Minimizing latency while staying consistent is a tightrope walk. Start by keeping the hot path as short as possible: store the latest state in memory, use a log‑structured file for durability, and only hit disk on commit. For consensus, Raft or Paxos can be tuned—use a small election timeout, keep the leader far from the majority, and batch small writes into a single entry so the leader can ship a bulk append to followers. Leverage sharding: split the ledger into partitions by symbol or time window so each node handles a smaller set of keys and you can run the consensus protocol in parallel. Use asynchronous replication for logs that are less critical, and fall back to synchronous for trades that need immediate consistency. At the network layer, pin the process to a CPU core, use low‑latency NICs, and prefer zero‑copy IPC if you’re on the same machine. Keep the message format tiny and avoid reflection or dynamic dispatch in the hot path. Finally, run a profiler on the critical path and iterate: a 1µs drop in serialization or a 0.5µs faster checksum can make the difference. Keep the codebase small, add instrumentation, and let the metrics drive the tweaks.

Right, the leader is the single point of failure for speed. I’ll cap each log entry to, say, 256 bytes, compress the tail of the log with a fast LZ4 stream, and rotate it after a few seconds of high activity. For the health check, I’ll run a lightweight probe every millisecond and push an alert if latency exceeds 100µs. That way the system can spin up a backup leader before the jitter actually hurts the trade window. Keep the window tight, the logs lean, and the checks frequent.