We’re launching Lyria 3.5 in Google Flow Music, with advances across musicality, lyrics, vocals, and creative control
deepmind.google·6d ago
AI Engineering
Lobsters·Jun 27·5 min read
Shard your locks: benchmarking 6 Go cache designs
TL;DR
In-memory caching can suffer from performance issues due to lock contention, especially under varying workloads. Six different cache designs were implemented using Go's standard library and benchmarked for efficiency.
✦ Why It Matters
Engineers should consider sharding locks in concurrent caching to optimize performance under heavy loads.
Key Takeaways
- Implement a 256-way sharded map for better performance.
- Avoid using sync.RWMutex for high write loads.
- Benchmark cache designs under various workloads.
- Pin processes to physical cores for accurate performance testing.
Full Summary
Lock contention in in-memory caching can significantly impact performance, particularly in multi-core environments. To explore this, six different cache designs were created using Go's standard library, focusing on a string-to-string mapping.
The benchmarks were conducted under read-heavy, balanced, and write-heavy loads across 1 to 8 CPU cores. Results showed that a 256-way sharded map was the most efficient, being up to 8 times faster than a single sync.Mutex at 8 cores.
Interestingly, using sync.RWMutex, which is often thought to improve read performance, did not yield significant benefits beyond two cores and was slower for write operations. The testing methodology involved pinning processes to physical cores to avoid OS-induced variability.
These findings suggest that sharding locks can greatly enhance cache performance in concurrent applications.