Problem
Low-latency software is easy to romanticize and hard to build well. This project matters because it demonstrates discipline around determinism, hot-path simplicity, and observability in a domain where vague correctness is not acceptable.
Ratna
Project Stash Vault
Engineering archive shaped for long-horizon work, not short-term portfolio churn.
Vault 02 / Systems
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Case file / 2025
Featured case fileLimit Order Book Engine
A compact matching engine centered on deterministic sequencing, cache-aware data structures, and replayable correctness.
Recommended starting point for the systems archive.
Public materials are still being curated. The internal case file below is the current source of truth until supporting assets are ready to publish.
C++20BenchmarkingReplay ToolingMarket Data Simulation
#matching engine#low latency#determinism#systems
20-second project scan
What to understand first
A fast scan for recruiters and engineers: ownership, technical depth, proof status, and current outcome in one place.
Category
Systems
Status
Operational
Proof ready
00
Stack surface
4 tools
My role
Core engine implementation, replay tooling, benchmark harness design, and code structure for a maintainable performance-sensitive system.
C++20BenchmarkingReplay ToolingMarket Data Simulation
Technical highlights
01
Optimized the hot path around predictable memory access and strict sequencing rather than layering abstractions over the matching core.
02
Separated replay and validation tooling from the engine itself so performance tuning could happen with a trustworthy feedback loop.
03
Focused on price-time priority as the non-negotiable behavior, then shaped data structures around fast, readable state transitions.
04
Used replay infrastructure to test realistic event streams instead of relying only on synthetic microbenchmarks.
Impact
Created a credible anchor project for latency-sensitive systems engineering.
Architecture overview
Problem, solution, and system shape
The project framed as a system: the problem, the solution boundary, and the architecture choices that make the implementation credible.
Solution
Limit Order Book Engine is a study in throughput-sensitive systems work. The project focuses on predictable memory behavior, strict price-time matching, and the tooling required to replay order flow and verify that performance work never drifts away from correctness.
Architecture
01
Optimized the hot path around predictable memory access and strict sequencing rather than layering abstractions over the matching core.
02
Separated replay and validation tooling from the engine itself so performance tuning could happen with a trustworthy feedback loop.
03
Kept the codebase compact enough to reason about while preserving clear boundaries for future adapter and feed-handling work.
Technical highlights
Where the engineering depth shows up
The implementation details a technical reviewer should notice before reading the full case file.
Highlight 01
Optimized the hot path around predictable memory access and strict sequencing rather than layering abstractions over the matching core.
Highlight 02
Separated replay and validation tooling from the engine itself so performance tuning could happen with a trustworthy feedback loop.
Highlight 03
Focused on price-time priority as the non-negotiable behavior, then shaped data structures around fast, readable state transitions.
Highlight 04
Used replay infrastructure to test realistic event streams instead of relying only on synthetic microbenchmarks.
Proof surface
Artifacts, references, and public access points
Ready links and planned proof artifacts are shown together so reviewers can distinguish published evidence from reserved case-study slots.
Trace
PlannedReplay trace excerpt
Reserved for an event-stream replay demonstrating price-time matching and deterministic state transitions.
Reserved for future publication once the supporting material is ready.
Capture
PlannedBenchmark and profile capture
Reserved for supporting measurements and hot-path inspection once the benchmark package is publication-ready.
Reserved for future publication once the supporting material is ready.
System view
Architecture and implementation
The architectural boundaries and implementation choices that make the system coherent, maintainable, and operationally meaningful.
Architecture
01
Optimized the hot path around predictable memory access and strict sequencing rather than layering abstractions over the matching core.
02
Separated replay and validation tooling from the engine itself so performance tuning could happen with a trustworthy feedback loop.
03
Kept the codebase compact enough to reason about while preserving clear boundaries for future adapter and feed-handling work.
Implementation Notes
01
Focused on price-time priority as the non-negotiable behavior, then shaped data structures around fast, readable state transitions.
02
Used replay infrastructure to test realistic event streams instead of relying only on synthetic microbenchmarks.
03
Treated maintainability as part of systems quality rather than something to revisit after performance work.
Metrics and outcomes
What is proven so far
Honest status, proof readiness, and results. Qualitative markers are used where exact production metrics are not available yet.
Status
Operational
Current maturity of the project record.
Proof
Trace planned
Reserved for an event-stream replay demonstrating price-time matching and deterministic state transitions.
Architecture
3 notes
Documented architecture decisions and boundaries.
Outcomes
01
Created a credible anchor project for latency-sensitive systems engineering.
02
Shows comfort with building compact infrastructure where behavior under load matters as much as API shape.
Future Work
01
Add feed adapters and persistence layers that preserve deterministic replay across recovery scenarios.
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View ProjectView Repo
Internal case file is live; public repo is not linked yet.