[WIP] Structured array for manifest

virtualizarr/manifests/__init__.py

@@ -2,9 +2,4 @@
 # This is just to avoid conflicting with some type of file called manifest that .gitignore recommends ignoring.
 
 from .array import ManifestArray  # type: ignore # noqa
-from .manifest import (  # type: ignore # noqa
-    ChunkEntry,
-    ChunkManifest,
-    concat_manifests,
-    stack_manifests,
-)
+from .manifest import ChunkEntry, ChunkManifest  # type: ignore # noqa

virtualizarr/manifests/array_api.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from ..zarr import Codec, ZArray
-from .manifest import concat_manifests, stack_manifests
+from .manifest import ChunkManifest
 
 if TYPE_CHECKING:
     from .array import ManifestArray
@@ -123,10 +123,11 @@ def concatenate(
     new_shape = list(first_shape)
     new_shape[axis] = new_length_along_concat_axis
 
-    concatenated_manifest = concat_manifests(
-        [arr.manifest for arr in arrays],
+    concatenated_manifest_entries = np.concatenate(
+        [arr.manifest.entries for arr in arrays],
         axis=axis,
     )
+    concatenated_manifest = ChunkManifest(entries=concatenated_manifest_entries)
 
     new_zarray = ZArray(
         chunks=first_arr.chunks,
@@ -206,10 +207,11 @@ def stack(
     new_shape = list(first_shape)
     new_shape.insert(axis, length_along_new_stacked_axis)
 
-    stacked_manifest = stack_manifests(
-        [arr.manifest for arr in arrays],
+    stacked_manifest_entries = np.stack(
+        [arr.manifest.entries for arr in arrays],
         axis=axis,
     )
+    stacked_manifest = ChunkManifest(entries=stacked_manifest_entries)
 
     # chunk size has changed because a length-1 axis has been inserted
     old_chunks = first_arr.chunks

virtualizarr/manifests/manifest.py

@@ -1,9 +1,8 @@
-import itertools
 import re
-from typing import Any, Iterable, Iterator, List, Mapping, Tuple, Union, cast
+from typing import Any, Iterable, Iterator, List, NewType, Tuple, Union, cast
 
 import numpy as np
-from pydantic import BaseModel, ConfigDict, field_validator
+from pydantic import BaseModel, ConfigDict
 
 from ..types import ChunkKey
 
@@ -14,6 +13,9 @@
 _CHUNK_KEY = rf"^{_INTEGER}+({_SEPARATOR}{_INTEGER})*$"  # matches 1 integer, optionally followed by more integers each separated by a separator (i.e. a period)
 
 
+ChunkDict = NewType("ChunkDict", dict[ChunkKey, dict[str, Union[str, int]]])
+
+
 class ChunkEntry(BaseModel):
     """
     Information for a single chunk in the manifest.
@@ -42,11 +44,20 @@ def to_kerchunk(self) -> List[Union[str, int]]:
         return [self.path, self.offset, self.length]
 
 
+# TODO we want the path field to contain a variable-length string, but that's not available until numpy 2.0
+# See https://numpy.org/neps/nep-0055-string_dtype.html
+MANIFEST_STRUCTURED_ARRAY_DTYPES = np.dtype(
+    [("path", "<U32"), ("offset", np.int32), ("length", np.int32)]
+)
+
+
 class ChunkManifest(BaseModel):
     """
     In-memory representation of a single Zarr chunk manifest.
 
-    Stores the manifest as a dictionary under the .chunks attribute, in this form:
+    Stores the manifest internally as a numpy structured array.
+
+    The manifest can be converted to or from a dictionary form looking like this
 
     {
         "0.0.0": {"path": "s3://bucket/foo.nc", "offset": 100, "length": 100},
@@ -55,24 +66,45 @@ class ChunkManifest(BaseModel):
         "0.1.1": {"path": "s3://bucket/foo.nc", "offset": 400, "length": 100},
     }
 
+    using the .from_dict() and .dict() methods, so users of this class can think of the manifest as if it were a dict.
+
     See the chunk manifest SPEC proposal in https://github.com/zarr-developers/zarr-specs/issues/287 .
 
     Validation is done when this object is instatiated, and this class is immutable,
     so it's not possible to have a ChunkManifest object that does not represent a complete valid grid of chunks.
     """
 
-    model_config = ConfigDict(frozen=True)
+    model_config = ConfigDict(
+        frozen=True,
+        arbitrary_types_allowed=True,  # so pydantic doesn't complain about the numpy array field
+    )
 
-    entries: Mapping[ChunkKey, ChunkEntry]
-    # shape_chunk_grid: Tuple[int, ...]  # TODO do we need this for anything?
+    # TODO how to type hint to indicate a numpy structured array with specifically-typed fields?
+    entries: np.ndarray
 
-    @field_validator("entries")
     @classmethod
-    def validate_chunks(cls, entries: Any) -> Mapping[ChunkKey, ChunkEntry]:
-        validate_chunk_keys(list(entries.keys()))
+    def from_dict(cls, chunks: ChunkDict) -> "ChunkManifest":
+        # TODO do some input validation here first?
+        validate_chunk_keys(chunks.keys())
+
+        # TODO should we actually pass shape in, in case there are not enough chunks to give correct idea of full shape?
+        shape = get_chunk_grid_shape(chunks.keys())
 
-        # TODO what if pydantic adjusts anything during validation?
-        return entries
+        # Initializing to empty implies that entries with path='' are treated as missing chunks
+        entries = np.empty(shape=shape, dtype=MANIFEST_STRUCTURED_ARRAY_DTYPES)
+
+        # populate the array
+        for key, entry in chunks.items():
+            try:
+                entries[split(key)] = tuple(entry.values())
+            except (ValueError, TypeError) as e:
+                msg = (
+                    "Each chunk entry must be of the form dict(path=<str>, offset=<int>, length=<int>), "
+                    f"but got {entry}"
+                )
+                raise ValueError(msg) from e
+
+        return ChunkManifest(entries=entries)
 
     @property
     def ndim_chunk_grid(self) -> int:
@@ -81,7 +113,7 @@ def ndim_chunk_grid(self) -> int:
 
         Not the same as the dimension of an array backed by this chunk manifest.
         """
-        return get_ndim_from_key(list(self.entries.keys())[0])
+        return self.entries.ndim
 
     @property
     def shape_chunk_grid(self) -> Tuple[int, ...]:
@@ -90,23 +122,57 @@ def shape_chunk_grid(self) -> Tuple[int, ...]:
 
         Not the same as the shape of an array backed by this chunk manifest.
         """
-        return get_chunk_grid_shape(list(self.entries.keys()))
+        return self.entries.shape
 
     def __repr__(self) -> str:
         return f"ChunkManifest<shape={self.shape_chunk_grid}>"
 
     def __getitem__(self, key: ChunkKey) -> ChunkEntry:
-        return self.chunks[key]
+        indices = split(key)
+        return ChunkEntry(self.entries[indices])
 
     def __iter__(self) -> Iterator[ChunkKey]:
         return iter(self.chunks.keys())
 
     def __len__(self) -> int:
-        return len(self.chunks)
+        return self.entries.size
 
-    def dict(self) -> dict[str, dict[str, Union[str, int]]]:
-        """Converts the entire manifest to a nested dictionary."""
-        return {k: dict(entry) for k, entry in self.entries.items()}
+    def dict(self) -> ChunkDict:
+        """
+        Converts the entire manifest to a nested dictionary, of the form
+
+        {
+            "0.0.0": {"path": "s3://bucket/foo.nc", "offset": 100, "length": 100},
+            "0.0.1": {"path": "s3://bucket/foo.nc", "offset": 200, "length": 100},
+            "0.1.0": {"path": "s3://bucket/foo.nc", "offset": 300, "length": 100},
+            "0.1.1": {"path": "s3://bucket/foo.nc", "offset": 400, "length": 100},
+        }
+        """
+
+        def _entry_to_dict(entry: Tuple[str, int, int]) -> dict[str, Union[str, int]]:
+            return {
+                "path": entry[0],
+                "offset": entry[1],
+                "length": entry[2],
+            }
+
+        coord_vectors = np.mgrid[
+            tuple(slice(None, length) for length in self.shape_chunk_grid)
+        ]
+
+        return cast(
+            ChunkDict,
+            {
+                join(inds): _entry_to_dict(entry.item())
+                for *inds, entry in np.nditer([*coord_vectors, self.entries])
+                if entry.item()[0]
+                != ""  # don't include entry if path='' (i.e. empty chunk)
+            },
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        """Two manifests are equal if all of their entries are identical."""
+        return (self.entries == other.entries).all()
 
     @staticmethod
     def from_zarr_json(filepath: str) -> "ChunkManifest":
@@ -122,15 +188,15 @@ def from_kerchunk_chunk_dict(cls, kerchunk_chunk_dict) -> "ChunkManifest":
         chunkentries = {
             k: ChunkEntry.from_kerchunk(v) for k, v in kerchunk_chunk_dict.items()
         }
-        return ChunkManifest(entries=chunkentries)
+        return ChunkManifest.from_dict(chunkentries)
 
 
-def split(key: ChunkKey) -> List[int]:
-    return list(int(i) for i in key.split("."))
+def split(key: ChunkKey) -> Tuple[int, ...]:
+    return tuple(int(i) for i in key.split("."))
 
 
-def join(inds: Iterable[int]) -> ChunkKey:
-    return cast(ChunkKey, ".".join(str(i) for i in inds))
+def join(inds: Iterable[Any]) -> ChunkKey:
+    return cast(ChunkKey, ".".join(str(i) for i in list(inds)))
 
 
 def get_ndim_from_key(key: str) -> int:
@@ -154,9 +220,6 @@ def validate_chunk_keys(chunk_keys: Iterable[ChunkKey]):
                 f"Inconsistent number of dimensions between chunk key {key} and {first_key}: {other_ndim} vs {ndim}"
             )
 
-    # Check that the keys collectively form a complete grid
-    check_keys_form_grid(chunk_keys)
-
 
 def get_chunk_grid_shape(chunk_keys: Iterable[ChunkKey]) -> Tuple[int, ...]:
     # find max chunk index along each dimension
@@ -165,99 +228,3 @@ def get_chunk_grid_shape(chunk_keys: Iterable[ChunkKey]) -> Tuple[int, ...]:
         max(indices_along_one_dim) + 1 for indices_along_one_dim in zipped_indices
     )
     return chunk_grid_shape
-
-
-def check_keys_form_grid(chunk_keys: Iterable[ChunkKey]):
-    """Check that the chunk keys collectively form a complete grid"""
-
-    chunk_grid_shape = get_chunk_grid_shape(chunk_keys)
-
-    # create every possible combination
-    all_possible_combos = itertools.product(
-        *[range(length) for length in chunk_grid_shape]
-    )
-    all_required_chunk_keys: set[ChunkKey] = set(
-        join(inds) for inds in all_possible_combos
-    )
-
-    # check that every possible combination is represented once in the list of chunk keys
-    if set(chunk_keys) != all_required_chunk_keys:
-        raise ValueError("Chunk keys do not form a complete grid")
-
-
-def concat_manifests(manifests: List["ChunkManifest"], axis: int) -> "ChunkManifest":
-    """
-    Concatenate manifests along an existing dimension.
-
-    This only requires adjusting one index of chunk keys along a single dimension.
-
-    Note axis is not expected to be negative.
-    """
-    if len(manifests) == 1:
-        return manifests[0]
-
-    chunk_grid_shapes = [manifest.shape_chunk_grid for manifest in manifests]
-    lengths_along_concat_dim = [shape[axis] for shape in chunk_grid_shapes]
-
-    # Note we do not need to change the keys of the first manifest
-    chunk_index_offsets = np.cumsum(lengths_along_concat_dim)[:-1]
-    new_entries = [
-        adjust_chunk_keys(manifest.entries, axis, offset)
-        for manifest, offset in zip(manifests[1:], chunk_index_offsets)
-    ]
-    all_entries = [manifests[0].entries] + new_entries
-    merged_entries = dict((k, v) for d in all_entries for k, v in d.items())
-
-    # Arguably don't need to re-perform validation checks on a manifest we created out of already-validated manifests
-    # Could use pydantic's model_construct classmethod to skip these checks
-    # But we should actually performance test it because it might be pointless, and current implementation is safer
-    return ChunkManifest(entries=merged_entries)
-
-
-def adjust_chunk_keys(
-    entries: Mapping[ChunkKey, ChunkEntry], axis: int, offset: int
-) -> Mapping[ChunkKey, ChunkEntry]:
-    """Replace all chunk keys with keys which have been offset along one axis."""
-
-    def offset_key(key: ChunkKey, axis: int, offset: int) -> ChunkKey:
-        inds = split(key)
-        inds[axis] += offset
-        return join(inds)
-
-    return {offset_key(k, axis, offset): v for k, v in entries.items()}
-
-
-def stack_manifests(manifests: List[ChunkManifest], axis: int) -> "ChunkManifest":
-    """
-    Stack manifests along a new dimension.
-
-    This only requires inserting one index into all chunk keys to add a new dimension.
-
-    Note axis is not expected to be negative.
-    """
-
-    # even if there is only one manifest it still needs a new axis inserted
-    chunk_indexes_along_new_dim = range(len(manifests))
-    new_entries = [
-        insert_new_axis_into_chunk_keys(manifest.entries, axis, new_index_value)
-        for manifest, new_index_value in zip(manifests, chunk_indexes_along_new_dim)
-    ]
-    merged_entries = dict((k, v) for d in new_entries for k, v in d.items())
-
-    # Arguably don't need to re-perform validation checks on a manifest we created out of already-validated manifests
-    # Could use pydantic's model_construct classmethod to skip these checks
-    # But we should actually performance test it because it might be pointless, and current implementation is safer
-    return ChunkManifest(entries=merged_entries)
-
-
-def insert_new_axis_into_chunk_keys(
-    entries: Mapping[ChunkKey, ChunkEntry], axis: int, new_index_value: int
-) -> Mapping[ChunkKey, ChunkEntry]:
-    """Replace all chunk keys with keys which have a new axis inserted, with a given value."""
-
-    def insert_axis(key: ChunkKey, new_axis: int, index_value: int) -> ChunkKey:
-        inds = split(key)
-        inds.insert(new_axis, index_value)
-        return join(inds)
-
-    return {insert_axis(k, axis, new_index_value): v for k, v in entries.items()}