diff --git a/python/python/lancedb/table.py b/python/python/lancedb/table.py index 84ae4e836..95b10422a 100644 --- a/python/python/lancedb/table.py +++ b/python/python/lancedb/table.py @@ -634,26 +634,43 @@ def _align_field_types( return new_fields -def _align_list_value_field( - value_field: pa.Field, target_value_field: pa.Field -) -> pa.Field: - # A list has exactly one child, so the inferred child name ("item") aligns - # positionally and adopts the table's child name; pa.Table.cast renames it. - return _align_field(value_field, target_value_field).with_name( - target_value_field.name - ) +def _align_container_child(child: pa.Field, target_child: pa.Field) -> pa.Field: + # A list has one child, a map one key and one item, so an inferred child name + # ("item") aligns positionally and adopts the table's; pa.Table.cast renames it. + return _align_field(child, target_child).with_name(target_child.name) + + +def _arrow_json_storage_type(input_type: pa.DataType) -> Optional[pa.DataType]: + """The storage type arrow.json would use for ``input_type``. + + Returns None if the type cannot hold JSON text. + """ + if pa.types.is_string(input_type) or pa.types.is_string_view(input_type): + return pa.string() + if pa.types.is_large_string(input_type): + return pa.large_string() + return None def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field: - # Preserve arrow.json input until it reaches Lance. LanceDB exposes stored - # JSON columns as lance.json (JSONB-backed LargeBinary), but casting the - # input to that storage type here merely relabels the raw JSON bytes as + # LanceDB exposes stored JSON columns as lance.json (JSONB-backed LargeBinary), but + # casting the input to that storage type here merely relabels the raw JSON bytes as # JSONB. Lance must see arrow.json so it can perform the JSONB encoding. - if ( - _field_extension_name(field) == "arrow.json" - and _field_extension_name(target_field) == "lance.json" - ): - return field + if _field_extension_name(target_field) == "lance.json": + if _field_extension_name(field) == "arrow.json": + return field + # Plain JSON text, which is what pyarrow infers for a column of `str`, only + # needs the arrow.json label. + json_storage = _arrow_json_storage_type(field.type) + if json_storage is not None: + # Labelled through metadata rather than pa.json_(), which only exists on + # newer PyArrow; Lance reads the extension name off the field either way. + return pa.field( + field.name, + json_storage, + field.nullable, + {"ARROW:extension:name": "arrow.json"}, + ) if pa.types.is_struct(target_field.type): if pa.types.is_struct(field.type): new_type = pa.struct( @@ -667,7 +684,7 @@ def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field: elif pa.types.is_list(target_field.type): if _is_list_like(field.type): new_type = pa.list_( - _align_list_value_field( + _align_container_child( field.type.value_field, target_field.type.value_field ) ) @@ -676,7 +693,7 @@ def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field: elif pa.types.is_large_list(target_field.type): if _is_list_like(field.type): new_type = pa.large_list( - _align_list_value_field( + _align_container_child( field.type.value_field, target_field.type.value_field ) ) @@ -685,13 +702,28 @@ def _align_field(field: pa.Field, target_field: pa.Field) -> pa.Field: elif pa.types.is_fixed_size_list(target_field.type): if _is_list_like(field.type): new_type = pa.list_( - _align_list_value_field( + _align_container_child( field.type.value_field, target_field.type.value_field ), target_field.type.list_size, ) else: new_type = target_field.type + elif pa.types.is_map(target_field.type): + if pa.types.is_map(field.type): + # A map has exactly one key and one item field, so like a list's child they + # align positionally and adopt the table's names. + new_type = pa.map_( + _align_container_child( + field.type.key_field, target_field.type.key_field + ), + _align_container_child( + field.type.item_field, target_field.type.item_field + ), + keys_sorted=target_field.type.keys_sorted, + ) + else: + new_type = target_field.type else: new_type = target_field.type return pa.field(field.name, new_type, field.nullable, target_field.metadata) diff --git a/python/python/tests/test_blob.py b/python/python/tests/test_blob.py index 298b021df..0d3a89c21 100644 --- a/python/python/tests/test_blob.py +++ b/python/python/tests/test_blob.py @@ -751,6 +751,80 @@ def test_fetch_blobs_preserves_null_and_empty_values(): assert blobs[3].as_py() == b"present" +def test_add_all_null_list_to_blob_column(): + table = _blob_table("all_null_add", [{"id": 1, "image": None}]) + + hits = table.search().to_arrow() + blobs = table.fetch_blobs("image", hits) + assert len(blobs) == 1 + assert blobs[0].as_py() is None + + +def test_add_all_null_list_to_blob_column_with_sanitizer(): + db = lancedb.connect("memory:///") + schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")]) + table = db.create_table("all_null_sanitized_add", schema=schema) + + table.add([{"id": 1, "image": None}], on_bad_vectors="fill") + + hits = table.search().to_arrow() + blobs = table.fetch_blobs("image", hits) + assert len(blobs) == 1 + assert blobs[0].as_py() is None + + +def test_add_all_null_list_to_nested_blob_column(): + db = lancedb.connect("memory:///") + blob_field = lancedb.blob("image") + info_field = pa.field("info", pa.struct([blob_field])) + info = pa.StructArray.from_arrays( + [_blob_array("image", [b"seed"])], fields=[blob_field] + ) + seed = pa.Table.from_arrays( + [pa.array([0], type=pa.int64()), info], + schema=pa.schema([pa.field("id", pa.int64()), info_field]), + ) + table = db.create_table("nested_null_add", data=seed) + + table.add([{"id": 1, "info": {"image": None}}]) + table.add([{"id": 2, "info": {"image": None}}], on_bad_vectors="fill") + + hits = table.search().where("id > 0").to_arrow() + blobs = table.fetch_blobs("info.image", hits) + assert len(blobs) == 2 + assert all(blob.as_py() is None for blob in blobs) + + +@pytest.mark.parametrize("large_list", [False, True], ids=["list", "large_list"]) +def test_add_list_of_dicts_to_blob_list_column(large_list): + db = lancedb.connect("memory:///") + blob_field = lancedb.blob("image") + blob_values = _blob_array("image", [b"seed"]) + if large_list: + items_field = pa.field("items", pa.large_list(blob_field)) + items = pa.LargeListArray.from_arrays( + pa.array([0, 1], type=pa.int64()), blob_values + ) + else: + items_field = pa.field("items", pa.list_(blob_field)) + items = pa.ListArray.from_arrays(pa.array([0, 1], type=pa.int32()), blob_values) + seed = pa.Table.from_arrays( + [pa.array([0], type=pa.int64()), items], + schema=pa.schema([pa.field("id", pa.int64()), items_field]), + ) + table = db.create_table(f"blob_{large_list}_list_add", data=seed) + + table.add([{"id": 1, "items": [None]}]) + table.add( + [{"id": 2, "items": [b"a", None]}], + on_bad_vectors="fill", + ) + + ids = table.search().select(["id"]).to_arrow()["id"].to_pylist() + assert sorted(ids) == [0, 1, 2] + assert pa.types.is_large_list(table.schema.field("items").type) is large_list + + def test_fetch_blob_ranges_aligns_repeated_ranges_and_nulls(): table = _blob_table( "range_alignment", diff --git a/python/python/tests/test_table.py b/python/python/tests/test_table.py index ac7dc660d..a3cbf68f1 100644 --- a/python/python/tests/test_table.py +++ b/python/python/tests/test_table.py @@ -2979,28 +2979,29 @@ async def test_merge_insert_async(mem_db_async: AsyncConnection): assert (await table.to_arrow()).sort_by("a") == expected +def _json_arrow_table(schema, rows): + json_type = schema.field("j").type + json_values = pa.ExtensionArray.from_storage( + json_type, + pa.array([value for _, value in rows], type=json_type.storage_type), + ) + return pa.Table.from_arrays( + [pa.array([row_id for row_id, _ in rows]), json_values], schema=schema + ) + + @pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") @pytest.mark.asyncio async def test_merge_insert_encodes_json(mem_db_async: AsyncConnection): - json_type = pa.json_() - schema = pa.schema([pa.field("id", pa.string()), pa.field("j", json_type)]) - - def json_table(rows): - json_values = pa.ExtensionArray.from_storage( - json_type, - pa.array([value for _, value in rows], type=json_type.storage_type), - ) - return pa.Table.from_arrays( - [pa.array([row_id for row_id, _ in rows]), json_values], schema=schema - ) + schema = pa.schema([pa.field("id", pa.string()), pa.field("j", pa.json_())]) table = await mem_db_async.create_table("json_merge", schema=schema) - await table.add(json_table([("a", '{"k": 1}'), ("b", '{"k": 9}')])) + await table.add(_json_arrow_table(schema, [("a", '{"k": 1}'), ("b", '{"k": 9}')])) await ( table.merge_insert("id") .when_matched_update_all() - .execute(json_table([("a", '{"k": 2}')])) + .execute(_json_arrow_table(schema, [("a", '{"k": 2}')])) ) rows = sorted(await table.query().to_list(), key=lambda row: row["id"]) @@ -3015,20 +3016,176 @@ async def test_merge_insert_encodes_json(mem_db_async: AsyncConnection): @pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") @pytest.mark.asyncio async def test_add_sanitization_encodes_json(mem_db_async: AsyncConnection): - json_type = pa.json_() - schema = pa.schema([pa.field("id", pa.string()), pa.field("j", json_type)]) - json_values = pa.ExtensionArray.from_storage( - json_type, pa.array(['{"k": 3}'], type=json_type.storage_type) - ) - data = pa.Table.from_arrays([pa.array(["c"]), json_values], schema=schema) + schema = pa.schema([pa.field("id", pa.string()), pa.field("j", pa.json_())]) table = await mem_db_async.create_table("json_add", schema=schema) - await table.add(data, on_bad_vectors="fill") + await table.add( + _json_arrow_table(schema, [("c", '{"k": 3}')]), on_bad_vectors="fill" + ) rows = await table.query().where("json_extract(j, '$.k') = '3'").to_list() assert rows == [{"id": "c", "j": '{"k":3}'}] +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +@pytest.mark.asyncio +async def test_add_all_null_json_batch(mem_db_async: AsyncConnection): + """A batch of dicts whose json values are all None infers as pa.null(), which used + to fail with a `json` vs `large_binary` schema mismatch. A row-at-a-time insert of + an optional json column always looks like this.""" + schema = pa.schema([pa.field("id", pa.string()), pa.field("j", pa.json_())]) + table = await mem_db_async.create_table("json_nulls", schema=schema) + + await table.add([{"id": "a", "j": None}]) + assert await table.count_rows() == 1 + + # ... and again once real JSON has been written. + await table.add(_json_arrow_table(schema, [("b", '{"k": 9}')])) + await table.add([{"id": "c", "j": None}]) + + rows = sorted(await table.query().to_list(), key=lambda row: row["id"]) + assert rows == [ + {"id": "a", "j": None}, + {"id": "b", "j": '{"k":9}'}, + {"id": "c", "j": None}, + ] + + # The nulls must not disturb reads of the column. + filtered = await table.query().where("json_extract(j, '$.k') = '9'").to_list() + assert filtered == [{"id": "b", "j": '{"k":9}'}] + assert len(await table.query().where("j IS NULL").to_list()) == 2 + + +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +def test_add_all_null_json_batch_sync(mem_db: DBConnection): + schema = pa.schema([pa.field("id", pa.string()), pa.field("j", pa.json_())]) + table = mem_db.create_table("json_nulls_sync", schema=schema) + + table.add([{"id": "a", "j": None}]) + + assert table.count_rows() == 1 + assert table.to_arrow()["j"].to_pylist() == [None] + + +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("values", "expected"), + [ + ([None], [None]), + ([None, '{"k": 1}'], [None, '{"k":1}']), + (['{"k": 2}'], ['{"k":2}']), + ], +) +async def test_add_list_of_dicts_to_json_column( + mem_db_async: AsyncConnection, values, expected +): + schema = pa.schema([pa.field("id", pa.int64()), pa.field("value", pa.json_())]) + table = await mem_db_async.create_table("json_list_add", schema=schema) + + await table.add([{"id": idx, "value": value} for idx, value in enumerate(values)]) + + rows = (await table.to_arrow()).sort_by("id").to_pylist() + assert [row["value"] for row in rows] == expected + + +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +@pytest.mark.asyncio +async def test_add_list_of_dicts_to_nested_json_column( + mem_db_async: AsyncConnection, +): + json_field = pa.field("value", pa.json_()) + info_field = pa.field("info", pa.struct([json_field])) + info = pa.StructArray.from_arrays( + [pa.array(['{"seed": 0}'], type=pa.json_())], fields=[json_field] + ) + seed = pa.Table.from_arrays( + [pa.array([0], type=pa.int64()), info], + schema=pa.schema([pa.field("id", pa.int64()), info_field]), + ) + table = await mem_db_async.create_table("nested_json_list_add", data=seed) + + await table.add([{"id": 1, "info": {"value": '{"k": 1}'}}]) + await table.add([{"id": 2, "info": {"value": '{"k": 2}'}}], on_bad_vectors="fill") + + rows = (await table.to_arrow()).sort_by("id").to_pylist() + assert rows == [ + {"id": 0, "info": {"value": '{"seed":0}'}}, + {"id": 1, "info": {"value": '{"k":1}'}}, + {"id": 2, "info": {"value": '{"k":2}'}}, + ] + + +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +@pytest.mark.asyncio +async def test_add_list_of_dicts_to_json_list_column(mem_db_async: AsyncConnection): + """JSON inside a list must be JSONB-encoded, not stored as the raw text. + + Storing raw text appends without error but leaves the column unreadable, so the + round trip is checked with a filter as well as by value. + """ + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("docs", pa.list_(pa.field("item", pa.json_()))), + ] + ) + table = await mem_db_async.create_table("json_list_add", schema=schema) + + await table.add([{"id": 1, "docs": ['{"k": 1}', '{"k": 2}']}]) + await table.add([{"id": 2, "docs": ['{"k": 3}']}], on_bad_vectors="fill") + + rows = (await table.to_arrow()).sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "docs": ['{"k":1}', '{"k":2}']}, + {"id": 2, "docs": ['{"k":3}']}, + ] + + matched = await table.query().where("json_extract(docs[1], '$.k') = 3").to_arrow() + assert matched.column("id").to_pylist() == [2] + + +@pytest.mark.skipif(not hasattr(pa, "json_"), reason="requires PyArrow JSON type") +@pytest.mark.asyncio +async def test_add_map_of_json_values(mem_db_async: AsyncConnection): + """JSON in a map's values needs the same encoding a list's items do.""" + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("m", pa.map_(pa.string(), pa.json_())), + ] + ) + table = await mem_db_async.create_table( + "json_map_add", + schema=schema, + storage_options={"new_table_data_storage_version": "2.2"}, + ) + + def batch(row_id: int, text: str) -> pa.Table: + return pa.table( + { + "id": pa.array([row_id], type=pa.int64()), + "m": pa.array([[("k", text)]], type=pa.map_(pa.string(), pa.string())), + } + ) + + await table.add(batch(1, '{"x": 1}')) + await table.add(batch(2, '{"x": 2}'), on_bad_vectors="fill") + + rows = (await table.to_arrow()).sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "m": [("k", '{"x":1}')]}, + {"id": 2, "m": [("k", '{"x":2}')]}, + ] + + matched = ( + await table.query() + .where("json_extract(element_at(m, 'k')[1], '$.x') = '2'") + .to_arrow() + ) + assert matched.column("id").to_pylist() == [2] + + def test_create_with_embedding_function(mem_db: DBConnection): class MyTable(LanceModel): text: str diff --git a/rust/lancedb/src/table/add_data.rs b/rust/lancedb/src/table/add_data.rs index 15ccf9f66..0bfec9d68 100644 --- a/rust/lancedb/src/table/add_data.rs +++ b/rust/lancedb/src/table/add_data.rs @@ -1104,4 +1104,220 @@ mod tests { } } } + + /// A batch whose json values are all null infers as `DataType::Null` (this is what + /// pyarrow produces for a one-row insert with no value). The column's lance.json + /// identity lives in the field metadata, so dropping it while casting used to make + /// lance-core reject the batch as a schema mismatch. + #[tokio::test] + async fn test_add_all_null_json_column() { + use arrow_array::{Array, cast::AsArray, new_null_array}; + + let table_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + lance_arrow::json::json_field("data", true), + ])); + + let db = connect("memory://").execute().await.unwrap(); + let table = db + .create_empty_table("json_nulls", table_schema) + .execute() + .await + .unwrap(); + + let null_batch = |ids: Vec| { + let len = ids.len(); + RecordBatch::try_new( + Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("data", DataType::Null, true), + ])), + vec![ + Arc::new(arrow_array::Int64Array::from(ids)), + new_null_array(&DataType::Null, len), + ], + ) + .unwrap() + }; + + // A single all-null row as the very first write, then again after real JSON has + // been written - both scenarios from the bug report. + table.add(null_batch(vec![1])).execute().await.unwrap(); + + let arrow_json_field = Field::new("data", DataType::Utf8, true).with_metadata( + std::collections::HashMap::from([( + lance_arrow::ARROW_EXT_NAME_KEY.to_string(), + lance_arrow::json::ARROW_JSON_EXT_NAME.to_string(), + )]), + ); + let populated = RecordBatch::try_new( + Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + arrow_json_field, + ])), + vec![ + Arc::new(arrow_array::Int64Array::from(vec![2])), + Arc::new(arrow_array::StringArray::from(vec![Some(r#"{"a": 1}"#)])), + ], + ) + .unwrap(); + table.add(populated).execute().await.unwrap(); + table.add(null_batch(vec![3])).execute().await.unwrap(); + + assert_eq!(table.count_rows(None).await.unwrap(), 3); + + let results: Vec = table + .query() + .execute() + .await + .unwrap() + .try_collect() + .await + .unwrap(); + let batch = arrow_select::concat::concat_batches(&results[0].schema(), &results).unwrap(); + let ids = batch + .column_by_name("id") + .unwrap() + .as_primitive::(); + let json_strs = batch.column_by_name("data").unwrap().as_string::(); + for row in 0..batch.num_rows() { + match ids.value(row) { + 2 => assert_eq!(json_strs.value(row), r#"{"a":1}"#), + _ => assert!(json_strs.is_null(row), "row {row} expected null"), + } + } + } + + /// JSON text with no arrow.json label - what pyarrow infers for a column of `str` - is + /// encoded as JSONB rather than stored verbatim, at the top level and inside a struct. + #[tokio::test] + async fn test_add_unlabelled_json_strings() { + use arrow_array::{Array, cast::AsArray}; + use arrow_schema::Fields; + + let table_schema = Arc::new(Schema::new(vec![ + lance_arrow::json::json_field("data", true), + Field::new( + "info", + DataType::Struct(vec![lance_arrow::json::json_field("value", true)].into()), + true, + ), + ])); + + let db = connect("memory://").execute().await.unwrap(); + let table = db + .create_empty_table("json_strings", table_schema) + .execute() + .await + .unwrap(); + + let nested_children: Fields = vec![Field::new("value", DataType::Utf8, true)].into(); + let input_schema = Arc::new(Schema::new(vec![ + Field::new("data", DataType::Utf8, true), + Field::new("info", DataType::Struct(nested_children.clone()), true), + ])); + let batch = RecordBatch::try_new( + input_schema, + vec![ + Arc::new(arrow_array::StringArray::from(vec![ + Some(r#"{"a": 1}"#), + None, + ])), + Arc::new(arrow_array::StructArray::new( + nested_children, + vec![Arc::new(arrow_array::StringArray::from(vec![ + Some(r#"{"b": 2}"#), + None, + ]))], + None, + )), + ], + ) + .unwrap(); + table.add(batch).execute().await.unwrap(); + + let results: Vec = table + .query() + .execute() + .await + .unwrap() + .try_collect() + .await + .unwrap(); + let batch = arrow_select::concat::concat_batches(&results[0].schema(), &results).unwrap(); + assert_eq!(batch.num_rows(), 2); + + let data = batch.column_by_name("data").unwrap().as_string::(); + assert_eq!(data.value(0), r#"{"a":1}"#); + assert!(data.is_null(1)); + + let nested = batch + .column_by_name("info") + .unwrap() + .as_struct() + .column_by_name("value") + .unwrap() + .as_string::(); + assert_eq!(nested.value(0), r#"{"b":2}"#); + assert!(nested.is_null(1)); + } + + /// A null struct row survives a cast of one of its children, even when that child is + /// non-nullable. Lance checks a non-nullable child for nulls without applying the + /// parent's validity, so rebuilding the struct must leave the children untouched. + #[tokio::test] + async fn test_add_null_struct_with_non_nullable_child() { + use arrow_array::{Array, cast::AsArray}; + use arrow_schema::Fields; + + let table_schema = Arc::new(Schema::new(vec![Field::new( + "s", + DataType::Struct(vec![Field::new("x", DataType::Int64, false)].into()), + true, + )])); + + let db = connect("memory://").execute().await.unwrap(); + let table = db + .create_empty_table("null_struct", table_schema) + .execute() + .await + .unwrap(); + + // Int32 rather than the table's Int64, so the struct goes through reconstruction. + let input_children: Fields = vec![Field::new("x", DataType::Int32, false)].into(); + let input_schema = Arc::new(Schema::new(vec![Field::new( + "s", + DataType::Struct(input_children.clone()), + true, + )])); + let batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(arrow_array::StructArray::new( + input_children, + vec![Arc::new(arrow_array::Int32Array::from(vec![0, 6]))], + Some(arrow::buffer::NullBuffer::from(vec![false, true])), + ))], + ) + .unwrap(); + table.add(batch).execute().await.unwrap(); + + let results: Vec = table + .query() + .execute() + .await + .unwrap() + .try_collect() + .await + .unwrap(); + let batch = arrow_select::concat::concat_batches(&results[0].schema(), &results).unwrap(); + let s = batch.column_by_name("s").unwrap().as_struct(); + assert!(s.is_null(0)); + assert_eq!( + s.column_by_name("x") + .unwrap() + .as_primitive::() + .value(1), + 6 + ); + } } diff --git a/rust/lancedb/src/table/datafusion/cast.rs b/rust/lancedb/src/table/datafusion/cast.rs index bc47caeb2..0df1cb4ba 100644 --- a/rust/lancedb/src/table/datafusion/cast.rs +++ b/rust/lancedb/src/table/datafusion/cast.rs @@ -1,20 +1,25 @@ // SPDX-License-Identifier: Apache-2.0 // SPDX-FileCopyrightText: Copyright The LanceDB Authors -use std::sync::Arc; +use std::collections::HashMap; +use std::sync::{Arc, LazyLock}; +use arrow_array::StructArray; +use arrow_array::cast::AsArray; use arrow_cast::can_cast_types; use arrow_schema::{DataType, Field, FieldRef, Fields, Schema}; use datafusion::functions::core::{get_field, named_struct}; -use datafusion_common::ScalarValue; use datafusion_common::config::ConfigOptions; +use datafusion_common::metadata::FieldMetadata; +use datafusion_common::{DataFusionError, Result as DFResult, ScalarValue}; +use datafusion_expr::{ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility}; use datafusion_physical_expr::ScalarFunctionExpr; use datafusion_physical_expr::expressions::{CastExpr, Literal}; use datafusion_physical_plan::expressions::Column; use datafusion_physical_plan::projection::ProjectionExec; use datafusion_physical_plan::{ExecutionPlan, PhysicalExpr}; -use lance_arrow::FieldExt; -use lance_arrow::json::{is_arrow_json_field, is_json_field}; +use lance_arrow::json::{ARROW_JSON_EXT_NAME, has_json_fields, is_arrow_json_field, is_json_field}; +use lance_arrow::{ARROW_EXT_NAME_KEY, FieldExt}; use super::blob_coerce::coerce_blob_expr; use crate::{Error, Result}; @@ -67,18 +72,33 @@ fn build_field_exprs( let input_field = &input_fields[input_idx]; let input_expr = get_input_expr(input_idx); - // Special case: input is arrow.json (PyArrow pa.json_() extension type backed by - // Utf8/LargeUtf8) and the table field is lance.json (backed by LargeBinary). - // Lance-core's write path already handles the arrow.json → lance.json conversion - // (including JSONB encoding), so we pass the expression through unchanged and let - // lance-core deal with it. Attempting to cast Utf8 → LargeBinary here would - // produce a field whose metadata still identifies it as arrow.json, which then - // causes a schema-mismatch error inside lance-core. - if is_arrow_json_field(input_field) && is_json_field(table_field) { + // PyArrow's pa.json_() is already labelled arrow.json, which is what lance-core wants + // to see, so pass it straight through. + if is_json_field(table_field) && is_arrow_json_field(input_field) { result.push((input_expr, Arc::clone(input_field) as FieldRef)); continue; } + // Anything else destined for a json column needs its JSON leaves labelled; see + // `json_write_target`. Structs are excluded because the recursion below rebuilds them + // field by field, which also handles reordered and partial input. + if !matches!(table_field.data_type(), DataType::Struct(_)) + && let Some(target) = json_write_target(input_field, table_field) + && can_cast_types(input_field.data_type(), target.data_type()) + { + // The label goes on the cast's target field rather than the field returned + // alongside it, because DataFusion derives the projection's output schema from + // `PhysicalExpr::return_field`. + let target: FieldRef = Arc::new(target); + let expr = Arc::new(CastExpr::new_with_target_field( + input_expr, + target.clone(), + None, + )); + result.push((expr, target)); + continue; + } + // Blob columns accept raw binary on write; exact matches pass through below. if table_field.is_blob_v2() && input_field.as_ref() != table_field.as_ref() { result.push(coerce_blob_expr( @@ -90,6 +110,18 @@ fn build_field_exprs( continue; } + // A column whose values are all null infers as `Null` (pyarrow does this for a list of + // dicts), so there is no input type to cast from. Emit typed nulls carrying the table + // field verbatim: a plain cast would drop the field metadata, and extension columns + // such as lance.json are identified by that metadata alone, so lance-core would then + // reject the batch as a schema mismatch. + if matches!(input_field.data_type(), DataType::Null) + && !matches!(table_field.data_type(), DataType::Null) + { + result.push((null_literal(table_field)?, table_field.clone())); + continue; + } + let expr = match (input_field.data_type(), table_field.data_type()) { // Both are structs: recurse into sub-fields to handle subschemas and casts. (DataType::Struct(in_children), DataType::Struct(tbl_children)) @@ -137,7 +169,16 @@ fn build_field_exprs( config.clone(), )); - result.push((ns_expr, output_field)); + result.push(( + restore_struct_validity( + ns_expr, + input_expr, + input_field, + &output_field, + config.clone(), + ), + output_field, + )); continue; } // Types match: pass through. @@ -171,6 +212,193 @@ fn build_field_exprs( Ok(result) } +// `named_struct` returns a struct with no null bitmap, and the `get_field` calls feeding it +// read each child without applying the parent's validity, so a null input struct would come +// back non-null with its masked children exposed. Move the input's null buffer onto the +// rebuilt struct. +fn restore_struct_validity( + rebuilt: Arc, + input_expr: Arc, + input_field: &FieldRef, + output_field: &FieldRef, + config: Arc, +) -> Arc { + if !input_field.is_nullable() { + return rebuilt; + } + + Arc::new(ScalarFunctionExpr::new( + &format!("restore_validity({})", output_field.name()), + RESTORE_VALIDITY_UDF.clone(), + vec![rebuilt, input_expr], + output_field.clone(), + config, + )) +} + +static RESTORE_VALIDITY_UDF: LazyLock> = + LazyLock::new(|| Arc::new(datafusion_expr::ScalarUDF::from(RestoreValidityUdf::new()))); + +/// Returns its first argument, a struct, carrying the null buffer of its second. +/// +/// Selecting a typed null for the null rows instead would nullify their children too, which +/// Lance rejects outright for a non-nullable child, even where the parent masks it. Children +/// therefore have to survive the round trip byte for byte. +#[derive(Debug, Hash, PartialEq, Eq)] +struct RestoreValidityUdf { + signature: Signature, +} + +impl RestoreValidityUdf { + fn new() -> Self { + Self { + signature: Signature::any(2, Volatility::Immutable), + } + } +} + +impl ScalarUDFImpl for RestoreValidityUdf { + fn name(&self) -> &str { + "restore_validity" + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, arg_types: &[DataType]) -> DFResult { + Ok(arg_types[0].clone()) + } + + fn invoke_with_args(&self, args: ScalarFunctionArgs) -> DFResult { + let rows = args.number_rows; + let rebuilt = args.args[0].to_array(rows)?; + let nulls_from = args.args[1].to_array(rows)?; + + let rebuilt = rebuilt.as_struct_opt().ok_or_else(|| { + DataFusionError::Internal(format!( + "restore_validity expects a struct, got {}", + rebuilt.data_type() + )) + })?; + + let nulls = nulls_from.logical_nulls(); + let (fields, columns, _) = rebuilt.clone().into_parts(); + let restored = StructArray::try_new_with_length(fields, columns, nulls, rows)?; + Ok(ColumnarValue::Array(Arc::new(restored))) + } +} + +// The storage type arrow.json would use for `input`, or None if it cannot hold JSON text. +fn arrow_json_storage_type(input: &DataType) -> Option { + match input { + // arrow.json only recognises Utf8 and LargeUtf8 storage, so a view has to be cast. + DataType::Utf8 | DataType::Utf8View => Some(DataType::Utf8), + DataType::LargeUtf8 => Some(DataType::LargeUtf8), + _ => None, + } +} + +fn arrow_json_field(name: &str, storage: DataType, nullable: bool) -> Field { + Field::new(name, storage, nullable).with_metadata(HashMap::from([( + ARROW_EXT_NAME_KEY.to_string(), + ARROW_JSON_EXT_NAME.to_string(), + )])) +} + +/// Rewrite `table_field` so that every lance.json leaf the input supplies as text becomes an +/// arrow.json leaf, leaving the rest of the shape untouched. +/// +/// Lance-core encodes JSON text into JSONB on write, but only for leaves labelled arrow.json. +/// Casting to the lance.json storage type instead relabels raw text as JSONB, which appends +/// successfully but leaves the column unreadable, so the label has to reach every leaf however +/// deeply it is nested. Returns `None` when there is nothing to relabel. +fn json_write_target(input_field: &Field, table_field: &Field) -> Option { + if is_json_field(table_field) { + let storage = if is_arrow_json_field(input_field) { + input_field.data_type().clone() + } else { + arrow_json_storage_type(input_field.data_type())? + }; + return Some(arrow_json_field( + table_field.name(), + storage, + table_field.is_nullable(), + )); + } + + if !has_json_fields(table_field) { + return None; + } + + let relabelled = match (input_field.data_type(), table_field.data_type()) { + ( + DataType::List(input_item) + | DataType::LargeList(input_item) + | DataType::FixedSizeList(input_item, _), + DataType::List(table_item) + | DataType::LargeList(table_item) + | DataType::FixedSizeList(table_item, _), + ) => { + let item: FieldRef = Arc::new(json_write_target(input_item, table_item)?); + match table_field.data_type() { + DataType::List(_) => DataType::List(item), + DataType::LargeList(_) => DataType::LargeList(item), + DataType::FixedSizeList(_, len) => DataType::FixedSizeList(item, *len), + _ => unreachable!("matched a list type above"), + } + } + (DataType::Map(input_entries, _), DataType::Map(table_entries, sorted)) => { + let entries = json_write_target(input_entries, table_entries)?; + DataType::Map(Arc::new(entries), *sorted) + } + (DataType::Struct(input_children), DataType::Struct(table_children)) => { + let mut children = Vec::with_capacity(table_children.len()); + let mut relabelled_any = false; + for table_child in table_children { + let relabelled_child = input_children + .iter() + .find(|f| f.name() == table_child.name()) + .and_then(|input_child| json_write_target(input_child, table_child)); + match relabelled_child { + Some(child) => { + relabelled_any = true; + children.push(Arc::new(child)); + } + None => children.push(table_child.clone()), + } + } + if !relabelled_any { + return None; + } + DataType::Struct(children.into()) + } + _ => return None, + }; + + Some( + Field::new(table_field.name(), relabelled, table_field.is_nullable()) + .with_metadata(table_field.metadata().clone()), + ) +} + +// The field's metadata is attached to the literal itself, because DataFusion derives the +// projection's output schema from `PhysicalExpr::return_field` rather than from the field we +// return alongside the expression. +fn null_literal(field: &FieldRef) -> Result> { + let scalar = ScalarValue::try_new_null(field.data_type()).map_err(|e| Error::InvalidInput { + message: format!( + "cannot build null literal for column '{}' of type {}: {e}", + field.name(), + field.data_type() + ), + })?; + Ok(Arc::new(Literal::new_with_metadata( + scalar, + Some(FieldMetadata::from(field.as_ref())), + ))) +} + #[cfg(test)] mod tests { use std::sync::Arc; @@ -715,4 +943,379 @@ mod tests { assert!(result.column(0).is_null(1)); assert_eq!(v2, r#"{"y": 2}"#); } + + /// Plain JSON text (what pyarrow infers for a column of `str`, and what a caller writing + /// JSON by hand supplies) has to be labelled arrow.json so lance-core encodes it as JSONB. + /// Casting it to the table field's LargeBinary storage type would store the raw text. + #[rstest::rstest] + #[case::utf8(DataType::Utf8, DataType::Utf8)] + #[case::large_utf8(DataType::LargeUtf8, DataType::LargeUtf8)] + #[case::utf8_view(DataType::Utf8View, DataType::Utf8)] + #[tokio::test] + async fn test_unlabelled_string_into_lance_json_gets_arrow_json_label( + #[case] input_type: DataType, + #[case] expected_type: DataType, + ) { + use lance_arrow::json::{is_arrow_json_field, json_field}; + + let table_schema = Schema::new(vec![json_field("data", true)]); + + let input_schema = Arc::new(Schema::new(vec![Field::new("data", input_type, true)])); + let values = vec![Some(r#"{"x": 1}"#), None]; + let input_array = arrow_cast::cast( + &StringArray::from(values) as &dyn arrow_array::Array, + input_schema.field(0).data_type(), + ) + .unwrap(); + let input_batch = RecordBatch::try_new(input_schema, vec![input_array]).unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let out_field = projected.schema().field_with_name("data").unwrap().clone(); + assert_eq!(out_field.data_type(), &expected_type); + assert!( + is_arrow_json_field(&out_field), + "output field must be labelled arrow.json, got {:?}", + out_field.metadata() + ); + + let result = collect(projected).await; + assert_eq!(result.num_rows(), 2); + assert_eq!(result.column(0).null_count(), 1); + } + + /// A json leaf inside a list is relabelled too. The outer field is a list, so without + /// recursing into it the generic container cast would turn the text into LargeBinary + /// labelled lance.json - an append that succeeds but stores unreadable JSON. + #[rstest::rstest] + #[case::unlabelled(DataType::Utf8, false)] + #[case::already_labelled(DataType::Utf8, true)] + #[tokio::test] + async fn test_unlabelled_list_item_into_lance_json_gets_arrow_json_label( + #[case] item_type: DataType, + #[case] input_labelled: bool, + ) { + use lance_arrow::json::{is_arrow_json_field, json_field}; + + use super::arrow_json_field; + + let table_schema = Schema::new(vec![Field::new( + "docs", + DataType::List(Arc::new(json_field("item", true))), + true, + )]); + + let input_item = if input_labelled { + Arc::new(arrow_json_field("item", item_type, true)) + } else { + Arc::new(Field::new("item", item_type, true)) + }; + let input_schema = Arc::new(Schema::new(vec![Field::new( + "docs", + DataType::List(input_item.clone()), + true, + )])); + let values = StringArray::from(vec![Some(r#"{"k": 1}"#), Some(r#"{"k": 2}"#)]); + let input_batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(ListArray::new( + input_item, + OffsetBuffer::new(vec![0, 1, 2].into()), + Arc::new(values), + None, + ))], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let out_field = projected.schema().field_with_name("docs").unwrap().clone(); + let DataType::List(out_item) = out_field.data_type() else { + panic!("expected a list, got {}", out_field.data_type()); + }; + assert!( + is_arrow_json_field(out_item), + "the list item must be labelled arrow.json, got {out_item:?}" + ); + + let result = collect(projected).await; + assert_eq!(result.num_rows(), 2); + } + + /// The same, for a json column nested inside a struct: the struct is rebuilt from its + /// children, so the label has to travel on the child field. + #[tokio::test] + async fn test_unlabelled_struct_child_into_lance_json_gets_arrow_json_label() { + use lance_arrow::json::{is_arrow_json_field, json_field}; + + let table_schema = Schema::new(vec![Field::new( + "info", + DataType::Struct( + vec![ + Field::new("id", DataType::Int64, true), + json_field("value", true), + ] + .into(), + ), + true, + )]); + + let input_children: Fields = vec![ + Field::new("id", DataType::Int64, true), + Field::new("value", DataType::Utf8, true), + ] + .into(); + let input_schema = Arc::new(Schema::new(vec![Field::new( + "info", + DataType::Struct(input_children.clone()), + true, + )])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(StructArray::new( + input_children, + vec![ + Arc::new(Int64Array::from(vec![1, 2])), + Arc::new(StringArray::from(vec![Some(r#"{"a": 1}"#), None])), + ], + None, + ))], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let out_field = projected.schema().field_with_name("info").unwrap().clone(); + let DataType::Struct(out_children) = out_field.data_type() else { + panic!("expected a struct, got {}", out_field.data_type()); + }; + let value = out_children.iter().find(|f| f.name() == "value").unwrap(); + assert!( + is_arrow_json_field(value), + "nested field must be labelled arrow.json, got {:?}", + value.metadata() + ); + + let result = collect(projected).await; + let info: &StructArray = result.column(0).as_any().downcast_ref().unwrap(); + assert_eq!(info.column_by_name("value").unwrap().null_count(), 1); + } + + /// An all-null column comes through as `DataType::Null` (pyarrow infers that for a batch + /// of dicts whose values are all `None`). The lance.json extension metadata lives on the + /// field alone, so it has to be carried into the output schema or lance-core rejects the + /// batch with a "json vs large_binary" schema mismatch. + #[tokio::test] + async fn test_null_column_into_lance_json_keeps_extension_metadata() { + use lance_arrow::ARROW_EXT_NAME_KEY; + use lance_arrow::json::{JSON_EXT_NAME, json_field}; + + let table_schema = Schema::new(vec![ + Field::new("id", DataType::Int64, false), + json_field("data", true), + ]); + + let input_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("data", DataType::Null, true), + ])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![ + Arc::new(Int64Array::from(vec![0, 1, 2])), + arrow_array::new_null_array(&DataType::Null, 3), + ], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let out_field = projected.schema().field_with_name("data").unwrap().clone(); + assert_eq!(out_field.data_type(), &DataType::LargeBinary); + assert_eq!( + out_field + .metadata() + .get(ARROW_EXT_NAME_KEY) + .map(|s| s.as_str()), + Some(JSON_EXT_NAME), + "output field must still identify itself as lance.json" + ); + + let result = collect(projected).await; + assert_eq!(result.num_rows(), 3); + assert_eq!(result.column_by_name("data").unwrap().null_count(), 3); + } + + /// The same, for a lance.json column nested inside a struct: the struct is rebuilt from + /// its children, so each child field must keep its own metadata, and a null struct must + /// stay null even though it is rebuilt child by child. + #[tokio::test] + async fn test_null_struct_child_into_lance_json_keeps_extension_metadata() { + use lance_arrow::ARROW_EXT_NAME_KEY; + use lance_arrow::json::{JSON_EXT_NAME, json_field}; + + let table_schema = Schema::new(vec![Field::new( + "meta", + DataType::Struct( + vec![ + Field::new("id", DataType::Int64, true), + json_field("doc", true), + ] + .into(), + ), + true, + )]); + + let input_children: Fields = vec![ + Field::new("id", DataType::Int64, true), + Field::new("doc", DataType::Null, true), + ] + .into(); + let input_schema = Arc::new(Schema::new(vec![Field::new( + "meta", + DataType::Struct(input_children.clone()), + true, + )])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(StructArray::new( + input_children, + vec![ + Arc::new(Int64Array::from(vec![7, 8])), + arrow_array::new_null_array(&DataType::Null, 2), + ], + Some(arrow::buffer::NullBuffer::from(vec![false, true])), + ))], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let out_field = projected.schema().field_with_name("meta").unwrap().clone(); + let DataType::Struct(out_children) = out_field.data_type() else { + panic!("expected a struct, got {}", out_field.data_type()); + }; + let doc = out_children.iter().find(|f| f.name() == "doc").unwrap(); + assert_eq!(doc.data_type(), &DataType::LargeBinary); + assert_eq!( + doc.metadata().get(ARROW_EXT_NAME_KEY).map(|s| s.as_str()), + Some(JSON_EXT_NAME) + ); + + let result = collect(projected).await; + let meta: &StructArray = result.column(0).as_any().downcast_ref().unwrap(); + assert!(meta.is_null(0), "a null struct must stay null once rebuilt"); + assert!(meta.is_valid(1)); + assert_eq!(meta.column_by_name("doc").unwrap().null_count(), 2); + } + + /// Any struct whose children need adjusting is rebuilt child by child, so the parent's + /// nulls have to be restored afterwards - not only for the extension-column cases. + #[tokio::test] + async fn test_null_struct_stays_null_when_child_is_cast() { + let input_children: Fields = vec![Field::new("x", DataType::Int32, true)].into(); + let table_schema = Schema::new(vec![Field::new( + "s", + DataType::Struct(vec![Field::new("x", DataType::Int64, true)].into()), + true, + )]); + + let input_schema = Arc::new(Schema::new(vec![Field::new( + "s", + DataType::Struct(input_children.clone()), + true, + )])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(StructArray::new( + input_children, + vec![Arc::new(Int32Array::from(vec![5, 6]))], + Some(arrow::buffer::NullBuffer::from(vec![false, true])), + ))], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let result = collect(projected).await; + let s: &StructArray = result.column(0).as_any().downcast_ref().unwrap(); + assert!(s.is_null(0)); + assert!(s.is_valid(1)); + let x: &Int64Array = s.column(0).as_any().downcast_ref().unwrap(); + assert_eq!(x.value(1), 6); + } + + /// Lance rejects a non-nullable child that carries nulls even where the parent masks + /// them, so the null rows have to keep the placeholder children the input gave them. + #[tokio::test] + async fn test_null_struct_keeps_children_of_a_non_nullable_child() { + let input_children: Fields = vec![Field::new("x", DataType::Int32, false)].into(); + let table_schema = Schema::new(vec![Field::new( + "s", + DataType::Struct(vec![Field::new("x", DataType::Int64, false)].into()), + true, + )]); + + let input_schema = Arc::new(Schema::new(vec![Field::new( + "s", + DataType::Struct(input_children.clone()), + true, + )])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![Arc::new(StructArray::new( + input_children, + vec![Arc::new(Int32Array::from(vec![0, 6]))], + Some(arrow::buffer::NullBuffer::from(vec![false, true])), + ))], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + + let result = collect(projected).await; + let s: &StructArray = result.column(0).as_any().downcast_ref().unwrap(); + assert!(s.is_null(0)); + assert!(s.is_valid(1)); + let x: &Int64Array = s.column(0).as_any().downcast_ref().unwrap(); + assert_eq!(x.null_count(), 0); + assert_eq!(x.value(1), 6); + } + + /// A `Null` input column against a plain table column writes nulls too, including for + /// target types that a DataFusion cast would not reach. + #[tokio::test] + async fn test_null_column_into_struct_column() { + let children: Fields = vec![Field::new("x", DataType::Int32, true)].into(); + let table_schema = Schema::new(vec![Field::new( + "s", + DataType::Struct(children.clone()), + true, + )]); + + let input_schema = Arc::new(Schema::new(vec![Field::new("s", DataType::Null, true)])); + let input_batch = RecordBatch::try_new( + input_schema, + vec![arrow_array::new_null_array(&DataType::Null, 2)], + ) + .unwrap(); + + let plan = plan_from_batch(input_batch).await; + let projected = cast_to_table_schema(plan, &table_schema).unwrap(); + assert_eq!( + projected.schema().field_with_name("s").unwrap().data_type(), + &DataType::Struct(children) + ); + + let result = collect(projected).await; + assert_eq!(result.num_rows(), 2); + assert_eq!(result.column(0).null_count(), 2); + } } diff --git a/rust/lancedb/tests/blob_integration.rs b/rust/lancedb/tests/blob_integration.rs index 118ecf5c9..dd4051657 100644 --- a/rust/lancedb/tests/blob_integration.rs +++ b/rust/lancedb/tests/blob_integration.rs @@ -47,6 +47,21 @@ fn binary_input_batch(ids: &[i64], payloads: &[Option<&[u8]>]) -> RecordBatch { .unwrap() } +/// What pyarrow infers for a batch of dicts whose blob values are all `None`. +fn null_typed_input_batch(ids: &[i64]) -> RecordBatch { + RecordBatch::try_new( + Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("image", DataType::Null, true), + ])), + vec![ + Arc::new(Int64Array::from(ids.to_vec())), + new_null_array(&DataType::Null, ids.len()), + ], + ) + .unwrap() +} + async fn create_inline_blob_table( db: &Connection, name: &str, @@ -255,6 +270,38 @@ async fn add_accepts_null_blob_rows() -> Result<()> { Ok(()) } +/// A batch whose blob values are all null carries no type information — pyarrow infers +/// `DataType::Null` for it, which is what a row-at-a-time insert of an optional blob column +/// looks like. Such a batch must be accepted, both as the first write and after bytes have +/// already been written. +#[tokio::test] +async fn add_accepts_all_null_typed_blob_column() -> Result<()> { + let tmp = tempdir().unwrap(); + let db = connect(tmp.path().to_str().unwrap()).execute().await?; + let table = db + .create_empty_table("t", blob_table_schema()) + .execute() + .await?; + + table.add(null_typed_input_batch(&[1])).execute().await?; + assert_eq!(table.count_rows(None).await?, 1); + assert!(query_image_struct(&table).await.is_null(0)); + + table + .add(binary_input_batch(&[2], &[Some(b"bytes".as_slice())])) + .execute() + .await?; + table.add(null_typed_input_batch(&[3])).execute().await?; + assert_eq!(table.count_rows(None).await?, 3); + + let row_ids = collect_row_ids(&table).await?; + let bytes = table.fetch_blobs("image", &row_ids).await?; + assert!(bytes.is_null(0)); + assert_eq!(bytes.value(1), b"bytes"); + assert!(bytes.is_null(2)); + Ok(()) +} + #[tokio::test] async fn add_rejects_uncoercible_blob_input() -> Result<()> { let tmp = tempdir().unwrap();