docs(node): clarify full-text search filtering

<!-- lance-gatekeeper-fix:v1 agent=5c80c44c083b3b8ad0da595419d468fc
generation=1 -->

## Root cause

The legacy synchronous Python table called `delete` on a shared, mutable
`lance.Dataset`. Concurrent table operations could hold a PyO3 borrow
while delete requested an exclusive borrow, producing `RuntimeError:
Already borrowed`. The current async-backed binding fixes this by
cloning its thread-safe Rust table handle before awaiting, but that
concurrency contract had no regression coverage.

## Fix

- Document why delete must clone the Rust table handle before entering
its async future.
- Add a barrier-synchronized regression test that deletes distinct rows
through one shared table from eight Python threads.
- Verify every delete commits exactly one row, every commit gets a
distinct version, and no rows remain.

## Validation

- `cargo check --quiet --features remote --tests --examples`
- `cargo fmt --all -- --check`
- `uv run --extra tests --extra dev ruff format --check
python/tests/test_table.py`
- `uv run --extra tests --extra dev ruff check
python/tests/test_table.py`
- `uv run --extra tests --extra dev pytest
python/tests/test_table.py::test_concurrent_deletes_are_thread_safe
python/tests/test_table.py::test_delete
python/tests/test_table.py::test_delete_expr
python/tests/test_table.py::test_delete_expr_async -q` (4 passed)
- Manual stress reproduction: 100 concurrent deletes on one table
completed at versions 2–101 with zero rows remaining.

Fixes #530

Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>

docs/src/js/classes/Query.md

@@ -541,7 +541,17 @@ where(predicate): this
 
 A filter statement to be applied to this query.
 
-The filter should be supplied as an SQL query string.  For example:
+Filters are applied before full-text and vector searches by default; no
+separate prefilter call is needed. For vector searches only, use
+[VectorQuery#postfilter](VectorQuery.md#postfilter) to apply the filter after the search.
+
+The filter should be supplied as an SQL query string.
+
+Filtering performance can often be improved by creating a scalar index
+on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
 
 #### Parameters
 
@@ -551,18 +561,20 @@ The filter should be supplied as an SQL query string.  For example:
 
 `this`
 
-#### Example
+#### Examples
 
 ```ts
-x > 10
-y > 0 AND y < 100
-x > 5 OR y = 'test'
+const results = await table
+  .search("puppy", "fts")
+  .where("meta = 'foo'")
+  .limit(10)
+  .toArray();
+```
 
-Filtering performance can often be improved by creating a scalar index
-on the filter column(s).
-
-Calling this multiple times combines the filters with a logical AND rather
-than replacing the previous filter.
+```ts
+query.where("x > 10");
+query.where("y > 0 AND y < 100");
+query.where("x > 5 OR y = 'test'");
 ```
 
 #### Inherited from

docs/src/js/classes/VectorQuery.md

@@ -560,6 +560,9 @@ postfilter(): VectorQuery
 If this is called then filtering will happen after the vector search instead of
 before.
 
+This method is only available for vector search queries. Full-text search
+filters are always applied before the search.
+
 By default filtering will be performed before the vector search.  This is how
 filtering is typically understood to work.  This prefilter step does add some
 additional latency.  Creating a scalar index on the filter column(s) can
@@ -790,7 +793,17 @@ where(predicate): this
 
 A filter statement to be applied to this query.
 
-The filter should be supplied as an SQL query string.  For example:
+Filters are applied before full-text and vector searches by default; no
+separate prefilter call is needed. For vector searches only, use
+[VectorQuery#postfilter](VectorQuery.md#postfilter) to apply the filter after the search.
+
+The filter should be supplied as an SQL query string.
+
+Filtering performance can often be improved by creating a scalar index
+on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
 
 #### Parameters
 
@@ -800,18 +813,20 @@ The filter should be supplied as an SQL query string.  For example:
 
 `this`
 
-#### Example
+#### Examples
 
 ```ts
-x > 10
-y > 0 AND y < 100
-x > 5 OR y = 'test'
+const results = await table
+  .search("puppy", "fts")
+  .where("meta = 'foo'")
+  .limit(10)
+  .toArray();
+```
 
-Filtering performance can often be improved by creating a scalar index
-on the filter column(s).
-
-Calling this multiple times combines the filters with a logical AND rather
-than replacing the previous filter.
+```ts
+query.where("x > 10");
+query.where("y > 0 AND y < 100");
+query.where("x > 5 OR y = 'test'");
 ```
 
 #### Inherited from

nodejs/examples/filtering.test.ts

@@ -47,5 +47,22 @@ test("filtering examples", async () => {
       .limit(5)
       .toArray();
     // --8<-- [end:orderby_search]
+
+    const ftsTable = await db.createTable("myFts", [
+      { text: "Frodo was a happy puppy", category: "pet" },
+      { text: "A puppy training guide", category: "guide" },
+      { text: "There are several kittens playing", category: "pet" },
+    ]);
+    await ftsTable.createIndex("text", { config: lancedb.Index.fts() });
+
+    // --8<-- [start:fts_prefilter]
+    const ftsResults = await ftsTable
+      .search("puppy", "fts")
+      .where("category = 'pet'")
+      .limit(10)
+      .toArray();
+    // --8<-- [end:fts_prefilter]
+    expect(ftsResults).toHaveLength(1);
+    expect(ftsResults[0].category).toBe("pet");
   });
 });

nodejs/lancedb/query.ts

@@ -363,17 +363,33 @@ export class StandardQueryBase<
   /**
    * A filter statement to be applied to this query.
    *
-   * The filter should be supplied as an SQL query string.  For example:
-   * @example
-   * x > 10
-   * y > 0 AND y < 100
-   * x > 5 OR y = 'test'
+   * Filters are applied before full-text and vector searches by default; no
+   * separate prefilter call is needed. For vector searches only, use
+   * {@link VectorQuery#postfilter} to apply the filter after the search.
+   *
+   * The filter should be supplied as an SQL query string.
    *
    * Filtering performance can often be improved by creating a scalar index
    * on the filter column(s).
    *
    * Calling this multiple times combines the filters with a logical AND rather
    * than replacing the previous filter.
+   *
+   * @example Filter a full-text search
+   * ```ts
+   * const results = await table
+   *   .search("puppy", "fts")
+   *   .where("meta = 'foo'")
+   *   .limit(10)
+   *   .toArray();
+   * ```
+   *
+   * @example SQL filter expressions
+   * ```ts
+   * query.where("x > 10");
+   * query.where("y > 0 AND y < 100");
+   * query.where("x > 5 OR y = 'test'");
+   * ```
    */
   where(predicate: string): this {
     this.doCall((inner: NativeQueryType) => inner.onlyIf(predicate));
@@ -669,6 +685,9 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {
    * If this is called then filtering will happen after the vector search instead of
    * before.
    *
+   * This method is only available for vector search queries. Full-text search
+   * filters are always applied before the search.
+   *
    * By default filtering will be performed before the vector search.  This is how
    * filtering is typically understood to work.  This prefilter step does add some
    * additional latency.  Creating a scalar index on the filter column(s) can

python/python/lancedb/db.py

@@ -707,6 +707,9 @@ class LanceDBConnection(DBConnection):
         self._namespace_client_properties = namespace_client_properties
         if _inner is not None:
             self._conn = _inner
+            # Native-derived wrappers resolve this in their async reconstruction
+            # path so construction never synchronously re-enters LOOP.
+            self._read_consistency_interval = read_consistency_interval
             self._cached_namespace_client = None
             return
 
@@ -756,11 +759,14 @@ class LanceDBConnection(DBConnection):
         # storage_options.  Also, this class really shouldn't be holding any state
         # beyond _conn.
         self._conn = AsyncConnection(LOOP.run(do_connect()))
+        # Keep property access synchronous so debugger introspection cannot wait on
+        # the background loop while that thread is suspended at a breakpoint.
+        self._read_consistency_interval = read_consistency_interval
         self._cached_namespace_client: Optional[LanceNamespace] = None
 
     @property
     def read_consistency_interval(self) -> Optional[timedelta]:
-        return LOOP.run(self._conn.get_read_consistency_interval())
+        return self._read_consistency_interval
 
     @property
     def session(self) -> Optional[Session]:
@@ -771,8 +777,16 @@ class LanceDBConnection(DBConnection):
         return self._conn.uri
 
     @classmethod
-    def from_inner(cls, inner: LanceDbConnection):
-        return cls(None, _inner=inner)
+    def from_inner(
+        cls,
+        inner: LanceDbConnection,
+        read_consistency_interval: Optional[timedelta],
+    ):
+        return cls(
+            None,
+            read_consistency_interval=read_consistency_interval,
+            _inner=inner,
+        )
 
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(uri={self._conn.uri!r})"

python/python/lancedb/permutation.py

@@ -226,7 +226,7 @@ class PermutationBuilder:
 
         async def do_execute():
             inner_tbl = await self._async.execute()
-            return LanceTable.from_inner(inner_tbl)
+            return await LanceTable.from_inner(inner_tbl)
 
         return LOOP.run(do_execute())
 

python/python/lancedb/table.py

@@ -2182,11 +2182,15 @@ class LanceTable(Table):
         return self.name
 
     @classmethod
-    def from_inner(cls, tbl: LanceDBTable):
-        from .db import LanceDBConnection
+    async def from_inner(cls, tbl: LanceDBTable):
+        from .db import AsyncConnection, LanceDBConnection
 
         async_tbl = AsyncTable(tbl)
-        conn = LanceDBConnection.from_inner(tbl.database())
+        inner_conn = tbl.database()
+        read_consistency_interval = await AsyncConnection(
+            inner_conn
+        ).get_read_consistency_interval()
+        conn = LanceDBConnection.from_inner(inner_conn, read_consistency_interval)
         return cls(
             conn,
             async_tbl.name,

python/python/tests/test_db.py

@@ -77,6 +77,23 @@ def test_sync_repr_does_not_use_background_loop(tmp_path, monkeypatch):
     assert repr(table) == f"LanceTable(name='test', _conn={db!r})"
 
 
+def test_read_consistency_interval_does_not_use_background_loop(tmp_path, monkeypatch):
+    from lancedb.background_loop import LOOP
+    from lancedb.db import LanceDBConnection
+
+    consistency_interval = timedelta(seconds=5)
+    db = lancedb.connect(tmp_path, read_consistency_interval=consistency_interval)
+    db_from_inner = LanceDBConnection.from_inner(db._inner, consistency_interval)
+
+    def fail_run(*args, **kwargs):
+        raise AssertionError("properties should not use the Python background loop")
+
+    monkeypatch.setattr(LOOP, "run", fail_run)
+
+    assert db.read_consistency_interval == consistency_interval
+    assert db_from_inner.read_consistency_interval == consistency_interval
+
+
 def test_ingest_pd(tmp_path):
     db = lancedb.connect(tmp_path)
 

python/python/tests/test_permutation.py

@@ -6,6 +6,7 @@ import math
 import pytest
 
 from lancedb import DBConnection, Table, connect
+from lancedb.background_loop import LOOP
 from lancedb.permutation import Permutation, Permutations, permutation_builder
 
 
@@ -31,6 +32,25 @@ def test_split_random_ratios(mem_db):
     assert 65 <= split_1_count <= 75  # ~70% ± tolerance
 
 
+def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
+    import threading
+
+    db = connect(tmp_path)
+    tbl = db.create_table("test_table", pa.table({"x": range(10)}))
+    original_run = LOOP.run
+
+    def fail_on_reentry(future):
+        assert threading.current_thread() is not LOOP.thread
+        return original_run(future)
+
+    monkeypatch.setattr(LOOP, "run", fail_on_reentry)
+
+    permutation_tbl = permutation_builder(tbl).execute()
+
+    assert permutation_tbl.count_rows() == 10
+    assert permutation_tbl._conn.read_consistency_interval is None
+
+
 def test_split_random_counts(mem_db):
     """Test random splitting with absolute counts."""
     tbl = mem_db.create_table(

python/python/tests/test_table.py

@@ -6,6 +6,7 @@ import os
 import sys
 import threading
 import warnings
+from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
 from time import sleep
 from typing import List
@@ -2124,6 +2125,27 @@ def test_delete(mem_db: DBConnection):
     assert table.to_arrow()["id"].to_pylist() == [1]
 
 
+def test_concurrent_deletes_are_thread_safe(mem_db: DBConnection):
+    num_workers = 8
+    table = mem_db.create_table(
+        "my_table", data=[{"id": row_id} for row_id in range(num_workers)]
+    )
+    barrier = threading.Barrier(num_workers)
+
+    def delete(row_id: int):
+        barrier.wait()
+        return table.delete(f"id = {row_id}")
+
+    with ThreadPoolExecutor(max_workers=num_workers) as pool:
+        results = list(pool.map(delete, range(num_workers)))
+
+    assert all(result.num_deleted_rows == 1 for result in results)
+    assert sorted(result.version for result in results) == list(
+        range(2, num_workers + 2)
+    )
+    assert table.count_rows() == 0
+
+
 def test_delete_expr(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",

python/src/table.rs

@@ -745,6 +745,9 @@ impl Table {
 
     #[allow(private_interfaces)]
     pub fn delete(self_: PyRef<'_, Self>, condition: PredicateArg) -> PyResult<Bound<'_, PyAny>> {
+        // Do not hold the Python borrow across the await.  The cloned Rust table
+        // handle is thread-safe and allows deletes on the same Python table to
+        // run concurrently without PyO3 reporting "Already borrowed".
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let result = match &condition {

rust/lancedb/src/connection.rs

@@ -420,11 +420,6 @@ impl Connection {
     ///
     /// * `name` - The name of the table
     /// * `initial_data` - The initial data to write to the table
-    ///
-    /// Floating-point `List` columns named `vec`, or with `vector` or `embedding`
-    /// in their name, are inferred as vector columns when the first batch has a
-    /// uniform, non-zero list length. The inferred dimension is validated for all
-    /// subsequent batches and stored as a `FixedSizeList`.
     pub fn create_table<T: Scannable + 'static>(
         &self,
         name: impl Into<String>,

rust/lancedb/src/connection/create_table.rs

@@ -8,7 +8,7 @@ use lance_io::object_store::StorageOptionsProvider;
 use crate::{
     Error, Result, Table,
     connection::{merge_storage_options, set_storage_options_provider},
-    data::scannable::{Scannable, WithEmbeddingsScannable, maybe_infer_vector_schema},
+    data::scannable::{Scannable, WithEmbeddingsScannable},
     database::{CreateTableMode, CreateTableRequest, Database},
     embeddings::{EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry},
     table::WriteOptions,
@@ -147,8 +147,6 @@ impl CreateTableBuilder {
         let embedding_registry = self.embedding_registry.clone();
         let parent = self.parent.clone();
 
-        self.request.data = maybe_infer_vector_schema(self.request.data).await?;
-
         // If embeddings were configured via add_embedding(), wrap the data
         if !self.embeddings.is_empty() {
             let wrapped_data: Box<dyn Scannable> = Box::new(WithEmbeddingsScannable::try_new(

rust/lancedb/src/data/scannable.rs

@@ -18,16 +18,13 @@ use crate::embeddings::{
 };
 use crate::table::{ColumnDefinition, ColumnKind, TableDefinition};
 use crate::{Error, Result};
-use arrow_array::{ArrayRef, RecordBatch, RecordBatchIterator, RecordBatchReader, cast::AsArray};
-use arrow_cast::{CastOptions, cast_with_options};
-use arrow_schema::{ArrowError, DataType, Schema, SchemaRef};
+use arrow_array::{ArrayRef, RecordBatch, RecordBatchIterator, RecordBatchReader};
+use arrow_schema::{ArrowError, SchemaRef};
 use async_trait::async_trait;
 use futures::StreamExt;
 use futures::stream::once;
 use lance_datafusion::utils::StreamingWriteSource;
 
-use super::inspect::infer_dimension;
-
 pub trait Scannable: Send {
     /// Returns the schema of the data.
     fn schema(&self) -> SchemaRef;
@@ -500,146 +497,6 @@ impl Scannable for PeekedScannable {
     }
 }
 
-fn name_suggests_vector_column(name: &str) -> bool {
-    let name = name.to_ascii_lowercase();
-    name == "vec" || name.contains("vector") || name.contains("embedding")
-}
-
-/// Infer fixed dimensions for vector-like floating-point list columns.
-///
-/// Lance vector search requires `FixedSizeList` columns, but Arrow data assembled
-/// from runtime embedding models is often represented as `List`. For vector-like
-/// column names, inspect the first batch and convert uniform, non-empty lists to a
-/// fixed-size schema. Every subsequent batch is cast with strict length checking.
-pub(crate) async fn maybe_infer_vector_schema(
-    data: Box<dyn Scannable>,
-) -> Result<Box<dyn Scannable>> {
-    let input_schema = data.schema();
-    let candidates = input_schema
-        .fields()
-        .iter()
-        .enumerate()
-        .filter(|(_, field)| {
-            name_suggests_vector_column(field.name())
-                && matches!(
-                    field.data_type(),
-                    DataType::List(item) | DataType::LargeList(item)
-                        if item.data_type().is_floating()
-                )
-        })
-        .map(|(index, _)| index)
-        .collect::<Vec<_>>();
-    if candidates.is_empty() {
-        return Ok(data);
-    }
-
-    let mut peeked = PeekedScannable::new(data);
-    let Some(first_batch) = peeked.peek().await else {
-        return Ok(Box::new(peeked));
-    };
-
-    let mut fields = input_schema.fields().iter().cloned().collect::<Vec<_>>();
-    let mut changed = false;
-    for index in candidates {
-        let array = first_batch.column(index);
-        let dimension = match array.data_type() {
-            DataType::List(_) => {
-                infer_dimension::<arrow_array::types::Int32Type>(array.as_list::<i32>())?
-                    .map(i64::from)
-            }
-            DataType::LargeList(_) => {
-                infer_dimension::<arrow_array::types::Int64Type>(array.as_list::<i64>())?
-            }
-            _ => unreachable!(),
-        };
-        let Some(dimension) = dimension.filter(|dimension| *dimension > 0) else {
-            continue;
-        };
-        let dimension = i32::try_from(dimension).map_err(|_| Error::InvalidInput {
-            message: format!(
-                "Vector column '{}' has a dimension larger than i32::MAX",
-                fields[index].name()
-            ),
-        })?;
-        let item = match fields[index].data_type() {
-            DataType::List(item) | DataType::LargeList(item) => item.clone(),
-            _ => unreachable!(),
-        };
-        fields[index] = Arc::new(
-            fields[index]
-                .as_ref()
-                .clone()
-                .with_data_type(DataType::FixedSizeList(item, dimension)),
-        );
-        changed = true;
-    }
-
-    if !changed {
-        return Ok(Box::new(peeked));
-    }
-
-    let output_schema = Arc::new(Schema::new_with_metadata(
-        fields,
-        input_schema.metadata().clone(),
-    ));
-    Ok(Box::new(InferredVectorScannable {
-        inner: peeked,
-        output_schema,
-    }))
-}
-
-struct InferredVectorScannable {
-    inner: PeekedScannable,
-    output_schema: SchemaRef,
-}
-
-impl Scannable for InferredVectorScannable {
-    fn schema(&self) -> SchemaRef {
-        self.output_schema.clone()
-    }
-
-    fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
-        let output_schema = self.output_schema.clone();
-        let stream_schema = output_schema.clone();
-        let stream = self.inner.scan_as_stream().map(move |batch| {
-            let batch = batch?;
-            let columns = batch
-                .columns()
-                .iter()
-                .zip(output_schema.fields())
-                .map(|(array, field)| {
-                    if array.data_type() == field.data_type() {
-                        Ok(array.clone())
-                    } else {
-                        cast_with_options(
-                            array,
-                            field.data_type(),
-                            &CastOptions {
-                                safe: false,
-                                ..Default::default()
-                            },
-                        )
-                        .map_err(Error::from)
-                    }
-                })
-                .collect::<Result<Vec<_>>>()?;
-            Ok(RecordBatch::try_new(output_schema.clone(), columns)?)
-        });
-        Box::pin(SimpleRecordBatchStream {
-            schema: stream_schema,
-            stream,
-        })
-    }
-
-    fn num_rows(&self) -> Option<usize> {
-        self.inner.num_rows()
-    }
-
-    fn rescannable(&self) -> bool {
-        self.inner.rescannable()
-    }
-}
-
 /// Compute the number of write partitions based on data size estimates.
 ///
 /// `sample_bytes` and `sample_rows` come from a representative batch and are

rust/lancedb/src/lib.rs

@@ -72,9 +72,7 @@
 //!
 //! LanceDB uses [arrow-rs](https://github.com/apache/arrow-rs) to define schema, data types and array itself.
 //! It treats [`FixedSizeList<Float16/Float32>`](https://docs.rs/arrow/latest/arrow/array/struct.FixedSizeListArray.html)
-//! columns as vector columns. When creating a table with a floating-point `List`
-//! column named `vec`, or with `vector` or `embedding` in its name, LanceDB infers
-//! a uniform dimension from the first batch and stores it as a `FixedSizeList`.
+//! columns as vector columns.
 //!
 //! For more details, please refer to the [LanceDB documentation](https://docs.lancedb.com).
 //!
@@ -84,8 +82,6 @@
 //! schema of the `RecordBatch` determines the schema of the table.
 //!
 //! Vector columns should be represented as `FixedSizeList<Float16/Float32>` data type.
-//! A vector-like `List<Float16/Float32>` input is also accepted when every vector
-//! has the same runtime dimension.
 //!
 //! ```rust
 //! # use std::sync::Arc;

rust/lancedb/src/query.rs

@@ -1648,8 +1648,8 @@ mod tests {
     use super::*;
     use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
     use arrow_array::{
-        FixedSizeListArray, Float32Array, Int32Array, ListArray, RecordBatch, StringArray,
-        cast::AsArray, types::Float32Type,
+        FixedSizeListArray, Float32Array, Int32Array, RecordBatch, StringArray, cast::AsArray,
+        types::Float32Type,
     };
     use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
     use futures::{StreamExt, TryStreamExt};
@@ -2282,91 +2282,6 @@ mod tests {
         );
     }
 
-    #[tokio::test]
-    async fn vector_search_infers_dimension_from_list_array() {
-        let tmp_dir = tempdir().unwrap();
-        let schema = Arc::new(ArrowSchema::new(vec![
-            ArrowField::new("id", DataType::Int32, false),
-            ArrowField::new(
-                "vec",
-                DataType::List(Arc::new(ArrowField::new("item", DataType::Float32, true))),
-                true,
-            ),
-        ]));
-        let vectors = ListArray::from_iter_primitive::<Float32Type, _, _>([
-            Some([Some(0.0), Some(0.0)]),
-            Some([Some(1.0), Some(1.0)]),
-        ]);
-        let batch = RecordBatch::try_new(
-            schema,
-            vec![Arc::new(Int32Array::from(vec![0, 1])), Arc::new(vectors)],
-        )
-        .unwrap();
-        let table = connect(tmp_dir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap()
-            .create_table("vectors", batch)
-            .execute()
-            .await
-            .unwrap();
-        assert!(matches!(
-            table.schema().await.unwrap().field(1).data_type(),
-            DataType::FixedSizeList(_, 2)
-        ));
-
-        let results = table
-            .vector_search(&[0.0, 0.0])
-            .unwrap()
-            .limit(1)
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-
-        assert_eq!(results[0]["id"].as_primitive::<Int32Type>().value(0), 0);
-    }
-
-    #[tokio::test]
-    async fn inferred_vector_dimension_is_validated_across_batches() {
-        let tmp_dir = tempdir().unwrap();
-        let schema = Arc::new(ArrowSchema::new(vec![ArrowField::new(
-            "vec",
-            DataType::List(Arc::new(ArrowField::new("item", DataType::Float32, true))),
-            true,
-        )]));
-        let first = RecordBatch::try_new(
-            schema.clone(),
-            vec![Arc::new(
-                ListArray::from_iter_primitive::<Float32Type, _, _>([Some([Some(0.0), Some(0.0)])]),
-            )],
-        )
-        .unwrap();
-        let wrong_dimension = RecordBatch::try_new(
-            schema,
-            vec![Arc::new(
-                ListArray::from_iter_primitive::<Float32Type, _, _>([Some([
-                    Some(1.0),
-                    Some(1.0),
-                    Some(1.0),
-                ])]),
-            )],
-        )
-        .unwrap();
-
-        let result = connect(tmp_dir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap()
-            .create_table("vectors", vec![first, wrong_dimension])
-            .execute()
-            .await;
-
-        assert!(result.is_err());
-    }
-
     #[tokio::test]
     async fn test_fast_search_plan() {
         let tmp_dir = tempdir().unwrap();