feat: add python Permutation class to mimic hugging face dataset and provide pytorch dataloader (#2725)

docs/src/js/classes/PermutationBuilder.md

@@ -64,6 +64,36 @@ builder.filter("age > 18 AND status = 'active'");
 
 ***
 
+### persist()
+
+```ts
+persist(connection, tableName): PermutationBuilder
+```
+
+Configure the permutation to be persisted.
+
+#### Parameters
+
+* **connection**: [`Connection`](Connection.md)
+    The connection to persist the permutation to
+
+* **tableName**: `string`
+    The name of the table to create
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+builder.persist(connection, "permutation_table");
+```
+
+***
+
 ### shuffle()
 
 ```ts
@@ -98,15 +128,15 @@ builder.shuffle({ seed: 42, clumpSize: 10 });
 ### splitCalculated()
 
 ```ts
-splitCalculated(calculation): PermutationBuilder
+splitCalculated(options): PermutationBuilder
 ```
 
 Configure calculated splits for the permutation.
 
 #### Parameters
 
-* **calculation**: `string`
-    SQL expression for calculating splits
+* **options**: [`SplitCalculatedOptions`](../interfaces/SplitCalculatedOptions.md)
+    Configuration for calculated splitting
 
 #### Returns
 

docs/src/js/globals.md

@@ -77,6 +77,7 @@
 - [RemovalStats](interfaces/RemovalStats.md)
 - [RetryConfig](interfaces/RetryConfig.md)
 - [ShuffleOptions](interfaces/ShuffleOptions.md)
+- [SplitCalculatedOptions](interfaces/SplitCalculatedOptions.md)
 - [SplitHashOptions](interfaces/SplitHashOptions.md)
 - [SplitRandomOptions](interfaces/SplitRandomOptions.md)
 - [SplitSequentialOptions](interfaces/SplitSequentialOptions.md)

docs/src/js/interfaces/SplitCalculatedOptions.md

@@ -0,0 +1,23 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SplitCalculatedOptions
+
+# Interface: SplitCalculatedOptions
+
+## Properties
+
+### calculation
+
+```ts
+calculation: string;
+```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

docs/src/js/interfaces/SplitHashOptions.md

@@ -24,6 +24,14 @@ optional discardWeight: number;
 
 ***
 
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```
+
+***
+
 ### splitWeights
 
 ```ts

docs/src/js/interfaces/SplitRandomOptions.md

@@ -37,3 +37,11 @@ optional ratios: number[];
 ```ts
 optional seed: number;
 ```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

docs/src/js/interfaces/SplitSequentialOptions.md

@@ -29,3 +29,11 @@ optional fixed: number;
 ```ts
 optional ratios: number[];
 ```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

nodejs/__test__/permutation.test.ts

@@ -138,7 +138,9 @@ describe("PermutationBuilder", () => {
   });
 
   test("should create permutation with calculated splits", async () => {
-    const builder = permutationBuilder(table).splitCalculated("id % 2");
+    const builder = permutationBuilder(table).splitCalculated({
+      calculation: "id % 2",
+    });
 
     const permutationTable = await builder.execute();
     const rowCount = await permutationTable.countRows();
@@ -224,4 +226,146 @@ describe("PermutationBuilder", () => {
     // Should throw error on second execution
     await expect(builder.execute()).rejects.toThrow("Builder already consumed");
   });
+
+  test("should accept custom split names with random splits", async () => {
+    const builder = permutationBuilder(table).splitRandom({
+      ratios: [0.3, 0.7],
+      seed: 42,
+      splitNames: ["train", "test"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric (0, 1, etc.)
+    // The names are metadata that can be used by higher-level APIs
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should accept custom split names with hash splits", async () => {
+    const builder = permutationBuilder(table).splitHash({
+      columns: ["id"],
+      splitWeights: [50, 50],
+      discardWeight: 0,
+      splitNames: ["set_a", "set_b"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should accept custom split names with sequential splits", async () => {
+    const builder = permutationBuilder(table).splitSequential({
+      ratios: [0.5, 0.5],
+      splitNames: ["first", "second"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBe(5);
+    expect(split1Count).toBe(5);
+  });
+
+  test("should accept custom split names with calculated splits", async () => {
+    const builder = permutationBuilder(table).splitCalculated({
+      calculation: "id % 2",
+      splitNames: ["even", "odd"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should persist permutation to a new table", async () => {
+    const db = await connect(tmpDir.name);
+    const builder = permutationBuilder(table)
+      .splitRandom({
+        ratios: [0.7, 0.3],
+        seed: 42,
+        splitNames: ["train", "validation"],
+      })
+      .persist(db, "my_permutation");
+
+    // Execute the builder which will persist the table
+    const permutationTable = await builder.execute();
+
+    // Verify the persisted table exists and can be opened
+    const persistedTable = await db.openTable("my_permutation");
+    expect(persistedTable).toBeDefined();
+
+    // Verify the persisted table has the correct number of rows
+    const rowCount = await persistedTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Verify splits exist (numeric split_id values)
+    const split0Count = await persistedTable.countRows("split_id = 0");
+    const split1Count = await persistedTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+
+    // Verify the table returned by execute is the same as the persisted one
+    const executedRowCount = await permutationTable.countRows();
+    expect(executedRowCount).toBe(10);
+  });
+
+  test("should persist permutation with multiple operations", async () => {
+    const db = await connect(tmpDir.name);
+    const builder = permutationBuilder(table)
+      .filter("value > 30")
+      .splitRandom({ ratios: [0.5, 0.5], seed: 123, splitNames: ["a", "b"] })
+      .shuffle({ seed: 456 })
+      .persist(db, "filtered_permutation");
+
+    // Execute the builder
+    const permutationTable = await builder.execute();
+
+    // Verify the persisted table
+    const persistedTable = await db.openTable("filtered_permutation");
+    const rowCount = await persistedTable.countRows();
+    expect(rowCount).toBe(7); // Values 40, 50, 60, 70, 80, 90, 100
+
+    // Verify splits exist (numeric split_id values)
+    const split0Count = await persistedTable.countRows("split_id = 0");
+    const split1Count = await persistedTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(7);
+
+    // Verify the executed table matches
+    const executedRowCount = await permutationTable.countRows();
+    expect(executedRowCount).toBe(7);
+  });
 });

nodejs/lancedb/index.ts

@@ -43,6 +43,7 @@ export {
   DeleteResult,
   DropColumnsResult,
   UpdateResult,
+  SplitCalculatedOptions,
   SplitRandomOptions,
   SplitHashOptions,
   SplitSequentialOptions,

nodejs/lancedb/permutation.ts

@@ -1,10 +1,12 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+import { Connection, LocalConnection } from "./connection.js";
 import {
   PermutationBuilder as NativePermutationBuilder,
   Table as NativeTable,
   ShuffleOptions,
+  SplitCalculatedOptions,
   SplitHashOptions,
   SplitRandomOptions,
   SplitSequentialOptions,
@@ -29,6 +31,23 @@ export class PermutationBuilder {
     this.inner = inner;
   }
 
+  /**
+   * Configure the permutation to be persisted.
+   *
+   * @param connection - The connection to persist the permutation to
+   * @param tableName - The name of the table to create
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * builder.persist(connection, "permutation_table");
+   * ```
+   */
+  persist(connection: Connection, tableName: string): PermutationBuilder {
+    const localConnection = connection as LocalConnection;
+    const newInner = this.inner.persist(localConnection.inner, tableName);
+    return new PermutationBuilder(newInner);
+  }
+
   /**
    * Configure random splits for the permutation.
    *
@@ -95,15 +114,15 @@ export class PermutationBuilder {
   /**
    * Configure calculated splits for the permutation.
    *
-   * @param calculation - SQL expression for calculating splits
+   * @param options - Configuration for calculated splitting
    * @returns A new PermutationBuilder instance
    * @example
    * ```ts
    * builder.splitCalculated("user_id % 3");
    * ```
    */
-  splitCalculated(calculation: string): PermutationBuilder {
-    const newInner = this.inner.splitCalculated(calculation);
+  splitCalculated(options: SplitCalculatedOptions): PermutationBuilder {
+    const newInner = this.inner.splitCalculated(options);
     return new PermutationBuilder(newInner);
   }
 

nodejs/src/connection.rs

@@ -4,7 +4,7 @@
 use std::collections::HashMap;
 use std::sync::Arc;
 
-use lancedb::database::CreateTableMode;
+use lancedb::database::{CreateTableMode, Database};
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
@@ -41,6 +41,10 @@ impl Connection {
             _ => Err(napi::Error::from_reason(format!("Invalid mode {}", mode))),
         }
     }
+
+    pub fn database(&self) -> napi::Result<Arc<dyn Database>> {
+        Ok(self.get_inner()?.database().clone())
+    }
 }
 
 #[napi]

nodejs/src/permutation.rs

@@ -16,6 +16,7 @@ pub struct SplitRandomOptions {
     pub counts: Option<Vec<i64>>,
     pub fixed: Option<i64>,
     pub seed: Option<i64>,
+    pub split_names: Option<Vec<String>>,
 }
 
 #[napi(object)]
@@ -23,6 +24,7 @@ pub struct SplitHashOptions {
     pub columns: Vec<String>,
     pub split_weights: Vec<i64>,
     pub discard_weight: Option<i64>,
+    pub split_names: Option<Vec<String>>,
 }
 
 #[napi(object)]
@@ -30,6 +32,13 @@ pub struct SplitSequentialOptions {
     pub ratios: Option<Vec<f64>>,
     pub counts: Option<Vec<i64>>,
     pub fixed: Option<i64>,
+    pub split_names: Option<Vec<String>>,
+}
+
+#[napi(object)]
+pub struct SplitCalculatedOptions {
+    pub calculation: String,
+    pub split_names: Option<Vec<String>>,
 }
 
 #[napi(object)]
@@ -76,6 +85,16 @@ impl PermutationBuilder {
 
 #[napi]
 impl PermutationBuilder {
+    #[napi]
+    pub fn persist(
+        &self,
+        connection: &crate::connection::Connection,
+        table_name: String,
+    ) -> napi::Result<Self> {
+        let database = connection.database()?;
+        self.modify(|builder| builder.persist(database, table_name))
+    }
+
     /// Configure random splits
     #[napi]
     pub fn split_random(&self, options: SplitRandomOptions) -> napi::Result<Self> {
@@ -107,7 +126,12 @@ impl PermutationBuilder {
 
         let seed = options.seed.map(|s| s as u64);
 
-        self.modify(|builder| builder.with_split_strategy(SplitStrategy::Random { seed, sizes }))
+        self.modify(|builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Random { seed, sizes },
+                options.split_names.clone(),
+            )
+        })
     }
 
     /// Configure hash-based splits
@@ -120,12 +144,15 @@ impl PermutationBuilder {
             .collect();
         let discard_weight = options.discard_weight.unwrap_or(0) as u64;
 
-        self.modify(|builder| {
-            builder.with_split_strategy(SplitStrategy::Hash {
-                columns: options.columns,
-                split_weights,
-                discard_weight,
-            })
+        self.modify(move |builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Hash {
+                    columns: options.columns,
+                    split_weights,
+                    discard_weight,
+                },
+                options.split_names,
+            )
         })
     }
 
@@ -158,14 +185,21 @@ impl PermutationBuilder {
             unreachable!("One of the split arguments must be provided");
         };
 
-        self.modify(|builder| builder.with_split_strategy(SplitStrategy::Sequential { sizes }))
+        self.modify(move |builder| {
+            builder.with_split_strategy(SplitStrategy::Sequential { sizes }, options.split_names)
+        })
     }
 
     /// Configure calculated splits
     #[napi]
-    pub fn split_calculated(&self, calculation: String) -> napi::Result<Self> {
-        self.modify(|builder| {
-            builder.with_split_strategy(SplitStrategy::Calculated { calculation })
+    pub fn split_calculated(&self, options: SplitCalculatedOptions) -> napi::Result<Self> {
+        self.modify(move |builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Calculated {
+                    calculation: options.calculation,
+                },
+                options.split_names,
+            )
         })
     }
 

python/python/lancedb/__init__.py

@@ -17,7 +17,7 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .schema import vector
-from .table import AsyncTable
+from .table import AsyncTable, Table
 from ._lancedb import Session
 from .namespace import connect_namespace, LanceNamespaceDBConnection
 
@@ -233,6 +233,7 @@ __all__ = [
     "LanceNamespaceDBConnection",
     "RemoteDBConnection",
     "Session",
+    "Table",
     "__version__",
 ]
 

python/python/lancedb/_lancedb.pyi

@@ -340,3 +340,6 @@ def async_permutation_builder(
     table: Table, dest_table_name: str
 ) -> AsyncPermutationBuilder: ...
 def fts_query_to_json(query: Any) -> str: ...
+
+class PermutationReader:
+    def __init__(self, base_table: Table, permutation_table: Table): ...

python/python/lancedb/permutation.py

@@ -1,18 +1,63 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-from ._lancedb import async_permutation_builder
+from deprecation import deprecated
+from lancedb import AsyncConnection, DBConnection
+import pyarrow as pa
+import json
+
+from ._lancedb import async_permutation_builder, PermutationReader
 from .table import LanceTable
 from .background_loop import LOOP
-from typing import Optional
+from .util import batch_to_tensor
+from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
+
+if TYPE_CHECKING:
+    from lancedb.dependencies import pandas as pd, numpy as np, polars as pl
 
 
 class PermutationBuilder:
+    """
+    A utility for creating a "permutation table" which is a table that defines an
+    ordering on a base table.
+
+    The permutation table does not store the actual data.  It only stores row
+    ids and split ids to define the ordering.  The [Permutation] class can be used to
+    read the data from the base table in the order defined by the permutation table.
+
+    Permutations can split, shuffle, and filter the data in the base table.
+
+    A filter limits the rows that are included in the permutation.
+    Splits divide the data into subsets (for example, a test/train split, or K
+    different splits for cross-validation).
+    Shuffling randomizes the order of the rows in the permutation.
+
+    Splits can optionally be named.  If names are provided it will enable them to
+    be referenced by name in the future.  If names are not provided then they can only
+    be referenced by their ordinal index.  There is no requirement to name every split.
+
+    By default, the permutation will be stored in memory and will be lost when the
+    program exits.  To persist the permutation (for very large datasets or to share
+    the permutation across multiple workers) use the [persist](#persist) method to
+    create a permanent table.
+    """
+
     def __init__(self, table: LanceTable):
+        """
+        Creates a new permutation builder for the given table.
+
+        By default, the permutation builder will create a single split that contains all
+        rows in the same order as the base table.
+        """
         self._async = async_permutation_builder(table)
 
-    def select(self, projections: dict[str, str]) -> "PermutationBuilder":
-        self._async.select(projections)
+    def persist(
+        self, database: Union[DBConnection, AsyncConnection], table_name: str
+    ) -> "PermutationBuilder":
+        """
+        Persist the permutation to the given database.
+        """
+        self._async.persist(database, table_name)
         return self
 
     def split_random(
@@ -22,8 +67,38 @@ class PermutationBuilder:
         counts: Optional[list[int]] = None,
         fixed: Optional[int] = None,
         seed: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
     ) -> "PermutationBuilder":
-        self._async.split_random(ratios=ratios, counts=counts, fixed=fixed, seed=seed)
+        """
+        Configure random splits for the permutation.
+
+        One of ratios, counts, or fixed must be provided.
+
+        If ratios are provided, they will be used to determine the relative size of each
+        split. For example, if ratios are [0.3, 0.7] then the first split will contain
+        30% of the rows and the second split will contain 70% of the rows.
+
+        If counts are provided, they will be used to determine the absolute number of
+        rows in each split. For example, if counts are [100, 200] then the first split
+        will contain 100 rows and the second split will contain 200 rows.
+
+        If fixed is provided, it will be used to determine the number of splits.
+        For example, if fixed is 3 then the permutation will be split evenly into 3
+        splits.
+
+        Rows will be randomly assigned to splits.  The optional seed can be provided to
+        make the assignment deterministic.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_random(
+            ratios=ratios,
+            counts=counts,
+            fixed=fixed,
+            seed=seed,
+            split_names=split_names,
+        )
         return self
 
     def split_hash(
@@ -32,8 +107,33 @@ class PermutationBuilder:
         split_weights: list[int],
         *,
         discard_weight: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
     ) -> "PermutationBuilder":
-        self._async.split_hash(columns, split_weights, discard_weight=discard_weight)
+        """
+        Configure hash-based splits for the permutation.
+
+        First, a hash will be calculated over the specified columns.  The splits weights
+        are then used to determine how many rows to assign to each split.  For example,
+        if split weights are [1, 2] then the first split will contain 1/3 of the rows
+        and the second split will contain 2/3 of the rows.
+
+        The optional discard weight can be provided to determine what percentage of rows
+        should be discarded.  For example, if split weights are [1, 2] and discard
+        weight is 1 then 25% of the rows will be discarded.
+
+        Hash-based splits are useful if you want the split to be more or less random but
+        you don't want the split assignments to change if rows are added or removed
+        from the table.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_hash(
+            columns,
+            split_weights,
+            discard_weight=discard_weight,
+            split_names=split_names,
+        )
         return self
 
     def split_sequential(
@@ -42,25 +142,85 @@ class PermutationBuilder:
         ratios: Optional[list[float]] = None,
         counts: Optional[list[int]] = None,
         fixed: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
     ) -> "PermutationBuilder":
-        self._async.split_sequential(ratios=ratios, counts=counts, fixed=fixed)
+        """
+        Configure sequential splits for the permutation.
+
+        One of ratios, counts, or fixed must be provided.
+
+        If ratios are provided, they will be used to determine the relative size of each
+        split. For example, if ratios are [0.3, 0.7] then the first split will contain
+        30% of the rows and the second split will contain 70% of the rows.
+
+        If counts are provided, they will be used to determine the absolute number of
+        rows in each split. For example, if counts are [100, 200] then the first split
+        will contain 100 rows and the second split will contain 200 rows.
+
+        If fixed is provided, it will be used to determine the number of splits.
+        For example, if fixed is 3 then the permutation will be split evenly into 3
+        splits.
+
+        Rows will be assigned to splits sequentially.  The first N1 rows are assigned to
+        split 1, the next N2 rows are assigned to split 2, etc.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_sequential(
+            ratios=ratios, counts=counts, fixed=fixed, split_names=split_names
+        )
         return self
 
-    def split_calculated(self, calculation: str) -> "PermutationBuilder":
-        self._async.split_calculated(calculation)
+    def split_calculated(
+        self, calculation: str, split_names: Optional[list[str]] = None
+    ) -> "PermutationBuilder":
+        """
+        Use pre-calculated splits for the permutation.
+
+        The calculation should be an SQL statement that returns an integer value between
+        0 and the number of splits - 1.  For example, if you have 3 splits then the
+        calculation should return 0 for the first split, 1 for the second split, and 2
+        for the third split.
+
+        This can be used to implement any kind of user-defined split strategy.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_calculated(calculation, split_names=split_names)
         return self
 
     def shuffle(
         self, *, seed: Optional[int] = None, clump_size: Optional[int] = None
     ) -> "PermutationBuilder":
+        """
+        Randomly shuffle the rows in the permutation.
+
+        An optional seed can be provided to make the shuffle deterministic.
+
+        If a clump size is provided, then data will be shuffled as small "clumps"
+        of contiguous rows.  This allows for a balance between randomization and
+        I/O performance.  It can be useful when reading from cloud storage.
+        """
         self._async.shuffle(seed=seed, clump_size=clump_size)
         return self
 
     def filter(self, filter: str) -> "PermutationBuilder":
+        """
+        Configure a filter for the permutation.
+
+        The filter should be an SQL statement that returns a boolean value for each row.
+        Only rows where the filter is true will be included in the permutation.
+        """
         self._async.filter(filter)
         return self
 
     def execute(self) -> LanceTable:
+        """
+        Execute the configuration and create the permutation table.
+        """
+
         async def do_execute():
             inner_tbl = await self._async.execute()
             return LanceTable.from_inner(inner_tbl)
@@ -70,3 +230,592 @@ class PermutationBuilder:
 
 def permutation_builder(table: LanceTable) -> PermutationBuilder:
     return PermutationBuilder(table)
+
+
+class Permutations:
+    """
+    A collection of permutations indexed by name or ordinal index.
+
+    Splits are defined when the permutation is created.  Splits can always be referenced
+    by their ordinal index.  If names were provided when the permutation was created
+    then they can also be referenced by name.
+
+    Each permutation or "split" is a view of a portion of the base table.  For more
+    details see [Permutation].
+
+    Attributes
+    ----------
+    base_table: LanceTable
+        The base table that the permutations are based on.
+    permutation_table: LanceTable
+        The permutation table that defines the splits.
+    split_names: list[str]
+        The names of the splits.
+    split_dict: dict[str, int]
+        A dictionary mapping split names to their ordinal index.
+
+    Examples
+    --------
+    >>> # Initial data
+    >>> import lancedb
+    >>> db = lancedb.connect("memory:///")
+    >>> tbl = db.create_table("tbl", data=[{"x": x} for x in range(1000)])
+    >>> # Create a permutation
+    >>> perm_tbl = (
+    ...    permutation_builder(tbl)
+    ...    .split_random(ratios=[0.95, 0.05], split_names=["train", "test"])
+    ...    .shuffle()
+    ...    .execute()
+    ... )
+    >>> # Read the permutations
+    >>> permutations = Permutations(tbl, perm_tbl)
+    >>> permutations["train"]
+    <lancedb.permutation.Permutation ...>
+    >>> permutations[0]
+    <lancedb.permutation.Permutation ...>
+    >>> permutations.split_names
+    ['train', 'test']
+    >>> permutations.split_dict
+    {'train': 0, 'test': 1}
+    """
+
+    def __init__(self, base_table: LanceTable, permutation_table: LanceTable):
+        self.base_table = base_table
+        self.permutation_table = permutation_table
+
+        if permutation_table.schema.metadata is not None:
+            split_names = permutation_table.schema.metadata.get(
+                b"split_names", None
+            ).decode("utf-8")
+            if split_names is not None:
+                self.split_names = json.loads(split_names)
+                self.split_dict = {
+                    name: idx for idx, name in enumerate(self.split_names)
+                }
+            else:
+                # No split names are defined in the permutation table
+                self.split_names = []
+                self.split_dict = {}
+        else:
+            # No metadata is defined in the permutation table
+            self.split_names = []
+            self.split_dict = {}
+
+    def get_by_name(self, name: str) -> "Permutation":
+        """
+        Get a permutation by name.
+
+        If no split named `name` is found then an error will be raised.
+        """
+        idx = self.split_dict.get(name, None)
+        if idx is None:
+            raise ValueError(f"No split named `{name}` found")
+        return self.get_by_index(idx)
+
+    def get_by_index(self, index: int) -> "Permutation":
+        """
+        Get a permutation by index.
+        """
+        return Permutation.from_tables(self.base_table, self.permutation_table, index)
+
+    def __getitem__(self, name: Union[str, int]) -> "Permutation":
+        if isinstance(name, str):
+            return self.get_by_name(name)
+        elif isinstance(name, int):
+            return self.get_by_index(name)
+        else:
+            raise TypeError(f"Invalid split name or index: {name}")
+
+
+class Transforms:
+    """
+    Namespace for common transformation functions
+    """
+
+    @staticmethod
+    def arrow2python(batch: pa.RecordBatch) -> dict[str, list[Any]]:
+        return batch.to_pydict()
+
+    @staticmethod
+    def arrow2arrow(batch: pa.RecordBatch) -> pa.RecordBatch:
+        return batch
+
+    @staticmethod
+    def arrow2numpy(batch: pa.RecordBatch) -> "np.ndarray":
+        return batch.to_pandas().to_numpy()
+
+    @staticmethod
+    def arrow2pandas(batch: pa.RecordBatch) -> "pd.DataFrame":
+        return batch.to_pandas()
+
+    @staticmethod
+    def arrow2polars() -> "pl.DataFrame":
+        import polars as pl
+
+        def impl(batch: pa.RecordBatch) -> pl.DataFrame:
+            return pl.from_arrow(batch)
+
+        return impl
+
+
+# HuggingFace uses 10 which is pretty small
+DEFAULT_BATCH_SIZE = 100
+
+
+class Permutation:
+    """
+    A Permutation is a view of a dataset that can be used as input to model training
+    and evaluation.
+
+    A Permutation fulfills the pytorch Dataset contract and is loosely modeled after the
+    huggingface Dataset so it should be easy to use with existing code.
+
+    A permutation is not a "materialized view" or copy of the underlying data.  It is
+    calculated on the fly from the base table.  As a result, it is truly "lazy" and does
+    not require materializing the entire dataset in memory.
+    """
+
+    def __init__(
+        self,
+        reader: PermutationReader,
+        selection: dict[str, str],
+        batch_size: int,
+        transform_fn: Callable[pa.RecordBatch, Any],
+    ):
+        """
+        Internal constructor.  Use [from_tables](#from_tables) instead.
+        """
+        assert reader is not None, "reader is required"
+        assert selection is not None, "selection is required"
+        self.reader = reader
+        self.selection = selection
+        self.transform_fn = transform_fn
+        self.batch_size = batch_size
+
+    def _with_selection(self, selection: dict[str, str]) -> "Permutation":
+        """
+        Creates a new permutation with the given selection
+
+        Does not validation of the selection and it replaces it entirely.  This is not
+        intended for public use.
+        """
+        return Permutation(self.reader, selection, self.batch_size, self.transform_fn)
+
+    def _with_reader(self, reader: PermutationReader) -> "Permutation":
+        """
+        Creates a new permutation with the given reader
+
+        This is an internal method and should not be used directly.
+        """
+        return Permutation(reader, self.selection, self.batch_size, self.transform_fn)
+
+    def with_batch_size(self, batch_size: int) -> "Permutation":
+        """
+        Creates a new permutation with the given batch size
+        """
+        return Permutation(self.reader, self.selection, batch_size, self.transform_fn)
+
+    @classmethod
+    def identity(cls, table: LanceTable) -> "Permutation":
+        """
+        Creates an identity permutation for the given table.
+        """
+        return Permutation.from_tables(table, None, None)
+
+    @classmethod
+    def from_tables(
+        cls,
+        base_table: LanceTable,
+        permutation_table: Optional[LanceTable] = None,
+        split: Optional[Union[str, int]] = None,
+    ) -> "Permutation":
+        """
+        Creates a permutation from the given base table and permutation table.
+
+        A permutation table identifies which rows, and in what order, the data should
+        be read from the base table.  For more details see the [PermutationBuilder]
+        class.
+
+        If no permutation table is provided, then the identity permutation will be
+        created.  An identity permutation is a permutation that reads all rows in the
+        base table in the order they are stored.
+
+        The split parameter identifies which split to use.  If no split is provided
+        then the first split will be used.
+        """
+        assert base_table is not None, "base_table is required"
+        if split is not None:
+            if permutation_table is None:
+                raise ValueError(
+                    "Cannot create a permutation on split `{split}`"
+                    " because no permutation table is provided"
+                )
+            if isinstance(split, str):
+                if permutation_table.schema.metadata is None:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        " because no split names are defined in the permutation table"
+                    )
+                split_names = permutation_table.schema.metadata.get(
+                    b"split_names", None
+                ).decode("utf-8")
+                if split_names is None:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        " because no split names are defined in the permutation table"
+                    )
+                split_names = json.loads(split_names)
+                try:
+                    split = split_names.index(split)
+                except ValueError:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        f" because split `{split}` is not defined in the "
+                        "permutation table"
+                    )
+            elif isinstance(split, int):
+                split = split
+            else:
+                raise TypeError(f"Invalid split: {split}")
+        else:
+            split = 0
+
+        async def do_from_tables():
+            reader = await PermutationReader.from_tables(
+                base_table, permutation_table, split
+            )
+            schema = await reader.output_schema(None)
+            initial_selection = {name: name for name in schema.names}
+            return cls(
+                reader, initial_selection, DEFAULT_BATCH_SIZE, Transforms.arrow2python
+            )
+
+        return LOOP.run(do_from_tables())
+
+    @property
+    def schema(self) -> pa.Schema:
+        async def do_output_schema():
+            return await self.reader.output_schema(self.selection)
+
+        return LOOP.run(do_output_schema())
+
+    @property
+    def num_columns(self) -> int:
+        """
+        The number of columns in the permutation
+        """
+        return len(self.schema)
+
+    @property
+    def num_rows(self) -> int:
+        """
+        The number of rows in the permutation
+        """
+        return self.reader.count_rows()
+
+    @property
+    def column_names(self) -> list[str]:
+        """
+        The names of the columns in the permutation
+        """
+        return self.schema.names
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        """
+        The shape of the permutation
+
+        This will return self.num_rows, self.num_columns
+        """
+        return self.num_rows, self.num_columns
+
+    def __len__(self) -> int:
+        """
+        The number of rows in the permutation
+
+        This is an alias for [num_rows][lancedb.permutation.Permutation.num_rows]
+        """
+        return self.num_rows
+
+    def unique(self, _column: str) -> list[Any]:
+        """
+        Get the unique values in the given column
+        """
+        raise Exception("unique is not yet implemented")
+
+    def flatten(self) -> "Permutation":
+        """
+        Flatten the permutation
+
+        Each column with a struct type will be flattened into multiple columns.
+
+        This flattening operation happens at read time as a post-processing step
+        so this call is cheap and no data is copied or modified in the underlying
+        dataset.
+        """
+        raise Exception("flatten is not yet implemented")
+
+    def remove_columns(self, columns: list[str]) -> "Permutation":
+        """
+        Remove the given columns from the permutation
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        which columns are visible from this permutation.  Also, this does not introduce
+        a post-processing step.  Instead, we simply do not read those columns in the
+        first place.
+
+        If any of the provided columns does not exist in the current permutation then it
+        will be ignored (no error is raised for missing columns)
+
+        Returns a new permutation with the given columns removed.  This does not modify
+        self.
+        """
+        assert columns is not None, "columns is required"
+
+        new_selection = {
+            name: value for name, value in self.selection.items() if name not in columns
+        }
+
+        if len(new_selection) == 0:
+            raise ValueError("Cannot remove all columns")
+
+        return self._with_selection(new_selection)
+
+    def rename_column(self, old_name: str, new_name: str) -> "Permutation":
+        """
+        Rename a column in the permutation
+
+        If there is no column named old_name then an error will be raised
+        If there is already a column named new_name then an error will be raised
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        the name of the column that is visible from this permutation.  This is a
+        post-processing step but done at the batch level and so it is very cheap.
+        No data will be copied.
+        """
+        assert old_name is not None, "old_name is required"
+        assert new_name is not None, "new_name is required"
+        if old_name not in self.selection:
+            raise ValueError(
+                f"Cannot rename column `{old_name}` because it does not exist"
+            )
+        if new_name in self.selection:
+            raise ValueError(
+                f"Cannot rename column `{old_name}` to `{new_name}` because a column "
+                "with that name already exists"
+            )
+        new_selection = self.selection.copy()
+        new_selection[new_name] = new_selection[old_name]
+        del new_selection[old_name]
+        return self._with_selection(new_selection)
+
+    def rename_columns(self, column_map: dict[str, str]) -> "Permutation":
+        """
+        Rename the given columns in the permutation
+
+        If any of the columns do not exist then an error will be raised
+        If any of the new names already exist then an error will be raised
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        the name of the column that is visible from this permutation.  This is a
+        post-processing step but done at the batch level and so it is very cheap.
+        No data will be copied.
+        """
+        assert column_map is not None, "column_map is required"
+
+        new_permutation = self
+        for old_name, new_name in column_map.items():
+            new_permutation = new_permutation.rename_column(old_name, new_name)
+        return new_permutation
+
+    def select_columns(self, columns: list[str]) -> "Permutation":
+        """
+        Select the given columns from the permutation
+
+        This method refines the current selection, potentially removing columns.  It
+        will not add back columns that were previously removed.
+
+        If any of the columns do not exist then an error will be raised
+
+        This does not introduce a post-processing step.  It simply reduces the amount
+        of data we read.
+        """
+        assert columns is not None, "columns is required"
+        if len(columns) == 0:
+            raise ValueError("Must select at least one column")
+
+        new_selection = {}
+        for name in columns:
+            value = self.selection.get(name, None)
+            if value is None:
+                raise ValueError(
+                    f"Cannot select column `{name}` because it does not exist"
+                )
+            new_selection[name] = value
+        return self._with_selection(new_selection)
+
+    def __iter__(self) -> Iterator[dict[str, Any]]:
+        """
+        Iterate over the permutation
+        """
+        return self.iter(self.batch_size, skip_last_batch=True)
+
+    def iter(
+        self, batch_size: int, skip_last_batch: bool = False
+    ) -> Iterator[dict[str, Any]]:
+        """
+        Iterate over the permutation in batches
+
+        If skip_last_batch is True, the last batch will be skipped if it is not a
+        multiple of batch_size.
+        """
+
+        async def get_iter():
+            return await self.reader.read(self.selection, batch_size=batch_size)
+
+        async_iter = LOOP.run(get_iter())
+
+        async def get_next():
+            return await async_iter.__anext__()
+
+        try:
+            while True:
+                batch = LOOP.run(get_next())
+                if batch.num_rows == batch_size or not skip_last_batch:
+                    yield self.transform_fn(batch)
+        except StopAsyncIteration:
+            return
+
+    def with_format(
+        self, format: Literal["numpy", "python", "pandas", "arrow", "torch", "polars"]
+    ) -> "Permutation":
+        """
+        Set the format for batches
+
+        If this method is not called, the "python" format will be used.
+
+        The format can be one of:
+        - "numpy" - the batch will be a dict of numpy arrays (one per column)
+        - "python" - the batch will be a dict of lists (one per column)
+        - "pandas" - the batch will be a pandas DataFrame
+        - "arrow" - the batch will be a pyarrow RecordBatch
+        - "torch" - the batch will be a two dimensional torch tensor
+        - "polars" - the batch will be a polars DataFrame
+
+        Conversion may or may not involve a data copy.  Lance uses Arrow internally
+        and so it is able to zero-copy to the arrow and polars.
+
+        Conversion to torch will be zero-copy but will only support a subset of data
+        types (numeric types).
+
+        Conversion to numpy and/or pandas will typically be zero-copy for numeric
+        types.  Conversion of strings, lists, and structs will require creating python
+        objects and this is not zero-copy.
+
+        For custom formatting, use [with_transform](#with_transform) which overrides
+        this method.
+        """
+        assert format is not None, "format is required"
+        if format == "python":
+            return self.with_transform(Transforms.arrow2python)
+        elif format == "numpy":
+            return self.with_transform(Transforms.arrow2numpy)
+        elif format == "pandas":
+            return self.with_transform(Transforms.arrow2pandas)
+        elif format == "arrow":
+            return self.with_transform(Transforms.arrow2arrow)
+        elif format == "torch":
+            return self.with_transform(batch_to_tensor)
+        elif format == "polars":
+            return self.with_transform(Transforms.arrow2polars())
+        else:
+            raise ValueError(f"Invalid format: {format}")
+
+    def with_transform(self, transform: Callable[pa.RecordBatch, Any]) -> "Permutation":
+        """
+        Set a custom transform for the permutation
+
+        The transform is a callable that will be invoked with each record batch.  The
+        return value will be used as the batch for iteration.
+
+        Note: transforms are not invoked in parallel.  This method is not a good place
+        for expensive operations such as image decoding.
+        """
+        assert transform is not None, "transform is required"
+        return Permutation(self.reader, self.selection, self.batch_size, transform)
+
+    def __getitem__(self, index: int) -> Any:
+        """
+        Return a single row from the permutation
+
+        The output will always be a python dictionary regardless of the format.
+
+        This method is mostly useful for debugging and exploration.  For actual
+        processing use [iter](#iter) or a torch data loader to perform batched
+        processing.
+        """
+        pass
+
+    @deprecated(details="Use with_skip instead")
+    def skip(self, skip: int) -> "Permutation":
+        """
+        Skip the first `skip` rows of the permutation
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_skip](#with_skip) instead to avoid confusion.
+        """
+        return self.with_skip(skip)
+
+    def with_skip(self, skip: int) -> "Permutation":
+        """
+        Skip the first `skip` rows of the permutation
+        """
+
+        async def do_with_skip():
+            reader = await self.reader.with_offset(skip)
+            return self._with_reader(reader)
+
+        return LOOP.run(do_with_skip())
+
+    @deprecated(details="Use with_take instead")
+    def take(self, limit: int) -> "Permutation":
+        """
+        Limit the permutation to `limit` rows (following any `skip`)
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_take](#with_take) instead to avoid confusion.
+        """
+        return self.with_take(limit)
+
+    def with_take(self, limit: int) -> "Permutation":
+        """
+        Limit the permutation to `limit` rows (following any `skip`)
+        """
+
+        async def do_with_take():
+            reader = await self.reader.with_limit(limit)
+            return self._with_reader(reader)
+
+        return LOOP.run(do_with_take())
+
+    @deprecated(details="Use with_repeat instead")
+    def repeat(self, times: int) -> "Permutation":
+        """
+        Repeat the permutation `times` times
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_repeat](#with_repeat) instead to avoid confusion.
+        """
+        return self.with_repeat(times)
+
+    def with_repeat(self, times: int) -> "Permutation":
+        """
+        Repeat the permutation `times` times
+        """
+        raise Exception("with_repeat is not yet implemented")

python/python/lancedb/util.py

@@ -366,3 +366,56 @@ def add_note(base_exception: BaseException, note: str):
         )
     else:
         raise ValueError("Cannot add note to exception")
+
+
+def tbl_to_tensor(tbl: pa.Table):
+    """
+    Convert a PyArrow Table to a PyTorch Tensor.
+
+    Each column is converted to a tensor (using zero-copy via DLPack)
+    and the columns are then stacked into a single tensor.
+
+    Fails if torch is not installed.
+    Fails if any column is more than one chunk.
+    Fails if a column's data type is not supported by PyTorch.
+
+    Parameters
+    ----------
+    tbl : pa.Table or pa.RecordBatch
+        The table or record batch to convert to a tensor.
+
+    Returns
+    -------
+    torch.Tensor: The tensor containing the columns of the table.
+    """
+    torch = attempt_import_or_raise("torch", "torch")
+
+    def to_tensor(col: pa.ChunkedArray):
+        if col.num_chunks > 1:
+            raise Exception("Single batch was too large to fit into a one-chunk table")
+        return torch.from_dlpack(col.chunk(0))
+
+    return torch.stack([to_tensor(tbl.column(i)) for i in range(tbl.num_columns)])
+
+
+def batch_to_tensor(batch: pa.RecordBatch):
+    """
+    Convert a PyArrow RecordBatch to a PyTorch Tensor.
+
+    Each column is converted to a tensor (using zero-copy via DLPack)
+    and the columns are then stacked into a single tensor.
+
+    Fails if torch is not installed.
+    Fails if a column's data type is not supported by PyTorch.
+
+    Parameters
+    ----------
+    batch : pa.RecordBatch
+        The record batch to convert to a tensor.
+
+    Returns
+    -------
+    torch.Tensor: The tensor containing the columns of the record batch.
+    """
+    torch = attempt_import_or_raise("torch", "torch")
+    return torch.stack([torch.from_dlpack(col) for col in batch.columns])

python/python/tests/test_permutation.py

@@ -2,9 +2,26 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import pyarrow as pa
+import math
 import pytest
 
-from lancedb.permutation import permutation_builder
+from lancedb import DBConnection, Table, connect
+from lancedb.permutation import Permutation, Permutations, permutation_builder
+
+
+def test_permutation_persistence(tmp_path):
+    db = connect(tmp_path)
+    tbl = db.create_table("test_table", pa.table({"x": range(100), "y": range(100)}))
+
+    permutation_tbl = (
+        permutation_builder(tbl).shuffle().persist(db, "test_permutation").execute()
+    )
+    assert permutation_tbl.count_rows() == 100
+
+    re_open = db.open_table("test_permutation")
+    assert re_open.count_rows() == 100
+
+    assert permutation_tbl.to_arrow() == re_open.to_arrow()
 
 
 def test_split_random_ratios(mem_db):
@@ -195,21 +212,33 @@ def test_split_error_cases(mem_db):
     tbl = mem_db.create_table("test_table", pa.table({"x": range(10), "y": range(10)}))
 
     # Test split_random with no parameters
-    with pytest.raises(Exception):
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
         permutation_builder(tbl).split_random().execute()
 
     # Test split_random with multiple parameters
-    with pytest.raises(Exception):
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
         permutation_builder(tbl).split_random(
             ratios=[0.5, 0.5], counts=[5, 5]
         ).execute()
 
     # Test split_sequential with no parameters
-    with pytest.raises(Exception):
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
         permutation_builder(tbl).split_sequential().execute()
 
     # Test split_sequential with multiple parameters
-    with pytest.raises(Exception):
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
         permutation_builder(tbl).split_sequential(ratios=[0.5, 0.5], fixed=2).execute()
 
 
@@ -460,3 +489,455 @@ def test_filter_empty_result(mem_db):
     )
 
     assert permutation_tbl.count_rows() == 0
+
+
+@pytest.fixture
+def mem_db() -> DBConnection:
+    return connect("memory:///")
+
+
+@pytest.fixture
+def some_table(mem_db: DBConnection) -> Table:
+    data = pa.table(
+        {
+            "id": range(1000),
+            "value": range(1000),
+        }
+    )
+    return mem_db.create_table("some_table", data)
+
+
+def test_no_split_names(some_table: Table):
+    perm_tbl = (
+        permutation_builder(some_table).split_sequential(counts=[500, 500]).execute()
+    )
+    permutations = Permutations(some_table, perm_tbl)
+    assert permutations.split_names == []
+    assert permutations.split_dict == {}
+    assert permutations[0].num_rows == 500
+    assert permutations[1].num_rows == 500
+
+
+@pytest.fixture
+def some_perm_table(some_table: Table) -> Table:
+    return (
+        permutation_builder(some_table)
+        .split_random(ratios=[0.95, 0.05], seed=42, split_names=["train", "test"])
+        .shuffle(seed=42)
+        .execute()
+    )
+
+
+def test_nonexistent_split(some_table: Table, some_perm_table: Table):
+    # Reference by name and name does not exist
+    with pytest.raises(ValueError, match="split `nonexistent` is not defined"):
+        Permutation.from_tables(some_table, some_perm_table, "nonexistent")
+
+    # Reference by ordinal and there are no rows
+    with pytest.raises(ValueError, match="No rows found"):
+        Permutation.from_tables(some_table, some_perm_table, 5)
+
+
+def test_permutations(some_table: Table, some_perm_table: Table):
+    permutations = Permutations(some_table, some_perm_table)
+    assert permutations.split_names == ["train", "test"]
+    assert permutations.split_dict == {"train": 0, "test": 1}
+    assert permutations["train"].num_rows == 950
+    assert permutations[0].num_rows == 950
+    assert permutations["test"].num_rows == 50
+    assert permutations[1].num_rows == 50
+
+    with pytest.raises(ValueError, match="No split named `nonexistent` found"):
+        permutations["nonexistent"]
+    with pytest.raises(ValueError, match="No rows found"):
+        permutations[5]
+
+
+@pytest.fixture
+def some_permutation(some_table: Table, some_perm_table: Table) -> Permutation:
+    return Permutation.from_tables(some_table, some_perm_table)
+
+
+def test_num_rows(some_permutation: Permutation):
+    assert some_permutation.num_rows == 950
+
+
+def test_num_columns(some_permutation: Permutation):
+    assert some_permutation.num_columns == 2
+
+
+def test_column_names(some_permutation: Permutation):
+    assert some_permutation.column_names == ["id", "value"]
+
+
+def test_shape(some_permutation: Permutation):
+    assert some_permutation.shape == (950, 2)
+
+
+def test_schema(some_permutation: Permutation):
+    assert some_permutation.schema == pa.schema(
+        [("id", pa.int64()), ("value", pa.int64())]
+    )
+
+
+def test_limit_offset(some_permutation: Permutation):
+    assert some_permutation.with_take(100).num_rows == 100
+    assert some_permutation.with_skip(100).num_rows == 850
+    assert some_permutation.with_take(100).with_skip(100).num_rows == 100
+
+    with pytest.raises(Exception):
+        some_permutation.with_take(1000000).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_skip(1000000).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_take(500).with_skip(500).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_skip(500).with_take(500).num_rows
+
+
+def test_remove_columns(some_permutation: Permutation):
+    assert some_permutation.remove_columns(["value"]).schema == pa.schema(
+        [("id", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot remove all columns
+    with pytest.raises(ValueError, match="Cannot remove all columns"):
+        some_permutation.remove_columns(["id", "value"])
+
+
+def test_rename_column(some_permutation: Permutation):
+    assert some_permutation.rename_column("value", "new_value").schema == pa.schema(
+        [("id", pa.int64()), ("new_value", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot rename to an existing column
+    with pytest.raises(
+        ValueError,
+        match="a column with that name already exists",
+    ):
+        some_permutation.rename_column("value", "id")
+    # Cannot rename a non-existent column
+    with pytest.raises(
+        ValueError,
+        match="does not exist",
+    ):
+        some_permutation.rename_column("non_existent", "new_value")
+
+
+def test_rename_columns(some_permutation: Permutation):
+    assert some_permutation.rename_columns({"value": "new_value"}).schema == pa.schema(
+        [("id", pa.int64()), ("new_value", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot rename to an existing column
+    with pytest.raises(ValueError, match="a column with that name already exists"):
+        some_permutation.rename_columns({"value": "id"})
+
+
+def test_select_columns(some_permutation: Permutation):
+    assert some_permutation.select_columns(["id"]).schema == pa.schema(
+        [("id", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot select a non-existent column
+    with pytest.raises(ValueError, match="does not exist"):
+        some_permutation.select_columns(["non_existent"])
+    # Empty selection is not allowed
+    with pytest.raises(ValueError, match="select at least one column"):
+        some_permutation.select_columns([])
+
+
+def test_iter_basic(some_permutation: Permutation):
+    """Test basic iteration with custom batch size."""
+    batch_size = 100
+    batches = list(some_permutation.iter(batch_size, skip_last_batch=False))
+
+    # Check that we got the expected number of batches
+    expected_batches = (950 + batch_size - 1) // batch_size  # ceiling division
+    assert len(batches) == expected_batches
+
+    # Check that all batches are dicts (default python format)
+    assert all(isinstance(batch, dict) for batch in batches)
+
+    # Check that batches have the correct structure
+    for batch in batches:
+        assert "id" in batch
+        assert "value" in batch
+        assert isinstance(batch["id"], list)
+        assert isinstance(batch["value"], list)
+
+    # Check that all batches except the last have the correct size
+    for batch in batches[:-1]:
+        assert len(batch["id"]) == batch_size
+        assert len(batch["value"]) == batch_size
+
+    # Last batch might be smaller
+    assert len(batches[-1]["id"]) <= batch_size
+
+
+def test_iter_skip_last_batch(some_permutation: Permutation):
+    """Test iteration with skip_last_batch=True."""
+    batch_size = 300
+    batches_with_skip = list(some_permutation.iter(batch_size, skip_last_batch=True))
+    batches_without_skip = list(
+        some_permutation.iter(batch_size, skip_last_batch=False)
+    )
+
+    # With skip_last_batch=True, we should have fewer batches if the last one is partial
+    num_full_batches = 950 // batch_size
+    assert len(batches_with_skip) == num_full_batches
+
+    # Without skip_last_batch, we should have one more batch if there's a remainder
+    if 950 % batch_size != 0:
+        assert len(batches_without_skip) == num_full_batches + 1
+        # Last batch should be smaller
+        assert len(batches_without_skip[-1]["id"]) == 950 % batch_size
+
+    # All batches with skip_last_batch should be full size
+    for batch in batches_with_skip:
+        assert len(batch["id"]) == batch_size
+
+
+def test_iter_different_batch_sizes(some_permutation: Permutation):
+    """Test iteration with different batch sizes."""
+
+    # Test with small batch size
+    small_batches = list(some_permutation.iter(100, skip_last_batch=False))
+    assert len(small_batches) == 10  # ceiling(950 / 100)
+
+    # Test with large batch size
+    large_batches = list(some_permutation.iter(400, skip_last_batch=False))
+    assert len(large_batches) == 3  # ceiling(950 / 400)
+
+    # Test with batch size equal to total rows
+    single_batch = list(some_permutation.iter(950, skip_last_batch=False))
+    assert len(single_batch) == 1
+    assert len(single_batch[0]["id"]) == 950
+
+    # Test with batch size larger than total rows
+    oversized_batch = list(some_permutation.iter(10000, skip_last_batch=False))
+    assert len(oversized_batch) == 1
+    assert len(oversized_batch[0]["id"]) == 950
+
+
+def test_dunder_iter(some_permutation: Permutation):
+    """Test the __iter__ method."""
+    # __iter__ should use DEFAULT_BATCH_SIZE (100) and skip_last_batch=True
+    batches = list(some_permutation)
+
+    # With DEFAULT_BATCH_SIZE=100 and skip_last_batch=True, we should get 9 batches
+    assert len(batches) == 9  # ceiling(950 / 100)
+
+    # All batches should be full size
+    for batch in batches:
+        assert len(batch["id"]) == 100
+        assert len(batch["value"]) == 100
+
+    some_permutation = some_permutation.with_batch_size(400)
+    batches = list(some_permutation)
+    assert len(batches) == 2  # floor(950 / 400) since skip_last_batch=True
+    for batch in batches:
+        assert len(batch["id"]) == 400
+        assert len(batch["value"]) == 400
+
+
+def test_iter_with_different_formats(some_permutation: Permutation):
+    """Test iteration with different output formats."""
+    batch_size = 100
+
+    # Test with arrow format
+    arrow_perm = some_permutation.with_format("arrow")
+    arrow_batches = list(arrow_perm.iter(batch_size, skip_last_batch=False))
+    assert all(isinstance(batch, pa.RecordBatch) for batch in arrow_batches)
+
+    # Test with python format (default)
+    python_perm = some_permutation.with_format("python")
+    python_batches = list(python_perm.iter(batch_size, skip_last_batch=False))
+    assert all(isinstance(batch, dict) for batch in python_batches)
+
+    # Test with pandas format
+    pandas_perm = some_permutation.with_format("pandas")
+    pandas_batches = list(pandas_perm.iter(batch_size, skip_last_batch=False))
+    # Import pandas to check the type
+    import pandas as pd
+
+    assert all(isinstance(batch, pd.DataFrame) for batch in pandas_batches)
+
+
+def test_iter_with_column_selection(some_permutation: Permutation):
+    """Test iteration after column selection."""
+    # Select only the id column
+    id_only = some_permutation.select_columns(["id"])
+    batches = list(id_only.iter(100, skip_last_batch=False))
+
+    # Check that batches only contain the id column
+    for batch in batches:
+        assert "id" in batch
+        assert "value" not in batch
+
+
+def test_iter_with_column_rename(some_permutation: Permutation):
+    """Test iteration after renaming columns."""
+    renamed = some_permutation.rename_column("value", "data")
+    batches = list(renamed.iter(100, skip_last_batch=False))
+
+    # Check that batches have the renamed column
+    for batch in batches:
+        assert "id" in batch
+        assert "data" in batch
+        assert "value" not in batch
+
+
+def test_iter_with_limit_offset(some_permutation: Permutation):
+    """Test iteration with limit and offset."""
+    # Test with offset
+    offset_perm = some_permutation.with_skip(100)
+    offset_batches = list(offset_perm.iter(100, skip_last_batch=False))
+    # Should have 850 rows (950 - 100)
+    expected_batches = math.ceil(850 / 100)
+    assert len(offset_batches) == expected_batches
+
+    # Test with limit
+    limit_perm = some_permutation.with_take(500)
+    limit_batches = list(limit_perm.iter(100, skip_last_batch=False))
+    # Should have 5 batches (500 / 100)
+    assert len(limit_batches) == 5
+
+    no_skip = some_permutation.iter(101, skip_last_batch=False)
+    row_100 = next(no_skip)["id"][100]
+
+    # Test with both limit and offset
+    limited_perm = some_permutation.with_skip(100).with_take(300)
+    limited_batches = list(limited_perm.iter(100, skip_last_batch=False))
+    # Should have 3 batches (300 / 100)
+    assert len(limited_batches) == 3
+    assert limited_batches[0]["id"][0] == row_100
+
+
+def test_iter_empty_permutation(mem_db):
+    """Test iteration over an empty permutation."""
+    # Create a table and filter it to be empty
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation_tbl = permutation_builder(tbl).filter("value > 100").execute()
+    with pytest.raises(ValueError, match="No rows found"):
+        Permutation.from_tables(tbl, permutation_tbl)
+
+
+def test_iter_single_row(mem_db):
+    """Test iteration over a permutation with a single row."""
+    tbl = mem_db.create_table("test_table", pa.table({"id": [42], "value": [100]}))
+    permutation_tbl = permutation_builder(tbl).execute()
+    perm = Permutation.from_tables(tbl, permutation_tbl)
+
+    # With skip_last_batch=False, should get one batch
+    batches = list(perm.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    assert len(batches[0]["id"]) == 1
+
+    # With skip_last_batch=True, should skip the single row (since it's < batch_size)
+    batches_skip = list(perm.iter(10, skip_last_batch=True))
+    assert len(batches_skip) == 0
+
+
+def test_identity_permutation(mem_db):
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    assert permutation.num_rows == 10
+    assert permutation.num_columns == 2
+
+    batches = list(permutation.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    assert len(batches[0]["id"]) == 10
+    assert len(batches[0]["value"]) == 10
+
+    permutation = permutation.remove_columns(["value"])
+    assert permutation.num_columns == 1
+    assert permutation.schema == pa.schema([("id", pa.int64())])
+    assert permutation.column_names == ["id"]
+    assert permutation.shape == (10, 1)
+
+
+def test_transform_fn(mem_db):
+    import numpy as np
+    import pandas as pd
+    import polars as pl
+
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    np_result = list(permutation.with_format("numpy").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert np_result.shape == (10, 2)
+    assert np_result.dtype == np.int64
+    assert isinstance(np_result, np.ndarray)
+
+    pd_result = list(permutation.with_format("pandas").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert pd_result.shape == (10, 2)
+    assert pd_result.dtypes.tolist() == [np.int64, np.int64]
+    assert isinstance(pd_result, pd.DataFrame)
+
+    pl_result = list(permutation.with_format("polars").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert pl_result.shape == (10, 2)
+    assert pl_result.dtypes == [pl.Int64, pl.Int64]
+    assert isinstance(pl_result, pl.DataFrame)
+
+    py_result = list(permutation.with_format("python").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert len(py_result) == 2
+    assert len(py_result["id"]) == 10
+    assert len(py_result["value"]) == 10
+    assert isinstance(py_result, dict)
+
+    try:
+        import torch
+
+        torch_result = list(
+            permutation.with_format("torch").iter(10, skip_last_batch=False)
+        )[0]
+        assert torch_result.shape == (2, 10)
+        assert torch_result.dtype == torch.int64
+        assert isinstance(torch_result, torch.Tensor)
+    except ImportError:
+        # Skip check if torch is not installed
+        pass
+
+    arrow_result = list(
+        permutation.with_format("arrow").iter(10, skip_last_batch=False)
+    )[0]
+    assert arrow_result.shape == (10, 2)
+    assert arrow_result.schema == pa.schema([("id", pa.int64()), ("value", pa.int64())])
+    assert isinstance(arrow_result, pa.RecordBatch)
+
+
+def test_custom_transform(mem_db):
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    def transform(batch: pa.RecordBatch) -> pa.RecordBatch:
+        return batch.select(["id"])
+
+    transformed = permutation.with_transform(transform)
+    batches = list(transformed.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    batch = batches[0]
+
+    assert batch == pa.record_batch([range(10)], ["id"])

python/python/tests/test_torch.py

@@ -3,19 +3,11 @@
 
 import pyarrow as pa
 import pytest
+from lancedb.util import tbl_to_tensor
 
 torch = pytest.importorskip("torch")
 
 
-def tbl_to_tensor(tbl):
-    def to_tensor(col: pa.ChunkedArray):
-        if col.num_chunks > 1:
-            raise Exception("Single batch was too large to fit into a one-chunk table")
-        return torch.from_dlpack(col.chunk(0))
-
-    return torch.stack([to_tensor(tbl.column(i)) for i in range(tbl.num_columns)])
-
-
 def test_table_dataloader(mem_db):
     table = mem_db.create_table("test_table", pa.table({"a": range(1000)}))
     dataloader = torch.utils.data.DataLoader(

python/src/connection.rs

@@ -6,7 +6,7 @@ use std::{collections::HashMap, sync::Arc, time::Duration};
 use arrow::{datatypes::Schema, ffi_stream::ArrowArrayStreamReader, pyarrow::FromPyArrow};
 use lancedb::{
     connection::Connection as LanceConnection,
-    database::{CreateTableMode, ReadConsistency},
+    database::{CreateTableMode, Database, ReadConsistency},
 };
 use pyo3::{
     exceptions::{PyRuntimeError, PyValueError},
@@ -42,6 +42,10 @@ impl Connection {
             _ => Err(PyValueError::new_err(format!("Invalid mode {}", mode))),
         }
     }
+
+    pub fn database(&self) -> PyResult<Arc<dyn Database>> {
+        Ok(self.get_inner()?.database().clone())
+    }
 }
 
 #[pymethods]

python/src/lib.rs

@@ -5,7 +5,7 @@ use arrow::RecordBatchStream;
 use connection::{connect, Connection};
 use env_logger::Env;
 use index::IndexConfig;
-use permutation::PyAsyncPermutationBuilder;
+use permutation::{PyAsyncPermutationBuilder, PyPermutationReader};
 use pyo3::{
     pymodule,
     types::{PyModule, PyModuleMethods},
@@ -52,6 +52,7 @@ pub fn _lancedb(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<DropColumnsResult>()?;
     m.add_class::<UpdateResult>()?;
     m.add_class::<PyAsyncPermutationBuilder>()?;
+    m.add_class::<PyPermutationReader>()?;
     m.add_function(wrap_pyfunction!(connect, m)?)?;
     m.add_function(wrap_pyfunction!(permutation::async_permutation_builder, m)?)?;
     m.add_function(wrap_pyfunction!(util::validate_table_name, m)?)?;

python/src/permutation.rs

@@ -3,14 +3,23 @@
 
 use std::sync::{Arc, Mutex};
 
-use crate::{error::PythonErrorExt, table::Table};
-use lancedb::dataloader::{
-    permutation::builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
-    permutation::split::{SplitSizes, SplitStrategy},
+use crate::{
+    arrow::RecordBatchStream, connection::Connection, error::PythonErrorExt, table::Table,
+};
+use arrow::pyarrow::ToPyArrow;
+use lancedb::{
+    dataloader::permutation::{
+        builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
+        reader::PermutationReader,
+        split::{SplitSizes, SplitStrategy},
+    },
+    query::Select,
 };
 use pyo3::{
-    exceptions::PyRuntimeError, pyclass, pymethods, types::PyAnyMethods, Bound, PyAny, PyRefMut,
-    PyResult,
+    exceptions::PyRuntimeError,
+    pyclass, pymethods,
+    types::{PyAnyMethods, PyDict, PyDictMethods, PyType},
+    Bound, PyAny, PyRef, PyRefMut, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;
 
@@ -56,13 +65,32 @@ impl PyAsyncPermutationBuilder {
 
 #[pymethods]
 impl PyAsyncPermutationBuilder {
-    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None))]
+    #[pyo3(signature = (database, table_name))]
+    pub fn persist(
+        slf: PyRefMut<'_, Self>,
+        database: Bound<'_, PyAny>,
+        table_name: String,
+    ) -> PyResult<Self> {
+        let conn = if database.hasattr("_conn")? {
+            database
+                .getattr("_conn")?
+                .getattr("_inner")?
+                .downcast_into::<Connection>()?
+        } else {
+            database.getattr("_inner")?.downcast_into::<Connection>()?
+        };
+        let database = conn.borrow().database()?;
+        slf.modify(|builder| builder.persist(database, table_name))
+    }
+
+    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None, split_names=None))]
     pub fn split_random(
         slf: PyRefMut<'_, Self>,
         ratios: Option<Vec<f64>>,
         counts: Option<Vec<u64>>,
         fixed: Option<u64>,
         seed: Option<u64>,
+        split_names: Option<Vec<String>>,
     ) -> PyResult<Self> {
         // Check that exactly one split type is provided
         let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
@@ -86,31 +114,38 @@ impl PyAsyncPermutationBuilder {
             unreachable!("One of the split arguments must be provided");
         };
 
-        slf.modify(|builder| builder.with_split_strategy(SplitStrategy::Random { seed, sizes }))
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Random { seed, sizes }, split_names)
+        })
     }
 
-    #[pyo3(signature = (columns, split_weights, *, discard_weight=0))]
+    #[pyo3(signature = (columns, split_weights, *, discard_weight=0, split_names=None))]
     pub fn split_hash(
         slf: PyRefMut<'_, Self>,
         columns: Vec<String>,
         split_weights: Vec<u64>,
         discard_weight: u64,
+        split_names: Option<Vec<String>>,
     ) -> PyResult<Self> {
         slf.modify(|builder| {
-            builder.with_split_strategy(SplitStrategy::Hash {
-                columns,
-                split_weights,
-                discard_weight,
-            })
+            builder.with_split_strategy(
+                SplitStrategy::Hash {
+                    columns,
+                    split_weights,
+                    discard_weight,
+                },
+                split_names,
+            )
         })
     }
 
-    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None))]
+    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, split_names=None))]
     pub fn split_sequential(
         slf: PyRefMut<'_, Self>,
         ratios: Option<Vec<f64>>,
         counts: Option<Vec<u64>>,
         fixed: Option<u64>,
+        split_names: Option<Vec<String>>,
     ) -> PyResult<Self> {
         // Check that exactly one split type is provided
         let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
@@ -134,11 +169,19 @@ impl PyAsyncPermutationBuilder {
             unreachable!("One of the split arguments must be provided");
         };
 
-        slf.modify(|builder| builder.with_split_strategy(SplitStrategy::Sequential { sizes }))
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Sequential { sizes }, split_names)
+        })
     }
 
-    pub fn split_calculated(slf: PyRefMut<'_, Self>, calculation: String) -> PyResult<Self> {
-        slf.modify(|builder| builder.with_split_strategy(SplitStrategy::Calculated { calculation }))
+    pub fn split_calculated(
+        slf: PyRefMut<'_, Self>,
+        calculation: String,
+        split_names: Option<Vec<String>>,
+    ) -> PyResult<Self> {
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Calculated { calculation }, split_names)
+        })
     }
 
     pub fn shuffle(
@@ -168,3 +211,121 @@ impl PyAsyncPermutationBuilder {
         })
     }
 }
+
+#[pyclass(name = "PermutationReader")]
+pub struct PyPermutationReader {
+    reader: Arc<PermutationReader>,
+}
+
+impl PyPermutationReader {
+    fn from_reader(reader: PermutationReader) -> Self {
+        Self {
+            reader: Arc::new(reader),
+        }
+    }
+
+    fn parse_selection(selection: Option<Bound<'_, PyAny>>) -> PyResult<Select> {
+        let Some(selection) = selection else {
+            return Ok(Select::All);
+        };
+        let selection = selection.downcast_into::<PyDict>()?;
+        let selection = selection
+            .iter()
+            .map(|(key, value)| {
+                let key = key.extract::<String>()?;
+                let value = value.extract::<String>()?;
+                Ok((key, value))
+            })
+            .collect::<PyResult<Vec<_>>>()?;
+        Ok(Select::dynamic(&selection))
+    }
+}
+
+#[pymethods]
+impl PyPermutationReader {
+    #[classmethod]
+    pub fn from_tables<'py>(
+        cls: &Bound<'py, PyType>,
+        base_table: Bound<'py, PyAny>,
+        permutation_table: Option<Bound<'py, PyAny>>,
+        split: u64,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let base_table = base_table.getattr("_inner")?.downcast_into::<Table>()?;
+        let permutation_table = permutation_table
+            .map(|p| PyResult::Ok(p.getattr("_inner")?.downcast_into::<Table>()?))
+            .transpose()?;
+
+        let base_table = base_table.borrow().inner_ref()?.base_table().clone();
+        let permutation_table = permutation_table
+            .map(|p| PyResult::Ok(p.borrow().inner_ref()?.base_table().clone()))
+            .transpose()?;
+
+        future_into_py(cls.py(), async move {
+            let reader = if let Some(permutation_table) = permutation_table {
+                PermutationReader::try_from_tables(base_table, permutation_table, split)
+                    .await
+                    .infer_error()?
+            } else {
+                PermutationReader::identity(base_table).await
+            };
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (selection=None))]
+    pub fn output_schema<'py>(
+        slf: PyRef<'py, Self>,
+        selection: Option<Bound<'py, PyAny>>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let selection = Self::parse_selection(selection)?;
+        let reader = slf.reader.clone();
+        future_into_py(slf.py(), async move {
+            let schema = reader.output_schema(selection).await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
+    #[pyo3(signature = ())]
+    pub fn count_rows<'py>(slf: PyRef<'py, Self>) -> u64 {
+        slf.reader.count_rows()
+    }
+
+    #[pyo3(signature = (offset))]
+    pub fn with_offset<'py>(slf: PyRef<'py, Self>, offset: u64) -> PyResult<Bound<'py, PyAny>> {
+        let reader = slf.reader.as_ref().clone();
+        future_into_py(slf.py(), async move {
+            let reader = reader.with_offset(offset).await.infer_error()?;
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (limit))]
+    pub fn with_limit<'py>(slf: PyRef<'py, Self>, limit: u64) -> PyResult<Bound<'py, PyAny>> {
+        let reader = slf.reader.as_ref().clone();
+        future_into_py(slf.py(), async move {
+            let reader = reader.with_limit(limit).await.infer_error()?;
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (selection=None, *, batch_size=None))]
+    pub fn read<'py>(
+        slf: PyRef<'py, Self>,
+        selection: Option<Bound<'py, PyAny>>,
+        batch_size: Option<u32>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let selection = Self::parse_selection(selection)?;
+        let reader = slf.reader.clone();
+        let batch_size = batch_size.unwrap_or(1024);
+        future_into_py(slf.py(), async move {
+            use lancedb::query::QueryExecutionOptions;
+            let mut execution_options = QueryExecutionOptions::default();
+            execution_options.max_batch_length = batch_size;
+            let stream = reader
+                .read(selection, execution_options)
+                .await
+                .infer_error()?;
+            Ok(RecordBatchStream::new(stream))
+        })
+    }
+}

rust/lancedb/src/dataloader/permutation/builder.rs

@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::sync::Arc;
+use std::{collections::HashMap, sync::Arc};
 
 use datafusion::prelude::{SessionConfig, SessionContext};
 use datafusion_execution::{disk_manager::DiskManagerBuilder, runtime_env::RuntimeEnvBuilder};
@@ -25,6 +25,8 @@ use crate::{
 
 pub const SRC_ROW_ID_COL: &str = "row_id";
 
+pub const SPLIT_NAMES_CONFIG_KEY: &str = "split_names";
+
 /// Where to store the permutation table
 #[derive(Debug, Clone, Default)]
 enum PermutationDestination {
@@ -40,6 +42,8 @@ enum PermutationDestination {
 pub struct PermutationConfig {
     /// Splitting configuration
     split_strategy: SplitStrategy,
+    /// Optional names for the splits
+    split_names: Option<Vec<String>>,
     /// Shuffle strategy
     shuffle_strategy: ShuffleStrategy,
     /// Optional filter to apply to the base table
@@ -112,8 +116,16 @@ impl PermutationBuilder {
     /// multiple processes and multiple nodes.
     ///
     /// The default is a single split that contains all rows.
-    pub fn with_split_strategy(mut self, split_strategy: SplitStrategy) -> Self {
+    ///
+    /// An optional list of names can be provided for the splits.  This is for convenience and the names
+    /// will be stored in the permutation table's config metadata.
+    pub fn with_split_strategy(
+        mut self,
+        split_strategy: SplitStrategy,
+        split_names: Option<Vec<String>>,
+    ) -> Self {
         self.config.split_strategy = split_strategy;
+        self.config.split_names = split_names;
         self
     }
 
@@ -193,6 +205,30 @@ impl PermutationBuilder {
         Ok(Box::pin(SimpleRecordBatchStream { schema, stream }))
     }
 
+    fn add_split_names(
+        data: SendableRecordBatchStream,
+        split_names: &[String],
+    ) -> Result<SendableRecordBatchStream> {
+        let schema = data
+            .schema()
+            .as_ref()
+            .clone()
+            .with_metadata(HashMap::from([(
+                SPLIT_NAMES_CONFIG_KEY.to_string(),
+                serde_json::to_string(split_names).map_err(|e| Error::Other {
+                    message: format!("Failed to serialize split names: {}", e),
+                    source: Some(e.into()),
+                })?,
+            )]));
+        let schema = Arc::new(schema);
+        let schema_clone = schema.clone();
+        let stream = data.map_ok(move |batch| batch.with_schema(schema.clone()).unwrap());
+        Ok(Box::pin(SimpleRecordBatchStream {
+            schema: schema_clone,
+            stream,
+        }))
+    }
+
     /// Builds the permutation table and stores it in the given database.
     pub async fn build(self) -> Result<Table> {
         // First pass, apply filter and load row ids
@@ -249,6 +285,12 @@ impl PermutationBuilder {
         // Rename _rowid to row_id
         let renamed = rename_column(sorted, ROW_ID, SRC_ROW_ID_COL)?;
 
+        let streaming_data = if let Some(split_names) = &self.config.split_names {
+            Self::add_split_names(renamed, split_names)?
+        } else {
+            renamed
+        };
+
         let (name, database) = match &self.config.destination {
             PermutationDestination::Permanent(database, table_name) => {
                 (table_name.as_str(), database.clone())
@@ -259,10 +301,13 @@ impl PermutationBuilder {
             }
         };
 
-        let create_table_request =
-            CreateTableRequest::new(name.to_string(), CreateTableData::StreamingData(renamed));
+        let create_table_request = CreateTableRequest::new(
+            name.to_string(),
+            CreateTableData::StreamingData(streaming_data),
+        );
 
         let table = database.create_table(create_table_request).await?;
+
         Ok(Table::new(table, database))
     }
 }
@@ -296,10 +341,13 @@ mod tests {
 
         let permutation_table = PermutationBuilder::new(data_table.clone())
             .with_filter("some_value > 57".to_string())
-            .with_split_strategy(SplitStrategy::Random {
-                seed: Some(42),
-                sizes: SplitSizes::Percentages(vec![0.05, 0.30]),
-            })
+            .with_split_strategy(
+                SplitStrategy::Random {
+                    seed: Some(42),
+                    sizes: SplitSizes::Percentages(vec![0.05, 0.30]),
+                },
+                None,
+            )
             .build()
             .await
             .unwrap();

rust/lancedb/src/dataloader/permutation/reader.rs

@@ -11,14 +11,19 @@ use crate::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
 use crate::dataloader::permutation::builder::SRC_ROW_ID_COL;
 use crate::dataloader::permutation::split::SPLIT_ID_COLUMN;
 use crate::error::Error;
-use crate::query::{QueryExecutionOptions, QueryFilter, QueryRequest, Select};
-use crate::table::{AnyQuery, BaseTable};
-use crate::Result;
+use crate::query::{
+    ExecutableQuery, QueryBase, QueryExecutionOptions, QueryFilter, QueryRequest, Select,
+};
+use crate::table::{AnyQuery, BaseTable, Filter};
+use crate::{Result, Table};
 use arrow::array::AsArray;
+use arrow::compute::concat_batches;
 use arrow::datatypes::UInt64Type;
 use arrow_array::{RecordBatch, UInt64Array};
+use arrow_schema::SchemaRef;
 use futures::{StreamExt, TryStreamExt};
 use lance::dataset::scanner::DatasetRecordBatchStream;
+use lance::io::RecordBatchStream;
 use lance_arrow::RecordBatchExt;
 use lance_core::error::LanceOptionExt;
 use lance_core::ROW_ID;
@@ -26,43 +31,140 @@ use std::collections::HashMap;
 use std::sync::Arc;
 
 /// Reads a permutation of a source table based on row IDs stored in a separate table
+#[derive(Clone)]
 pub struct PermutationReader {
     base_table: Arc<dyn BaseTable>,
-    permutation_table: Arc<dyn BaseTable>,
+    permutation_table: Option<Arc<dyn BaseTable>>,
+    offset: Option<u64>,
+    limit: Option<u64>,
+    available_rows: u64,
+    split: u64,
 }
 
 impl std::fmt::Debug for PermutationReader {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         write!(
             f,
-            "PermutationReader(base={}, permutation={})",
+            "PermutationReader(base={}, permutation={}, split={}, offset={:?}, limit={:?})",
             self.base_table.name(),
-            self.permutation_table.name(),
+            self.permutation_table
+                .as_ref()
+                .map(|t| t.name())
+                .unwrap_or("--"),
+            self.split,
+            self.offset,
+            self.limit,
         )
     }
 }
 
 impl PermutationReader {
     /// Create a new PermutationReader
-    pub async fn try_new(
+    pub async fn inner_new(
         base_table: Arc<dyn BaseTable>,
-        permutation_table: Arc<dyn BaseTable>,
+        permutation_table: Option<Arc<dyn BaseTable>>,
+        split: u64,
     ) -> Result<Self> {
-        let schema = permutation_table.schema().await?;
-        if schema.column_with_name(SRC_ROW_ID_COL).is_none() {
-            return Err(Error::InvalidInput {
-                message: "Permutation table must contain a column named row_id".to_string(),
-            });
-        }
-        if schema.column_with_name(SPLIT_ID_COLUMN).is_none() {
-            return Err(Error::InvalidInput {
-                message: "Permutation table must contain a column named split_id".to_string(),
-            });
-        }
-        Ok(Self {
+        let mut slf = Self {
             base_table,
             permutation_table,
-        })
+            offset: None,
+            limit: None,
+            available_rows: 0,
+            split,
+        };
+        slf.validate().await?;
+        // Calculate the number of available rows
+        slf.available_rows = slf.verify_limit_offset(None, None).await?;
+        if slf.available_rows == 0 {
+            return Err(Error::InvalidInput {
+                message: "No rows found in the permutation table for the given split".to_string(),
+            });
+        }
+        Ok(slf)
+    }
+
+    pub async fn try_from_tables(
+        base_table: Arc<dyn BaseTable>,
+        permutation_table: Arc<dyn BaseTable>,
+        split: u64,
+    ) -> Result<Self> {
+        Self::inner_new(base_table, Some(permutation_table), split).await
+    }
+
+    pub async fn identity(base_table: Arc<dyn BaseTable>) -> Self {
+        Self::inner_new(base_table, None, 0).await.unwrap()
+    }
+
+    /// Validates the limit and offset and returns the number of rows that will be read
+    fn validate_limit_offset(
+        limit: Option<u64>,
+        offset: Option<u64>,
+        available_rows: u64,
+    ) -> Result<u64> {
+        match (limit, offset) {
+            (Some(limit), Some(offset)) => {
+                if offset + limit > available_rows {
+                    Err(Error::InvalidInput {
+                        message: "Offset + limit is greater than the number of rows in the permutation table"
+                            .to_string(),
+                    })
+                } else {
+                    Ok(limit)
+                }
+            }
+            (None, Some(offset)) => {
+                if offset > available_rows {
+                    Err(Error::InvalidInput {
+                        message:
+                            "Offset is greater than the number of rows in the permutation table"
+                                .to_string(),
+                    })
+                } else {
+                    Ok(available_rows - offset)
+                }
+            }
+            (Some(limit), None) => {
+                if limit > available_rows {
+                    Err(Error::InvalidInput {
+                        message:
+                            "Limit is greater than the number of rows in the permutation table"
+                                .to_string(),
+                    })
+                } else {
+                    Ok(limit)
+                }
+            }
+            (None, None) => Ok(available_rows),
+        }
+    }
+
+    async fn verify_limit_offset(&self, limit: Option<u64>, offset: Option<u64>) -> Result<u64> {
+        let available_rows = if let Some(permutation_table) = &self.permutation_table {
+            permutation_table
+                .count_rows(Some(Filter::Sql(format!(
+                    "{} = {}",
+                    SPLIT_ID_COLUMN, self.split
+                ))))
+                .await? as u64
+        } else {
+            self.base_table.count_rows(None).await? as u64
+        };
+        Self::validate_limit_offset(limit, offset, available_rows)
+    }
+
+    pub async fn with_offset(mut self, offset: u64) -> Result<Self> {
+        let available_rows = self.verify_limit_offset(self.limit, Some(offset)).await?;
+        self.offset = Some(offset);
+        self.available_rows = available_rows;
+        Ok(self)
+    }
+
+    pub async fn with_limit(mut self, limit: u64) -> Result<Self> {
+        let available_rows = self.verify_limit_offset(Some(limit), self.offset).await?;
+        self.available_rows = available_rows;
+        self.limit = Some(limit);
+        Ok(self)
     }
 
     fn is_sorted_already<'a, T: Iterator<Item = &'a u64>>(iter: T) -> bool {
@@ -103,7 +205,7 @@ impl PermutationReader {
             ..Default::default()
         };
 
-        let mut data = base_table
+        let data = base_table
             .query(
                 &AnyQuery::Query(base_query),
                 QueryExecutionOptions {
@@ -112,25 +214,29 @@ impl PermutationReader {
                 },
             )
             .await?;
+        let schema = data.schema();
 
-        let Some(batch) = data.try_next().await? else {
+        let batches = data.try_collect::<Vec<_>>().await?;
+
+        if batches.is_empty() {
             return Err(Error::InvalidInput {
                 message: "Base table returned no batches".to_string(),
             });
-        };
-        if data.try_next().await?.is_some() {
-            return Err(Error::InvalidInput {
-                message: "Base table returned more than one batch".to_string(),
-            });
         }
 
-        if batch.num_rows() != num_rows {
+        if batches.iter().map(|b| b.num_rows()).sum::<usize>() != num_rows {
             return Err(Error::InvalidInput {
                 message: "Base table returned different number of rows than the number of row IDs"
                     .to_string(),
             });
         }
 
+        let batch = if batches.len() == 1 {
+            batches.into_iter().next().unwrap()
+        } else {
+            concat_batches(&schema, &batches)?
+        };
+
         // There is no guarantee the result order will match the order provided
         // so may need to restore order
         let actual_row_ids = batch
@@ -230,26 +336,75 @@ impl PermutationReader {
         }
     }
 
-    pub async fn read_split(
+    async fn validate(&self) -> Result<()> {
+        if let Some(permutation_table) = &self.permutation_table {
+            let schema = permutation_table.schema().await?;
+            if schema.column_with_name(SRC_ROW_ID_COL).is_none() {
+                return Err(Error::InvalidInput {
+                    message: "Permutation table must contain a column named row_id".to_string(),
+                });
+            }
+            if schema.column_with_name(SPLIT_ID_COLUMN).is_none() {
+                return Err(Error::InvalidInput {
+                    message: "Permutation table must contain a column named split_id".to_string(),
+                });
+            }
+        }
+        let avail_rows = if let Some(permutation_table) = &self.permutation_table {
+            permutation_table.count_rows(None).await? as u64
+        } else {
+            self.base_table.count_rows(None).await? as u64
+        };
+        Self::validate_limit_offset(self.limit, self.offset, avail_rows)?;
+        Ok(())
+    }
+
+    pub async fn read(
         &self,
-        split: u64,
         selection: Select,
         execution_options: QueryExecutionOptions,
     ) -> Result<SendableRecordBatchStream> {
-        let row_ids = self
-            .permutation_table
-            .query(
-                &AnyQuery::Query(QueryRequest {
-                    select: Select::Columns(vec![SRC_ROW_ID_COL.to_string()]),
-                    filter: Some(QueryFilter::Sql(format!("{} = {}", SPLIT_ID_COLUMN, split))),
-                    ..Default::default()
-                }),
-                execution_options,
-            )
-            .await?;
-
+        // Note: this relies on the row ids query here being returned in consistent order
+        let row_ids = if let Some(permutation_table) = &self.permutation_table {
+            permutation_table
+                .query(
+                    &AnyQuery::Query(QueryRequest {
+                        select: Select::Columns(vec![SRC_ROW_ID_COL.to_string()]),
+                        filter: Some(QueryFilter::Sql(format!(
+                            "{} = {}",
+                            SPLIT_ID_COLUMN, self.split
+                        ))),
+                        offset: self.offset.map(|o| o as usize),
+                        limit: self.limit.map(|l| l as usize),
+                        ..Default::default()
+                    }),
+                    execution_options,
+                )
+                .await?
+        } else {
+            self.base_table
+                .query(
+                    &AnyQuery::Query(QueryRequest {
+                        select: Select::Columns(vec![ROW_ID.to_string()]),
+                        offset: self.offset.map(|o| o as usize),
+                        limit: self.limit.map(|l| l as usize),
+                        ..Default::default()
+                    }),
+                    execution_options,
+                )
+                .await?
+        };
         Self::row_ids_to_batches(self.base_table.clone(), row_ids, selection).await
     }
+
+    pub async fn output_schema(&self, selection: Select) -> Result<SchemaRef> {
+        let table = Table::from(self.base_table.clone());
+        table.query().select(selection).output_schema().await
+    }
+
+    pub fn count_rows(&self) -> u64 {
+        self.available_rows
+    }
 }
 
 #[cfg(test)]
@@ -321,17 +476,17 @@ mod tests {
         .unwrap();
         let row_ids_table = virtual_table("row_ids", &permutation_batch).await;
 
-        let reader = PermutationReader::try_new(
+        let reader = PermutationReader::try_from_tables(
             base_table.base_table().clone(),
             row_ids_table.base_table().clone(),
+            0,
         )
         .await
         .unwrap();
 
         // Read split 0
         let mut stream = reader
-            .read_split(
-                0,
+            .read(
                 Select::All,
                 QueryExecutionOptions {
                     max_batch_length: 3,
@@ -366,9 +521,16 @@ mod tests {
         assert!(stream.try_next().await.unwrap().is_none());
 
         // Read split 1
+        let reader = PermutationReader::try_from_tables(
+            base_table.base_table().clone(),
+            row_ids_table.base_table().clone(),
+            1,
+        )
+        .await
+        .unwrap();
+
         let mut stream = reader
-            .read_split(
-                1,
+            .read(
                 Select::All,
                 QueryExecutionOptions {
                     max_batch_length: 3,

rust/lancedb/src/query.rs

@@ -34,7 +34,7 @@ pub(crate) const DEFAULT_TOP_K: usize = 10;
 /// Which columns should be retrieved from the database
 #[derive(Debug, Clone)]
 pub enum Select {
-    /// Select all columns
+    /// Select all non-system columns
     ///
     /// Warning: This will always be slower than selecting only the columns you need.
     All,

rust/lancedb/src/table.rs

@@ -620,7 +620,7 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
 #[derive(Clone, Debug)]
 pub struct Table {
     inner: Arc<dyn BaseTable>,
-    database: Arc<dyn Database>,
+    database: Option<Arc<dyn Database>>,
     embedding_registry: Arc<dyn EmbeddingRegistry>,
 }
 
@@ -644,7 +644,7 @@ mod test_utils {
             let database = Arc::new(crate::remote::db::RemoteDatabase::new_mock(handler));
             Self {
                 inner,
-                database,
+                database: Some(database),
                 // Registry is unused.
                 embedding_registry: Arc::new(MemoryRegistry::new()),
             }
@@ -666,7 +666,7 @@ mod test_utils {
             let database = Arc::new(crate::remote::db::RemoteDatabase::new_mock(handler));
             Self {
                 inner,
-                database,
+                database: Some(database),
                 // Registry is unused.
                 embedding_registry: Arc::new(MemoryRegistry::new()),
             }
@@ -680,11 +680,21 @@ impl std::fmt::Display for Table {
     }
 }
 
+impl From<Arc<dyn BaseTable>> for Table {
+    fn from(inner: Arc<dyn BaseTable>) -> Self {
+        Self {
+            inner,
+            database: None,
+            embedding_registry: Arc::new(MemoryRegistry::new()),
+        }
+    }
+}
+
 impl Table {
     pub fn new(inner: Arc<dyn BaseTable>, database: Arc<dyn Database>) -> Self {
         Self {
             inner,
-            database,
+            database: Some(database),
             embedding_registry: Arc::new(MemoryRegistry::new()),
         }
     }
@@ -694,7 +704,7 @@ impl Table {
     }
 
     pub fn database(&self) -> &Arc<dyn Database> {
-        &self.database
+        self.database.as_ref().unwrap()
     }
 
     pub fn embedding_registry(&self) -> &Arc<dyn EmbeddingRegistry> {
@@ -708,7 +718,7 @@ impl Table {
     ) -> Self {
         Self {
             inner,
-            database,
+            database: Some(database),
             embedding_registry,
         }
     }