Client background and business context

  • Client profile: largest European electronics retailer 
  • Project: performance testing implementation 


  • Capacity limitation: the client’s solution platform needed initial gauging in real-world scenario conditions. This would allow to better assess risks under a normal daily load or during peak sales periods. Based on the client’s existing measurements, the application was able to handle 400 transactions per second (TPS). As the company grew and diversified its needs, this number of transactions represented a potential risk.  
  • Undefined performance test strategy: performance testing revolved around a rarely conclusive end-to-end (E2E) approach, preventing early issue detection, by overlooking components, features, and endpoint-level isolated assessment.  
  • Limited test platform coverage: limited test scripts customisations at country or brand level. Limitations were also present in terms of request distribution, usage profile and load-sourcing patterns (mobile, web, app). 
  • Deprecated test-data script sourcing that were put in place for previous testing sessions 

Our approach

Setting up the performance testing strategy was our prio 0 aspect. While keeping close contact with the client’s development, infrastructure, and middle-ware team, we’ve started implementing and documenting a long-term, sustainable approach for performance testing and expanding coverage, in a prioritised manner. In doing so, we’ve provided our client with an early detection mechanism, accurate prediction strategies, invaluable load-model emulation, and scalable cross-brand and cross-country test coverage. All these were vital assets in ultimately fulfilling the end-customers’ desire for a customer-experience oriented shopping experience

Technologies used in this case study


  • Apache JMeter and HP LoadRunner to replicate user load on the SUT
  • BlazeMeter and Octoperf 3rd party cloud provider
  • Multi-zone load distribution using AWS, Scaleway, DigitalOcean
  • Grafana , DynaTrace, Munin, AppDynamics, NewRelic, Kibana as monitoring tools (APM)
  • Jenkins, Taurus for CI / CD integration of Performance Validation in the pipeline
  • JAVA, C, Groovy, BeanShell as scripting languages
  • Containerised load generating infrastructure deployed on client premise

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