libhdf5 encodes a version-4 layout's chunk dimensions in (log2(max) +
8) / 8 bytes, and HDF5 2.0.0 (h5py 3.16) refuses any other width:
"stored chunk dimension encoding length does not match value calculated
from chunk dimensions". The writer rounded 3 bytes up to 4, so h5py
could not open a dataset we wrote with a chunk dimension from 65 536 to
16 777 215, for every chunk index (single chunk, fixed and extensible
array, v2 B-tree). The three encoders now share push_v4_chunk_dims,
which writes the exact width.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The maxshape checks added to chunked_write use format!, which a no_std
build has to import from alloc (scripts/check-nostd.sh).
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
A dataset with more than one unlimited dimension got an Extensible Array
index, which libhdf5 refuses ("already found unlimited dimension"), so
the whole file failed to open in h5py and h5dump. The previous commit
turned that into a write error; this one writes what the library itself
uses there: a version-2 B-tree chunk index (record type 10/11), as a
single leaf of the library's 2048-byte node size, or a larger leaf when
the records do not fit. The root's record count is 16-bit, so more than
65535 chunks is still refused rather than written wrong.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The writer indexed chunks by their position in the current shape, the
same mistake the reader had. With a finite maxshape larger than the shape
the Fixed Array was sized for the shape, so libhdf5 looked up chunks past
its end ("addr overflow"); with the unlimited dimension anywhere but first,
e.g. maxshape (20, None), libhdf5 swizzles that dimension to the slowest
position and read our Extensible Array scrambled. Two unlimited dimensions
produced a file libhdf5 refused to open ("already found unlimited
dimension").
Chunks are now placed with the shared chunk_grid linearisation: Fixed
Array slots cover every chunk of the maximum extent (unwritten ones
undefined), Extensible Array indexes are swizzled, Single Chunk is only
used when the maximum extent is one chunk, and a maxshape that is smaller
than the shape, has more than one unlimited dimension, or would need an
absurd Fixed Array is an error instead of a bad file.
build_chunked_data_from_precompressed now returns a Result.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The Extensible Array writer only filled the index block's 4 inline
elements and the 6 data blocks it addresses directly (240 elements); its
super block addresses were always undefined. Chunks from index 244 on were
written to the file but never indexed, so they read back as fill values in
our reader and in libhdf5, without an error.
The writer now lays out data blocks and super blocks for any element
count as H5EA__hdr_init sizes them, pages data blocks larger than 1024
elements (page-init bits in the owning super block), leaves blocks with no
defined element unallocated, and records real header statistics
(max_idx_set is one past the highest defined index).
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Layout message v4 flag bit 0 (H5D_CHUNK_DONT_FILTER_PARTIAL_CHUNKS, set
with H5Pset_chunk_opts) makes libhdf5 store every chunk that extends past
the dataset's extent without the filter pipeline, while its filter mask
still reads 0. The parser ignored the flag, so readers tried to inflate
raw bytes: libhdf5's own h5fc_edge_v3.h5 failed with "deflate: ...
unknown compression method".
DataLayout::Chunked gains dont_filter_partial_edge_chunks (always false
for v3), and list_chunks — the one place every read path gets its chunk
list from — marks such partial chunks as having skipped every filter, so
the full, cached, indexed, parallel and selection readers all copy them
as-is. chunked_write.rs gets `..` in one exhaustive test pattern for the
new field.
Regression: libhdf5_edge_chunk_fixture_reads (h5fc_edge_v3.h5 from the
HDF5 tools test files, committed as a 2.5 KB fixture), and
h5py_unfiltered_partial_edge_chunks_read (the flag set through h5py's
bundled libhdf5 via ctypes, as h5py has no binding for it: fixed array,
extensible array and B-tree v2 indexes, 1-D and 2-D, plus a hyperslab
of the last chunk), and v4_chunked_dont_filter_partial_edge_chunks_flag.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The Fixed Array writer always packed every element into one data block
behind one checksum. Past 2^10 elements libhdf5 (and our reader) expect a
paged block: a page-init bitmap after the prefix, then one checksummed page
per 1024 elements. Any dataset with more than 1024 chunks and no unlimited
dimension failed with "incorrect metadata checksum" in h5py, h5dump and
our own reader.
build_fixed_array_at now takes one Option<WrittenChunk> per array slot so
later fixes can leave unallocated slots.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Pcodec chunks were written as filter 32023, which the HDF Group registry
assigns to Granular BitRound (GBR). Pcodec has no registered ID (checked
2026-09-25 against hdf5_plugins/docs/RegisteredFilterPlugins.md, which
ends at 32033 with no pcodec entry). GBR's decode is a pass-through, so
libhdf5 with that plugin loaded would have returned the compressed bytes
as the dataset's values.
Write pcodec as 480, from the registry's testing/private range (256-511),
named "pcodec (clawhdf5 private)", and document it as non-interoperable:
only clawhdf5 with the `pcodec` feature reads it. Chunks under 32023 are
still read as pcodec when the filter is named exactly "pcodec" (what
clawhdf5 <= 2.7.0 wrote); any other 32023 is UnsupportedFilter.
Test: pcodec_uses_private_id_and_reads_legacy_32023.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Every Python interop suite had stopped running on this machine: the h5py
writer round-trips, the facade suite, netCDF4 and the reference files.
`python3` is 3.14, nothing on the box has h5py, and PEP 668 refuses to
install it into a system interpreter at all — so the availability probes
all returned false and each suite skipped without failing.
A silent skip here is exactly how the v5 compound-datatype bug reached a
release, so the probes now read `CLAWHDF5_PYTHON` and `ci-test.sh` picks
up `.venv/bin/python` on its own. The detection sits at the top of the
script rather than beside the interop step, because the non-ignored
suites run in the earlier `cargo test` step and would otherwise still
miss it. `CLAWHDF5_REQUIRE_INTEROP=1` continues to turn a skip into a
failure.
Verified against a venv with h5py 3.16 / HDF5 2.0.0: 94 interop tests
across the four suites, all passing.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Requesting a filter without chunk dimensions made the whole dataset a single
chunk. Any read, even one row, then decompresses everything, and a large
dataset cannot be decoded in parallel — which also made the new partial reads
pointless for such files.
auto_chunk_dims keeps datasets up to 1 MiB as one chunk (unchanged behaviour)
and splits larger ones by halving the dimensions in turn, so chunks keep
roughly the dataset's proportions, until a chunk is at most 1 MiB — h5py's
approach. An empty (unlimited, unwritten) dimension is treated as 1024. The
writer passes the element size through resolve_chunk_dims_for; the old
resolve_chunk_dims assumes 8-byte elements. Explicit with_chunks always wins.
Interop test: h5py reads an auto-chunked 13 MB deflate dataset, sees chunks
between 128 KiB and 1 MiB, and a small dataset still has one chunk.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
- Add .gitea/workflows/ci.yml running scripts/ci-test.sh (fmt, clippy,
test, no_std check) on push/PR to main.
- Fix stale rustyhdf5-py/rustyhdf5-format package names in
ci-test.sh/check-nostd.sh, which had been silently no-op'ing those
checks (cargo warns but doesn't fail on an unknown --exclude/-p
target).
- With those checks actually running, fix the real issues they surface:
- clippy: useless_conversion in chunked_write.rs, byte_char_slices in
global_heap.rs/object_header.rs.
- cargo fmt: apply formatting across the workspace (whitespace only).
- no_std (thumbv7em-none-eabihf) build errors in clawhdf5-format:
core::sync::atomic::AtomicU64 doesn't exist on that target (no
native 64-bit atomics) — switch profiling.rs's counters to
portable-atomic, which falls back to a CAS-based emulation there
and is a no-op wrapper elsewhere. Add missing alloc imports for
Box (filters.rs), Vec (filters_szip.rs), and format! (dict_encoding.rs)
on no_std paths. Replace f64::powi (std/libm-only) with a small
local exponentiation-by-squaring helper in the scale-offset filter.
Enables Rayon parallel compression for typical 4-chunk workloads (e.g.,
128×128 matrix with 32-row chunks). Rayon's dispatch overhead is ~2 µs,
worthwhile at ≥3 chunks with real compression work per chunk.
Previously the threshold was "> 4" which excluded 4-chunk datasets entirely
from parallel compression. Now "> 2" covers 3+ chunks.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
Following arXiv:2506.18062 (TDT pre-filter) and matching h5py default
behavior: the shuffle filter is now automatically applied before any
compression codec (deflate, Zstd, LZ4, Pcodec) unless explicitly
disabled with .without_shuffle().
Benchmark results (f32 matrices, shuffle+codec vs unshuffled baseline):
- Zstd-3 at 512×512: 610 → 764 MiB/s (+25%)
- Deflate-6 at 128×128: 132 → 401 MiB/s (+204%)
- Deflate-6 at 512×512: 280 → 745 MiB/s (+166%)
Both codecs now reach parity at ~750 MiB/s for large matrices.
Changes:
- Add no_shuffle field to ChunkOptions (opt-out via .without_shuffle())
- Auto-add FILTER_SHUFFLE in build_pipeline() when compression is active
- Add DatasetBuilder.without_shuffle() method
- Update pipeline tests to reflect new 2-filter default
- Add chunk_options_pipeline_deflate_no_shuffle test
- Update BENCHMARKS.md with measured throughput improvements
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
Implements Pcodec (filter ID 32023) via the `pco` 1.0.x crate as a new
optional compression codec. Pcodec achieves 30–94% better compression
ratio than Zstd for f32/f64 columnar data at 1–5 GiB/s decompression
speed, making it ideal for write-once/read-many embedding archives.
Write throughput at 512×512: 591 MiB/s (parity with Zstd-3 at 610 MiB/s).
For smaller chunks Zstd-3 remains faster due to Pcodec's fixed per-chunk
distributional analysis overhead.
- Add FILTER_PCODEC = 32023 constant to filter_pipeline.rs
- Add pcodec_compress/pcodec_decompress using pco::standalone API
- Wire into compress_chunk/decompress_chunk dispatch
- Add ChunkOptions.pcodec field and DatasetBuilder.with_pcodec() method
- Enable pcodec as highest-priority codec in build_pipeline()
- Add pco dep (optional, feature = "pcodec") to clawhdf5-format/clawhdf5
- Add write_2d_chunked_pcodec benchmark comparing pcodec vs zstd-3
- Document results in BENCHMARKS.md
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
Four independent write-path improvements:
1. Cache compressed chunks between Pass 1 and Pass 2 (chunked_write.rs,
file_writer.rs): the two-pass layout writer previously called
build_chunked_data_at_ext() twice per chunked dataset — once in Pass 1
to get blob sizes and once in Pass 2 with real addresses. Add
PrecompressedChunks / precompress_chunks() / build_chunked_data_from_
precompressed() to compress once in Pass 1, cache the result, and only
rebuild the address-dependent index structures in Pass 2. Expected
~2× speedup for chunked+deflate writes (512×512 deflate: 3.33ms → ~1.7ms).
2. SIMD-vectorisable shuffle filter (filters.rs): replace the naïve O(N·S)
nested loop with an unrolled u32-load path for 4-byte elements (f32) and
a cache-blocked tile loop for all other sizes. LLVM auto-vectorises the
4-byte path into SSE2/AVX2/NEON byte-deinterleave sequences.
3. Zstd benchmark variant (h5bench_write.rs): add write_2d_chunked_zstd
group measuring Zstd level 3 vs deflate level 6 side-by-side. Also fix
the existing write_2d_chunked benchmark — the clawhdf5 path was missing
.with_deflate(6), making the comparison apples-to-oranges. Add arXiv-
backed doc recommendation on DatasetBuilder::with_zstd().
4. Zero-copy HNSW save (hnsw.rs, clawhdf5-io/lib.rs): add
FileWriter::write_bytes_owned(Vec<u8>) that takes ownership to avoid the
full-file clone in write_all_bytes(&[u8]). HNSW::save_to_hdf5 uses it.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
build_chunked_data_at_ext now compresses all chunks via compress_all_chunks
(previously dead code) before laying them out, so compression runs across
rayon threads under the `parallel` feature when there are >4 filtered chunks.
Layout is unchanged — compression preserves chunk order, so on-disk bytes are
identical to the sequential path. The agent crate enables `parallel`, so this
speeds up compressed embedding writes.
Removes the #[allow(dead_code)] on compress_all_chunks and gates
PARALLEL_COMPRESS_THRESHOLD behind the `parallel` feature.
Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
