What is Chaos Engineering

Every developer has experienced it: the application works perfectly in development, passes all tests, and then crashes spectacularly in production. A payment API times out. A database returns null where it shouldn't. A third-party service rate-limits your requests. These failures are inevitable in distributed systems, but they don't have to be surprising.

This is where Chaos Engineering comes in.

The Problem with Traditional Testing

Traditional testing assumes everything works as expected. Unit tests verify your logic. Integration tests check that components communicate correctly. End-to-end tests simulate user journeys. But none of these answer a critical question: What happens when things go wrong?

In production, things always go wrong:

• Networks have latency spikes
• Services return errors randomly
• APIs get rate-limited during traffic surges
• Responses arrive incomplete or corrupted
• Databases become temporarily unavailable

If you've never tested these scenarios, you're essentially hoping your application handles them gracefully. Hope is not a strategy.

What is Chaos Engineering?

Chaos Engineering is the discipline of experimenting on a system to build confidence in its ability to withstand turbulent conditions in production. Instead of waiting for failures to happen, you intentionally inject failures in a controlled environment to see how your application responds.

The core principle is simple: if you're going to fail, fail on your terms.

Netflix pioneered this approach with their famous "Chaos Monkey" tool, which randomly terminates production instances to ensure their systems can survive infrastructure failures. But you don't need to operate at Netflix scale to benefit from chaos engineering. Any application that depends on external services—which is essentially every modern application—can benefit from testing failure scenarios.

Four Types of Chaos

When testing how applications handle failures, there are four main categories of chaos you should consider:

1. Latency

Real-world networks are unpredictable. Sometimes a request takes 50 milliseconds, sometimes 5 seconds. How does your application behave when responses are slow? Do your loading states work correctly? Does your timeout configuration actually protect users from hanging indefinitely?

The most realistic latency simulation follows a log-normal distribution: most requests are reasonably fast, but occasionally some are significantly slower. This matches real production traffic patterns better than fixed delays.

2. Errors

APIs fail. Services return 500 errors during deployments. Gateways timeout when backends are overloaded. Your application needs to handle these gracefully—showing appropriate error messages, retrying when sensible, and degrading functionality without crashing entirely.

Testing with random error injection reveals whether your error handling actually works. A 10% error rate quickly exposes missing try-catch blocks and inadequate user feedback.

3. Data Corruption

What happens when an API returns unexpected data? A field that's normally present is suddenly null. A response is truncated mid-stream. The JSON is malformed.

These scenarios are surprisingly common in production, especially when APIs evolve and backwards compatibility breaks subtly. Testing data corruption ensures your parsing logic is defensive and your UI handles missing data gracefully.

4. Rate Limiting

Every third-party API has rate limits. When you hit them, you receive 429 responses. Does your application detect this? Does it back off and retry? Or does it hammer the API repeatedly, making the situation worse?

Rate limit simulation is essential for any application that integrates with external services—payment providers, email services, social media APIs, or any SaaS platform.

Chaos Engineering with Mocklantis

Mocklantis provides a comprehensive fault injection system that lets you test all four chaos scenarios directly in your mock API endpoints. Instead of setting up complex infrastructure or writing custom failure simulation code, you configure chaos settings per-endpoint through a simple interface.

Latency Injection

Add artificial delay to responses with three modes: fixed delay for predictable testing, random delay within a range for unpredictable conditions, or log-normal distribution for production-like patterns. The log-normal mode is particularly powerful—set a median latency of 200ms with a sigma of 0.8, and you'll see most requests complete quickly while occasionally experiencing the "long tail" latency that causes real production issues.

Error Injection

Configure a failure rate (0-100%) and define multiple error responses with different status codes and bodies. When an error triggers, Mocklantis randomly selects from your defined errors. This simulates real-world variety—sometimes you get a 500, sometimes a 502, sometimes a 503.

Response Corruption

Test defensive programming with four corruption types: drop specific fields from responses, set fields to null, truncate the response mid-stream, or inject malformed JSON. You can target nested fields using dot notation (e.g., user.profile.email), testing exactly the scenarios you're worried about.

Rate Limiting

Simulate API throttling with configurable limits and time windows. When the limit is exceeded, requests receive 429 responses with custom bodies. This tests whether your application respects rate limits and implements proper backoff strategies.

The Real Power: Combining Effects

What makes Mocklantis chaos engineering particularly effective is the ability to combine all four effects on a single endpoint. A realistic production simulation might include:

• Log-normal latency with 200ms median
• 5% error rate with various 5xx responses
• 2% chance of dropping optional fields
• Rate limit of 100 requests per minute

This creates the kind of "mostly works, occasionally fails in various ways" behavior that real production environments exhibit.

Building Confidence Through Failure

Chaos Engineering isn't about breaking things for fun. It's about building confidence. When you've tested your application against simulated failures, you know—not hope, know—that it handles them correctly.

Every timeout you test is one less surprise in production. Every error scenario you handle is one fewer support ticket. Every rate limit you respect is one less angry email from a third-party provider.

Start with low chaos percentages. Watch how your application behaves. Increase the chaos gradually. Fix what breaks. Repeat.

Your users will thank you for the bugs they never see.