FileEditor re-opened its path to plan each edit but wrote through the file
it held open, and the Python 'r+' handle re-opened the path after every
edit to read. When the path came to name another file between edits (a
rename or replacement, or a relative path after os.chdir), an edit was laid
out from the other file's metadata and written into the held one,
corrupting it, and later reads came from the other file (the review's
repro: h5py then reports "invalid dataset size, likely file corruption").
The editor now plans from a mapping of its own file (a clone of the held
descriptor, dropped before the edit writes) and canonicalises its path at
open. New FileEditor::reader() opens the held file anew for reading,
without sharing the editor's flock (a mapping of a cloned descriptor holds
the lock until unmapped): through /proc/self/fd on Linux, which follows a
renamed file; elsewhere by path, refused on Unix when the path no longer
names the held file. The Python handle reads through it and keeps no path;
a 'w' file is written at the absolute path it was opened with.
Tests: edit_tests.rs edits_go_to_the_file_held_not_the_path; test_edit.py
test_relative_path_and_chdir and test_path_replaced_between_edits.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
clawhdf5.File(path, 'r+') (and 'a' on an existing file) holds a
FileEditor, and with it the file's exclusive lock, until close():
- ds[key] = value: h5py's keys and broadcasting (numpy's rules for
slices and integers with extra leading 1-axes allowed; the exact shape
for an index list, a scalar only where h5py expands it). Arrays are
converted as libhdf5 converts them in native byte order (integers
saturate, floats truncate toward zero and clip, integers go into h5py's
bool enum by value); other values through
numpy.asarray(value, dtype=ds.dtype), as h5py does. NaN into an integer
dataset is a ValueError instead of libhdf5's arbitrary value. The value
preparation is a small Python module compiled into the extension
(src/edit_helpers.py).
- ds.resize(shape) / ds.resize(n, axis=k) with h5py's argument rules.
- attrs[name] = value, attrs.create(name, data, shape, dtype),
attrs.modify: numeric, bool, complex, bytes and str data of any shape,
with h5py's HDF5 types; str is stored as fixed-length UTF-8 (the editor
cannot write variable-length strings).
- File.mode, File.flush(), Dataset.chunks.
Each edit runs with the GIL released under the file handle's write lock
(no read sees a half-written edit), then the file is reopened;
datasets and attrs objects re-read their shape and attributes when the
handle's edit generation moved. What the editor cannot do is
NotImplementedError before anything is written: deleting attributes or
objects, creating datasets or groups, compound fields by name,
variable-length data, and FileEditor's own limits.
Where libhdf5 2.0 (h5py 3.16) converts inconsistently -- its soft
conversions in non-native byte order (a float in (-1, 0) becomes the
integer minimum, same-size unsigned->signed wraps) and native casts that
are undefined in C (half floats into unsigned, float(max) rounded up) --
clawhdf5 saturates as libhdf5's native path does; listed in
docs/known-issues.md.
Tests (tests/test_edit.py): every edit applied by h5py and by clawhdf5 to
copies of the same file and both read back through h5py after each edit,
on h5py files (libver earliest, v114, latest) and a clawhdf5 file: a fixed
sequence over every chunk index kind, compact/contiguous/gzip layouts and
numeric, bool, enum, complex, string and compound types, 16 random
sequences of 40 edits, and a numeric conversion matrix; a refused edit
must be refused by both and leave the file unchanged. Also dense
attributes, locking, objects seeing edits, readers racing a writer, and
h5dump (plus h5rs check in ci-test.sh) on every edited file. The
read-vs-h5py suite also runs on a file opened 'r+'.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
The Python bindings could not open a remote file: they parsed through
File::as_bytes() in eight places (path lookups, object headers,
dataspaces, attributes, group listings, the global heap of
variable-length data), which a storage-backed file does not have.
- Every object of a File now shares one handle (src/handle.rs) that
runs all file access, metadata included, with the GIL released and
parses through File::storage() and the clawhdf5_format *_in functions.
Local files take the same path (their storage is the mmap).
- clawhdf5.File(url) opens any scheme://... through
clawhdf5_remote::storage_for_url (read-only; another mode is a
ValueError). File.open_url(url, **options) takes the cache and HTTP
options (block_size, cache_size, headers, retries, timeout,
allow_full_download, max_full_download, require_validator,
max_redirects, max_parallel); File.remote_stats gives the block
cache's counters.
- Default build: plain HTTP only, no C. https (rustls/ring) and
s3/gcs/azure (aws-lc-rs) are opt-in features of clawhdf5-py, and
ci-test.sh's no-C check now covers the crate.
- A failed storage read (network error, file changed on the server) is an
OSError, never KeyError/ValueError and never data; `key in group`
raises it instead of answering False.
Tests: the read-vs-h5py suite runs locally and over HTTP (1 MiB and
1 KiB blocks) against a range-capable http.server in the test process
(conftest.RangeServer); test_remote.py covers request counts, cache
hits, a server without Range support, a changed file, a server that
hangs up, 16 threads, and a spinning thread that keeps running while a
read waits on 0.2 s requests.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
