Boosted our Kafka log with clever caching and cut the message overhead by ~14%. Turns out you don't need fancy algorithms when a good old cache hits the sweet spot.
In-memory cache for offset counters (bye bye redundant reads)
Optimistic CAS with cache-based hints (fewer retries = less traffic)
Cache invalidation on write conflicts (eventual consistency FTW)
Batched reads (5 at a time) to slash network overhead
Message prefetch based on access patterns
No fancy data structure needed - just better read patterns
Offset Caching: Cache-first CAS attempts cut ~50% of offset reads
Batch Reading: Sequential message groups slashed polling RPCs
Minimal Implementation: No complex distributed algorithms needed
| Metric | v2 (Original) | v3 (Optimized) | Improvement |
|---|---|---|---|
| Messages/op | 14.67 | 12.64 | 13.8% |
| Availability | 0.9994939 | 0.9995162 | 0.002% |
| Server messages/op | 12.2 | 10.29 | 15.7% |
Cut the fancy sharding, CRDT, and consensus tricks - a well-placed cache was all we needed. The read-through cache pattern brings most of the performance boost without the distributed systems PhD.
The classic CS lesson applies: optimize the common path first. We found that caching just the offset values and batching reads in groups of 5 accounted for most of the gains.
Eliminating ~2 messages per operation at 1000 ops/sec saves 2K round trips per second
Reduced CAS retries means lower tail latency for concurrent writes
All this while maintaining the same linearizable correctness guarantees
Could go wild with gossip protocols, RDMA, and cache coherence schemes - but nah, we'll save those for when the requirements actually call for them.
Sometimes the simplest solutions are the most effective.
