How Tape à l’œil protects digital performance during retail peaks with Gatling Enterprise

Sales surges, API complexity, and mobile memory bottlenecks demanded more than intuition: TAO needed simulation-based certainty.

• Past traffic spikes had caused outages during key events
• Non-functional requirements had to be validated under peak concurrency
• A mobile app architecture issue caused RAM spikes during login
• Complex promotional tools required realistic load to verify performance

Gatling helped the team shift from reactive troubleshooting to structured, scenario-based testing that replicates real customer behavior.

TAO faced growing pressure to validate performance, not just assume it. One critical case involved mobile app users triggering 8+ API calls on launch. The app worked in QA, but when real users hit the platform at scale, RAM usage spiked.

Another challenge came from the way TAO handled pre-sale carts. Thousands of customers would prepare baskets before big sales events, only to check out en masse at 8 AM on launch day. Without simulating that burst load—including the cart transfer and promotion logic—the backend couldn’t cope.

Even with auto-scaling enabled, their environments couldn’t be trusted blindly. “Autoscaling isn’t magic,” Nordine explained. “We needed to warm up our pods before the real load hit.”

• Reduced memory usage by consolidating multiple mobile API calls into one
• Validated performance improvements after switching JSON serializers
• Simulated complex carts of 10+ items for sales-day benchmarking
• Ran warm-up tests to trigger autoscaling before traffic surges
• Built dedicated test scripts for mobile and web customer journeys
• Migrated from Scala to Java for easier scripting and broader adoption
• Performed load testing exclusively in staging while mimicking production behavior

The team built specific load testing scenarios using Gatling Enterprise:

• A mobile script mimicking full user behavior: login, browse, cart, checkout
• A web simulation using closed injection models for homepage, product lists, and cart events
• Load tests for backend services like the commercial operations engine to validate complex pricing logic
• Performance regressions tracked using Gatling dashboards and correlated with Dynatrace and Datadog

• Improved memory usage by optimizing startup calls in the mobile app
• Detected and fixed issues in promotional pricing engines before campaign days
• Validated architectural changes (e.g., switching JSON libraries) through load simulation
• Reduced backend load by consolidating multiple endpoints into a single optimized one
• Logged 15–20 performance-related tickets annually, many directly linked to Gatling testing

• Automate tests into CI/CD via GitHub Actions
• Run monthly performance simulations to catch regressions before they hit production
• Adopt reference-tier benchmarking for performance thresholds
• Correlate Gatling results with Datadog metrics using upcoming integrations
• Explore AI-powered reporting and test suggestions via Gatling Studio
• Use Postman-to-Gatling imports to simplify test script creation