test(python): Add benchmark tests for join_where with inequalities

py-polars/tests/benchmark/test_join_where.py

@@ -0,0 +1,73 @@
+"""Benchmark tests for join_where with inequality conditions."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+import polars as pl
+
+pytestmark = pytest.mark.benchmark()
+
+
+def test_strict_inequalities(east_west: tuple[pl.DataFrame, pl.DataFrame]) -> None:
+    east, west = east_west
+    result = (
+        east.lazy()
+        .join_where(
+            west.lazy(),
+            [pl.col("dur") < pl.col("time"), pl.col("rev") > pl.col("cost")],
+        )
+        .collect()
+    )
+
+    assert len(result) > 0
+
+
+def test_non_strict_inequalities(east_west: tuple[pl.DataFrame, pl.DataFrame]) -> None:
+    east, west = east_west
+    result = (
+        east.lazy()
+        .join_where(
+            west.lazy(),
+            [pl.col("dur") <= pl.col("time"), pl.col("rev") >= pl.col("cost")],
+        )
+        .collect()
+    )
+
+    assert len(result) > 0
+
+
+@pytest.fixture(scope="module")
+def east_west() -> tuple[pl.DataFrame, pl.DataFrame]:
+    num_rows_left, num_rows_right = 50_000, 5_000
+    rng = np.random.default_rng(42)
+
+    # Generate two separate datasets where revenue/cost are linearly related to
+    # duration/time, but add some noise to the west table so that there are some
+    # rows where the cost for the same or greater time will be less than the east table.
+    east_dur = rng.integers(1_000, 50_000, num_rows_left)
+    east_rev = (east_dur * 0.123).astype(np.int32)
+    west_time = rng.integers(1_000, 50_000, num_rows_right)
+    west_cost = west_time * 0.123
+    west_cost += rng.normal(0.0, 1.0, num_rows_right)
+    west_cost = west_cost.astype(np.int32)
+
+    east = pl.DataFrame(
+        {
+            "id": np.arange(0, num_rows_left),
+            "dur": east_dur,
+            "rev": east_rev,
+            "cores": rng.integers(1, 10, num_rows_left),
+        }
+    )
+    west = pl.DataFrame(
+        {
+            "t_id": np.arange(0, num_rows_right),
+            "time": west_time,
+            "cost": west_cost,
+            "cores": rng.integers(1, 10, num_rows_right),
+        }
+    )
+
+    return east, west