CHANGELOG.md

@@ -359,6 +359,16 @@
   to each check. **Breaking (format crate):** `FormatError` gained
   `InvalidObjectHeader`, `InvalidDatatype`, `InvalidChunkDimensions` and
   `TruncatedFile`; an exhaustive `match` on it needs the new arms.
+- **Chunked datasets whose chunk dimensions take 3, 5, 6 or 7 bytes did not
+  open.** A version-4 layout (`libver="latest"`) stores each chunk dimension
+  in the fewest bytes that hold the largest one, so a chunk dimension from
+  65 536 to 16 777 215 (e.g. h5py `chunks=(70000,)`) takes 3 bytes; only 1, 2,
+  4 and 8 were read, and the rest failed with `UnexpectedEof`. Widths 1-8 are
+  read now, and 0 or more than 8 is refused as libhdf5 refuses it. A width
+  larger than needed is accepted: HDF5 2.0.0 refuses one ("stored chunk
+  dimension encoding length does not match"), but libhdf5 since
+  HDFGroup/hdf5@e124c36 (2026-06-05) reads it, and clawhdf5 itself wrote such
+  layouts.
 - `clawhdf5-format` VDS: variable-length and reference data from a source in
   another file is refused. Those elements are global-heap IDs and object
   addresses in the source file; copied into the virtual dataset they would

crates/clawhdf5-format/src/data_layout.rs

@@ -540,43 +540,30 @@ impl DataLayout {
                     ));
                 }
 
-                // dimension sizes
+                // Each dimension takes 1 to 8 bytes (libhdf5 writes the
+                // fewest that hold the largest one, so 3, 5, 6 and 7 occur:
+                // a chunk dimension of 70 000 takes 3). libhdf5 refuses 0
+                // and more than 8.
+                if dim_size_encoded_length == 0 || dim_size_encoded_length > 8 {
+                    return Err(FormatError::InvalidChunkDimensions(
+                        "encoded chunk dimension size is too large".into(),
+                    ));
+                }
                 ensure_len(data, p, dimensionality * dim_size_encoded_length)?;
                 let mut chunk_dimensions = Vec::with_capacity(dimensionality);
                 for _ in 0..dimensionality {
-                    let val = match dim_size_encoded_length {
+                    let val = data[p..p + dim_size_encoded_length]
-                        1 => data[p] as u32,
+                        .iter()
-                        2 => u16::from_le_bytes([data[p], data[p + 1]]) as u32,
+                        .rev()
-                        4 => u32::from_le_bytes([data[p], data[p + 1], data[p + 2], data[p + 3]]),
+                        .fold(0u64, |acc, &b| (acc << 8) | u64::from(b));
-                        8 => {
+                    // Chunk dimensions are held as u32; HDF5 2.0 can write
-                            // V4 chunked encodes dimension sizes as 8 bytes, but
+                    // larger ones (layout version 5), which are refused
-                            // our ChunkedStorageV4 stores them as u32. We read only
+                    // rather than truncated.
-                            // the low 4 bytes (little-endian). This silently
+                    let val = u32::try_from(val).map_err(|_| {
-                            // truncates dimensions > 4 GiB, which are not expected
+                        FormatError::InvalidChunkDimensions(format!(
-                            // in practice (HDF5 chunk dimensions are always small).
+                            "chunk dimension {val} is larger than 2^32 - 1, which is not supported"
-                            // If the high bytes are non-zero, the file is malformed
+                        ))
-                            // or uses dimensions we cannot represent.
+                    })?;
-                            let high = u32::from_le_bytes([
-                                data[p + 4],
-                                data[p + 5],
-                                data[p + 6],
-                                data[p + 7],
-                            ]);
-                            if high != 0 {
-                                return Err(FormatError::UnexpectedEof {
-                                    expected: p + 8,
-                                    available: data.len(),
-                                });
-                            }
-                            u32::from_le_bytes([data[p], data[p + 1], data[p + 2], data[p + 3]])
-                        }
-                        _ => {
-                            return Err(FormatError::UnexpectedEof {
-                                expected: p + dim_size_encoded_length,
-                                available: data.len(),
-                            });
-                        }
-                    };
                     chunk_dimensions.push(val);
                     p += dim_size_encoded_length;
                 }
@@ -838,6 +825,51 @@ mod tests {
         ));
     }
 
+    /// A v4 chunked layout (fixed array index) whose `dims` are each
+    /// encoded in `width` bytes.
+    fn v4_chunked_msg(width: u8, dims: &[u64]) -> Vec<u8> {
+        let mut m = vec![4u8, 2, 0, dims.len() as u8, width];
+        for &d in dims {
+            m.extend_from_slice(&d.to_le_bytes()[..width.min(8) as usize]);
+        }
+        m.push(3); // fixed array index
+        m.push(0); // page bits
+        m.extend_from_slice(&0x1000u64.to_le_bytes());
+        m
+    }
+
+    #[test]
+    fn v4_chunk_dimensions_take_1_to_8_bytes() {
+        // libhdf5 encodes each dimension in the fewest bytes that hold the
+        // largest: a chunk dimension of 70 000 takes 3, and 3, 5, 6 and 7
+        // were refused ("UnexpectedEof").
+        for width in 1..=8u8 {
+            let dims = [if width >= 3 { 70_000 } else { 200 }, 8];
+            let layout = DataLayout::parse(&v4_chunked_msg(width, &dims), 8, 8)
+                .unwrap_or_else(|e| panic!("width {width}: {e:?}"));
+            assert!(
+                matches!(&layout, DataLayout::Chunked { chunk_dimensions, .. }
+                    if chunk_dimensions.iter().map(|&d| u64::from(d)).eq(dims)),
+                "width {width}: {layout:?}"
+            );
+        }
+        // libhdf5 refuses 0 and more than 8 bytes.
+        for width in [0u8, 9] {
+            assert_eq!(
+                DataLayout::parse(&v4_chunked_msg(width, &[4, 8]), 8, 8).unwrap_err(),
+                FormatError::InvalidChunkDimensions(
+                    "encoded chunk dimension size is too large".into()
+                )
+            );
+        }
+        // A dimension past u32 cannot be represented and is refused, not
+        // truncated.
+        assert!(matches!(
+            DataLayout::parse(&v4_chunked_msg(5, &[1 << 32, 8]), 8, 8),
+            Err(FormatError::InvalidChunkDimensions(m)) if m.contains("2^32")
+        ));
+    }
+
     #[test]
     fn v1v2_rejects_bad_class_dimensionality_and_truncation() {
         assert_eq!(

crates/clawhdf5/tests/h5py_chunked_read_tests.rs

@@ -343,3 +343,41 @@ print("OK")
     let want: Vec<u8> = line[990..].iter().flat_map(|v| v.to_le_bytes()).collect();
     assert_eq!(tail, want);
 }
+
+// ---------------------------------------------------------------------------
+// Chunk dimension widths in layout version 4
+// ---------------------------------------------------------------------------
+
+/// A version-4 layout stores every chunk dimension in the fewest bytes that
+/// hold the largest one (the element size included). A chunk dimension of
+/// 70 000 takes 3 bytes and 2^32 + 1 elements would take 5; widths other
+/// than 1, 2, 4 and 8 were refused, so these h5py files did not open.
+#[test]
+fn h5py_layout_v4_chunk_dimensions_of_3_bytes_read() {
+    skip_if_no_python!();
+    let dir = tempfile::tempdir().unwrap();
+    let path = dir.path().join("enc3.h5");
+    let p = path.display().to_string();
+    run_python(&format!(
+        r#"
+import h5py, numpy as np
+with h5py.File("{p}", "w", libver="latest") as f:
+    f.create_dataset("single", data=np.arange(70000, dtype="<u4"), chunks=(70000,))
+    ds = f.create_dataset("ea", shape=(10,), maxshape=(None,), chunks=(70000,), dtype="<f8")
+    ds[:] = np.arange(10.0)
+    f.create_dataset("fa", data=np.arange(3 * 70000, dtype="<i4").reshape(3, 70000),
+                     chunks=(1, 70000), compression="gzip")
+with h5py.File("{p}", "r") as f:
+    raw = open("{p}", "rb").read()
+    # version 4, class 2 (chunked), flags, 2 or 3 dims, then 3 bytes each
+    assert raw.find(bytes([4, 2, 0, 2, 3])) > 0 or raw.find(bytes([4, 2, 1, 2, 3])) > 0
+"#
+    ));
+    let file = File::open(&path).unwrap();
+    let single = file.dataset("single").unwrap().read_u64().unwrap();
+    assert!(single.iter().copied().eq(0..70000), "single");
+    let ea = file.dataset("ea").unwrap().read_f64().unwrap();
+    assert_eq!(ea, (0..10).map(f64::from).collect::<Vec<_>>());
+    let fa = file.dataset("fa").unwrap().read_i32().unwrap();
+    assert!(fa.iter().copied().eq(0..3 * 70000), "fa");
+}