FillTime::to_byte had the fill-time field rotated against libhdf5
(H5D_FILL_TIME_ALLOC = 0, NEVER = 1, IFSET = 2): Never was written as
ALLOC, Alloc as IFSET and IfSet as NEVER, as h5py reported. The flags
byte is now late allocation plus the right code, and FillTime::from_byte
decodes it.
The default becomes IfSet, which is libhdf5's default and exactly the
byte (0x0a) every dataset was already written with, so default output
does not change; `Alloc` was documented as the C library's default but
never was. DatasetCreateProps follows.
DatasetBuilder::with_fill_value sets a user-defined fill value (one
element's stored bytes, checked against the datatype size), written as a
defined value in the fill value message. h5py reports it, and extending
the dataset in h5py fills the new elements with it.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
FileWriter::with_page_size wrote a "version 4" superblock with an extra
page-size field. HDF5 has no superblock version 4, so libhdf5 refused
every such file ("bad superblock version number").
A paged file is now what libhdf5 itself writes for fs_strategy="page":
a v3 superblock whose extension object header holds a File Space Info
message (strategy PAGE, the page size, free space not persisted; same
bytes and flags as HDF5 2.0), with the file padded to a whole page.
h5py opens it, reports the strategy and page size, and can modify it in
r+ mode. Page sizes outside libhdf5's 512 B..1 GiB are an error.
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]>
read_vl_bytes cut each element to the reference's length field, which
counts sequence elements, not bytes: a VL int32 [1, 2, 3] came back as
3 bytes. Return the whole global-heap object, which is element count x
base size bytes. No in-tree caller depended on the old behaviour.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
read_i64/read_u64/read_i32/read_f64/read_f32 refused enumeration
datatypes, including h5py's bool (an enum of int8), with a type
mismatch. Read them as their base type's integer values, the way array
datatypes already read through theirs.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Every 2-byte float was decoded as IEEE half, so bfloat16 (HDF5 2.0's
H5T_FLOAT_BFLOAT16*, or any custom 8-bit-exponent type) read wrong:
1.5 as 1.9375, +inf as NaN. 1-byte FP8 floats were refused.
Read the exponent/mantissa location and size and the bias from the
datatype message: IEEE half/single/double keep their existing paths
(half still through clawhdf5_format::float16), any other IEEE-style
layout up to 64 bits whose values fit f64 (bfloat16, FP8 E4M3/E5M2, ...)
is decoded generically, and the bulk-copy and zero-copy fast paths now
require the IEEE layout rather than just the size. Datatypes with fields
that describe no float still fall back to IEEE by size; layouts that
cannot be represented in f64 (x87 80-bit, binary128) remain an error.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Reading wider or differently-signed integers kept the low bits: i64
2^40+5 read as i32 was 5, u64::MAX read as i64 was -1, and -1 read as
u64 was 4294967295. u32 data read as i32 also took the bulk-copy fast
path meant for i32. Saturate at the target range like libhdf5's hard
conversions (a negative value read as unsigned is 0), and keep the i32
fast path to signed data.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
read_i32/read_i64/read_u64 on a floating-point dataset reinterpreted the
IEEE bits (1.5 read as i64 was 4609434218613702656). Convert like
libhdf5's hard conversions instead: truncate toward zero and saturate at
the target range; NaN reads as 0.
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]>
Datatype::serialize returned an empty message for these four classes, so
any dataset or attribute of them (including a Raw attribute copied from
another file) was unreadable by libhdf5 ("ran off end of input buffer
while decoding"). They now encode exactly as libhdf5 does: legacy object
and region references as datatype version 1, H5T_STD_REF kinds as version
4 with their encoding version, opaque tags NUL-padded to 8 bytes.
Parsing an opaque tag now stops at its first NUL, so libhdf5's padding
no longer becomes part of the tag. Datatype::check_encodable rejects
what has no encoding (an opaque tag over 248 bytes); FileWriter::finish
calls it for every dataset and attribute type.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Every decode stage was capped at the chunk's decoded size. That holds
only when every filter ahead of the codec preserves size; Fletcher32
does not (it appends a 4-byte checksum), so a pipeline with Fletcher32
before deflate (NetCDF-4's fletcher32 -> shuffle -> deflate ordering,
h5repack's "all filters") failed with "deflate: output exceeds size
limit" on every chunk.
decompress_chunk_masked now computes each stage's bound by running the
chunk size forward through the filters that precede it in write order
(and that the chunk's mask did not skip): shuffle keeps the size,
Fletcher32 adds 4, any codec adds at most n/8 + 64. The cap is still a
small constant factor of the chunk, so a decompression bomb is rejected
as before (tested).
Shuffle also had to learn libhdf5's handling of a length that is not a
whole number of elements (chunk + checksum): shuffle the whole elements
and leave the trailing bytes in place, in both directions. It used to
refuse such data.
Regression: h5py_fletcher32_before_deflate_reads (fletcher->shuffle->
gzip, fletcher->gzip, shuffle->fletcher->gzip, and a 2-D i32 grid),
fletcher32_ahead_of_deflate_stays_bounded and
shuffle_leaves_a_partial_trailing_element_in_place.
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]>
A chunk's filter mask has one bit per pipeline filter; bit i set means
filter i was not applied to that chunk (an optional filter that
declined, or a direct chunk write). Every read path treated any nonzero
mask as "no filters applied" and returned the stored bytes, so a chunk
that skipped only gzip in a shuffle+gzip pipeline came back still
shuffled (h5py write_direct_chunk with filter_mask=0b10: 8 of 32 values
wrong).
decompress_chunk_masked undoes the filters the mask leaves set and skips
the rest; an unsupported filter is no longer an error when the chunk
skipped it. The full, cached, sweep, indexed, parallel and selection
(partial_read) paths all use it, and a chunk is copied straight from the
file only when every filter was skipped. decompress_chunk is the mask-0
case.
Regression: h5py_partial_filter_mask_skips_only_masked_filters (1-D
shuffle+gzip with masks 0, 0b10 and 0b11; 2-D with 0b01; full and
hyperslab reads) and filter_mask_skips_only_the_masked_filters.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Filter 32015 chunks were written with the streaming encoder
(zstd::encode_all), whose frames carry no content size. The registered
HDF5 Zstandard filter (H5Zzstd.c, libhdf5 + hdf5plugin) sizes its output
from ZSTD_getFrameContentSize and fails on such frames, so h5py could not
read our zstd datasets ("filter returned failure during read"). Compress
with the one-shot API, which records the size.
Tests: zstd_frames_record_content_size (content size was None before),
hdf5plugin_reads_our_zstd (ignored interop test; failed before).
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Both indexes place each chunk at a linear index computed from the
dataset's maximum dimensions (libhdf5's max_down_chunks), and the
Extensible Array first swizzles its unlimited dimension to the slowest
position. We linearised by the current dimensions, so any dataset whose
shape was smaller than its maxshape, or whose unlimited dimension was not
the first, read back scrambled without an error: h5py libver="latest"
files with maxshape (10, None) or (20, 10), and the libhdf5 test files
h5fc_ext*.h5 and test_ld.h5.
The linearisation now lives in chunk_grid (shared with the writers), and
slots beyond the current extent are ignored as the library does.
read_fixed_array_chunks / read_extensible_array_chunks take the
dataspace's max dimensions.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
A v2 object header message has a 2-byte size field. The writer truncated
larger sizes to 16 bits, so an attribute over ~64 KiB (or a compact
dataset of 65532-65535 bytes, whose layout message adds 4 bytes) produced
a file libhdf5 rejects ("message of unshareable class flagged as
shareable", "bad flag combination").
ObjectHeaderWriter::serialize now returns a Result and fails on any message
over MAX_MESSAGE_SIZE; FileWriter::finish propagates it. Compact storage
falls back to contiguous above 65531 bytes, the real limit. Dense storage
for large attributes remains future work.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Filter 32004 chunks were framed as a 4-byte little-endian size plus one
LZ4 block. That is not the registered HDF5 LZ4 format (H5Zlz4.c: 8-byte
big-endian total size, 4-byte big-endian block size, then per block a
4-byte big-endian compressed length and the block, stored raw when the
length equals the block size), so libhdf5 + hdf5plugin could not read
our LZ4 datasets and we could not read theirs (h5ex_d_lz4.h5:
"lz4: 0 is not a valid match offset").
Write the registered format (cd_values[0] is honoured as the block size,
default 1 GiB like the plugin) and read it, multi-block and raw blocks
included. Chunks in the old framing stay readable: an HDF5 chunk is under
4 GiB, so a registered chunk always starts with four zero bytes and is at
least 12 bytes long, while an old one starts with four zero bytes only
when empty (5 bytes).
Tests: lz4_reads_registered_hdf5_format (chunk of the HDF Group's
h5ex_d_lz4.h5, block size 3), lz4_writes_registered_hdf5_format,
lz4_reads_legacy_clawhdf5_format, and hdf5plugin_reads_our_lz4 (ignored
interop test; failed before with "filter returned failure during read").
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
A type-1 (raw data chunk) B-tree key holds the chunk size, the filter
mask and one offset per dimension, and those offsets are always 8 bytes:
they are dataset coordinates, not file addresses. The reader used the
superblock's size-of-offsets for them, so in a file with 4-byte offsets
every key was misparsed. Unfiltered chunked datasets read as zeros (with
stray bytes where a misread address landed on data) and filtered ones
failed with "deflate: truncated stream".
Only the sibling and child addresses follow size-of-offsets now. The
unit-test B-tree builder wrote keys the same wrong way, which is why its
tests passed; it now matches the format.
Regression: h5py_four_byte_offsets_chunked_reads (h5py, set_sizes(4, 4)
and (4, 8); 1-D and 2-D, unfiltered and gzip) and the unit test
collect_chunks_with_four_byte_addresses.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
An 82-byte fuzz_btree_v2 crash input from 2026-09-20 was left untracked
in fuzz/artifacts. Replayed today it runs cleanly: the depth cap and
record budget added to B-tree v2 traversal that day fixed it. It is now
in the committed fuzz corpus, and a robustness test replays the fuzz
target's exact code path on it so a regression fails CI rather than
waiting for someone to run the fuzzer.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The setting was persisted in /meta and otherwise ignored: embeddings
were always written as f32. It now does what it says.
clawhdf5-format:
- `DatasetBuilder::with_f16_data` writes IEEE binary16 (numpy float16),
rounding to nearest-even, and `make_f16_type`.
- `clawhdf5_format::float16` holds the f32 <-> f16 conversions, the one
implementation the writer, the reader and the agent all use. Checked
against the `half` crate on 16.7M f32 values and round-trips all 65536
half values; the h5py interop tests confirm the rounding matches
numpy's bit for bit (4020 values incl. ties, subnormals, overflow).
- Reading little-endian float16 as f32 has a fast path.
clawhdf5-agent:
- A float16 store writes /memory/embeddings as half precision, and
`MemoryCache::half_precision` rounds each embedding as it enters the
cache (save, update, WAL replay, and on load of a store still f32 on
disk), so memory and file agree bit for bit and a store searches the
same before and after a reopen (tested).
- Values beyond +-65504 are refused with the new
`MemoryError::InvalidEntry` rather than stored as infinity, on every
save path; batches are all or nothing, and a rejected ephemeral entry
stays in the ephemeral tier. Breaking for exhaustive matches.
- CLI: `create --float16`. Off by default.
Measured on tank, 384-dim, six runs alternating order, medians
(search_harness --float16-study --full): at 100K the file goes from
154.0 to 80.8 MiB (-48%), checkpoint 752 -> 512 ms, open 300 -> 252 ms;
vector recall@10 against an exact scan and hybrid_search latency do not
change. At 10K open is 3 ms slower. Also a test that h5py opens a whole
agent store, f32 and float16, and decodes every dataset.
Docs: README, BENCHMARKS.md ("float16 embedding storage"), CHANGELOG
(including the h5py interop fixes in the previous commit), CLAUDE.md.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Two write-side bugs, both present in every release (the first at least
since v2.1.0), made libhdf5 refuse files written by clawhdf5. Our own
reader ignores both fields, and the interop suites only ever wrote f64
from our side, so nothing here caught them.
- Every f32 dataset: "sign bit position out of bounds". The float
datatype encoder hard-coded the sign bit's position (bits 8-15 of the
class bit field) to 63, which is right only for f64. It is now derived
from the type: bit_offset + bit_precision - 1. This covered every
agent store's embeddings, norms and activation weights.
- Every empty dataset: "invalid dataset size, likely file corruption".
It was written with a real address and size 0, which trips libhdf5's
`addr + size <= addr` overflow check. An empty contiguous dataset now
gets the undefined address, as libhdf5 writes it. This covered every
agent store without sessions or a knowledge graph.
Agent stores are rewritten in full at each checkpoint, so they become
readable at their next checkpoint on a fixed build; other files with f32
or empty datasets need rewriting. Both are recorded in
docs/known-issues.md.
Tests: the sign position byte for f32/f64, and h5py reading our f32
datasets (plain and chunked + deflate) bit for bit.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
rust-version = "1.92" in [workspace.package], inherited by every crate.
1.92 is the floor: wgpu (clawhdf5-gpu) requires it, and the whole
workspace, Python bindings included, checks cleanly on it. ci-test.sh
reads the version from Cargo.toml and checks the workspace on exactly
that toolchain, so the manifests and the README badge cannot drift from
what actually builds. The badge said 1.75, below edition 2024's own
floor.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The core crates (clawhdf5, -agent, -format, -io, -filters, -ann, -accel,
-netcdf4, -cli) now build no C by default: deflate defaults to zlib-rs,
a pure-Rust port of zlib-ng, and zlib-ng becomes the opt-in
`fast-deflate`, which overrides zlib-rs wherever it is enabled. A default
build no longer needs cmake or a C compiler.
Measured on tank, both builds run alternately, three rounds, medians:
zlib-rs is within 6% of zlib-ng on every HDF5 read and write (512x512
deflate-6 chunked write 1.458 vs 1.484 ms; 64 MB compressed read 64.4
vs 65.2 ms), and compressed output is byte-identical. Details in
BENCHMARKS.md, "Deflate backend".
Getting there took two fixes the first measurement exposed:
- zlib-rs needs `std` to detect SIMD at runtime. flate2 enables it via
its default `runtime_detection`, which `default-features = false` had
switched off, leaving zlib-rs 3.5x slower on inflate. The `zlib-rs`
features now enable it.
- Both deflate paths streamed through flate2's 32 KiB read/write
wrappers. They now hand the codec the whole chunk in one call, into a
buffer sized up front (~5% on chunked writes). This also fixes a
silent short read: the streaming reader returned a truncated stream's
bytes without an error; a truncated chunk is now DecompressionError.
In clawhdf5-filters, output longer than the stated size is now an
error rather than silently cut off.
CI: ci-test.sh lints and tests the zlib-ng path, and fails if a
C-building crate (*-sys, cc, cmake) enters a core crate's default
dependency tree. The arm64 job no longer installs cmake.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Every structure in both chunk indexes — header, index block, super
block, data block and each data block page — carries a Jenkins lookup3
checksum, and all of them were parsed past and ignored.
What that costs is not a warning but correct data. Flip one low bit of a
chunk address and the index still has the right shape, the address still
lands inside the file, and the reader returns whatever bytes now sit
there as that chunk's contents. Nothing else in the parse can tell.
Verified in both directions. The checksums accept files written by
HDF5 2.0 from 100 to 200 000 chunks — dense, sparse, gzip-filtered and
paged — which also confirms the block layouts byte for byte, since a
wrong offset would fail every file. And an interop test corrupts an
address to check the read fails instead of returning data: removing the
verification makes that test fail with "corruption produced data instead
of an error", which is what it is there to prove.
The first version of that test passed with verification disabled — it
corrupted a byte a structural check already rejected, so it proved
nothing. Worth recording, since a test that passes for the wrong reason
looks exactly like coverage.
Hand-built fixtures now stamp real checksums, as HDF5 writers do, and
the Extensible Array ones no longer describe the superseded layout.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
A dataset with exactly one unlimited dimension — the ordinary
append-only case — is indexed by an Extensible Array. Only its first
few chunk entries (4 by default) sit inline in the index block, and
everything past them was read with the wrong layout. In the default
shape the 37th chunk onward came back from the wrong place: a
400-chunk dataset returned 364 wrong values while reporting success,
and beyond about a thousand chunks the read failed outright. Silently
wrong data is the worse half of that.
It survived because the only Extensible Array fixture in the suite had
three chunks — inside the inline limit — so no test ever reached a data
block.
Four layout errors, each confirmed against files written by HDF5 2.0 and
against the library source rather than inferred:
- super block `u` owns 2^(u/2) data blocks, not 2^u;
- each holds 2^((u+1)/2) * data_blk_min_elmts elements — the two
quantities double every *other* level, a half step apart;
- a super block carries a block-offset field before its data block
addresses, which was not skipped;
- the page-init bitmap belongs to the super block, one bit per page
packed across all of its data blocks and read MSB-first, rather than
living inside the data block; a paged data block also ends its prefix
with a checksum before the first page.
Where the spec left room for doubt the file settled it: decoding a
paged block's elements and reading the chunk values they address
identifies the mapping exactly, and the bitmap's 68 set bits matched
the 34 data blocks x 2 pages that 200 000 elements need, which only
holds MSB-first.
New interop tests cross every boundary — 4, 37, 400, 5 000 and 200 000
chunks, the last with paged data blocks — plus sparse (uninitialised
pages taking fill values), gzip-filtered elements and a 2-D dataset.
All three fail against the old traversal.
Writing is untouched; this was a read-path bug.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
The existing target only called `BTreeV2Header::parse`, so the
recursive walk behind it — where a node that is its own child overflowed
the stack — was never fuzzed at all. Parsing also requires a valid
Jenkins checksum, which random input essentially never produces, so
almost every input stopped at the first branch.
The target now walks the tree after a successful parse, and also builds
a header straight from the input bytes so the traversal is reachable
without forging a checksum.
Checked both ways: against the unfixed traversal libFuzzer finds the
stack overflow (ASan: stack-overflow), and against the fix that same
input executes in 0 ms and 34.7 million further runs produce no crash,
timeout or OOM.
Corpora and crash artifacts stay out of the repository; the two crafted
inputs are covered by unit tests instead.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Traversal recursed one frame per level with the depth taken from the
file (a u16), and followed child addresses without asking whether they
were shared. Two crafted inputs, both reproduced before fixing:
- A node listing itself as its own child, under a header claiming 65 535
levels, overflowed the stack and aborted the process — SIGABRT, not an
error a caller can handle — from under 100 bytes.
- Levels whose children all point at one shared node below reached it
fan-out^depth times: 29.5 million records in 8 s from ~5 KB, and one
more level would exhaust memory.
Depth is now capped at 64, as the fractal heap already was; no real tree
approaches it, since even at the minimum fan-out of two that is over
2^64 records. And traversal stops once it has produced more records
than the file has bytes to hold them — a valid tree stores each record
once in its own bytes, so this bounds shared subtrees without trusting
the header's own `total_records`. Both inputs now fail in under a
millisecond.
Every B-tree v2 user goes through this collector: dense attributes, v2
groups, shared messages and chunk indexes. To show the budget never
refuses a real file, a new interop test has HDF5 2.0 write a depth-2
chunk index with 40 000 records and reads back all 160 000 values; it
fails when the budget is deliberately made too tight.
Also corrects `BM25Index::search`, which claimed to use Block-Max WAND.
It scores exhaustively, and pruning would not help the store:
`hybrid_search` needs every score because fusion normalises over them.
Co-Authored-By: Claude Opus 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]>
Bump all workspace crates, the node package and pyproject to 2.5.0, fold the
two unreleased sections together and add upgrade notes for the behaviour
changes.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
HDF5 1.12 revised the reference datatype (class 7) in datatype message version
4: reference types 2-4 are the new H5T_STD_REF object / dataset-region /
attribute references. Datatype::parse rejected them with
InvalidReferenceType, so any dataset of that type was unreadable.
h5py cannot write this type, which is why it had never been tested. A real
file was produced by calling the libhdf5 bundled in the h5py wheel through
ctypes (H5T_STD_REF_g, H5Rcreate_object, H5Dwrite); the 2 KB result is
committed as tests/fixtures/std_ref_hdf5_2_0.h5 with its generator,
gen_std_ref.py.
- ReferenceType gains Object2, DatasetRegion2 and Attribute, accepted only
from datatype version 4.
- read_object_references decodes Object2 elements: type(1) flags(1)
token_size(1) token, zero-padded to the element size; the token is the
target's object header address. A null reference decodes to the undefined
address; an external reference, a wrong type byte or a token that doesn't
fit is an error.
The fixture test follows both references and checks they resolve to the
objects they were created from.
Co-Authored-By: Claude Fable 5.1 <[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]>
shuffle_decompress — on the read path of every compressed dataset, since
shuffle is applied automatically before compression — was the naive
`result[i * es + j] = data[j * n + i]`: a multiply and two bounds checks per
byte. It now interleaves fixed-width arrays of byte planes for element sizes
2/4/8/16 (bounds checks hoisted, vectorisable), with a chunked generic
fallback. The write-side shuffle was already optimised; this was the asymmetry
the survey flagged. Modest wall-clock effect now that decode is parallel
(chunked+deflate full read ~70 -> ~66 ms). Round-trip test over element sizes
1-24 and several lengths.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Same-moment A/B on a 64 MB f64 dataset: chunked+deflate 110 -> 69 ms, chunked
72 -> 60 ms, contiguous 56 -> 30 ms.
- read_chunked_data_cached — the path the facade uses — decompressed chunks
one at a time; only the uncached reader was parallel. Cache misses are now
decoded in bounded batches (128), in parallel with the `parallel` feature.
- Every chunk was pushed into the 16 MiB chunk cache, which a larger dataset
just churns (insert, evict moments later). Chunks are cached only when the
whole dataset fits (new ChunkCache::max_bytes).
- Unfiltered chunks went file -> Vec -> aligned cache buffer -> output. They
are copied straight from the file bytes.
- The facade's typed reads convert a contiguous dataset straight from the
borrowed file bytes instead of copying it into a Vec first.
- The native little-endian fast paths allocated vec![0; n] and then overwrote
it; they now fill an uninitialised buffer in one copy (native_le_to_vec).
alloc_output requests zeroed memory from the allocator instead of reserving
and filling.
The unit test that expected unfiltered chunks to land in the decompressed
cache now asserts the new design (index reused, cache not involved).
Co-Authored-By: Claude Fable 5.1 <[email protected]>
With libver='latest', a chunked dataset with two or more unlimited dimensions
indexes its chunks with a v2 B-tree (layout v4, index type 5). Reading one
failed with "unsupported chunked layout version=4, index_type=Some(5)".
read_btree_v2_chunks decodes record types 10 (address + scaled offsets) and 11
(address, stored size, filter mask, scaled offsets). The width of the
stored-size field is taken from the record size the tree header declares
rather than re-deriving the library's formula. Scaled offsets are multiplied
back by the chunk dimensions with overflow checks.
The chunk-index dispatch existed four times (uncached, cached, sweep and
indexed readers). The three copies outside list_chunks now call it, so every
read path — and fill-value handling and partial reads — supports every index
type from one place.
h5py interop test: plain, gzip+shuffle, a 2500-chunk tree with internal nodes,
a sparse dataset with a fill value, and a strided hyperslab.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
read_raw_data_selection computed which chunks a selection intersects, threw
the answer away, decoded the entire dataset and picked elements out of it —
for contiguous layouts too. A 64x64 window of a 64 MB deflate dataset cost
105 ms, about half a full read; every selection cost the same whatever its
size.
New partial_read module: materialise only the selection's bounding box — the
overlapping rows of a contiguous dataset (straight from the file bytes) or the
overlapping chunks (only those are decompressed) — then run the existing
extractor over that buffer with the selection translated to the box origin, so
extraction semantics are exactly the full-read ones. It declines (falling back
to the old path) for All/None, compact/virtual/storage-less layouts, and boxes
covering more than half the dataset. That window now takes 0.39 ms, one row
2.7 ms, one column 5.2 ms.
Selections are validated against the dataset shape first. They were not: a
hyperslab past an edge came back padded with zeros and a point with an
out-of-range column wrapped into the next row, returning the wrong element
with no error. Now FormatError::SelectionOutOfBounds (also rank mismatch and
overlapping blocks); the facade's fill-aware path validates too.
Tests: equivalence against a reference extraction from a full read over 60
random hyperslabs/point lists per layout (contiguous, chunked, deflate) for
ranks 1-3. New read_harness bench binary with before/after in BENCHMARKS.md.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Bump all workspace crates, the node package and pyproject to 2.4.0, finalize
the changelog and add upgrade notes for the search behaviour changes.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Bump all workspace crates, the node package and pyproject to 2.3.0, finalize
the changelog and add upgrade notes for the behaviour changes.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
attrs() silently omitted any attribute whose datatype had no AttrValue variant
— including every Python bool, which h5py stores as an enum — plus complex,
compound and reference attributes, and cast unsigned 64-bit arrays to
I64Array so values above i64::MAX came back negative.
- numpy/h5py-style booleans (an enum of exactly FALSE=0 / TRUE=1 over an
integer base) decode as I64 / I64Array of 0/1.
- AttrValue::U64Array keeps unsigned arrays unsigned. Behaviour change: an
unsigned array attribute no longer arrives as I64Array; the netCDF-4 CF
helpers (_FillValue, valid_range) and the Python bindings handle it.
- AttrValue::Raw { datatype, shape, data } carries any other attribute
verbatim (also used when a value fails to decode as its declared type), so
the attribute list is always complete. Decodable with data_read against the
datatype; Python receives {"dtype", "shape", "data"}.
- Both new variants are writable, so attributes round-trip between files.
h5py interop tests cover reading 13 attribute kinds and h5py reading back a
compound and a u64 attribute written by clawhdf5.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
- Path resolution follows soft links in both old-style (symbol table, cache
type 2) and new-style (compact and dense Link message) groups: absolute and
relative targets, links to groups, links through links, with a depth limit
so a link cycle is NestingDepthExceeded rather than a hang. A dangling link
reports the target it could not find. Previously every soft link was
PathNotFound.
- An external link is FormatError::ExternalLinkUnsupported { filename,
object_path } instead of a misleading PathNotFound.
- Message 0x0007 (External Data Files) is now a known MessageType, and a
dataset carrying it is FormatError::ExternalDataFilesUnsupported. Such a
dataset has no data address in this file, so it would otherwise be read as
"never written" and answered with fill values — wrong data, no error.
- Dense link iteration is shared between hard-link listing and the new
symbolic-link lookup; entry listing behaviour is unchanged.
- h5py interop test for both libver settings.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
HDF5 allocates lazily: a chunk nobody wrote doesn't exist in the file, and a
dataset nobody wrote has no data address. Such regions must read as the
dataset's fill value. There was no Fill Value message parser at all, so:
- a sparse chunked dataset read its holes as zeros — silently wrong whenever
the fill value isn't zero (h5py `fillvalue=-1` came back as 0);
- a dataset that was created but never written failed with NoDataAllocated /
"no address for chunked layout" where h5py returns a filled array.
New clawhdf5_format::fill_value: parses Fill Value messages v1-v3 and the old
0x0004 message (validated against HDF5 2.0 output under default and latest
libver), builds a fully filled dataset when there is no storage, and writes the
fill value into exactly the chunk-grid cells absent from the chunk index —
never mistaking a stored zero for a hole, clipping edge chunks, any rank. It is
skipped entirely for the default (zero) fill value. The chunk index dispatch is
extracted from read_chunked_data into a reusable list_chunks.
The reader, lazy and mmap facades apply it on full reads; selection reads go
through a fill-aware full read when the fill value matters. h5py interop test
compares against h5py's own readback, including a sparse 2-D dataset and a
hyperslab straddling allocated and unallocated chunks.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
A dataset created from a committed (named) datatype stores only a shared-
message reference to it. The facade parsed those reference bytes as the
datatype itself, producing `Time { size: 0 }` and unreadable data, and an
attribute using a committed datatype was silently dropped.
- shared_message::parse_shared_ref had the encoding wrong: it skipped six
reserved bytes for version 2 (only version 1 has them) and had the version 3
types inverted (1 is the SOHM heap, 2 is "committed, in another object
header"). Verified against h5py 3.16 / HDF5 2.0, which writes
`02 02 <address>` under both default and latest libver bounds. Resolution
now dispatches on which field the reference carries.
- New shared_message::message_data resolves a header message through the
indirection; the reader, lazy and mmap facades use it for datatype,
dataspace and filter-pipeline messages.
- AttributeMessage honours the v2/v3 flags (bit 0 datatype shared, bit 1
dataspace shared) via the new parse_in_file, used everywhere file data is
available. Parsing a shared attribute without file access is now
FormatError::UnresolvedSharedMessage instead of a garbage datatype.
- h5py interop test covering both libver settings.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Dataspace and chunk dimensions are untrusted 64-bit fields, but the chunked
read paths computed `num_elements() as usize * elem_size` and
`chunk_dims.product() * elem_size` with plain arithmetic and fed the result to
`vec![0u8; n]`. A crafted file could wrap the product (under-sizing the output
buffer that chunks are then copied into) or request an allocation large enough
to abort the process.
- Dataspace::checked_num_elements, checked_byte_len, checked_chunk_byte_len
and alloc_output (try_reserve_exact) replace the plain products and
vec![0; n] at every chunked read site, plus the VDS and hyperslab paths.
Overflow and allocation failure are FormatError::Overflow.
- Dataspace::num_elements saturates instead of wrapping.
- A zero-element dataset returns early, which also keeps the stride products
in range when another dimension is huge.
- parallel_read.rs: the three `c_addr + size > len` bounds checks used a raw
add; they now use checked_add like the rest of the crate.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Formatting only. cargo fmt --check was already failing on main (accel SIMD
kernels, agent, format, migrate, bench); CI now enforces it.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
- clippy --all-targets plus a clawhdf5-format feature matrix (parallel, lz4,
zstd, pcodec, fast-checksum); fix the accumulated lint backlog in test,
bench and feature-gated code (no behaviour changes).
- Install python3 + h5py/numpy/netCDF4/xarray in the CI container and set
CLAWHDF5_REQUIRE_INTEROP=1, which makes a missing interop dependency a test
failure. Every h5py/netCDF4 interop test used to skip silently in CI. Run
the #[ignore]d writer_h5py_tests suite explicitly.
- cargo bench --no-run so benches can't rot; fix bench.rs and memory_bench.rs,
which no longer compiled against the current strategy/consolidation APIs.
- Optional fuzz smoke run via CLAWHDF5_FUZZ_SECONDS.
- CHANGELOG and docs/known-issues.md updated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Compound datasets written with default libver bounds (datatype message
version 1, i.e. plain h5py.File(path, 'w')) could not be read: the v1 member
layout has 28 bytes of legacy array fields after the byte offset
(dimensionality 1, reserved 3, permutation 4, reserved 4, four sizes 16) and
the parser skipped 24, so every following member was read 4 bytes off. v2 was
also wrong: it keeps the 8-byte name padding and has no array fields.
Found by adding a default-libver axis to the h5py-generated-file tests (HDF5
2.0 raised the default low bound to 1.8, so "default" files are a distinct
format path from libver='latest'). Adds byte-level v1/v2 regression tests, a
truncation test, and fuzz corpus seeds for v1 compound and native complex.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Bump all workspace crates, the node package and pyproject to 2.2.0 and
finalize the changelog. The repository URL in every manifest pointed at a
GitHub location that does not resolve; use the real origin.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
