Go maps reuse memory on overwrites, which is why orcaman achieves 0 B/op for pure updates. xsync's custom bucket structure allocates 24 B/op per write even when overwriting existing keys.
At 1M writes/second with 90% overwrites: xsync allocates ~27 MB/s, orcaman ~6 MB/s. The trade is 24 bytes/op for 2x speed under contention. Whether this matters depends on whether your bottleneck is CPU or memory allocation.
Benchmark code: standard Go testing framework, 8 workers, 100k keys.
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Looks good! There's an important thing missing from the benchmarks though:
- cpu usage under concurrency: many of these spin-lock or use atomics, which can use up to 100% cpu time just spinning.
- latency under concurrency: atomics cause cache-line bouncing which kills latency, especially p99 latency
Idk why but I tend to shy away from non std libs that use unsafe (like xsync). I'm sure the code is fine, but I'd rather take the performance hit I guess.
I don't write Go but respect to the author for trying to list trade-off considerations for each of the implementations tested, and not just proclaim their library the overal winner.
Will we also eventually get a generic sync.Map?
Almost certainly, since the internal HashTrieMap is already generic. But for now this author's package stands in nicely.
I ran benchmarks comparing xsync.Map's memory allocation against orcaman/concurrent-map.
Pure overwrite workload (pre-allocated values): xsync.Map: 24 B/op 1 alloc/op 31.89 ns/op orcaman/concurrent-map: 0 B/op 0 alloc/op 70.72 ns/op
Real-world mixed (80% overwrites, 20% new): xsync.Map: 57 B/op 2 allocs/op 218.1 ns/op orcaman/concurrent-map: 63 B/op 3 allocs/op 283.1 ns/op
Go maps reuse memory on overwrites, which is why orcaman achieves 0 B/op for pure updates. xsync's custom bucket structure allocates 24 B/op per write even when overwriting existing keys.
At 1M writes/second with 90% overwrites: xsync allocates ~27 MB/s, orcaman ~6 MB/s. The trade is 24 bytes/op for 2x speed under contention. Whether this matters depends on whether your bottleneck is CPU or memory allocation.
Benchmark code: standard Go testing framework, 8 workers, 100k keys.
Looks good! There's an important thing missing from the benchmarks though:
- cpu usage under concurrency: many of these spin-lock or use atomics, which can use up to 100% cpu time just spinning.
- latency under concurrency: atomics cause cache-line bouncing which kills latency, especially p99 latency
Idk why but I tend to shy away from non std libs that use unsafe (like xsync). I'm sure the code is fine, but I'd rather take the performance hit I guess.
I don't write Go but respect to the author for trying to list trade-off considerations for each of the implementations tested, and not just proclaim their library the overal winner.
Will we also eventually get a generic sync.Map?
Almost certainly, since the internal HashTrieMap is already generic. But for now this author's package stands in nicely.