Benchmark for lore storage fragment optimization#103
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write_fragmented called FastCDC::cut once per chunk, shipping the work to the compute pool, awaiting the result, and repeating. For a 1 GiB buffer with ~4 KiB chunks that is ~256k spawn dispatches, oneshot allocations and await yields — overhead that can rival the chunking work itself for small remaining chunks. Compute all FastCDC boundaries in a single compute_pool task by driving the FastCDC Iterator to completion. Fixed-size chunking has trivial per-step math so its boundaries are computed inline. The single-fragment fast path and the storage dispatch loop are unchanged. Verified by a new parity test that compares the batched boundaries against a reference FastCDC iteration on a 256 KiB random buffer, plus edge cases for empty, sub-min-size, all-zero, and non-aligned fixed-size inputs. Signed-off-by: Oleksii Habrusiev <alexgabrusev@gmail.com>
…nt-optimization Signed-off-by: Oleksii Habrusiev <alexgabrusev@gmail.com> # Conflicts: # lore-storage/src/fragment_engine.rs
…ation Signed-off-by: Oleksii Habrusiev <alexgabrusev@gmail.com>
Signed-off-by: Oleksii Habrusiev <alexgabrusev@gmail.com>
…c allocation in FastCDC chunking Signed-off-by: Oleksii Habrusiev <alexgabrusev@gmail.com>
…lore-storage-fragment-optimization
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Micro-benchmark results
Comparing the new batched approach against a simulation of the old per-chunk approach.
Same FastCDC config as the real code: min=32KB, expected=64KB, threshold=256KB, Level1.
What's being measured
Results
Fixed-size chunking (for reference): 252 ns at 16 MB (just arithmetic, no hashing).
Why it matters
At 64 KB the overhead of a single boundary is too small to notice. But for real-world
writes (1 MB and up) the batch approach is consistently 40-47% faster. A 1 GB file
with ~16K chunk boundaries saves ~16K spawn dispatches, oneshot allocations and await
yields — replacing them with a single dispatch.
Run locally:
cargo bench -p lore-storage --bench chunkingRaw log: