What Is Chaos Engineering: Overview

Denis Sautin

Author

Denis Sautin

Denis Sautin is a Senior Copywriter at PFLB. He works on producing and reviewing a wide range of technical and editorial content. Denis collaborates with product, marketing, and engineering teams to maintain consistency and structure. He has over 10 years of experience in managing and delivering content for tech companies.

Senior Copywriter

Reviewed by Boris Seleznev

Reviewed by

Boris Seleznev

Boris Seleznev is a seasoned performance engineer with over 10 years of experience in the field. Throughout his career, he has successfully delivered more than 200 load testing projects, both as an engineer and in managerial roles. Currently, Boris serves as the Professional Services Director at PFLB, where he leads a team of 150 skilled performance engineers.

Chaos engineering is a way to test how complex systems respond to unexpected problems. The idea is simple: introduce controlled failures and watch how the system behaves. This helps uncover weak points before they lead to costly outages. An approach that forces you to think about the unexpected, making it easier to build robust, fault-tolerant applications.

Done right, chaos engineering can make systems more resilient, reducing the risk of sudden breakdowns and improving overall performance. We also apply chaos engineering in our load testing services.

What is Chaos Engineering?

Chaos engineering is a discipline focused on testing how complex, distributed systems respond to unexpected failures. It involves intentionally introducing chaos engineering principles into production or staging environments to uncover weaknesses before they become costly outages.

The purpose of chaos engineering is to make systems more resilient by revealing how they behave under stress. Instead of waiting for failures to happen naturally, teams use controlled experiments to identify weak points and address them proactively. This approach forces teams to confront the messy, unpredictable nature of real-world traffic and usage, helping them design more robust, fault-tolerant architectures.

For example, you might simulate a server crash, introduce random latency, or cut off a critical service to see how your application responds. The goal is to learn from these controlled failures and build systems that can handle the unexpected.

Principles of Chaos Engineering

The principles of chaos engineering provide a structured approach to uncovering vulnerabilities in complex systems. They guide teams in designing experiments that reveal weak points without causing unnecessary disruptions. Here’s a breakdown:

Benefits of Chaos Engineering

The value of chaos engineering goes beyond just finding bugs. It forces teams to confront the often messy, unpredictable nature of real-world systems. Here’s what that actually means:

Chaos Engineering vs Traditional Testing

While both chaos engineering and traditional testing aim to improve system reliability, their approaches and goals are fundamentally different.

Types of Chaos Engineering Experiments

Chaos engineering experiments come in many forms, each targeting a different aspect of system reliability. Here are some of the most common types:

Latency Injection

Latency injection tests how a system handles delayed responses from critical services. This is particularly important in microservice architectures, where even minor delays can ripple through the system and create significant performance issues. For example, adding artificial delays to a key API can reveal bottleneck testing opportunities, where a single slow service impacts the overall user experience.

Fault Injection

Fault injection simulates the failure of individual components to see how the system responds. Killing server processes, dropping database connections, forcing timeout errors — it’s a way to test how well your failover mechanisms work and whether your error-handling logic can prevent a cascading failure.

Load Generation

Load generation involves simulating high traffic volumes to test the system’s scalability and capacity limits. It is similar to benchmark testing, where the goal is to find the system’s breaking point under extreme load. It’s a critical step for understanding how your architecture handles peak traffic.

Canary Testing

Canary testing involves gradually rolling out changes to a small subset of users before a full-scale release. Teams can catch issues early and roll back quickly if something goes wrong. It’s a less aggressive form of chaos engineering, but still valuable for identifying issues that only appear under real-world conditions.

Resource Starvation

Resource starvation tests push the limits of system resources, such as CPU, memory, or disk I/O. This can reveal how your application behaves when it’s competing for limited resources, potentially exposing deadlocks, memory leaks, or unoptimized code paths.

Network Partitioning

Network partitioning tests simulate network failures, like dropped packets or split-brain scenarios in distributed databases. Usually used with systems that rely on high availability and data consistency, as it reveals how well they handle communication breakdowns.

Best Practices for Chaos Engineering

Effective chaos practices and experiments go beyond basic break tests and require a deep understanding of your systems. Here’s a structured approach:

Understand Your System’s Normal State

Chaos experiments are only meaningful if you know what stability looks like. Establish clear performance baselines, including response times, error rates, and throughput. Without context, interpreting experiment results won’t lead you anywhere..

Set Clear Objectives for Each Experiment

Every chaos test should have a specific focus, and the goals need to be well-defined. Avoid unnecessary disruptions and ensure that experiments generate relevant insights.

Treat Failures as Learning Opportunities

Each failure is a valuable insight. And each unexpected behavior reveals a gap in system design or operational processes. Document the findings and adjust your architecture accordingly to prevent similar issues in the future.

Use Realistic Scenarios

Focus on real-world conditions. Instead of just pulling the plug on a server, simulate the subtle issues that are harder to catch, like intermittent network delays or slow database responses. This is the key chaos engineering approach that uncovers weaknesses that might otherwise go unnoticed.

Automate Chaos Experiments

Scaling chaos engineering requires automation. Manual testing can provide insights early on, but automated chaos experiments are more consistent and less prone to human error. Specialized chaos engineering tools can help standardize this process.

Make Continuous Improvement a Habit

Chaos engineering isn’t a one-time effort. The insights you gain should feed back into your development and operations processes. Regularly revisit past experiments, expand the scope of your tests, and set increasingly higher resilience goals.

Final Thoughts

Chaos engineering is a powerful way to find and fix the weak points in complex systems, but it’s just one piece of the puzzle. It’s great for uncovering hidden risks, but it doesn’t replace the basics — like proper unit testing, integration testing, and load testing.

If you’re serious about building reliable software, chaos engineering is a good start, but there’s a lot more to the story. The real work begins when you combine it with everything else you’re doing to keep your systems stable.