NAME VERSION STATUS AGE
md 11.8.5 Ready 94m
➤ # All nodes recovered:
md-0: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0.0145
md-1: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0
md-2: wsrep_cluster_size=3, Synced, wsrep_ready=ON, wsrep_flow_control_paused=0.0059
Verify data integrity:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3774570434, sbtest2=107955818, sbtest3=4222587866, sbtest4=2113239503
md-1: sbtest1=3774570434, sbtest2=107955818, sbtest3=4222587866, sbtest4=2113239503
md-2: sbtest1=3774570434, sbtest2=107955818, sbtest3=4222587866, sbtest4=2113239503
Result: PASS — IO latency caused the affected node to become unresponsive and expelled from Galera. The remaining 2 nodes continued serving 1450 TPS with zero errors. After chaos expired, the affected node auto-rejoined via IST. Zero data loss — all checksums match.
Clean up:
➤ kubectl delete -f 1-single-experiments/io-latency-primary.yaml
iochaos.chaos-mesh.org "mariadb-primary-io-latency" deleted
Chaos#5: Network Latency (1s)
We inject 1 second network latency between one node and all others. This tests how Galera certification handles slow cross-node communication — a common scenario with cross-region deployments or degraded network links.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-replication-latency
namespace: chaos-mesh
spec:
action: delay
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
target:
mode: all
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
delay:
latency: "1s"
jitter: "50ms"
duration: "10m"
direction: both
What this chaos does: Adds 1 second latency (+ 50ms jitter) between one node and all other cluster nodes. Every Galera certification message must wait 1 second each way.
-
Expected behavior: 1s latency on Galera traffic → synchronous certification requires ≥2s round trip → TPS collapses dramatically → but all 3 nodes stay
Synced(1s is below the wsrep-timeout threshold) → no errors, no split-brain → TPS recovers after chaos. Zero data loss — this is Galera’s synchronous safety trade-off. -
Actual result: TPS dropped from 1039 → 2.77 (99.7% reduction). 95p latency 2045ms (exactly one Paxos round-trip). All 3 nodes stayed
Synced, 0 errors, 0 reconnects. 25/25 tracking rows and checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/network-latency-primary-to-replicas.yaml
networkchaos.chaos-mesh.org/mariadb-replication-latency created
Check Galera — all 3 nodes stay Synced (1s latency isn’t enough to trigger expulsion):
➤ # All nodes:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0136
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0058
But sysbench throughput is severely impacted:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 2.40 qps: 63.19 (r/w/o: 44.79/8.40/10.00) lat (ms,95%): 1938.16 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 2.80 qps: 56.00 (r/w/o: 39.20/7.80/9.00) lat (ms,95%): 1869.60 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 3.00 qps: 60.00 (r/w/o: 42.00/7.00/11.00) lat (ms,95%): 1973.38 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 45 (2.77 per sec.)
queries: 900 (55.49 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Latency (ms):
min: 985.37
avg: 1418.98
max: 2090.38
95th percentile: 2045.74
TPS dropped from 1039 to 2.77 — a 99.7% reduction! This is because Galera uses synchronous replication: every write must be certified by all nodes. With 1s network latency, each certification round-trip takes ~2 seconds. The 95th percentile latency is 2045ms, confirming this.
However, note the key metrics: 0 errors, 0 reconnects. The cluster never broke — it just became extremely slow.
After removing the chaos, throughput returns to normal immediately. Verify data:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=620696091, sbtest2=702654982, sbtest3=2895801545, sbtest4=86505438
md-1: sbtest1=620696091, sbtest2=702654982, sbtest3=2895801545, sbtest4=86505438
md-2: sbtest1=620696091, sbtest2=702654982, sbtest3=2895801545, sbtest4=86505438
Result: PASS — 1s network latency caused severe throughput degradation (99.7% TPS drop) due to Galera’s synchronous certification, but the cluster remained operational with zero errors. No split-brain, no data loss. This is a key trade-off of synchronous replication: latency directly impacts throughput, but data safety is guaranteed.
Clean up:
➤ kubectl delete -f 1-single-experiments/network-latency-primary-to-replicas.yaml
networkchaos.chaos-mesh.org "mariadb-replication-latency" deleted
Chaos#6: CPU Stress (98%)
We apply 98% CPU stress on one node to see if the Galera cluster can handle a CPU-starved member.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: mariadb-primary-cpu-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
stressors:
cpu:
workers: 2
load: 98
duration: "5m"
What this chaos does: Consumes 98% CPU on one node using 2 stress workers. Tests whether the cluster maintains throughput and data consistency under extreme CPU pressure.
-
Expected behavior: 98% CPU stress on one node → MariaDB writes are mostly IO-bound, so impact should be modest → all 3 nodes stay
Synced→ TPS largely unchanged. Zero data loss. -
Actual result: 1034 TPS (99.5% of baseline — negligible impact). All 3 nodes stayed
Syncedwithwsrep_cluster_size=3. Zero errors, 25/25 tracking rows and checksums match. PASS — confirms Galera writes are IO-bound, not CPU-bound.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/stress-cpu-primary.yaml
stresschaos.chaos-mesh.org/mariadb-primary-cpu-stress created
All 3 nodes remain Synced:
➤ # Galera status during CPU stress:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0121
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0056
Run sysbench:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1033.26 qps: 20679.04 (r/w/o: 14476.27/2825.42/3377.35) lat (ms,95%): 5.18 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1026.65 qps: 20530.52 (r/w/o: 14371.44/2828.13/3330.95) lat (ms,95%): 5.09 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1041.79 qps: 20836.99 (r/w/o: 14586.25/2930.17/3320.57) lat (ms,95%): 5.00 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 15513 (1033.96 per sec.)
queries: 310260 (20679.19 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
1034 TPS — virtually no impact! MariaDB on Galera is largely IO-bound, not CPU-bound for write workloads.
Verify data:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3417352532, sbtest2=403445326
md-1: sbtest1=3417352532, sbtest2=403445326
md-2: sbtest1=3417352532, sbtest2=403445326
Result: PASS — 98% CPU stress had negligible impact on Galera cluster. 1034 TPS (99.5% of baseline). Zero errors, all nodes Synced throughout. MariaDB’s write path is IO-bound, not CPU-bound.
Clean up:
➤ kubectl delete -f 1-single-experiments/stress-cpu-primary.yaml
stresschaos.chaos-mesh.org "mariadb-primary-cpu-stress" deleted
Chaos#7: Packet Loss (30%)
We inject 30% packet loss across all cluster nodes. This simulates unreliable network infrastructure — congested switches, flaky NICs, or cloud provider network issues.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-cluster-packet-loss
namespace: chaos-mesh
spec:
action: loss
mode: all
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
loss:
loss: "30"
correlation: "25"
duration: "5m"
What this chaos does: Drops 30% of all network packets on every cluster node with 25% correlation (burst losses). This affects both Galera replication traffic and client connections.
-
Expected behavior: 30% packet loss → TCP retransmissions slow Galera certification massively → TPS collapses → all 3 nodes stay
Synced(losses don’t cross wsrep-timeout) → after chaos, TPS recovers. Zero data loss. -
Actual result: TPS collapsed from 1039 → 1.32 (-99.9%). 95p latency 4.5s. All 3 nodes stayed
Synced,wsrep_cluster_size=3, no expulsion, zero errors. 25/25 tracking rows and checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/packet-loss.yaml
networkchaos.chaos-mesh.org/mariadb-cluster-packet-loss created
All 3 nodes remain Synced — Galera handles retransmissions:
➤ # Galera status during 30% packet loss:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0124
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0077
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0064
But throughput is severely impacted due to TCP retransmissions and Galera certification delays:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1.00 qps: 28.99 (r/w/o: 21.80/3.20/4.00) lat (ms,95%): 4203.93 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1.60 qps: 33.40 (r/w/o: 23.40/4.40/5.60) lat (ms,95%): 5507.54 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 0.80 qps: 20.80 (r/w/o: 13.60/4.00/3.20) lat (ms,95%): 3326.55 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 21 (1.32 per sec.)
queries: 420 (26.40 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Latency (ms):
min: 1373.02
avg: 2987.19
max: 5524.30
95th percentile: 4517.90
TPS dropped from 1039 to 1.32 — even worse than 1s network latency! 30% packet loss causes massive TCP retransmissions, and each Galera certification round can take several seconds. But critically: zero errors, zero reconnects, no node expulsion.
Verify data integrity after cleanup:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=2426416412, sbtest2=1176558970, sbtest3=4292186888, sbtest4=1305861152
md-1: sbtest1=2426416412, sbtest2=1176558970, sbtest3=4292186888, sbtest4=1305861152
md-2: sbtest1=2426416412, sbtest2=1176558970, sbtest3=4292186888, sbtest4=1305861152
Result: PASS — 30% packet loss caused extreme throughput degradation (TPS: 1039 → 1.32) but no node was expelled and no data was lost. Galera’s TCP-based replication handles retransmissions correctly. All checksums match.
Clean up:
➤ kubectl delete -f 1-single-experiments/packet-loss.yaml
networkchaos.chaos-mesh.org "mariadb-cluster-packet-loss" deleted
Chaos#8: Full Cluster Kill
We kill ALL 3 pods simultaneously — the worst-case scenario. This tests Galera’s ability to bootstrap from a complete outage with no surviving member.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: mariadb-full-cluster-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: all
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
gracePeriod: 0
What this chaos does: Kills all 3 MariaDB pods simultaneously. No surviving node means the cluster must bootstrap from scratch using the data on PVCs.
-
Expected behavior: All 3 pods killed → cluster
NotReady, no primary component → coordinator identifies pod with highestseqno(via--wsrep-recover) → bootstraps that pod with--wsrep-new-cluster→ other pods rejoin via IST/SST → clusterReady. Zero data loss. -
Actual result: All 3 pods recreated. Coordinator detected full outage, ran seqno recovery, bootstrapped the highest-seqno node, other 2 rejoined. Full recovery in ~3 min. Post-recovery TPS 1024. 25/25 tracking rows and checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f full-cluster-kill.yaml
podchaos.chaos-mesh.org/mariadb-full-cluster-kill created
All pods are killed and recreated. The database immediately goes NotReady:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 NotReady 112m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 10s Unknown
pod/md-1 2/2 Running 0 10s Unknown
pod/md-2 2/2 Running 0 10s Unknown
All pods show role Unknown — the Galera cluster has no quorum, no primary component. The KubeDB coordinator detects this and initiates a Galera bootstrap sequence:
- The coordinator identifies the node with the most recent GTID (highest
seqno) - That node is bootstrapped as the new primary component (
--wsrep-new-cluster) - The other 2 nodes join the bootstrapped cluster via IST/SST
After approximately 3 minutes, all nodes rejoin and the cluster becomes Ready:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 115m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 2m59s Primary
pod/md-1 2/2 Running 0 2m59s Primary
pod/md-2 2/2 Running 0 2m59s Primary
All 3 nodes back to Primary, Galera fully operational:
➤ # All nodes:
md-0: wsrep_cluster_size=3, Synced, wsrep_ready=ON
md-1: wsrep_cluster_size=3, Synced, wsrep_ready=ON
md-2: wsrep_cluster_size=3, Synced, wsrep_ready=ON
Run sysbench to confirm throughput is restored:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1014.09 qps: 20295.00 lat (ms,95%): 5.47 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1027.82 qps: 20559.23 lat (ms,95%): 5.28 err/s: 0.20 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1030.61 qps: 20616.72 lat (ms,95%): 5.18 err/s: 0.20 reconn/s: 0.00
SQL statistics:
transactions: 15367 (1024.26 per sec.)
queries: 307372 (20487.38 per sec.)
ignored errors: 2 (0.13 per sec.)
reconnects: 0 (0.00 per sec.)
Verify data integrity:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3947591857, sbtest2=3914957582, sbtest3=901119555, sbtest4=4023705335
md-1: sbtest1=3947591857, sbtest2=3914957582, sbtest3=901119555, sbtest4=4023705335
md-2: sbtest1=3947591857, sbtest2=3914957582, sbtest3=901119555, sbtest4=4023705335
Result: PASS — Full cluster kill (all 3 pods) recovered automatically via Galera bootstrap in ~3 minutes. KubeDB coordinator handled the bootstrap sequence — no manual intervention needed. 1024 TPS post-recovery. Zero data loss — all 25 tracking rows preserved, all checksums match.
Clean up:
➤ kubectl delete podchaos mariadb-full-cluster-kill -n chaos-mesh
podchaos.chaos-mesh.org "mariadb-full-cluster-kill" deleted
Chaos#9: DNS Error
We inject DNS errors on one cluster node to see if it affects Galera replication.
apiVersion: chaos-mesh.org/v1alpha1
kind: DNSChaos
metadata:
name: mariadb-dns-error-primary
namespace: chaos-mesh
spec:
action: error
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
duration: "3m"
What this chaos does: Makes all DNS lookups fail on one MariaDB pod. Tests whether Galera replication (which uses IP addresses internally) is affected by DNS failures.
-
Expected behavior: DNS resolution blocked on one pod → Galera uses pre-resolved IPs for cluster communication, so no impact → all 3 nodes stay
Synced, TPS unchanged. Zero data loss. -
Actual result: All 3 nodes stayed
Syncedthroughout 3-minute DNS chaos. 1016 TPS (97.8% of baseline — noise-level impact). 25/25 tracking rows and checksums match. PASS.
Apply and check:
➤ kubectl apply -f 1-single-experiments/dns-error-primary.yaml
dnschaos.chaos-mesh.org/mariadb-dns-error-primary created
All 3 nodes remain Synced — Galera uses IP addresses for cluster communication, not DNS:
➤ # Galera status during DNS error:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0193
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0193
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0182
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1011.69 qps: 20251.63 lat (ms,95%): 5.47 err/s: 0.20 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1022.82 qps: 20449.23 lat (ms,95%): 5.09 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1013.21 qps: 20267.98 lat (ms,95%): 5.47 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 15242 (1015.94 per sec.)
queries: 304857 (20319.98 per sec.)
ignored errors: 1 (0.07 per sec.)
reconnects: 0 (0.00 per sec.)
1016 TPS — virtually no impact. Verify:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=286824257, sbtest2=4261772161
md-1: sbtest1=286824257, sbtest2=4261772161
md-2: sbtest1=286824257, sbtest2=4261772161
Result: PASS — DNS errors had no impact on Galera replication. Galera communicates via IP addresses, so DNS failures don’t affect cluster operations. 1016 TPS (97.8% of baseline).
Chaos#10: IO Fault (EIO 50%)
We inject IO errors (errno 5 = EIO) on 50% of disk operations on one node. This simulates disk corruption or failing storage hardware.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: mariadb-primary-io-fault
namespace: chaos-mesh
spec:
action: fault
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
volumePath: "/var/lib/mysql"
path: "/**"
errno: 5
percent: 50
duration: "3m"
What this chaos does: Returns EIO (Input/output error) for 50% of all disk operations on /var/lib/mysql. This is more severe than IO latency — it causes actual data access failures.
-
Expected behavior: Real I/O errors → MariaDB crashes on affected node → Galera expels it → remaining 2 nodes continue serving → after chaos ends, coordinator rejoins the crashed node via IST/SST → cluster
Ready. Zero data loss. -
Actual result: md-0 crashed (role became
Unknown). Remaining 2 nodes (md-1, md-2) stayedPrimary, served traffic. After chaos cleared, coordinator rejoined md-0 and cluster returned toReady. 25/25 tracking rows and checksums match. PASS.
Apply the chaos:
➤ kubectl apply -f 1-single-experiments/io-fault-primary.yaml
iochaos.chaos-mesh.org/mariadb-primary-io-fault created
The affected node (md-0) crashes with a segfault — MariaDB cannot handle random IO errors on its data files:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Critical 129m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 17m Unknown
pod/md-1 2/2 Running 0 17m Primary
pod/md-2 2/2 Running 0 17m Primary
➤ # md-0 (crashed):
ERROR 2002 (HY000): Can't connect to local server through socket '/run/mysqld/mysqld.sock' (111)
➤ # md-1 (healthy):
wsrep_flow_control_paused 0.0222942
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_ready ON
➤ # md-2 (healthy):
wsrep_flow_control_paused 0.0215375
wsrep_local_state_comment Synced
wsrep_cluster_size 2
wsrep_cluster_status Primary
wsrep_ready ON
MariaDB logs show: Segmentation fault (core dumped). The remaining 2 nodes continue serving traffic:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1391.02 qps: 27830.18 lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1409.46 qps: 28191.89 lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1411.61 qps: 28231.51 lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 21065 (1404.01 per sec.)
queries: 421300 (28080.23 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
1404 TPS on 2 nodes. After chaos expires, the KubeDB coordinator detects the crashed node, restarts it, and it rejoins via IST:
➤ # After recovery:
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=3560624458, sbtest2=1590320566
md-1: sbtest1=3560624458, sbtest2=1590320566
md-2: sbtest1=3560624458, sbtest2=1590320566
Result: PASS — IO faults caused MariaDB to segfault on the affected node, but the remaining 2 nodes continued at 1404 TPS. After chaos expired, the crashed node was recovered by the coordinator. Zero data loss.
Chaos#11: Clock Skew (-5 min)
We shift the clock backward by 5 minutes on one node. This tests whether time-dependent operations (certificates, timeouts, GTID ordering) are affected.
apiVersion: chaos-mesh.org/v1alpha1
kind: TimeChaos
metadata:
name: mariadb-primary-clock-skew
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
timeOffset: "-5m"
duration: "3m"
What this chaos does: Shifts the system clock backward by 5 minutes on one pod. This can confuse time-dependent operations like TLS certificate validation, timeout calculations, and log ordering.
-
Expected behavior: Clock skewed -5 min on one node → Galera certification uses write-set ordering (not wall clock) → minor impact at most → cluster stays
Syncedon all 3 nodes. Zero data loss. -
Actual result: 988 TPS (5% drop), 3 ignored errors (transient deadlocks from skewed timestamps). All 3 nodes stayed
Synced. 25/25 tracking rows and checksums match. PASS.
Apply and check:
➤ kubectl apply -f 1-single-experiments/clock-skew-primary.yaml
timechaos.chaos-mesh.org/mariadb-primary-clock-skew created
All 3 nodes remain Synced:
➤ # Galera status during clock skew:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0165
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0161
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 973.31 qps: 19488.47 lat (ms,95%): 6.09 err/s: 0.40 reconn/s: 0.00
[ 10s ] thds: 4 tps: 995.79 qps: 19915.27 lat (ms,95%): 5.88 err/s: 0.20 reconn/s: 0.00
[ 15s ] thds: 4 tps: 996.42 qps: 19930.23 lat (ms,95%): 5.47 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 14832 (988.48 per sec.)
queries: 296691 (19772.96 per sec.)
ignored errors: 3 (0.20 per sec.)
reconnects: 0 (0.00 per sec.)
988 TPS — minor 5% drop. 3 ignored errors (likely deadlocks from skewed timestamps). Verify:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=1081956110, sbtest2=4193306675
md-1: sbtest1=1081956110, sbtest2=4193306675
md-2: sbtest1=1081956110, sbtest2=4193306675
Result: PASS — 5-minute clock skew caused only a minor throughput dip (5%) and 3 ignored errors. Galera certification is based on write-set ordering, not wall-clock time, so clock skew has minimal impact.
Chaos#12: Bandwidth Throttle (1 mbps)
We throttle network bandwidth to 1 mbps on one node. This simulates a severely constrained network link.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-bandwidth-throttle
namespace: chaos-mesh
spec:
action: bandwidth
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
bandwidth:
rate: "1mbps"
limit: 20971520
buffer: 10000
duration: "3m"
What this chaos does: Limits the outbound bandwidth of one node to 1 mbps. This severely restricts the amount of data the node can send for Galera replication and client responses.
-
Expected behavior: Bandwidth throttled on one node → that node falls behind on write-set replication → Galera flow control pauses the writer side to let it catch up → TPS drops significantly but cluster stays
Synced→ zero errors. Zero data loss. -
Actual result: TPS dropped from 1039 → 280 (-73%).
wsrep_flow_control_pausedincreased to ~0.02 (Galera pausing writers). All 3 nodes stayedSynced, zero errors. 25/25 tracking rows and checksums match. PASS.
Apply and check:
➤ kubectl apply -f 1-single-experiments/bandwidth-throttle.yaml
networkchaos.chaos-mesh.org/mariadb-bandwidth-throttle created
All 3 nodes remain Synced, but flow control kicks in:
➤ # Galera status during bandwidth throttle:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0247
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0202
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0197
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 282.18 qps: 5651.74 (r/w/o: 3957.88/903.93/789.94) lat (ms,95%): 41.85 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 278.00 qps: 5565.80 (r/w/o: 3895.40/885.00/785.40) lat (ms,95%): 41.85 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 281.40 qps: 5627.60 (r/w/o: 3939.20/898.00/790.40) lat (ms,95%): 41.85 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 4212 (280.27 per sec.)
queries: 84240 (5605.35 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Latency (ms):
min: 2.80
avg: 14.25
max: 50.38
95th percentile: 41.85
TPS dropped from 1039 to 280 (73% reduction). The 1 mbps bandwidth limit constrains how fast write-sets can be replicated. P95 latency increased from 5ms to 42ms. But zero errors — the cluster self-throttles via Galera flow control.
Note the wsrep_flow_control_paused values are slightly elevated (0.02) — Galera is pausing writers to prevent the slow node from falling too far behind.
Verify data integrity after cleanup:
➤ # Tracking rows — all match
md-0 markers: 25
md-1 markers: 25
md-2 markers: 25
➤ # Checksums — all match
md-0: sbtest1=410521570, sbtest2=126349543, sbtest3=2842752298, sbtest4=2843785900
md-1: sbtest1=410521570, sbtest2=126349543, sbtest3=2842752298, sbtest4=2843785900
md-2: sbtest1=410521570, sbtest2=126349543, sbtest3=2842752298, sbtest4=2843785900
Result: PASS — Bandwidth throttle caused significant throughput degradation (73%) but Galera flow control kept all nodes Synced with zero errors. No data loss.
Chaos#13: Pod Failure (5 min pause)
Unlike pod-kill which deletes and recreates the pod, pod-failure pauses the pod — making it completely unresponsive while the container appears to be Running.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: mariadb-primary-pod-failure
namespace: chaos-mesh
spec:
action: pod-failure
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
duration: "5m"
What this chaos does: Freezes one pod completely — the container appears Running but is unresponsive. This simulates a hung process or kernel-level freeze.
-
Expected behavior: One pod frozen (appears Running but unresponsive) → Galera heartbeats fail → expelled from cluster → remaining 2 nodes serve traffic → after chaos, frozen pod resumes and rejoins via IST. Zero data loss.
-
Actual result: md-1 frozen, shown as
Unknownrole; remaining md-0/md-2 keptPrimarywithwsrep_cluster_size=2, served 1409 TPS with zero errors. After chaos expired, md-1 resumed and rejoined. 25/25 tracking rows and checksums match. PASS.
During chaos, the frozen pod (md-1) shows container not found when exec’d:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Critical 149m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 37m Primary
pod/md-1 2/2 Running 0 37m Unknown
pod/md-2 2/2 Running 0 37m Primary
➤ # md-0: wsrep_cluster_size=2, Synced, wsrep_ready=ON
➤ # md-1: error: container not found ("mariadb")
➤ # md-2: wsrep_cluster_size=2, Synced, wsrep_ready=ON
The remaining 2 nodes continue serving traffic:
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1415.21 qps: 28314.94 lat (ms,95%): 3.49 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1420.69 qps: 28413.41 lat (ms,95%): 3.43 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1390.60 qps: 27812.97 lat (ms,95%): 3.68 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 21137 (1408.83 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Result: PASS — Pod freeze handled gracefully. 1409 TPS on 2 nodes, 0 errors. After chaos expired, frozen pod restarted and rejoined. 25/25 markers, checksums match.
Chaos#14: Container Kill (mariadb process only)
We kill only the mariadb container, not the entire pod. This tests whether the pod-level restart (by kubelet) and the coordinator can recover the database process.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: mariadb-kill-mariadb-process
namespace: chaos-mesh
spec:
action: container-kill
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
containerNames:
- mariadb
duration: "30s"
What this chaos does: Kills only the mariadb container inside the pod. The coordinator container keeps running. Kubelet restarts the killed container automatically.
-
Expected behavior: mariadb container killed → kubelet restarts it → coordinator detects restart and rejoins the node to Galera → remaining 2 nodes serve during the restart window → all 3 back
Synced. Zero data loss. -
Actual result: md-1
mariadbcontainer killed (restart count 5). Remaining md-0/md-2 keptPrimarywithwsrep_cluster_size=2, served 1381 TPS. Coordinator rejoined md-1 after restart. 25/25 tracking rows and checksums match. PASS.
➤ kubectl get pods -n demo -L kubedb.com/role
NAME READY STATUS RESTARTS AGE ROLE
md-0 2/2 Running 0 16h Primary
md-1 2/2 Running 5 (15s ago) 16h Unknown
md-2 2/2 Running 0 16h Primary
➤ # md-0: wsrep_cluster_size=2, Synced, wsrep_flow_control_paused=0.0001
➤ # md-1: ERROR 2002 - Can't connect to local server through socket
➤ # md-2: wsrep_cluster_size=2, Synced, wsrep_flow_control_paused=0.0004
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1403.41 qps: 28080.43 lat (ms,95%): 3.55 err/s: 0.20 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1368.89 qps: 27381.54 lat (ms,95%): 3.82 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1370.59 qps: 27412.22 lat (ms,95%): 3.68 err/s: 0.20 reconn/s: 0.00
SQL statistics:
transactions: 20719 (1380.96 per sec.)
ignored errors: 2 (0.13 per sec.)
reconnects: 0 (0.00 per sec.)
Result: PASS — Container kill handled by kubelet restart. 1381 TPS on 2 nodes. Coordinator detected the restart and rejoined the node. 25/25 markers, checksums match.
Chaos#15: Packet Duplicate (50%)
We inject 50% packet duplication. Unlike packet loss, duplicated packets arrive multiple times, which can confuse protocols that aren’t idempotent.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-primary-packet-duplicate
namespace: chaos-mesh
spec:
action: duplicate
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
target:
mode: all
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
duplicate:
duplicate: "50"
correlation: "25"
duration: "10m"
direction: both
-
Expected behavior: 50% packet duplication → TCP dedups duplicates via sequence numbers → Galera certification unaffected → cluster stays
Syncedon all 3 nodes → minimal TPS impact. Zero data loss. -
Actual result: 995 TPS (4% drop). All 3 nodes stayed
Syncedthroughout. Zero errors, 25/25 tracking rows and checksums match. PASS — TCP handles duplicate packets natively.
All 3 nodes remain Synced:
➤ # Galera status during 50% packet duplication:
md-0: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0001
md-1: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0
md-2: wsrep_cluster_size=3, Synced, wsrep_flow_control_paused=0.0004
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 988.07 qps: 19773.62 lat (ms,95%): 5.67 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1009.84 qps: 20196.64 lat (ms,95%): 5.57 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 986.39 qps: 19727.30 lat (ms,95%): 5.57 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 14926 (994.79 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Result: PASS — 50% packet duplication caused only a minor 4% TPS drop (995 vs 1039). TCP handles duplicate packets natively (sequence numbers), so Galera is unaffected. Zero errors, all nodes Synced. 25/25 markers, checksums match.
Chaos#16: Packet Corrupt (50%)
We corrupt 50% of all packets. This is the most severe network chaos — corrupt packets fail TCP checksums, causing retransmissions that compound with Galera’s certification protocol.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mariadb-primary-packet-corrupt
namespace: chaos-mesh
spec:
action: corrupt
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
target:
mode: all
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
corrupt:
corrupt: "50"
correlation: "25"
duration: "10m"
direction: both
-
Expected behavior: 50% packet corruption → corrupt packets fail TCP checksum → massive retransmissions → Galera group communication breaks → all nodes may lose quorum (worst case). After chaos lifts, coordinator must bootstrap cluster. Zero data loss.
-
Actual result: All 3 nodes lost quorum (
wsrep_cluster_size=1, non-Primary, wsrep_ready=OFF). Sysbench got error 1047 (WSREP not ready). After chaos removed, coordinator detected full outage and automatically bootstrapped the cluster. 25/25 tracking rows and checksums match after recovery. PASS — worst-case Galera failure, but data survived.
This is the most severe chaos experiment. 50% packet corruption completely broke the Galera cluster:
➤ # ALL 3 nodes lost quorum:
md-0: wsrep_cluster_size=1, non-Primary, wsrep_ready=OFF, Initialized
md-1: wsrep_cluster_size=1, non-Primary, wsrep_ready=OFF, Initialized
md-2: wsrep_cluster_size=1, non-Primary, wsrep_ready=OFF, Initialized
➤ sysbench:
FATAL: mysql_stmt_execute() returned error 1047 (WSREP has not yet prepared node for application use)
The cluster was completely non-functional — all nodes became standalone (cluster_size=1) with non-Primary status. No node could accept writes (error 1047). This happened because corrupt packets broke the group communication protocol — nodes couldn’t exchange valid heartbeats or certification messages.
After removing the chaos, the KubeDB coordinator detected the complete outage and bootstrapped the cluster:
➤ kubectl get mariadb -n demo md
NAME VERSION STATUS AGE
md 11.8.5 Ready 18h
Result: PASS — 50% packet corruption caused a complete cluster outage (all nodes non-Primary). This is the expected worst case — Galera cannot function when half of all packets are corrupted. However, after chaos removal, the coordinator automatically bootstrapped the cluster with zero data loss. 25/25 markers, checksums match.
Chaos#17: IO Attr Override (read-only filesystem)
We override file attributes to make the data directory read-only. This simulates a read-only filesystem mount or permissions issue.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: mariadb-primary-io-attr-override
namespace: chaos-mesh
spec:
action: attrOverride
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
volumePath: /var/lib/mysql
path: /var/lib/mysql/**/*
attr:
perm: 444
percent: 100
duration: "10m"
containerNames:
- mariadb
What this chaos does: Makes all files in /var/lib/mysql read-only (perm 444). MariaDB cannot write WAL, flush pages, or commit transactions.
-
Expected behavior: Data dir becomes read-only on one node → MariaDB fails on writes and crashes → Galera expels the node → remaining 2 nodes continue serving → after chaos ends, coordinator recovers the crashed node (likely via SST). Zero data loss.
-
Actual result: md-2 crashed (role
Unknown, socket unavailable). md-0/md-1 keptPrimarywithwsrep_cluster_size=2, served 1388 TPS with zero errors. Coordinator recovered md-2 after chaos cleared. 25/25 tracking rows and checksums match. PASS.
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Critical 18h
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 16h Primary
pod/md-1 2/2 Running 5 16h Primary
pod/md-2 2/2 Running 0 16h Unknown
➤ # md-0: wsrep_cluster_size=2, Synced, wsrep_ready=ON
➤ # md-2 (affected): ERROR 2002 - Can't connect to local server
➤ # md-1: wsrep_cluster_size=2, Synced, wsrep_ready=ON
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1384.82 qps: 27707.45 lat (ms,95%): 3.62 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1381.28 qps: 27625.71 lat (ms,95%): 3.55 err/s: 0.20 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1397.80 qps: 27962.05 lat (ms,95%): 3.49 err/s: 0.20 reconn/s: 0.00
SQL statistics:
transactions: 20824 (1387.98 per sec.)
ignored errors: 2 (0.13 per sec.)
reconnects: 0 (0.00 per sec.)
Result: PASS — Read-only filesystem crashed MariaDB on the affected node. Remaining 2 nodes served 1388 TPS. After chaos expired, coordinator recovered the node. 25/25 markers, checksums match.
Chaos#18: IO Mistake (random data corruption in IO)
We inject random bytes into 50% of disk read/write operations. This is the most dangerous IO chaos — it corrupts actual data being read from or written to disk.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: mariadb-primary-io-mistake
namespace: chaos-mesh
spec:
action: mistake
mode: one
selector:
namespaces:
- demo
labelSelectors:
"app.kubernetes.io/instance": "md"
"kubedb.com/role": "Primary"
volumePath: /var/lib/mysql
path: /var/lib/mysql/**/*
mistake:
filling: random
maxOccurrences: 10
maxLength: 100
percent: 50
duration: "10m"
containerNames:
- mariadb
What this chaos does: Replaces up to 10 occurrences of up to 100 bytes with random data in 50% of IO operations. This corrupts InnoDB pages, WAL entries, and metadata.
-
Expected behavior: Random data corruption on disk → InnoDB page checksums detect corruption → MariaDB crashes on the affected node → Galera expels it → remaining 2 nodes continue → coordinator recovers the corrupted node via full SST (data cannot be salvaged). Zero data loss on surviving nodes.
-
Actual result: md-1 crashed (page corruption detected by InnoDB checksums). md-0/md-2 kept
Primarywithwsrep_cluster_size=2, served 1380 TPS with zero errors. Coordinator recovered md-1 via SST from a healthy node. 25/25 tracking rows and checksums match across all 3 nodes. PASS.
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Critical 18h
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 16h Primary
pod/md-1 2/2 Running 5 16h Unknown
pod/md-2 2/2 Running 0 16h Primary
➤ # md-0: wsrep_cluster_size=2, Synced, wsrep_ready=ON
➤ # md-1 (affected): ERROR 2002 - Can't connect
➤ # md-2: wsrep_cluster_size=2, Synced, wsrep_ready=ON
➤ sysbench oltp_read_write ... --time=15 --report-interval=5 run
[ 5s ] thds: 4 tps: 1352.04 qps: 27051.31 lat (ms,95%): 3.96 err/s: 0.00 reconn/s: 0.00
[ 10s ] thds: 4 tps: 1396.45 qps: 27929.02 lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
[ 15s ] thds: 4 tps: 1390.02 qps: 27800.64 lat (ms,95%): 3.55 err/s: 0.00 reconn/s: 0.00
SQL statistics:
transactions: 20697 (1379.51 per sec.)
ignored errors: 0 (0.00 per sec.)
reconnects: 0 (0.00 per sec.)
Result: PASS — Random IO corruption crashed MariaDB on the affected node (data page checksums detected the corruption). Remaining 2 nodes served 1380 TPS with zero errors. The corrupted node was recovered by the coordinator (likely via SST — full state transfer from a healthy node). 25/25 markers, checksums match across all 3 nodes.
MariaDB Replication with MaxScale (29 Experiments)
Now we test 29 chaos experiments on MariaDB Replication topology with MaxScale proxy — an extended pass that goes beyond the 18 Galera experiments above with Slave-side variants (pod kill slave, memory / IO stress on slave), MaxScale-targeted chaos, compound faults, and a rolling restart. Unlike Galera (synchronous multi-master), Replication uses asynchronous Master-Slave replication with MaxScale handling automatic failover and read-write splitting.
Replication Test Environment
| Component | Details |
|---|---|
| Cluster Topology | MariaDB Replication (1 Master + 2 Slaves) |
| Proxy | MaxScale (3 replicas) |
| MariaDB Version | 11.8.5 |
| Load via | md-mx service (MaxScale, port 3306) |
| Baseline TPS | ~926 (via MaxScale) |
Deploy MariaDB Replication with MaxScale
apiVersion: kubedb.com/v1
kind: MariaDB
metadata:
name: md
namespace: demo
spec:
deletionPolicy: Delete
replicas: 3
storage:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi
storageType: Durable
podTemplate:
spec:
containers:
- name: mariadb
resources:
limits:
memory: 1.5Gi
requests:
cpu: 500m
memory: 1.5Gi
topology:
mode: MariaDBReplication
maxscale:
replicas: 3
enableUI: true
storage:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 100Mi
storageType: Durable
version: 11.8.5
Key differences from Galera:
kubedb.com/role: Master(1 node) andkubedb.com/role: Slave(2 nodes) — not multi-master- MaxScale automatically routes writes to Master, reads to Slaves
- Async replication — Master doesn’t wait for Slave acknowledgment
- All sysbench load goes through
md-mx(MaxScale service)
Verify:
➤ kubectl get mariadb,pods -n demo -L kubedb.com/role
NAME VERSION STATUS AGE
mariadb.kubedb.com/md 11.8.5 Ready 3m
NAME READY STATUS RESTARTS AGE ROLE
pod/md-0 2/2 Running 0 3m Master
pod/md-1 2/2 Running 0 3m Slave
pod/md-2 2/2 Running 0 3m Slave
pod/md-mx-0 1/1 Running 0 3m
pod/md-mx-1 1/1 Running 0 3m
pod/md-mx-2 1/1 Running 0 3m
➤ # Replication status:
Slave_IO_Running: Yes
Slave_SQL_Running: Yes
Seconds_Behind_Master: 0
➤ # Baseline via MaxScale:
transactions: 13902 (926.35 per sec.)
reconnects: 0 (0.00 per sec.)
Standard Replication Chaos Experiments
A total of 29 chaos experiments were run on the Replication topology — deeper than Galera’s 18-test pass. We added Slave-side variants (pod kill slave, memory / IO stress on slave), MaxScale-targeted chaos, compound faults (master + MaxScale together), and a rolling restart.
26/29 experiments passed with zero data loss. Three IO-chaos tests (T12, T13, T15) exposed edge cases requiring operator intervention — documented in the test details below.
The verification methodology for each test follows the same pattern as Galera: sysbench load through MaxScale, tracking markers cross-node, and checksums matching across all nodes.
Chaos#1: Pod Kill — Master
- Force-delete the Master pod to verify MaxScale-driven failover.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-master-pod-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
gracePeriod: 0
- Expected behavior: Master pod deleted → MaxScale detects loss → one Slave promoted to Master → killed pod recreated and rejoins as Slave → cluster returns to
Readywith zero data loss. - Actual result (PASS): Failover md-2 → md-0 in ~5s. Sysbench continued at 1043 TPS, 0 reconnects. All 3 nodes show identical
CHECKSUM TABLEvalues post-chaos. MariaDB CR stayedReadythroughout — MaxScale kept serving.
Chaos#2: Pod Kill — Slave
- Kill one Slave pod; verify it’s recreated and rejoins replication automatically.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-slave-pod-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
gracePeriod: 0
- Expected behavior: Slave killed → pod recreated →
CHANGE MASTER TOcompletes →Slave_IO_Running=Yes,Slave_SQL_Running=Yes. Writes on master continue via MaxScale. - Actual result (PASS): Sysbench maintained 1125 TPS throughout — zero write impact. Slave back to
Slave_IO_Running=Yesin ~30s. Checksums match across 3 nodes.
Chaos#3: Pod Kill — MaxScale
- Kill one MaxScale proxy pod; remaining MaxScale pods should keep serving.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-maxscale-pod-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md-mx
gracePeriod: 0
- Expected behavior: One MaxScale pod dies → kubernetes Service re-routes client traffic to the surviving 2 MaxScale pods → at most a transient blip.
- Actual result (PASS): md-mx-0 restarted. Sysbench continued at 898 TPS via the remaining 2 MaxScale pods. No client reconnects.
Chaos#4: Container Kill — mariadb on Master
- Kill the
mariadbcontainer (not the whole pod) on Master to simulate a process crash.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-container-kill-mariadb
namespace: chaos-mesh
spec:
action: container-kill
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
containerNames: [mariadb]
duration: "30s"
- Expected behavior:
mariadbcontainer SIGKILLed → kubelet restarts the container → MaxScale briefly re-routes to a Slave promoted as new Master → original pod rejoins as Slave. - Actual result (PASS): Failover md-0 → md-1 completed. Sysbench dropped briefly then recovered. Steady state in ~30s, checksums match.
Chaos#5: Pod Failure (90s freeze) — Master
- Freeze the Master pod for 90 seconds to simulate kernel hang or long GC pause.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-master-pod-failure
namespace: chaos-mesh
spec:
action: pod-failure
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
duration: "90s"
- Expected behavior: Master unresponsive → MaxScale detects failure and promotes a Slave → frozen pod killed by kubelet mid-freeze → recreated, rejoins as Slave.
- Actual result (PASS): Kubelet killed the container 4× during the freeze (restart count reached 4 on md-1). Failover completed, sysbench ranged 750–1127 TPS during chaos, checksums match post-chaos.
Chaos#6: Memory Stress (1400MB) — Master
- Drive 1400MB of memory pressure against Master (1.5Gi limit) to trigger OOMKill.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: md-master-memory-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
stressors:
memory:
workers: 2
size: "1400MB"
duration: "3m"
- Expected behavior: OOMKiller fires → mariadb killed → MaxScale fails over to a Slave → killed pod recreated, rejoins as Slave.
- Actual result (PASS): Failover md-0 → md-1 triggered by OOMKill. Sysbench 775–950 TPS. Rejoined cluster after stress cleared; checksums match.
Chaos#7: Memory Stress (1400MB) — Slave
- Same stressor against a Slave — verify write path stays healthy.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: md-slave-memory-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
stressors:
memory:
workers: 2
size: "1400MB"
duration: "2m"
- Expected behavior: Slave OOMKilled → pod recreated → rejoins replication. No write impact (writes only go to master).
- Actual result (PASS): 1138 TPS maintained throughout — slave OOM completely invisible to writers via MaxScale. Slave caught up after restart; checksums match.
Chaos#8: CPU Stress (98%) — Master
- Peg Master CPU at 98%; verify it stays stable without failover.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: md-master-cpu-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
stressors:
cpu:
workers: 2
load: 98
duration: "90s"
- Expected behavior: Master CPU saturated → TPS degrades but no restart, no failover.
- Actual result (PASS): Sysbench 867–950 TPS during chaos (vs 926 baseline — <10% drop). No failover. Post-chaos checksums match.
Chaos#9: Combined Memory + CPU Stress — Master
- Simultaneous 1200MB memory + 90% CPU stress on Master.
apiVersion: chaos-mesh.org/v1alpha1
kind: StressChaos
metadata:
name: md-master-combined-stress
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
stressors:
memory:
workers: 2
size: "1200MB"
cpu:
workers: 2
load: 90
duration: "2m"
- Expected behavior: Degraded but stable; no OOMKill (below the 1400MB threshold from Chaos#6), no failover.
- Actual result (PASS): 749–958 TPS, no restart, no failover. Checksums match post-chaos.
Chaos#10: IO Latency (100ms) — Master
- Inject 100ms disk latency on Master’s datadir; expect TPS to collapse (single-master write bottleneck).
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-master-io-latency
namespace: chaos-mesh
spec:
action: latency
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
volumePath: /var/lib/mysql
path: "/**"
delay: "100ms"
percent: 100
duration: "2m"
containerNames: [mariadb]
- Expected behavior: Master disk-bound → TPS drops severely → no failover (disk still functional, just slow) → recovery when chaos expires.
- Actual result (PASS): TPS dropped to 5 (vs 926 baseline — extreme single-master bottleneck), 0 errors, no failover. Recovered within seconds of chaos clearing.
Chaos#11: IO Latency (100ms) — Slave
- Same latency but on Slave; expect write path unaffected.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-slave-io-latency
namespace: chaos-mesh
spec:
action: latency
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
volumePath: /var/lib/mysql
path: "/**"
delay: "100ms"
percent: 100
duration: "2m"
containerNames: [mariadb]
- Expected behavior: Slave falls behind but writes continue via MaxScale → master unaffected → slave catches up after chaos.
- Actual result (PASS): 1131 TPS maintained. Slave’s
Seconds_Behind_Mastergrew during chaos and caught up after chaos cleared.
Chaos#12: IOChaos fault (EIO 50%) — Master — FAIL, Defect #1 (High)
- Return EIO on 50% of file operations against Master’s datadir for 2 minutes.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-master-io-fault
namespace: chaos-mesh
spec:
action: fault
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
volumePath: /var/lib/mysql
path: "/**"
errno: 5
percent: 50
duration: "2m"
containerNames: [mariadb]
-
Expected behavior: Master crashes on first EIO → MaxScale fails over to a Slave → killed container restarted by kubelet → old master rejoins as Slave.
-
Actual result (FAIL — Defect #1): mariadbd crashes as expected, but the init script (
run-on-present.sh) stays alive in a 900-attempt ping loop and never restartsmariadbd. Pod reports2/2 Ready(tini is PID 1) but nomariadbdin the process tree:mysql 1 0 /scripts/tini -g -- /scripts/std-replication-run.sh mysql 14 1 bash /scripts/std-replication-run.sh mysql 20 14 bash ./scripts/std-replication-on-start.sh mysql 120 20 bash ./run-script/run-on-present.sh # no mariadbdMariaDB CR stays
Critical, role labelDown. Cluster stays broken until the init-script timeout forces container restart or the operator intervenes. -
Recovery: Delete the stuck pod to trigger a fresh restart:
kubectl delete pod md-0 -n demo. The PVC is intact and the pod comes back clean. If this was the active Master, MaxScale has already promoted a Slave, and the deleted pod rejoins as a Slave.
Chaos#13: IOChaos fault (EIO 50%) — Slave
- Same EIO fault injection targeting a Slave; confirms Defect #1 is role-independent.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-slave-io-fault
namespace: chaos-mesh
spec:
action: fault
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
volumePath: /var/lib/mysql
path: "/**"
errno: 5
percent: 50
duration: "2m"
containerNames: [mariadb]
- Expected behavior: Slave’s mariadbd crashes, pod recreated, rejoins. Zero write impact on master.
- Actual result (FAIL): Identical stuck pattern as Chaos#12 — the init script enters a prolonged ping-loop and fails to restart mariadbd. Writes on master continue via MaxScale — no data loss on the write path — but the slave stays offline until the pod is restarted.
- Recovery: Delete the stuck pod to trigger a fresh restart:
kubectl delete pod <stuck-slave> -n demo.
Chaos#14: IO AttrOverride (read-only datadir) — Master
- Force read-only (0444) file permissions on datadir for 3 minutes.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-master-io-attr-override
namespace: chaos-mesh
spec:
action: attrOverride
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
volumePath: /var/lib/mysql
path: /var/lib/mysql/**/*
attr:
perm: 444
percent: 100
duration: "3m"
containerNames: [mariadb]
- Expected behavior: Writes fail with EACCES → mariadbd either crashes or goes degraded → recovery after chaos clears and permissions are restored.
- Actual result (PASS): TPS dropped to 656, 0 errors visible to clients, no crash. Permissions restored post-chaos, full recovery. Checksums match across 3 nodes.
Chaos#15: IOChaos mistake (random corruption 50%) — Master — FAIL
- Write random bytes into files under
/var/lib/mysqlon Master for 2 minutes — the highest-severity fault in the suite.
apiVersion: chaos-mesh.org/v1alpha1
kind: IOChaos
metadata:
name: md-master-io-mistake
namespace: chaos-mesh
spec:
action: mistake
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
volumePath: /var/lib/mysql
path: /var/lib/mysql/**/*
mistake:
filling: random
maxOccurrences: 10
maxLength: 100
percent: 50
duration: "2m"
containerNames: [mariadb]
-
Expected behavior: Some files bit-flipped → mariadbd detects corruption (InnoDB checksum failure or binlog parse error) → node flagged, cluster recovers after operator intervention.
-
Actual result (FAIL — Defect #2): active binary log corrupted — replication breaks permanently on both slaves:
Slave_IO_Running: No Last_IO_Error: Got fatal error 1236 ... log event entry exceeded max_allowed_packetmariadb-binlogconfirms binlog garbage past the corruption offset (event_type: 232, invalid — valid types cap near ~165). Checksums diverge permanently between master and slaves. In worst-case runs, corruption lands onundo003and mariadbd refuses to restart:[ERROR] InnoDB: Failed to read page 3 from file './/undo003': Page read from tablespace is corrupted.KubeDB flips slave role labels to
Unknownbut does not auto-repair; MaxScale may silently serve stale reads from frozen slaves. -
Recovery options:
- Binlog only, master InnoDB intact: On the master, run
FLUSH BINARY LOGSto rotate to a clean binary log file, then rebuild each broken slave by deleting its PVC and pod so KubeDB re-provisions it via backup-stream from the master. Zero data loss because the master’s InnoDB tables are the source of truth. - InnoDB undo / data files also corrupted: Cluster-local recovery is not possible. Restore from an external backup (e.g., KubeStash) taken before the corruption, or bring the master up with
innodb_force_recovery=5to dump data read-only and reload into a fresh datadir. We strongly recommend pairing KubeDB with scheduled external backups in production.
- Binlog only, master InnoDB intact: On the master, run
Chaos#16: Network Partition — Master ↔ Slaves
- Cut network between Master and the two Slaves for 90 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-master-slaves-partition
namespace: chaos-mesh
spec:
action: partition
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
direction: both
duration: "90s"
- Expected behavior: Async replication lag grows on both slaves → no failover (master keeps accepting writes) → replication catches up after partition clears.
- Actual result (PASS): 1122 TPS maintained — master writes unaffected (async replication). Slaves’
Seconds_Behind_Mastergrew during partition, dropped to 0 after chaos cleared. Checksums match post-chaos.
Chaos#17: Network Partition — Slave isolated
- Isolate one Slave from the rest of the cluster for 90 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-slave-isolated
namespace: chaos-mesh
spec:
action: partition
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
direction: both
duration: "90s"
- Expected behavior: Isolated slave falls behind, MaxScale stops routing reads to it → master + other slave continue serving → slave catches up after chaos.
- Actual result (PASS): 964 TPS — no write impact. Isolated slave lag grew, then caught up after partition cleared.
Chaos#18: Network Partition — MaxScale ↔ MariaDB
- Cut network between all MaxScale pods and all MariaDB pods for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: mx-md-partition
namespace: chaos-mesh
spec:
action: partition
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md-mx
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
direction: both
duration: "60s"
- Expected behavior: Clients lose connection (MaxScale can’t reach backends) → no writes during partition → MaxScale reconnects when partition clears → full recovery.
- Actual result (PASS): TPS dropped to 0 during chaos (sysbench lost connections as expected). After partition cleared, sysbench reconnected and recovered to 955 TPS. Checksums match.
Chaos#19: Network Latency (1s) — Master ↔ Slaves
- Add 1 second of latency between Master and Slaves for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-net-latency-1s
namespace: chaos-mesh
spec:
action: delay
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Slave
delay:
latency: "1s"
jitter: "50ms"
duration: "60s"
direction: both
- Expected behavior: Slaves lag by ~1s, master unaffected (async replication doesn’t wait for slave ack).
- Actual result (PASS): 1148 TPS — zero impact. Key contrast: the same test on Galera tanks TPS to ~3 TPS because synchronous certification requires slave acknowledgment.
Chaos#20: Packet Loss (30%)
- Drop 30% of packets between Master and Slaves for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-packet-loss
namespace: chaos-mesh
spec:
action: loss
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
loss:
loss: "30"
correlation: "25"
duration: "60s"
direction: both
- Expected behavior: Master-slave replication slows due to TCP retransmits; master unaffected.
- Actual result (PASS): 954 TPS — no write impact. Slave lag grew, recovered after chaos.
Chaos#21: Packet Duplicate (50%)
- Duplicate 50% of packets between master and slaves.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-packet-duplicate
namespace: chaos-mesh
spec:
action: duplicate
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
duplicate:
duplicate: "50"
correlation: "25"
duration: "45s"
direction: both
- Expected behavior: TCP handles duplicate packets transparently → no impact.
- Actual result (PASS): 948 TPS, no impact as expected.
Chaos#22: Packet Corrupt (50%)
- Corrupt 50% of packets (bit-flip) between master and slaves.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-packet-corrupt
namespace: chaos-mesh
spec:
action: corrupt
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
target:
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
corrupt:
corrupt: "50"
correlation: "25"
duration: "45s"
direction: both
- Expected behavior: TCP checksums drop corrupt packets → retransmitted → replication slightly slower, master unaffected.
- Actual result (PASS): 1156 TPS — no impact. Stark contrast with Galera, where the same test causes complete outage because wsrep requires valid certification packets.
Chaos#23: Bandwidth 1 Mbps — Master
- Throttle Master’s network to 1 Mbps for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
name: md-bandwidth-1mbps
namespace: chaos-mesh
spec:
action: bandwidth
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
bandwidth:
rate: "1mbps"
limit: 20971520
buffer: 10000
duration: "60s"
- Expected behavior: Severely throttled TPS, 0 errors, instant recovery after chaos.
- Actual result (PASS): TPS dropped to 21 (-97%) — all clients connected, 0 errors. Recovered instantly after chaos cleared.
Chaos#24: DNS Error — Master
- Inject DNS resolution errors on Master for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: DNSChaos
metadata:
name: md-dns-error
namespace: chaos-mesh
spec:
action: error
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
duration: "60s"
- Expected behavior: No impact — MariaDB uses cached/resolved IPs for peer connections.
- Actual result (PASS): 956 TPS — completely unaffected.
Chaos#25: Clock Skew (-5 min) — Master
- Set Master’s clock back 5 minutes for 60 seconds.
apiVersion: chaos-mesh.org/v1alpha1
kind: TimeChaos
metadata:
name: md-clock-skew
namespace: chaos-mesh
spec:
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
timeOffset: "-5m"
duration: "60s"
- Expected behavior: Timestamps mildly affected, replication continues.
- Actual result (PASS): 879 TPS (minor dip from 926). No replication errors.
Chaos#26: Full MariaDB Cluster Kill
- Kill all 3 MariaDB pods simultaneously to test full-outage recovery.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-full-cluster-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
gracePeriod: 0
- Expected behavior: All 3 pods killed and recreated → KubeDB coordinator identifies the most up-to-date node and bootstraps the cluster → replication re-established.
- Actual result (PASS): Recovery ~75s. Coordinator bootstrapped the correct node, re-established master/slaves. 12/12 tracking markers preserved.
Chaos#27: Full MaxScale Kill
- Kill all 3 MaxScale pods simultaneously.
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-mx-full-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md-mx
gracePeriod: 0
- Expected behavior: All MaxScale pods killed, clients lose access, recover when MaxScale pods come back. MariaDB cluster itself unaffected.
- Actual result (PASS): ~3-minute client outage (MaxScale pods restarted sequentially). After recovery, sysbench resumed at 955 TPS. Master/slaves unaffected during the outage; checksums match.
Chaos#28: Compound — Master + all MaxScale kill
- Simultaneously kill Master and all 3 MaxScale pods.
kubectl apply -f - <<'EOF'
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-master-compound-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: one
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md
kubedb.com/role: Master
gracePeriod: 0
---
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
name: md-mx-compound-kill
namespace: chaos-mesh
spec:
action: pod-kill
mode: all
selector:
namespaces: [demo]
labelSelectors:
app.kubernetes.io/instance: md-mx
gracePeriod: 0
EOF
- Expected behavior: Both Master and MaxScale path broken → full outage → MaxScale restarts, Slave promoted to Master, recovery.
- Actual result (PASS): Recovery ~90s. md-0 re-elected Master. 14/14 tracking markers preserved.
Chaos#29: Rolling Restart (0 → 1 → 2, rapid)
- Delete md-0, md-1, md-2 sequentially with ~40s gaps (faster than KubeDB’s internal convergence).
kubectl delete pod md-0 -n demo --grace-period=0 --force
sleep 40
kubectl delete pod md-1 -n demo --grace-period=0 --force
sleep 40
kubectl delete pod md-2 -n demo --grace-period=0 --force
- Expected behavior: Each pod restarts and rejoins the cluster. May briefly show roles
Downduring rapid succession; eventually converges. - Actual result (PASS): All 3 roles briefly showed
Downduring the rapid sequence (kubelet didn’t have time to settle between deletes). Cluster recovered to Master + 2× Slave in ~2m30s. 15/15 tracking markers preserved. Coordinator handled the unusual sequence correctly.
Chaos Testing Results Summary
We ran 47 chaos experiments across 2 MariaDB topologies — 18 on Galera (all PASS) and 29 on Replication + MaxScale (26 PASS, 3 failures requiring manual intervention). All 44 passing experiments completed with zero data loss.
Galera Cluster Results (18 experiments, baseline ~1039 TPS)
| # | Experiment | TPS During | Impact | Recovery | Data |
|---|---|---|---|---|---|
| 1 | Pod Kill | 1061 | None | ~5s auto-rejoin | 25/25 |
| 2 | OOMKill (1200MB) | 1050 | None (survived) | N/A | 25/25 |
| 3 | Network Partition | 1430 (+37%) | TPS increased (2 nodes) | Auto-rejoin | 25/25 |
| 4 | IO Latency (100ms) | 1450 (2 nodes) | Node unresponsive | Auto-rejoin | 25/25 |
| 5 | Network Latency (1s) | 3 (-99.7%) | Severe | Instant | 25/25 |
| 6 | CPU Stress (98%) | 1034 | Negligible | N/A | 25/25 |
| 7 | Packet Loss (30%) | 1.3 (-99.9%) | Severe | Instant | 25/25 |
| 8 | Full Cluster Kill | 1024 | Full outage | ~3 min bootstrap | 25/25 |
| 9 | DNS Error | 1016 | None | N/A | 25/25 |
| 10 | IO Fault (EIO 50%) | 1404 (2 nodes) | Node crash | Coordinator | 25/25 |
| 11 | Clock Skew (-5 min) | 988 (-5%) | Minor | Instant | 25/25 |
| 12 | Bandwidth Throttle | 280 (-73%) | Significant | Instant | 25/25 |
| 13 | Pod Failure (freeze) | 1409 (2 nodes) | Node frozen | Auto-rejoin | 25/25 |
| 14 | Container Kill | 1381 (2 nodes) | Process killed | Kubelet restart | 25/25 |
| 15 | Packet Duplicate (50%) | 995 (-4%) | Minor | N/A | 25/25 |
| 16 | Packet Corrupt (50%) | 0 (all down) | Complete outage | Auto-bootstrap | 25/25 |
| 17 | IO Attr Override (r/o) | 1388 (2 nodes) | Node crash | Coordinator | 25/25 |
| 18 | IO Mistake (corruption) | 1380 (2 nodes) | Node crash | SST recovery | 25/25 |
Replication + MaxScale Results (29 experiments, baseline ~926 TPS)
| # | Experiment | Result | TPS During | Notes |
|---|---|---|---|---|
| 1 | Pod Kill — Master | PASS | 1043 | Failover md-2 → md-0, rejoin as Slave |
| 2 | Pod Kill — Slave | PASS | 1125 | Zero write impact |
| 3 | Pod Kill — MaxScale | PASS | 898 | Routing kept via remaining MaxScale pods |
| 4 | Container Kill — mariadb on Master | PASS | — | Failover, ~30s to steady state |
| 5 | Pod Failure (90s freeze) on Master | PASS | 750–1127 | Kubelet killed container 4× during freeze |
| 6 | Memory stress (1400MB) on Master | PASS | 775–950 | OOMKilled, failover, rejoined |
| 7 | Memory stress (1400MB) on Slave | PASS | 1138 | Writes via MaxScale → master unaffected |
| 8 | CPU stress 98% on Master | PASS | 867–950 | No failover, stable |
| 9 | Combined Memory + CPU on Master | PASS | 749–958 | Survived |
| 10 | IO Latency (100ms) on Master | PASS | 5 | Severe throttle, 0 errors |
| 11 | IO Latency (100ms) on Slave | PASS | 1131 | Writes unaffected |
| 12 | IOChaos fault (EIO 50%) on Master |
FAIL | — | mariadbd dies; init-script ping loop. Recovery requires pod delete. |
| 13 | IOChaos fault (EIO 50%) on Slave |
FAIL | — | Same pattern as T12. Recovery requires pod delete. |
| 14 | IO AttrOverride (read-only) on Master | PASS | 656 | Degraded, no crash, auto-recovered |
| 15 | IOChaos mistake (corruption 50%) on Master |
FAIL | — | Binlog + InnoDB undo corruption. Recovery requires manual intervention. |
| 16 | Network Partition (Master ↔ Slaves, 90s) | PASS | 1122 | No failover (async replication) |
| 17 | Network Partition (Slave isolated, 90s) | PASS | 964 | No write impact |
| 18 | Network Partition (MaxScale ↔ MariaDB, 60s) | PASS | 0 / 955 | Lost conn during, full recovery after |
| 19 | Network Latency 1s | PASS | 1148 | Zero impact (async) |
| 20 | Packet Loss 30% | PASS | 954 | No write impact |
| 21 | Packet Duplicate 50% | PASS | 948 | No impact |
| 22 | Packet Corrupt 50% | PASS | 1156 | No impact |
| 23 | Bandwidth 1 Mbps on Master | PASS | 21 | -97% TPS, 0 errors |
| 24 | DNS Error on Master | PASS | 956 | No impact |
| 25 | Clock Skew (-5 min) on Master | PASS | 879 | Minor dip |
| 26 | Full cluster kill (3 pods) | PASS | — | Recovery ~75s |
| 27 | Full MaxScale kill (3 pods) | PASS | 955 after | ~3 min client outage |
| 28 | Compound: Master + all MaxScale kill | PASS | — | Recovery ~90s |
| 29 | Rolling restart 0 → 1 → 2 | PASS | — | Recovery ~2m30s |
Key Findings
Galera vs Replication: Head-to-Head
| Scenario | Galera TPS | Replication TPS | Winner | Why |
|---|---|---|---|---|
| Network Latency 1s | 3 | 941 | Replication | Async replication doesn’t wait for acknowledgment |
| Packet Corrupt 50% | 0 (outage) | 967 | Replication | Galera needs valid certification packets |
| IO Latency 100ms | 1450 | 5 | Galera | Multi-master routes writes to healthy nodes |
| IO Fault (EIO) | 1404 | 0 (down) | Galera | Multi-master continues on remaining nodes |
| Bandwidth 1mbps | 280 | 22 | Galera | Multi-master distributes load |
| Pod Kill | 1061 | 958 | Tie | Both recover quickly |
| CPU Stress | 1034 | 933 | Tie | Both unaffected |
When to Choose Each Topology
Choose Galera Cluster when:
- IO-intensive workloads that benefit from multi-node write distribution
- Environments needing true multi-master writes
- Single-node failure should not block writes
- Synchronous replication guarantees are required
Choose Replication + MaxScale when:
- Networks with high latency or unreliable connections (WAN, cross-region)
- Environments prone to packet corruption or packet loss
- Read-heavy workloads where Slaves offload read traffic
- Simpler operational model with clear Master/Slave roles
Conclusion
The KubeDB operator demonstrated excellent recovery capabilities across 47 chaos experiments on 2 MariaDB topologies — 18 on Galera Cluster (all PASS) and 29 on Replication with MaxScale (26 PASS, 3 failures requiring manual intervention). All 44 passing experiments completed with zero data loss.
The operator handled every failure scenario we threw at it:
| Operator Capability | What We Tested | Result |
|---|---|---|
| Failure detection | Pod kill, container kill, pod freeze, IO crash, segfault | Coordinator detected all failures within seconds |
| Automatic failover | Kill master/primary during active writes | New master elected, replication reconfigured, MaxScale re-routed — all automatically |
| Pod recovery | OOMKill, IO latency, read-only filesystem, clean pod kill | Crashed pods restarted, rejoined cluster via IST/SST, data fully restored |
| Cluster bootstrap | Kill all 3 pods simultaneously | Coordinator bootstrapped from scratch in ~3 min — identified most up-to-date node, elected master, rejoined others |
| Network resilience | Partition, latency, packet loss/corrupt/duplicate, bandwidth throttle, DNS error | Cluster stayed operational (or recovered automatically after chaos expired) |
| MaxScale integration | All 29 replication experiments via MaxScale proxy | MaxScale detected every topology change and re-routed traffic automatically |
| IO corruption | IOChaos fault (EIO 50%) and mistake (random byte corruption 50%) on master/slave |
Requires operator intervention — see test details above for recovery procedures |
The KubeDB operator is the reason these chaos tests pass. MariaDB itself doesn’t know how to bootstrap a Galera cluster from a total outage, elect a new replication master, or reconfigure MaxScale routing. The operator’s coordinator sidecar handles all of this — making KubeDB MariaDB truly self-healing on Kubernetes for the pod / stress / network / time fault families.
For the IO-chaos family, recovery from latency, read-only filesystems, and clean pod kill works automatically. For random bit-level corruption and EIO faults, we recommend pairing KubeDB with external backups (e.g., KubeStash) in production environments. Both topologies achieve production-grade reliability for the vast majority of real-world failure scenarios.
What Next?
Please try the latest release and give us your valuable feedback.
- If you want to install KubeDB, please follow the installation instruction from here .
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