Validating Microservices Scalability in Volatile Market Conditions
Industry:
Financial Data & Trading Technology
Region:
Global (Buy-side asset managers, hedge funds, execution desks)
Technology:
Apache JMeter, Azure, Kafka, Oracle RAC
About the Client
A broker-neutral, multi-asset execution management system enables institutional traders to manage order flow, FIX connectivity, pre-trade and post-trade analytics, and automated order routing.
Challenges
Large buy-side clients relied on the platform to support mission-critical trading workflows with strict expectations for sub-second latency and uninterrupted availability. However, as concurrency and data throughput increased, the system began to show performance limitations, including:
- Latency under high-volume FX options allocations and multi-asset order batching
- Queuing delays and throughput issues in FIX gateway connections during market surges
- Scalability constraints in Kubernetes-based microservices when simulating real-world trading volatility
Solutions
A comprehensive performance engineering initiative was implemented to ensure the platform could meet the demands of modern, high-frequency trading environments.
Results
- Achieved <500ms average round-trip latency for order placement and routing under peak load
- FIX gateway throughput improved by 70%, handling burst events with zero message loss
- 24-hour soak tests passed with no container restarts or queue saturation issues
- Dynamically auto-scaled trading microservices in response to spike triggers using Dynatrace insights