Pod JVM trigger GC
Force the JVM in a target Kubernetes pod to run garbage collection on a configurable schedule so you can test how the application behaves under repeated GC pauses.
Pod JVM trigger GC is a Kubernetes pod-level chaos fault that forces a JVM running in a target container to run garbage collection repeatedly for a configurable duration. Only the targeted JVM is affected; other processes in the pod and other pods on the node are unaffected. When the fault ends, the JVM resumes normal GC scheduling immediately.
Use this fault to test how a Java application behaves under repeated GC pauses: spikes in request latency, missed scheduled tasks, paused background workers, or probe failures caused by the JVM stopping the world.
Use cases
Run this fault when you want to answer concrete questions like:
GC-pause tolerance: When the JVM stops the world more often than usual, do request handlers handle it gracefully or do upstream timeouts cascade?
GC algorithm comparison: Compare CMS, G1, ZGC, and Shenandoah under forced GC pressure to validate the choice for your workload.
Heap-sizing validation: Does the heap have enough headroom that forced GCs stay short, or does each cycle take seconds?
Probe sensitivity: Are readiness and liveness probes resilient to short stop-the-world events?
JIT and warmup interaction: Confirm that forced GC does not deoptimize hot paths in a way that hurts steady-state throughput.
Prerequisites
Kubernetes version: 1.21 or later. Go to What's supported to confirm distribution support.
Target pod is Running: The Java application pod is in the
Runningstate.Java agent attach available: The Java process allows agent attach. Utilities such as
ps,pgrep, andbashare present in the container, and the JVM is not built with a restricted runtime that strips attach modules.Privileged pods allowed: The cluster lets you schedule privileged pods in the chaos namespace. GKE Autopilot supports this fault but requires the one-time setup in Chaos on GKE Autopilot; other locked-down distributions may need similar exemptions.
Container runtime access: The chaos pod can reach the container runtime socket on the target node (
/run/containerd/containerd.sock,/var/run/docker.sock, or/var/run/crio/crio.sock).Workload selector defined: The chaos experiment knows the target workload by kind, namespace, and either names or labels.
Supported environments
Amazon EKS
Supported
Azure AKS
Supported
Google GKE
Supported
Red Hat OpenShift
Supported
Rancher
Supported
VMware Tanzu
Supported
Self-managed Kubernetes (CNCF-certified)
Supported
GKE Autopilot
Supported with Autopilot setup
EKS Fargate, ACI virtual nodes
Not supported (no access to container runtime sockets)
Permissions required
The fault runs under the chaos infrastructure's service account.
Resource (apiGroup)
Verbs
Why it is needed
pods ("")
get, list, create, delete, deletecollection, patch, update
Discover target pods and run the chaos pod on the same node
pods/log ("")
get, list, watch
Stream chaos pod logs for status and debugging
deployments, statefulsets, replicasets, daemonsets (apps)
get, list
Resolve the target workload to the pods it owns
events ("")
get, list, create, patch, update
Record fault progress as Kubernetes events
jobs (batch)
get, list, create, delete, deletecollection
Run the chaos job that drives the fault
The default Harness chaos infrastructure service account already includes these permissions.
Fault tunables
Configure the following fault parameters when you add Pod JVM trigger GC to an experiment in Chaos Studio. Defaults are shown for reference.
Chaos parameters
TOTAL_CHAOS_DURATION
Duration of the fault in seconds. The JVM is asked to run GC repeatedly across this window.
60
JVM
BYTEMAN_PORT
Port on which the Byteman agent listens inside the container. Must not conflict with any port already in use.
9091
JAVA_HOME
Absolute path to the Java installation inside the container. Empty auto-detects from PATH.
""
Targeting
TARGET_PODS
Comma-separated list of pod names to target. Empty selects from the workload's pods using POD_AFFECTED_PERCENTAGE.
""
TARGET_CONTAINER
Container in the pod running the JVM. Empty targets the first container in the pod spec.
""
NODE_LABEL
Label selector to filter target pods by the node they run on. Empty disables node-based filtering.
""
POD_AFFECTED_PERCENTAGE
Percentage of the workload's pods to target. 0 means one pod.
0
SEQUENCE
When multiple pods are targeted, inject parallel (all at once) or serial (one after another).
parallel
Runtime and helper
CONTAINER_RUNTIME
Container runtime on the target nodes. One of containerd, docker, crio.
containerd
SOCKET_PATH
Path to the container runtime socket on the target node. Set to match CONTAINER_RUNTIME.
/run/containerd/containerd.sock
RAMP_TIME
Wait period in seconds before and after the fault. Go to ramp time to read how it is applied.
0
Common pod selection tunables (TARGET_WORKLOAD_KIND, TARGET_WORKLOAD_NAMESPACE, TARGET_WORKLOAD_NAMES, TARGET_WORKLOAD_LABELS) are documented in common pod fault tunables. Tunables that apply to every fault are documented in common tunables for all faults.
Configure for your container runtime
Set CONTAINER_RUNTIME and SOCKET_PATH to match the runtime on the target node:
CONTAINER_RUNTIME
SOCKET_PATH
containerd (default)
/run/containerd/containerd.sock
docker
/var/run/docker.sock
crio
/var/run/crio/crio.sock
Fault execution in brief
Attaches a Java agent to the target JVM and calls System.gc() repeatedly for TOTAL_CHAOS_DURATION seconds.
Expected behavior during fault execution
Garbage collection runs more often than usual. Each cycle stops the world for at least a brief moment, depending on the GC algorithm and heap size.
Application request latency spikes during each pause; throughput drops correspondingly.
GC logs show a higher rate of full collections (or cycles, for concurrent collectors).
Allocator pressure may show as elevated CPU as the JVM compacts heap regions.
Signals to watch
Attach resilience probes to assert each layer:
GC pause time: Use a Prometheus probe on
jvm_gc_pause_secondsor equivalent micrometer/JMX metric.Application latency: Use an HTTP probe on a representative endpoint to detect tail-latency spikes.
Pod readiness: Use a Kubernetes probe to fail when the target pod oscillates
NotReady.
Verify the fault execution effect
While the experiment is running, confirm GCs are running:
Inspect GC counts.
YGCandFGCcounters should increment faster than the application's baseline rate.Confirm application-level impact.
Watch the application's p99 latency or an HTTP probe. Latency spikes should correlate with the GC events.
Recovery and cleanup
End of duration: The JVM resumes normal GC scheduling automatically.
Abort the experiment: Stopping the experiment from Chaos Studio triggers the same cleanup path.
Stuck JVM: If the application is wedged, restart the pod to clear the state.
Limitations
Serverless Kubernetes (EKS Fargate, ACI virtual nodes): These platforms do not expose container runtime sockets and reject the privileged access the fault needs. GKE Autopilot is supported once the one-time setup in Chaos on GKE Autopilot is in place.
Windows containers: This fault is supported on Linux pods only.
Non-JVM processes: This fault targets a Java process.
JVMs that ignore
System.gc(): Some configurations (-XX:+DisableExplicitGC) suppress explicit GC requests. Remove the flag or accept that this fault has no effect on that JVM.
Troubleshooting
Related faults
Pod JVM CPU stress: Saturate CPU inside the JVM.
Pod JVM method latency: Add latency to a Java method invocation.
Pod memory hog: Consume container memory until OOM.
Common pod fault tunables: Shared environment variables for selecting target pods and workloads.
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