This blog explores the challenges organizations face when relying solely on testing to validate deployments, highlights how chaos engineering can bridge these gaps, and showcases various adopters of chaos engineering practices along with their use cases.
Organizations are rolling out changes almost everyday with the help of CI/CD. You can perform automated, manual, unit and integration tests to validate features before deployment. But what if your system encounters unexpected failures?
What if there are network disruptions, application crashes or resource exhaustions that would impact the application resulting in downtime or cascading failures?
These unforeseen failures can’t be addressed using testing because traditional testing methodologies aren’t designed to simulate such complex, real-world conditions.
This is where chaos engineering comes into play.
You can address the gaps mentioned earlier by incorporating chaos engineering experimentation in your application to build resilient and reliable systems.
Described below are some organizations (by sector) that leverage chaos engineering to enhance their cloud-native applications and operations:
Technology & E-Commerce: The Challenges and Solutions
1. Flipkart: Handling High-Traffic Events Without Downtime
Challenge: During peak shopping events, Flipkart experienced traffic spikes that led to performance degradation and downtime. Traditional load testing failed to capture the full complexity of real-world failures like network congestion and database latency.
Impact: Shoppers faced checkout failures, slow page loads, and abandoned carts, affecting revenue and customer satisfaction.
Solution: Flipkart integrated chaos engineering to simulate failures in production-like environments, testing how microservices and databases handled stress conditions. This approach helped them optimize auto-scaling strategies and build robust failover mechanisms, ensuring seamless shopping experiences.
2. Delivery Hero: Ensuring Reliability in Food Delivery Services
Challenge: Delivery Hero operates a high-demand food delivery service, where real-time order processing and delivery tracking are critical. Network failures and API downtime led to order failures and frustrated customers.
Impact: Customers faced delays or lost orders, affecting restaurant partnerships and overall brand trust.
Solution: Using chaos engineering, Delivery Hero injected failures into their APIs, databases, and network connections to identify weak points. By proactively fixing these, they improved system redundancy, reduced downtime, and ensured smooth operations even during peak hours.
3. Talend: Improving Data Pipeline Resiliency
Challenge: Talend, a data integration company, processes massive datasets across multiple cloud environments. Issues like database failures or unexpected API rate limits disrupted data workflows, affecting analytics and reporting.
Impact: Businesses relying on Talend’s data pipelines faced inconsistencies in analytics, leading to misinformed decisions.
Solution: Talend used chaos engineering to introduce controlled disruptions in their ETL (Extract, Transform, Load) processes. This approach helped them refine retry logic, optimize failover configurations, and ensure consistent data processing even under failure conditions.
4. Kitopi: Enhancing Reliability in Cloud Kitchens
Challenge: Kitopi, a cloud kitchen platform, depends on efficient order routing and delivery logistics. Unforeseen infrastructure failures, like database slowdowns or application crashes, led to delayed or missed orders.
Impact: Customers experienced long wait times, and restaurant partners faced reduced efficiency.
Solution: Kitopi adopted chaos engineering to simulate database latencies and system crashes. By analyzing system responses, they improved recovery mechanisms, reduced downtime, and ensured reliable operations for food preparation and delivery.
5. Lenskart: Ensuring E-Commerce Stability During Scaling
Challenge: As Lenskart scaled its e-commerce operations, performance bottlenecks emerged due to unpredictable user behavior and traffic surges.
Impact: Checkout failures and slow load times led to cart abandonment and lost sales.
Solution: Lenskart employed chaos engineering to test their microservices against network failures and sudden traffic bursts. These experiments helped them fine-tune their cloud infrastructure for better scalability and higher uptime.
6. iFood: Strengthening Food Delivery Operations
Challenge: iFood operates a large-scale food delivery service that depends on real-time coordination between customers, restaurants, and delivery partners. System failures disrupted order processing and driver dispatching.
Impact: Delayed or canceled deliveries hurt customer satisfaction and restaurant partnerships.
Solution: iFood used chaos engineering to test service degradations, ensuring their systems could handle API timeouts and database failures. By enhancing their error-handling strategies, they minimized order disruptions and maintained seamless delivery services.
7. Wingie Enuygun Company: Improving Online Travel & Finance Platform Resilience
Challenge: Wingie Enuygun, an online travel and finance platform, faced outages due to third-party API failures and unpredictable network issues.
Impact: Users encountered booking failures and delayed transactions, leading to loss of trust.
Solution: By integrating chaos engineering, they simulated API slowdowns and network failures, allowing them to improve fallback strategies and system stability. As a result, they delivered a more reliable user experience.
Conclusion
Organizations that do not use chaos engineering risk falling into a cycle of operational inefficiencies and poor customer experiences. By injecting controlled failures, chaos engineering helps address these challenges, thereby building robust and scalable systems.
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