feat: add a permutation reader that can read a permutation view (#2712)

This adds a rust permutation builder. In the next PR I will have python
bindings and integration with pytorch.

python/python/lancedb/permutation.py

@@ -8,8 +8,8 @@ from typing import Optional
 
 
 class PermutationBuilder:
-    def __init__(self, table: LanceTable, dest_table_name: str):
-        self._async = async_permutation_builder(table, dest_table_name)
+    def __init__(self, table: LanceTable):
+        self._async = async_permutation_builder(table)
 
     def select(self, projections: dict[str, str]) -> "PermutationBuilder":
         self._async.select(projections)
@@ -68,5 +68,5 @@ class PermutationBuilder:
         return LOOP.run(do_execute())
 
 
-def permutation_builder(table: LanceTable, dest_table_name: str) -> PermutationBuilder:
-    return PermutationBuilder(table, dest_table_name)
+def permutation_builder(table: LanceTable) -> PermutationBuilder:
+    return PermutationBuilder(table)

python/python/tests/test_permutation.py

@@ -12,11 +12,7 @@ def test_split_random_ratios(mem_db):
     tbl = mem_db.create_table(
         "test_table", pa.table({"x": range(100), "y": range(100)})
     )
-    permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
-        .split_random(ratios=[0.3, 0.7])
-        .execute()
-    )
+    permutation_tbl = permutation_builder(tbl).split_random(ratios=[0.3, 0.7]).execute()
 
     # Check that the table was created and has data
     assert permutation_tbl.count_rows() == 100
@@ -38,11 +34,7 @@ def test_split_random_counts(mem_db):
     tbl = mem_db.create_table(
         "test_table", pa.table({"x": range(100), "y": range(100)})
     )
-    permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
-        .split_random(counts=[20, 30])
-        .execute()
-    )
+    permutation_tbl = permutation_builder(tbl).split_random(counts=[20, 30]).execute()
 
     # Check that we have exactly the requested counts
     assert permutation_tbl.count_rows() == 50
@@ -58,9 +50,7 @@ def test_split_random_fixed(mem_db):
     tbl = mem_db.create_table(
         "test_table", pa.table({"x": range(100), "y": range(100)})
     )
-    permutation_tbl = (
-        permutation_builder(tbl, "test_permutation").split_random(fixed=4).execute()
-    )
+    permutation_tbl = permutation_builder(tbl).split_random(fixed=4).execute()
 
     # Check that we have 4 splits with 25 rows each
     assert permutation_tbl.count_rows() == 100
@@ -78,17 +68,9 @@ def test_split_random_with_seed(mem_db):
     tbl = mem_db.create_table("test_table", pa.table({"x": range(50), "y": range(50)}))
 
     # Create two identical permutations with same seed
-    perm1 = (
-        permutation_builder(tbl, "perm1")
-        .split_random(ratios=[0.6, 0.4], seed=42)
-        .execute()
-    )
+    perm1 = permutation_builder(tbl).split_random(ratios=[0.6, 0.4], seed=42).execute()
 
-    perm2 = (
-        permutation_builder(tbl, "perm2")
-        .split_random(ratios=[0.6, 0.4], seed=42)
-        .execute()
-    )
+    perm2 = permutation_builder(tbl).split_random(ratios=[0.6, 0.4], seed=42).execute()
 
     # Results should be identical
     data1 = perm1.search(None).to_arrow().to_pydict()
@@ -112,7 +94,7 @@ def test_split_hash(mem_db):
     )
 
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .split_hash(["category"], [1, 1], discard_weight=0)
         .execute()
     )
@@ -133,7 +115,7 @@ def test_split_hash(mem_db):
     # Hash splits should be deterministic - same category should go to same split
     # Let's verify by creating another permutation and checking consistency
     perm2 = (
-        permutation_builder(tbl, "test_permutation2")
+        permutation_builder(tbl)
         .split_hash(["category"], [1, 1], discard_weight=0)
         .execute()
     )
@@ -150,7 +132,7 @@ def test_split_hash_with_discard(mem_db):
     )
 
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .split_hash(["category"], [1, 1], discard_weight=2)  # Should discard ~50%
         .execute()
     )
@@ -168,9 +150,7 @@ def test_split_sequential(mem_db):
     )
 
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
-        .split_sequential(counts=[30, 40])
-        .execute()
+        permutation_builder(tbl).split_sequential(counts=[30, 40]).execute()
     )
 
     assert permutation_tbl.count_rows() == 70
@@ -194,7 +174,7 @@ def test_split_calculated(mem_db):
     )
 
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .split_calculated("id % 3")  # Split based on id modulo 3
         .execute()
     )
@@ -216,23 +196,21 @@ def test_split_error_cases(mem_db):
 
     # Test split_random with no parameters
     with pytest.raises(Exception):
-        permutation_builder(tbl, "error1").split_random().execute()
+        permutation_builder(tbl).split_random().execute()
 
     # Test split_random with multiple parameters
     with pytest.raises(Exception):
-        permutation_builder(tbl, "error2").split_random(
+        permutation_builder(tbl).split_random(
             ratios=[0.5, 0.5], counts=[5, 5]
         ).execute()
 
     # Test split_sequential with no parameters
     with pytest.raises(Exception):
-        permutation_builder(tbl, "error3").split_sequential().execute()
+        permutation_builder(tbl).split_sequential().execute()
 
     # Test split_sequential with multiple parameters
     with pytest.raises(Exception):
-        permutation_builder(tbl, "error4").split_sequential(
-            ratios=[0.5, 0.5], fixed=2
-        ).execute()
+        permutation_builder(tbl).split_sequential(ratios=[0.5, 0.5], fixed=2).execute()
 
 
 def test_shuffle_no_seed(mem_db):
@@ -242,7 +220,7 @@ def test_shuffle_no_seed(mem_db):
     )
 
     # Create a permutation with shuffling (no seed)
-    permutation_tbl = permutation_builder(tbl, "test_permutation").shuffle().execute()
+    permutation_tbl = permutation_builder(tbl).shuffle().execute()
 
     assert permutation_tbl.count_rows() == 100
 
@@ -262,9 +240,9 @@ def test_shuffle_with_seed(mem_db):
     )
 
     # Create two identical permutations with same shuffle seed
-    perm1 = permutation_builder(tbl, "perm1").shuffle(seed=42).execute()
+    perm1 = permutation_builder(tbl).shuffle(seed=42).execute()
 
-    perm2 = permutation_builder(tbl, "perm2").shuffle(seed=42).execute()
+    perm2 = permutation_builder(tbl).shuffle(seed=42).execute()
 
     # Results should be identical due to same seed
     data1 = perm1.search(None).to_arrow().to_pydict()
@@ -282,7 +260,7 @@ def test_shuffle_with_clump_size(mem_db):
 
     # Create a permutation with shuffling using clumps
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .shuffle(clump_size=10)  # 10-row clumps
         .execute()
     )
@@ -304,19 +282,9 @@ def test_shuffle_different_seeds(mem_db):
     )
 
     # Create two permutations with different shuffle seeds
-    perm1 = (
-        permutation_builder(tbl, "perm1")
-        .split_random(fixed=2)
-        .shuffle(seed=42)
-        .execute()
-    )
+    perm1 = permutation_builder(tbl).split_random(fixed=2).shuffle(seed=42).execute()
 
-    perm2 = (
-        permutation_builder(tbl, "perm2")
-        .split_random(fixed=2)
-        .shuffle(seed=123)
-        .execute()
-    )
+    perm2 = permutation_builder(tbl).split_random(fixed=2).shuffle(seed=123).execute()
 
     # Results should be different due to different seeds
     data1 = perm1.search(None).to_arrow().to_pydict()
@@ -341,7 +309,7 @@ def test_shuffle_combined_with_splits(mem_db):
 
     # Test shuffle with random splits
     perm_random = (
-        permutation_builder(tbl, "perm_random")
+        permutation_builder(tbl)
         .split_random(ratios=[0.6, 0.4], seed=42)
         .shuffle(seed=123, clump_size=None)
         .execute()
@@ -349,7 +317,7 @@ def test_shuffle_combined_with_splits(mem_db):
 
     # Test shuffle with hash splits
     perm_hash = (
-        permutation_builder(tbl, "perm_hash")
+        permutation_builder(tbl)
         .split_hash(["category"], [1, 1], discard_weight=0)
         .shuffle(seed=456, clump_size=5)
         .execute()
@@ -357,7 +325,7 @@ def test_shuffle_combined_with_splits(mem_db):
 
     # Test shuffle with sequential splits
     perm_sequential = (
-        permutation_builder(tbl, "perm_sequential")
+        permutation_builder(tbl)
         .split_sequential(counts=[40, 35])
         .shuffle(seed=789, clump_size=None)
         .execute()
@@ -384,7 +352,7 @@ def test_no_shuffle_maintains_order(mem_db):
 
     # Create permutation without shuffle (should maintain some order)
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .split_sequential(counts=[25, 25])  # Sequential maintains order
         .execute()
     )
@@ -405,9 +373,7 @@ def test_filter_basic(mem_db):
     )
 
     # Filter to only include rows where id < 50
-    permutation_tbl = (
-        permutation_builder(tbl, "test_permutation").filter("id < 50").execute()
-    )
+    permutation_tbl = permutation_builder(tbl).filter("id < 50").execute()
 
     assert permutation_tbl.count_rows() == 50
 
@@ -433,7 +399,7 @@ def test_filter_with_splits(mem_db):
 
     # Filter to only category A and B, then split
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .filter("category IN ('A', 'B')")
         .split_random(ratios=[0.5, 0.5])
         .execute()
@@ -465,7 +431,7 @@ def test_filter_with_shuffle(mem_db):
 
     # Filter and shuffle
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .filter("category IN ('A', 'C')")
         .shuffle(seed=42)
         .execute()
@@ -488,7 +454,7 @@ def test_filter_empty_result(mem_db):
 
     # Filter that matches nothing
     permutation_tbl = (
-        permutation_builder(tbl, "test_permutation")
+        permutation_builder(tbl)
         .filter("value > 100")  # No values > 100 in our data
         .execute()
     )