Add consolidated structure family

docs/source/explanations/catalog.md

@@ -54,7 +54,8 @@ and `assets`, describes the format,  structure, and location of the data.
   to the Adapter
 - `management` --- enum indicating whether the data is registered `"external"` data
   or `"writable"` data managed by Tiled
-- `structure_family` --- enum of structure types (`"container"`, `"array"`, `"table"`, ...)
+- `structure_family` --- enum of structure types (`"container"`, `"array"`, `"table"`,
+  etc. -- except for `consolidated`, which can not be assigned to a Data Source)
 - `structure_id` --- a foreign key to the `structures` table
 - `node_id` --- foreign key to `nodes`
 - `id` --- integer primary key

docs/source/explanations/structures.md

@@ -11,7 +11,8 @@ The structure families are:
 
 * array --- a strided array, like a [numpy](https://numpy.org) array
 * awkward --- nested, variable-sized data (as implemented by [AwkwardArray](https://awkward-array.org/))
-* container --- a of other structures, akin to a dictionary or a directory
+* consolidated --- a container-like structure to combine tables and arrays in a common namespace
+* container --- a collection of other structures, akin to a dictionary or a directory
 * sparse --- a sparse array (i.e. an array which is mostly zeros)
 * table --- tabular data, as in [Apache Arrow](https://arrow.apache.org) or
   [pandas](https://pandas.pydata.org/)
@@ -575,3 +576,76 @@ response.
   "count": 5
 }
 ```
+
+### Consolidated
+
+This is a specialized container-like structure designed to link together multiple tables and arrays that store
+related scientific data. It does not support nesting but provides a common namespace across all columns of the
+contained tables along with the arrays (thus, name collisions are forbidden). This allows to further abstract out
+the disparate internal storage mechanisms (e.g. Parquet for tables and zarr for arrays) and present the user with a
+smooth homogeneous interface for data access. Consolidated structures do not support pagination and are not
+recommended for "wide" datasets with more than ~1000 items (cloumns and arrays) in the namespace.
+
+Below is an example of a Consolidated structure that describes two tables and two arrays of various sizes. Their
+respective structures are specfied in the `parts` list, and `all_keys` defines the internal namespace of directly
+addressible columns and arrays.
+
+```json
+{
+    "parts": [
+        {
+            "structure_family": "table",
+            "structure": {
+                "arrow_schema": "data:application/vnd.apache.arrow.file;base64,/////...FFFF",
+                "npartitions": 1,
+                "columns": ["A", "B"],
+                "resizable": false
+            },
+            "name": "table1"
+        },
+        {
+            "structure_family": "table",
+            "structure": {
+                "arrow_schema": "data:application/vnd.apache.arrow.file;base64,/////...FFFF",
+                "npartitions": 1,
+                "columns": ["C", "D", "E"],
+                "resizable": false
+            },
+            "name": "table2"
+        },
+        {
+            "structure_family": "array",
+            "structure": {
+                "data_type": {
+                    "endianness": "little",
+                    "kind": "f",
+                    "itemsize": 8,
+                    "dt_units": null
+                },
+                "chunks": [[3], [5]],
+                "shape": [3, 5],
+                "dims": null,
+                "resizable": false
+            },
+            "name": "F"
+        },
+        {
+            "structure_family": "array",
+            "structure": {
+                "data_type": {
+                    "endianness": "not_applicable",
+                    "kind": "u",
+                    "itemsize": 1,
+                    "dt_units": null
+                },
+                "chunks": [[5], [7], [3]],
+                "shape": [5, 7, 3],
+                "dims": null,
+                "resizable": false
+            },
+            "name": "G"
+        }
+    ],
+    "all_keys": ["A", "B", "C", "D", "E", "F", "G"]
+}
+```

docs/source/how-to/register.md

@@ -72,7 +72,10 @@ Sometimes it is necessary to take more manual control of this registration
 process, such as if you want to take advantage of particular knowledge
 about the files to specify particular `metadata` or `specs`.
 
-Use the Python client, as in this example.
+#### Registering external data
+
+To register data from external files in Tiled, one can use the Python client and
+construct Data Source object explicitly passing the list of assets, as in the following example.
 
 ```py
 import numpy
@@ -112,3 +115,36 @@ client.new(
     specs=[],
 )
 ```
+
+#### Writing a consolidated structure
+
+Similarly, to create a consolidated container structure, one needs to specify
+its constituents as separate Data Sources. For example, to consolidate a table
+and an array, consider the following example
+
+```python
+import pandas
+
+rng = numpy.random.default_rng(12345)
+arr = rng.random(size=(3, 5), dtype="float64")
+df = pandas.DataFrame({"A": ["one", "two", "three"], "B": [1, 2, 3]})
+
+node = client.create_consolidated(
+    [
+        DataSource(
+            structure_family=StructureFamily.table,
+            structure=TableStructure.from_pandas(df),
+            name="table1",
+        ),
+        DataSource(
+            structure_family=StructureFamily.array,
+            structure=ArrayStructure.from_array(arr),
+            name="C",
+        )
+    ]
+)
+
+# Write the data
+node.parts["table1"].write(df)
+node.parts["C"].write_block(arr, (0, 0))
+```

docs/source/reference/service.md

@@ -104,6 +104,8 @@ See {doc}`../explanations/structures` for more context.
    tiled.structures.array.BuiltinDtype
    tiled.structures.array.Endianness
    tiled.structures.array.Kind
+   tiled.structures.consolidated.ConsolidatedStructure
+   tiled.structures.consolidated.ConsolidatedStructurePart
    tiled.structures.core.Spec
    tiled.structures.core.StructureFamily
    tiled.structures.table.TableStructure

tiled/_tests/test_consolidated.py

@@ -0,0 +1,88 @@
+import numpy
+import pandas
+import pandas.testing
+import pytest
+
+from ..catalog import in_memory
+from ..client import Context, from_context
+from ..server.app import build_app
+from ..structures.array import ArrayStructure
+from ..structures.core import StructureFamily
+from ..structures.data_source import DataSource
+from ..structures.table import TableStructure
+
+rng = numpy.random.default_rng(12345)
+
+df1 = pandas.DataFrame({"A": ["one", "two", "three"], "B": [1, 2, 3]})
+df2 = pandas.DataFrame(
+    {
+        "C": ["red", "green", "blue", "white"],
+        "D": [10.0, 20.0, 30.0, 40.0],
+        "E": [0, 0, 0, 0],
+    }
+)
+arr1 = rng.random(size=(3, 5), dtype="float64")
+arr2 = rng.integers(0, 255, size=(5, 7, 3), dtype="uint8")
+md = {"md_key1": "md_val1", "md_key2": 2}
+
+
+@pytest.fixture(scope="module")
+def tree(tmp_path_factory):
+    return in_memory(writable_storage=tmp_path_factory.getbasetemp())
+
+
+@pytest.fixture(scope="module")
+def context(tree):
+    with Context.from_app(build_app(tree)) as context:
+        client = from_context(context)
+        x = client.create_consolidated(
+            [
+                DataSource(
+                    structure_family=StructureFamily.table,
+                    structure=TableStructure.from_pandas(df1),
+                    name="table1",
+                ),
+                DataSource(
+                    structure_family=StructureFamily.table,
+                    structure=TableStructure.from_pandas(df2),
+                    name="table2",
+                ),
+                DataSource(
+                    structure_family=StructureFamily.array,
+                    structure=ArrayStructure.from_array(arr1),
+                    name="F",
+                ),
+                DataSource(
+                    structure_family=StructureFamily.array,
+                    structure=ArrayStructure.from_array(arr2),
+                    name="G",
+                ),
+            ],
+            key="x",
+            metadata=md,
+        )
+        # Write by data source.
+        x.parts["table1"].write(df1)
+        x.parts["table2"].write(df2)
+        x.parts["F"].write_block(arr1, (0, 0))
+        x.parts["G"].write_block(arr2, (0, 0, 0))
+
+        yield context
+
+
+def test_iterate_parts(context):
+    client = from_context(context)
+    for part in client["x"].parts:
+        client["x"].parts[part].read()
+
+
+def test_iterate_columns(context):
+    client = from_context(context)
+    for col in client["x"]:
+        client["x"][col].read()
+        client[f"x/{col}"].read()
+
+
+def test_metadata(context):
+    client = from_context(context)
+    assert client["x"].metadata == md

tiled/_tests/test_dataframe.py

@@ -41,6 +41,17 @@
             pandas.DataFrame({f"column_{i:03d}": i * numpy.ones(5) for i in range(10)}),
             npartitions=1,
         ),
+        # a dataframe with mixed types
+        "diverse": DataFrameAdapter.from_pandas(
+            pandas.DataFrame(
+                {
+                    "A": numpy.array([1, 2, 3], dtype="|u8"),
+                    "B": numpy.array([1, 2, 3], dtype="<f8"),
+                    "C": ["one", "two", "three"],
+                }
+            ),
+            npartitions=1,
+        ),
     }
 )
 
@@ -100,6 +111,17 @@ def test_dataframe_single_partition(context):
     pandas.testing.assert_frame_equal(actual, expected)
 
 
+def test_reading_diverse_dtypes(context):
+    client = from_context(context)
+    expected = tree["diverse"].read()
+    actual = client["diverse"].read()
+    pandas.testing.assert_frame_equal(actual, expected)
+
+    for col in expected.columns:
+        actual = client["diverse"][col].read()
+        assert numpy.array_equal(expected[col], actual)
+
+
 def test_dask(context):
     client = from_context(context, "dask")["basic"]
     expected = tree["basic"].read()