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

Interactive market depth visualization with sub-50ms latency updates

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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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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

Production metrics: throughput, latencies, connection counts, error rates

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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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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.

Sonnet 4.5 was used in the re-writing of this README.md file.

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.