Chunked reads beat an h5py process pool; unlimited writer B-trees; Blosc2; 599/697 conformance #16

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osobh merged 48 commits from feat/p2b-scale into main 2026-09-26 17:42:16 +00:00
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## Unreleased
### Chunked full reads (2026-09-26)
- **Chunks are decoded straight into the output, into reused buffers.** A
full read of a chunked dataset faulted in about three times its size in
fresh pages: every chunk was decoded into a new buffer per filter stage
(the cached reader behind `read_*` decoded 128 chunks at a time before
placing any; the uncached one behind `MmapFile`, `LazyFile` and
`verify_provenance` decoded the whole dataset first), then assembled into
a byte buffer, which the typed readers copied once more. Now each chunk
is decoded into buffers the thread keeps between chunks and reads
(`clawhdf5_format::filters::DecodeScratch`,
`decompress_chunk_exact_with`: deflate inflates into a kept buffer with a
reset inflater, shuffle into the other one, Fletcher32 is checked in
place; other filters go through the registry as before) and copied
directly to its place in the output. Chunks still go into the file's
chunk cache when the whole dataset fits. Selection reads decode the
chunks they touch the same way.
- **Typed full reads of chunked data skip the byte buffer.** `read_f32`,
`read_f64`, `read_i32`, `read_i64` and `read_u64` (on `File`, `MmapFile`
and `LazyFile`) of a chunked dataset stored as that type in native byte
order decode every chunk into the returned `Vec` (huge-page backed when
large, like the byte readers' output); other types and byte orders
convert as before. New public
`clawhdf5_format::data_read::read_chunked_native`.
- **Reading threads no longer wait for a busy rayon pool.** A full read
handed its chunks to rayon and the calling thread slept until the pool had
decoded them, so with a small pool (2-4 threads) readers outside it queued
behind its workers. The calling thread now decodes too, and pool workers
join in only when free; a helper the pool starts after the read has
finished returns at once. A single read still spreads over the default
pool. This replaces the one-thread-pool special case below for full
reads. Chunks are placed from several threads only when the chunk index
puts them on the chunk grid at distinct places; a corrupt index is read
one chunk at a time, and the error reported is still the first failing
chunk's. New test `crates/clawhdf5/tests/busy_decode_pool.rs`.
- **Fixed:** in a filtered dataset, a chunk stored with every filter skipped
(filter mask) and shorter than a chunk read with zeros in place of its
missing part through `File`'s `read_*`; it is now an error naming the
chunk, as `MmapFile`/`LazyFile` already made it.
- New h5py comparison `crates/clawhdf5/tests/chunked_read_paths_interop.rs`:
every chunked read path (cached and uncached full reads, `MmapFile`,
`LazyFile`, small, strided and point selections, with and without the
`parallel` feature) for 1-8-byte integers and 2-8-byte floats in both
byte orders, through deflate, shuffle, Fletcher32, LZF, SZIP and Blosc,
with partial edge chunks, sparse datasets and fill values, and datasets
larger than the chunk cache.
### Concurrent reads (2026-09-26)
- **Full reads of chunked datasets scale with threads again when rayon's
pool has one thread.** Each full read handed its chunks to rayon to
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## Concurrent and contiguous read performance (measured 2026-09-26)
**Status:** open for chunked full reads (one cause fixed 2026-09-26); the
contiguous item is fixed (2026-09-26). Measured on
**Status:** open for chunked full reads at 16 threads until re-measured
(the causes identified below are fixed as of 2026-09-26); the contiguous
item is fixed (2026-09-26). Measured on
tank with `concurrent_read` against h5py 3.16 / HDF5 2.0 (`BENCHMARKS.md`,
"Concurrent reads"):
- **Partly fixed 2026-09-26.** Full reads of chunked datasets from several threads
@@ -48,9 +49,19 @@ tank with `concurrent_read` against h5py 3.16 / HDF5 2.0 (`BENCHMARKS.md`,
readers outside it still wait on its workers. Datasets larger than the
cache's budget were already read without inserting into it, and skipping
its lookups entirely gained only a few percent at 16 threads. Remaining
per-read overhead, not yet addressed: each full `read_f32` of a chunked
dataset faults in about three times its size in fresh pages (the output,
the `f32` copy of it, and a new buffer per decoded chunk).
per-read overhead: each full `read_f32` of a chunked dataset faults in
about three times its size in fresh pages (the output, the `f32` copy of
it, and a new buffer per decoded chunk).
**Fixed 2026-09-26** (both causes; see `CHANGELOG.md`, "Chunked full
reads"): chunks are decoded into buffers each thread reuses and copied
straight into the output, and the typed readers decode into the `Vec<T>`
they return, so a full read no longer faults in a buffer per chunk or a
second copy of its output; and the reading
thread decodes its own chunks with pool workers helping when free, so no
reader waits on a small or busy pool
(`crates/clawhdf5/tests/busy_decode_pool.rs`). The 16-thread comparison
with h5py processes has not been re-measured yet (tank was busy with
other work); this item stays open until it is.
- Contiguous datasets read 4x slower than h5py on one thread (2.5 vs
9.8 GB/s full, 0.12x for 256 x 256 hyperslabs).
**Fixed 2026-09-26** (re-measured on tank at `408f69e`: 13665 MB/s