The efficiency pressure continues! This time with even tighter constraints - lower latency targets and message limits. Time to see if our simple approach still holds up.
Same core approach as Part I, but now we need to handle:
Stricter message-per-operation limits
Lower latency requirements
Higher node counts with more network delay
Made small adjustments to the ticker-based approach:
Reduced sync interval slightly (400ms instead of 500ms)
Better batching logic
Slight optimizations in message handling
With the adjusted timing:
Still well under the message limits
Latency stayed within bounds
The simple periodic sync continues to work
The beauty of the ticker approach:
Natural Load Balancing: Spreads messages over time
Batch Efficiency: Multiple messages share sync costs
Self-Tuning: Works across different network conditions
Robust: No complex failure modes
Sometimes the simple solution scales better than complex ones
Natural batching can be more effective than explicit optimization
Periodic sync patterns are surprisingly robust
Don't optimize prematurely - measure first
The core insight remains: instead of trying to be clever about exactly when to send what, just periodically share everything you know. It's simple, robust, and often surprisingly efficient.
