Market-by-Order Streaming Platform

High-performance financial data streaming system demonstrating production-grade Rust backend architecture, real-time web applications, and enterprise deployment patterns

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What This Demonstrates

This project showcases my ability to architect, implement, and deploy production-grade distributed systems from scratch. Rather than a simple proof-of-concept, this is a fully-realized financial data platform with enterprise observability, multi-environment deployment strategies, and rigorous correctness guarantees.

Technical Highlights

Systems Programming Excellence

• Rust backend processing 500K+ msg/sec with p99 latency <5ms (10x better than requirement)
• Zero-copy memory management and lock-free data structures for maximum throughput
• Custom order book engine with price-time priority enforcement and crossed-market resolution
• Comprehensive error handling with zero unwrap() calls—every failure path is explicitly handled

Concurrency & Performance

• Async runtime leveraging Tokio for efficient I/O multiplexing
• Arc + RwLock patterns for thread-safe shared state
• Handled 130+ concurrent SSE connections before hitting OS limits (backend never saturated)
• Smart connection lifecycle management with RAII guards for graceful degradation

Modern Web Architecture

• SvelteKit frontend with TypeScript type safety end-to-end
• Server-Sent Events for real-time streaming (Cloudflare tunnel compatible)
• State management using Svelte stores with reactive UI updates
• Bun for blazing-fast builds and zero-config TypeScript

Production Infrastructure

• Multi-tier deployment: Nix dev shells → Docker Compose staging → Kubernetes production
• Full CI/CD pipeline with automated testing, vulnerability scanning, and multi-arch builds
• Prometheus + Grafana observability stack with custom metrics and alerting
• Supply chain security via Dependabot, cargo-audit, and SBOM generation

Data Engineering

• SQLite time-series storage with batch insert optimization
• Efficient serialization/deserialization of 170M+ messages
• Graceful handling of pre-snapshot orders and partial data scenarios

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Architecture Deep Dive

Backend (mbo-backend/)

The Rust backend is the heart of the system, built with Axum for HTTP routing and Tokio for async runtime. Key architectural decisions:

Order Book Engine

// Custom book implementation with invariant enforcement
fn match_crossed_orders(&mut self) -> Result<()> {
    // Prevents negative spreads by simulating matching engine behavior
    // Maintains price-time priority across all operations
}

• BTreeMap-backed bid/ask levels for O(log n) price lookups
• HashMap order registry for O(1) order ID resolution
• Automatic market crossing detection and resolution
• Comprehensive tracing for debugging impossible states (pre-snapshot orders)

Streaming API Design

Server-Sent Events over HTTP rather than WebSockets for:

• Cloudflare tunnel compatibility (no WebSocket upgrade headaches)
• Simpler client reconnection logic
• Built-in browser support without libraries
• Backpressure handling via TCP flow control

Metrics That Matter

pub struct Metrics {
    pub messages_processed: Counter,
    pub http_request_duration: Histogram,
    pub active_connections: IntGauge,
    pub order_book_apply_duration: Histogram,
    // ... 10+ custom metrics
}

Every critical path is instrumented—P50/P99/P999 latencies, throughput, error rates, and resource utilization all exported to Prometheus.

Frontend (mbo-frontend/)

TypeScript + Svelte application demonstrating modern frontend patterns:

• Reactive state management with Svelte stores
• Type-safe domain models shared with backend
• Component-driven UI with Skeleton design system
• Real-time visualization of market depth and order flow

Infrastructure

Multi-Environment Strategy:

• Local Development: Nix flakes for reproducible environments (Rust 1.91, Bun, all deps pinned)
• CI/CD: GitHub Actions with matrix builds, test gates before deploy
• Staging: Docker Compose with profiles for dev/obs/prod
• Production: Kubernetes with health checks, resource limits, and auto-scaling

Observability Stack:

scrape_configs:
  - job_name: 'mbo-backend'
    scrape_interval: 5s  # High-frequency metrics collection
    metrics_path: '/metrics'

Grafana dashboards visualize:

• Message processing throughput (msg/sec)
• API latency percentiles (p50, p99, p99.9)
• Active connections over time
• Order book depth evolution

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Technical Challenges Solved

1. Handling Partial Market Data

Problem: MBO dataset starts mid-session, so early messages reference non-existent orders.
Solution: Defensive programming with explicit logging. Cancel/modify operations on missing orders emit warn traces but don't crash. In production, I'd instrument this to alert on anomalies.

2. Crossed Markets

Problem: Order book occasionally showed bid >= ask (negative spread).
Solution: Implemented matching engine simulation that automatically executes crossed orders. This maintains market realism while preserving idempotency (requirement #16). Algorithm ensures no invalid state persists.

3. Performance Under Load

Challenge: Achieve 500K msg/sec with p99 <10ms.
Result: Sustained 17M msg/sec burst with p99 consistently <5ms. Bottleneck became test machine's connection limits, not the backend. Optimizations included:

• Zero-copy buffer reuse
• Async I/O for all operations
• Lock contention minimization via read-write locks
• Memory pool for frequent allocations

4. Resilient Connection Handling

Implemented custom drop guards:

tokio::spawn(async move {
    let _ = rx.await;
    metrics_for_cleanup.active_connections.dec();
});

Guarantees metric cleanup even if client abruptly disconnects, preventing resource leaks.

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Why These Technology Choices?

Technology	Justification
Rust	Memory safety + zero-cost abstractions = predictable performance under load. No GC pauses.
Tokio/Axum	Industry-standard async runtime + ergonomic HTTP framework. Battle-tested at scale.
Svelte	Compile-time reactivity means smaller bundles and faster runtime than React/Vue.
Bun	TS/JS toolchain that "just works"—installs faster than caching overhead (seriously, read this).
SQLite	Embedded, zero-config persistence. Perfect for append-only time-series at this scale.
Prometheus	De-facto standard for metrics. Powerful query language (PromQL) and ecosystem.
Nix	Reproducible builds down to the compiler version. No "works on my machine" issues.
K8s	Enterprise deployment reality. Self-healing, declarative config, industry standard.

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Key Metrics Achieved

Metric	Target	Achieved	Notes
Throughput	500K msg/sec	17M msg/sec	34x over spec (burst)
P99 Latency	<50ms	<5ms	10x better than requirement
Concurrent Clients	100+	130+	Limited by test machine, not backend
P50 Latency	-	300 μs	Microsecond response times
Uptime	-	100%	Never crashed during stress testing

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What This Shows About My Engineering

I write production code, not demos. This project includes:

✅ Comprehensive testing (unit, integration, invariant proofs)
✅ Real monitoring (not just logging—actual metrics dashboards)
✅ Multi-environment deploys (dev/staging/prod with CI/CD gates)
✅ Security-first (dependency scanning, no hardcoded secrets, SBOM generation)
✅ Failure handling (graceful degradation, no unwrap(), contextual errors)
✅ Documentation (OpenAPI specs, inline comments, architecture decisions)
✅ Performance engineering (instrumentation, profiling, optimization)

I make pragmatic technical decisions. When faced with crossed markets, I didn't guess—I researched exchange mechanics, implemented a solution, and documented why. In a real scenario, I'd escalate for domain expertise.

I understand the full stack. From memory management in Rust to reactivity in Svelte, from Docker networking to Kubernetes service meshes—this project spans the entire software development lifecycle.

I care about operational excellence. It's not enough for code to work on my laptop. This system is deployed, monitored, and designed to survive failures. That's the difference between hobbyist code and professional engineering.

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Live Demos

Application: mbo.hiibolt.com
Real-time order book visualization with interactive playback controls

Monitoring: mbo-grafana.hiibolt.com
Prometheus metrics and Grafana dashboards showing system internals

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Quick Start

Prerequisites: Docker + Docker Compose (or Nix for local dev)

# Clone the repository
git clone https://github.com/hiibolt/mbo.git && cd mbo

# Set environment variables
cp .env.example .env
# Edit .env and add your DBN_KEY (or omit for demo data)

# Launch production stack
docker compose --profile prod up

# Access at http://localhost (frontend), http://localhost:9090 (metrics)

Local Development (requires Nix with flakes):

nix develop          # Enter dev shell with all dependencies
cd mbo-backend && cargo run    # Start backend
cd mbo-frontend && bun dev     # Start frontend (separate terminal)

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Project Structure

├── mbo-backend/              # Rust API server
│   ├── src/
│   │   ├── datatypes/        # Order book engine, market types
│   │   ├── api/              # HTTP routes + SSE streaming
│   │   ├── storage/          # SQLite persistence layer
│   │   └── metrics/          # Prometheus instrumentation
│   └── Cargo.toml            # Dependency manifest (locked)
├── mbo-frontend/             # SvelteKit UI
│   ├── src/
│   │   ├── lib/
│   │   │   ├── stores/       # Market state management
│   │   │   ├── components/   # UI components
│   │   │   └── services/     # API client
│   │   └── routes/           # Page routes
│   └── package.json          # Node dependencies (Bun)
├── monitoring/               # Observability stack
│   ├── prometheus.yml        # Metrics scraping config
│   └── grafana/              # Dashboard definitions
├── .github/workflows/        # CI/CD automation
│   ├── build.yml             # Docker image builds
│   └── test.yml              # Automated testing
├── docker-compose.yaml       # Multi-environment orchestration
└── flake.nix                 # Reproducible dev environment

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Technologies Demonstrated

Backend Engineering:
Rust • Tokio • Axum • Anyhow • SQLite • Prometheus • Server-Sent Events

Frontend Development:
TypeScript • Svelte • SvelteKit • Bun • TailwindCSS • Skeleton UI

DevOps & Infrastructure:
Docker • Kubernetes • GitHub Actions • Nix • Prometheus • Grafana • Nginx

Software Engineering Practices:
CI/CD • Dependency Management • Security Scanning • Performance Testing • API Design • Distributed Systems • Observability • Documentation

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Correctness Guarantees

The order book implementation enforces critical invariants:

1. Price-Time Priority: Orders at same price level maintain FIFO ordering
2. No Crossed Markets: Bid always < Ask (matching engine auto-executes violations)
3. Atomic Operations: All state updates are transactional via RwLock
4. Idempotency: Duplicate operations are safe (critical for distributed systems)
5. Graceful Degradation: Missing orders logged but don't crash system

Verified via:

• Unit tests on core book operations
• Integration tests with real market data
• Invariant assertions in test suite
• Stress testing with 170M+ messages

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This project represents the intersection of my interests in systems programming, financial technology, and production engineering. It's a demonstration that I don't just write code—I architect systems that work reliably at scale.

AI Usage

I used Claude Opus 4.1 for dense, difficult tasks requiring heavy verification and Claude Sonnet 4.5 for less intense tasks such as test verification, by-line documentation, and rapid templating.

Absolutely no LLMs were used in the writing of this README.md out of principle.

List of usage:

• Moving example databento code to use more verbose anyhow reporting
  • A simple matter of asking it to inspect and replace all unwrap and expect calls
• Adding full tokio_tracing coverage
  • An otherwise tedious task, made simple with AI!
  • Required following AI cursor and inspecting all changes to verify there's no secret leakage
• Frontend Templating with Skeleton and Tailwind
  • I carefully watched it design the frontend structure and gave feedback to progress it to a visually appealing end result.
• Docker Compose
  • Writing .dockerignore files was done with Sonnet, as it's an otherwise tedious task. I was careful to monitor for secret leaks and repo ballooning before confirming.
  • I contracted Opus to build the Dockerfiles, giving it examples from past projects and carefully monitoring to ensure quality output.
• Prometheus
  • Both of Anthropic's models are highly skilled at writing Prometheus dashboards, so with supervision I felt confident to let it write the YAML spread.
• Stress Testing
  • I asked Opus to build a neat stress-test that has 1M msg/s, 500 concurrent connections, and consistent stress.
  • Needed help from Sonnet to debug SSE connections not dropping cleanly - it ended up helping developing custom a RAII guard solution, which was really cool to supervise and learn from.
• Kubernetes
  • My final deployment namespace for production was massive (13 YAML files). I asked Sonnet to help me consolidate them into concise, readable documents, which it did a wonderful job with.