Performance Engineering Framework

Load testing strategy

Performance requirements were translated into concrete, testable budgets: P95 latency per critical API, target concurrent users per workflow, and acceptable error rates under sustained load.

Test types mapped to real risks: baseline runs on every release, stress tests before customer onboardings, soak tests to catch memory leaks, and spike tests modelling end-of-quarter document volumes typical in banking.

Test architecture

JMeter test plans parameterized per environment, executed through Azure Load Testing for cloud-scale load generation without maintaining injector fleets. Test data pools sized so no document or user is reused within a run, keeping cache behaviour honest.

Bottleneck analysis

Load results correlated with infrastructure metrics in Grafana: CPU, memory, DB connection pools, queue depths. The pattern that mattered most: latency cliffs almost always traced to a saturated dependency (a connection pool, a thread pool, a downstream API) rather than raw compute.

Each finding shipped as a specific, actionable recommendation: pool size changes, index additions, async offloading of document processing steps.

Scaling recommendations

Capacity plans gave the business an answer to 'how many concurrent users can we onboard?' backed by test evidence rather than estimates. The work delivered a 30% scalability improvement on the same infrastructure footprint.