fix: register dimensions without coordinates as columns

tests/test_sql.py

@@ -488,3 +488,116 @@ def test_table_names_is_keyword_only(self, mixed_ds):
         ctx = XarrayContext()
         with pytest.raises(TypeError):
             ctx.from_dataset("era5", mixed_ds, {("time",): "x"})
+
+    @pytest.fixture
+    def coordless_dims_ds(self):
+        """Mirror the fashion-mnist layout: a dimension
+        coordinate (``sample``) alongside dimensions without coordinates
+        (``channel``/``height``/``width``)."""
+        n_sample, n_channel, n_height, n_width = 4, 1, 3, 3
+        return xr.Dataset(
+            {
+                "images": (
+                    ["sample", "channel", "height", "width"],
+                    np.arange(
+                        n_sample * n_channel * n_height * n_width,
+                        dtype="float32",
+                    ).reshape(n_sample, n_channel, n_height, n_width),
+                ),
+                "labels": (["sample"], np.arange(n_sample, dtype="int64")),
+            },
+            coords={"sample": ("sample", np.arange(n_sample, dtype="int64"))},
+        ).chunk({"sample": 1})
+
+    def test_coordless_dims_appear_as_columns(self, coordless_dims_ds):
+        """Dimensions without coordinates must still be emitted as columns,
+        not silently dropped from the schema."""
+        ctx = XarrayContext()
+        ctx.from_dataset(
+            "mnist",
+            coordless_dims_ds,
+            table_names={
+                ("sample", "channel", "height", "width"): "X",
+                ("sample",): "y",
+            },
+        )
+        result = ctx.sql('SELECT * FROM mnist."X"').to_pandas()
+        assert set(result.columns) == {
+            "sample",
+            "channel",
+            "height",
+            "width",
+            "images",
+        }
+
+    def test_coordless_dims_values_match_xarray(self, coordless_dims_ds):
+        """The X table's rows must match xarray's own pivot exactly, including
+        the synthesized index values for the coordinate-less dimensions."""
+        ctx = XarrayContext()
+        ctx.from_dataset(
+            "mnist",
+            coordless_dims_ds,
+            table_names={
+                ("sample", "channel", "height", "width"): "X",
+                ("sample",): "y",
+            },
+        )
+        dim_cols = ["sample", "channel", "height", "width"]
+        result = (
+            ctx.sql('SELECT * FROM mnist."X"')
+            .to_pandas()
+            .sort_values(dim_cols)
+            .reset_index(drop=True)
+        )
+        expected = (
+            coordless_dims_ds[["images"]]
+            .to_dataframe()
+            .reset_index()
+            .sort_values(dim_cols)
+            .reset_index(drop=True)
+        )
+        pd.testing.assert_frame_equal(
+            result, expected, check_dtype=False, check_like=True
+        )
+
+    def test_coordless_dims_y_table_unaffected(self, coordless_dims_ds):
+        """The 1-D ``y`` group (sample coordinate + labels) is unchanged."""
+        ctx = XarrayContext()
+        ctx.from_dataset(
+            "mnist",
+            coordless_dims_ds,
+            table_names={
+                ("sample", "channel", "height", "width"): "X",
+                ("sample",): "y",
+            },
+        )
+        result = ctx.sql('SELECT * FROM mnist."y"').to_pandas()
+        assert set(result.columns) == {"sample", "labels"}
+        assert len(result) == coordless_dims_ds.sizes["sample"]
+
+    def test_single_table_all_coordless_dims(self):
+        """A uniform-dim dataset whose dims lack coordinates registers as one
+        table with every dimension present as a column, and the coordinate-less
+        dimensions carry their ABSOLUTE index even when chunked."""
+        ds = xr.Dataset(
+            {"a": (("x", "y"), np.arange(6, dtype="float32").reshape(3, 2))}
+        ).chunk({"x": 1})  # chunked along the coordinate-less 'x' dim
+        ctx = XarrayContext()
+        ctx.from_dataset("grid", ds)
+        result = (
+            ctx.sql("SELECT * FROM grid")
+            .to_pandas()
+            .sort_values(["x", "y"])
+            .reset_index(drop=True)
+        )
+        assert set(result.columns) == {"x", "y", "a"}
+        # x must span 0..2 across the three chunks, not restart at 0 each block.
+        expected = (
+            ds.to_dataframe()
+            .reset_index()
+            .sort_values(["x", "y"])
+            .reset_index(drop=True)
+        )
+        pd.testing.assert_frame_equal(
+            result, expected, check_dtype=False, check_like=True
+        )

xarray_sql/df.py

@@ -73,6 +73,24 @@ def resolve_chunks(
     return {d: tuple(c) for d, c in ds.chunks.items()}
 
 
+def _ensure_default_indexes(ds: xr.Dataset) -> xr.Dataset:
+    """Attach a default integer index coordinate to every dimension lacking one.
+
+    xarray allows "dimensions without coordinates"; these are absent from
+    ``ds.coords``, so they are dropped from the SQL schema and, once a block is
+    sliced out with ``isel``, their position is synthesized *relative to the
+    block* (restarting at 0 in every partition). Materialising an explicit
+    ``arange`` index up front turns them into ordinary dimension coordinates, so
+    they appear as columns and carry their absolute position through chunked
+    reads. Datasets whose dimensions already have coordinates are returned
+    unchanged.
+    """
+    missing = {
+        dim: np.arange(ds.sizes[dim]) for dim in ds.dims if dim not in ds.coords
+    }
+    return ds.assign_coords(missing) if missing else ds
+
+
 def _block_slices_from_resolved(
     ds: xr.Dataset, resolved: Mapping[Hashable, tuple[int, ...]]
 ) -> Iterator[Block]:
@@ -371,6 +389,11 @@ def iter_record_batches(
 def _parse_schema(ds: xr.Dataset) -> pa.Schema:
     """Extracts a `pa.Schema` from the Dataset, treating dims and data_vars as columns.
 
+    Only *dimension coordinates* become dimension columns, so a dimension
+    without a coordinate would be dropped. Callers must run the Dataset through
+    :func:`_ensure_default_indexes` first (the readers do) so every dimension
+    has a coordinate and appears as a column.
+
     Uses the xarray index type to detect cftime coordinates without
     materializing their data — important for Dask/Zarr-backed datasets
     where .values would trigger eager computation.

xarray_sql/reader.py

@@ -24,6 +24,7 @@
     _block_len,
     _block_metadata,
     _block_slices_from_resolved,
+    _ensure_default_indexes,
     _parse_schema,
     block_slices,
     iter_record_batches,
@@ -185,6 +186,7 @@ def read_xarray(ds: xr.Dataset, chunks: Chunks = None) -> pa.RecordBatchReader:
       A PyArrow RecordBatchReader, which is a table representation of the input
       Dataset.
     """
+    ds = _ensure_default_indexes(ds)
     reader = XarrayRecordBatchReader(ds, chunks=chunks)
     return pa.RecordBatchReader.from_stream(reader)
 
@@ -254,6 +256,7 @@ def read_xarray_table(
     """
     from ._native import LazyArrowStreamTable
 
+    ds = _ensure_default_indexes(ds)
     schema = _parse_schema(ds)
 
     # Hoist coordinate reads once; avoids N_partitions remote I/O calls for