搭建加密货币交易所：寻求可靠快速的嵌入式数据库（带C/C++ API）

Hey Jason, great question—order data for crypto exchanges is mission-critical, so picking the right storage solution (especially key-value stores, which excel at high-throughput order book operations) is make-or-break. Let’s break down your viable options, and address the self-build question at the end:

Top Reliable, Cost-Effective Storage Options with C/C++ APIs #

RocksDB #

• Background: Facebook’s enhanced fork of LevelDB, specifically designed to fix LevelDB’s reliability gaps while boosting performance. It’s now an industry standard for high-throughput workloads.
• Why it fits: Built-in write-ahead logging (WAL) for crash recovery, robust data corruption protection, and support for massive concurrent read/write operations—perfect for the low-latency, high-volume demands of crypto order processing. It has a mature C++ API, extensive documentation, and a huge active community (many top exchanges like Binance rely on it for order storage).
• Note: You’ll need to tune parameters like WAL configuration and compression strategies to match your throughput needs, but there’s no shortage of community best practices to guide you.

LMDB (Lightning Memory-Mapped Database) #

• Background: A lightweight, memory-mapped KV store with strict ACID compliance—reliability is its core selling point.
• Why it fits: Zero-copy design delivers exceptional read performance (great for querying historical orders), and its transaction model ensures atomicity for critical operations like order placement/cancellation. It has a native C API, minimal dependencies, and is trivial to deploy. For most mid-sized exchanges, its single-threaded write model won’t be a bottleneck; if you need higher write concurrency, you can implement sharding easily.
• Note: It’s a great pick if you prioritize simplicity and data integrity over extreme write concurrency.

WiredTiger #

• Background: Best known as MongoDB’s default storage engine, but it can be used as a standalone KV store.
• Why it fits: Supports multi-version concurrency control (MVCC) for stable high-concurrency performance, built-in compression, WAL, and crash recovery. Its C API is fully featured, and it handles complex transactional workflows (like batch order processing) smoothly. It’s been battle-tested at scale by MongoDB, so you know it’s reliable.
• Note: Community traction is slightly lower than RocksDB, but documentation is thorough enough to get you up and running quickly.

FoundationDB #

• Background: An open-source distributed KV store acquired by Apple, focused on strong consistency and ACID transactions.
• Why it fits: If you anticipate scaling your exchange to a distributed architecture (e.g., multi-region order processing), FoundationDB’s native clustering and fault tolerance are a huge win. It has a native C++ API, built-in data validation, and automatic failover—so you won’t lose order data if a node goes down. Best of all, it’s free to use.
• Note: Deployment and maintenance are a bit more complex than single-node stores, but the scalability payoff is worth it for long-term growth.

Should You Build Your Own Key-Value Store? #

Short answer: Almost certainly not, unless you have a dedicated team of database engineers with years of experience building and maintaining production-grade storage systems. Here’s why:

• Reliability is brutally hard: Building a store that can survive crashes, power outages, and data corruption without losing critical order data requires months (if not years) of rigorous testing. Even a tiny bug could lead to catastrophic data loss—something no crypto exchange can afford.
• Performance optimization takes time: The tools above have been refined over years to handle edge cases like disk I/O bottlenecks, concurrency conflicts, and efficient compression. Replicating that level of performance from scratch would divert your team’s focus away from your core exchange business.
• Maintenance is a full-time job: Once you build it, you’ll need to fix bugs, patch security vulnerabilities, and optimize for new hardware indefinitely. That’s a massive resource drain.

The only scenario where self-building makes sense is if you have hyper-specialized requirements (e.g., custom ultra-low-latency data structures for order matching that can’t be layered on top of existing tools). Even then, you’re better off extending an open-source solution (like RocksDB) rather than starting from zero.

内容的提问来源于stack exchange，提问作者Jason