(chore): good chunk size

src/anndata/_core/merge.py

@@ -1096,20 +1096,28 @@ def make_dask_col_from_extension_dtype(
 
     from anndata._io.specs.lazy_methods import (
         compute_chunk_layout_for_axis_size,
+        get_chunksize,
         maybe_open_h5,
     )
-    from anndata.experimental import read_lazy
+    from anndata.experimental import read_elem_lazy
     from anndata.experimental.backed._compat import DataArray
     from anndata.experimental.backed._compat import xarray as xr
 
     base_path_or_zarr_group = col.attrs.get("base_path_or_zarr_group")
     elem_name = col.attrs.get("elem_name")
     dims = col.dims
     coords = col.coords.copy()
+    with maybe_open_h5(base_path_or_zarr_group, elem_name) as f:
+        maybe_chunk_size = get_chunksize(read_elem_lazy(f))
+        chunk_size = (
+            compute_chunk_layout_for_axis_size(
+                1000 if maybe_chunk_size is None else maybe_chunk_size[0], col.shape[0]
+            ),
+        )
 
     def get_chunk(block_info=None):
         with maybe_open_h5(base_path_or_zarr_group, elem_name) as f:
-            v = read_lazy(f)
+            v = read_elem_lazy(f)
             variable = xr.Variable(
                 data=xr.core.indexing.LazilyIndexedArray(v), dims=dims
             )
@@ -1128,10 +1136,9 @@ def get_chunk(block_info=None):
         dtype = "object"
     else:
         dtype = col.dtype.numpy_dtype
-    # TODO: get good chunk size?
     return da.map_blocks(
         get_chunk,
-        chunks=(compute_chunk_layout_for_axis_size(1000, col.shape[0]),),
+        chunks=chunk_size,
         meta=np.array([], dtype=dtype),
         dtype=dtype,
     )
@@ -1185,7 +1192,7 @@ def get_attrs(annotations: Iterable[Dataset2D]) -> dict:
     """
     index_names = np.unique([a.index.name for a in annotations])
     assert len(index_names) == 1, "All annotations must have the same index name."
-    if any(a.index.dtype == "int64" for a in annotations):
+    if any(np.issubdtype(a.index.dtype, np.integer) for a in annotations):
         msg = "Concatenating with a pandas numeric index among the indices.  Index may likely not be unique."
         warn(msg, UserWarning)
     index_keys = [

src/anndata/_io/specs/lazy_methods.py

@@ -2,7 +2,7 @@
 
 import re
 from contextlib import contextmanager
-from functools import partial
+from functools import partial, singledispatch
 from pathlib import Path
 from typing import TYPE_CHECKING, overload
 
@@ -92,6 +92,13 @@ def make_dask_chunk(
     return chunk
 
 
+@singledispatch
+def get_chunksize(obj) -> tuple[int, ...]:
+    if hasattr(obj, "chunks"):
+        return obj.chunks
+    raise ValueError("object of type {type(obj)} has no recognized chunks")
+
+
 @_LAZY_REGISTRY.register_read(H5Group, IOSpec("csc_matrix", "0.1.0"))
 @_LAZY_REGISTRY.register_read(H5Group, IOSpec("csr_matrix", "0.1.0"))
 @_LAZY_REGISTRY.register_read(ZarrGroup, IOSpec("csc_matrix", "0.1.0"))

src/anndata/experimental/backed/_lazy_arrays.py

@@ -7,6 +7,7 @@
 
 from anndata._core.index import _subset
 from anndata._core.views import as_view
+from anndata._io.specs.lazy_methods import get_chunksize
 from anndata.compat import H5Array, ZarrArray
 
 from ..._settings import settings
@@ -28,6 +29,7 @@
 
 class ZarrOrHDF5Wrapper(ZarrArrayWrapper, Generic[K]):
     def __init__(self, array: K):
+        self.chunks = array.chunks
         if isinstance(array, ZarrArray):
             return super().__init__(array)
         self._array = array
@@ -152,3 +154,13 @@ def _subset_masked(a: DataArray, subset_idx: Index):
 @as_view.register(DataArray)
 def _view_pd_boolean_array(a: DataArray, view_args):
     return a
+
+
+@get_chunksize.register(MaskedArray)
+def _(a: MaskedArray):
+    return get_chunksize(a._values)
+
+
+@get_chunksize.register(CategoricalArray)
+def _(a: CategoricalArray):
+    return get_chunksize(a._codes)