Chaos Engineering

Chaos engineering is the practice of intentionally introducing controlled disruptions or failures into a system to test its resilience and reliability. The goal is to identify vulnerabilities, understand system behavior under stress, and build confidence in its ability to withstand unexpected conditions.

Key Principles

• Build a Hypothesis Around Steady State: Define what "normal" behavior looks like for your system, such as response times, throughput, or error rates.

• Introduce Real-World Events: Simulate failures like network latency, server crashes, or data center outages to mimic real-world scenarios.

• Run Experiments in Production or Close to Production: Conduct tests in environments that closely replicate production to ensure findings are realistic.

• Minimize Blast Radius: Start small and limit the impact of experiments to avoid causing widespread disruption.

• Measure and Learn: Analyze the results of experiments to identify weaknesses and improve system design and processes.

Benefits

• Improved system reliability and fault tolerance.

• Enhanced understanding of system behavior.

• Proactive identification and resolution of vulnerabilities.

Tools for Chaos Engineering

• Chaos Monkey: Developed by Netflix, it randomly disables production instances to ensure system resilience.

• Gremlin: A commercial tool for running controlled chaos experiments.

• LitmusChaos: Open-source tool for chaos engineering in Kubernetes environments.

Use Case Example

Fintech Industry

In the fintech industry, where reliability, security, and real-time processing are crucial, chaos engineering can help ensure systems are resilient to failures that could disrupt services, cause financial loss, or breach customer trust. Here are examples of chaos engineering experiments specifically relevant to fintech:

Payment Gateway Failures

• Scenario: Simulate the unavailability of a third-party payment gateway during peak transaction hours.

• Objective: Ensure the system can route transactions to alternative gateways and provide appropriate error handling and communication to users.

• Outcome: Identify bottlenecks in fallback mechanisms and test customer notification systems.

• Example using gremlin:

  # Drops all matching network traffic.
  gremlin attack new \
  --type "blackhole" \
  --target "host" \
  --host "payment-gateway-url" \
  --tags "env=production"

Database Latency

• Scenario: Introduce artificial latency in database queries, particularly during balance checks or transaction processing.

• Objective: Assess the system's ability to maintain performance under degraded conditions and prevent cascading failures.

• Outcome: Optimize query performance, caching strategies, and retry mechanisms.

API Rate-Limiting

• Scenario: Simulate rate-limiting or throttling of partner APIs, such as for credit scoring or fraud detection services.

• Objective: Test how gracefully the system handles API rate limits and whether it prioritizes critical transactions.

• Outcome: Develop strategies for fallback data sources or pre-emptive caching.

• Tools: gremlin

Transaction Duplication

• Scenario: Inject a failure causing a transaction to be processed multiple times (e.g., double charging a customer).

• Objective: Test detection mechanisms for duplicate transactions and ensure timely rollback or refunds.

• Outcome: Improve reconciliation processes and error correction protocols.

Network Partition

• Scenario: Simulate a network partition between microservices handling customer account management and transaction processing.

• Objective: Verify the system’s ability to operate in a degraded state without losing or corrupting data.

• Outcome: Strengthen data consistency mechanisms and eventual recovery processes.

Service Dependency Failures

• Scenario: Disable key services, such as fraud detection, currency conversion, or KYC verification.

• Objective: Ensure the system can continue partial operations, like processing transactions that don’t require the disabled service.

• Outcome: Build robust service isolation and failover strategies.

High-Volume Traffic Simulations

• Scenario: Introduce a sudden spike in user activity, mimicking Black Friday or a popular financial product launch.

• Objective: Test scalability, load balancers, and auto-scaling features.

• Outcome: Optimize system architecture for high availability and performance under load.

• Example using k6 load testing:

  import http from 'k6/http';
  import { sleep } from 'k6';
  
  export const options = {
    stages: [
      { duration: '1m', target: 1000 },
      { duration: '3m', target: 2000 },
      { duration: '1m', target: 0 },
    ],
  };
  
  export default function () {
    http.get('https://your-host/transactions');
    sleep(1);
  }

Card Issuance Delays

• Scenario: Inject delays or errors in card issuance systems (e.g., for virtual debit cards).

• Objective: Validate user-facing communication and queue management for delayed card issuance.

• Outcome: Enhance user experience during operational delays.

• Example using gremlin:

  # add latency to target container
  gremlin attack new \
  --type "latency" \
  --target "container" \
  --container-names "card-issuance-service" \
  --delay 5000 \
  --jitter 1000

Fraudulent Transactions Surge

• Scenario: Simulate a spike in transactions flagged as suspicious by fraud detection systems.

• Objective: Test the system's ability to process genuine transactions while isolating fraudulent ones efficiently.

• Outcome: Identify bottlenecks in fraud detection pipelines and enhance real-time response mechanisms.

• Tools: k6

Compliance Auditing System Outage

• Scenario: Simulate downtime in compliance auditing systems during reporting periods.

• Objective: Ensure the ability to queue audit logs and synchronize data once the system is restored.

• Outcome: Maintain compliance standards even during outages.