Made the multi-node log efficient! Focus on reducing message overhead and latency while maintaining correctness.
Batched Replication: Group log entries for efficient transfer
Smart Polling: Reduce redundant reads
Async Operations: Overlap network operations
Optimized Routing: Smart request forwarding
Batch Processing: Amortize network costs across multiple operations
Lazy Replication: Don't replicate immediately for every write
Read Optimization: Cache recent entries locally
Smart Forwarding: Route requests efficiently
Write Batching: Group multiple sends into single replication
Async Replication: Don't block writes on replication
Read Caching: Keep recent log entries locally
Efficient Polling: Avoid unnecessary network calls
Need to balance:
Consistency vs Performance
Latency vs Throughput
Complexity vs Simplicity
Batch operations where possible
Async processing for non-critical paths
Smart caching for frequently accessed data
Efficient network protocols
Lower message overhead per operation
Better throughput under load
Maintained consistency guarantees
Reduced latency for common operations
Batching is almost always worth it
Async can hide network latency
Caching helps with read-heavy workloads
Simple optimizations often give big wins
This shows how distributed systems can be made practical - start with correctness, then optimize for real-world performance.
