DOC_ID: SUPRA-LABS-BENCHMARKING

High-Throughput Benchmarking for a BFT Consensus Layer

TIMESTAMP: 2025-09-01

The Challenge

Supra Labs was developing a novel BFT consensus mechanism and needed to validate its performance and resilience under extreme conditions. The primary challenge was to simulate realistic, high-throughput, and often adversarial network traffic to identify potential bottlenecks and stress-test the peer-to-peer and consensus layers before production deployment.

The Solution

We were brought on to design and develop a sophisticated benchmarking suite entirely in Rust. The key features of this framework included:

  • High-Throughput Traffic Simulation: The suite was capable of generating a massive volume of transactions to accurately model a network under heavy load.
  • Realistic & Adversarial Profiles: We implemented plug-and-play traffic profiles that could emulate a wide range of production scenarios, including heterogeneous mixes of payments, complex smart-contract calls, and governance operations. This allowed us to simulate not just average use, but also worst-case, adversarial conditions.
  • Automated Reporting: Custom Python scripts generated comprehensive performance reports and charts, automatically uploading results to Google Drive for easy access by the leadership team.

The Impact

The benchmarking suite became a critical tool in the development lifecycle, providing invaluable data that directly influenced the project's direction.

  • Identified Critical Bottlenecks: The stress tests uncovered several performance bottlenecks that were subsequently resolved, leading to a more robust and scalable system.
  • Informed Leadership Decisions: We presented statistical findings and performance data to leadership on a fortnightly basis, aligning engineering and product stakeholders on timelines and resourcing.
  • Increased Confidence in Production Readiness: By thoroughly validating the system's limits, the benchmarking suite gave the team high confidence in the consensus layer's stability and performance ahead of its launch.

Technologies Used: Rust, Docker, Python, Google Drive API