Chaos Engineering Practices: A Beginner's Guide to Building Resilient Systems

Updated on Nov 24, 2025

6 min read

Chaos engineering is the practice of conducting controlled experiments that introduce faults into systems, aiming to uncover vulnerabilities before they impact users. This approach goes beyond traditional unit and integration tests by simulating real-world performance under stress, often in production or similar environments. By intentionally challenging system resilience through chaos engineering, teams can bolster their systems’ reliability, offering users a seamless experience even during failures. This guide is tailored for beginners in Site Reliability Engineering (SRE), platform engineering, and development teams who seek to implement chaos engineering safely and effectively.

Core Principles of Chaos Engineering

Chaos engineering is not merely about breaking things for amusement; it adheres to several core principles that ensure experiments are both valuable and safe:

Hypothesis-driven experiments: Each experiment begins with a clear hypothesis regarding system behavior under stress. For instance, “If 100ms latency is added to the payments API, the checkout success rate will remain >= 99% and 95th-percentile latency stays under 1 second.”
Define steady-state behavior: Establish the metrics that indicate normal operations—these may include latency percentiles, error rates, and business metrics like checkout rates.
Minimize and control blast radius: Start with small experiments affecting only a fraction of traffic or specific services to limit potential disruption.
Automate and run experiments continuously: Implement automation for scheduling and reporting to reduce human error and ensure repeatability. Tools like Gremlin can assist with this.
Learn and iterate from failures: Treat each experiment as an opportunity for learning and refinement, updating practices based on findings.

Prerequisites — What You Need Before You Start

Before diving into chaos experiments, ensure the following elements are established within your organization:

Observability and metrics: Ensure you have comprehensive logging, tracing, and monitoring solutions in place. Key metrics include latency histograms and SLO integrations. Guidance for Windows Performance Monitor can help monitor your testing environment effectively.
Testing environments and production safety: Begin in local development or dedicated staging environments. Consider constructing a test lab for more robust testing scenarios.
Deployment automation and rollback: Use CI/CD pipelines and automated rollback mechanisms. A helpful reference for Windows Deployment Services can guide recovery procedures.
Team readiness and culture: Foster a culture focused on learning and accountability, ensuring postmortem practices accompany every experiment.

Common Failure Modes to Test

Testing various faults can reveal critical insights about system behavior. Common tests include:

Network faults: latency, packet loss, and DNS failures can be simulated using tools such as Toxiproxy.
Resource exhaustion: Test system limits with CPU and memory stress, observing impacts on performance.
Dependency failures: Simulate downstream service outages to understand the resilience of user flows.
Infrastructure failures: Practice handling instance or zone outages and misconfigurations.
Configuration errors: Evaluate the system’s response to intentional deployment mishaps.

Prioritize testing on critical business processes first, like checkout flows or authentication systems.

Designing an Experiment — Step-by-Step

Designing a chaos experiment follows a structured template:

Title: Provide a succinct name for the experiment.
Owner: Identify a responsible person.
Hypothesis: State a clear and testable hypothesis.
Steady-state metrics: Set numerical thresholds that need to be met.
Variables and blast radius: Define fault type, scope, and the percentage of traffic affected.
Abort criteria: Outline metrics thresholds that will halt the experiment.
Rollback plan: Document the steps required to revert changes.
Observation plan: Specify the tools and metrics used for monitoring during the experiment.

Starter Experiment Example

Goal: Validate functionality under increased latency from a downstream service.

Hypothesis: Checkouts will remain >= 99% successful with a 100ms added delay.
Scope: Conduct in the staging environment on a single service instance.
Duration: Run the test for 5 minutes.
Abort criteria: If success rate dips below 99% for more than 1 minute or latency exceeds 1.5 seconds.

Commands for a Linux host using tc/netem:

# Add 100ms latency to eth0
sudo tc qdisc add dev eth0 root netem delay 100ms
# Rollback command
sudo tc qdisc del dev eth0 root netem

Tools and Platforms for Beginners

Select tools based on your environment:

Tool	Type	Strengths	Best for
Chaos Mesh	Kubernetes-native	CRD-based experiments	K8s clusters
LitmusChaos	Kubernetes-native	Rich experiment library	K8s with CI integration
Toxiproxy	Dependency proxy	Fine-grained latency control	API-level fault injection
Gremlin	Commercial/SaaS	Guided playbooks	Low-risk beginner experiments
Pumba	Docker-level	Simplified fault simulation	Legacy Docker environments
AWS Fault Injection Simulator	Cloud-native	Seamless AWS integration	AWS-hosted applications

Beginners are encouraged to experiment with Toxiproxy or Gremlin’s user-friendly guides for initial chaos engineering projects. For Kubernetes environments, consider tools like Chaos Mesh.

Best Practices for Running Experiments Safely

Implement these safety patterns:

Start with small experiments, gradually increasing scope.
Enforce clear safety checks and time constraints.
Document rollback procedures thoroughly.
Maintain communication with all relevant teams before experimentation.

Measuring Impact and Learning from Results

Evaluate both quantitative data (metrics comparison) and qualitative feedback (observations from users). Regular analysis promotes resilience through informed architecture adjustments.

Common Pitfalls to Avoid

Steer clear of these mistakes:

Lack of a hypothesis or vague metrics.
Running large-scale experiments without proper control.
Neglecting observability gaps.

A consistent culture of small, frequent experiments will mitigate these issues and enhance overall resilience.

Starter Playbook and Checklist

Follow this 5-step starter experiment process:

Secure approvals and confirm the experiment plan.
Validate observability metrics.
Execute a small-scale experiment in staging.
Monitor results and abort if necessary.
Conduct a post-experiment review to refine practices.

Final Checklist:

[ ] Experiment owner and approvers identified
[ ] Baseline metrics recorded
[ ] Blast radius defined
[ ] Abort criteria established
[ ] Rollback steps documented
[ ] Notification to all teams confirmed
[ ] Post-experiment review scheduled