System Design: Designing a High-Performance Trading Platform
Designing a stock or crypto trading platform is the ultimate test of low-latency engineering. You need to process millions of orders per second, maintain a perfectly consistent Order Book, and ensure that trades are executed in the exact order they were received.
1. Core Requirements
- Order Placement: Support Limit, Market, and Stop-Loss orders.
- Matching Engine: Match buy and sell orders with zero errors.
- Market Data: Stream real-time price updates to millions of users.
- Reporting: Maintaining a durable audit trail of every execution.
- Latency: Sub-millisecond execution is a requirement for competitive trading.
2. The Heart of the System: The Matching Engine
The matching engine is typically a single-threaded, in-memory process.
- Why Single-Threaded? To avoid the massive overhead of locks and context switching. By keeping the Order Book in RAM and processing sequentially, you can achieve millions of matches per second.
- Data Structure: Use two TreeMaps or Priority Queues for each trading pair:
- Bids (Buy): Sorted by price (descending) and time (ascending).
- Asks (Sell): Sorted by price (ascending) and time (ascending).
3. The LMAX Disruptor Pattern
To feed the single-threaded engine without a bottleneck, we use the Disruptor Pattern (a high-performance inter-thread messaging library).
- Input Disrupter: Collects orders from multiple network threads and serializes them into a ring buffer.
- Matching Engine: Consumes from the ring buffer, matches orders, and updates the in-memory state.
- Output Disrupter: Publishes execution results to the database and market data streams.
4. Durability: The Replay Strategy
Since the matching engine is in-memory, a crash would lose the entire Order Book.
- The Solution: Event Sourcing. Every incoming order is first appended to a high-speed Sequencer (Write-Ahead Log or Kafka).
- Recovery: If the engine crashes, it reboots and replays the log from the last snapshot to reconstruct the Order Book state exactly as it was.
5. Scaling: Multi-Symmetry
You can't shard a single trading pair (like BTC/USD) because matching requires global knowledge of all orders for that pair.
- The Solution: Symmetric Sharding. Different matching engine instances handle different trading pairs. Engine A handles AAPL, Engine B handles TSLA.
6. Market Data Streaming (WebSockets)
Users need to see the "Order Book Depth" (L2 data) in real-time.
- Optimization: Don't send the whole book on every change. Send a full snapshot once, then send only the Deltas (changes) via WebSockets to minimize bandwidth.
Summary
The engineering of a trading platform is about Mechanical Sympathy—designing software that works with the hardware, not against it. By using in-memory processing, the Disruptor pattern, and Event Sourcing, you can build a matching engine that handles the world's most aggressive trading volumes.
