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Welcome to Chaos Engineering! This discipline helps us build confidence in our systems by proactively injecting failures.

It's not about randomly breaking things, but about learning from controlled breakdowns to make systems more resilient.

Modern software systems are incredibly complex. Failures are inevitable, whether it's a network glitch or a database hiccup.

Chaos Engineering helps us uncover these weaknesses before they cause real incidents, improving overall system reliability and stability.

Chaos Engineering is guided by four key principles:

• Formulate a hypothesis: Predict how your system *should* react to a failure.
• Vary real-world events: Simulate actual problems your system might face.
• Run experiments in production (or close): Test where it matters most.
• Minimize blast radius: Limit the impact of your experiment.

A hypothesis in Chaos Engineering is an educated guess about how your system will behave under specific failure conditions.

For example: "If the user authentication service experiences high latency, the application's login page will gracefully display a 'retry' button without crashing."

Once you have a hypothesis, you design an experiment:

• Identify a 'steady state': Define what "normal" looks like for your system (e.g., CPU usage, error rates).
• Introduce a variable: Inject the specific failure (e.g., high latency, service crash).
• Observe impact: Monitor the system's behavior against your steady state.
• Verify hypothesis: Did the system behave as expected?

The blast radius is the potential impact area of your chaos experiment. It's crucial to keep this as small as possible, especially when starting out.

Always begin with experiments that affect a very limited set of users or services. You can gradually expand the scope as you gain confidence.

What kind of failures can you inject? Here are some common types:

• Network issues: Latency, packet loss, partitioning.
• Resource exhaustion: High CPU, low memory, full disk.
• Service failures: Crashing instances, restarting services.
• Dependency failures: Database unavailability, API timeouts.

You can't do Chaos Engineering without strong observability.

Robust monitoring, logging, and tracing are essential to understand what's happening before, during, and after an experiment. Without it, you're just breaking things blindly!

Chaos Engineering is an iterative process. It's a continuous cycle of:

• Running experiments.
• Finding weaknesses.
• Fixing those weaknesses.
• Repeating the process.

Each cycle helps you learn more about your system and build greater resilience.

Let's test your knowledge of Chaos Engineering principles.

In this lesson, we explored the core principles of Chaos Engineering.

We learned that it's a proactive approach to build resilient systems by formulating hypotheses, designing controlled experiments, minimizing blast radius, and relying heavily on observability to learn and improve.